| 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/EmptyTensor.h> |
| 52 | #include <ATen/ops/_adaptive_avg_pool2d_backward_cpu_dispatch.h> |
| 53 | #include <ATen/ops/_adaptive_avg_pool2d_backward_native.h> |
| 54 | #include <ATen/ops/_adaptive_avg_pool2d_cpu_dispatch.h> |
| 55 | #include <ATen/ops/_adaptive_avg_pool2d_native.h> |
| 56 | #include <ATen/ops/_adaptive_avg_pool3d_backward_cpu_dispatch.h> |
| 57 | #include <ATen/ops/_adaptive_avg_pool3d_backward_native.h> |
| 58 | #include <ATen/ops/_adaptive_avg_pool3d_cpu_dispatch.h> |
| 59 | #include <ATen/ops/_adaptive_avg_pool3d_native.h> |
| 60 | #include <ATen/ops/_add_relu_cpu_dispatch.h> |
| 61 | #include <ATen/ops/_add_relu_native.h> |
| 62 | #include <ATen/ops/_addmm_activation_cpu_dispatch.h> |
| 63 | #include <ATen/ops/_addmm_activation_native.h> |
| 64 | #include <ATen/ops/_aminmax_cpu_dispatch.h> |
| 65 | #include <ATen/ops/_aminmax_native.h> |
| 66 | #include <ATen/ops/_assert_async_cpu_dispatch.h> |
| 67 | #include <ATen/ops/_assert_async_native.h> |
| 68 | #include <ATen/ops/_cdist_backward_cpu_dispatch.h> |
| 69 | #include <ATen/ops/_cdist_backward_native.h> |
| 70 | #include <ATen/ops/_cdist_forward_cpu_dispatch.h> |
| 71 | #include <ATen/ops/_cdist_forward_native.h> |
| 72 | #include <ATen/ops/_cholesky_solve_helper_cpu_dispatch.h> |
| 73 | #include <ATen/ops/_cholesky_solve_helper_native.h> |
| 74 | #include <ATen/ops/_compute_linear_combination_cpu_dispatch.h> |
| 75 | #include <ATen/ops/_compute_linear_combination_native.h> |
| 76 | #include <ATen/ops/_convert_indices_from_coo_to_csr_cpu_dispatch.h> |
| 77 | #include <ATen/ops/_convert_indices_from_coo_to_csr_native.h> |
| 78 | #include <ATen/ops/_convert_indices_from_csr_to_coo_cpu_dispatch.h> |
| 79 | #include <ATen/ops/_convert_indices_from_csr_to_coo_native.h> |
| 80 | #include <ATen/ops/_ctc_loss_backward_cpu_dispatch.h> |
| 81 | #include <ATen/ops/_ctc_loss_backward_native.h> |
| 82 | #include <ATen/ops/_ctc_loss_cpu_dispatch.h> |
| 83 | #include <ATen/ops/_ctc_loss_native.h> |
| 84 | #include <ATen/ops/_cummax_helper_cpu_dispatch.h> |
| 85 | #include <ATen/ops/_cummax_helper_native.h> |
| 86 | #include <ATen/ops/_cummin_helper_cpu_dispatch.h> |
| 87 | #include <ATen/ops/_cummin_helper_native.h> |
| 88 | #include <ATen/ops/_dirichlet_grad_cpu_dispatch.h> |
| 89 | #include <ATen/ops/_dirichlet_grad_native.h> |
| 90 | #include <ATen/ops/_efficientzerotensor_cpu_dispatch.h> |
| 91 | #include <ATen/ops/_efficientzerotensor_native.h> |
| 92 | #include <ATen/ops/_embedding_bag_cpu_dispatch.h> |
| 93 | #include <ATen/ops/_embedding_bag_dense_backward_cpu_dispatch.h> |
| 94 | #include <ATen/ops/_embedding_bag_dense_backward_native.h> |
| 95 | #include <ATen/ops/_embedding_bag_forward_only_cpu_dispatch.h> |
| 96 | #include <ATen/ops/_embedding_bag_forward_only_native.h> |
| 97 | #include <ATen/ops/_embedding_bag_native.h> |
| 98 | #include <ATen/ops/_embedding_bag_per_sample_weights_backward_cpu_dispatch.h> |
| 99 | #include <ATen/ops/_embedding_bag_per_sample_weights_backward_native.h> |
| 100 | #include <ATen/ops/_empty_affine_quantized_cpu_dispatch.h> |
| 101 | #include <ATen/ops/_empty_affine_quantized_native.h> |
| 102 | #include <ATen/ops/_empty_per_channel_affine_quantized_cpu_dispatch.h> |
| 103 | #include <ATen/ops/_empty_per_channel_affine_quantized_native.h> |
| 104 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward_cpu_dispatch.h> |
| 105 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward_native.h> |
| 106 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_cpu_dispatch.h> |
| 107 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_native.h> |
| 108 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward_cpu_dispatch.h> |
| 109 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward_native.h> |
| 110 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_cpu_dispatch.h> |
| 111 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_native.h> |
| 112 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_cpu_dispatch.h> |
| 113 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_native.h> |
| 114 | #include <ATen/ops/_fft_c2c_cpu_dispatch.h> |
| 115 | #include <ATen/ops/_fft_c2c_native.h> |
| 116 | #include <ATen/ops/_fft_c2r_cpu_dispatch.h> |
| 117 | #include <ATen/ops/_fft_c2r_native.h> |
| 118 | #include <ATen/ops/_fft_r2c_cpu_dispatch.h> |
| 119 | #include <ATen/ops/_fft_r2c_native.h> |
| 120 | #include <ATen/ops/_foobar_cpu_dispatch.h> |
| 121 | #include <ATen/ops/_foobar_native.h> |
| 122 | #include <ATen/ops/_foreach_abs_cpu_dispatch.h> |
| 123 | #include <ATen/ops/_foreach_abs_native.h> |
| 124 | #include <ATen/ops/_foreach_acos_cpu_dispatch.h> |
| 125 | #include <ATen/ops/_foreach_acos_native.h> |
| 126 | #include <ATen/ops/_foreach_add_cpu_dispatch.h> |
| 127 | #include <ATen/ops/_foreach_add_native.h> |
| 128 | #include <ATen/ops/_foreach_addcdiv_cpu_dispatch.h> |
| 129 | #include <ATen/ops/_foreach_addcdiv_native.h> |
| 130 | #include <ATen/ops/_foreach_addcmul_cpu_dispatch.h> |
| 131 | #include <ATen/ops/_foreach_addcmul_native.h> |
| 132 | #include <ATen/ops/_foreach_asin_cpu_dispatch.h> |
| 133 | #include <ATen/ops/_foreach_asin_native.h> |
| 134 | #include <ATen/ops/_foreach_atan_cpu_dispatch.h> |
| 135 | #include <ATen/ops/_foreach_atan_native.h> |
| 136 | #include <ATen/ops/_foreach_ceil_cpu_dispatch.h> |
| 137 | #include <ATen/ops/_foreach_ceil_native.h> |
| 138 | #include <ATen/ops/_foreach_clamp_max_cpu_dispatch.h> |
| 139 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 140 | #include <ATen/ops/_foreach_clamp_min_cpu_dispatch.h> |
| 141 | #include <ATen/ops/_foreach_clamp_min_native.h> |
| 142 | #include <ATen/ops/_foreach_cos_cpu_dispatch.h> |
| 143 | #include <ATen/ops/_foreach_cos_native.h> |
| 144 | #include <ATen/ops/_foreach_cosh_cpu_dispatch.h> |
| 145 | #include <ATen/ops/_foreach_cosh_native.h> |
| 146 | #include <ATen/ops/_foreach_div_cpu_dispatch.h> |
| 147 | #include <ATen/ops/_foreach_div_native.h> |
| 148 | #include <ATen/ops/_foreach_erf_cpu_dispatch.h> |
| 149 | #include <ATen/ops/_foreach_erf_native.h> |
| 150 | #include <ATen/ops/_foreach_erfc_cpu_dispatch.h> |
| 151 | #include <ATen/ops/_foreach_erfc_native.h> |
| 152 | #include <ATen/ops/_foreach_exp_cpu_dispatch.h> |
| 153 | #include <ATen/ops/_foreach_exp_native.h> |
| 154 | #include <ATen/ops/_foreach_expm1_cpu_dispatch.h> |
| 155 | #include <ATen/ops/_foreach_expm1_native.h> |
| 156 | #include <ATen/ops/_foreach_floor_cpu_dispatch.h> |
| 157 | #include <ATen/ops/_foreach_floor_native.h> |
| 158 | #include <ATen/ops/_foreach_frac_cpu_dispatch.h> |
| 159 | #include <ATen/ops/_foreach_frac_native.h> |
| 160 | #include <ATen/ops/_foreach_lerp_cpu_dispatch.h> |
| 161 | #include <ATen/ops/_foreach_lerp_native.h> |
| 162 | #include <ATen/ops/_foreach_lgamma_cpu_dispatch.h> |
| 163 | #include <ATen/ops/_foreach_lgamma_native.h> |
| 164 | #include <ATen/ops/_foreach_log10_cpu_dispatch.h> |
| 165 | #include <ATen/ops/_foreach_log10_native.h> |
| 166 | #include <ATen/ops/_foreach_log1p_cpu_dispatch.h> |
| 167 | #include <ATen/ops/_foreach_log1p_native.h> |
| 168 | #include <ATen/ops/_foreach_log2_cpu_dispatch.h> |
| 169 | #include <ATen/ops/_foreach_log2_native.h> |
| 170 | #include <ATen/ops/_foreach_log_cpu_dispatch.h> |
| 171 | #include <ATen/ops/_foreach_log_native.h> |
| 172 | #include <ATen/ops/_foreach_maximum_cpu_dispatch.h> |
| 173 | #include <ATen/ops/_foreach_maximum_native.h> |
| 174 | #include <ATen/ops/_foreach_minimum_cpu_dispatch.h> |
| 175 | #include <ATen/ops/_foreach_minimum_native.h> |
| 176 | #include <ATen/ops/_foreach_mul_cpu_dispatch.h> |
| 177 | #include <ATen/ops/_foreach_mul_native.h> |
| 178 | #include <ATen/ops/_foreach_neg_cpu_dispatch.h> |
| 179 | #include <ATen/ops/_foreach_neg_native.h> |
| 180 | #include <ATen/ops/_foreach_norm_cpu_dispatch.h> |
| 181 | #include <ATen/ops/_foreach_norm_native.h> |
| 182 | #include <ATen/ops/_foreach_reciprocal_cpu_dispatch.h> |
| 183 | #include <ATen/ops/_foreach_reciprocal_native.h> |
| 184 | #include <ATen/ops/_foreach_round_cpu_dispatch.h> |
| 185 | #include <ATen/ops/_foreach_round_native.h> |
| 186 | #include <ATen/ops/_foreach_sigmoid_cpu_dispatch.h> |
| 187 | #include <ATen/ops/_foreach_sigmoid_native.h> |
| 188 | #include <ATen/ops/_foreach_sin_cpu_dispatch.h> |
| 189 | #include <ATen/ops/_foreach_sin_native.h> |
| 190 | #include <ATen/ops/_foreach_sinh_cpu_dispatch.h> |
| 191 | #include <ATen/ops/_foreach_sinh_native.h> |
| 192 | #include <ATen/ops/_foreach_sqrt_cpu_dispatch.h> |
| 193 | #include <ATen/ops/_foreach_sqrt_native.h> |
| 194 | #include <ATen/ops/_foreach_sub_cpu_dispatch.h> |
| 195 | #include <ATen/ops/_foreach_sub_native.h> |
| 196 | #include <ATen/ops/_foreach_tan_cpu_dispatch.h> |
| 197 | #include <ATen/ops/_foreach_tan_native.h> |
| 198 | #include <ATen/ops/_foreach_tanh_cpu_dispatch.h> |
| 199 | #include <ATen/ops/_foreach_tanh_native.h> |
| 200 | #include <ATen/ops/_foreach_trunc_cpu_dispatch.h> |
| 201 | #include <ATen/ops/_foreach_trunc_native.h> |
| 202 | #include <ATen/ops/_foreach_zero_cpu_dispatch.h> |
| 203 | #include <ATen/ops/_foreach_zero_native.h> |
| 204 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_cpu_dispatch.h> |
| 205 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_native.h> |
| 206 | #include <ATen/ops/_fused_sdp_choice_cpu_dispatch.h> |
| 207 | #include <ATen/ops/_fused_sdp_choice_native.h> |
| 208 | #include <ATen/ops/_histogramdd_bin_edges_cpu_dispatch.h> |
| 209 | #include <ATen/ops/_histogramdd_bin_edges_native.h> |
| 210 | #include <ATen/ops/_histogramdd_from_bin_cts_cpu_dispatch.h> |
| 211 | #include <ATen/ops/_histogramdd_from_bin_cts_native.h> |
| 212 | #include <ATen/ops/_histogramdd_from_bin_tensors_cpu_dispatch.h> |
| 213 | #include <ATen/ops/_histogramdd_from_bin_tensors_native.h> |
| 214 | #include <ATen/ops/_index_put_impl_cpu_dispatch.h> |
| 215 | #include <ATen/ops/_index_put_impl_native.h> |
| 216 | #include <ATen/ops/_linalg_det_cpu_dispatch.h> |
| 217 | #include <ATen/ops/_linalg_det_native.h> |
| 218 | #include <ATen/ops/_linalg_eigh_cpu_dispatch.h> |
| 219 | #include <ATen/ops/_linalg_eigh_native.h> |
| 220 | #include <ATen/ops/_linalg_slogdet_cpu_dispatch.h> |
| 221 | #include <ATen/ops/_linalg_slogdet_native.h> |
| 222 | #include <ATen/ops/_linalg_solve_ex_cpu_dispatch.h> |
| 223 | #include <ATen/ops/_linalg_solve_ex_native.h> |
| 224 | #include <ATen/ops/_linalg_svd_cpu_dispatch.h> |
| 225 | #include <ATen/ops/_linalg_svd_native.h> |
| 226 | #include <ATen/ops/_local_scalar_dense_cpu_dispatch.h> |
| 227 | #include <ATen/ops/_local_scalar_dense_native.h> |
| 228 | #include <ATen/ops/_log_softmax_backward_data_cpu_dispatch.h> |
| 229 | #include <ATen/ops/_log_softmax_backward_data_native.h> |
| 230 | #include <ATen/ops/_log_softmax_cpu_dispatch.h> |
| 231 | #include <ATen/ops/_log_softmax_native.h> |
| 232 | #include <ATen/ops/_logcumsumexp_cpu_dispatch.h> |
| 233 | #include <ATen/ops/_logcumsumexp_native.h> |
| 234 | #include <ATen/ops/_make_per_channel_quantized_tensor_cpu_dispatch.h> |
| 235 | #include <ATen/ops/_make_per_channel_quantized_tensor_native.h> |
| 236 | #include <ATen/ops/_make_per_tensor_quantized_tensor_cpu_dispatch.h> |
| 237 | #include <ATen/ops/_make_per_tensor_quantized_tensor_native.h> |
| 238 | #include <ATen/ops/_masked_softmax_backward_cpu_dispatch.h> |
| 239 | #include <ATen/ops/_masked_softmax_backward_native.h> |
| 240 | #include <ATen/ops/_masked_softmax_cpu_dispatch.h> |
| 241 | #include <ATen/ops/_masked_softmax_native.h> |
| 242 | #include <ATen/ops/_native_batch_norm_legit_cpu_dispatch.h> |
| 243 | #include <ATen/ops/_native_batch_norm_legit_native.h> |
| 244 | #include <ATen/ops/_native_decoder_only_multi_head_attention_cpu_dispatch.h> |
| 245 | #include <ATen/ops/_native_decoder_only_multi_head_attention_native.h> |
| 246 | #include <ATen/ops/_native_multi_head_attention_cpu_dispatch.h> |
| 247 | #include <ATen/ops/_native_multi_head_attention_native.h> |
| 248 | #include <ATen/ops/_nested_from_padded_cpu_dispatch.h> |
| 249 | #include <ATen/ops/_nested_from_padded_native.h> |
| 250 | #include <ATen/ops/_nested_tensor_from_mask_cpu_dispatch.h> |
| 251 | #include <ATen/ops/_nested_tensor_from_mask_left_aligned_cpu_dispatch.h> |
| 252 | #include <ATen/ops/_nested_tensor_from_mask_left_aligned_native.h> |
| 253 | #include <ATen/ops/_nested_tensor_from_mask_native.h> |
| 254 | #include <ATen/ops/_nested_view_from_buffer_cpu_dispatch.h> |
| 255 | #include <ATen/ops/_nested_view_from_buffer_native.h> |
| 256 | #include <ATen/ops/_pdist_backward_cpu_dispatch.h> |
| 257 | #include <ATen/ops/_pdist_backward_native.h> |
| 258 | #include <ATen/ops/_pdist_forward_cpu_dispatch.h> |
| 259 | #include <ATen/ops/_pdist_forward_native.h> |
| 260 | #include <ATen/ops/_prelu_kernel_backward_cpu_dispatch.h> |
| 261 | #include <ATen/ops/_prelu_kernel_backward_native.h> |
| 262 | #include <ATen/ops/_prelu_kernel_cpu_dispatch.h> |
| 263 | #include <ATen/ops/_prelu_kernel_native.h> |
| 264 | #include <ATen/ops/_reshape_alias_cpu_dispatch.h> |
| 265 | #include <ATen/ops/_reshape_alias_native.h> |
| 266 | #include <ATen/ops/_sample_dirichlet_cpu_dispatch.h> |
| 267 | #include <ATen/ops/_sample_dirichlet_native.h> |
| 268 | #include <ATen/ops/_segment_reduce_backward_cpu_dispatch.h> |
| 269 | #include <ATen/ops/_segment_reduce_backward_native.h> |
| 270 | #include <ATen/ops/_slow_conv2d_backward_cpu_dispatch.h> |
| 271 | #include <ATen/ops/_slow_conv2d_backward_native.h> |
| 272 | #include <ATen/ops/_slow_conv2d_forward_cpu_dispatch.h> |
| 273 | #include <ATen/ops/_slow_conv2d_forward_native.h> |
| 274 | #include <ATen/ops/_softmax_backward_data_cpu_dispatch.h> |
| 275 | #include <ATen/ops/_softmax_backward_data_native.h> |
| 276 | #include <ATen/ops/_softmax_cpu_dispatch.h> |
| 277 | #include <ATen/ops/_softmax_native.h> |
| 278 | #include <ATen/ops/_spdiags_cpu_dispatch.h> |
| 279 | #include <ATen/ops/_spdiags_native.h> |
| 280 | #include <ATen/ops/_stack_cpu_dispatch.h> |
| 281 | #include <ATen/ops/_stack_native.h> |
| 282 | #include <ATen/ops/_standard_gamma_cpu_dispatch.h> |
| 283 | #include <ATen/ops/_standard_gamma_grad_cpu_dispatch.h> |
| 284 | #include <ATen/ops/_standard_gamma_grad_native.h> |
| 285 | #include <ATen/ops/_standard_gamma_native.h> |
| 286 | #include <ATen/ops/_test_optional_filled_intlist_cpu_dispatch.h> |
| 287 | #include <ATen/ops/_test_optional_filled_intlist_native.h> |
| 288 | #include <ATen/ops/_test_optional_floatlist_cpu_dispatch.h> |
| 289 | #include <ATen/ops/_test_optional_floatlist_native.h> |
| 290 | #include <ATen/ops/_test_optional_intlist_cpu_dispatch.h> |
| 291 | #include <ATen/ops/_test_optional_intlist_native.h> |
| 292 | #include <ATen/ops/_transform_bias_rescale_qkv_cpu_dispatch.h> |
| 293 | #include <ATen/ops/_transform_bias_rescale_qkv_native.h> |
| 294 | #include <ATen/ops/_transformer_decoder_only_layer_fwd_cpu_dispatch.h> |
| 295 | #include <ATen/ops/_transformer_decoder_only_layer_fwd_native.h> |
| 296 | #include <ATen/ops/_transformer_encoder_layer_fwd_cpu_dispatch.h> |
| 297 | #include <ATen/ops/_transformer_encoder_layer_fwd_native.h> |
| 298 | #include <ATen/ops/_unique2_cpu_dispatch.h> |
| 299 | #include <ATen/ops/_unique2_native.h> |
| 300 | #include <ATen/ops/_unique_cpu_dispatch.h> |
| 301 | #include <ATen/ops/_unique_native.h> |
| 302 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_cpu_dispatch.h> |
| 303 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_native.h> |
| 304 | #include <ATen/ops/_upsample_bicubic2d_aa_cpu_dispatch.h> |
| 305 | #include <ATen/ops/_upsample_bicubic2d_aa_native.h> |
| 306 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_cpu_dispatch.h> |
| 307 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_native.h> |
| 308 | #include <ATen/ops/_upsample_bilinear2d_aa_cpu_dispatch.h> |
| 309 | #include <ATen/ops/_upsample_bilinear2d_aa_native.h> |
| 310 | #include <ATen/ops/_upsample_nearest_exact1d_backward_cpu_dispatch.h> |
| 311 | #include <ATen/ops/_upsample_nearest_exact1d_backward_native.h> |
| 312 | #include <ATen/ops/_upsample_nearest_exact1d_cpu_dispatch.h> |
| 313 | #include <ATen/ops/_upsample_nearest_exact1d_native.h> |
| 314 | #include <ATen/ops/_upsample_nearest_exact2d_backward_cpu_dispatch.h> |
| 315 | #include <ATen/ops/_upsample_nearest_exact2d_backward_native.h> |
| 316 | #include <ATen/ops/_upsample_nearest_exact2d_cpu_dispatch.h> |
| 317 | #include <ATen/ops/_upsample_nearest_exact2d_native.h> |
| 318 | #include <ATen/ops/_upsample_nearest_exact3d_backward_cpu_dispatch.h> |
| 319 | #include <ATen/ops/_upsample_nearest_exact3d_backward_native.h> |
| 320 | #include <ATen/ops/_upsample_nearest_exact3d_cpu_dispatch.h> |
| 321 | #include <ATen/ops/_upsample_nearest_exact3d_native.h> |
| 322 | #include <ATen/ops/_validate_compressed_sparse_indices_cpu_dispatch.h> |
| 323 | #include <ATen/ops/_validate_compressed_sparse_indices_native.h> |
| 324 | #include <ATen/ops/_weight_norm_interface_backward_cpu_dispatch.h> |
| 325 | #include <ATen/ops/_weight_norm_interface_backward_native.h> |
| 326 | #include <ATen/ops/_weight_norm_interface_cpu_dispatch.h> |
| 327 | #include <ATen/ops/_weight_norm_interface_native.h> |
| 328 | #include <ATen/ops/abs_cpu_dispatch.h> |
| 329 | #include <ATen/ops/abs_native.h> |
| 330 | #include <ATen/ops/acos_cpu_dispatch.h> |
| 331 | #include <ATen/ops/acos_native.h> |
| 332 | #include <ATen/ops/acosh_cpu_dispatch.h> |
| 333 | #include <ATen/ops/acosh_native.h> |
| 334 | #include <ATen/ops/adaptive_avg_pool2d_cpu_dispatch.h> |
| 335 | #include <ATen/ops/adaptive_avg_pool2d_native.h> |
| 336 | #include <ATen/ops/adaptive_avg_pool3d_backward_cpu_dispatch.h> |
| 337 | #include <ATen/ops/adaptive_avg_pool3d_backward_native.h> |
| 338 | #include <ATen/ops/adaptive_avg_pool3d_cpu_dispatch.h> |
| 339 | #include <ATen/ops/adaptive_avg_pool3d_native.h> |
| 340 | #include <ATen/ops/adaptive_max_pool2d_backward_cpu_dispatch.h> |
| 341 | #include <ATen/ops/adaptive_max_pool2d_backward_native.h> |
| 342 | #include <ATen/ops/adaptive_max_pool2d_cpu_dispatch.h> |
| 343 | #include <ATen/ops/adaptive_max_pool2d_native.h> |
| 344 | #include <ATen/ops/adaptive_max_pool3d_backward_cpu_dispatch.h> |
| 345 | #include <ATen/ops/adaptive_max_pool3d_backward_native.h> |
| 346 | #include <ATen/ops/adaptive_max_pool3d_cpu_dispatch.h> |
| 347 | #include <ATen/ops/adaptive_max_pool3d_native.h> |
| 348 | #include <ATen/ops/add_cpu_dispatch.h> |
| 349 | #include <ATen/ops/add_native.h> |
| 350 | #include <ATen/ops/addbmm_cpu_dispatch.h> |
| 351 | #include <ATen/ops/addbmm_native.h> |
| 352 | #include <ATen/ops/addcdiv_cpu_dispatch.h> |
| 353 | #include <ATen/ops/addcdiv_native.h> |
| 354 | #include <ATen/ops/addcmul_cpu_dispatch.h> |
| 355 | #include <ATen/ops/addcmul_native.h> |
| 356 | #include <ATen/ops/addmm_cpu_dispatch.h> |
| 357 | #include <ATen/ops/addmm_native.h> |
| 358 | #include <ATen/ops/addmv_cpu_dispatch.h> |
| 359 | #include <ATen/ops/addmv_native.h> |
| 360 | #include <ATen/ops/addr_cpu_dispatch.h> |
| 361 | #include <ATen/ops/addr_native.h> |
| 362 | #include <ATen/ops/all_cpu_dispatch.h> |
| 363 | #include <ATen/ops/all_native.h> |
| 364 | #include <ATen/ops/amax_cpu_dispatch.h> |
| 365 | #include <ATen/ops/amax_native.h> |
| 366 | #include <ATen/ops/amin_cpu_dispatch.h> |
| 367 | #include <ATen/ops/amin_native.h> |
| 368 | #include <ATen/ops/aminmax_cpu_dispatch.h> |
| 369 | #include <ATen/ops/aminmax_native.h> |
| 370 | #include <ATen/ops/angle_cpu_dispatch.h> |
| 371 | #include <ATen/ops/angle_native.h> |
| 372 | #include <ATen/ops/any_cpu_dispatch.h> |
| 373 | #include <ATen/ops/any_native.h> |
| 374 | #include <ATen/ops/arange_cpu_dispatch.h> |
| 375 | #include <ATen/ops/arange_native.h> |
| 376 | #include <ATen/ops/argmax_cpu_dispatch.h> |
| 377 | #include <ATen/ops/argmax_native.h> |
| 378 | #include <ATen/ops/argmin_cpu_dispatch.h> |
| 379 | #include <ATen/ops/argmin_native.h> |
| 380 | #include <ATen/ops/argsort_cpu_dispatch.h> |
| 381 | #include <ATen/ops/argsort_native.h> |
| 382 | #include <ATen/ops/as_strided_cpu_dispatch.h> |
| 383 | #include <ATen/ops/as_strided_native.h> |
| 384 | #include <ATen/ops/asin_cpu_dispatch.h> |
| 385 | #include <ATen/ops/asin_native.h> |
| 386 | #include <ATen/ops/asinh_cpu_dispatch.h> |
| 387 | #include <ATen/ops/asinh_native.h> |
| 388 | #include <ATen/ops/atan2_cpu_dispatch.h> |
| 389 | #include <ATen/ops/atan2_native.h> |
| 390 | #include <ATen/ops/atan_cpu_dispatch.h> |
| 391 | #include <ATen/ops/atan_native.h> |
| 392 | #include <ATen/ops/atanh_cpu_dispatch.h> |
| 393 | #include <ATen/ops/atanh_native.h> |
| 394 | #include <ATen/ops/avg_pool2d_backward_cpu_dispatch.h> |
| 395 | #include <ATen/ops/avg_pool2d_backward_native.h> |
| 396 | #include <ATen/ops/avg_pool2d_cpu_dispatch.h> |
| 397 | #include <ATen/ops/avg_pool2d_native.h> |
| 398 | #include <ATen/ops/avg_pool3d_backward_cpu_dispatch.h> |
| 399 | #include <ATen/ops/avg_pool3d_backward_native.h> |
| 400 | #include <ATen/ops/avg_pool3d_cpu_dispatch.h> |
| 401 | #include <ATen/ops/avg_pool3d_native.h> |
| 402 | #include <ATen/ops/baddbmm_cpu_dispatch.h> |
| 403 | #include <ATen/ops/baddbmm_native.h> |
| 404 | #include <ATen/ops/batch_norm_update_stats_cpu_dispatch.h> |
| 405 | #include <ATen/ops/batch_norm_update_stats_native.h> |
| 406 | #include <ATen/ops/bernoulli_cpu_dispatch.h> |
| 407 | #include <ATen/ops/bernoulli_native.h> |
| 408 | #include <ATen/ops/binary_cross_entropy_backward_cpu_dispatch.h> |
| 409 | #include <ATen/ops/binary_cross_entropy_backward_native.h> |
| 410 | #include <ATen/ops/binary_cross_entropy_cpu_dispatch.h> |
| 411 | #include <ATen/ops/binary_cross_entropy_native.h> |
| 412 | #include <ATen/ops/bincount_cpu_dispatch.h> |
| 413 | #include <ATen/ops/bincount_native.h> |
| 414 | #include <ATen/ops/binomial_cpu_dispatch.h> |
| 415 | #include <ATen/ops/binomial_native.h> |
| 416 | #include <ATen/ops/bitwise_and_cpu_dispatch.h> |
| 417 | #include <ATen/ops/bitwise_and_native.h> |
| 418 | #include <ATen/ops/bitwise_left_shift_cpu_dispatch.h> |
| 419 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 420 | #include <ATen/ops/bitwise_not_cpu_dispatch.h> |
| 421 | #include <ATen/ops/bitwise_not_native.h> |
| 422 | #include <ATen/ops/bitwise_or_cpu_dispatch.h> |
| 423 | #include <ATen/ops/bitwise_or_native.h> |
| 424 | #include <ATen/ops/bitwise_right_shift_cpu_dispatch.h> |
| 425 | #include <ATen/ops/bitwise_right_shift_native.h> |
| 426 | #include <ATen/ops/bitwise_xor_cpu_dispatch.h> |
| 427 | #include <ATen/ops/bitwise_xor_native.h> |
| 428 | #include <ATen/ops/bmm_cpu_dispatch.h> |
| 429 | #include <ATen/ops/bmm_native.h> |
| 430 | #include <ATen/ops/bucketize_cpu_dispatch.h> |
| 431 | #include <ATen/ops/bucketize_native.h> |
| 432 | #include <ATen/ops/cat_cpu_dispatch.h> |
| 433 | #include <ATen/ops/cat_native.h> |
| 434 | #include <ATen/ops/cauchy_cpu_dispatch.h> |
| 435 | #include <ATen/ops/cauchy_native.h> |
| 436 | #include <ATen/ops/ceil_cpu_dispatch.h> |
| 437 | #include <ATen/ops/ceil_native.h> |
| 438 | #include <ATen/ops/channel_shuffle_cpu_dispatch.h> |
| 439 | #include <ATen/ops/channel_shuffle_native.h> |
| 440 | #include <ATen/ops/cholesky_cpu_dispatch.h> |
| 441 | #include <ATen/ops/cholesky_inverse_cpu_dispatch.h> |
| 442 | #include <ATen/ops/cholesky_inverse_native.h> |
| 443 | #include <ATen/ops/cholesky_native.h> |
| 444 | #include <ATen/ops/clamp_cpu_dispatch.h> |
| 445 | #include <ATen/ops/clamp_max_cpu_dispatch.h> |
| 446 | #include <ATen/ops/clamp_max_native.h> |
| 447 | #include <ATen/ops/clamp_min_cpu_dispatch.h> |
| 448 | #include <ATen/ops/clamp_min_native.h> |
| 449 | #include <ATen/ops/clamp_native.h> |
| 450 | #include <ATen/ops/col2im_cpu_dispatch.h> |
| 451 | #include <ATen/ops/col2im_native.h> |
| 452 | #include <ATen/ops/complex_cpu_dispatch.h> |
| 453 | #include <ATen/ops/complex_native.h> |
| 454 | #include <ATen/ops/conj_physical_cpu_dispatch.h> |
| 455 | #include <ATen/ops/conj_physical_native.h> |
| 456 | #include <ATen/ops/copysign_cpu_dispatch.h> |
| 457 | #include <ATen/ops/copysign_native.h> |
| 458 | #include <ATen/ops/cos_cpu_dispatch.h> |
| 459 | #include <ATen/ops/cos_native.h> |
| 460 | #include <ATen/ops/cosh_cpu_dispatch.h> |
| 461 | #include <ATen/ops/cosh_native.h> |
| 462 | #include <ATen/ops/count_nonzero_cpu_dispatch.h> |
| 463 | #include <ATen/ops/count_nonzero_native.h> |
| 464 | #include <ATen/ops/cumprod_cpu_dispatch.h> |
| 465 | #include <ATen/ops/cumprod_native.h> |
| 466 | #include <ATen/ops/cumsum_cpu_dispatch.h> |
| 467 | #include <ATen/ops/cumsum_native.h> |
| 468 | #include <ATen/ops/dense_dim_cpu_dispatch.h> |
| 469 | #include <ATen/ops/dense_dim_native.h> |
| 470 | #include <ATen/ops/dequantize_cpu_dispatch.h> |
| 471 | #include <ATen/ops/dequantize_native.h> |
| 472 | #include <ATen/ops/digamma_cpu_dispatch.h> |
| 473 | #include <ATen/ops/digamma_native.h> |
| 474 | #include <ATen/ops/div_cpu_dispatch.h> |
| 475 | #include <ATen/ops/div_native.h> |
| 476 | #include <ATen/ops/dot_cpu_dispatch.h> |
| 477 | #include <ATen/ops/dot_native.h> |
| 478 | #include <ATen/ops/elu_backward_cpu_dispatch.h> |
| 479 | #include <ATen/ops/elu_backward_native.h> |
| 480 | #include <ATen/ops/elu_cpu_dispatch.h> |
| 481 | #include <ATen/ops/elu_native.h> |
| 482 | #include <ATen/ops/embedding_dense_backward_cpu_dispatch.h> |
| 483 | #include <ATen/ops/embedding_dense_backward_native.h> |
| 484 | #include <ATen/ops/embedding_renorm_cpu_dispatch.h> |
| 485 | #include <ATen/ops/embedding_renorm_native.h> |
| 486 | #include <ATen/ops/empty_cpu_dispatch.h> |
| 487 | #include <ATen/ops/empty_native.h> |
| 488 | #include <ATen/ops/empty_strided_cpu_dispatch.h> |
| 489 | #include <ATen/ops/empty_strided_native.h> |
| 490 | #include <ATen/ops/eq_cpu_dispatch.h> |
| 491 | #include <ATen/ops/eq_native.h> |
| 492 | #include <ATen/ops/equal_cpu_dispatch.h> |
| 493 | #include <ATen/ops/equal_native.h> |
| 494 | #include <ATen/ops/erf_cpu_dispatch.h> |
| 495 | #include <ATen/ops/erf_native.h> |
| 496 | #include <ATen/ops/erfc_cpu_dispatch.h> |
| 497 | #include <ATen/ops/erfc_native.h> |
| 498 | #include <ATen/ops/erfinv_cpu_dispatch.h> |
| 499 | #include <ATen/ops/erfinv_native.h> |
| 500 | #include <ATen/ops/exp2_cpu_dispatch.h> |
| 501 | #include <ATen/ops/exp2_native.h> |
| 502 | #include <ATen/ops/exp_cpu_dispatch.h> |
| 503 | #include <ATen/ops/exp_native.h> |
| 504 | #include <ATen/ops/expm1_cpu_dispatch.h> |
| 505 | #include <ATen/ops/expm1_native.h> |
| 506 | #include <ATen/ops/exponential_cpu_dispatch.h> |
| 507 | #include <ATen/ops/exponential_native.h> |
| 508 | #include <ATen/ops/eye_cpu_dispatch.h> |
| 509 | #include <ATen/ops/eye_native.h> |
| 510 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_cpu_dispatch.h> |
| 511 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_native.h> |
| 512 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_cpu_dispatch.h> |
| 513 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_native.h> |
| 514 | #include <ATen/ops/fill_cpu_dispatch.h> |
| 515 | #include <ATen/ops/fill_native.h> |
| 516 | #include <ATen/ops/flip_cpu_dispatch.h> |
| 517 | #include <ATen/ops/flip_native.h> |
| 518 | #include <ATen/ops/floor_cpu_dispatch.h> |
| 519 | #include <ATen/ops/floor_divide_cpu_dispatch.h> |
| 520 | #include <ATen/ops/floor_divide_native.h> |
| 521 | #include <ATen/ops/floor_native.h> |
| 522 | #include <ATen/ops/fmax_cpu_dispatch.h> |
| 523 | #include <ATen/ops/fmax_native.h> |
| 524 | #include <ATen/ops/fmin_cpu_dispatch.h> |
| 525 | #include <ATen/ops/fmin_native.h> |
| 526 | #include <ATen/ops/fmod_cpu_dispatch.h> |
| 527 | #include <ATen/ops/fmod_native.h> |
| 528 | #include <ATen/ops/frac_cpu_dispatch.h> |
| 529 | #include <ATen/ops/frac_native.h> |
| 530 | #include <ATen/ops/fractional_max_pool2d_backward_cpu_dispatch.h> |
| 531 | #include <ATen/ops/fractional_max_pool2d_backward_native.h> |
| 532 | #include <ATen/ops/fractional_max_pool2d_cpu_dispatch.h> |
| 533 | #include <ATen/ops/fractional_max_pool2d_native.h> |
| 534 | #include <ATen/ops/fractional_max_pool3d_backward_cpu_dispatch.h> |
| 535 | #include <ATen/ops/fractional_max_pool3d_backward_native.h> |
| 536 | #include <ATen/ops/fractional_max_pool3d_cpu_dispatch.h> |
| 537 | #include <ATen/ops/fractional_max_pool3d_native.h> |
| 538 | #include <ATen/ops/frexp_cpu_dispatch.h> |
| 539 | #include <ATen/ops/frexp_native.h> |
| 540 | #include <ATen/ops/from_file_cpu_dispatch.h> |
| 541 | #include <ATen/ops/from_file_native.h> |
| 542 | #include <ATen/ops/gather_cpu_dispatch.h> |
| 543 | #include <ATen/ops/gather_native.h> |
| 544 | #include <ATen/ops/gcd_cpu_dispatch.h> |
| 545 | #include <ATen/ops/gcd_native.h> |
| 546 | #include <ATen/ops/ge_cpu_dispatch.h> |
| 547 | #include <ATen/ops/ge_native.h> |
| 548 | #include <ATen/ops/gelu_backward_cpu_dispatch.h> |
| 549 | #include <ATen/ops/gelu_backward_native.h> |
| 550 | #include <ATen/ops/gelu_cpu_dispatch.h> |
| 551 | #include <ATen/ops/gelu_native.h> |
| 552 | #include <ATen/ops/geometric_cpu_dispatch.h> |
| 553 | #include <ATen/ops/geometric_native.h> |
| 554 | #include <ATen/ops/geqrf_cpu_dispatch.h> |
| 555 | #include <ATen/ops/geqrf_native.h> |
| 556 | #include <ATen/ops/glu_backward_cpu_dispatch.h> |
| 557 | #include <ATen/ops/glu_backward_jvp_cpu_dispatch.h> |
| 558 | #include <ATen/ops/glu_backward_jvp_native.h> |
| 559 | #include <ATen/ops/glu_backward_native.h> |
| 560 | #include <ATen/ops/glu_cpu_dispatch.h> |
| 561 | #include <ATen/ops/glu_jvp_cpu_dispatch.h> |
| 562 | #include <ATen/ops/glu_jvp_native.h> |
| 563 | #include <ATen/ops/glu_native.h> |
| 564 | #include <ATen/ops/grid_sampler_2d_backward_cpu_dispatch.h> |
| 565 | #include <ATen/ops/grid_sampler_2d_backward_native.h> |
| 566 | #include <ATen/ops/grid_sampler_2d_cpu_dispatch.h> |
| 567 | #include <ATen/ops/grid_sampler_2d_native.h> |
| 568 | #include <ATen/ops/grid_sampler_3d_backward_cpu_dispatch.h> |
| 569 | #include <ATen/ops/grid_sampler_3d_backward_native.h> |
| 570 | #include <ATen/ops/grid_sampler_3d_cpu_dispatch.h> |
| 571 | #include <ATen/ops/grid_sampler_3d_native.h> |
| 572 | #include <ATen/ops/gt_cpu_dispatch.h> |
| 573 | #include <ATen/ops/gt_native.h> |
| 574 | #include <ATen/ops/hardshrink_backward_cpu_dispatch.h> |
| 575 | #include <ATen/ops/hardshrink_backward_native.h> |
| 576 | #include <ATen/ops/hardshrink_cpu_dispatch.h> |
| 577 | #include <ATen/ops/hardshrink_native.h> |
| 578 | #include <ATen/ops/hardsigmoid_backward_cpu_dispatch.h> |
| 579 | #include <ATen/ops/hardsigmoid_backward_native.h> |
| 580 | #include <ATen/ops/hardsigmoid_cpu_dispatch.h> |
| 581 | #include <ATen/ops/hardsigmoid_native.h> |
| 582 | #include <ATen/ops/hardswish_backward_cpu_dispatch.h> |
| 583 | #include <ATen/ops/hardswish_backward_native.h> |
| 584 | #include <ATen/ops/hardswish_cpu_dispatch.h> |
| 585 | #include <ATen/ops/hardswish_native.h> |
| 586 | #include <ATen/ops/hardtanh_backward_cpu_dispatch.h> |
| 587 | #include <ATen/ops/hardtanh_backward_native.h> |
| 588 | #include <ATen/ops/hardtanh_cpu_dispatch.h> |
| 589 | #include <ATen/ops/hardtanh_native.h> |
| 590 | #include <ATen/ops/heaviside_cpu_dispatch.h> |
| 591 | #include <ATen/ops/heaviside_native.h> |
| 592 | #include <ATen/ops/histc_cpu_dispatch.h> |
| 593 | #include <ATen/ops/histc_native.h> |
| 594 | #include <ATen/ops/histogram_cpu_dispatch.h> |
| 595 | #include <ATen/ops/histogram_native.h> |
| 596 | #include <ATen/ops/huber_loss_backward_cpu_dispatch.h> |
| 597 | #include <ATen/ops/huber_loss_backward_native.h> |
| 598 | #include <ATen/ops/huber_loss_cpu_dispatch.h> |
| 599 | #include <ATen/ops/huber_loss_native.h> |
| 600 | #include <ATen/ops/hypot_cpu_dispatch.h> |
| 601 | #include <ATen/ops/hypot_native.h> |
| 602 | #include <ATen/ops/i0_cpu_dispatch.h> |
| 603 | #include <ATen/ops/i0_native.h> |
| 604 | #include <ATen/ops/igamma_cpu_dispatch.h> |
| 605 | #include <ATen/ops/igamma_native.h> |
| 606 | #include <ATen/ops/igammac_cpu_dispatch.h> |
| 607 | #include <ATen/ops/igammac_native.h> |
| 608 | #include <ATen/ops/im2col_cpu_dispatch.h> |
| 609 | #include <ATen/ops/im2col_native.h> |
| 610 | #include <ATen/ops/index_add_cpu_dispatch.h> |
| 611 | #include <ATen/ops/index_add_native.h> |
| 612 | #include <ATen/ops/index_copy_cpu_dispatch.h> |
| 613 | #include <ATen/ops/index_copy_native.h> |
| 614 | #include <ATen/ops/index_cpu_dispatch.h> |
| 615 | #include <ATen/ops/index_fill_cpu_dispatch.h> |
| 616 | #include <ATen/ops/index_fill_native.h> |
| 617 | #include <ATen/ops/index_native.h> |
| 618 | #include <ATen/ops/index_reduce_cpu_dispatch.h> |
| 619 | #include <ATen/ops/index_reduce_native.h> |
| 620 | #include <ATen/ops/index_select_cpu_dispatch.h> |
| 621 | #include <ATen/ops/index_select_native.h> |
| 622 | #include <ATen/ops/is_set_to_cpu_dispatch.h> |
| 623 | #include <ATen/ops/is_set_to_native.h> |
| 624 | #include <ATen/ops/isin_cpu_dispatch.h> |
| 625 | #include <ATen/ops/isin_native.h> |
| 626 | #include <ATen/ops/isnan_cpu_dispatch.h> |
| 627 | #include <ATen/ops/isnan_native.h> |
| 628 | #include <ATen/ops/isneginf_cpu_dispatch.h> |
| 629 | #include <ATen/ops/isneginf_native.h> |
| 630 | #include <ATen/ops/isposinf_cpu_dispatch.h> |
| 631 | #include <ATen/ops/isposinf_native.h> |
| 632 | #include <ATen/ops/kthvalue_cpu_dispatch.h> |
| 633 | #include <ATen/ops/kthvalue_native.h> |
| 634 | #include <ATen/ops/lcm_cpu_dispatch.h> |
| 635 | #include <ATen/ops/lcm_native.h> |
| 636 | #include <ATen/ops/le_cpu_dispatch.h> |
| 637 | #include <ATen/ops/le_native.h> |
| 638 | #include <ATen/ops/leaky_relu_backward_cpu_dispatch.h> |
| 639 | #include <ATen/ops/leaky_relu_backward_native.h> |
| 640 | #include <ATen/ops/leaky_relu_cpu_dispatch.h> |
| 641 | #include <ATen/ops/leaky_relu_native.h> |
| 642 | #include <ATen/ops/lerp_cpu_dispatch.h> |
| 643 | #include <ATen/ops/lerp_native.h> |
| 644 | #include <ATen/ops/lgamma_cpu_dispatch.h> |
| 645 | #include <ATen/ops/lgamma_native.h> |
| 646 | #include <ATen/ops/linalg_cholesky_ex_cpu_dispatch.h> |
| 647 | #include <ATen/ops/linalg_cholesky_ex_native.h> |
| 648 | #include <ATen/ops/linalg_cross_cpu_dispatch.h> |
| 649 | #include <ATen/ops/linalg_cross_native.h> |
| 650 | #include <ATen/ops/linalg_eig_cpu_dispatch.h> |
| 651 | #include <ATen/ops/linalg_eig_native.h> |
| 652 | #include <ATen/ops/linalg_householder_product_cpu_dispatch.h> |
| 653 | #include <ATen/ops/linalg_householder_product_native.h> |
| 654 | #include <ATen/ops/linalg_inv_ex_cpu_dispatch.h> |
| 655 | #include <ATen/ops/linalg_inv_ex_native.h> |
| 656 | #include <ATen/ops/linalg_ldl_factor_ex_cpu_dispatch.h> |
| 657 | #include <ATen/ops/linalg_ldl_factor_ex_native.h> |
| 658 | #include <ATen/ops/linalg_ldl_solve_cpu_dispatch.h> |
| 659 | #include <ATen/ops/linalg_ldl_solve_native.h> |
| 660 | #include <ATen/ops/linalg_lstsq_cpu_dispatch.h> |
| 661 | #include <ATen/ops/linalg_lstsq_native.h> |
| 662 | #include <ATen/ops/linalg_lu_cpu_dispatch.h> |
| 663 | #include <ATen/ops/linalg_lu_factor_ex_cpu_dispatch.h> |
| 664 | #include <ATen/ops/linalg_lu_factor_ex_native.h> |
| 665 | #include <ATen/ops/linalg_lu_native.h> |
| 666 | #include <ATen/ops/linalg_lu_solve_cpu_dispatch.h> |
| 667 | #include <ATen/ops/linalg_lu_solve_native.h> |
| 668 | #include <ATen/ops/linalg_matrix_exp_cpu_dispatch.h> |
| 669 | #include <ATen/ops/linalg_matrix_exp_native.h> |
| 670 | #include <ATen/ops/linalg_qr_cpu_dispatch.h> |
| 671 | #include <ATen/ops/linalg_qr_native.h> |
| 672 | #include <ATen/ops/linalg_solve_triangular_cpu_dispatch.h> |
| 673 | #include <ATen/ops/linalg_solve_triangular_native.h> |
| 674 | #include <ATen/ops/linalg_vector_norm_cpu_dispatch.h> |
| 675 | #include <ATen/ops/linalg_vector_norm_native.h> |
| 676 | #include <ATen/ops/linspace_cpu_dispatch.h> |
| 677 | #include <ATen/ops/linspace_native.h> |
| 678 | #include <ATen/ops/log10_cpu_dispatch.h> |
| 679 | #include <ATen/ops/log10_native.h> |
| 680 | #include <ATen/ops/log1p_cpu_dispatch.h> |
| 681 | #include <ATen/ops/log1p_native.h> |
| 682 | #include <ATen/ops/log2_cpu_dispatch.h> |
| 683 | #include <ATen/ops/log2_native.h> |
| 684 | #include <ATen/ops/log_cpu_dispatch.h> |
| 685 | #include <ATen/ops/log_native.h> |
| 686 | #include <ATen/ops/log_normal_cpu_dispatch.h> |
| 687 | #include <ATen/ops/log_normal_native.h> |
| 688 | #include <ATen/ops/log_sigmoid_backward_cpu_dispatch.h> |
| 689 | #include <ATen/ops/log_sigmoid_backward_native.h> |
| 690 | #include <ATen/ops/log_sigmoid_forward_cpu_dispatch.h> |
| 691 | #include <ATen/ops/log_sigmoid_forward_native.h> |
| 692 | #include <ATen/ops/logaddexp2_cpu_dispatch.h> |
| 693 | #include <ATen/ops/logaddexp2_native.h> |
| 694 | #include <ATen/ops/logaddexp_cpu_dispatch.h> |
| 695 | #include <ATen/ops/logaddexp_native.h> |
| 696 | #include <ATen/ops/logical_and_cpu_dispatch.h> |
| 697 | #include <ATen/ops/logical_and_native.h> |
| 698 | #include <ATen/ops/logical_not_cpu_dispatch.h> |
| 699 | #include <ATen/ops/logical_not_native.h> |
| 700 | #include <ATen/ops/logical_or_cpu_dispatch.h> |
| 701 | #include <ATen/ops/logical_or_native.h> |
| 702 | #include <ATen/ops/logical_xor_cpu_dispatch.h> |
| 703 | #include <ATen/ops/logical_xor_native.h> |
| 704 | #include <ATen/ops/logit_backward_cpu_dispatch.h> |
| 705 | #include <ATen/ops/logit_backward_native.h> |
| 706 | #include <ATen/ops/logit_cpu_dispatch.h> |
| 707 | #include <ATen/ops/logit_native.h> |
| 708 | #include <ATen/ops/logspace_cpu_dispatch.h> |
| 709 | #include <ATen/ops/logspace_native.h> |
| 710 | #include <ATen/ops/lshift_cpu_dispatch.h> |
| 711 | #include <ATen/ops/lshift_native.h> |
| 712 | #include <ATen/ops/lt_cpu_dispatch.h> |
| 713 | #include <ATen/ops/lt_native.h> |
| 714 | #include <ATen/ops/lu_unpack_cpu_dispatch.h> |
| 715 | #include <ATen/ops/lu_unpack_native.h> |
| 716 | #include <ATen/ops/masked_fill_cpu_dispatch.h> |
| 717 | #include <ATen/ops/masked_fill_native.h> |
| 718 | #include <ATen/ops/masked_scatter_cpu_dispatch.h> |
| 719 | #include <ATen/ops/masked_scatter_native.h> |
| 720 | #include <ATen/ops/masked_select_cpu_dispatch.h> |
| 721 | #include <ATen/ops/masked_select_native.h> |
| 722 | #include <ATen/ops/max_cpu_dispatch.h> |
| 723 | #include <ATen/ops/max_native.h> |
| 724 | #include <ATen/ops/max_pool2d_with_indices_backward_cpu_dispatch.h> |
| 725 | #include <ATen/ops/max_pool2d_with_indices_backward_native.h> |
| 726 | #include <ATen/ops/max_pool2d_with_indices_cpu_dispatch.h> |
| 727 | #include <ATen/ops/max_pool2d_with_indices_native.h> |
| 728 | #include <ATen/ops/max_pool3d_with_indices_backward_cpu_dispatch.h> |
| 729 | #include <ATen/ops/max_pool3d_with_indices_backward_native.h> |
| 730 | #include <ATen/ops/max_pool3d_with_indices_cpu_dispatch.h> |
| 731 | #include <ATen/ops/max_pool3d_with_indices_native.h> |
| 732 | #include <ATen/ops/max_unpool2d_cpu_dispatch.h> |
| 733 | #include <ATen/ops/max_unpool2d_native.h> |
| 734 | #include <ATen/ops/max_unpool3d_cpu_dispatch.h> |
| 735 | #include <ATen/ops/max_unpool3d_native.h> |
| 736 | #include <ATen/ops/maximum_cpu_dispatch.h> |
| 737 | #include <ATen/ops/maximum_native.h> |
| 738 | #include <ATen/ops/mean_cpu_dispatch.h> |
| 739 | #include <ATen/ops/mean_native.h> |
| 740 | #include <ATen/ops/median_cpu_dispatch.h> |
| 741 | #include <ATen/ops/median_native.h> |
| 742 | #include <ATen/ops/min_cpu_dispatch.h> |
| 743 | #include <ATen/ops/min_native.h> |
| 744 | #include <ATen/ops/minimum_cpu_dispatch.h> |
| 745 | #include <ATen/ops/minimum_native.h> |
| 746 | #include <ATen/ops/mish_backward_cpu_dispatch.h> |
| 747 | #include <ATen/ops/mish_backward_native.h> |
| 748 | #include <ATen/ops/mish_cpu_dispatch.h> |
| 749 | #include <ATen/ops/mish_native.h> |
| 750 | #include <ATen/ops/mkldnn_rnn_layer_backward_cpu_dispatch.h> |
| 751 | #include <ATen/ops/mkldnn_rnn_layer_backward_native.h> |
| 752 | #include <ATen/ops/mkldnn_rnn_layer_cpu_dispatch.h> |
| 753 | #include <ATen/ops/mkldnn_rnn_layer_native.h> |
| 754 | #include <ATen/ops/mm_cpu_dispatch.h> |
| 755 | #include <ATen/ops/mm_native.h> |
| 756 | #include <ATen/ops/mode_cpu_dispatch.h> |
| 757 | #include <ATen/ops/mode_native.h> |
| 758 | #include <ATen/ops/mse_loss_backward_cpu_dispatch.h> |
| 759 | #include <ATen/ops/mse_loss_backward_native.h> |
| 760 | #include <ATen/ops/mse_loss_cpu_dispatch.h> |
| 761 | #include <ATen/ops/mse_loss_native.h> |
| 762 | #include <ATen/ops/mul_cpu_dispatch.h> |
| 763 | #include <ATen/ops/mul_native.h> |
| 764 | #include <ATen/ops/multi_margin_loss_backward_cpu_dispatch.h> |
| 765 | #include <ATen/ops/multi_margin_loss_backward_native.h> |
| 766 | #include <ATen/ops/multi_margin_loss_cpu_dispatch.h> |
| 767 | #include <ATen/ops/multi_margin_loss_native.h> |
| 768 | #include <ATen/ops/multilabel_margin_loss_backward_cpu_dispatch.h> |
| 769 | #include <ATen/ops/multilabel_margin_loss_backward_native.h> |
| 770 | #include <ATen/ops/multilabel_margin_loss_forward_cpu_dispatch.h> |
| 771 | #include <ATen/ops/multilabel_margin_loss_forward_native.h> |
| 772 | #include <ATen/ops/multinomial_cpu_dispatch.h> |
| 773 | #include <ATen/ops/multinomial_native.h> |
| 774 | #include <ATen/ops/mvlgamma_cpu_dispatch.h> |
| 775 | #include <ATen/ops/mvlgamma_native.h> |
| 776 | #include <ATen/ops/nan_to_num_cpu_dispatch.h> |
| 777 | #include <ATen/ops/nan_to_num_native.h> |
| 778 | #include <ATen/ops/nanmedian_cpu_dispatch.h> |
| 779 | #include <ATen/ops/nanmedian_native.h> |
| 780 | #include <ATen/ops/nansum_cpu_dispatch.h> |
| 781 | #include <ATen/ops/nansum_native.h> |
| 782 | #include <ATen/ops/narrow_copy_cpu_dispatch.h> |
| 783 | #include <ATen/ops/narrow_copy_native.h> |
| 784 | #include <ATen/ops/native_batch_norm_backward_cpu_dispatch.h> |
| 785 | #include <ATen/ops/native_batch_norm_backward_native.h> |
| 786 | #include <ATen/ops/native_batch_norm_cpu_dispatch.h> |
| 787 | #include <ATen/ops/native_batch_norm_native.h> |
| 788 | #include <ATen/ops/native_channel_shuffle_cpu_dispatch.h> |
| 789 | #include <ATen/ops/native_channel_shuffle_native.h> |
| 790 | #include <ATen/ops/native_dropout_backward_cpu_dispatch.h> |
| 791 | #include <ATen/ops/native_dropout_backward_native.h> |
| 792 | #include <ATen/ops/native_dropout_cpu_dispatch.h> |
| 793 | #include <ATen/ops/native_dropout_native.h> |
| 794 | #include <ATen/ops/native_group_norm_backward_cpu_dispatch.h> |
| 795 | #include <ATen/ops/native_group_norm_backward_native.h> |
| 796 | #include <ATen/ops/native_group_norm_cpu_dispatch.h> |
| 797 | #include <ATen/ops/native_group_norm_native.h> |
| 798 | #include <ATen/ops/native_layer_norm_backward_cpu_dispatch.h> |
| 799 | #include <ATen/ops/native_layer_norm_backward_native.h> |
| 800 | #include <ATen/ops/native_layer_norm_cpu_dispatch.h> |
| 801 | #include <ATen/ops/native_layer_norm_native.h> |
| 802 | #include <ATen/ops/ne_cpu_dispatch.h> |
| 803 | #include <ATen/ops/ne_native.h> |
| 804 | #include <ATen/ops/neg_cpu_dispatch.h> |
| 805 | #include <ATen/ops/neg_native.h> |
| 806 | #include <ATen/ops/nextafter_cpu_dispatch.h> |
| 807 | #include <ATen/ops/nextafter_native.h> |
| 808 | #include <ATen/ops/nll_loss2d_backward_cpu_dispatch.h> |
| 809 | #include <ATen/ops/nll_loss2d_backward_native.h> |
| 810 | #include <ATen/ops/nll_loss2d_forward_cpu_dispatch.h> |
| 811 | #include <ATen/ops/nll_loss2d_forward_native.h> |
| 812 | #include <ATen/ops/nll_loss_backward_cpu_dispatch.h> |
| 813 | #include <ATen/ops/nll_loss_backward_native.h> |
| 814 | #include <ATen/ops/nll_loss_forward_cpu_dispatch.h> |
| 815 | #include <ATen/ops/nll_loss_forward_native.h> |
| 816 | #include <ATen/ops/nonzero_cpu_dispatch.h> |
| 817 | #include <ATen/ops/nonzero_native.h> |
| 818 | #include <ATen/ops/norm_cpu_dispatch.h> |
| 819 | #include <ATen/ops/norm_native.h> |
| 820 | #include <ATen/ops/normal_cpu_dispatch.h> |
| 821 | #include <ATen/ops/normal_native.h> |
| 822 | #include <ATen/ops/ormqr_cpu_dispatch.h> |
| 823 | #include <ATen/ops/ormqr_native.h> |
| 824 | #include <ATen/ops/pixel_shuffle_cpu_dispatch.h> |
| 825 | #include <ATen/ops/pixel_shuffle_native.h> |
| 826 | #include <ATen/ops/pixel_unshuffle_cpu_dispatch.h> |
| 827 | #include <ATen/ops/pixel_unshuffle_native.h> |
| 828 | #include <ATen/ops/poisson_cpu_dispatch.h> |
| 829 | #include <ATen/ops/poisson_native.h> |
| 830 | #include <ATen/ops/polar_cpu_dispatch.h> |
| 831 | #include <ATen/ops/polar_native.h> |
| 832 | #include <ATen/ops/polygamma_cpu_dispatch.h> |
| 833 | #include <ATen/ops/polygamma_native.h> |
| 834 | #include <ATen/ops/pow_cpu_dispatch.h> |
| 835 | #include <ATen/ops/pow_native.h> |
| 836 | #include <ATen/ops/prod_cpu_dispatch.h> |
| 837 | #include <ATen/ops/prod_native.h> |
| 838 | #include <ATen/ops/put_cpu_dispatch.h> |
| 839 | #include <ATen/ops/put_native.h> |
| 840 | #include <ATen/ops/quantize_per_channel_cpu_dispatch.h> |
| 841 | #include <ATen/ops/quantize_per_channel_native.h> |
| 842 | #include <ATen/ops/quantize_per_tensor_cpu_dispatch.h> |
| 843 | #include <ATen/ops/quantize_per_tensor_dynamic_cpu_dispatch.h> |
| 844 | #include <ATen/ops/quantize_per_tensor_dynamic_native.h> |
| 845 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 846 | #include <ATen/ops/random_cpu_dispatch.h> |
| 847 | #include <ATen/ops/random_native.h> |
| 848 | #include <ATen/ops/randperm_cpu_dispatch.h> |
| 849 | #include <ATen/ops/randperm_native.h> |
| 850 | #include <ATen/ops/range_cpu_dispatch.h> |
| 851 | #include <ATen/ops/range_native.h> |
| 852 | #include <ATen/ops/reciprocal_cpu_dispatch.h> |
| 853 | #include <ATen/ops/reciprocal_native.h> |
| 854 | #include <ATen/ops/reflection_pad1d_backward_cpu_dispatch.h> |
| 855 | #include <ATen/ops/reflection_pad1d_backward_native.h> |
| 856 | #include <ATen/ops/reflection_pad1d_cpu_dispatch.h> |
| 857 | #include <ATen/ops/reflection_pad1d_native.h> |
| 858 | #include <ATen/ops/reflection_pad2d_backward_cpu_dispatch.h> |
| 859 | #include <ATen/ops/reflection_pad2d_backward_native.h> |
| 860 | #include <ATen/ops/reflection_pad2d_cpu_dispatch.h> |
| 861 | #include <ATen/ops/reflection_pad2d_native.h> |
| 862 | #include <ATen/ops/reflection_pad3d_backward_cpu_dispatch.h> |
| 863 | #include <ATen/ops/reflection_pad3d_backward_native.h> |
| 864 | #include <ATen/ops/reflection_pad3d_cpu_dispatch.h> |
| 865 | #include <ATen/ops/reflection_pad3d_native.h> |
| 866 | #include <ATen/ops/relu_cpu_dispatch.h> |
| 867 | #include <ATen/ops/relu_native.h> |
| 868 | #include <ATen/ops/remainder_cpu_dispatch.h> |
| 869 | #include <ATen/ops/remainder_native.h> |
| 870 | #include <ATen/ops/renorm_cpu_dispatch.h> |
| 871 | #include <ATen/ops/renorm_native.h> |
| 872 | #include <ATen/ops/repeat_interleave_cpu_dispatch.h> |
| 873 | #include <ATen/ops/repeat_interleave_native.h> |
| 874 | #include <ATen/ops/replication_pad1d_backward_cpu_dispatch.h> |
| 875 | #include <ATen/ops/replication_pad1d_backward_native.h> |
| 876 | #include <ATen/ops/replication_pad1d_cpu_dispatch.h> |
| 877 | #include <ATen/ops/replication_pad1d_native.h> |
| 878 | #include <ATen/ops/replication_pad2d_backward_cpu_dispatch.h> |
| 879 | #include <ATen/ops/replication_pad2d_backward_native.h> |
| 880 | #include <ATen/ops/replication_pad2d_cpu_dispatch.h> |
| 881 | #include <ATen/ops/replication_pad2d_native.h> |
| 882 | #include <ATen/ops/replication_pad3d_backward_cpu_dispatch.h> |
| 883 | #include <ATen/ops/replication_pad3d_backward_native.h> |
| 884 | #include <ATen/ops/replication_pad3d_cpu_dispatch.h> |
| 885 | #include <ATen/ops/replication_pad3d_native.h> |
| 886 | #include <ATen/ops/resize_cpu_dispatch.h> |
| 887 | #include <ATen/ops/resize_native.h> |
| 888 | #include <ATen/ops/roll_cpu_dispatch.h> |
| 889 | #include <ATen/ops/roll_native.h> |
| 890 | #include <ATen/ops/round_cpu_dispatch.h> |
| 891 | #include <ATen/ops/round_native.h> |
| 892 | #include <ATen/ops/rrelu_with_noise_cpu_dispatch.h> |
| 893 | #include <ATen/ops/rrelu_with_noise_native.h> |
| 894 | #include <ATen/ops/rshift_cpu_dispatch.h> |
| 895 | #include <ATen/ops/rshift_native.h> |
| 896 | #include <ATen/ops/rsqrt_cpu_dispatch.h> |
| 897 | #include <ATen/ops/rsqrt_native.h> |
| 898 | #include <ATen/ops/rsub_cpu_dispatch.h> |
| 899 | #include <ATen/ops/rsub_native.h> |
| 900 | #include <ATen/ops/scatter_add_cpu_dispatch.h> |
| 901 | #include <ATen/ops/scatter_add_native.h> |
| 902 | #include <ATen/ops/scatter_cpu_dispatch.h> |
| 903 | #include <ATen/ops/scatter_native.h> |
| 904 | #include <ATen/ops/scatter_reduce_cpu_dispatch.h> |
| 905 | #include <ATen/ops/scatter_reduce_native.h> |
| 906 | #include <ATen/ops/searchsorted_cpu_dispatch.h> |
| 907 | #include <ATen/ops/searchsorted_native.h> |
| 908 | #include <ATen/ops/segment_reduce_cpu_dispatch.h> |
| 909 | #include <ATen/ops/segment_reduce_native.h> |
| 910 | #include <ATen/ops/set_cpu_dispatch.h> |
| 911 | #include <ATen/ops/set_native.h> |
| 912 | #include <ATen/ops/sgn_cpu_dispatch.h> |
| 913 | #include <ATen/ops/sgn_native.h> |
| 914 | #include <ATen/ops/sigmoid_backward_cpu_dispatch.h> |
| 915 | #include <ATen/ops/sigmoid_backward_native.h> |
| 916 | #include <ATen/ops/sigmoid_cpu_dispatch.h> |
| 917 | #include <ATen/ops/sigmoid_native.h> |
| 918 | #include <ATen/ops/sign_cpu_dispatch.h> |
| 919 | #include <ATen/ops/sign_native.h> |
| 920 | #include <ATen/ops/signbit_cpu_dispatch.h> |
| 921 | #include <ATen/ops/signbit_native.h> |
| 922 | #include <ATen/ops/silu_backward_cpu_dispatch.h> |
| 923 | #include <ATen/ops/silu_backward_native.h> |
| 924 | #include <ATen/ops/silu_cpu_dispatch.h> |
| 925 | #include <ATen/ops/silu_native.h> |
| 926 | #include <ATen/ops/sin_cpu_dispatch.h> |
| 927 | #include <ATen/ops/sin_native.h> |
| 928 | #include <ATen/ops/sinc_cpu_dispatch.h> |
| 929 | #include <ATen/ops/sinc_native.h> |
| 930 | #include <ATen/ops/sinh_cpu_dispatch.h> |
| 931 | #include <ATen/ops/sinh_native.h> |
| 932 | #include <ATen/ops/slow_conv3d_forward_cpu_dispatch.h> |
| 933 | #include <ATen/ops/slow_conv3d_forward_native.h> |
| 934 | #include <ATen/ops/slow_conv_dilated2d_cpu_dispatch.h> |
| 935 | #include <ATen/ops/slow_conv_dilated2d_native.h> |
| 936 | #include <ATen/ops/slow_conv_dilated3d_cpu_dispatch.h> |
| 937 | #include <ATen/ops/slow_conv_dilated3d_native.h> |
| 938 | #include <ATen/ops/slow_conv_transpose2d_cpu_dispatch.h> |
| 939 | #include <ATen/ops/slow_conv_transpose2d_native.h> |
| 940 | #include <ATen/ops/slow_conv_transpose3d_cpu_dispatch.h> |
| 941 | #include <ATen/ops/slow_conv_transpose3d_native.h> |
| 942 | #include <ATen/ops/smooth_l1_loss_backward_cpu_dispatch.h> |
| 943 | #include <ATen/ops/smooth_l1_loss_backward_native.h> |
| 944 | #include <ATen/ops/smooth_l1_loss_cpu_dispatch.h> |
| 945 | #include <ATen/ops/smooth_l1_loss_native.h> |
| 946 | #include <ATen/ops/softplus_backward_cpu_dispatch.h> |
| 947 | #include <ATen/ops/softplus_backward_native.h> |
| 948 | #include <ATen/ops/softplus_cpu_dispatch.h> |
| 949 | #include <ATen/ops/softplus_native.h> |
| 950 | #include <ATen/ops/softshrink_backward_cpu_dispatch.h> |
| 951 | #include <ATen/ops/softshrink_backward_native.h> |
| 952 | #include <ATen/ops/softshrink_cpu_dispatch.h> |
| 953 | #include <ATen/ops/softshrink_native.h> |
| 954 | #include <ATen/ops/sort_cpu_dispatch.h> |
| 955 | #include <ATen/ops/sort_native.h> |
| 956 | #include <ATen/ops/sparse_dim_cpu_dispatch.h> |
| 957 | #include <ATen/ops/sparse_dim_native.h> |
| 958 | #include <ATen/ops/special_airy_ai_cpu_dispatch.h> |
| 959 | #include <ATen/ops/special_airy_ai_native.h> |
| 960 | #include <ATen/ops/special_bessel_j0_cpu_dispatch.h> |
| 961 | #include <ATen/ops/special_bessel_j0_native.h> |
| 962 | #include <ATen/ops/special_bessel_j1_cpu_dispatch.h> |
| 963 | #include <ATen/ops/special_bessel_j1_native.h> |
| 964 | #include <ATen/ops/special_bessel_y0_cpu_dispatch.h> |
| 965 | #include <ATen/ops/special_bessel_y0_native.h> |
| 966 | #include <ATen/ops/special_bessel_y1_cpu_dispatch.h> |
| 967 | #include <ATen/ops/special_bessel_y1_native.h> |
| 968 | #include <ATen/ops/special_chebyshev_polynomial_t_cpu_dispatch.h> |
| 969 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 970 | #include <ATen/ops/special_chebyshev_polynomial_u_cpu_dispatch.h> |
| 971 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 972 | #include <ATen/ops/special_chebyshev_polynomial_v_cpu_dispatch.h> |
| 973 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 974 | #include <ATen/ops/special_chebyshev_polynomial_w_cpu_dispatch.h> |
| 975 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 976 | #include <ATen/ops/special_entr_cpu_dispatch.h> |
| 977 | #include <ATen/ops/special_entr_native.h> |
| 978 | #include <ATen/ops/special_erfcx_cpu_dispatch.h> |
| 979 | #include <ATen/ops/special_erfcx_native.h> |
| 980 | #include <ATen/ops/special_hermite_polynomial_h_cpu_dispatch.h> |
| 981 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 982 | #include <ATen/ops/special_hermite_polynomial_he_cpu_dispatch.h> |
| 983 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 984 | #include <ATen/ops/special_i0e_cpu_dispatch.h> |
| 985 | #include <ATen/ops/special_i0e_native.h> |
| 986 | #include <ATen/ops/special_i1_cpu_dispatch.h> |
| 987 | #include <ATen/ops/special_i1_native.h> |
| 988 | #include <ATen/ops/special_i1e_cpu_dispatch.h> |
| 989 | #include <ATen/ops/special_i1e_native.h> |
| 990 | #include <ATen/ops/special_laguerre_polynomial_l_cpu_dispatch.h> |
| 991 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 992 | #include <ATen/ops/special_legendre_polynomial_p_cpu_dispatch.h> |
| 993 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 994 | #include <ATen/ops/special_log_ndtr_cpu_dispatch.h> |
| 995 | #include <ATen/ops/special_log_ndtr_native.h> |
| 996 | #include <ATen/ops/special_modified_bessel_i0_cpu_dispatch.h> |
| 997 | #include <ATen/ops/special_modified_bessel_i0_native.h> |
| 998 | #include <ATen/ops/special_modified_bessel_i1_cpu_dispatch.h> |
| 999 | #include <ATen/ops/special_modified_bessel_i1_native.h> |
| 1000 | #include <ATen/ops/special_modified_bessel_k0_cpu_dispatch.h> |
| 1001 | #include <ATen/ops/special_modified_bessel_k0_native.h> |
| 1002 | #include <ATen/ops/special_modified_bessel_k1_cpu_dispatch.h> |
| 1003 | #include <ATen/ops/special_modified_bessel_k1_native.h> |
| 1004 | #include <ATen/ops/special_ndtri_cpu_dispatch.h> |
| 1005 | #include <ATen/ops/special_ndtri_native.h> |
| 1006 | #include <ATen/ops/special_scaled_modified_bessel_k0_cpu_dispatch.h> |
| 1007 | #include <ATen/ops/special_scaled_modified_bessel_k0_native.h> |
| 1008 | #include <ATen/ops/special_scaled_modified_bessel_k1_cpu_dispatch.h> |
| 1009 | #include <ATen/ops/special_scaled_modified_bessel_k1_native.h> |
| 1010 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_cpu_dispatch.h> |
| 1011 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1012 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_cpu_dispatch.h> |
| 1013 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1014 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_cpu_dispatch.h> |
| 1015 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1016 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_cpu_dispatch.h> |
| 1017 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1018 | #include <ATen/ops/special_spherical_bessel_j0_cpu_dispatch.h> |
| 1019 | #include <ATen/ops/special_spherical_bessel_j0_native.h> |
| 1020 | #include <ATen/ops/special_xlog1py_cpu_dispatch.h> |
| 1021 | #include <ATen/ops/special_xlog1py_native.h> |
| 1022 | #include <ATen/ops/special_zeta_cpu_dispatch.h> |
| 1023 | #include <ATen/ops/special_zeta_native.h> |
| 1024 | #include <ATen/ops/sqrt_cpu_dispatch.h> |
| 1025 | #include <ATen/ops/sqrt_native.h> |
| 1026 | #include <ATen/ops/sspaddmm_cpu_dispatch.h> |
| 1027 | #include <ATen/ops/sspaddmm_native.h> |
| 1028 | #include <ATen/ops/std_cpu_dispatch.h> |
| 1029 | #include <ATen/ops/std_mean_cpu_dispatch.h> |
| 1030 | #include <ATen/ops/std_mean_native.h> |
| 1031 | #include <ATen/ops/std_native.h> |
| 1032 | #include <ATen/ops/sub_cpu_dispatch.h> |
| 1033 | #include <ATen/ops/sub_native.h> |
| 1034 | #include <ATen/ops/sum_cpu_dispatch.h> |
| 1035 | #include <ATen/ops/sum_native.h> |
| 1036 | #include <ATen/ops/take_cpu_dispatch.h> |
| 1037 | #include <ATen/ops/take_native.h> |
| 1038 | #include <ATen/ops/tan_cpu_dispatch.h> |
| 1039 | #include <ATen/ops/tan_native.h> |
| 1040 | #include <ATen/ops/tanh_backward_cpu_dispatch.h> |
| 1041 | #include <ATen/ops/tanh_backward_native.h> |
| 1042 | #include <ATen/ops/tanh_cpu_dispatch.h> |
| 1043 | #include <ATen/ops/tanh_native.h> |
| 1044 | #include <ATen/ops/tensordot_cpu_dispatch.h> |
| 1045 | #include <ATen/ops/tensordot_native.h> |
| 1046 | #include <ATen/ops/threshold_backward_cpu_dispatch.h> |
| 1047 | #include <ATen/ops/threshold_backward_native.h> |
| 1048 | #include <ATen/ops/threshold_cpu_dispatch.h> |
| 1049 | #include <ATen/ops/threshold_native.h> |
| 1050 | #include <ATen/ops/to_mkldnn_cpu_dispatch.h> |
| 1051 | #include <ATen/ops/to_mkldnn_native.h> |
| 1052 | #include <ATen/ops/to_sparse_bsc_cpu_dispatch.h> |
| 1053 | #include <ATen/ops/to_sparse_bsc_native.h> |
| 1054 | #include <ATen/ops/to_sparse_bsr_cpu_dispatch.h> |
| 1055 | #include <ATen/ops/to_sparse_bsr_native.h> |
| 1056 | #include <ATen/ops/to_sparse_cpu_dispatch.h> |
| 1057 | #include <ATen/ops/to_sparse_csc_cpu_dispatch.h> |
| 1058 | #include <ATen/ops/to_sparse_csc_native.h> |
| 1059 | #include <ATen/ops/to_sparse_csr_cpu_dispatch.h> |
| 1060 | #include <ATen/ops/to_sparse_csr_native.h> |
| 1061 | #include <ATen/ops/to_sparse_native.h> |
| 1062 | #include <ATen/ops/topk_cpu_dispatch.h> |
| 1063 | #include <ATen/ops/topk_native.h> |
| 1064 | #include <ATen/ops/trace_cpu_dispatch.h> |
| 1065 | #include <ATen/ops/trace_native.h> |
| 1066 | #include <ATen/ops/triangular_solve_cpu_dispatch.h> |
| 1067 | #include <ATen/ops/triangular_solve_native.h> |
| 1068 | #include <ATen/ops/tril_cpu_dispatch.h> |
| 1069 | #include <ATen/ops/tril_indices_cpu_dispatch.h> |
| 1070 | #include <ATen/ops/tril_indices_native.h> |
| 1071 | #include <ATen/ops/tril_native.h> |
| 1072 | #include <ATen/ops/triu_cpu_dispatch.h> |
| 1073 | #include <ATen/ops/triu_indices_cpu_dispatch.h> |
| 1074 | #include <ATen/ops/triu_indices_native.h> |
| 1075 | #include <ATen/ops/triu_native.h> |
| 1076 | #include <ATen/ops/trunc_cpu_dispatch.h> |
| 1077 | #include <ATen/ops/trunc_native.h> |
| 1078 | #include <ATen/ops/unfold_backward_cpu_dispatch.h> |
| 1079 | #include <ATen/ops/unfold_backward_native.h> |
| 1080 | #include <ATen/ops/unfold_cpu_dispatch.h> |
| 1081 | #include <ATen/ops/unfold_native.h> |
| 1082 | #include <ATen/ops/uniform_cpu_dispatch.h> |
| 1083 | #include <ATen/ops/uniform_native.h> |
| 1084 | #include <ATen/ops/unique_consecutive_cpu_dispatch.h> |
| 1085 | #include <ATen/ops/unique_consecutive_native.h> |
| 1086 | #include <ATen/ops/unique_dim_consecutive_cpu_dispatch.h> |
| 1087 | #include <ATen/ops/unique_dim_consecutive_native.h> |
| 1088 | #include <ATen/ops/unique_dim_cpu_dispatch.h> |
| 1089 | #include <ATen/ops/unique_dim_native.h> |
| 1090 | #include <ATen/ops/upsample_bicubic2d_backward_cpu_dispatch.h> |
| 1091 | #include <ATen/ops/upsample_bicubic2d_backward_native.h> |
| 1092 | #include <ATen/ops/upsample_bicubic2d_cpu_dispatch.h> |
| 1093 | #include <ATen/ops/upsample_bicubic2d_native.h> |
| 1094 | #include <ATen/ops/upsample_bilinear2d_backward_cpu_dispatch.h> |
| 1095 | #include <ATen/ops/upsample_bilinear2d_backward_native.h> |
| 1096 | #include <ATen/ops/upsample_bilinear2d_cpu_dispatch.h> |
| 1097 | #include <ATen/ops/upsample_bilinear2d_native.h> |
| 1098 | #include <ATen/ops/upsample_linear1d_backward_cpu_dispatch.h> |
| 1099 | #include <ATen/ops/upsample_linear1d_backward_native.h> |
| 1100 | #include <ATen/ops/upsample_linear1d_cpu_dispatch.h> |
| 1101 | #include <ATen/ops/upsample_linear1d_native.h> |
| 1102 | #include <ATen/ops/upsample_nearest1d_backward_cpu_dispatch.h> |
| 1103 | #include <ATen/ops/upsample_nearest1d_backward_native.h> |
| 1104 | #include <ATen/ops/upsample_nearest1d_cpu_dispatch.h> |
| 1105 | #include <ATen/ops/upsample_nearest1d_native.h> |
| 1106 | #include <ATen/ops/upsample_nearest2d_backward_cpu_dispatch.h> |
| 1107 | #include <ATen/ops/upsample_nearest2d_backward_native.h> |
| 1108 | #include <ATen/ops/upsample_nearest2d_cpu_dispatch.h> |
| 1109 | #include <ATen/ops/upsample_nearest2d_native.h> |
| 1110 | #include <ATen/ops/upsample_nearest3d_backward_cpu_dispatch.h> |
| 1111 | #include <ATen/ops/upsample_nearest3d_backward_native.h> |
| 1112 | #include <ATen/ops/upsample_nearest3d_cpu_dispatch.h> |
| 1113 | #include <ATen/ops/upsample_nearest3d_native.h> |
| 1114 | #include <ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h> |
| 1115 | #include <ATen/ops/upsample_trilinear3d_backward_native.h> |
| 1116 | #include <ATen/ops/upsample_trilinear3d_cpu_dispatch.h> |
| 1117 | #include <ATen/ops/upsample_trilinear3d_native.h> |
| 1118 | #include <ATen/ops/var_cpu_dispatch.h> |
| 1119 | #include <ATen/ops/var_mean_cpu_dispatch.h> |
| 1120 | #include <ATen/ops/var_mean_native.h> |
| 1121 | #include <ATen/ops/var_native.h> |
| 1122 | #include <ATen/ops/vdot_cpu_dispatch.h> |
| 1123 | #include <ATen/ops/vdot_native.h> |
| 1124 | #include <ATen/ops/view_as_complex_cpu_dispatch.h> |
| 1125 | #include <ATen/ops/view_as_complex_native.h> |
| 1126 | #include <ATen/ops/view_as_real_cpu_dispatch.h> |
| 1127 | #include <ATen/ops/view_as_real_native.h> |
| 1128 | #include <ATen/ops/view_cpu_dispatch.h> |
| 1129 | #include <ATen/ops/view_native.h> |
| 1130 | #include <ATen/ops/where_cpu_dispatch.h> |
| 1131 | #include <ATen/ops/where_native.h> |
| 1132 | #include <ATen/ops/xlogy_cpu_dispatch.h> |
| 1133 | #include <ATen/ops/xlogy_native.h> |
| 1134 | #include <ATen/ops/zero_cpu_dispatch.h> |
| 1135 | #include <ATen/ops/zero_native.h> |
| 1136 | |
| 1137 | // See template file RegisterDispatchDefinitions.ini |
| 1138 | namespace at { |
| 1139 | // NB: TORCH_LIBRARY_IMPL must be in an anonymous namespace to avoid |
| 1140 | // ambiguity with conflicting identifiers that may have been defined in |
| 1141 | // at namespace already. |
| 1142 | namespace { |
| 1143 | Tensor create_out(IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) { |
| 1144 | if (strides.empty()) { |
| 1145 | return at::detail::empty_cpu(sizes, options); |
| 1146 | } else { |
| 1147 | return at::detail::empty_strided_cpu(sizes, strides, options); |
| 1148 | } |
| 1149 | } |
| 1150 | void resize_out(const Tensor &out, IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) { |
| 1151 | TORCH_CHECK(options.dtype() == out.dtype(), |
| 1152 | "Expected out tensor to have dtype ", options.dtype(), ", but got ", out.dtype(), " instead"); |
| 1153 | TORCH_CHECK(options.device() == out.device(), |
| 1154 | "Expected out tensor to have device ", options.device(), ", but got ", out.device(), " instead"); |
| 1155 | const bool resized = at::native::resize_output(out, sizes); |
| 1156 | // Only restride if a resize occurred; otherwise we ignore the (advisory) |
| 1157 | // strides from the meta function and directly use the output tensor's |
| 1158 | // preexisting strides |
| 1159 | if (resized) { |
| 1160 | if (!strides.empty()) { |
| 1161 | TORCH_INTERNAL_ASSERT(!options.memory_format_opt().has_value()); |
| 1162 | // TODO: avoid the redispatch here |
| 1163 | out.as_strided_(sizes, strides); |
| 1164 | } else if (options.memory_format_opt().has_value()) { |
| 1165 | out.unsafeGetTensorImpl()->empty_tensor_restride(*options.memory_format_opt()); |
| 1166 | } |
| 1167 | } |
| 1168 | } |
| 1169 | void check_inplace(const Tensor &self, IntArrayRef sizes, const TensorOptions &options) { |
| 1170 | // These checks are needed on those operators that: |
| 1171 | // 1) don't use 'TensorIterator' (e.g. 'addmm' and 'baddbmm') |
| 1172 | // 2) have particular typing rules (e.g. 'cumsum' and 'cumprod') |
| 1173 | // For other operators (e.g. 'add'), 'TensorIterator' already checks |
| 1174 | // these things separately. |
| 1175 | TORCH_CHECK(options.dtype() == self.dtype(), |
| 1176 | "Bad in-place call: ", |
| 1177 | "input tensor dtype ", self.dtype(), " and output tensor dtype ", options.dtype(), " should match"); |
| 1178 | TORCH_CHECK(options.device() == self.device(), |
| 1179 | "Bad in-place call: ", |
| 1180 | "input tensor device ", self.device(), " and output tensor device ", options.device(), " should match"); |
| 1181 | TORCH_CHECK(sizes == self.sizes(), |
| 1182 | "Bad in-place call: ", |
| 1183 | "input tensor size ", self.sizes(), " and output tensor size ", sizes, " should match"); |
| 1184 | } |
| 1185 | c10::optional<Tensor> maybe_create_proxy(const Tensor &out, IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) { |
| 1186 | if (out.strides() != strides) { |
| 1187 | return at::detail::empty_strided_cpu(sizes, strides, options); |
| 1188 | } |
| 1189 | return c10::nullopt; |
| 1190 | } |
| 1191 | namespace { |
| 1192 | void wrapper_CPU___assert_async(const at::Tensor & self) { |
| 1193 | // No device check |
| 1194 | // DeviceGuard omitted |
| 1195 | return at::native::_assert_async_cpu(self); |
| 1196 | } |
| 1197 | } // anonymous namespace |
| 1198 | namespace { |
| 1199 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__native_dropout(const at::Tensor & input, double p, c10::optional<bool> train) { |
| 1200 | // No device check |
| 1201 | // DeviceGuard omitted |
| 1202 | return at::native::native_dropout_cpu(input, p, train); |
| 1203 | } |
| 1204 | } // anonymous namespace |
| 1205 | namespace { |
| 1206 | at::Tensor wrapper_CPU__native_dropout_backward(const at::Tensor & grad_output, const at::Tensor & mask, double scale) { |
| 1207 | // No device check |
| 1208 | // DeviceGuard omitted |
| 1209 | return at::native::native_dropout_backward(grad_output, mask, scale); |
| 1210 | } |
| 1211 | } // anonymous namespace |
| 1212 | namespace { |
| 1213 | at::Tensor & wrapper_CPU_out_abs_out(const at::Tensor & self, at::Tensor & out) { |
| 1214 | // No device check |
| 1215 | // DeviceGuard omitted |
| 1216 | return at::native::abs_out(self, out); |
| 1217 | } |
| 1218 | } // anonymous namespace |
| 1219 | namespace { |
| 1220 | at::Tensor wrapper_CPU__angle(const at::Tensor & self) { |
| 1221 | // No device check |
| 1222 | // DeviceGuard omitted |
| 1223 | return at::native::angle(self); |
| 1224 | } |
| 1225 | } // anonymous namespace |
| 1226 | namespace { |
| 1227 | at::Tensor & wrapper_CPU_out_angle_out(const at::Tensor & self, at::Tensor & out) { |
| 1228 | // No device check |
| 1229 | // DeviceGuard omitted |
| 1230 | return at::native::angle_out(self, out); |
| 1231 | } |
| 1232 | } // anonymous namespace |
| 1233 | namespace { |
| 1234 | at::Tensor wrapper_CPU__view_as_real(const at::Tensor & self) { |
| 1235 | // No device check |
| 1236 | // DeviceGuard omitted |
| 1237 | return at::native::view_as_real(self); |
| 1238 | } |
| 1239 | } // anonymous namespace |
| 1240 | namespace { |
| 1241 | at::Tensor wrapper_CPU__view_as_complex(const at::Tensor & self) { |
| 1242 | // No device check |
| 1243 | // DeviceGuard omitted |
| 1244 | return at::native::view_as_complex(self); |
| 1245 | } |
| 1246 | } // anonymous namespace |
| 1247 | struct structured_sgn_out_functional final : public at::native::structured_sgn_out { |
| 1248 | void set_output_strided( |
| 1249 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1250 | TensorOptions options, DimnameList names |
| 1251 | ) override { |
| 1252 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1253 | if (!names.empty()) { |
| 1254 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1255 | } |
| 1256 | // super must happen after, so that downstream can use maybe_get_output |
| 1257 | // to retrieve the output |
| 1258 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1259 | } |
| 1260 | void set_output_raw_strided( |
| 1261 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1262 | TensorOptions options, DimnameList names |
| 1263 | ) override { |
| 1264 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1265 | if (!names.empty()) { |
| 1266 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1267 | } |
| 1268 | // super must happen after, so that downstream can use maybe_get_output |
| 1269 | // to retrieve the output |
| 1270 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1271 | } |
| 1272 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1273 | return *outputs_[output_idx]; |
| 1274 | } |
| 1275 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1276 | }; |
| 1277 | at::Tensor wrapper_CPU_sgn(const at::Tensor & self) { |
| 1278 | structured_sgn_out_functional op; |
| 1279 | op.meta(self); |
| 1280 | op.impl(self, *op.outputs_[0]); |
| 1281 | return std::move(op.outputs_[0]).take(); |
| 1282 | } |
| 1283 | struct structured_sgn_out_out final : public at::native::structured_sgn_out { |
| 1284 | structured_sgn_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1285 | void set_output_strided( |
| 1286 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1287 | TensorOptions options, DimnameList names |
| 1288 | ) override { |
| 1289 | const auto& out = outputs_[output_idx].get(); |
| 1290 | resize_out(out, sizes, strides, options); |
| 1291 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1292 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1293 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1294 | } |
| 1295 | if (!names.empty()) { |
| 1296 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1297 | } |
| 1298 | // super must happen after, so that downstream can use maybe_get_output |
| 1299 | // to retrieve the output |
| 1300 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1301 | } |
| 1302 | void set_output_raw_strided( |
| 1303 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1304 | TensorOptions options, DimnameList names |
| 1305 | ) override { |
| 1306 | const auto& out = outputs_[output_idx].get(); |
| 1307 | resize_out(out, sizes, strides, options); |
| 1308 | if (!names.empty()) { |
| 1309 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1310 | } |
| 1311 | // super must happen after, so that downstream can use maybe_get_output |
| 1312 | // to retrieve the output |
| 1313 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1314 | } |
| 1315 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1316 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1317 | } |
| 1318 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1319 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1320 | }; |
| 1321 | at::Tensor & wrapper_CPU_sgn_out_out(const at::Tensor & self, at::Tensor & out) { |
| 1322 | structured_sgn_out_out op(out); |
| 1323 | op.meta(self); |
| 1324 | op.impl(self, op.maybe_get_output(0)); |
| 1325 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1326 | return out; |
| 1327 | } |
| 1328 | struct structured_sgn_out_inplace final : public at::native::structured_sgn_out { |
| 1329 | structured_sgn_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1330 | void set_output_strided( |
| 1331 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1332 | TensorOptions options, DimnameList names |
| 1333 | ) override { |
| 1334 | const auto& out = outputs_[output_idx].get(); |
| 1335 | check_inplace(out, sizes, options); |
| 1336 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1337 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1338 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1339 | } |
| 1340 | if (!names.empty()) { |
| 1341 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1342 | } |
| 1343 | // super must happen after, so that downstream can use maybe_get_output |
| 1344 | // to retrieve the output |
| 1345 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1346 | } |
| 1347 | void set_output_raw_strided( |
| 1348 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1349 | TensorOptions options, DimnameList names |
| 1350 | ) override { |
| 1351 | const auto& out = outputs_[output_idx].get(); |
| 1352 | check_inplace(out, sizes, options); |
| 1353 | if (!names.empty()) { |
| 1354 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1355 | } |
| 1356 | // super must happen after, so that downstream can use maybe_get_output |
| 1357 | // to retrieve the output |
| 1358 | at::native::structured_sgn_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1359 | } |
| 1360 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1361 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1362 | } |
| 1363 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1364 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1365 | }; |
| 1366 | at::Tensor & wrapper_CPU_sgn_(at::Tensor & self) { |
| 1367 | structured_sgn_out_inplace op(self); |
| 1368 | op.meta(self); |
| 1369 | op.impl(self, op.outputs_[0]); |
| 1370 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1371 | return self; |
| 1372 | } |
| 1373 | namespace { |
| 1374 | at::Tensor & wrapper_CPU_out_conj_physical_out(const at::Tensor & self, at::Tensor & out) { |
| 1375 | // No device check |
| 1376 | // DeviceGuard omitted |
| 1377 | return at::native::conj_physical_out(self, out); |
| 1378 | } |
| 1379 | } // anonymous namespace |
| 1380 | struct structured_acos_out_functional final : public at::native::structured_acos_out { |
| 1381 | void set_output_strided( |
| 1382 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1383 | TensorOptions options, DimnameList names |
| 1384 | ) override { |
| 1385 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1386 | if (!names.empty()) { |
| 1387 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1388 | } |
| 1389 | // super must happen after, so that downstream can use maybe_get_output |
| 1390 | // to retrieve the output |
| 1391 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1392 | } |
| 1393 | void set_output_raw_strided( |
| 1394 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1395 | TensorOptions options, DimnameList names |
| 1396 | ) override { |
| 1397 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1398 | if (!names.empty()) { |
| 1399 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1400 | } |
| 1401 | // super must happen after, so that downstream can use maybe_get_output |
| 1402 | // to retrieve the output |
| 1403 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1404 | } |
| 1405 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1406 | return *outputs_[output_idx]; |
| 1407 | } |
| 1408 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1409 | }; |
| 1410 | at::Tensor wrapper_CPU_acos(const at::Tensor & self) { |
| 1411 | structured_acos_out_functional op; |
| 1412 | op.meta(self); |
| 1413 | op.impl(self, *op.outputs_[0]); |
| 1414 | return std::move(op.outputs_[0]).take(); |
| 1415 | } |
| 1416 | struct structured_acos_out_out final : public at::native::structured_acos_out { |
| 1417 | structured_acos_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1418 | void set_output_strided( |
| 1419 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1420 | TensorOptions options, DimnameList names |
| 1421 | ) override { |
| 1422 | const auto& out = outputs_[output_idx].get(); |
| 1423 | resize_out(out, sizes, strides, options); |
| 1424 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1425 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1426 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1427 | } |
| 1428 | if (!names.empty()) { |
| 1429 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1430 | } |
| 1431 | // super must happen after, so that downstream can use maybe_get_output |
| 1432 | // to retrieve the output |
| 1433 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1434 | } |
| 1435 | void set_output_raw_strided( |
| 1436 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1437 | TensorOptions options, DimnameList names |
| 1438 | ) override { |
| 1439 | const auto& out = outputs_[output_idx].get(); |
| 1440 | resize_out(out, sizes, strides, options); |
| 1441 | if (!names.empty()) { |
| 1442 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1443 | } |
| 1444 | // super must happen after, so that downstream can use maybe_get_output |
| 1445 | // to retrieve the output |
| 1446 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1447 | } |
| 1448 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1449 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1450 | } |
| 1451 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1452 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1453 | }; |
| 1454 | at::Tensor & wrapper_CPU_acos_out_out(const at::Tensor & self, at::Tensor & out) { |
| 1455 | structured_acos_out_out op(out); |
| 1456 | op.meta(self); |
| 1457 | op.impl(self, op.maybe_get_output(0)); |
| 1458 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1459 | return out; |
| 1460 | } |
| 1461 | struct structured_acos_out_inplace final : public at::native::structured_acos_out { |
| 1462 | structured_acos_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1463 | void set_output_strided( |
| 1464 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1465 | TensorOptions options, DimnameList names |
| 1466 | ) override { |
| 1467 | const auto& out = outputs_[output_idx].get(); |
| 1468 | check_inplace(out, sizes, options); |
| 1469 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1470 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1471 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1472 | } |
| 1473 | if (!names.empty()) { |
| 1474 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1475 | } |
| 1476 | // super must happen after, so that downstream can use maybe_get_output |
| 1477 | // to retrieve the output |
| 1478 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1479 | } |
| 1480 | void set_output_raw_strided( |
| 1481 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1482 | TensorOptions options, DimnameList names |
| 1483 | ) override { |
| 1484 | const auto& out = outputs_[output_idx].get(); |
| 1485 | check_inplace(out, sizes, options); |
| 1486 | if (!names.empty()) { |
| 1487 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1488 | } |
| 1489 | // super must happen after, so that downstream can use maybe_get_output |
| 1490 | // to retrieve the output |
| 1491 | at::native::structured_acos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1492 | } |
| 1493 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1494 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1495 | } |
| 1496 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1497 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1498 | }; |
| 1499 | at::Tensor & wrapper_CPU_acos_(at::Tensor & self) { |
| 1500 | structured_acos_out_inplace op(self); |
| 1501 | op.meta(self); |
| 1502 | op.impl(self, op.outputs_[0]); |
| 1503 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1504 | return self; |
| 1505 | } |
| 1506 | struct structured_ufunc_add_CPU_functional final : public at::native::structured_ufunc_add_CPU { |
| 1507 | void set_output_strided( |
| 1508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1509 | TensorOptions options, DimnameList names |
| 1510 | ) override { |
| 1511 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1512 | if (!names.empty()) { |
| 1513 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1514 | } |
| 1515 | // super must happen after, so that downstream can use maybe_get_output |
| 1516 | // to retrieve the output |
| 1517 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1518 | } |
| 1519 | void set_output_raw_strided( |
| 1520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1521 | TensorOptions options, DimnameList names |
| 1522 | ) override { |
| 1523 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1524 | if (!names.empty()) { |
| 1525 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1526 | } |
| 1527 | // super must happen after, so that downstream can use maybe_get_output |
| 1528 | // to retrieve the output |
| 1529 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1530 | } |
| 1531 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1532 | return *outputs_[output_idx]; |
| 1533 | } |
| 1534 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1535 | }; |
| 1536 | at::Tensor wrapper_CPU_add_Tensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1537 | structured_ufunc_add_CPU_functional op; |
| 1538 | op.meta(self, other, alpha); |
| 1539 | op.impl(self, other, alpha, *op.outputs_[0]); |
| 1540 | return std::move(op.outputs_[0]).take(); |
| 1541 | } |
| 1542 | struct structured_ufunc_add_CPU_out final : public at::native::structured_ufunc_add_CPU { |
| 1543 | structured_ufunc_add_CPU_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1544 | void set_output_strided( |
| 1545 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1546 | TensorOptions options, DimnameList names |
| 1547 | ) override { |
| 1548 | const auto& out = outputs_[output_idx].get(); |
| 1549 | resize_out(out, sizes, strides, options); |
| 1550 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1551 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1552 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1553 | } |
| 1554 | if (!names.empty()) { |
| 1555 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1556 | } |
| 1557 | // super must happen after, so that downstream can use maybe_get_output |
| 1558 | // to retrieve the output |
| 1559 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1560 | } |
| 1561 | void set_output_raw_strided( |
| 1562 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1563 | TensorOptions options, DimnameList names |
| 1564 | ) override { |
| 1565 | const auto& out = outputs_[output_idx].get(); |
| 1566 | resize_out(out, sizes, strides, options); |
| 1567 | if (!names.empty()) { |
| 1568 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1569 | } |
| 1570 | // super must happen after, so that downstream can use maybe_get_output |
| 1571 | // to retrieve the output |
| 1572 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1573 | } |
| 1574 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1575 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1576 | } |
| 1577 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1578 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1579 | }; |
| 1580 | at::Tensor & wrapper_CPU_add_out_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 1581 | structured_ufunc_add_CPU_out op(out); |
| 1582 | op.meta(self, other, alpha); |
| 1583 | op.impl(self, other, alpha, op.maybe_get_output(0)); |
| 1584 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1585 | return out; |
| 1586 | } |
| 1587 | struct structured_ufunc_add_CPU_inplace final : public at::native::structured_ufunc_add_CPU { |
| 1588 | structured_ufunc_add_CPU_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1589 | void set_output_strided( |
| 1590 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1591 | TensorOptions options, DimnameList names |
| 1592 | ) override { |
| 1593 | const auto& out = outputs_[output_idx].get(); |
| 1594 | check_inplace(out, sizes, options); |
| 1595 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1596 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1597 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1598 | } |
| 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 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1605 | } |
| 1606 | void set_output_raw_strided( |
| 1607 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1608 | TensorOptions options, DimnameList names |
| 1609 | ) override { |
| 1610 | const auto& out = outputs_[output_idx].get(); |
| 1611 | check_inplace(out, sizes, options); |
| 1612 | if (!names.empty()) { |
| 1613 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1614 | } |
| 1615 | // super must happen after, so that downstream can use maybe_get_output |
| 1616 | // to retrieve the output |
| 1617 | at::native::structured_ufunc_add_CPU::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1618 | } |
| 1619 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1620 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1621 | } |
| 1622 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1623 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1624 | }; |
| 1625 | at::Tensor & wrapper_CPU_add__Tensor(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1626 | structured_ufunc_add_CPU_inplace op(self); |
| 1627 | op.meta(self, other, alpha); |
| 1628 | op.impl(self, other, alpha, op.outputs_[0]); |
| 1629 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1630 | return self; |
| 1631 | } |
| 1632 | namespace { |
| 1633 | at::Tensor wrapper_CPU_Tensor__add_relu(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1634 | // No device check |
| 1635 | // DeviceGuard omitted |
| 1636 | return at::native::add_relu(self, other, alpha); |
| 1637 | } |
| 1638 | } // anonymous namespace |
| 1639 | namespace { |
| 1640 | at::Tensor & wrapper_CPU_out__add_relu_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 1641 | // No device check |
| 1642 | // DeviceGuard omitted |
| 1643 | return at::native::add_relu_out(self, other, alpha, out); |
| 1644 | } |
| 1645 | } // anonymous namespace |
| 1646 | namespace { |
| 1647 | at::Tensor & wrapper_CPU_Tensor__add_relu_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1648 | // No device check |
| 1649 | // DeviceGuard omitted |
| 1650 | return at::native::add_relu_(self, other, alpha); |
| 1651 | } |
| 1652 | } // anonymous namespace |
| 1653 | namespace { |
| 1654 | at::Tensor wrapper_CPU_Scalar__add_relu(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 1655 | // No device check |
| 1656 | // DeviceGuard omitted |
| 1657 | return at::native::add_relu(self, other, alpha); |
| 1658 | } |
| 1659 | } // anonymous namespace |
| 1660 | namespace { |
| 1661 | at::Tensor & wrapper_CPU_Scalar__add_relu_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 1662 | // No device check |
| 1663 | // DeviceGuard omitted |
| 1664 | return at::native::add_relu_(self, other, alpha); |
| 1665 | } |
| 1666 | } // anonymous namespace |
| 1667 | struct structured_addmv_out_cpu_functional final : public at::native::structured_addmv_out_cpu { |
| 1668 | void set_output_strided( |
| 1669 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1670 | TensorOptions options, DimnameList names |
| 1671 | ) override { |
| 1672 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1673 | if (!names.empty()) { |
| 1674 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1675 | } |
| 1676 | // super must happen after, so that downstream can use maybe_get_output |
| 1677 | // to retrieve the output |
| 1678 | } |
| 1679 | void set_output_raw_strided( |
| 1680 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1681 | TensorOptions options, DimnameList names |
| 1682 | ) override { |
| 1683 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1684 | if (!names.empty()) { |
| 1685 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1686 | } |
| 1687 | // super must happen after, so that downstream can use maybe_get_output |
| 1688 | // to retrieve the output |
| 1689 | } |
| 1690 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1691 | return *outputs_[output_idx]; |
| 1692 | } |
| 1693 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1694 | }; |
| 1695 | at::Tensor wrapper_CPU_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1696 | structured_addmv_out_cpu_functional op; |
| 1697 | op.meta(self, mat, vec, beta, alpha); |
| 1698 | op.impl(self, mat, vec, beta, alpha, *op.outputs_[0]); |
| 1699 | return std::move(op.outputs_[0]).take(); |
| 1700 | } |
| 1701 | struct structured_addmv_out_cpu_out final : public at::native::structured_addmv_out_cpu { |
| 1702 | structured_addmv_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1703 | void set_output_strided( |
| 1704 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1705 | TensorOptions options, DimnameList names |
| 1706 | ) override { |
| 1707 | const auto& out = outputs_[output_idx].get(); |
| 1708 | resize_out(out, sizes, strides, options); |
| 1709 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1710 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1711 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1712 | } |
| 1713 | if (!names.empty()) { |
| 1714 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1715 | } |
| 1716 | // super must happen after, so that downstream can use maybe_get_output |
| 1717 | // to retrieve the output |
| 1718 | } |
| 1719 | void set_output_raw_strided( |
| 1720 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1721 | TensorOptions options, DimnameList names |
| 1722 | ) override { |
| 1723 | const auto& out = outputs_[output_idx].get(); |
| 1724 | resize_out(out, sizes, strides, options); |
| 1725 | if (!names.empty()) { |
| 1726 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1727 | } |
| 1728 | // super must happen after, so that downstream can use maybe_get_output |
| 1729 | // to retrieve the output |
| 1730 | } |
| 1731 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1732 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1733 | } |
| 1734 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1735 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1736 | }; |
| 1737 | at::Tensor & wrapper_CPU_addmv_out_out(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1738 | structured_addmv_out_cpu_out op(out); |
| 1739 | op.meta(self, mat, vec, beta, alpha); |
| 1740 | op.impl(self, mat, vec, beta, alpha, op.maybe_get_output(0)); |
| 1741 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1742 | return out; |
| 1743 | } |
| 1744 | struct structured_addmv_out_cpu_inplace final : public at::native::structured_addmv_out_cpu { |
| 1745 | structured_addmv_out_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1746 | void set_output_strided( |
| 1747 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1748 | TensorOptions options, DimnameList names |
| 1749 | ) override { |
| 1750 | const auto& out = outputs_[output_idx].get(); |
| 1751 | check_inplace(out, sizes, options); |
| 1752 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1753 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1754 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1755 | } |
| 1756 | if (!names.empty()) { |
| 1757 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1758 | } |
| 1759 | // super must happen after, so that downstream can use maybe_get_output |
| 1760 | // to retrieve the output |
| 1761 | } |
| 1762 | void set_output_raw_strided( |
| 1763 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1764 | TensorOptions options, DimnameList names |
| 1765 | ) override { |
| 1766 | const auto& out = outputs_[output_idx].get(); |
| 1767 | check_inplace(out, sizes, options); |
| 1768 | if (!names.empty()) { |
| 1769 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1770 | } |
| 1771 | // super must happen after, so that downstream can use maybe_get_output |
| 1772 | // to retrieve the output |
| 1773 | } |
| 1774 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1775 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1776 | } |
| 1777 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1778 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1779 | }; |
| 1780 | at::Tensor & wrapper_CPU_addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1781 | structured_addmv_out_cpu_inplace op(self); |
| 1782 | op.meta(self, mat, vec, beta, alpha); |
| 1783 | op.impl(self, mat, vec, beta, alpha, op.outputs_[0]); |
| 1784 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1785 | return self; |
| 1786 | } |
| 1787 | namespace { |
| 1788 | at::Tensor wrapper_CPU__addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1789 | // No device check |
| 1790 | // DeviceGuard omitted |
| 1791 | return at::native::addr(self, vec1, vec2, beta, alpha); |
| 1792 | } |
| 1793 | } // anonymous namespace |
| 1794 | namespace { |
| 1795 | at::Tensor & wrapper_CPU_out_addr_out(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1796 | // No device check |
| 1797 | // DeviceGuard omitted |
| 1798 | return at::native::addr_out(self, vec1, vec2, beta, alpha, out); |
| 1799 | } |
| 1800 | } // anonymous namespace |
| 1801 | struct structured_all_out_functional final : public at::native::structured_all_out { |
| 1802 | void set_output_strided( |
| 1803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1804 | TensorOptions options, DimnameList names |
| 1805 | ) override { |
| 1806 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1807 | if (!names.empty()) { |
| 1808 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1809 | } |
| 1810 | // super must happen after, so that downstream can use maybe_get_output |
| 1811 | // to retrieve the output |
| 1812 | } |
| 1813 | void set_output_raw_strided( |
| 1814 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1815 | TensorOptions options, DimnameList names |
| 1816 | ) override { |
| 1817 | outputs_[output_idx] = create_out(sizes, strides, 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 | } |
| 1824 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1825 | return *outputs_[output_idx]; |
| 1826 | } |
| 1827 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1828 | }; |
| 1829 | at::Tensor wrapper_CPU_all_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 1830 | structured_all_out_functional op; |
| 1831 | auto precompute = op.meta(self, dim, keepdim); |
| 1832 | (void)precompute; |
| 1833 | op.impl(self, precompute.dim, keepdim, *op.outputs_[0]); |
| 1834 | return std::move(op.outputs_[0]).take(); |
| 1835 | } |
| 1836 | struct structured_all_out_out final : public at::native::structured_all_out { |
| 1837 | structured_all_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1838 | void set_output_strided( |
| 1839 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1840 | TensorOptions options, DimnameList names |
| 1841 | ) override { |
| 1842 | const auto& out = outputs_[output_idx].get(); |
| 1843 | resize_out(out, sizes, strides, options); |
| 1844 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1845 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1846 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1847 | } |
| 1848 | if (!names.empty()) { |
| 1849 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1850 | } |
| 1851 | // super must happen after, so that downstream can use maybe_get_output |
| 1852 | // to retrieve the output |
| 1853 | } |
| 1854 | void set_output_raw_strided( |
| 1855 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1856 | TensorOptions options, DimnameList names |
| 1857 | ) override { |
| 1858 | const auto& out = outputs_[output_idx].get(); |
| 1859 | resize_out(out, sizes, strides, options); |
| 1860 | if (!names.empty()) { |
| 1861 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1862 | } |
| 1863 | // super must happen after, so that downstream can use maybe_get_output |
| 1864 | // to retrieve the output |
| 1865 | } |
| 1866 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1867 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1868 | } |
| 1869 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1870 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1871 | }; |
| 1872 | at::Tensor & wrapper_CPU_all_out_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 1873 | structured_all_out_out op(out); |
| 1874 | auto precompute = op.meta(self, dim, keepdim); |
| 1875 | (void)precompute; |
| 1876 | op.impl(self, precompute.dim, keepdim, op.maybe_get_output(0)); |
| 1877 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1878 | return out; |
| 1879 | } |
| 1880 | struct structured_any_out_functional final : public at::native::structured_any_out { |
| 1881 | void set_output_strided( |
| 1882 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1883 | TensorOptions options, DimnameList names |
| 1884 | ) override { |
| 1885 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1886 | if (!names.empty()) { |
| 1887 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1888 | } |
| 1889 | // super must happen after, so that downstream can use maybe_get_output |
| 1890 | // to retrieve the output |
| 1891 | } |
| 1892 | void set_output_raw_strided( |
| 1893 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1894 | TensorOptions options, DimnameList names |
| 1895 | ) override { |
| 1896 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1897 | if (!names.empty()) { |
| 1898 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1899 | } |
| 1900 | // super must happen after, so that downstream can use maybe_get_output |
| 1901 | // to retrieve the output |
| 1902 | } |
| 1903 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1904 | return *outputs_[output_idx]; |
| 1905 | } |
| 1906 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1907 | }; |
| 1908 | at::Tensor wrapper_CPU_any_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 1909 | structured_any_out_functional op; |
| 1910 | auto precompute = op.meta(self, dim, keepdim); |
| 1911 | (void)precompute; |
| 1912 | op.impl(self, precompute.dim, keepdim, *op.outputs_[0]); |
| 1913 | return std::move(op.outputs_[0]).take(); |
| 1914 | } |
| 1915 | struct structured_any_out_out final : public at::native::structured_any_out { |
| 1916 | structured_any_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 1917 | void set_output_strided( |
| 1918 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1919 | TensorOptions options, DimnameList names |
| 1920 | ) override { |
| 1921 | const auto& out = outputs_[output_idx].get(); |
| 1922 | resize_out(out, sizes, strides, options); |
| 1923 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1924 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1925 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1926 | } |
| 1927 | if (!names.empty()) { |
| 1928 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1929 | } |
| 1930 | // super must happen after, so that downstream can use maybe_get_output |
| 1931 | // to retrieve the output |
| 1932 | } |
| 1933 | void set_output_raw_strided( |
| 1934 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1935 | TensorOptions options, DimnameList names |
| 1936 | ) override { |
| 1937 | const auto& out = outputs_[output_idx].get(); |
| 1938 | resize_out(out, sizes, strides, options); |
| 1939 | if (!names.empty()) { |
| 1940 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1941 | } |
| 1942 | // super must happen after, so that downstream can use maybe_get_output |
| 1943 | // to retrieve the output |
| 1944 | } |
| 1945 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1946 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1947 | } |
| 1948 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1949 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1950 | }; |
| 1951 | at::Tensor & wrapper_CPU_any_out_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 1952 | structured_any_out_out op(out); |
| 1953 | auto precompute = op.meta(self, dim, keepdim); |
| 1954 | (void)precompute; |
| 1955 | op.impl(self, precompute.dim, keepdim, op.maybe_get_output(0)); |
| 1956 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1957 | return out; |
| 1958 | } |
| 1959 | namespace { |
| 1960 | at::Tensor & wrapper_CPU_start_out_arange_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 1961 | // No device check |
| 1962 | // DeviceGuard omitted |
| 1963 | return at::native::arange_out(start, end, step, out); |
| 1964 | } |
| 1965 | } // anonymous namespace |
| 1966 | struct structured_argmax_out_functional final : public at::native::structured_argmax_out { |
| 1967 | void set_output_strided( |
| 1968 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1969 | TensorOptions options, DimnameList names |
| 1970 | ) override { |
| 1971 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1972 | if (!names.empty()) { |
| 1973 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1974 | } |
| 1975 | // super must happen after, so that downstream can use maybe_get_output |
| 1976 | // to retrieve the output |
| 1977 | } |
| 1978 | void set_output_raw_strided( |
| 1979 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1980 | TensorOptions options, DimnameList names |
| 1981 | ) override { |
| 1982 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1983 | if (!names.empty()) { |
| 1984 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1985 | } |
| 1986 | // super must happen after, so that downstream can use maybe_get_output |
| 1987 | // to retrieve the output |
| 1988 | } |
| 1989 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1990 | return *outputs_[output_idx]; |
| 1991 | } |
| 1992 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1993 | }; |
| 1994 | at::Tensor wrapper_CPU_argmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 1995 | structured_argmax_out_functional op; |
| 1996 | op.meta(self, dim, keepdim); |
| 1997 | op.impl(self, dim, keepdim, *op.outputs_[0]); |
| 1998 | return std::move(op.outputs_[0]).take(); |
| 1999 | } |
| 2000 | struct structured_argmax_out_out final : public at::native::structured_argmax_out { |
| 2001 | structured_argmax_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2002 | void set_output_strided( |
| 2003 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2004 | TensorOptions options, DimnameList names |
| 2005 | ) override { |
| 2006 | const auto& out = outputs_[output_idx].get(); |
| 2007 | resize_out(out, sizes, strides, options); |
| 2008 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2009 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2010 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2011 | } |
| 2012 | if (!names.empty()) { |
| 2013 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2014 | } |
| 2015 | // super must happen after, so that downstream can use maybe_get_output |
| 2016 | // to retrieve the output |
| 2017 | } |
| 2018 | void set_output_raw_strided( |
| 2019 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2020 | TensorOptions options, DimnameList names |
| 2021 | ) override { |
| 2022 | const auto& out = outputs_[output_idx].get(); |
| 2023 | resize_out(out, sizes, strides, options); |
| 2024 | if (!names.empty()) { |
| 2025 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2026 | } |
| 2027 | // super must happen after, so that downstream can use maybe_get_output |
| 2028 | // to retrieve the output |
| 2029 | } |
| 2030 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2031 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2032 | } |
| 2033 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2034 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2035 | }; |
| 2036 | at::Tensor & wrapper_CPU_argmax_out_out(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 2037 | structured_argmax_out_out op(out); |
| 2038 | op.meta(self, dim, keepdim); |
| 2039 | op.impl(self, dim, keepdim, op.maybe_get_output(0)); |
| 2040 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2041 | return out; |
| 2042 | } |
| 2043 | struct structured_argmin_out_functional final : public at::native::structured_argmin_out { |
| 2044 | void set_output_strided( |
| 2045 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2046 | TensorOptions options, DimnameList names |
| 2047 | ) override { |
| 2048 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2049 | if (!names.empty()) { |
| 2050 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2051 | } |
| 2052 | // super must happen after, so that downstream can use maybe_get_output |
| 2053 | // to retrieve the output |
| 2054 | } |
| 2055 | void set_output_raw_strided( |
| 2056 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2057 | TensorOptions options, DimnameList names |
| 2058 | ) override { |
| 2059 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2060 | if (!names.empty()) { |
| 2061 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2062 | } |
| 2063 | // super must happen after, so that downstream can use maybe_get_output |
| 2064 | // to retrieve the output |
| 2065 | } |
| 2066 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2067 | return *outputs_[output_idx]; |
| 2068 | } |
| 2069 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2070 | }; |
| 2071 | at::Tensor wrapper_CPU_argmin(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 2072 | structured_argmin_out_functional op; |
| 2073 | op.meta(self, dim, keepdim); |
| 2074 | op.impl(self, dim, keepdim, *op.outputs_[0]); |
| 2075 | return std::move(op.outputs_[0]).take(); |
| 2076 | } |
| 2077 | struct structured_argmin_out_out final : public at::native::structured_argmin_out { |
| 2078 | structured_argmin_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2079 | void set_output_strided( |
| 2080 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2081 | TensorOptions options, DimnameList names |
| 2082 | ) override { |
| 2083 | const auto& out = outputs_[output_idx].get(); |
| 2084 | resize_out(out, sizes, strides, options); |
| 2085 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2086 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2087 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2088 | } |
| 2089 | if (!names.empty()) { |
| 2090 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2091 | } |
| 2092 | // super must happen after, so that downstream can use maybe_get_output |
| 2093 | // to retrieve the output |
| 2094 | } |
| 2095 | void set_output_raw_strided( |
| 2096 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2097 | TensorOptions options, DimnameList names |
| 2098 | ) override { |
| 2099 | const auto& out = outputs_[output_idx].get(); |
| 2100 | resize_out(out, sizes, strides, options); |
| 2101 | if (!names.empty()) { |
| 2102 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2103 | } |
| 2104 | // super must happen after, so that downstream can use maybe_get_output |
| 2105 | // to retrieve the output |
| 2106 | } |
| 2107 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2108 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2109 | } |
| 2110 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2111 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2112 | }; |
| 2113 | at::Tensor & wrapper_CPU_argmin_out_out(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 2114 | structured_argmin_out_out op(out); |
| 2115 | op.meta(self, dim, keepdim); |
| 2116 | op.impl(self, dim, keepdim, op.maybe_get_output(0)); |
| 2117 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2118 | return out; |
| 2119 | } |
| 2120 | struct structured_acosh_out_functional final : public at::native::structured_acosh_out { |
| 2121 | void set_output_strided( |
| 2122 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2123 | TensorOptions options, DimnameList names |
| 2124 | ) override { |
| 2125 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2126 | if (!names.empty()) { |
| 2127 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2128 | } |
| 2129 | // super must happen after, so that downstream can use maybe_get_output |
| 2130 | // to retrieve the output |
| 2131 | at::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2132 | } |
| 2133 | void set_output_raw_strided( |
| 2134 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2135 | TensorOptions options, DimnameList names |
| 2136 | ) override { |
| 2137 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 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::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2144 | } |
| 2145 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2146 | return *outputs_[output_idx]; |
| 2147 | } |
| 2148 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2149 | }; |
| 2150 | at::Tensor wrapper_CPU_acosh(const at::Tensor & self) { |
| 2151 | structured_acosh_out_functional op; |
| 2152 | op.meta(self); |
| 2153 | op.impl(self, *op.outputs_[0]); |
| 2154 | return std::move(op.outputs_[0]).take(); |
| 2155 | } |
| 2156 | struct structured_acosh_out_out final : public at::native::structured_acosh_out { |
| 2157 | structured_acosh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2158 | void set_output_strided( |
| 2159 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2160 | TensorOptions options, DimnameList names |
| 2161 | ) override { |
| 2162 | const auto& out = outputs_[output_idx].get(); |
| 2163 | resize_out(out, sizes, strides, options); |
| 2164 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2165 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2166 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2167 | } |
| 2168 | if (!names.empty()) { |
| 2169 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2170 | } |
| 2171 | // super must happen after, so that downstream can use maybe_get_output |
| 2172 | // to retrieve the output |
| 2173 | at::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2174 | } |
| 2175 | void set_output_raw_strided( |
| 2176 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2177 | TensorOptions options, DimnameList names |
| 2178 | ) override { |
| 2179 | const auto& out = outputs_[output_idx].get(); |
| 2180 | resize_out(out, sizes, strides, options); |
| 2181 | if (!names.empty()) { |
| 2182 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2183 | } |
| 2184 | // super must happen after, so that downstream can use maybe_get_output |
| 2185 | // to retrieve the output |
| 2186 | at::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2187 | } |
| 2188 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2189 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2190 | } |
| 2191 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2192 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2193 | }; |
| 2194 | at::Tensor & wrapper_CPU_acosh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 2195 | structured_acosh_out_out op(out); |
| 2196 | op.meta(self); |
| 2197 | op.impl(self, op.maybe_get_output(0)); |
| 2198 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2199 | return out; |
| 2200 | } |
| 2201 | struct structured_acosh_out_inplace final : public at::native::structured_acosh_out { |
| 2202 | structured_acosh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2203 | void set_output_strided( |
| 2204 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2205 | TensorOptions options, DimnameList names |
| 2206 | ) override { |
| 2207 | const auto& out = outputs_[output_idx].get(); |
| 2208 | check_inplace(out, sizes, options); |
| 2209 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2210 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2211 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2212 | } |
| 2213 | if (!names.empty()) { |
| 2214 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2215 | } |
| 2216 | // super must happen after, so that downstream can use maybe_get_output |
| 2217 | // to retrieve the output |
| 2218 | at::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2219 | } |
| 2220 | void set_output_raw_strided( |
| 2221 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2222 | TensorOptions options, DimnameList names |
| 2223 | ) override { |
| 2224 | const auto& out = outputs_[output_idx].get(); |
| 2225 | check_inplace(out, sizes, options); |
| 2226 | if (!names.empty()) { |
| 2227 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2228 | } |
| 2229 | // super must happen after, so that downstream can use maybe_get_output |
| 2230 | // to retrieve the output |
| 2231 | at::native::structured_acosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2232 | } |
| 2233 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2234 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2235 | } |
| 2236 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2237 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2238 | }; |
| 2239 | at::Tensor & wrapper_CPU_acosh_(at::Tensor & self) { |
| 2240 | structured_acosh_out_inplace op(self); |
| 2241 | op.meta(self); |
| 2242 | op.impl(self, op.outputs_[0]); |
| 2243 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2244 | return self; |
| 2245 | } |
| 2246 | struct structured_asinh_out_functional final : public at::native::structured_asinh_out { |
| 2247 | void set_output_strided( |
| 2248 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2249 | TensorOptions options, DimnameList names |
| 2250 | ) override { |
| 2251 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2252 | if (!names.empty()) { |
| 2253 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2254 | } |
| 2255 | // super must happen after, so that downstream can use maybe_get_output |
| 2256 | // to retrieve the output |
| 2257 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2258 | } |
| 2259 | void set_output_raw_strided( |
| 2260 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2261 | TensorOptions options, DimnameList names |
| 2262 | ) override { |
| 2263 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2264 | if (!names.empty()) { |
| 2265 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2266 | } |
| 2267 | // super must happen after, so that downstream can use maybe_get_output |
| 2268 | // to retrieve the output |
| 2269 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2270 | } |
| 2271 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2272 | return *outputs_[output_idx]; |
| 2273 | } |
| 2274 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2275 | }; |
| 2276 | at::Tensor wrapper_CPU_asinh(const at::Tensor & self) { |
| 2277 | structured_asinh_out_functional op; |
| 2278 | op.meta(self); |
| 2279 | op.impl(self, *op.outputs_[0]); |
| 2280 | return std::move(op.outputs_[0]).take(); |
| 2281 | } |
| 2282 | struct structured_asinh_out_out final : public at::native::structured_asinh_out { |
| 2283 | structured_asinh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2284 | void set_output_strided( |
| 2285 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2286 | TensorOptions options, DimnameList names |
| 2287 | ) override { |
| 2288 | const auto& out = outputs_[output_idx].get(); |
| 2289 | resize_out(out, sizes, strides, options); |
| 2290 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2291 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2292 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2293 | } |
| 2294 | if (!names.empty()) { |
| 2295 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2296 | } |
| 2297 | // super must happen after, so that downstream can use maybe_get_output |
| 2298 | // to retrieve the output |
| 2299 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2300 | } |
| 2301 | void set_output_raw_strided( |
| 2302 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2303 | TensorOptions options, DimnameList names |
| 2304 | ) override { |
| 2305 | const auto& out = outputs_[output_idx].get(); |
| 2306 | resize_out(out, sizes, strides, options); |
| 2307 | if (!names.empty()) { |
| 2308 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2309 | } |
| 2310 | // super must happen after, so that downstream can use maybe_get_output |
| 2311 | // to retrieve the output |
| 2312 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2313 | } |
| 2314 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2315 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2316 | } |
| 2317 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2318 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2319 | }; |
| 2320 | at::Tensor & wrapper_CPU_asinh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 2321 | structured_asinh_out_out op(out); |
| 2322 | op.meta(self); |
| 2323 | op.impl(self, op.maybe_get_output(0)); |
| 2324 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2325 | return out; |
| 2326 | } |
| 2327 | struct structured_asinh_out_inplace final : public at::native::structured_asinh_out { |
| 2328 | structured_asinh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2329 | void set_output_strided( |
| 2330 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2331 | TensorOptions options, DimnameList names |
| 2332 | ) override { |
| 2333 | const auto& out = outputs_[output_idx].get(); |
| 2334 | check_inplace(out, sizes, options); |
| 2335 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2336 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2337 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2338 | } |
| 2339 | if (!names.empty()) { |
| 2340 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2341 | } |
| 2342 | // super must happen after, so that downstream can use maybe_get_output |
| 2343 | // to retrieve the output |
| 2344 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2345 | } |
| 2346 | void set_output_raw_strided( |
| 2347 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2348 | TensorOptions options, DimnameList names |
| 2349 | ) override { |
| 2350 | const auto& out = outputs_[output_idx].get(); |
| 2351 | check_inplace(out, sizes, options); |
| 2352 | if (!names.empty()) { |
| 2353 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2354 | } |
| 2355 | // super must happen after, so that downstream can use maybe_get_output |
| 2356 | // to retrieve the output |
| 2357 | at::native::structured_asinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2358 | } |
| 2359 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2360 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2361 | } |
| 2362 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2363 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2364 | }; |
| 2365 | at::Tensor & wrapper_CPU_asinh_(at::Tensor & self) { |
| 2366 | structured_asinh_out_inplace op(self); |
| 2367 | op.meta(self); |
| 2368 | op.impl(self, op.outputs_[0]); |
| 2369 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2370 | return self; |
| 2371 | } |
| 2372 | struct structured_atanh_out_functional final : public at::native::structured_atanh_out { |
| 2373 | void set_output_strided( |
| 2374 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2375 | TensorOptions options, DimnameList names |
| 2376 | ) override { |
| 2377 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2378 | if (!names.empty()) { |
| 2379 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2380 | } |
| 2381 | // super must happen after, so that downstream can use maybe_get_output |
| 2382 | // to retrieve the output |
| 2383 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2384 | } |
| 2385 | void set_output_raw_strided( |
| 2386 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2387 | TensorOptions options, DimnameList names |
| 2388 | ) override { |
| 2389 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2390 | if (!names.empty()) { |
| 2391 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2392 | } |
| 2393 | // super must happen after, so that downstream can use maybe_get_output |
| 2394 | // to retrieve the output |
| 2395 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2396 | } |
| 2397 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2398 | return *outputs_[output_idx]; |
| 2399 | } |
| 2400 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2401 | }; |
| 2402 | at::Tensor wrapper_CPU_atanh(const at::Tensor & self) { |
| 2403 | structured_atanh_out_functional op; |
| 2404 | op.meta(self); |
| 2405 | op.impl(self, *op.outputs_[0]); |
| 2406 | return std::move(op.outputs_[0]).take(); |
| 2407 | } |
| 2408 | struct structured_atanh_out_out final : public at::native::structured_atanh_out { |
| 2409 | structured_atanh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2410 | void set_output_strided( |
| 2411 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2412 | TensorOptions options, DimnameList names |
| 2413 | ) override { |
| 2414 | const auto& out = outputs_[output_idx].get(); |
| 2415 | resize_out(out, sizes, strides, options); |
| 2416 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2417 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2418 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2419 | } |
| 2420 | if (!names.empty()) { |
| 2421 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2422 | } |
| 2423 | // super must happen after, so that downstream can use maybe_get_output |
| 2424 | // to retrieve the output |
| 2425 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2426 | } |
| 2427 | void set_output_raw_strided( |
| 2428 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2429 | TensorOptions options, DimnameList names |
| 2430 | ) override { |
| 2431 | const auto& out = outputs_[output_idx].get(); |
| 2432 | resize_out(out, sizes, strides, options); |
| 2433 | if (!names.empty()) { |
| 2434 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2435 | } |
| 2436 | // super must happen after, so that downstream can use maybe_get_output |
| 2437 | // to retrieve the output |
| 2438 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2439 | } |
| 2440 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2441 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2442 | } |
| 2443 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2444 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2445 | }; |
| 2446 | at::Tensor & wrapper_CPU_atanh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 2447 | structured_atanh_out_out op(out); |
| 2448 | op.meta(self); |
| 2449 | op.impl(self, op.maybe_get_output(0)); |
| 2450 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2451 | return out; |
| 2452 | } |
| 2453 | struct structured_atanh_out_inplace final : public at::native::structured_atanh_out { |
| 2454 | structured_atanh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2455 | void set_output_strided( |
| 2456 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2457 | TensorOptions options, DimnameList names |
| 2458 | ) override { |
| 2459 | const auto& out = outputs_[output_idx].get(); |
| 2460 | check_inplace(out, sizes, options); |
| 2461 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2462 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2463 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2464 | } |
| 2465 | if (!names.empty()) { |
| 2466 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2467 | } |
| 2468 | // super must happen after, so that downstream can use maybe_get_output |
| 2469 | // to retrieve the output |
| 2470 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2471 | } |
| 2472 | void set_output_raw_strided( |
| 2473 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2474 | TensorOptions options, DimnameList names |
| 2475 | ) override { |
| 2476 | const auto& out = outputs_[output_idx].get(); |
| 2477 | check_inplace(out, sizes, options); |
| 2478 | if (!names.empty()) { |
| 2479 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2480 | } |
| 2481 | // super must happen after, so that downstream can use maybe_get_output |
| 2482 | // to retrieve the output |
| 2483 | at::native::structured_atanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2484 | } |
| 2485 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2486 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2487 | } |
| 2488 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2489 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2490 | }; |
| 2491 | at::Tensor & wrapper_CPU_atanh_(at::Tensor & self) { |
| 2492 | structured_atanh_out_inplace op(self); |
| 2493 | op.meta(self); |
| 2494 | op.impl(self, op.outputs_[0]); |
| 2495 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2496 | return self; |
| 2497 | } |
| 2498 | namespace { |
| 2499 | at::Tensor wrapper_CPU__as_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 2500 | // No device check |
| 2501 | // DeviceGuard omitted |
| 2502 | return at::native::as_strided_tensorimpl(self, C10_AS_INTARRAYREF_SLOW(size), C10_AS_INTARRAYREF_SLOW(stride), storage_offset.has_value() ? c10::make_optional(storage_offset->expect_int()) : c10::nullopt); |
| 2503 | } |
| 2504 | } // anonymous namespace |
| 2505 | struct structured_asin_out_functional final : public at::native::structured_asin_out { |
| 2506 | void set_output_strided( |
| 2507 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2508 | TensorOptions options, DimnameList names |
| 2509 | ) override { |
| 2510 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2511 | if (!names.empty()) { |
| 2512 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2513 | } |
| 2514 | // super must happen after, so that downstream can use maybe_get_output |
| 2515 | // to retrieve the output |
| 2516 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2517 | } |
| 2518 | void set_output_raw_strided( |
| 2519 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2520 | TensorOptions options, DimnameList names |
| 2521 | ) override { |
| 2522 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2523 | if (!names.empty()) { |
| 2524 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2525 | } |
| 2526 | // super must happen after, so that downstream can use maybe_get_output |
| 2527 | // to retrieve the output |
| 2528 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2529 | } |
| 2530 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2531 | return *outputs_[output_idx]; |
| 2532 | } |
| 2533 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2534 | }; |
| 2535 | at::Tensor wrapper_CPU_asin(const at::Tensor & self) { |
| 2536 | structured_asin_out_functional op; |
| 2537 | op.meta(self); |
| 2538 | op.impl(self, *op.outputs_[0]); |
| 2539 | return std::move(op.outputs_[0]).take(); |
| 2540 | } |
| 2541 | struct structured_asin_out_out final : public at::native::structured_asin_out { |
| 2542 | structured_asin_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2543 | void set_output_strided( |
| 2544 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2545 | TensorOptions options, DimnameList names |
| 2546 | ) override { |
| 2547 | const auto& out = outputs_[output_idx].get(); |
| 2548 | resize_out(out, sizes, strides, options); |
| 2549 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2550 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2551 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2552 | } |
| 2553 | if (!names.empty()) { |
| 2554 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2555 | } |
| 2556 | // super must happen after, so that downstream can use maybe_get_output |
| 2557 | // to retrieve the output |
| 2558 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2559 | } |
| 2560 | void set_output_raw_strided( |
| 2561 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2562 | TensorOptions options, DimnameList names |
| 2563 | ) override { |
| 2564 | const auto& out = outputs_[output_idx].get(); |
| 2565 | resize_out(out, sizes, strides, options); |
| 2566 | if (!names.empty()) { |
| 2567 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2568 | } |
| 2569 | // super must happen after, so that downstream can use maybe_get_output |
| 2570 | // to retrieve the output |
| 2571 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2572 | } |
| 2573 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2574 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2575 | } |
| 2576 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2577 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2578 | }; |
| 2579 | at::Tensor & wrapper_CPU_asin_out_out(const at::Tensor & self, at::Tensor & out) { |
| 2580 | structured_asin_out_out op(out); |
| 2581 | op.meta(self); |
| 2582 | op.impl(self, op.maybe_get_output(0)); |
| 2583 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2584 | return out; |
| 2585 | } |
| 2586 | struct structured_asin_out_inplace final : public at::native::structured_asin_out { |
| 2587 | structured_asin_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2588 | void set_output_strided( |
| 2589 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2590 | TensorOptions options, DimnameList names |
| 2591 | ) override { |
| 2592 | const auto& out = outputs_[output_idx].get(); |
| 2593 | check_inplace(out, sizes, options); |
| 2594 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2595 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2596 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2597 | } |
| 2598 | if (!names.empty()) { |
| 2599 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2600 | } |
| 2601 | // super must happen after, so that downstream can use maybe_get_output |
| 2602 | // to retrieve the output |
| 2603 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2604 | } |
| 2605 | void set_output_raw_strided( |
| 2606 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2607 | TensorOptions options, DimnameList names |
| 2608 | ) override { |
| 2609 | const auto& out = outputs_[output_idx].get(); |
| 2610 | check_inplace(out, sizes, options); |
| 2611 | if (!names.empty()) { |
| 2612 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2613 | } |
| 2614 | // super must happen after, so that downstream can use maybe_get_output |
| 2615 | // to retrieve the output |
| 2616 | at::native::structured_asin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2617 | } |
| 2618 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2619 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2620 | } |
| 2621 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2622 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2623 | }; |
| 2624 | at::Tensor & wrapper_CPU_asin_(at::Tensor & self) { |
| 2625 | structured_asin_out_inplace op(self); |
| 2626 | op.meta(self); |
| 2627 | op.impl(self, op.outputs_[0]); |
| 2628 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2629 | return self; |
| 2630 | } |
| 2631 | struct structured_atan_out_functional final : public at::native::structured_atan_out { |
| 2632 | void set_output_strided( |
| 2633 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2634 | TensorOptions options, DimnameList names |
| 2635 | ) override { |
| 2636 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2637 | if (!names.empty()) { |
| 2638 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2639 | } |
| 2640 | // super must happen after, so that downstream can use maybe_get_output |
| 2641 | // to retrieve the output |
| 2642 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2643 | } |
| 2644 | void set_output_raw_strided( |
| 2645 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2646 | TensorOptions options, DimnameList names |
| 2647 | ) override { |
| 2648 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2649 | if (!names.empty()) { |
| 2650 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2651 | } |
| 2652 | // super must happen after, so that downstream can use maybe_get_output |
| 2653 | // to retrieve the output |
| 2654 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2655 | } |
| 2656 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2657 | return *outputs_[output_idx]; |
| 2658 | } |
| 2659 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2660 | }; |
| 2661 | at::Tensor wrapper_CPU_atan(const at::Tensor & self) { |
| 2662 | structured_atan_out_functional op; |
| 2663 | op.meta(self); |
| 2664 | op.impl(self, *op.outputs_[0]); |
| 2665 | return std::move(op.outputs_[0]).take(); |
| 2666 | } |
| 2667 | struct structured_atan_out_out final : public at::native::structured_atan_out { |
| 2668 | structured_atan_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2669 | void set_output_strided( |
| 2670 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2671 | TensorOptions options, DimnameList names |
| 2672 | ) override { |
| 2673 | const auto& out = outputs_[output_idx].get(); |
| 2674 | resize_out(out, sizes, strides, options); |
| 2675 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2676 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2677 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2678 | } |
| 2679 | if (!names.empty()) { |
| 2680 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2681 | } |
| 2682 | // super must happen after, so that downstream can use maybe_get_output |
| 2683 | // to retrieve the output |
| 2684 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2685 | } |
| 2686 | void set_output_raw_strided( |
| 2687 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2688 | TensorOptions options, DimnameList names |
| 2689 | ) override { |
| 2690 | const auto& out = outputs_[output_idx].get(); |
| 2691 | resize_out(out, sizes, strides, options); |
| 2692 | if (!names.empty()) { |
| 2693 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2694 | } |
| 2695 | // super must happen after, so that downstream can use maybe_get_output |
| 2696 | // to retrieve the output |
| 2697 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2698 | } |
| 2699 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2700 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2701 | } |
| 2702 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2703 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2704 | }; |
| 2705 | at::Tensor & wrapper_CPU_atan_out_out(const at::Tensor & self, at::Tensor & out) { |
| 2706 | structured_atan_out_out op(out); |
| 2707 | op.meta(self); |
| 2708 | op.impl(self, op.maybe_get_output(0)); |
| 2709 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2710 | return out; |
| 2711 | } |
| 2712 | struct structured_atan_out_inplace final : public at::native::structured_atan_out { |
| 2713 | structured_atan_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2714 | void set_output_strided( |
| 2715 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2716 | TensorOptions options, DimnameList names |
| 2717 | ) override { |
| 2718 | const auto& out = outputs_[output_idx].get(); |
| 2719 | check_inplace(out, sizes, options); |
| 2720 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2721 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2722 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2723 | } |
| 2724 | if (!names.empty()) { |
| 2725 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2726 | } |
| 2727 | // super must happen after, so that downstream can use maybe_get_output |
| 2728 | // to retrieve the output |
| 2729 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2730 | } |
| 2731 | void set_output_raw_strided( |
| 2732 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2733 | TensorOptions options, DimnameList names |
| 2734 | ) override { |
| 2735 | const auto& out = outputs_[output_idx].get(); |
| 2736 | check_inplace(out, sizes, options); |
| 2737 | if (!names.empty()) { |
| 2738 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2739 | } |
| 2740 | // super must happen after, so that downstream can use maybe_get_output |
| 2741 | // to retrieve the output |
| 2742 | at::native::structured_atan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2743 | } |
| 2744 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2745 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2746 | } |
| 2747 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2748 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2749 | }; |
| 2750 | at::Tensor & wrapper_CPU_atan_(at::Tensor & self) { |
| 2751 | structured_atan_out_inplace op(self); |
| 2752 | op.meta(self); |
| 2753 | op.impl(self, op.outputs_[0]); |
| 2754 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2755 | return self; |
| 2756 | } |
| 2757 | struct structured_baddbmm_out_cpu_functional final : public at::native::structured_baddbmm_out_cpu { |
| 2758 | void set_output_strided( |
| 2759 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2760 | TensorOptions options, DimnameList names |
| 2761 | ) override { |
| 2762 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2763 | if (!names.empty()) { |
| 2764 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2765 | } |
| 2766 | // super must happen after, so that downstream can use maybe_get_output |
| 2767 | // to retrieve the output |
| 2768 | } |
| 2769 | void set_output_raw_strided( |
| 2770 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2771 | TensorOptions options, DimnameList names |
| 2772 | ) override { |
| 2773 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2774 | if (!names.empty()) { |
| 2775 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2776 | } |
| 2777 | // super must happen after, so that downstream can use maybe_get_output |
| 2778 | // to retrieve the output |
| 2779 | } |
| 2780 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2781 | return *outputs_[output_idx]; |
| 2782 | } |
| 2783 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2784 | }; |
| 2785 | at::Tensor wrapper_CPU_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2786 | structured_baddbmm_out_cpu_functional op; |
| 2787 | op.meta(self, batch1, batch2, beta, alpha); |
| 2788 | op.impl(self, batch1, batch2, beta, alpha, *op.outputs_[0]); |
| 2789 | return std::move(op.outputs_[0]).take(); |
| 2790 | } |
| 2791 | struct structured_baddbmm_out_cpu_out final : public at::native::structured_baddbmm_out_cpu { |
| 2792 | structured_baddbmm_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2793 | void set_output_strided( |
| 2794 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2795 | TensorOptions options, DimnameList names |
| 2796 | ) override { |
| 2797 | const auto& out = outputs_[output_idx].get(); |
| 2798 | resize_out(out, sizes, strides, options); |
| 2799 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2800 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2801 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2802 | } |
| 2803 | if (!names.empty()) { |
| 2804 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2805 | } |
| 2806 | // super must happen after, so that downstream can use maybe_get_output |
| 2807 | // to retrieve the output |
| 2808 | } |
| 2809 | void set_output_raw_strided( |
| 2810 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2811 | TensorOptions options, DimnameList names |
| 2812 | ) override { |
| 2813 | const auto& out = outputs_[output_idx].get(); |
| 2814 | resize_out(out, sizes, strides, 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 | } |
| 2821 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2822 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2823 | } |
| 2824 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2825 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2826 | }; |
| 2827 | at::Tensor & wrapper_CPU_baddbmm_out_out(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 2828 | structured_baddbmm_out_cpu_out op(out); |
| 2829 | op.meta(self, batch1, batch2, beta, alpha); |
| 2830 | op.impl(self, batch1, batch2, beta, alpha, op.maybe_get_output(0)); |
| 2831 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2832 | return out; |
| 2833 | } |
| 2834 | struct structured_baddbmm_out_cpu_inplace final : public at::native::structured_baddbmm_out_cpu { |
| 2835 | structured_baddbmm_out_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2836 | void set_output_strided( |
| 2837 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2838 | TensorOptions options, DimnameList names |
| 2839 | ) override { |
| 2840 | const auto& out = outputs_[output_idx].get(); |
| 2841 | check_inplace(out, sizes, options); |
| 2842 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2843 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2844 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2845 | } |
| 2846 | if (!names.empty()) { |
| 2847 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2848 | } |
| 2849 | // super must happen after, so that downstream can use maybe_get_output |
| 2850 | // to retrieve the output |
| 2851 | } |
| 2852 | void set_output_raw_strided( |
| 2853 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2854 | TensorOptions options, DimnameList names |
| 2855 | ) override { |
| 2856 | const auto& out = outputs_[output_idx].get(); |
| 2857 | check_inplace(out, sizes, options); |
| 2858 | if (!names.empty()) { |
| 2859 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2860 | } |
| 2861 | // super must happen after, so that downstream can use maybe_get_output |
| 2862 | // to retrieve the output |
| 2863 | } |
| 2864 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2865 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2866 | } |
| 2867 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2868 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2869 | }; |
| 2870 | at::Tensor & wrapper_CPU_baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2871 | structured_baddbmm_out_cpu_inplace op(self); |
| 2872 | op.meta(self, batch1, batch2, beta, alpha); |
| 2873 | op.impl(self, batch1, batch2, beta, alpha, op.outputs_[0]); |
| 2874 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2875 | return self; |
| 2876 | } |
| 2877 | namespace { |
| 2878 | at::Tensor & wrapper_CPU_out_bernoulli_out(const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 2879 | // No device check |
| 2880 | // DeviceGuard omitted |
| 2881 | return at::native::bernoulli_out(self, generator, out); |
| 2882 | } |
| 2883 | } // anonymous namespace |
| 2884 | namespace { |
| 2885 | at::Tensor & wrapper_CPU_Tensor_bernoulli_(at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator) { |
| 2886 | // No device check |
| 2887 | // DeviceGuard omitted |
| 2888 | return at::native::bernoulli_(self, p, generator); |
| 2889 | } |
| 2890 | } // anonymous namespace |
| 2891 | namespace { |
| 2892 | at::Tensor & wrapper_CPU_float_bernoulli_(at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 2893 | // No device check |
| 2894 | // DeviceGuard omitted |
| 2895 | return at::native::bernoulli_(self, p, generator); |
| 2896 | } |
| 2897 | } // anonymous namespace |
| 2898 | namespace { |
| 2899 | at::Tensor wrapper_CPU__binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 2900 | // No device check |
| 2901 | // DeviceGuard omitted |
| 2902 | return at::native::binary_cross_entropy_cpu(self, target, weight, reduction); |
| 2903 | } |
| 2904 | } // anonymous namespace |
| 2905 | namespace { |
| 2906 | at::Tensor & wrapper_CPU_out_binary_cross_entropy_out(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 2907 | // No device check |
| 2908 | // DeviceGuard omitted |
| 2909 | return at::native::binary_cross_entropy_out_cpu(self, target, weight, reduction, out); |
| 2910 | } |
| 2911 | } // anonymous namespace |
| 2912 | namespace { |
| 2913 | at::Tensor wrapper_CPU__binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 2914 | // No device check |
| 2915 | // DeviceGuard omitted |
| 2916 | return at::native::binary_cross_entropy_backward_cpu(grad_output, self, target, weight, reduction); |
| 2917 | } |
| 2918 | } // anonymous namespace |
| 2919 | namespace { |
| 2920 | at::Tensor & wrapper_CPU_grad_input_binary_cross_entropy_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) { |
| 2921 | // No device check |
| 2922 | // DeviceGuard omitted |
| 2923 | return at::native::binary_cross_entropy_backward_out_cpu(grad_output, self, target, weight, reduction, grad_input); |
| 2924 | } |
| 2925 | } // anonymous namespace |
| 2926 | namespace { |
| 2927 | at::Tensor wrapper_CPU__bincount(const at::Tensor & self, const c10::optional<at::Tensor> & weights, int64_t minlength) { |
| 2928 | // No device check |
| 2929 | // DeviceGuard omitted |
| 2930 | return at::native::_bincount_cpu(self, weights, minlength); |
| 2931 | } |
| 2932 | } // anonymous namespace |
| 2933 | struct structured_bitwise_not_out_functional final : public at::native::structured_bitwise_not_out { |
| 2934 | void set_output_strided( |
| 2935 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2936 | TensorOptions options, DimnameList names |
| 2937 | ) override { |
| 2938 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2939 | if (!names.empty()) { |
| 2940 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2941 | } |
| 2942 | // super must happen after, so that downstream can use maybe_get_output |
| 2943 | // to retrieve the output |
| 2944 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2945 | } |
| 2946 | void set_output_raw_strided( |
| 2947 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2948 | TensorOptions options, DimnameList names |
| 2949 | ) override { |
| 2950 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2951 | if (!names.empty()) { |
| 2952 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2953 | } |
| 2954 | // super must happen after, so that downstream can use maybe_get_output |
| 2955 | // to retrieve the output |
| 2956 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2957 | } |
| 2958 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2959 | return *outputs_[output_idx]; |
| 2960 | } |
| 2961 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2962 | }; |
| 2963 | at::Tensor wrapper_CPU_bitwise_not(const at::Tensor & self) { |
| 2964 | structured_bitwise_not_out_functional op; |
| 2965 | op.meta(self); |
| 2966 | op.impl(self, *op.outputs_[0]); |
| 2967 | return std::move(op.outputs_[0]).take(); |
| 2968 | } |
| 2969 | struct structured_bitwise_not_out_out final : public at::native::structured_bitwise_not_out { |
| 2970 | structured_bitwise_not_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 2971 | void set_output_strided( |
| 2972 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2973 | TensorOptions options, DimnameList names |
| 2974 | ) override { |
| 2975 | const auto& out = outputs_[output_idx].get(); |
| 2976 | resize_out(out, sizes, strides, options); |
| 2977 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2978 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2979 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2980 | } |
| 2981 | if (!names.empty()) { |
| 2982 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2983 | } |
| 2984 | // super must happen after, so that downstream can use maybe_get_output |
| 2985 | // to retrieve the output |
| 2986 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2987 | } |
| 2988 | void set_output_raw_strided( |
| 2989 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2990 | TensorOptions options, DimnameList names |
| 2991 | ) override { |
| 2992 | const auto& out = outputs_[output_idx].get(); |
| 2993 | resize_out(out, sizes, strides, options); |
| 2994 | if (!names.empty()) { |
| 2995 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2996 | } |
| 2997 | // super must happen after, so that downstream can use maybe_get_output |
| 2998 | // to retrieve the output |
| 2999 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3000 | } |
| 3001 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3002 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3003 | } |
| 3004 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3005 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3006 | }; |
| 3007 | at::Tensor & wrapper_CPU_bitwise_not_out_out(const at::Tensor & self, at::Tensor & out) { |
| 3008 | structured_bitwise_not_out_out op(out); |
| 3009 | op.meta(self); |
| 3010 | op.impl(self, op.maybe_get_output(0)); |
| 3011 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3012 | return out; |
| 3013 | } |
| 3014 | struct structured_bitwise_not_out_inplace final : public at::native::structured_bitwise_not_out { |
| 3015 | structured_bitwise_not_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3016 | void set_output_strided( |
| 3017 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3018 | TensorOptions options, DimnameList names |
| 3019 | ) override { |
| 3020 | const auto& out = outputs_[output_idx].get(); |
| 3021 | check_inplace(out, sizes, options); |
| 3022 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3023 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3024 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3025 | } |
| 3026 | if (!names.empty()) { |
| 3027 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3028 | } |
| 3029 | // super must happen after, so that downstream can use maybe_get_output |
| 3030 | // to retrieve the output |
| 3031 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3032 | } |
| 3033 | void set_output_raw_strided( |
| 3034 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3035 | TensorOptions options, DimnameList names |
| 3036 | ) override { |
| 3037 | const auto& out = outputs_[output_idx].get(); |
| 3038 | check_inplace(out, sizes, options); |
| 3039 | if (!names.empty()) { |
| 3040 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3041 | } |
| 3042 | // super must happen after, so that downstream can use maybe_get_output |
| 3043 | // to retrieve the output |
| 3044 | at::native::structured_bitwise_not_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3045 | } |
| 3046 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3047 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3048 | } |
| 3049 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3050 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3051 | }; |
| 3052 | at::Tensor & wrapper_CPU_bitwise_not_(at::Tensor & self) { |
| 3053 | structured_bitwise_not_out_inplace op(self); |
| 3054 | op.meta(self); |
| 3055 | op.impl(self, op.outputs_[0]); |
| 3056 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3057 | return self; |
| 3058 | } |
| 3059 | struct structured_copysign_out_functional final : public at::native::structured_copysign_out { |
| 3060 | void set_output_strided( |
| 3061 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3062 | TensorOptions options, DimnameList names |
| 3063 | ) override { |
| 3064 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3065 | if (!names.empty()) { |
| 3066 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3067 | } |
| 3068 | // super must happen after, so that downstream can use maybe_get_output |
| 3069 | // to retrieve the output |
| 3070 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3071 | } |
| 3072 | void set_output_raw_strided( |
| 3073 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3074 | TensorOptions options, DimnameList names |
| 3075 | ) override { |
| 3076 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3077 | if (!names.empty()) { |
| 3078 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3079 | } |
| 3080 | // super must happen after, so that downstream can use maybe_get_output |
| 3081 | // to retrieve the output |
| 3082 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3083 | } |
| 3084 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3085 | return *outputs_[output_idx]; |
| 3086 | } |
| 3087 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3088 | }; |
| 3089 | at::Tensor wrapper_CPU_copysign_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 3090 | structured_copysign_out_functional op; |
| 3091 | op.meta(self, other); |
| 3092 | op.impl(self, other, *op.outputs_[0]); |
| 3093 | return std::move(op.outputs_[0]).take(); |
| 3094 | } |
| 3095 | struct structured_copysign_out_out final : public at::native::structured_copysign_out { |
| 3096 | structured_copysign_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3097 | void set_output_strided( |
| 3098 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3099 | TensorOptions options, DimnameList names |
| 3100 | ) override { |
| 3101 | const auto& out = outputs_[output_idx].get(); |
| 3102 | resize_out(out, sizes, strides, options); |
| 3103 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3104 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3105 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3106 | } |
| 3107 | if (!names.empty()) { |
| 3108 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3109 | } |
| 3110 | // super must happen after, so that downstream can use maybe_get_output |
| 3111 | // to retrieve the output |
| 3112 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3113 | } |
| 3114 | void set_output_raw_strided( |
| 3115 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3116 | TensorOptions options, DimnameList names |
| 3117 | ) override { |
| 3118 | const auto& out = outputs_[output_idx].get(); |
| 3119 | resize_out(out, sizes, strides, options); |
| 3120 | if (!names.empty()) { |
| 3121 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3122 | } |
| 3123 | // super must happen after, so that downstream can use maybe_get_output |
| 3124 | // to retrieve the output |
| 3125 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3126 | } |
| 3127 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3128 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3129 | } |
| 3130 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3131 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3132 | }; |
| 3133 | at::Tensor & wrapper_CPU_copysign_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3134 | structured_copysign_out_out op(out); |
| 3135 | op.meta(self, other); |
| 3136 | op.impl(self, other, op.maybe_get_output(0)); |
| 3137 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3138 | return out; |
| 3139 | } |
| 3140 | struct structured_copysign_out_inplace final : public at::native::structured_copysign_out { |
| 3141 | structured_copysign_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3142 | void set_output_strided( |
| 3143 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3144 | TensorOptions options, DimnameList names |
| 3145 | ) override { |
| 3146 | const auto& out = outputs_[output_idx].get(); |
| 3147 | check_inplace(out, sizes, options); |
| 3148 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3149 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3150 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3151 | } |
| 3152 | if (!names.empty()) { |
| 3153 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3154 | } |
| 3155 | // super must happen after, so that downstream can use maybe_get_output |
| 3156 | // to retrieve the output |
| 3157 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3158 | } |
| 3159 | void set_output_raw_strided( |
| 3160 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3161 | TensorOptions options, DimnameList names |
| 3162 | ) override { |
| 3163 | const auto& out = outputs_[output_idx].get(); |
| 3164 | check_inplace(out, sizes, options); |
| 3165 | if (!names.empty()) { |
| 3166 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3167 | } |
| 3168 | // super must happen after, so that downstream can use maybe_get_output |
| 3169 | // to retrieve the output |
| 3170 | at::native::structured_copysign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3171 | } |
| 3172 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3173 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3174 | } |
| 3175 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3176 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3177 | }; |
| 3178 | at::Tensor & wrapper_CPU_copysign__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 3179 | structured_copysign_out_inplace op(self); |
| 3180 | op.meta(self, other); |
| 3181 | op.impl(self, other, op.outputs_[0]); |
| 3182 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3183 | return self; |
| 3184 | } |
| 3185 | namespace { |
| 3186 | at::Tensor & wrapper_CPU_out_logical_not_out(const at::Tensor & self, at::Tensor & out) { |
| 3187 | // No device check |
| 3188 | // DeviceGuard omitted |
| 3189 | return at::native::logical_not_out(self, out); |
| 3190 | } |
| 3191 | } // anonymous namespace |
| 3192 | namespace { |
| 3193 | at::Tensor & wrapper_CPU_out_logical_xor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3194 | // No device check |
| 3195 | // DeviceGuard omitted |
| 3196 | return at::native::logical_xor_out(self, other, out); |
| 3197 | } |
| 3198 | } // anonymous namespace |
| 3199 | namespace { |
| 3200 | at::Tensor & wrapper_CPU_out_logical_and_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3201 | // No device check |
| 3202 | // DeviceGuard omitted |
| 3203 | return at::native::logical_and_out(self, other, out); |
| 3204 | } |
| 3205 | } // anonymous namespace |
| 3206 | namespace { |
| 3207 | at::Tensor & wrapper_CPU_out_logical_or_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3208 | // No device check |
| 3209 | // DeviceGuard omitted |
| 3210 | return at::native::logical_or_out(self, other, out); |
| 3211 | } |
| 3212 | } // anonymous namespace |
| 3213 | struct structured_bmm_out_cpu_functional final : public at::native::structured_bmm_out_cpu { |
| 3214 | void set_output_strided( |
| 3215 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3216 | TensorOptions options, DimnameList names |
| 3217 | ) override { |
| 3218 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3219 | if (!names.empty()) { |
| 3220 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3221 | } |
| 3222 | // super must happen after, so that downstream can use maybe_get_output |
| 3223 | // to retrieve the output |
| 3224 | } |
| 3225 | void set_output_raw_strided( |
| 3226 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3227 | TensorOptions options, DimnameList names |
| 3228 | ) override { |
| 3229 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3230 | if (!names.empty()) { |
| 3231 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3232 | } |
| 3233 | // super must happen after, so that downstream can use maybe_get_output |
| 3234 | // to retrieve the output |
| 3235 | } |
| 3236 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3237 | return *outputs_[output_idx]; |
| 3238 | } |
| 3239 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3240 | }; |
| 3241 | at::Tensor wrapper_CPU_bmm(const at::Tensor & self, const at::Tensor & mat2) { |
| 3242 | structured_bmm_out_cpu_functional op; |
| 3243 | op.meta(self, mat2); |
| 3244 | op.impl(self, mat2, *op.outputs_[0]); |
| 3245 | return std::move(op.outputs_[0]).take(); |
| 3246 | } |
| 3247 | struct structured_bmm_out_cpu_out final : public at::native::structured_bmm_out_cpu { |
| 3248 | structured_bmm_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3249 | void set_output_strided( |
| 3250 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3251 | TensorOptions options, DimnameList names |
| 3252 | ) override { |
| 3253 | const auto& out = outputs_[output_idx].get(); |
| 3254 | resize_out(out, sizes, strides, options); |
| 3255 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3256 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3257 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3258 | } |
| 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 | } |
| 3265 | void set_output_raw_strided( |
| 3266 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3267 | TensorOptions options, DimnameList names |
| 3268 | ) override { |
| 3269 | const auto& out = outputs_[output_idx].get(); |
| 3270 | resize_out(out, sizes, strides, options); |
| 3271 | if (!names.empty()) { |
| 3272 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3273 | } |
| 3274 | // super must happen after, so that downstream can use maybe_get_output |
| 3275 | // to retrieve the output |
| 3276 | } |
| 3277 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3278 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3279 | } |
| 3280 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3281 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3282 | }; |
| 3283 | at::Tensor & wrapper_CPU_bmm_out_out(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 3284 | structured_bmm_out_cpu_out op(out); |
| 3285 | op.meta(self, mat2); |
| 3286 | op.impl(self, mat2, op.maybe_get_output(0)); |
| 3287 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3288 | return out; |
| 3289 | } |
| 3290 | struct structured_cat_out_cpu_functional final : public at::native::structured_cat_out_cpu { |
| 3291 | void set_output_strided( |
| 3292 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3293 | TensorOptions options, DimnameList names |
| 3294 | ) override { |
| 3295 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3296 | if (!names.empty()) { |
| 3297 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3298 | } |
| 3299 | // super must happen after, so that downstream can use maybe_get_output |
| 3300 | // to retrieve the output |
| 3301 | } |
| 3302 | void set_output_raw_strided( |
| 3303 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3304 | TensorOptions options, DimnameList names |
| 3305 | ) override { |
| 3306 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3307 | if (!names.empty()) { |
| 3308 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3309 | } |
| 3310 | // super must happen after, so that downstream can use maybe_get_output |
| 3311 | // to retrieve the output |
| 3312 | } |
| 3313 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3314 | return *outputs_[output_idx]; |
| 3315 | } |
| 3316 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3317 | }; |
| 3318 | at::Tensor wrapper_CPU_cat(const at::ITensorListRef & tensors, int64_t dim) { |
| 3319 | structured_cat_out_cpu_functional op; |
| 3320 | auto precompute = op.meta(tensors, dim); |
| 3321 | (void)precompute; |
| 3322 | op.impl(tensors, precompute.dim, precompute.valid, precompute.all_contiguous, precompute.all_same_dtype, precompute.all_same_sizes_and_stride, precompute.memory_format, *op.outputs_[0]); |
| 3323 | return std::move(op.outputs_[0]).take(); |
| 3324 | } |
| 3325 | struct structured_cat_out_cpu_out final : public at::native::structured_cat_out_cpu { |
| 3326 | structured_cat_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3327 | void set_output_strided( |
| 3328 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3329 | TensorOptions options, DimnameList names |
| 3330 | ) override { |
| 3331 | const auto& out = outputs_[output_idx].get(); |
| 3332 | resize_out(out, sizes, strides, options); |
| 3333 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3334 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3335 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3336 | } |
| 3337 | if (!names.empty()) { |
| 3338 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3339 | } |
| 3340 | // super must happen after, so that downstream can use maybe_get_output |
| 3341 | // to retrieve the output |
| 3342 | } |
| 3343 | void set_output_raw_strided( |
| 3344 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3345 | TensorOptions options, DimnameList names |
| 3346 | ) override { |
| 3347 | const auto& out = outputs_[output_idx].get(); |
| 3348 | resize_out(out, sizes, strides, options); |
| 3349 | if (!names.empty()) { |
| 3350 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3351 | } |
| 3352 | // super must happen after, so that downstream can use maybe_get_output |
| 3353 | // to retrieve the output |
| 3354 | } |
| 3355 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3356 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3357 | } |
| 3358 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3359 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3360 | }; |
| 3361 | at::Tensor & wrapper_CPU_cat_out_out(const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out) { |
| 3362 | structured_cat_out_cpu_out op(out); |
| 3363 | auto precompute = op.meta(tensors, dim); |
| 3364 | (void)precompute; |
| 3365 | op.impl(tensors, precompute.dim, precompute.valid, precompute.all_contiguous, precompute.all_same_dtype, precompute.all_same_sizes_and_stride, precompute.memory_format, op.maybe_get_output(0)); |
| 3366 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3367 | return out; |
| 3368 | } |
| 3369 | struct structured_ceil_out_functional final : public at::native::structured_ceil_out { |
| 3370 | void set_output_strided( |
| 3371 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3372 | TensorOptions options, DimnameList names |
| 3373 | ) override { |
| 3374 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3375 | if (!names.empty()) { |
| 3376 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3377 | } |
| 3378 | // super must happen after, so that downstream can use maybe_get_output |
| 3379 | // to retrieve the output |
| 3380 | at::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3381 | } |
| 3382 | void set_output_raw_strided( |
| 3383 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3384 | TensorOptions options, DimnameList names |
| 3385 | ) override { |
| 3386 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3387 | if (!names.empty()) { |
| 3388 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3389 | } |
| 3390 | // super must happen after, so that downstream can use maybe_get_output |
| 3391 | // to retrieve the output |
| 3392 | at::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3393 | } |
| 3394 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3395 | return *outputs_[output_idx]; |
| 3396 | } |
| 3397 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3398 | }; |
| 3399 | at::Tensor wrapper_CPU_ceil(const at::Tensor & self) { |
| 3400 | structured_ceil_out_functional op; |
| 3401 | op.meta(self); |
| 3402 | op.impl(self, *op.outputs_[0]); |
| 3403 | return std::move(op.outputs_[0]).take(); |
| 3404 | } |
| 3405 | struct structured_ceil_out_out final : public at::native::structured_ceil_out { |
| 3406 | structured_ceil_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3407 | void set_output_strided( |
| 3408 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3409 | TensorOptions options, DimnameList names |
| 3410 | ) override { |
| 3411 | const auto& out = outputs_[output_idx].get(); |
| 3412 | resize_out(out, sizes, strides, options); |
| 3413 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3414 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3415 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3416 | } |
| 3417 | if (!names.empty()) { |
| 3418 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3419 | } |
| 3420 | // super must happen after, so that downstream can use maybe_get_output |
| 3421 | // to retrieve the output |
| 3422 | at::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3423 | } |
| 3424 | void set_output_raw_strided( |
| 3425 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3426 | TensorOptions options, DimnameList names |
| 3427 | ) override { |
| 3428 | const auto& out = outputs_[output_idx].get(); |
| 3429 | resize_out(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::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3436 | } |
| 3437 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3438 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3439 | } |
| 3440 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3441 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3442 | }; |
| 3443 | at::Tensor & wrapper_CPU_ceil_out_out(const at::Tensor & self, at::Tensor & out) { |
| 3444 | structured_ceil_out_out op(out); |
| 3445 | op.meta(self); |
| 3446 | op.impl(self, op.maybe_get_output(0)); |
| 3447 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3448 | return out; |
| 3449 | } |
| 3450 | struct structured_ceil_out_inplace final : public at::native::structured_ceil_out { |
| 3451 | structured_ceil_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3452 | void set_output_strided( |
| 3453 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3454 | TensorOptions options, DimnameList names |
| 3455 | ) override { |
| 3456 | const auto& out = outputs_[output_idx].get(); |
| 3457 | check_inplace(out, sizes, options); |
| 3458 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3459 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3460 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3461 | } |
| 3462 | if (!names.empty()) { |
| 3463 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3464 | } |
| 3465 | // super must happen after, so that downstream can use maybe_get_output |
| 3466 | // to retrieve the output |
| 3467 | at::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3468 | } |
| 3469 | void set_output_raw_strided( |
| 3470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3471 | TensorOptions options, DimnameList names |
| 3472 | ) override { |
| 3473 | const auto& out = outputs_[output_idx].get(); |
| 3474 | check_inplace(out, sizes, options); |
| 3475 | if (!names.empty()) { |
| 3476 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3477 | } |
| 3478 | // super must happen after, so that downstream can use maybe_get_output |
| 3479 | // to retrieve the output |
| 3480 | at::native::structured_ceil_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3481 | } |
| 3482 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3483 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3484 | } |
| 3485 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3486 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3487 | }; |
| 3488 | at::Tensor & wrapper_CPU_ceil_(at::Tensor & self) { |
| 3489 | structured_ceil_out_inplace op(self); |
| 3490 | op.meta(self); |
| 3491 | op.impl(self, op.outputs_[0]); |
| 3492 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3493 | return self; |
| 3494 | } |
| 3495 | struct structured_clamp_out_functional final : public at::native::structured_clamp_out { |
| 3496 | void set_output_strided( |
| 3497 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3498 | TensorOptions options, DimnameList names |
| 3499 | ) override { |
| 3500 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3501 | if (!names.empty()) { |
| 3502 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3503 | } |
| 3504 | // super must happen after, so that downstream can use maybe_get_output |
| 3505 | // to retrieve the output |
| 3506 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3507 | } |
| 3508 | void set_output_raw_strided( |
| 3509 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3510 | TensorOptions options, DimnameList names |
| 3511 | ) override { |
| 3512 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3513 | if (!names.empty()) { |
| 3514 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3515 | } |
| 3516 | // super must happen after, so that downstream can use maybe_get_output |
| 3517 | // to retrieve the output |
| 3518 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3519 | } |
| 3520 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3521 | return *outputs_[output_idx]; |
| 3522 | } |
| 3523 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3524 | }; |
| 3525 | at::Tensor wrapper_CPU_clamp(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 3526 | structured_clamp_out_functional op; |
| 3527 | op.meta(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef())); |
| 3528 | op.impl(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef()), *op.outputs_[0]); |
| 3529 | return std::move(op.outputs_[0]).take(); |
| 3530 | } |
| 3531 | struct structured_clamp_out_out final : public at::native::structured_clamp_out { |
| 3532 | structured_clamp_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3533 | void set_output_strided( |
| 3534 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3535 | TensorOptions options, DimnameList names |
| 3536 | ) override { |
| 3537 | const auto& out = outputs_[output_idx].get(); |
| 3538 | resize_out(out, sizes, strides, options); |
| 3539 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3540 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3541 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3542 | } |
| 3543 | if (!names.empty()) { |
| 3544 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3545 | } |
| 3546 | // super must happen after, so that downstream can use maybe_get_output |
| 3547 | // to retrieve the output |
| 3548 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3549 | } |
| 3550 | void set_output_raw_strided( |
| 3551 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3552 | TensorOptions options, DimnameList names |
| 3553 | ) override { |
| 3554 | const auto& out = outputs_[output_idx].get(); |
| 3555 | resize_out(out, sizes, strides, options); |
| 3556 | if (!names.empty()) { |
| 3557 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3558 | } |
| 3559 | // super must happen after, so that downstream can use maybe_get_output |
| 3560 | // to retrieve the output |
| 3561 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3562 | } |
| 3563 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3564 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3565 | } |
| 3566 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3567 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3568 | }; |
| 3569 | at::Tensor & wrapper_CPU_clamp_out_out(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max, at::Tensor & out) { |
| 3570 | structured_clamp_out_out op(out); |
| 3571 | op.meta(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef())); |
| 3572 | op.impl(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef()), op.maybe_get_output(0)); |
| 3573 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3574 | return out; |
| 3575 | } |
| 3576 | struct structured_clamp_out_inplace final : public at::native::structured_clamp_out { |
| 3577 | structured_clamp_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3578 | void set_output_strided( |
| 3579 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3580 | TensorOptions options, DimnameList names |
| 3581 | ) override { |
| 3582 | const auto& out = outputs_[output_idx].get(); |
| 3583 | check_inplace(out, sizes, options); |
| 3584 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3585 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3586 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3587 | } |
| 3588 | if (!names.empty()) { |
| 3589 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3590 | } |
| 3591 | // super must happen after, so that downstream can use maybe_get_output |
| 3592 | // to retrieve the output |
| 3593 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3594 | } |
| 3595 | void set_output_raw_strided( |
| 3596 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3597 | TensorOptions options, DimnameList names |
| 3598 | ) override { |
| 3599 | const auto& out = outputs_[output_idx].get(); |
| 3600 | check_inplace(out, sizes, options); |
| 3601 | if (!names.empty()) { |
| 3602 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3603 | } |
| 3604 | // super must happen after, so that downstream can use maybe_get_output |
| 3605 | // to retrieve the output |
| 3606 | at::native::structured_clamp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3607 | } |
| 3608 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3609 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3610 | } |
| 3611 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3612 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3613 | }; |
| 3614 | at::Tensor & wrapper_CPU_clamp_(at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 3615 | structured_clamp_out_inplace op(self); |
| 3616 | op.meta(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef())); |
| 3617 | op.impl(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef()), op.outputs_[0]); |
| 3618 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3619 | return self; |
| 3620 | } |
| 3621 | struct structured_clamp_Tensor_out_functional final : public at::native::structured_clamp_Tensor_out { |
| 3622 | void set_output_strided( |
| 3623 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3624 | TensorOptions options, DimnameList names |
| 3625 | ) override { |
| 3626 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3627 | if (!names.empty()) { |
| 3628 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3629 | } |
| 3630 | // super must happen after, so that downstream can use maybe_get_output |
| 3631 | // to retrieve the output |
| 3632 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3633 | } |
| 3634 | void set_output_raw_strided( |
| 3635 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3636 | TensorOptions options, DimnameList names |
| 3637 | ) override { |
| 3638 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3639 | if (!names.empty()) { |
| 3640 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3641 | } |
| 3642 | // super must happen after, so that downstream can use maybe_get_output |
| 3643 | // to retrieve the output |
| 3644 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3645 | } |
| 3646 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3647 | return *outputs_[output_idx]; |
| 3648 | } |
| 3649 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3650 | }; |
| 3651 | at::Tensor wrapper_CPU_clamp_Tensor(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 3652 | structured_clamp_Tensor_out_functional op; |
| 3653 | op.meta(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef())); |
| 3654 | op.impl(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef()), *op.outputs_[0]); |
| 3655 | return std::move(op.outputs_[0]).take(); |
| 3656 | } |
| 3657 | struct structured_clamp_Tensor_out_out final : public at::native::structured_clamp_Tensor_out { |
| 3658 | structured_clamp_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3659 | void set_output_strided( |
| 3660 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3661 | TensorOptions options, DimnameList names |
| 3662 | ) override { |
| 3663 | const auto& out = outputs_[output_idx].get(); |
| 3664 | resize_out(out, sizes, strides, options); |
| 3665 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3666 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3667 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3668 | } |
| 3669 | if (!names.empty()) { |
| 3670 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3671 | } |
| 3672 | // super must happen after, so that downstream can use maybe_get_output |
| 3673 | // to retrieve the output |
| 3674 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3675 | } |
| 3676 | void set_output_raw_strided( |
| 3677 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3678 | TensorOptions options, DimnameList names |
| 3679 | ) override { |
| 3680 | const auto& out = outputs_[output_idx].get(); |
| 3681 | resize_out(out, sizes, strides, options); |
| 3682 | if (!names.empty()) { |
| 3683 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3684 | } |
| 3685 | // super must happen after, so that downstream can use maybe_get_output |
| 3686 | // to retrieve the output |
| 3687 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3688 | } |
| 3689 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3690 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3691 | } |
| 3692 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3693 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3694 | }; |
| 3695 | at::Tensor & wrapper_CPU_clamp_out_Tensor_out(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max, at::Tensor & out) { |
| 3696 | structured_clamp_Tensor_out_out op(out); |
| 3697 | op.meta(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef())); |
| 3698 | op.impl(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef()), op.maybe_get_output(0)); |
| 3699 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3700 | return out; |
| 3701 | } |
| 3702 | struct structured_clamp_Tensor_out_inplace final : public at::native::structured_clamp_Tensor_out { |
| 3703 | structured_clamp_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3704 | void set_output_strided( |
| 3705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3706 | TensorOptions options, DimnameList names |
| 3707 | ) override { |
| 3708 | const auto& out = outputs_[output_idx].get(); |
| 3709 | check_inplace(out, sizes, options); |
| 3710 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3711 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3712 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3713 | } |
| 3714 | if (!names.empty()) { |
| 3715 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3716 | } |
| 3717 | // super must happen after, so that downstream can use maybe_get_output |
| 3718 | // to retrieve the output |
| 3719 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3720 | } |
| 3721 | void set_output_raw_strided( |
| 3722 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3723 | TensorOptions options, DimnameList names |
| 3724 | ) override { |
| 3725 | const auto& out = outputs_[output_idx].get(); |
| 3726 | check_inplace(out, sizes, options); |
| 3727 | if (!names.empty()) { |
| 3728 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3729 | } |
| 3730 | // super must happen after, so that downstream can use maybe_get_output |
| 3731 | // to retrieve the output |
| 3732 | at::native::structured_clamp_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3733 | } |
| 3734 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3735 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3736 | } |
| 3737 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3738 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3739 | }; |
| 3740 | at::Tensor & wrapper_CPU_clamp__Tensor(at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 3741 | structured_clamp_Tensor_out_inplace op(self); |
| 3742 | op.meta(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef())); |
| 3743 | op.impl(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef()), op.outputs_[0]); |
| 3744 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3745 | return self; |
| 3746 | } |
| 3747 | struct structured_clamp_max_out_functional final : public at::native::structured_clamp_max_out { |
| 3748 | void set_output_strided( |
| 3749 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3750 | TensorOptions options, DimnameList names |
| 3751 | ) override { |
| 3752 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3753 | if (!names.empty()) { |
| 3754 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3755 | } |
| 3756 | // super must happen after, so that downstream can use maybe_get_output |
| 3757 | // to retrieve the output |
| 3758 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3759 | } |
| 3760 | void set_output_raw_strided( |
| 3761 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3762 | TensorOptions options, DimnameList names |
| 3763 | ) override { |
| 3764 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3765 | if (!names.empty()) { |
| 3766 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3767 | } |
| 3768 | // super must happen after, so that downstream can use maybe_get_output |
| 3769 | // to retrieve the output |
| 3770 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3771 | } |
| 3772 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3773 | return *outputs_[output_idx]; |
| 3774 | } |
| 3775 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3776 | }; |
| 3777 | at::Tensor wrapper_CPU_clamp_max(const at::Tensor & self, const at::Scalar & max) { |
| 3778 | structured_clamp_max_out_functional op; |
| 3779 | op.meta(self, max); |
| 3780 | op.impl(self, max, *op.outputs_[0]); |
| 3781 | return std::move(op.outputs_[0]).take(); |
| 3782 | } |
| 3783 | struct structured_clamp_max_out_out final : public at::native::structured_clamp_max_out { |
| 3784 | structured_clamp_max_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3785 | void set_output_strided( |
| 3786 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3787 | TensorOptions options, DimnameList names |
| 3788 | ) override { |
| 3789 | const auto& out = outputs_[output_idx].get(); |
| 3790 | resize_out(out, sizes, strides, 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 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3801 | } |
| 3802 | void set_output_raw_strided( |
| 3803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3804 | TensorOptions options, DimnameList names |
| 3805 | ) override { |
| 3806 | const auto& out = outputs_[output_idx].get(); |
| 3807 | resize_out(out, sizes, strides, options); |
| 3808 | if (!names.empty()) { |
| 3809 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3810 | } |
| 3811 | // super must happen after, so that downstream can use maybe_get_output |
| 3812 | // to retrieve the output |
| 3813 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3814 | } |
| 3815 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3816 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3817 | } |
| 3818 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3819 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3820 | }; |
| 3821 | at::Tensor & wrapper_CPU_clamp_max_out_out(const at::Tensor & self, const at::Scalar & max, at::Tensor & out) { |
| 3822 | structured_clamp_max_out_out op(out); |
| 3823 | op.meta(self, max); |
| 3824 | op.impl(self, max, op.maybe_get_output(0)); |
| 3825 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3826 | return out; |
| 3827 | } |
| 3828 | struct structured_clamp_max_out_inplace final : public at::native::structured_clamp_max_out { |
| 3829 | structured_clamp_max_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3830 | void set_output_strided( |
| 3831 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3832 | TensorOptions options, DimnameList names |
| 3833 | ) override { |
| 3834 | const auto& out = outputs_[output_idx].get(); |
| 3835 | check_inplace(out, sizes, options); |
| 3836 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3837 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3838 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3839 | } |
| 3840 | if (!names.empty()) { |
| 3841 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3842 | } |
| 3843 | // super must happen after, so that downstream can use maybe_get_output |
| 3844 | // to retrieve the output |
| 3845 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3846 | } |
| 3847 | void set_output_raw_strided( |
| 3848 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3849 | TensorOptions options, DimnameList names |
| 3850 | ) override { |
| 3851 | const auto& out = outputs_[output_idx].get(); |
| 3852 | check_inplace(out, sizes, options); |
| 3853 | if (!names.empty()) { |
| 3854 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3855 | } |
| 3856 | // super must happen after, so that downstream can use maybe_get_output |
| 3857 | // to retrieve the output |
| 3858 | at::native::structured_clamp_max_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3859 | } |
| 3860 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3861 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3862 | } |
| 3863 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3864 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3865 | }; |
| 3866 | at::Tensor & wrapper_CPU_clamp_max_(at::Tensor & self, const at::Scalar & max) { |
| 3867 | structured_clamp_max_out_inplace op(self); |
| 3868 | op.meta(self, max); |
| 3869 | op.impl(self, max, op.outputs_[0]); |
| 3870 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3871 | return self; |
| 3872 | } |
| 3873 | struct structured_clamp_max_Tensor_out_functional final : public at::native::structured_clamp_max_Tensor_out { |
| 3874 | void set_output_strided( |
| 3875 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3876 | TensorOptions options, DimnameList names |
| 3877 | ) override { |
| 3878 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3879 | if (!names.empty()) { |
| 3880 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3881 | } |
| 3882 | // super must happen after, so that downstream can use maybe_get_output |
| 3883 | // to retrieve the output |
| 3884 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3885 | } |
| 3886 | void set_output_raw_strided( |
| 3887 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3888 | TensorOptions options, DimnameList names |
| 3889 | ) override { |
| 3890 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3891 | if (!names.empty()) { |
| 3892 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3893 | } |
| 3894 | // super must happen after, so that downstream can use maybe_get_output |
| 3895 | // to retrieve the output |
| 3896 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3897 | } |
| 3898 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3899 | return *outputs_[output_idx]; |
| 3900 | } |
| 3901 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3902 | }; |
| 3903 | at::Tensor wrapper_CPU_clamp_max_Tensor(const at::Tensor & self, const at::Tensor & max) { |
| 3904 | structured_clamp_max_Tensor_out_functional op; |
| 3905 | op.meta(self, max); |
| 3906 | op.impl(self, max, *op.outputs_[0]); |
| 3907 | return std::move(op.outputs_[0]).take(); |
| 3908 | } |
| 3909 | struct structured_clamp_max_Tensor_out_out final : public at::native::structured_clamp_max_Tensor_out { |
| 3910 | structured_clamp_max_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 3911 | void set_output_strided( |
| 3912 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3913 | TensorOptions options, DimnameList names |
| 3914 | ) override { |
| 3915 | const auto& out = outputs_[output_idx].get(); |
| 3916 | resize_out(out, sizes, strides, options); |
| 3917 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3918 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3919 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3920 | } |
| 3921 | if (!names.empty()) { |
| 3922 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3923 | } |
| 3924 | // super must happen after, so that downstream can use maybe_get_output |
| 3925 | // to retrieve the output |
| 3926 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3927 | } |
| 3928 | void set_output_raw_strided( |
| 3929 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3930 | TensorOptions options, DimnameList names |
| 3931 | ) override { |
| 3932 | const auto& out = outputs_[output_idx].get(); |
| 3933 | resize_out(out, sizes, strides, options); |
| 3934 | if (!names.empty()) { |
| 3935 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3936 | } |
| 3937 | // super must happen after, so that downstream can use maybe_get_output |
| 3938 | // to retrieve the output |
| 3939 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3940 | } |
| 3941 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3942 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3943 | } |
| 3944 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3945 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3946 | }; |
| 3947 | at::Tensor & wrapper_CPU_clamp_max_out_Tensor_out(const at::Tensor & self, const at::Tensor & max, at::Tensor & out) { |
| 3948 | structured_clamp_max_Tensor_out_out op(out); |
| 3949 | op.meta(self, max); |
| 3950 | op.impl(self, max, op.maybe_get_output(0)); |
| 3951 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3952 | return out; |
| 3953 | } |
| 3954 | struct structured_clamp_max_Tensor_out_inplace final : public at::native::structured_clamp_max_Tensor_out { |
| 3955 | structured_clamp_max_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3956 | void set_output_strided( |
| 3957 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3958 | TensorOptions options, DimnameList names |
| 3959 | ) override { |
| 3960 | const auto& out = outputs_[output_idx].get(); |
| 3961 | check_inplace(out, sizes, options); |
| 3962 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3963 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3964 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3965 | } |
| 3966 | if (!names.empty()) { |
| 3967 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3968 | } |
| 3969 | // super must happen after, so that downstream can use maybe_get_output |
| 3970 | // to retrieve the output |
| 3971 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3972 | } |
| 3973 | void set_output_raw_strided( |
| 3974 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3975 | TensorOptions options, DimnameList names |
| 3976 | ) override { |
| 3977 | const auto& out = outputs_[output_idx].get(); |
| 3978 | check_inplace(out, sizes, options); |
| 3979 | if (!names.empty()) { |
| 3980 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3981 | } |
| 3982 | // super must happen after, so that downstream can use maybe_get_output |
| 3983 | // to retrieve the output |
| 3984 | at::native::structured_clamp_max_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3985 | } |
| 3986 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3987 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3988 | } |
| 3989 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3990 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3991 | }; |
| 3992 | at::Tensor & wrapper_CPU_clamp_max__Tensor(at::Tensor & self, const at::Tensor & max) { |
| 3993 | structured_clamp_max_Tensor_out_inplace op(self); |
| 3994 | op.meta(self, max); |
| 3995 | op.impl(self, max, op.outputs_[0]); |
| 3996 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3997 | return self; |
| 3998 | } |
| 3999 | struct structured_clamp_min_out_functional final : public at::native::structured_clamp_min_out { |
| 4000 | void set_output_strided( |
| 4001 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4002 | TensorOptions options, DimnameList names |
| 4003 | ) override { |
| 4004 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4005 | if (!names.empty()) { |
| 4006 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4007 | } |
| 4008 | // super must happen after, so that downstream can use maybe_get_output |
| 4009 | // to retrieve the output |
| 4010 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4011 | } |
| 4012 | void set_output_raw_strided( |
| 4013 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4014 | TensorOptions options, DimnameList names |
| 4015 | ) override { |
| 4016 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4017 | if (!names.empty()) { |
| 4018 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4019 | } |
| 4020 | // super must happen after, so that downstream can use maybe_get_output |
| 4021 | // to retrieve the output |
| 4022 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4023 | } |
| 4024 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4025 | return *outputs_[output_idx]; |
| 4026 | } |
| 4027 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4028 | }; |
| 4029 | at::Tensor wrapper_CPU_clamp_min(const at::Tensor & self, const at::Scalar & min) { |
| 4030 | structured_clamp_min_out_functional op; |
| 4031 | op.meta(self, min); |
| 4032 | op.impl(self, min, *op.outputs_[0]); |
| 4033 | return std::move(op.outputs_[0]).take(); |
| 4034 | } |
| 4035 | struct structured_clamp_min_out_out final : public at::native::structured_clamp_min_out { |
| 4036 | structured_clamp_min_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4037 | void set_output_strided( |
| 4038 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4039 | TensorOptions options, DimnameList names |
| 4040 | ) override { |
| 4041 | const auto& out = outputs_[output_idx].get(); |
| 4042 | resize_out(out, sizes, strides, options); |
| 4043 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4044 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4045 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4046 | } |
| 4047 | if (!names.empty()) { |
| 4048 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4049 | } |
| 4050 | // super must happen after, so that downstream can use maybe_get_output |
| 4051 | // to retrieve the output |
| 4052 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4053 | } |
| 4054 | void set_output_raw_strided( |
| 4055 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4056 | TensorOptions options, DimnameList names |
| 4057 | ) override { |
| 4058 | const auto& out = outputs_[output_idx].get(); |
| 4059 | resize_out(out, sizes, strides, options); |
| 4060 | if (!names.empty()) { |
| 4061 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4062 | } |
| 4063 | // super must happen after, so that downstream can use maybe_get_output |
| 4064 | // to retrieve the output |
| 4065 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4066 | } |
| 4067 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4068 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4069 | } |
| 4070 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4071 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4072 | }; |
| 4073 | at::Tensor & wrapper_CPU_clamp_min_out_out(const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { |
| 4074 | structured_clamp_min_out_out op(out); |
| 4075 | op.meta(self, min); |
| 4076 | op.impl(self, min, op.maybe_get_output(0)); |
| 4077 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4078 | return out; |
| 4079 | } |
| 4080 | struct structured_clamp_min_out_inplace final : public at::native::structured_clamp_min_out { |
| 4081 | structured_clamp_min_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4082 | void set_output_strided( |
| 4083 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4084 | TensorOptions options, DimnameList names |
| 4085 | ) override { |
| 4086 | const auto& out = outputs_[output_idx].get(); |
| 4087 | check_inplace(out, sizes, options); |
| 4088 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4089 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4090 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4091 | } |
| 4092 | if (!names.empty()) { |
| 4093 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4094 | } |
| 4095 | // super must happen after, so that downstream can use maybe_get_output |
| 4096 | // to retrieve the output |
| 4097 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4098 | } |
| 4099 | void set_output_raw_strided( |
| 4100 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4101 | TensorOptions options, DimnameList names |
| 4102 | ) override { |
| 4103 | const auto& out = outputs_[output_idx].get(); |
| 4104 | check_inplace(out, sizes, options); |
| 4105 | if (!names.empty()) { |
| 4106 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4107 | } |
| 4108 | // super must happen after, so that downstream can use maybe_get_output |
| 4109 | // to retrieve the output |
| 4110 | at::native::structured_clamp_min_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4111 | } |
| 4112 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4113 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4114 | } |
| 4115 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4116 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4117 | }; |
| 4118 | at::Tensor & wrapper_CPU_clamp_min_(at::Tensor & self, const at::Scalar & min) { |
| 4119 | structured_clamp_min_out_inplace op(self); |
| 4120 | op.meta(self, min); |
| 4121 | op.impl(self, min, op.outputs_[0]); |
| 4122 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4123 | return self; |
| 4124 | } |
| 4125 | struct structured_clamp_min_Tensor_out_functional final : public at::native::structured_clamp_min_Tensor_out { |
| 4126 | void set_output_strided( |
| 4127 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4128 | TensorOptions options, DimnameList names |
| 4129 | ) override { |
| 4130 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4131 | if (!names.empty()) { |
| 4132 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4133 | } |
| 4134 | // super must happen after, so that downstream can use maybe_get_output |
| 4135 | // to retrieve the output |
| 4136 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4137 | } |
| 4138 | void set_output_raw_strided( |
| 4139 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4140 | TensorOptions options, DimnameList names |
| 4141 | ) override { |
| 4142 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4143 | if (!names.empty()) { |
| 4144 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4145 | } |
| 4146 | // super must happen after, so that downstream can use maybe_get_output |
| 4147 | // to retrieve the output |
| 4148 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4149 | } |
| 4150 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4151 | return *outputs_[output_idx]; |
| 4152 | } |
| 4153 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4154 | }; |
| 4155 | at::Tensor wrapper_CPU_clamp_min_Tensor(const at::Tensor & self, const at::Tensor & min) { |
| 4156 | structured_clamp_min_Tensor_out_functional op; |
| 4157 | op.meta(self, min); |
| 4158 | op.impl(self, min, *op.outputs_[0]); |
| 4159 | return std::move(op.outputs_[0]).take(); |
| 4160 | } |
| 4161 | struct structured_clamp_min_Tensor_out_out final : public at::native::structured_clamp_min_Tensor_out { |
| 4162 | structured_clamp_min_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4163 | void set_output_strided( |
| 4164 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4165 | TensorOptions options, DimnameList names |
| 4166 | ) override { |
| 4167 | const auto& out = outputs_[output_idx].get(); |
| 4168 | resize_out(out, sizes, strides, options); |
| 4169 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4170 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4171 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4172 | } |
| 4173 | if (!names.empty()) { |
| 4174 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4175 | } |
| 4176 | // super must happen after, so that downstream can use maybe_get_output |
| 4177 | // to retrieve the output |
| 4178 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4179 | } |
| 4180 | void set_output_raw_strided( |
| 4181 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4182 | TensorOptions options, DimnameList names |
| 4183 | ) override { |
| 4184 | const auto& out = outputs_[output_idx].get(); |
| 4185 | resize_out(out, sizes, strides, options); |
| 4186 | if (!names.empty()) { |
| 4187 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4188 | } |
| 4189 | // super must happen after, so that downstream can use maybe_get_output |
| 4190 | // to retrieve the output |
| 4191 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4192 | } |
| 4193 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4194 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4195 | } |
| 4196 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4197 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4198 | }; |
| 4199 | at::Tensor & wrapper_CPU_clamp_min_out_Tensor_out(const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { |
| 4200 | structured_clamp_min_Tensor_out_out op(out); |
| 4201 | op.meta(self, min); |
| 4202 | op.impl(self, min, op.maybe_get_output(0)); |
| 4203 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4204 | return out; |
| 4205 | } |
| 4206 | struct structured_clamp_min_Tensor_out_inplace final : public at::native::structured_clamp_min_Tensor_out { |
| 4207 | structured_clamp_min_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4208 | void set_output_strided( |
| 4209 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4210 | TensorOptions options, DimnameList names |
| 4211 | ) override { |
| 4212 | const auto& out = outputs_[output_idx].get(); |
| 4213 | check_inplace(out, sizes, options); |
| 4214 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4215 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4216 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4217 | } |
| 4218 | if (!names.empty()) { |
| 4219 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4220 | } |
| 4221 | // super must happen after, so that downstream can use maybe_get_output |
| 4222 | // to retrieve the output |
| 4223 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4224 | } |
| 4225 | void set_output_raw_strided( |
| 4226 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4227 | TensorOptions options, DimnameList names |
| 4228 | ) override { |
| 4229 | const auto& out = outputs_[output_idx].get(); |
| 4230 | check_inplace(out, sizes, options); |
| 4231 | if (!names.empty()) { |
| 4232 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4233 | } |
| 4234 | // super must happen after, so that downstream can use maybe_get_output |
| 4235 | // to retrieve the output |
| 4236 | at::native::structured_clamp_min_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4237 | } |
| 4238 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4239 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4240 | } |
| 4241 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4242 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4243 | }; |
| 4244 | at::Tensor & wrapper_CPU_clamp_min__Tensor(at::Tensor & self, const at::Tensor & min) { |
| 4245 | structured_clamp_min_Tensor_out_inplace op(self); |
| 4246 | op.meta(self, min); |
| 4247 | op.impl(self, min, op.outputs_[0]); |
| 4248 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4249 | return self; |
| 4250 | } |
| 4251 | namespace { |
| 4252 | at::Tensor & wrapper_CPU_out_complex_out(const at::Tensor & real, const at::Tensor & imag, at::Tensor & out) { |
| 4253 | // No device check |
| 4254 | // DeviceGuard omitted |
| 4255 | return at::native::complex_out(real, imag, out); |
| 4256 | } |
| 4257 | } // anonymous namespace |
| 4258 | namespace { |
| 4259 | at::Tensor & wrapper_CPU_out_polar_out(const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out) { |
| 4260 | // No device check |
| 4261 | // DeviceGuard omitted |
| 4262 | return at::native::polar_out(abs, angle, out); |
| 4263 | } |
| 4264 | } // anonymous namespace |
| 4265 | struct structured_cos_out_functional final : public at::native::structured_cos_out { |
| 4266 | void set_output_strided( |
| 4267 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4268 | TensorOptions options, DimnameList names |
| 4269 | ) override { |
| 4270 | outputs_[output_idx] = create_out(sizes, strides, 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::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4277 | } |
| 4278 | void set_output_raw_strided( |
| 4279 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4280 | TensorOptions options, DimnameList names |
| 4281 | ) override { |
| 4282 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4283 | if (!names.empty()) { |
| 4284 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4285 | } |
| 4286 | // super must happen after, so that downstream can use maybe_get_output |
| 4287 | // to retrieve the output |
| 4288 | at::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4289 | } |
| 4290 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4291 | return *outputs_[output_idx]; |
| 4292 | } |
| 4293 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4294 | }; |
| 4295 | at::Tensor wrapper_CPU_cos(const at::Tensor & self) { |
| 4296 | structured_cos_out_functional op; |
| 4297 | op.meta(self); |
| 4298 | op.impl(self, *op.outputs_[0]); |
| 4299 | return std::move(op.outputs_[0]).take(); |
| 4300 | } |
| 4301 | struct structured_cos_out_out final : public at::native::structured_cos_out { |
| 4302 | structured_cos_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4303 | void set_output_strided( |
| 4304 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4305 | TensorOptions options, DimnameList names |
| 4306 | ) override { |
| 4307 | const auto& out = outputs_[output_idx].get(); |
| 4308 | resize_out(out, sizes, strides, options); |
| 4309 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4310 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4311 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4312 | } |
| 4313 | if (!names.empty()) { |
| 4314 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4315 | } |
| 4316 | // super must happen after, so that downstream can use maybe_get_output |
| 4317 | // to retrieve the output |
| 4318 | at::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4319 | } |
| 4320 | void set_output_raw_strided( |
| 4321 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4322 | TensorOptions options, DimnameList names |
| 4323 | ) override { |
| 4324 | const auto& out = outputs_[output_idx].get(); |
| 4325 | resize_out(out, sizes, strides, options); |
| 4326 | if (!names.empty()) { |
| 4327 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4328 | } |
| 4329 | // super must happen after, so that downstream can use maybe_get_output |
| 4330 | // to retrieve the output |
| 4331 | at::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4332 | } |
| 4333 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4334 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4335 | } |
| 4336 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4337 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4338 | }; |
| 4339 | at::Tensor & wrapper_CPU_cos_out_out(const at::Tensor & self, at::Tensor & out) { |
| 4340 | structured_cos_out_out op(out); |
| 4341 | op.meta(self); |
| 4342 | op.impl(self, op.maybe_get_output(0)); |
| 4343 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4344 | return out; |
| 4345 | } |
| 4346 | struct structured_cos_out_inplace final : public at::native::structured_cos_out { |
| 4347 | structured_cos_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4348 | void set_output_strided( |
| 4349 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4350 | TensorOptions options, DimnameList names |
| 4351 | ) override { |
| 4352 | const auto& out = outputs_[output_idx].get(); |
| 4353 | check_inplace(out, sizes, options); |
| 4354 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4355 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4356 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4357 | } |
| 4358 | if (!names.empty()) { |
| 4359 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4360 | } |
| 4361 | // super must happen after, so that downstream can use maybe_get_output |
| 4362 | // to retrieve the output |
| 4363 | at::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4364 | } |
| 4365 | void set_output_raw_strided( |
| 4366 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4367 | TensorOptions options, DimnameList names |
| 4368 | ) override { |
| 4369 | const auto& out = outputs_[output_idx].get(); |
| 4370 | check_inplace(out, sizes, options); |
| 4371 | if (!names.empty()) { |
| 4372 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4373 | } |
| 4374 | // super must happen after, so that downstream can use maybe_get_output |
| 4375 | // to retrieve the output |
| 4376 | at::native::structured_cos_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4377 | } |
| 4378 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4379 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4380 | } |
| 4381 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4382 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4383 | }; |
| 4384 | at::Tensor & wrapper_CPU_cos_(at::Tensor & self) { |
| 4385 | structured_cos_out_inplace op(self); |
| 4386 | op.meta(self); |
| 4387 | op.impl(self, op.outputs_[0]); |
| 4388 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4389 | return self; |
| 4390 | } |
| 4391 | struct structured_cosh_out_functional final : public at::native::structured_cosh_out { |
| 4392 | void set_output_strided( |
| 4393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4394 | TensorOptions options, DimnameList names |
| 4395 | ) override { |
| 4396 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4397 | if (!names.empty()) { |
| 4398 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4399 | } |
| 4400 | // super must happen after, so that downstream can use maybe_get_output |
| 4401 | // to retrieve the output |
| 4402 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4403 | } |
| 4404 | void set_output_raw_strided( |
| 4405 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4406 | TensorOptions options, DimnameList names |
| 4407 | ) override { |
| 4408 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4409 | if (!names.empty()) { |
| 4410 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4411 | } |
| 4412 | // super must happen after, so that downstream can use maybe_get_output |
| 4413 | // to retrieve the output |
| 4414 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4415 | } |
| 4416 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4417 | return *outputs_[output_idx]; |
| 4418 | } |
| 4419 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4420 | }; |
| 4421 | at::Tensor wrapper_CPU_cosh(const at::Tensor & self) { |
| 4422 | structured_cosh_out_functional op; |
| 4423 | op.meta(self); |
| 4424 | op.impl(self, *op.outputs_[0]); |
| 4425 | return std::move(op.outputs_[0]).take(); |
| 4426 | } |
| 4427 | struct structured_cosh_out_out final : public at::native::structured_cosh_out { |
| 4428 | structured_cosh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4429 | void set_output_strided( |
| 4430 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4431 | TensorOptions options, DimnameList names |
| 4432 | ) override { |
| 4433 | const auto& out = outputs_[output_idx].get(); |
| 4434 | resize_out(out, sizes, strides, options); |
| 4435 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4436 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4437 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4438 | } |
| 4439 | if (!names.empty()) { |
| 4440 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4441 | } |
| 4442 | // super must happen after, so that downstream can use maybe_get_output |
| 4443 | // to retrieve the output |
| 4444 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4445 | } |
| 4446 | void set_output_raw_strided( |
| 4447 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4448 | TensorOptions options, DimnameList names |
| 4449 | ) override { |
| 4450 | const auto& out = outputs_[output_idx].get(); |
| 4451 | resize_out(out, sizes, strides, options); |
| 4452 | if (!names.empty()) { |
| 4453 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4454 | } |
| 4455 | // super must happen after, so that downstream can use maybe_get_output |
| 4456 | // to retrieve the output |
| 4457 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4458 | } |
| 4459 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4460 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4461 | } |
| 4462 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4463 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4464 | }; |
| 4465 | at::Tensor & wrapper_CPU_cosh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 4466 | structured_cosh_out_out op(out); |
| 4467 | op.meta(self); |
| 4468 | op.impl(self, op.maybe_get_output(0)); |
| 4469 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4470 | return out; |
| 4471 | } |
| 4472 | struct structured_cosh_out_inplace final : public at::native::structured_cosh_out { |
| 4473 | structured_cosh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4474 | void set_output_strided( |
| 4475 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4476 | TensorOptions options, DimnameList names |
| 4477 | ) override { |
| 4478 | const auto& out = outputs_[output_idx].get(); |
| 4479 | check_inplace(out, sizes, options); |
| 4480 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4481 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4482 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4483 | } |
| 4484 | if (!names.empty()) { |
| 4485 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4486 | } |
| 4487 | // super must happen after, so that downstream can use maybe_get_output |
| 4488 | // to retrieve the output |
| 4489 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4490 | } |
| 4491 | void set_output_raw_strided( |
| 4492 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4493 | TensorOptions options, DimnameList names |
| 4494 | ) override { |
| 4495 | const auto& out = outputs_[output_idx].get(); |
| 4496 | check_inplace(out, sizes, options); |
| 4497 | if (!names.empty()) { |
| 4498 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4499 | } |
| 4500 | // super must happen after, so that downstream can use maybe_get_output |
| 4501 | // to retrieve the output |
| 4502 | at::native::structured_cosh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4503 | } |
| 4504 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4505 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4506 | } |
| 4507 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4508 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4509 | }; |
| 4510 | at::Tensor & wrapper_CPU_cosh_(at::Tensor & self) { |
| 4511 | structured_cosh_out_inplace op(self); |
| 4512 | op.meta(self); |
| 4513 | op.impl(self, op.outputs_[0]); |
| 4514 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4515 | return self; |
| 4516 | } |
| 4517 | namespace { |
| 4518 | at::Tensor wrapper_CPU_dim_IntList_count_nonzero(const at::Tensor & self, at::IntArrayRef dim) { |
| 4519 | // No device check |
| 4520 | // DeviceGuard omitted |
| 4521 | return at::native::count_nonzero_cpu(self, dim); |
| 4522 | } |
| 4523 | } // anonymous namespace |
| 4524 | namespace { |
| 4525 | void wrapper_CPU___cummax_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 4526 | // No device check |
| 4527 | // DeviceGuard omitted |
| 4528 | return at::native::cummax_helper_cpu(self, values, indices, dim); |
| 4529 | } |
| 4530 | } // anonymous namespace |
| 4531 | namespace { |
| 4532 | void wrapper_CPU___cummin_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 4533 | // No device check |
| 4534 | // DeviceGuard omitted |
| 4535 | return at::native::cummin_helper_cpu(self, values, indices, dim); |
| 4536 | } |
| 4537 | } // anonymous namespace |
| 4538 | struct structured_cumprod_out_functional final : public at::native::structured_cumprod_out { |
| 4539 | void set_output_strided( |
| 4540 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4541 | TensorOptions options, DimnameList names |
| 4542 | ) override { |
| 4543 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4544 | if (!names.empty()) { |
| 4545 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4546 | } |
| 4547 | // super must happen after, so that downstream can use maybe_get_output |
| 4548 | // to retrieve the output |
| 4549 | } |
| 4550 | void set_output_raw_strided( |
| 4551 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4552 | TensorOptions options, DimnameList names |
| 4553 | ) override { |
| 4554 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4555 | if (!names.empty()) { |
| 4556 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4557 | } |
| 4558 | // super must happen after, so that downstream can use maybe_get_output |
| 4559 | // to retrieve the output |
| 4560 | } |
| 4561 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4562 | return *outputs_[output_idx]; |
| 4563 | } |
| 4564 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4565 | }; |
| 4566 | at::Tensor wrapper_CPU_cumprod(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4567 | structured_cumprod_out_functional op; |
| 4568 | op.meta(self, dim, dtype); |
| 4569 | op.impl(self, dim, dtype, *op.outputs_[0]); |
| 4570 | return std::move(op.outputs_[0]).take(); |
| 4571 | } |
| 4572 | struct structured_cumprod_out_out final : public at::native::structured_cumprod_out { |
| 4573 | structured_cumprod_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4574 | void set_output_strided( |
| 4575 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4576 | TensorOptions options, DimnameList names |
| 4577 | ) override { |
| 4578 | const auto& out = outputs_[output_idx].get(); |
| 4579 | resize_out(out, sizes, strides, 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 | } |
| 4590 | void set_output_raw_strided( |
| 4591 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4592 | TensorOptions options, DimnameList names |
| 4593 | ) override { |
| 4594 | const auto& out = outputs_[output_idx].get(); |
| 4595 | resize_out(out, sizes, strides, options); |
| 4596 | if (!names.empty()) { |
| 4597 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4598 | } |
| 4599 | // super must happen after, so that downstream can use maybe_get_output |
| 4600 | // to retrieve the output |
| 4601 | } |
| 4602 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4603 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4604 | } |
| 4605 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4606 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4607 | }; |
| 4608 | at::Tensor & wrapper_CPU_cumprod_out_out(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4609 | structured_cumprod_out_out op(out); |
| 4610 | op.meta(self, dim, dtype); |
| 4611 | op.impl(self, dim, dtype, op.maybe_get_output(0)); |
| 4612 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4613 | return out; |
| 4614 | } |
| 4615 | struct structured_cumprod_out_inplace final : public at::native::structured_cumprod_out { |
| 4616 | structured_cumprod_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4617 | void set_output_strided( |
| 4618 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4619 | TensorOptions options, DimnameList names |
| 4620 | ) override { |
| 4621 | const auto& out = outputs_[output_idx].get(); |
| 4622 | check_inplace(out, sizes, options); |
| 4623 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4624 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4625 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4626 | } |
| 4627 | if (!names.empty()) { |
| 4628 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4629 | } |
| 4630 | // super must happen after, so that downstream can use maybe_get_output |
| 4631 | // to retrieve the output |
| 4632 | } |
| 4633 | void set_output_raw_strided( |
| 4634 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4635 | TensorOptions options, DimnameList names |
| 4636 | ) override { |
| 4637 | const auto& out = outputs_[output_idx].get(); |
| 4638 | check_inplace(out, sizes, options); |
| 4639 | if (!names.empty()) { |
| 4640 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4641 | } |
| 4642 | // super must happen after, so that downstream can use maybe_get_output |
| 4643 | // to retrieve the output |
| 4644 | } |
| 4645 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4646 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4647 | } |
| 4648 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4649 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4650 | }; |
| 4651 | at::Tensor & wrapper_CPU_cumprod_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4652 | structured_cumprod_out_inplace op(self); |
| 4653 | op.meta(self, dim, dtype); |
| 4654 | op.impl(self, dim, dtype, op.outputs_[0]); |
| 4655 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4656 | return self; |
| 4657 | } |
| 4658 | struct structured_cumsum_out_functional final : public at::native::structured_cumsum_out { |
| 4659 | void set_output_strided( |
| 4660 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4661 | TensorOptions options, DimnameList names |
| 4662 | ) override { |
| 4663 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4664 | if (!names.empty()) { |
| 4665 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4666 | } |
| 4667 | // super must happen after, so that downstream can use maybe_get_output |
| 4668 | // to retrieve the output |
| 4669 | } |
| 4670 | void set_output_raw_strided( |
| 4671 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4672 | TensorOptions options, DimnameList names |
| 4673 | ) override { |
| 4674 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4675 | if (!names.empty()) { |
| 4676 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4677 | } |
| 4678 | // super must happen after, so that downstream can use maybe_get_output |
| 4679 | // to retrieve the output |
| 4680 | } |
| 4681 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4682 | return *outputs_[output_idx]; |
| 4683 | } |
| 4684 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4685 | }; |
| 4686 | at::Tensor wrapper_CPU_cumsum(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4687 | structured_cumsum_out_functional op; |
| 4688 | op.meta(self, dim, dtype); |
| 4689 | op.impl(self, dim, dtype, *op.outputs_[0]); |
| 4690 | return std::move(op.outputs_[0]).take(); |
| 4691 | } |
| 4692 | struct structured_cumsum_out_out final : public at::native::structured_cumsum_out { |
| 4693 | structured_cumsum_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4694 | void set_output_strided( |
| 4695 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4696 | TensorOptions options, DimnameList names |
| 4697 | ) override { |
| 4698 | const auto& out = outputs_[output_idx].get(); |
| 4699 | resize_out(out, sizes, strides, options); |
| 4700 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4701 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4702 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4703 | } |
| 4704 | if (!names.empty()) { |
| 4705 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4706 | } |
| 4707 | // super must happen after, so that downstream can use maybe_get_output |
| 4708 | // to retrieve the output |
| 4709 | } |
| 4710 | void set_output_raw_strided( |
| 4711 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4712 | TensorOptions options, DimnameList names |
| 4713 | ) override { |
| 4714 | const auto& out = outputs_[output_idx].get(); |
| 4715 | resize_out(out, sizes, strides, options); |
| 4716 | if (!names.empty()) { |
| 4717 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4718 | } |
| 4719 | // super must happen after, so that downstream can use maybe_get_output |
| 4720 | // to retrieve the output |
| 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 | }; |
| 4728 | at::Tensor & wrapper_CPU_cumsum_out_out(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4729 | structured_cumsum_out_out op(out); |
| 4730 | op.meta(self, dim, dtype); |
| 4731 | op.impl(self, dim, dtype, op.maybe_get_output(0)); |
| 4732 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4733 | return out; |
| 4734 | } |
| 4735 | struct structured_cumsum_out_inplace final : public at::native::structured_cumsum_out { |
| 4736 | structured_cumsum_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4737 | void set_output_strided( |
| 4738 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4739 | TensorOptions options, DimnameList names |
| 4740 | ) override { |
| 4741 | const auto& out = outputs_[output_idx].get(); |
| 4742 | check_inplace(out, sizes, options); |
| 4743 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4744 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4745 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4746 | } |
| 4747 | if (!names.empty()) { |
| 4748 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4749 | } |
| 4750 | // super must happen after, so that downstream can use maybe_get_output |
| 4751 | // to retrieve the output |
| 4752 | } |
| 4753 | void set_output_raw_strided( |
| 4754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4755 | TensorOptions options, DimnameList names |
| 4756 | ) override { |
| 4757 | const auto& out = outputs_[output_idx].get(); |
| 4758 | check_inplace(out, sizes, options); |
| 4759 | if (!names.empty()) { |
| 4760 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4761 | } |
| 4762 | // super must happen after, so that downstream can use maybe_get_output |
| 4763 | // to retrieve the output |
| 4764 | } |
| 4765 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4766 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4767 | } |
| 4768 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4769 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4770 | }; |
| 4771 | at::Tensor & wrapper_CPU_cumsum_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4772 | structured_cumsum_out_inplace op(self); |
| 4773 | op.meta(self, dim, dtype); |
| 4774 | op.impl(self, dim, dtype, op.outputs_[0]); |
| 4775 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4776 | return self; |
| 4777 | } |
| 4778 | namespace { |
| 4779 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity) { |
| 4780 | // No device check |
| 4781 | // DeviceGuard omitted |
| 4782 | return at::native::ctc_loss_cpu(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); |
| 4783 | } |
| 4784 | } // anonymous namespace |
| 4785 | namespace { |
| 4786 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_Tensor__ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity) { |
| 4787 | // No device check |
| 4788 | // DeviceGuard omitted |
| 4789 | return at::native::ctc_loss_tensor(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); |
| 4790 | } |
| 4791 | } // anonymous namespace |
| 4792 | namespace { |
| 4793 | at::Tensor wrapper_CPU___ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { |
| 4794 | // No device check |
| 4795 | // DeviceGuard omitted |
| 4796 | return at::native::ctc_loss_backward_cpu(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); |
| 4797 | } |
| 4798 | } // anonymous namespace |
| 4799 | namespace { |
| 4800 | at::Tensor wrapper_CPU_Tensor__ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { |
| 4801 | // No device check |
| 4802 | // DeviceGuard omitted |
| 4803 | return at::native::ctc_loss_backward_tensor(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); |
| 4804 | } |
| 4805 | } // anonymous namespace |
| 4806 | struct structured_div_out_functional final : public at::native::structured_div_out { |
| 4807 | void set_output_strided( |
| 4808 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4809 | TensorOptions options, DimnameList names |
| 4810 | ) override { |
| 4811 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4812 | if (!names.empty()) { |
| 4813 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4814 | } |
| 4815 | // super must happen after, so that downstream can use maybe_get_output |
| 4816 | // to retrieve the output |
| 4817 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4818 | } |
| 4819 | void set_output_raw_strided( |
| 4820 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4821 | TensorOptions options, DimnameList names |
| 4822 | ) override { |
| 4823 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4824 | if (!names.empty()) { |
| 4825 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4826 | } |
| 4827 | // super must happen after, so that downstream can use maybe_get_output |
| 4828 | // to retrieve the output |
| 4829 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4830 | } |
| 4831 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4832 | return *outputs_[output_idx]; |
| 4833 | } |
| 4834 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4835 | }; |
| 4836 | at::Tensor wrapper_CPU_div_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 4837 | structured_div_out_functional op; |
| 4838 | op.meta(self, other); |
| 4839 | op.impl(self, other, *op.outputs_[0]); |
| 4840 | return std::move(op.outputs_[0]).take(); |
| 4841 | } |
| 4842 | struct structured_div_out_out final : public at::native::structured_div_out { |
| 4843 | structured_div_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4844 | void set_output_strided( |
| 4845 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4846 | TensorOptions options, DimnameList names |
| 4847 | ) override { |
| 4848 | const auto& out = outputs_[output_idx].get(); |
| 4849 | resize_out(out, sizes, strides, options); |
| 4850 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4851 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4852 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4853 | } |
| 4854 | if (!names.empty()) { |
| 4855 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4856 | } |
| 4857 | // super must happen after, so that downstream can use maybe_get_output |
| 4858 | // to retrieve the output |
| 4859 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4860 | } |
| 4861 | void set_output_raw_strided( |
| 4862 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4863 | TensorOptions options, DimnameList names |
| 4864 | ) override { |
| 4865 | const auto& out = outputs_[output_idx].get(); |
| 4866 | resize_out(out, sizes, strides, options); |
| 4867 | if (!names.empty()) { |
| 4868 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4869 | } |
| 4870 | // super must happen after, so that downstream can use maybe_get_output |
| 4871 | // to retrieve the output |
| 4872 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4873 | } |
| 4874 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4875 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4876 | } |
| 4877 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4878 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4879 | }; |
| 4880 | at::Tensor & wrapper_CPU_div_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 4881 | structured_div_out_out op(out); |
| 4882 | op.meta(self, other); |
| 4883 | op.impl(self, other, op.maybe_get_output(0)); |
| 4884 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4885 | return out; |
| 4886 | } |
| 4887 | struct structured_div_out_inplace final : public at::native::structured_div_out { |
| 4888 | structured_div_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4889 | void set_output_strided( |
| 4890 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4891 | TensorOptions options, DimnameList names |
| 4892 | ) override { |
| 4893 | const auto& out = outputs_[output_idx].get(); |
| 4894 | check_inplace(out, sizes, options); |
| 4895 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4896 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4897 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4898 | } |
| 4899 | if (!names.empty()) { |
| 4900 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4901 | } |
| 4902 | // super must happen after, so that downstream can use maybe_get_output |
| 4903 | // to retrieve the output |
| 4904 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4905 | } |
| 4906 | void set_output_raw_strided( |
| 4907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4908 | TensorOptions options, DimnameList names |
| 4909 | ) override { |
| 4910 | const auto& out = outputs_[output_idx].get(); |
| 4911 | check_inplace(out, sizes, options); |
| 4912 | if (!names.empty()) { |
| 4913 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4914 | } |
| 4915 | // super must happen after, so that downstream can use maybe_get_output |
| 4916 | // to retrieve the output |
| 4917 | at::native::structured_div_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4918 | } |
| 4919 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4920 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4921 | } |
| 4922 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4923 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4924 | }; |
| 4925 | at::Tensor & wrapper_CPU_div__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 4926 | structured_div_out_inplace op(self); |
| 4927 | op.meta(self, other); |
| 4928 | op.impl(self, other, op.outputs_[0]); |
| 4929 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4930 | return self; |
| 4931 | } |
| 4932 | struct structured_div_out_mode_functional final : public at::native::structured_div_out_mode { |
| 4933 | void set_output_strided( |
| 4934 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4935 | TensorOptions options, DimnameList names |
| 4936 | ) override { |
| 4937 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4938 | if (!names.empty()) { |
| 4939 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4940 | } |
| 4941 | // super must happen after, so that downstream can use maybe_get_output |
| 4942 | // to retrieve the output |
| 4943 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4944 | } |
| 4945 | void set_output_raw_strided( |
| 4946 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4947 | TensorOptions options, DimnameList names |
| 4948 | ) override { |
| 4949 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4950 | if (!names.empty()) { |
| 4951 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4952 | } |
| 4953 | // super must happen after, so that downstream can use maybe_get_output |
| 4954 | // to retrieve the output |
| 4955 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4956 | } |
| 4957 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4958 | return *outputs_[output_idx]; |
| 4959 | } |
| 4960 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4961 | }; |
| 4962 | at::Tensor wrapper_CPU_div_Tensor_mode(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 4963 | structured_div_out_mode_functional op; |
| 4964 | op.meta(self, other, rounding_mode); |
| 4965 | op.impl(self, other, rounding_mode, *op.outputs_[0]); |
| 4966 | return std::move(op.outputs_[0]).take(); |
| 4967 | } |
| 4968 | struct structured_div_out_mode_out final : public at::native::structured_div_out_mode { |
| 4969 | structured_div_out_mode_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 4970 | void set_output_strided( |
| 4971 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4972 | TensorOptions options, DimnameList names |
| 4973 | ) override { |
| 4974 | const auto& out = outputs_[output_idx].get(); |
| 4975 | resize_out(out, sizes, strides, options); |
| 4976 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4977 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4978 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4979 | } |
| 4980 | if (!names.empty()) { |
| 4981 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4982 | } |
| 4983 | // super must happen after, so that downstream can use maybe_get_output |
| 4984 | // to retrieve the output |
| 4985 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4986 | } |
| 4987 | void set_output_raw_strided( |
| 4988 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4989 | TensorOptions options, DimnameList names |
| 4990 | ) override { |
| 4991 | const auto& out = outputs_[output_idx].get(); |
| 4992 | resize_out(out, sizes, strides, options); |
| 4993 | if (!names.empty()) { |
| 4994 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4995 | } |
| 4996 | // super must happen after, so that downstream can use maybe_get_output |
| 4997 | // to retrieve the output |
| 4998 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4999 | } |
| 5000 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5001 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5002 | } |
| 5003 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5004 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5005 | }; |
| 5006 | at::Tensor & wrapper_CPU_div_out_out_mode(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 5007 | structured_div_out_mode_out op(out); |
| 5008 | op.meta(self, other, rounding_mode); |
| 5009 | op.impl(self, other, rounding_mode, op.maybe_get_output(0)); |
| 5010 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5011 | return out; |
| 5012 | } |
| 5013 | struct structured_div_out_mode_inplace final : public at::native::structured_div_out_mode { |
| 5014 | structured_div_out_mode_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5015 | void set_output_strided( |
| 5016 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5017 | TensorOptions options, DimnameList names |
| 5018 | ) override { |
| 5019 | const auto& out = outputs_[output_idx].get(); |
| 5020 | check_inplace(out, sizes, options); |
| 5021 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5022 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5023 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5024 | } |
| 5025 | if (!names.empty()) { |
| 5026 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5027 | } |
| 5028 | // super must happen after, so that downstream can use maybe_get_output |
| 5029 | // to retrieve the output |
| 5030 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5031 | } |
| 5032 | void set_output_raw_strided( |
| 5033 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5034 | TensorOptions options, DimnameList names |
| 5035 | ) override { |
| 5036 | const auto& out = outputs_[output_idx].get(); |
| 5037 | check_inplace(out, sizes, options); |
| 5038 | if (!names.empty()) { |
| 5039 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5040 | } |
| 5041 | // super must happen after, so that downstream can use maybe_get_output |
| 5042 | // to retrieve the output |
| 5043 | at::native::structured_div_out_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5044 | } |
| 5045 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5046 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5047 | } |
| 5048 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5049 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5050 | }; |
| 5051 | at::Tensor & wrapper_CPU_div__Tensor_mode(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 5052 | structured_div_out_mode_inplace op(self); |
| 5053 | op.meta(self, other, rounding_mode); |
| 5054 | op.impl(self, other, rounding_mode, op.outputs_[0]); |
| 5055 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5056 | return self; |
| 5057 | } |
| 5058 | namespace { |
| 5059 | at::Tensor wrapper_CPU__dot(const at::Tensor & self, const at::Tensor & tensor) { |
| 5060 | // No device check |
| 5061 | // DeviceGuard omitted |
| 5062 | return at::native::dot(self, tensor); |
| 5063 | } |
| 5064 | } // anonymous namespace |
| 5065 | namespace { |
| 5066 | at::Tensor wrapper_CPU__vdot(const at::Tensor & self, const at::Tensor & other) { |
| 5067 | // No device check |
| 5068 | // DeviceGuard omitted |
| 5069 | return at::native::vdot(self, other); |
| 5070 | } |
| 5071 | } // anonymous namespace |
| 5072 | namespace { |
| 5073 | at::Tensor wrapper_CPU__embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) { |
| 5074 | // No device check |
| 5075 | // DeviceGuard omitted |
| 5076 | return at::native::embedding_dense_backward_cpu(grad_output, indices, num_weights.expect_int(), padding_idx.expect_int(), scale_grad_by_freq); |
| 5077 | } |
| 5078 | } // anonymous namespace |
| 5079 | namespace { |
| 5080 | at::Tensor & wrapper_CPU__embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { |
| 5081 | // No device check |
| 5082 | // DeviceGuard omitted |
| 5083 | return at::native::embedding_renorm_cpu_(self, indices, max_norm, norm_type); |
| 5084 | } |
| 5085 | } // anonymous namespace |
| 5086 | namespace { |
| 5087 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___embedding_bag_forward_only(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 5088 | // No device check |
| 5089 | // DeviceGuard omitted |
| 5090 | return at::native::_embedding_bag_forward_only_cpu(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 5091 | } |
| 5092 | } // anonymous namespace |
| 5093 | namespace { |
| 5094 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 5095 | // No device check |
| 5096 | // DeviceGuard omitted |
| 5097 | return at::native::_embedding_bag_cpu(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 5098 | } |
| 5099 | } // anonymous namespace |
| 5100 | namespace { |
| 5101 | at::Tensor wrapper_CPU___embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx) { |
| 5102 | // No device check |
| 5103 | // DeviceGuard omitted |
| 5104 | return at::native::_embedding_bag_dense_backward_cpu(grad, indices, offset2bag, bag_size, maximum_indices, num_weights.expect_int(), scale_grad_by_freq, mode, per_sample_weights, padding_idx); |
| 5105 | } |
| 5106 | } // anonymous namespace |
| 5107 | namespace { |
| 5108 | at::Tensor wrapper_CPU___embedding_bag_per_sample_weights_backward(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx) { |
| 5109 | // No device check |
| 5110 | // DeviceGuard omitted |
| 5111 | return at::native::_embedding_bag_per_sample_weights_backward_cpu(grad, weight, indices, offsets, offset2bag, mode, padding_idx); |
| 5112 | } |
| 5113 | } // anonymous namespace |
| 5114 | namespace { |
| 5115 | at::Tensor wrapper_CPU_memory_format_empty(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 5116 | // No device check |
| 5117 | // DeviceGuard omitted |
| 5118 | return at::native::empty_cpu(C10_AS_INTARRAYREF_SLOW(size), dtype, layout, device, pin_memory, memory_format); |
| 5119 | } |
| 5120 | } // anonymous namespace |
| 5121 | namespace { |
| 5122 | at::Tensor wrapper_CPU___empty_affine_quantized(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format) { |
| 5123 | // No device check |
| 5124 | // DeviceGuard omitted |
| 5125 | return at::native::empty_affine_quantized_other_backends_stub(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); |
| 5126 | } |
| 5127 | } // anonymous namespace |
| 5128 | namespace { |
| 5129 | at::Tensor wrapper_CPU___empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 5130 | // No device check |
| 5131 | // DeviceGuard omitted |
| 5132 | return at::native::empty_per_channel_affine_quantized_other_backends_stub(size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format); |
| 5133 | } |
| 5134 | } // anonymous namespace |
| 5135 | namespace { |
| 5136 | const at::Tensor & wrapper_CPU__resize_(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::MemoryFormat> memory_format) { |
| 5137 | // No device check |
| 5138 | // DeviceGuard omitted |
| 5139 | return at::native::resize_(self, C10_AS_INTARRAYREF_SLOW(size), memory_format); |
| 5140 | } |
| 5141 | } // anonymous namespace |
| 5142 | namespace { |
| 5143 | at::Tensor wrapper_CPU__empty_strided(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) { |
| 5144 | // No device check |
| 5145 | // DeviceGuard omitted |
| 5146 | return at::native::empty_strided_cpu(C10_AS_INTARRAYREF_SLOW(size), C10_AS_INTARRAYREF_SLOW(stride), dtype, layout, device, pin_memory); |
| 5147 | } |
| 5148 | } // anonymous namespace |
| 5149 | struct structured_erf_out_functional final : public at::native::structured_erf_out { |
| 5150 | void set_output_strided( |
| 5151 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5152 | TensorOptions options, DimnameList names |
| 5153 | ) override { |
| 5154 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5155 | if (!names.empty()) { |
| 5156 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5157 | } |
| 5158 | // super must happen after, so that downstream can use maybe_get_output |
| 5159 | // to retrieve the output |
| 5160 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5161 | } |
| 5162 | void set_output_raw_strided( |
| 5163 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5164 | TensorOptions options, DimnameList names |
| 5165 | ) override { |
| 5166 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5167 | if (!names.empty()) { |
| 5168 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5169 | } |
| 5170 | // super must happen after, so that downstream can use maybe_get_output |
| 5171 | // to retrieve the output |
| 5172 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5173 | } |
| 5174 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5175 | return *outputs_[output_idx]; |
| 5176 | } |
| 5177 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5178 | }; |
| 5179 | at::Tensor wrapper_CPU_erf(const at::Tensor & self) { |
| 5180 | structured_erf_out_functional op; |
| 5181 | op.meta(self); |
| 5182 | op.impl(self, *op.outputs_[0]); |
| 5183 | return std::move(op.outputs_[0]).take(); |
| 5184 | } |
| 5185 | struct structured_erf_out_out final : public at::native::structured_erf_out { |
| 5186 | structured_erf_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5187 | void set_output_strided( |
| 5188 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5189 | TensorOptions options, DimnameList names |
| 5190 | ) override { |
| 5191 | const auto& out = outputs_[output_idx].get(); |
| 5192 | resize_out(out, sizes, strides, options); |
| 5193 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5194 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5195 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5196 | } |
| 5197 | if (!names.empty()) { |
| 5198 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5199 | } |
| 5200 | // super must happen after, so that downstream can use maybe_get_output |
| 5201 | // to retrieve the output |
| 5202 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5203 | } |
| 5204 | void set_output_raw_strided( |
| 5205 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5206 | TensorOptions options, DimnameList names |
| 5207 | ) override { |
| 5208 | const auto& out = outputs_[output_idx].get(); |
| 5209 | resize_out(out, sizes, strides, options); |
| 5210 | if (!names.empty()) { |
| 5211 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5212 | } |
| 5213 | // super must happen after, so that downstream can use maybe_get_output |
| 5214 | // to retrieve the output |
| 5215 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5216 | } |
| 5217 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5218 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5219 | } |
| 5220 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5221 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5222 | }; |
| 5223 | at::Tensor & wrapper_CPU_erf_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5224 | structured_erf_out_out op(out); |
| 5225 | op.meta(self); |
| 5226 | op.impl(self, op.maybe_get_output(0)); |
| 5227 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5228 | return out; |
| 5229 | } |
| 5230 | struct structured_erf_out_inplace final : public at::native::structured_erf_out { |
| 5231 | structured_erf_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5232 | void set_output_strided( |
| 5233 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5234 | TensorOptions options, DimnameList names |
| 5235 | ) override { |
| 5236 | const auto& out = outputs_[output_idx].get(); |
| 5237 | check_inplace(out, sizes, options); |
| 5238 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5239 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5240 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5241 | } |
| 5242 | if (!names.empty()) { |
| 5243 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5244 | } |
| 5245 | // super must happen after, so that downstream can use maybe_get_output |
| 5246 | // to retrieve the output |
| 5247 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5248 | } |
| 5249 | void set_output_raw_strided( |
| 5250 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5251 | TensorOptions options, DimnameList names |
| 5252 | ) override { |
| 5253 | const auto& out = outputs_[output_idx].get(); |
| 5254 | check_inplace(out, sizes, options); |
| 5255 | if (!names.empty()) { |
| 5256 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5257 | } |
| 5258 | // super must happen after, so that downstream can use maybe_get_output |
| 5259 | // to retrieve the output |
| 5260 | at::native::structured_erf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5261 | } |
| 5262 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5263 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5264 | } |
| 5265 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5266 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5267 | }; |
| 5268 | at::Tensor & wrapper_CPU_erf_(at::Tensor & self) { |
| 5269 | structured_erf_out_inplace op(self); |
| 5270 | op.meta(self); |
| 5271 | op.impl(self, op.outputs_[0]); |
| 5272 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5273 | return self; |
| 5274 | } |
| 5275 | struct structured_erfc_out_functional final : public at::native::structured_erfc_out { |
| 5276 | void set_output_strided( |
| 5277 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5278 | TensorOptions options, DimnameList names |
| 5279 | ) override { |
| 5280 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5281 | if (!names.empty()) { |
| 5282 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5283 | } |
| 5284 | // super must happen after, so that downstream can use maybe_get_output |
| 5285 | // to retrieve the output |
| 5286 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5287 | } |
| 5288 | void set_output_raw_strided( |
| 5289 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5290 | TensorOptions options, DimnameList names |
| 5291 | ) override { |
| 5292 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5293 | if (!names.empty()) { |
| 5294 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5295 | } |
| 5296 | // super must happen after, so that downstream can use maybe_get_output |
| 5297 | // to retrieve the output |
| 5298 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5299 | } |
| 5300 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5301 | return *outputs_[output_idx]; |
| 5302 | } |
| 5303 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5304 | }; |
| 5305 | at::Tensor wrapper_CPU_erfc(const at::Tensor & self) { |
| 5306 | structured_erfc_out_functional op; |
| 5307 | op.meta(self); |
| 5308 | op.impl(self, *op.outputs_[0]); |
| 5309 | return std::move(op.outputs_[0]).take(); |
| 5310 | } |
| 5311 | struct structured_erfc_out_out final : public at::native::structured_erfc_out { |
| 5312 | structured_erfc_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5313 | void set_output_strided( |
| 5314 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5315 | TensorOptions options, DimnameList names |
| 5316 | ) override { |
| 5317 | const auto& out = outputs_[output_idx].get(); |
| 5318 | resize_out(out, sizes, strides, options); |
| 5319 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5320 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5321 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5322 | } |
| 5323 | if (!names.empty()) { |
| 5324 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5325 | } |
| 5326 | // super must happen after, so that downstream can use maybe_get_output |
| 5327 | // to retrieve the output |
| 5328 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5329 | } |
| 5330 | void set_output_raw_strided( |
| 5331 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5332 | TensorOptions options, DimnameList names |
| 5333 | ) override { |
| 5334 | const auto& out = outputs_[output_idx].get(); |
| 5335 | resize_out(out, sizes, strides, options); |
| 5336 | if (!names.empty()) { |
| 5337 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5338 | } |
| 5339 | // super must happen after, so that downstream can use maybe_get_output |
| 5340 | // to retrieve the output |
| 5341 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5342 | } |
| 5343 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5344 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5345 | } |
| 5346 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5347 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5348 | }; |
| 5349 | at::Tensor & wrapper_CPU_erfc_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5350 | structured_erfc_out_out op(out); |
| 5351 | op.meta(self); |
| 5352 | op.impl(self, op.maybe_get_output(0)); |
| 5353 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5354 | return out; |
| 5355 | } |
| 5356 | struct structured_erfc_out_inplace final : public at::native::structured_erfc_out { |
| 5357 | structured_erfc_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5358 | void set_output_strided( |
| 5359 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5360 | TensorOptions options, DimnameList names |
| 5361 | ) override { |
| 5362 | const auto& out = outputs_[output_idx].get(); |
| 5363 | check_inplace(out, sizes, options); |
| 5364 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5365 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5366 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5367 | } |
| 5368 | if (!names.empty()) { |
| 5369 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5370 | } |
| 5371 | // super must happen after, so that downstream can use maybe_get_output |
| 5372 | // to retrieve the output |
| 5373 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5374 | } |
| 5375 | void set_output_raw_strided( |
| 5376 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5377 | TensorOptions options, DimnameList names |
| 5378 | ) override { |
| 5379 | const auto& out = outputs_[output_idx].get(); |
| 5380 | check_inplace(out, sizes, options); |
| 5381 | if (!names.empty()) { |
| 5382 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5383 | } |
| 5384 | // super must happen after, so that downstream can use maybe_get_output |
| 5385 | // to retrieve the output |
| 5386 | at::native::structured_erfc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5387 | } |
| 5388 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5389 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5390 | } |
| 5391 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5392 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5393 | }; |
| 5394 | at::Tensor & wrapper_CPU_erfc_(at::Tensor & self) { |
| 5395 | structured_erfc_out_inplace op(self); |
| 5396 | op.meta(self); |
| 5397 | op.impl(self, op.outputs_[0]); |
| 5398 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5399 | return self; |
| 5400 | } |
| 5401 | struct structured_exp_out_functional final : public at::native::structured_exp_out { |
| 5402 | void set_output_strided( |
| 5403 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5404 | TensorOptions options, DimnameList names |
| 5405 | ) override { |
| 5406 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5407 | if (!names.empty()) { |
| 5408 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5409 | } |
| 5410 | // super must happen after, so that downstream can use maybe_get_output |
| 5411 | // to retrieve the output |
| 5412 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5413 | } |
| 5414 | void set_output_raw_strided( |
| 5415 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5416 | TensorOptions options, DimnameList names |
| 5417 | ) override { |
| 5418 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5419 | if (!names.empty()) { |
| 5420 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5421 | } |
| 5422 | // super must happen after, so that downstream can use maybe_get_output |
| 5423 | // to retrieve the output |
| 5424 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5425 | } |
| 5426 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5427 | return *outputs_[output_idx]; |
| 5428 | } |
| 5429 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5430 | }; |
| 5431 | at::Tensor wrapper_CPU_exp(const at::Tensor & self) { |
| 5432 | structured_exp_out_functional op; |
| 5433 | op.meta(self); |
| 5434 | op.impl(self, *op.outputs_[0]); |
| 5435 | return std::move(op.outputs_[0]).take(); |
| 5436 | } |
| 5437 | struct structured_exp_out_out final : public at::native::structured_exp_out { |
| 5438 | structured_exp_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5439 | void set_output_strided( |
| 5440 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5441 | TensorOptions options, DimnameList names |
| 5442 | ) override { |
| 5443 | const auto& out = outputs_[output_idx].get(); |
| 5444 | resize_out(out, sizes, strides, options); |
| 5445 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5446 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5447 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5448 | } |
| 5449 | if (!names.empty()) { |
| 5450 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5451 | } |
| 5452 | // super must happen after, so that downstream can use maybe_get_output |
| 5453 | // to retrieve the output |
| 5454 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5455 | } |
| 5456 | void set_output_raw_strided( |
| 5457 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5458 | TensorOptions options, DimnameList names |
| 5459 | ) override { |
| 5460 | const auto& out = outputs_[output_idx].get(); |
| 5461 | resize_out(out, sizes, strides, options); |
| 5462 | if (!names.empty()) { |
| 5463 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5464 | } |
| 5465 | // super must happen after, so that downstream can use maybe_get_output |
| 5466 | // to retrieve the output |
| 5467 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5468 | } |
| 5469 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5470 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5471 | } |
| 5472 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5473 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5474 | }; |
| 5475 | at::Tensor & wrapper_CPU_exp_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5476 | structured_exp_out_out op(out); |
| 5477 | op.meta(self); |
| 5478 | op.impl(self, op.maybe_get_output(0)); |
| 5479 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5480 | return out; |
| 5481 | } |
| 5482 | struct structured_exp_out_inplace final : public at::native::structured_exp_out { |
| 5483 | structured_exp_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5484 | void set_output_strided( |
| 5485 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5486 | TensorOptions options, DimnameList names |
| 5487 | ) override { |
| 5488 | const auto& out = outputs_[output_idx].get(); |
| 5489 | check_inplace(out, sizes, options); |
| 5490 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5491 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5492 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5493 | } |
| 5494 | if (!names.empty()) { |
| 5495 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5496 | } |
| 5497 | // super must happen after, so that downstream can use maybe_get_output |
| 5498 | // to retrieve the output |
| 5499 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5500 | } |
| 5501 | void set_output_raw_strided( |
| 5502 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5503 | TensorOptions options, DimnameList names |
| 5504 | ) override { |
| 5505 | const auto& out = outputs_[output_idx].get(); |
| 5506 | check_inplace(out, sizes, options); |
| 5507 | if (!names.empty()) { |
| 5508 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5509 | } |
| 5510 | // super must happen after, so that downstream can use maybe_get_output |
| 5511 | // to retrieve the output |
| 5512 | at::native::structured_exp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5513 | } |
| 5514 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5515 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5516 | } |
| 5517 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5518 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5519 | }; |
| 5520 | at::Tensor & wrapper_CPU_exp_(at::Tensor & self) { |
| 5521 | structured_exp_out_inplace op(self); |
| 5522 | op.meta(self); |
| 5523 | op.impl(self, op.outputs_[0]); |
| 5524 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5525 | return self; |
| 5526 | } |
| 5527 | struct structured_exp2_out_functional final : public at::native::structured_exp2_out { |
| 5528 | void set_output_strided( |
| 5529 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5530 | TensorOptions options, DimnameList names |
| 5531 | ) override { |
| 5532 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5533 | if (!names.empty()) { |
| 5534 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5535 | } |
| 5536 | // super must happen after, so that downstream can use maybe_get_output |
| 5537 | // to retrieve the output |
| 5538 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5539 | } |
| 5540 | void set_output_raw_strided( |
| 5541 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5542 | TensorOptions options, DimnameList names |
| 5543 | ) override { |
| 5544 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5545 | if (!names.empty()) { |
| 5546 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5547 | } |
| 5548 | // super must happen after, so that downstream can use maybe_get_output |
| 5549 | // to retrieve the output |
| 5550 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5551 | } |
| 5552 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5553 | return *outputs_[output_idx]; |
| 5554 | } |
| 5555 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5556 | }; |
| 5557 | at::Tensor wrapper_CPU_exp2(const at::Tensor & self) { |
| 5558 | structured_exp2_out_functional op; |
| 5559 | op.meta(self); |
| 5560 | op.impl(self, *op.outputs_[0]); |
| 5561 | return std::move(op.outputs_[0]).take(); |
| 5562 | } |
| 5563 | struct structured_exp2_out_out final : public at::native::structured_exp2_out { |
| 5564 | structured_exp2_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5565 | void set_output_strided( |
| 5566 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5567 | TensorOptions options, DimnameList names |
| 5568 | ) override { |
| 5569 | const auto& out = outputs_[output_idx].get(); |
| 5570 | resize_out(out, sizes, strides, options); |
| 5571 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5572 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5573 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5574 | } |
| 5575 | if (!names.empty()) { |
| 5576 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5577 | } |
| 5578 | // super must happen after, so that downstream can use maybe_get_output |
| 5579 | // to retrieve the output |
| 5580 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5581 | } |
| 5582 | void set_output_raw_strided( |
| 5583 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5584 | TensorOptions options, DimnameList names |
| 5585 | ) override { |
| 5586 | const auto& out = outputs_[output_idx].get(); |
| 5587 | resize_out(out, sizes, strides, options); |
| 5588 | if (!names.empty()) { |
| 5589 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5590 | } |
| 5591 | // super must happen after, so that downstream can use maybe_get_output |
| 5592 | // to retrieve the output |
| 5593 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5594 | } |
| 5595 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5596 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5597 | } |
| 5598 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5599 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5600 | }; |
| 5601 | at::Tensor & wrapper_CPU_exp2_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5602 | structured_exp2_out_out op(out); |
| 5603 | op.meta(self); |
| 5604 | op.impl(self, op.maybe_get_output(0)); |
| 5605 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5606 | return out; |
| 5607 | } |
| 5608 | struct structured_exp2_out_inplace final : public at::native::structured_exp2_out { |
| 5609 | structured_exp2_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5610 | void set_output_strided( |
| 5611 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5612 | TensorOptions options, DimnameList names |
| 5613 | ) override { |
| 5614 | const auto& out = outputs_[output_idx].get(); |
| 5615 | check_inplace(out, sizes, options); |
| 5616 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5617 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5618 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5619 | } |
| 5620 | if (!names.empty()) { |
| 5621 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5622 | } |
| 5623 | // super must happen after, so that downstream can use maybe_get_output |
| 5624 | // to retrieve the output |
| 5625 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5626 | } |
| 5627 | void set_output_raw_strided( |
| 5628 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5629 | TensorOptions options, DimnameList names |
| 5630 | ) override { |
| 5631 | const auto& out = outputs_[output_idx].get(); |
| 5632 | check_inplace(out, sizes, options); |
| 5633 | if (!names.empty()) { |
| 5634 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5635 | } |
| 5636 | // super must happen after, so that downstream can use maybe_get_output |
| 5637 | // to retrieve the output |
| 5638 | at::native::structured_exp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5639 | } |
| 5640 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5641 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5642 | } |
| 5643 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5644 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5645 | }; |
| 5646 | at::Tensor & wrapper_CPU_exp2_(at::Tensor & self) { |
| 5647 | structured_exp2_out_inplace op(self); |
| 5648 | op.meta(self); |
| 5649 | op.impl(self, op.outputs_[0]); |
| 5650 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5651 | return self; |
| 5652 | } |
| 5653 | struct structured_expm1_out_functional final : public at::native::structured_expm1_out { |
| 5654 | void set_output_strided( |
| 5655 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5656 | TensorOptions options, DimnameList names |
| 5657 | ) override { |
| 5658 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5659 | if (!names.empty()) { |
| 5660 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5661 | } |
| 5662 | // super must happen after, so that downstream can use maybe_get_output |
| 5663 | // to retrieve the output |
| 5664 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5665 | } |
| 5666 | void set_output_raw_strided( |
| 5667 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5668 | TensorOptions options, DimnameList names |
| 5669 | ) override { |
| 5670 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5671 | if (!names.empty()) { |
| 5672 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5673 | } |
| 5674 | // super must happen after, so that downstream can use maybe_get_output |
| 5675 | // to retrieve the output |
| 5676 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5677 | } |
| 5678 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5679 | return *outputs_[output_idx]; |
| 5680 | } |
| 5681 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5682 | }; |
| 5683 | at::Tensor wrapper_CPU_expm1(const at::Tensor & self) { |
| 5684 | structured_expm1_out_functional op; |
| 5685 | op.meta(self); |
| 5686 | op.impl(self, *op.outputs_[0]); |
| 5687 | return std::move(op.outputs_[0]).take(); |
| 5688 | } |
| 5689 | struct structured_expm1_out_out final : public at::native::structured_expm1_out { |
| 5690 | structured_expm1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5691 | void set_output_strided( |
| 5692 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5693 | TensorOptions options, DimnameList names |
| 5694 | ) override { |
| 5695 | const auto& out = outputs_[output_idx].get(); |
| 5696 | resize_out(out, sizes, strides, options); |
| 5697 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5698 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5699 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5700 | } |
| 5701 | if (!names.empty()) { |
| 5702 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5703 | } |
| 5704 | // super must happen after, so that downstream can use maybe_get_output |
| 5705 | // to retrieve the output |
| 5706 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5707 | } |
| 5708 | void set_output_raw_strided( |
| 5709 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5710 | TensorOptions options, DimnameList names |
| 5711 | ) override { |
| 5712 | const auto& out = outputs_[output_idx].get(); |
| 5713 | resize_out(out, sizes, strides, options); |
| 5714 | if (!names.empty()) { |
| 5715 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5716 | } |
| 5717 | // super must happen after, so that downstream can use maybe_get_output |
| 5718 | // to retrieve the output |
| 5719 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5720 | } |
| 5721 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5722 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5723 | } |
| 5724 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5725 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5726 | }; |
| 5727 | at::Tensor & wrapper_CPU_expm1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5728 | structured_expm1_out_out op(out); |
| 5729 | op.meta(self); |
| 5730 | op.impl(self, op.maybe_get_output(0)); |
| 5731 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5732 | return out; |
| 5733 | } |
| 5734 | struct structured_expm1_out_inplace final : public at::native::structured_expm1_out { |
| 5735 | structured_expm1_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5736 | void set_output_strided( |
| 5737 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5738 | TensorOptions options, DimnameList names |
| 5739 | ) override { |
| 5740 | const auto& out = outputs_[output_idx].get(); |
| 5741 | check_inplace(out, sizes, options); |
| 5742 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5743 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5744 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5745 | } |
| 5746 | if (!names.empty()) { |
| 5747 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5748 | } |
| 5749 | // super must happen after, so that downstream can use maybe_get_output |
| 5750 | // to retrieve the output |
| 5751 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5752 | } |
| 5753 | void set_output_raw_strided( |
| 5754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5755 | TensorOptions options, DimnameList names |
| 5756 | ) override { |
| 5757 | const auto& out = outputs_[output_idx].get(); |
| 5758 | check_inplace(out, sizes, options); |
| 5759 | if (!names.empty()) { |
| 5760 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5761 | } |
| 5762 | // super must happen after, so that downstream can use maybe_get_output |
| 5763 | // to retrieve the output |
| 5764 | at::native::structured_expm1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5765 | } |
| 5766 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5767 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5768 | } |
| 5769 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5770 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5771 | }; |
| 5772 | at::Tensor & wrapper_CPU_expm1_(at::Tensor & self) { |
| 5773 | structured_expm1_out_inplace op(self); |
| 5774 | op.meta(self); |
| 5775 | op.impl(self, op.outputs_[0]); |
| 5776 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5777 | return self; |
| 5778 | } |
| 5779 | namespace { |
| 5780 | at::Tensor & wrapper_CPU_out_eye_out(int64_t n, at::Tensor & out) { |
| 5781 | // No device check |
| 5782 | // DeviceGuard omitted |
| 5783 | return at::native::eye_out_cpu(n, out); |
| 5784 | } |
| 5785 | } // anonymous namespace |
| 5786 | namespace { |
| 5787 | at::Tensor & wrapper_CPU_m_out_eye_out(int64_t n, int64_t m, at::Tensor & out) { |
| 5788 | // No device check |
| 5789 | // DeviceGuard omitted |
| 5790 | return at::native::eye_out_cpu(n, m, out); |
| 5791 | } |
| 5792 | } // anonymous namespace |
| 5793 | namespace { |
| 5794 | at::Tensor & wrapper_CPU_Scalar_fill_(at::Tensor & self, const at::Scalar & value) { |
| 5795 | // No device check |
| 5796 | // DeviceGuard omitted |
| 5797 | return at::native::fill_(self, value); |
| 5798 | } |
| 5799 | } // anonymous namespace |
| 5800 | namespace { |
| 5801 | at::Tensor & wrapper_CPU_Tensor_fill_(at::Tensor & self, const at::Tensor & value) { |
| 5802 | // No device check |
| 5803 | // DeviceGuard omitted |
| 5804 | return at::native::fill_(self, value); |
| 5805 | } |
| 5806 | } // anonymous namespace |
| 5807 | struct structured_floor_out_functional final : public at::native::structured_floor_out { |
| 5808 | void set_output_strided( |
| 5809 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5810 | TensorOptions options, DimnameList names |
| 5811 | ) override { |
| 5812 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5813 | if (!names.empty()) { |
| 5814 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5815 | } |
| 5816 | // super must happen after, so that downstream can use maybe_get_output |
| 5817 | // to retrieve the output |
| 5818 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5819 | } |
| 5820 | void set_output_raw_strided( |
| 5821 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5822 | TensorOptions options, DimnameList names |
| 5823 | ) override { |
| 5824 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5825 | if (!names.empty()) { |
| 5826 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5827 | } |
| 5828 | // super must happen after, so that downstream can use maybe_get_output |
| 5829 | // to retrieve the output |
| 5830 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5831 | } |
| 5832 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5833 | return *outputs_[output_idx]; |
| 5834 | } |
| 5835 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5836 | }; |
| 5837 | at::Tensor wrapper_CPU_floor(const at::Tensor & self) { |
| 5838 | structured_floor_out_functional op; |
| 5839 | op.meta(self); |
| 5840 | op.impl(self, *op.outputs_[0]); |
| 5841 | return std::move(op.outputs_[0]).take(); |
| 5842 | } |
| 5843 | struct structured_floor_out_out final : public at::native::structured_floor_out { |
| 5844 | structured_floor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5845 | void set_output_strided( |
| 5846 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5847 | TensorOptions options, DimnameList names |
| 5848 | ) override { |
| 5849 | const auto& out = outputs_[output_idx].get(); |
| 5850 | resize_out(out, sizes, strides, options); |
| 5851 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5852 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5853 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5854 | } |
| 5855 | if (!names.empty()) { |
| 5856 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5857 | } |
| 5858 | // super must happen after, so that downstream can use maybe_get_output |
| 5859 | // to retrieve the output |
| 5860 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5861 | } |
| 5862 | void set_output_raw_strided( |
| 5863 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5864 | TensorOptions options, DimnameList names |
| 5865 | ) override { |
| 5866 | const auto& out = outputs_[output_idx].get(); |
| 5867 | resize_out(out, sizes, strides, options); |
| 5868 | if (!names.empty()) { |
| 5869 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5870 | } |
| 5871 | // super must happen after, so that downstream can use maybe_get_output |
| 5872 | // to retrieve the output |
| 5873 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5874 | } |
| 5875 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5876 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5877 | } |
| 5878 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5879 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5880 | }; |
| 5881 | at::Tensor & wrapper_CPU_floor_out_out(const at::Tensor & self, at::Tensor & out) { |
| 5882 | structured_floor_out_out op(out); |
| 5883 | op.meta(self); |
| 5884 | op.impl(self, op.maybe_get_output(0)); |
| 5885 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5886 | return out; |
| 5887 | } |
| 5888 | struct structured_floor_out_inplace final : public at::native::structured_floor_out { |
| 5889 | structured_floor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5890 | void set_output_strided( |
| 5891 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5892 | TensorOptions options, DimnameList names |
| 5893 | ) override { |
| 5894 | const auto& out = outputs_[output_idx].get(); |
| 5895 | check_inplace(out, sizes, options); |
| 5896 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5897 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5898 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5899 | } |
| 5900 | if (!names.empty()) { |
| 5901 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5902 | } |
| 5903 | // super must happen after, so that downstream can use maybe_get_output |
| 5904 | // to retrieve the output |
| 5905 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5906 | } |
| 5907 | void set_output_raw_strided( |
| 5908 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5909 | TensorOptions options, DimnameList names |
| 5910 | ) override { |
| 5911 | const auto& out = outputs_[output_idx].get(); |
| 5912 | check_inplace(out, sizes, options); |
| 5913 | if (!names.empty()) { |
| 5914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5915 | } |
| 5916 | // super must happen after, so that downstream can use maybe_get_output |
| 5917 | // to retrieve the output |
| 5918 | at::native::structured_floor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5919 | } |
| 5920 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5921 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5922 | } |
| 5923 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5924 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5925 | }; |
| 5926 | at::Tensor & wrapper_CPU_floor_(at::Tensor & self) { |
| 5927 | structured_floor_out_inplace op(self); |
| 5928 | op.meta(self); |
| 5929 | op.impl(self, op.outputs_[0]); |
| 5930 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5931 | return self; |
| 5932 | } |
| 5933 | namespace { |
| 5934 | at::Tensor wrapper_CPU__floor_divide(const at::Tensor & self, const at::Tensor & other) { |
| 5935 | // No device check |
| 5936 | // DeviceGuard omitted |
| 5937 | return at::native::floor_divide(self, other); |
| 5938 | } |
| 5939 | } // anonymous namespace |
| 5940 | namespace { |
| 5941 | at::Tensor & wrapper_CPU_out_floor_divide_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 5942 | // No device check |
| 5943 | // DeviceGuard omitted |
| 5944 | return at::native::floor_divide_out(self, other, out); |
| 5945 | } |
| 5946 | } // anonymous namespace |
| 5947 | namespace { |
| 5948 | at::Tensor & wrapper_CPU_Tensor_floor_divide_(at::Tensor & self, const at::Tensor & other) { |
| 5949 | // No device check |
| 5950 | // DeviceGuard omitted |
| 5951 | return at::native::floor_divide_(self, other); |
| 5952 | } |
| 5953 | } // anonymous namespace |
| 5954 | struct structured_frac_out_functional final : public at::native::structured_frac_out { |
| 5955 | void set_output_strided( |
| 5956 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5957 | TensorOptions options, DimnameList names |
| 5958 | ) override { |
| 5959 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5960 | if (!names.empty()) { |
| 5961 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5962 | } |
| 5963 | // super must happen after, so that downstream can use maybe_get_output |
| 5964 | // to retrieve the output |
| 5965 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5966 | } |
| 5967 | void set_output_raw_strided( |
| 5968 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5969 | TensorOptions options, DimnameList names |
| 5970 | ) override { |
| 5971 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5972 | if (!names.empty()) { |
| 5973 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5974 | } |
| 5975 | // super must happen after, so that downstream can use maybe_get_output |
| 5976 | // to retrieve the output |
| 5977 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5978 | } |
| 5979 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5980 | return *outputs_[output_idx]; |
| 5981 | } |
| 5982 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5983 | }; |
| 5984 | at::Tensor wrapper_CPU_frac(const at::Tensor & self) { |
| 5985 | structured_frac_out_functional op; |
| 5986 | op.meta(self); |
| 5987 | op.impl(self, *op.outputs_[0]); |
| 5988 | return std::move(op.outputs_[0]).take(); |
| 5989 | } |
| 5990 | struct structured_frac_out_out final : public at::native::structured_frac_out { |
| 5991 | structured_frac_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 5992 | void set_output_strided( |
| 5993 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5994 | TensorOptions options, DimnameList names |
| 5995 | ) override { |
| 5996 | const auto& out = outputs_[output_idx].get(); |
| 5997 | resize_out(out, sizes, strides, options); |
| 5998 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5999 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6000 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6001 | } |
| 6002 | if (!names.empty()) { |
| 6003 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6004 | } |
| 6005 | // super must happen after, so that downstream can use maybe_get_output |
| 6006 | // to retrieve the output |
| 6007 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6008 | } |
| 6009 | void set_output_raw_strided( |
| 6010 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6011 | TensorOptions options, DimnameList names |
| 6012 | ) override { |
| 6013 | const auto& out = outputs_[output_idx].get(); |
| 6014 | resize_out(out, sizes, strides, options); |
| 6015 | if (!names.empty()) { |
| 6016 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6017 | } |
| 6018 | // super must happen after, so that downstream can use maybe_get_output |
| 6019 | // to retrieve the output |
| 6020 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6021 | } |
| 6022 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6023 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6024 | } |
| 6025 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6026 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6027 | }; |
| 6028 | at::Tensor & wrapper_CPU_frac_out_out(const at::Tensor & self, at::Tensor & out) { |
| 6029 | structured_frac_out_out op(out); |
| 6030 | op.meta(self); |
| 6031 | op.impl(self, op.maybe_get_output(0)); |
| 6032 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6033 | return out; |
| 6034 | } |
| 6035 | struct structured_frac_out_inplace final : public at::native::structured_frac_out { |
| 6036 | structured_frac_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6037 | void set_output_strided( |
| 6038 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6039 | TensorOptions options, DimnameList names |
| 6040 | ) override { |
| 6041 | const auto& out = outputs_[output_idx].get(); |
| 6042 | check_inplace(out, sizes, options); |
| 6043 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6044 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6045 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6046 | } |
| 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 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6053 | } |
| 6054 | void set_output_raw_strided( |
| 6055 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6056 | TensorOptions options, DimnameList names |
| 6057 | ) override { |
| 6058 | const auto& out = outputs_[output_idx].get(); |
| 6059 | check_inplace(out, sizes, options); |
| 6060 | if (!names.empty()) { |
| 6061 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6062 | } |
| 6063 | // super must happen after, so that downstream can use maybe_get_output |
| 6064 | // to retrieve the output |
| 6065 | at::native::structured_frac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6066 | } |
| 6067 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6068 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6069 | } |
| 6070 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6071 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6072 | }; |
| 6073 | at::Tensor & wrapper_CPU_frac_(at::Tensor & self) { |
| 6074 | structured_frac_out_inplace op(self); |
| 6075 | op.meta(self); |
| 6076 | op.impl(self, op.outputs_[0]); |
| 6077 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6078 | return self; |
| 6079 | } |
| 6080 | namespace { |
| 6081 | at::Tensor wrapper_CPU__from_file(c10::string_view filename, c10::optional<bool> shared, c10::optional<int64_t> size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6082 | // No device check |
| 6083 | // DeviceGuard omitted |
| 6084 | return at::native::from_file(filename, shared, size, dtype, layout, device, pin_memory); |
| 6085 | } |
| 6086 | } // anonymous namespace |
| 6087 | struct structured_gcd_out_functional final : public at::native::structured_gcd_out { |
| 6088 | void set_output_strided( |
| 6089 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6090 | TensorOptions options, DimnameList names |
| 6091 | ) override { |
| 6092 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6093 | if (!names.empty()) { |
| 6094 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6095 | } |
| 6096 | // super must happen after, so that downstream can use maybe_get_output |
| 6097 | // to retrieve the output |
| 6098 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6099 | } |
| 6100 | void set_output_raw_strided( |
| 6101 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6102 | TensorOptions options, DimnameList names |
| 6103 | ) override { |
| 6104 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6105 | if (!names.empty()) { |
| 6106 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6107 | } |
| 6108 | // super must happen after, so that downstream can use maybe_get_output |
| 6109 | // to retrieve the output |
| 6110 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6111 | } |
| 6112 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6113 | return *outputs_[output_idx]; |
| 6114 | } |
| 6115 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6116 | }; |
| 6117 | at::Tensor wrapper_CPU_gcd(const at::Tensor & self, const at::Tensor & other) { |
| 6118 | structured_gcd_out_functional op; |
| 6119 | op.meta(self, other); |
| 6120 | op.impl(self, other, *op.outputs_[0]); |
| 6121 | return std::move(op.outputs_[0]).take(); |
| 6122 | } |
| 6123 | struct structured_gcd_out_out final : public at::native::structured_gcd_out { |
| 6124 | structured_gcd_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6125 | void set_output_strided( |
| 6126 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6127 | TensorOptions options, DimnameList names |
| 6128 | ) override { |
| 6129 | const auto& out = outputs_[output_idx].get(); |
| 6130 | resize_out(out, sizes, strides, options); |
| 6131 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6132 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6133 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6134 | } |
| 6135 | if (!names.empty()) { |
| 6136 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6137 | } |
| 6138 | // super must happen after, so that downstream can use maybe_get_output |
| 6139 | // to retrieve the output |
| 6140 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6141 | } |
| 6142 | void set_output_raw_strided( |
| 6143 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6144 | TensorOptions options, DimnameList names |
| 6145 | ) override { |
| 6146 | const auto& out = outputs_[output_idx].get(); |
| 6147 | resize_out(out, sizes, strides, options); |
| 6148 | if (!names.empty()) { |
| 6149 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6150 | } |
| 6151 | // super must happen after, so that downstream can use maybe_get_output |
| 6152 | // to retrieve the output |
| 6153 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6154 | } |
| 6155 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6156 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6157 | } |
| 6158 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6159 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6160 | }; |
| 6161 | at::Tensor & wrapper_CPU_gcd_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6162 | structured_gcd_out_out op(out); |
| 6163 | op.meta(self, other); |
| 6164 | op.impl(self, other, op.maybe_get_output(0)); |
| 6165 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6166 | return out; |
| 6167 | } |
| 6168 | struct structured_gcd_out_inplace final : public at::native::structured_gcd_out { |
| 6169 | structured_gcd_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6170 | void set_output_strided( |
| 6171 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6172 | TensorOptions options, DimnameList names |
| 6173 | ) override { |
| 6174 | const auto& out = outputs_[output_idx].get(); |
| 6175 | check_inplace(out, sizes, options); |
| 6176 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6177 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6178 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6179 | } |
| 6180 | if (!names.empty()) { |
| 6181 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6182 | } |
| 6183 | // super must happen after, so that downstream can use maybe_get_output |
| 6184 | // to retrieve the output |
| 6185 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6186 | } |
| 6187 | void set_output_raw_strided( |
| 6188 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6189 | TensorOptions options, DimnameList names |
| 6190 | ) override { |
| 6191 | const auto& out = outputs_[output_idx].get(); |
| 6192 | check_inplace(out, sizes, options); |
| 6193 | if (!names.empty()) { |
| 6194 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6195 | } |
| 6196 | // super must happen after, so that downstream can use maybe_get_output |
| 6197 | // to retrieve the output |
| 6198 | at::native::structured_gcd_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6199 | } |
| 6200 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6201 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6202 | } |
| 6203 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6204 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6205 | }; |
| 6206 | at::Tensor & wrapper_CPU_gcd_(at::Tensor & self, const at::Tensor & other) { |
| 6207 | structured_gcd_out_inplace op(self); |
| 6208 | op.meta(self, other); |
| 6209 | op.impl(self, other, op.outputs_[0]); |
| 6210 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6211 | return self; |
| 6212 | } |
| 6213 | struct structured_lcm_out_functional final : public at::native::structured_lcm_out { |
| 6214 | void set_output_strided( |
| 6215 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6216 | TensorOptions options, DimnameList names |
| 6217 | ) override { |
| 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 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6225 | } |
| 6226 | void set_output_raw_strided( |
| 6227 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6228 | TensorOptions options, DimnameList names |
| 6229 | ) override { |
| 6230 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6231 | if (!names.empty()) { |
| 6232 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6233 | } |
| 6234 | // super must happen after, so that downstream can use maybe_get_output |
| 6235 | // to retrieve the output |
| 6236 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6237 | } |
| 6238 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6239 | return *outputs_[output_idx]; |
| 6240 | } |
| 6241 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6242 | }; |
| 6243 | at::Tensor wrapper_CPU_lcm(const at::Tensor & self, const at::Tensor & other) { |
| 6244 | structured_lcm_out_functional op; |
| 6245 | op.meta(self, other); |
| 6246 | op.impl(self, other, *op.outputs_[0]); |
| 6247 | return std::move(op.outputs_[0]).take(); |
| 6248 | } |
| 6249 | struct structured_lcm_out_out final : public at::native::structured_lcm_out { |
| 6250 | structured_lcm_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6251 | void set_output_strided( |
| 6252 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6253 | TensorOptions options, DimnameList names |
| 6254 | ) override { |
| 6255 | const auto& out = outputs_[output_idx].get(); |
| 6256 | resize_out(out, sizes, strides, options); |
| 6257 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6258 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6259 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6260 | } |
| 6261 | if (!names.empty()) { |
| 6262 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6263 | } |
| 6264 | // super must happen after, so that downstream can use maybe_get_output |
| 6265 | // to retrieve the output |
| 6266 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6267 | } |
| 6268 | void set_output_raw_strided( |
| 6269 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6270 | TensorOptions options, DimnameList names |
| 6271 | ) override { |
| 6272 | const auto& out = outputs_[output_idx].get(); |
| 6273 | resize_out(out, sizes, strides, options); |
| 6274 | if (!names.empty()) { |
| 6275 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6276 | } |
| 6277 | // super must happen after, so that downstream can use maybe_get_output |
| 6278 | // to retrieve the output |
| 6279 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6280 | } |
| 6281 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6282 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6283 | } |
| 6284 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6285 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6286 | }; |
| 6287 | at::Tensor & wrapper_CPU_lcm_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6288 | structured_lcm_out_out op(out); |
| 6289 | op.meta(self, other); |
| 6290 | op.impl(self, other, op.maybe_get_output(0)); |
| 6291 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6292 | return out; |
| 6293 | } |
| 6294 | struct structured_lcm_out_inplace final : public at::native::structured_lcm_out { |
| 6295 | structured_lcm_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6296 | void set_output_strided( |
| 6297 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6298 | TensorOptions options, DimnameList names |
| 6299 | ) override { |
| 6300 | const auto& out = outputs_[output_idx].get(); |
| 6301 | check_inplace(out, sizes, options); |
| 6302 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6303 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6304 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6305 | } |
| 6306 | if (!names.empty()) { |
| 6307 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6308 | } |
| 6309 | // super must happen after, so that downstream can use maybe_get_output |
| 6310 | // to retrieve the output |
| 6311 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6312 | } |
| 6313 | void set_output_raw_strided( |
| 6314 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6315 | TensorOptions options, DimnameList names |
| 6316 | ) override { |
| 6317 | const auto& out = outputs_[output_idx].get(); |
| 6318 | check_inplace(out, sizes, options); |
| 6319 | if (!names.empty()) { |
| 6320 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6321 | } |
| 6322 | // super must happen after, so that downstream can use maybe_get_output |
| 6323 | // to retrieve the output |
| 6324 | at::native::structured_lcm_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6325 | } |
| 6326 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6327 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6328 | } |
| 6329 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6330 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6331 | }; |
| 6332 | at::Tensor & wrapper_CPU_lcm_(at::Tensor & self, const at::Tensor & other) { |
| 6333 | structured_lcm_out_inplace op(self); |
| 6334 | op.meta(self, other); |
| 6335 | op.impl(self, other, op.outputs_[0]); |
| 6336 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6337 | return self; |
| 6338 | } |
| 6339 | namespace { |
| 6340 | at::Tensor wrapper_CPU__grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 6341 | // No device check |
| 6342 | // DeviceGuard omitted |
| 6343 | return at::native::grid_sampler_2d_cpu(input, grid, interpolation_mode, padding_mode, align_corners); |
| 6344 | } |
| 6345 | } // anonymous namespace |
| 6346 | namespace { |
| 6347 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 6348 | // No device check |
| 6349 | // DeviceGuard omitted |
| 6350 | return at::native::grid_sampler_2d_backward_cpu(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 6351 | } |
| 6352 | } // anonymous namespace |
| 6353 | namespace { |
| 6354 | at::Tensor wrapper_CPU__grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 6355 | // No device check |
| 6356 | // DeviceGuard omitted |
| 6357 | return at::native::grid_sampler_3d_cpu(input, grid, interpolation_mode, padding_mode, align_corners); |
| 6358 | } |
| 6359 | } // anonymous namespace |
| 6360 | namespace { |
| 6361 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 6362 | // No device check |
| 6363 | // DeviceGuard omitted |
| 6364 | return at::native::grid_sampler_3d_backward_cpu(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 6365 | } |
| 6366 | } // anonymous namespace |
| 6367 | namespace { |
| 6368 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_group_norm(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { |
| 6369 | // No device check |
| 6370 | // DeviceGuard omitted |
| 6371 | return at::native::native_group_norm(input, weight, bias, N.expect_int(), C.expect_int(), HxW.expect_int(), group, eps); |
| 6372 | } |
| 6373 | } // anonymous namespace |
| 6374 | namespace { |
| 6375 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) { |
| 6376 | // No device check |
| 6377 | // DeviceGuard omitted |
| 6378 | return at::native::native_group_norm_backward(grad_out, input, mean, rstd, weight, N.expect_int(), C.expect_int(), HxW.expect_int(), group, output_mask); |
| 6379 | } |
| 6380 | } // anonymous namespace |
| 6381 | namespace { |
| 6382 | at::Tensor wrapper_CPU___fft_r2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { |
| 6383 | // No device check |
| 6384 | // DeviceGuard omitted |
| 6385 | return at::native::_fft_r2c_mkl(self, dim, normalization, onesided); |
| 6386 | } |
| 6387 | } // anonymous namespace |
| 6388 | namespace { |
| 6389 | at::Tensor & wrapper_CPU_out__fft_r2c_out(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { |
| 6390 | // No device check |
| 6391 | // DeviceGuard omitted |
| 6392 | return at::native::_fft_r2c_mkl_out(self, dim, normalization, onesided, out); |
| 6393 | } |
| 6394 | } // anonymous namespace |
| 6395 | namespace { |
| 6396 | at::Tensor wrapper_CPU___fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) { |
| 6397 | // No device check |
| 6398 | // DeviceGuard omitted |
| 6399 | return at::native::_fft_c2r_mkl(self, dim, normalization, last_dim_size); |
| 6400 | } |
| 6401 | } // anonymous namespace |
| 6402 | namespace { |
| 6403 | at::Tensor & wrapper_CPU_out__fft_c2r_out(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size, at::Tensor & out) { |
| 6404 | // No device check |
| 6405 | // DeviceGuard omitted |
| 6406 | return at::native::_fft_c2r_mkl_out(self, dim, normalization, last_dim_size, out); |
| 6407 | } |
| 6408 | } // anonymous namespace |
| 6409 | namespace { |
| 6410 | at::Tensor wrapper_CPU___fft_c2c(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { |
| 6411 | // No device check |
| 6412 | // DeviceGuard omitted |
| 6413 | return at::native::_fft_c2c_mkl(self, C10_AS_INTARRAYREF_SLOW(dim), normalization, forward); |
| 6414 | } |
| 6415 | } // anonymous namespace |
| 6416 | namespace { |
| 6417 | at::Tensor & wrapper_CPU_out__fft_c2c_out(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 6418 | // No device check |
| 6419 | // DeviceGuard omitted |
| 6420 | return at::native::_fft_c2c_mkl_out(self, C10_AS_INTARRAYREF_SLOW(dim), normalization, forward, out); |
| 6421 | } |
| 6422 | } // anonymous namespace |
| 6423 | namespace { |
| 6424 | void wrapper_CPU___validate_compressed_sparse_indices(bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { |
| 6425 | // No device check |
| 6426 | // DeviceGuard omitted |
| 6427 | return at::native::_validate_compressed_sparse_indices_cpu(is_crow, compressed_idx, plain_idx, cdim, dim, nnz); |
| 6428 | } |
| 6429 | } // anonymous namespace |
| 6430 | struct structured_index_out_functional final : public at::native::structured_index_out { |
| 6431 | void set_output_strided( |
| 6432 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6433 | TensorOptions options, DimnameList names |
| 6434 | ) override { |
| 6435 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6436 | if (!names.empty()) { |
| 6437 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6438 | } |
| 6439 | // super must happen after, so that downstream can use maybe_get_output |
| 6440 | // to retrieve the output |
| 6441 | at::native::structured_index_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6442 | } |
| 6443 | void set_output_raw_strided( |
| 6444 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6445 | TensorOptions options, DimnameList names |
| 6446 | ) override { |
| 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 | at::native::structured_index_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6454 | } |
| 6455 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6456 | return *outputs_[output_idx]; |
| 6457 | } |
| 6458 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6459 | }; |
| 6460 | at::Tensor wrapper_CPU_index_Tensor(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 6461 | structured_index_out_functional op; |
| 6462 | auto precompute = op.meta(self, at::IOptTensorListRef(indices)); |
| 6463 | (void)precompute; |
| 6464 | op.impl(self, precompute.sizes, precompute.strides, *op.outputs_[0]); |
| 6465 | return std::move(op.outputs_[0]).take(); |
| 6466 | } |
| 6467 | struct structured_index_out_out final : public at::native::structured_index_out { |
| 6468 | structured_index_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6469 | void set_output_strided( |
| 6470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6471 | TensorOptions options, DimnameList names |
| 6472 | ) override { |
| 6473 | const auto& out = outputs_[output_idx].get(); |
| 6474 | resize_out(out, sizes, strides, options); |
| 6475 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6476 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6477 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6478 | } |
| 6479 | if (!names.empty()) { |
| 6480 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6481 | } |
| 6482 | // super must happen after, so that downstream can use maybe_get_output |
| 6483 | // to retrieve the output |
| 6484 | at::native::structured_index_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6485 | } |
| 6486 | void set_output_raw_strided( |
| 6487 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6488 | TensorOptions options, DimnameList names |
| 6489 | ) override { |
| 6490 | const auto& out = outputs_[output_idx].get(); |
| 6491 | resize_out(out, sizes, strides, options); |
| 6492 | if (!names.empty()) { |
| 6493 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6494 | } |
| 6495 | // super must happen after, so that downstream can use maybe_get_output |
| 6496 | // to retrieve the output |
| 6497 | at::native::structured_index_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6498 | } |
| 6499 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6500 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6501 | } |
| 6502 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6503 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6504 | }; |
| 6505 | at::Tensor & wrapper_CPU_index_out_Tensor_out(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, at::Tensor & out) { |
| 6506 | structured_index_out_out op(out); |
| 6507 | auto precompute = op.meta(self, at::IOptTensorListRef(indices)); |
| 6508 | (void)precompute; |
| 6509 | op.impl(self, precompute.sizes, precompute.strides, op.maybe_get_output(0)); |
| 6510 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6511 | return out; |
| 6512 | } |
| 6513 | struct structured_index_copy_out_functional final : public at::native::structured_index_copy_out { |
| 6514 | void set_output_strided( |
| 6515 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6516 | TensorOptions options, DimnameList names |
| 6517 | ) override { |
| 6518 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6519 | if (!names.empty()) { |
| 6520 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6521 | } |
| 6522 | // super must happen after, so that downstream can use maybe_get_output |
| 6523 | // to retrieve the output |
| 6524 | } |
| 6525 | void set_output_raw_strided( |
| 6526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6527 | TensorOptions options, DimnameList names |
| 6528 | ) override { |
| 6529 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6530 | if (!names.empty()) { |
| 6531 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6532 | } |
| 6533 | // super must happen after, so that downstream can use maybe_get_output |
| 6534 | // to retrieve the output |
| 6535 | } |
| 6536 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6537 | return *outputs_[output_idx]; |
| 6538 | } |
| 6539 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6540 | }; |
| 6541 | at::Tensor wrapper_CPU_index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 6542 | structured_index_copy_out_functional op; |
| 6543 | auto precompute = op.meta(self, dim, index, source); |
| 6544 | (void)precompute; |
| 6545 | op.impl(self, precompute.dim, index, source, *op.outputs_[0]); |
| 6546 | return std::move(op.outputs_[0]).take(); |
| 6547 | } |
| 6548 | struct structured_index_copy_out_out final : public at::native::structured_index_copy_out { |
| 6549 | structured_index_copy_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6550 | void set_output_strided( |
| 6551 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6552 | TensorOptions options, DimnameList names |
| 6553 | ) override { |
| 6554 | const auto& out = outputs_[output_idx].get(); |
| 6555 | resize_out(out, sizes, strides, options); |
| 6556 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6557 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6558 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6559 | } |
| 6560 | if (!names.empty()) { |
| 6561 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6562 | } |
| 6563 | // super must happen after, so that downstream can use maybe_get_output |
| 6564 | // to retrieve the output |
| 6565 | } |
| 6566 | void set_output_raw_strided( |
| 6567 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6568 | TensorOptions options, DimnameList names |
| 6569 | ) override { |
| 6570 | const auto& out = outputs_[output_idx].get(); |
| 6571 | resize_out(out, sizes, strides, options); |
| 6572 | if (!names.empty()) { |
| 6573 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6574 | } |
| 6575 | // super must happen after, so that downstream can use maybe_get_output |
| 6576 | // to retrieve the output |
| 6577 | } |
| 6578 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6579 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6580 | } |
| 6581 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6582 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6583 | }; |
| 6584 | at::Tensor & wrapper_CPU_index_copy_out_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out) { |
| 6585 | structured_index_copy_out_out op(out); |
| 6586 | auto precompute = op.meta(self, dim, index, source); |
| 6587 | (void)precompute; |
| 6588 | op.impl(self, precompute.dim, index, source, op.maybe_get_output(0)); |
| 6589 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6590 | return out; |
| 6591 | } |
| 6592 | struct structured_index_copy_out_inplace final : public at::native::structured_index_copy_out { |
| 6593 | structured_index_copy_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6594 | void set_output_strided( |
| 6595 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6596 | TensorOptions options, DimnameList names |
| 6597 | ) override { |
| 6598 | const auto& out = outputs_[output_idx].get(); |
| 6599 | check_inplace(out, sizes, options); |
| 6600 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6601 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6602 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6603 | } |
| 6604 | if (!names.empty()) { |
| 6605 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6606 | } |
| 6607 | // super must happen after, so that downstream can use maybe_get_output |
| 6608 | // to retrieve the output |
| 6609 | } |
| 6610 | void set_output_raw_strided( |
| 6611 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6612 | TensorOptions options, DimnameList names |
| 6613 | ) override { |
| 6614 | const auto& out = outputs_[output_idx].get(); |
| 6615 | check_inplace(out, sizes, options); |
| 6616 | if (!names.empty()) { |
| 6617 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6618 | } |
| 6619 | // super must happen after, so that downstream can use maybe_get_output |
| 6620 | // to retrieve the output |
| 6621 | } |
| 6622 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6623 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6624 | } |
| 6625 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6626 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6627 | }; |
| 6628 | at::Tensor & wrapper_CPU_index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 6629 | structured_index_copy_out_inplace op(self); |
| 6630 | auto precompute = op.meta(self, dim, index, source); |
| 6631 | (void)precompute; |
| 6632 | op.impl(self, precompute.dim, index, source, op.outputs_[0]); |
| 6633 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6634 | return self; |
| 6635 | } |
| 6636 | namespace { |
| 6637 | at::Tensor & wrapper_CPU___index_put_impl_(at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe) { |
| 6638 | // No device check |
| 6639 | // DeviceGuard omitted |
| 6640 | return at::native::_index_put_impl_(self, indices, values, accumulate, unsafe); |
| 6641 | } |
| 6642 | } // anonymous namespace |
| 6643 | struct structured_isin_Tensor_Tensor_out_functional final : public at::native::structured_isin_Tensor_Tensor_out { |
| 6644 | void set_output_strided( |
| 6645 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6646 | TensorOptions options, DimnameList names |
| 6647 | ) override { |
| 6648 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6649 | if (!names.empty()) { |
| 6650 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6651 | } |
| 6652 | // super must happen after, so that downstream can use maybe_get_output |
| 6653 | // to retrieve the output |
| 6654 | } |
| 6655 | void set_output_raw_strided( |
| 6656 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6657 | TensorOptions options, DimnameList names |
| 6658 | ) override { |
| 6659 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6660 | if (!names.empty()) { |
| 6661 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6662 | } |
| 6663 | // super must happen after, so that downstream can use maybe_get_output |
| 6664 | // to retrieve the output |
| 6665 | } |
| 6666 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6667 | return *outputs_[output_idx]; |
| 6668 | } |
| 6669 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6670 | }; |
| 6671 | at::Tensor wrapper_CPU_isin_Tensor_Tensor(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 6672 | structured_isin_Tensor_Tensor_out_functional op; |
| 6673 | op.meta(elements, test_elements, assume_unique, invert); |
| 6674 | op.impl(elements, test_elements, assume_unique, invert, *op.outputs_[0]); |
| 6675 | return std::move(op.outputs_[0]).take(); |
| 6676 | } |
| 6677 | struct structured_isin_Tensor_Tensor_out_out final : public at::native::structured_isin_Tensor_Tensor_out { |
| 6678 | structured_isin_Tensor_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6679 | void set_output_strided( |
| 6680 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6681 | TensorOptions options, DimnameList names |
| 6682 | ) override { |
| 6683 | const auto& out = outputs_[output_idx].get(); |
| 6684 | resize_out(out, sizes, strides, options); |
| 6685 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6686 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6687 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6688 | } |
| 6689 | if (!names.empty()) { |
| 6690 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6691 | } |
| 6692 | // super must happen after, so that downstream can use maybe_get_output |
| 6693 | // to retrieve the output |
| 6694 | } |
| 6695 | void set_output_raw_strided( |
| 6696 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6697 | TensorOptions options, DimnameList names |
| 6698 | ) override { |
| 6699 | const auto& out = outputs_[output_idx].get(); |
| 6700 | resize_out(out, sizes, strides, options); |
| 6701 | if (!names.empty()) { |
| 6702 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6703 | } |
| 6704 | // super must happen after, so that downstream can use maybe_get_output |
| 6705 | // to retrieve the output |
| 6706 | } |
| 6707 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6708 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6709 | } |
| 6710 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6711 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6712 | }; |
| 6713 | at::Tensor & wrapper_CPU_isin_out_Tensor_Tensor_out(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 6714 | structured_isin_Tensor_Tensor_out_out op(out); |
| 6715 | op.meta(elements, test_elements, assume_unique, invert); |
| 6716 | op.impl(elements, test_elements, assume_unique, invert, op.maybe_get_output(0)); |
| 6717 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6718 | return out; |
| 6719 | } |
| 6720 | struct structured_isin_Tensor_Scalar_out_functional final : public at::native::structured_isin_Tensor_Scalar_out { |
| 6721 | void set_output_strided( |
| 6722 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6723 | TensorOptions options, DimnameList names |
| 6724 | ) override { |
| 6725 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6726 | if (!names.empty()) { |
| 6727 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6728 | } |
| 6729 | // super must happen after, so that downstream can use maybe_get_output |
| 6730 | // to retrieve the output |
| 6731 | } |
| 6732 | void set_output_raw_strided( |
| 6733 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6734 | TensorOptions options, DimnameList names |
| 6735 | ) override { |
| 6736 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6737 | if (!names.empty()) { |
| 6738 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6739 | } |
| 6740 | // super must happen after, so that downstream can use maybe_get_output |
| 6741 | // to retrieve the output |
| 6742 | } |
| 6743 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6744 | return *outputs_[output_idx]; |
| 6745 | } |
| 6746 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6747 | }; |
| 6748 | at::Tensor wrapper_CPU_isin_Tensor_Scalar(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { |
| 6749 | structured_isin_Tensor_Scalar_out_functional op; |
| 6750 | op.meta(elements, test_element, assume_unique, invert); |
| 6751 | op.impl(elements, test_element, assume_unique, invert, *op.outputs_[0]); |
| 6752 | return std::move(op.outputs_[0]).take(); |
| 6753 | } |
| 6754 | struct structured_isin_Tensor_Scalar_out_out final : public at::native::structured_isin_Tensor_Scalar_out { |
| 6755 | structured_isin_Tensor_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6756 | void set_output_strided( |
| 6757 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6758 | TensorOptions options, DimnameList names |
| 6759 | ) override { |
| 6760 | const auto& out = outputs_[output_idx].get(); |
| 6761 | resize_out(out, sizes, strides, options); |
| 6762 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6763 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6764 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6765 | } |
| 6766 | if (!names.empty()) { |
| 6767 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6768 | } |
| 6769 | // super must happen after, so that downstream can use maybe_get_output |
| 6770 | // to retrieve the output |
| 6771 | } |
| 6772 | void set_output_raw_strided( |
| 6773 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6774 | TensorOptions options, DimnameList names |
| 6775 | ) override { |
| 6776 | const auto& out = outputs_[output_idx].get(); |
| 6777 | resize_out(out, sizes, strides, options); |
| 6778 | if (!names.empty()) { |
| 6779 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6780 | } |
| 6781 | // super must happen after, so that downstream can use maybe_get_output |
| 6782 | // to retrieve the output |
| 6783 | } |
| 6784 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6785 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6786 | } |
| 6787 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6788 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6789 | }; |
| 6790 | at::Tensor & wrapper_CPU_isin_out_Tensor_Scalar_out(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) { |
| 6791 | structured_isin_Tensor_Scalar_out_out op(out); |
| 6792 | op.meta(elements, test_element, assume_unique, invert); |
| 6793 | op.impl(elements, test_element, assume_unique, invert, op.maybe_get_output(0)); |
| 6794 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6795 | return out; |
| 6796 | } |
| 6797 | struct structured_isin_Scalar_Tensor_out_functional final : public at::native::structured_isin_Scalar_Tensor_out { |
| 6798 | void set_output_strided( |
| 6799 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6800 | TensorOptions options, DimnameList names |
| 6801 | ) override { |
| 6802 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6803 | if (!names.empty()) { |
| 6804 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6805 | } |
| 6806 | // super must happen after, so that downstream can use maybe_get_output |
| 6807 | // to retrieve the output |
| 6808 | } |
| 6809 | void set_output_raw_strided( |
| 6810 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6811 | TensorOptions options, DimnameList names |
| 6812 | ) override { |
| 6813 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6814 | if (!names.empty()) { |
| 6815 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6816 | } |
| 6817 | // super must happen after, so that downstream can use maybe_get_output |
| 6818 | // to retrieve the output |
| 6819 | } |
| 6820 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6821 | return *outputs_[output_idx]; |
| 6822 | } |
| 6823 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6824 | }; |
| 6825 | at::Tensor wrapper_CPU_isin_Scalar_Tensor(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 6826 | structured_isin_Scalar_Tensor_out_functional op; |
| 6827 | op.meta(element, test_elements, assume_unique, invert); |
| 6828 | op.impl(element, test_elements, assume_unique, invert, *op.outputs_[0]); |
| 6829 | return std::move(op.outputs_[0]).take(); |
| 6830 | } |
| 6831 | struct structured_isin_Scalar_Tensor_out_out final : public at::native::structured_isin_Scalar_Tensor_out { |
| 6832 | structured_isin_Scalar_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6833 | void set_output_strided( |
| 6834 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6835 | TensorOptions options, DimnameList names |
| 6836 | ) override { |
| 6837 | const auto& out = outputs_[output_idx].get(); |
| 6838 | resize_out(out, sizes, strides, options); |
| 6839 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6840 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6841 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6842 | } |
| 6843 | if (!names.empty()) { |
| 6844 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6845 | } |
| 6846 | // super must happen after, so that downstream can use maybe_get_output |
| 6847 | // to retrieve the output |
| 6848 | } |
| 6849 | void set_output_raw_strided( |
| 6850 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6851 | TensorOptions options, DimnameList names |
| 6852 | ) override { |
| 6853 | const auto& out = outputs_[output_idx].get(); |
| 6854 | resize_out(out, sizes, strides, options); |
| 6855 | if (!names.empty()) { |
| 6856 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6857 | } |
| 6858 | // super must happen after, so that downstream can use maybe_get_output |
| 6859 | // to retrieve the output |
| 6860 | } |
| 6861 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6862 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6863 | } |
| 6864 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6865 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6866 | }; |
| 6867 | at::Tensor & wrapper_CPU_isin_out_Scalar_Tensor_out(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 6868 | structured_isin_Scalar_Tensor_out_out op(out); |
| 6869 | op.meta(element, test_elements, assume_unique, invert); |
| 6870 | op.impl(element, test_elements, assume_unique, invert, op.maybe_get_output(0)); |
| 6871 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6872 | return out; |
| 6873 | } |
| 6874 | namespace { |
| 6875 | at::Tensor wrapper_CPU__isnan(const at::Tensor & self) { |
| 6876 | // No device check |
| 6877 | // DeviceGuard omitted |
| 6878 | return at::native::isnan(self); |
| 6879 | } |
| 6880 | } // anonymous namespace |
| 6881 | namespace { |
| 6882 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_values_kthvalue_out(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6883 | // No device check |
| 6884 | // DeviceGuard omitted |
| 6885 | return at::native::kthvalue_out_cpu(self, k, dim, keepdim, values, indices); |
| 6886 | } |
| 6887 | } // anonymous namespace |
| 6888 | namespace { |
| 6889 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_layer_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps) { |
| 6890 | // No device check |
| 6891 | // DeviceGuard omitted |
| 6892 | return at::native::layer_norm_cpu(input, C10_AS_INTARRAYREF_SLOW(normalized_shape), weight, bias, eps); |
| 6893 | } |
| 6894 | } // anonymous namespace |
| 6895 | namespace { |
| 6896 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) { |
| 6897 | // No device check |
| 6898 | // DeviceGuard omitted |
| 6899 | return at::native::layer_norm_backward_cpu(grad_out, input, C10_AS_INTARRAYREF_SLOW(normalized_shape), mean, rstd, weight, bias, output_mask); |
| 6900 | } |
| 6901 | } // anonymous namespace |
| 6902 | namespace { |
| 6903 | at::Tensor & wrapper_CPU_out_nan_to_num_out(const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out) { |
| 6904 | // No device check |
| 6905 | // DeviceGuard omitted |
| 6906 | return at::native::nan_to_num_out(self, nan, posinf, neginf, out); |
| 6907 | } |
| 6908 | } // anonymous namespace |
| 6909 | namespace { |
| 6910 | at::Tensor & wrapper_CPU_out_linspace_out(const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out) { |
| 6911 | // No device check |
| 6912 | // DeviceGuard omitted |
| 6913 | return at::native::linspace_out(start, end, steps, out); |
| 6914 | } |
| 6915 | } // anonymous namespace |
| 6916 | struct structured_log_out_functional final : public at::native::structured_log_out { |
| 6917 | void set_output_strided( |
| 6918 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6919 | TensorOptions options, DimnameList names |
| 6920 | ) override { |
| 6921 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6922 | if (!names.empty()) { |
| 6923 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6924 | } |
| 6925 | // super must happen after, so that downstream can use maybe_get_output |
| 6926 | // to retrieve the output |
| 6927 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6928 | } |
| 6929 | void set_output_raw_strided( |
| 6930 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6931 | TensorOptions options, DimnameList names |
| 6932 | ) override { |
| 6933 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6934 | if (!names.empty()) { |
| 6935 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6936 | } |
| 6937 | // super must happen after, so that downstream can use maybe_get_output |
| 6938 | // to retrieve the output |
| 6939 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6940 | } |
| 6941 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6942 | return *outputs_[output_idx]; |
| 6943 | } |
| 6944 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6945 | }; |
| 6946 | at::Tensor wrapper_CPU_log(const at::Tensor & self) { |
| 6947 | structured_log_out_functional op; |
| 6948 | op.meta(self); |
| 6949 | op.impl(self, *op.outputs_[0]); |
| 6950 | return std::move(op.outputs_[0]).take(); |
| 6951 | } |
| 6952 | struct structured_log_out_out final : public at::native::structured_log_out { |
| 6953 | structured_log_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 6954 | void set_output_strided( |
| 6955 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6956 | TensorOptions options, DimnameList names |
| 6957 | ) override { |
| 6958 | const auto& out = outputs_[output_idx].get(); |
| 6959 | resize_out(out, sizes, strides, options); |
| 6960 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6961 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6962 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6963 | } |
| 6964 | if (!names.empty()) { |
| 6965 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6966 | } |
| 6967 | // super must happen after, so that downstream can use maybe_get_output |
| 6968 | // to retrieve the output |
| 6969 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6970 | } |
| 6971 | void set_output_raw_strided( |
| 6972 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6973 | TensorOptions options, DimnameList names |
| 6974 | ) override { |
| 6975 | const auto& out = outputs_[output_idx].get(); |
| 6976 | resize_out(out, sizes, strides, options); |
| 6977 | if (!names.empty()) { |
| 6978 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6979 | } |
| 6980 | // super must happen after, so that downstream can use maybe_get_output |
| 6981 | // to retrieve the output |
| 6982 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6983 | } |
| 6984 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6985 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6986 | } |
| 6987 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6988 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6989 | }; |
| 6990 | at::Tensor & wrapper_CPU_log_out_out(const at::Tensor & self, at::Tensor & out) { |
| 6991 | structured_log_out_out op(out); |
| 6992 | op.meta(self); |
| 6993 | op.impl(self, op.maybe_get_output(0)); |
| 6994 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6995 | return out; |
| 6996 | } |
| 6997 | struct structured_log_out_inplace final : public at::native::structured_log_out { |
| 6998 | structured_log_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6999 | void set_output_strided( |
| 7000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7001 | TensorOptions options, DimnameList names |
| 7002 | ) override { |
| 7003 | const auto& out = outputs_[output_idx].get(); |
| 7004 | check_inplace(out, sizes, options); |
| 7005 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7006 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7007 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7008 | } |
| 7009 | if (!names.empty()) { |
| 7010 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7011 | } |
| 7012 | // super must happen after, so that downstream can use maybe_get_output |
| 7013 | // to retrieve the output |
| 7014 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7015 | } |
| 7016 | void set_output_raw_strided( |
| 7017 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7018 | TensorOptions options, DimnameList names |
| 7019 | ) override { |
| 7020 | const auto& out = outputs_[output_idx].get(); |
| 7021 | check_inplace(out, sizes, options); |
| 7022 | if (!names.empty()) { |
| 7023 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7024 | } |
| 7025 | // super must happen after, so that downstream can use maybe_get_output |
| 7026 | // to retrieve the output |
| 7027 | at::native::structured_log_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7028 | } |
| 7029 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7030 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7031 | } |
| 7032 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7033 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7034 | }; |
| 7035 | at::Tensor & wrapper_CPU_log_(at::Tensor & self) { |
| 7036 | structured_log_out_inplace op(self); |
| 7037 | op.meta(self); |
| 7038 | op.impl(self, op.outputs_[0]); |
| 7039 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7040 | return self; |
| 7041 | } |
| 7042 | struct structured_log10_out_functional final : public at::native::structured_log10_out { |
| 7043 | void set_output_strided( |
| 7044 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7045 | TensorOptions options, DimnameList names |
| 7046 | ) override { |
| 7047 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7048 | if (!names.empty()) { |
| 7049 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7050 | } |
| 7051 | // super must happen after, so that downstream can use maybe_get_output |
| 7052 | // to retrieve the output |
| 7053 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7054 | } |
| 7055 | void set_output_raw_strided( |
| 7056 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7057 | TensorOptions options, DimnameList names |
| 7058 | ) override { |
| 7059 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7060 | if (!names.empty()) { |
| 7061 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7062 | } |
| 7063 | // super must happen after, so that downstream can use maybe_get_output |
| 7064 | // to retrieve the output |
| 7065 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7066 | } |
| 7067 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7068 | return *outputs_[output_idx]; |
| 7069 | } |
| 7070 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7071 | }; |
| 7072 | at::Tensor wrapper_CPU_log10(const at::Tensor & self) { |
| 7073 | structured_log10_out_functional op; |
| 7074 | op.meta(self); |
| 7075 | op.impl(self, *op.outputs_[0]); |
| 7076 | return std::move(op.outputs_[0]).take(); |
| 7077 | } |
| 7078 | struct structured_log10_out_out final : public at::native::structured_log10_out { |
| 7079 | structured_log10_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7080 | void set_output_strided( |
| 7081 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7082 | TensorOptions options, DimnameList names |
| 7083 | ) override { |
| 7084 | const auto& out = outputs_[output_idx].get(); |
| 7085 | resize_out(out, sizes, strides, options); |
| 7086 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7087 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7088 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7089 | } |
| 7090 | if (!names.empty()) { |
| 7091 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7092 | } |
| 7093 | // super must happen after, so that downstream can use maybe_get_output |
| 7094 | // to retrieve the output |
| 7095 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7096 | } |
| 7097 | void set_output_raw_strided( |
| 7098 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7099 | TensorOptions options, DimnameList names |
| 7100 | ) override { |
| 7101 | const auto& out = outputs_[output_idx].get(); |
| 7102 | resize_out(out, sizes, strides, options); |
| 7103 | if (!names.empty()) { |
| 7104 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7105 | } |
| 7106 | // super must happen after, so that downstream can use maybe_get_output |
| 7107 | // to retrieve the output |
| 7108 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7109 | } |
| 7110 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7111 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7112 | } |
| 7113 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7114 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7115 | }; |
| 7116 | at::Tensor & wrapper_CPU_log10_out_out(const at::Tensor & self, at::Tensor & out) { |
| 7117 | structured_log10_out_out op(out); |
| 7118 | op.meta(self); |
| 7119 | op.impl(self, op.maybe_get_output(0)); |
| 7120 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7121 | return out; |
| 7122 | } |
| 7123 | struct structured_log10_out_inplace final : public at::native::structured_log10_out { |
| 7124 | structured_log10_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7125 | void set_output_strided( |
| 7126 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7127 | TensorOptions options, DimnameList names |
| 7128 | ) override { |
| 7129 | const auto& out = outputs_[output_idx].get(); |
| 7130 | check_inplace(out, sizes, options); |
| 7131 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7132 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7133 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7134 | } |
| 7135 | if (!names.empty()) { |
| 7136 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7137 | } |
| 7138 | // super must happen after, so that downstream can use maybe_get_output |
| 7139 | // to retrieve the output |
| 7140 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7141 | } |
| 7142 | void set_output_raw_strided( |
| 7143 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7144 | TensorOptions options, DimnameList names |
| 7145 | ) override { |
| 7146 | const auto& out = outputs_[output_idx].get(); |
| 7147 | check_inplace(out, sizes, options); |
| 7148 | if (!names.empty()) { |
| 7149 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7150 | } |
| 7151 | // super must happen after, so that downstream can use maybe_get_output |
| 7152 | // to retrieve the output |
| 7153 | at::native::structured_log10_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7154 | } |
| 7155 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7156 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7157 | } |
| 7158 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7159 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7160 | }; |
| 7161 | at::Tensor & wrapper_CPU_log10_(at::Tensor & self) { |
| 7162 | structured_log10_out_inplace op(self); |
| 7163 | op.meta(self); |
| 7164 | op.impl(self, op.outputs_[0]); |
| 7165 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7166 | return self; |
| 7167 | } |
| 7168 | struct structured_log1p_out_functional final : public at::native::structured_log1p_out { |
| 7169 | void set_output_strided( |
| 7170 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7171 | TensorOptions options, DimnameList names |
| 7172 | ) override { |
| 7173 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7174 | if (!names.empty()) { |
| 7175 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7176 | } |
| 7177 | // super must happen after, so that downstream can use maybe_get_output |
| 7178 | // to retrieve the output |
| 7179 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7180 | } |
| 7181 | void set_output_raw_strided( |
| 7182 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7183 | TensorOptions options, DimnameList names |
| 7184 | ) override { |
| 7185 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7186 | if (!names.empty()) { |
| 7187 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7188 | } |
| 7189 | // super must happen after, so that downstream can use maybe_get_output |
| 7190 | // to retrieve the output |
| 7191 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7192 | } |
| 7193 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7194 | return *outputs_[output_idx]; |
| 7195 | } |
| 7196 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7197 | }; |
| 7198 | at::Tensor wrapper_CPU_log1p(const at::Tensor & self) { |
| 7199 | structured_log1p_out_functional op; |
| 7200 | op.meta(self); |
| 7201 | op.impl(self, *op.outputs_[0]); |
| 7202 | return std::move(op.outputs_[0]).take(); |
| 7203 | } |
| 7204 | struct structured_log1p_out_out final : public at::native::structured_log1p_out { |
| 7205 | structured_log1p_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7206 | void set_output_strided( |
| 7207 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7208 | TensorOptions options, DimnameList names |
| 7209 | ) override { |
| 7210 | const auto& out = outputs_[output_idx].get(); |
| 7211 | resize_out(out, sizes, strides, options); |
| 7212 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7213 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7214 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7215 | } |
| 7216 | if (!names.empty()) { |
| 7217 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7218 | } |
| 7219 | // super must happen after, so that downstream can use maybe_get_output |
| 7220 | // to retrieve the output |
| 7221 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7222 | } |
| 7223 | void set_output_raw_strided( |
| 7224 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7225 | TensorOptions options, DimnameList names |
| 7226 | ) override { |
| 7227 | const auto& out = outputs_[output_idx].get(); |
| 7228 | resize_out(out, sizes, strides, options); |
| 7229 | if (!names.empty()) { |
| 7230 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7231 | } |
| 7232 | // super must happen after, so that downstream can use maybe_get_output |
| 7233 | // to retrieve the output |
| 7234 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7235 | } |
| 7236 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7237 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7238 | } |
| 7239 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7240 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7241 | }; |
| 7242 | at::Tensor & wrapper_CPU_log1p_out_out(const at::Tensor & self, at::Tensor & out) { |
| 7243 | structured_log1p_out_out op(out); |
| 7244 | op.meta(self); |
| 7245 | op.impl(self, op.maybe_get_output(0)); |
| 7246 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7247 | return out; |
| 7248 | } |
| 7249 | struct structured_log1p_out_inplace final : public at::native::structured_log1p_out { |
| 7250 | structured_log1p_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7251 | void set_output_strided( |
| 7252 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7253 | TensorOptions options, DimnameList names |
| 7254 | ) override { |
| 7255 | const auto& out = outputs_[output_idx].get(); |
| 7256 | check_inplace(out, sizes, options); |
| 7257 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7258 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7259 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7260 | } |
| 7261 | if (!names.empty()) { |
| 7262 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7263 | } |
| 7264 | // super must happen after, so that downstream can use maybe_get_output |
| 7265 | // to retrieve the output |
| 7266 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7267 | } |
| 7268 | void set_output_raw_strided( |
| 7269 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7270 | TensorOptions options, DimnameList names |
| 7271 | ) override { |
| 7272 | const auto& out = outputs_[output_idx].get(); |
| 7273 | check_inplace(out, sizes, options); |
| 7274 | if (!names.empty()) { |
| 7275 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7276 | } |
| 7277 | // super must happen after, so that downstream can use maybe_get_output |
| 7278 | // to retrieve the output |
| 7279 | at::native::structured_log1p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7280 | } |
| 7281 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7282 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7283 | } |
| 7284 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7285 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7286 | }; |
| 7287 | at::Tensor & wrapper_CPU_log1p_(at::Tensor & self) { |
| 7288 | structured_log1p_out_inplace op(self); |
| 7289 | op.meta(self); |
| 7290 | op.impl(self, op.outputs_[0]); |
| 7291 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7292 | return self; |
| 7293 | } |
| 7294 | struct structured_log2_out_functional final : public at::native::structured_log2_out { |
| 7295 | void set_output_strided( |
| 7296 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7297 | TensorOptions options, DimnameList names |
| 7298 | ) override { |
| 7299 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7300 | if (!names.empty()) { |
| 7301 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7302 | } |
| 7303 | // super must happen after, so that downstream can use maybe_get_output |
| 7304 | // to retrieve the output |
| 7305 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7306 | } |
| 7307 | void set_output_raw_strided( |
| 7308 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7309 | TensorOptions options, DimnameList names |
| 7310 | ) override { |
| 7311 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7312 | if (!names.empty()) { |
| 7313 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7314 | } |
| 7315 | // super must happen after, so that downstream can use maybe_get_output |
| 7316 | // to retrieve the output |
| 7317 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7318 | } |
| 7319 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7320 | return *outputs_[output_idx]; |
| 7321 | } |
| 7322 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7323 | }; |
| 7324 | at::Tensor wrapper_CPU_log2(const at::Tensor & self) { |
| 7325 | structured_log2_out_functional op; |
| 7326 | op.meta(self); |
| 7327 | op.impl(self, *op.outputs_[0]); |
| 7328 | return std::move(op.outputs_[0]).take(); |
| 7329 | } |
| 7330 | struct structured_log2_out_out final : public at::native::structured_log2_out { |
| 7331 | structured_log2_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7332 | void set_output_strided( |
| 7333 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7334 | TensorOptions options, DimnameList names |
| 7335 | ) override { |
| 7336 | const auto& out = outputs_[output_idx].get(); |
| 7337 | resize_out(out, sizes, strides, options); |
| 7338 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7339 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7340 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7341 | } |
| 7342 | if (!names.empty()) { |
| 7343 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7344 | } |
| 7345 | // super must happen after, so that downstream can use maybe_get_output |
| 7346 | // to retrieve the output |
| 7347 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7348 | } |
| 7349 | void set_output_raw_strided( |
| 7350 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7351 | TensorOptions options, DimnameList names |
| 7352 | ) override { |
| 7353 | const auto& out = outputs_[output_idx].get(); |
| 7354 | resize_out(out, sizes, strides, options); |
| 7355 | if (!names.empty()) { |
| 7356 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7357 | } |
| 7358 | // super must happen after, so that downstream can use maybe_get_output |
| 7359 | // to retrieve the output |
| 7360 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7361 | } |
| 7362 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7363 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7364 | } |
| 7365 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7366 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7367 | }; |
| 7368 | at::Tensor & wrapper_CPU_log2_out_out(const at::Tensor & self, at::Tensor & out) { |
| 7369 | structured_log2_out_out op(out); |
| 7370 | op.meta(self); |
| 7371 | op.impl(self, op.maybe_get_output(0)); |
| 7372 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7373 | return out; |
| 7374 | } |
| 7375 | struct structured_log2_out_inplace final : public at::native::structured_log2_out { |
| 7376 | structured_log2_out_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 | const auto& out = outputs_[output_idx].get(); |
| 7382 | check_inplace(out, sizes, options); |
| 7383 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7384 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7385 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7386 | } |
| 7387 | if (!names.empty()) { |
| 7388 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7389 | } |
| 7390 | // super must happen after, so that downstream can use maybe_get_output |
| 7391 | // to retrieve the output |
| 7392 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7393 | } |
| 7394 | void set_output_raw_strided( |
| 7395 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7396 | TensorOptions options, DimnameList names |
| 7397 | ) override { |
| 7398 | const auto& out = outputs_[output_idx].get(); |
| 7399 | check_inplace(out, sizes, options); |
| 7400 | if (!names.empty()) { |
| 7401 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7402 | } |
| 7403 | // super must happen after, so that downstream can use maybe_get_output |
| 7404 | // to retrieve the output |
| 7405 | at::native::structured_log2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7406 | } |
| 7407 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7408 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7409 | } |
| 7410 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7411 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7412 | }; |
| 7413 | at::Tensor & wrapper_CPU_log2_(at::Tensor & self) { |
| 7414 | structured_log2_out_inplace op(self); |
| 7415 | op.meta(self); |
| 7416 | op.impl(self, op.outputs_[0]); |
| 7417 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7418 | return self; |
| 7419 | } |
| 7420 | struct structured_logaddexp_out_functional final : public at::native::structured_logaddexp_out { |
| 7421 | void set_output_strided( |
| 7422 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7423 | TensorOptions options, DimnameList names |
| 7424 | ) override { |
| 7425 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7426 | if (!names.empty()) { |
| 7427 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7428 | } |
| 7429 | // super must happen after, so that downstream can use maybe_get_output |
| 7430 | // to retrieve the output |
| 7431 | at::native::structured_logaddexp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7432 | } |
| 7433 | void set_output_raw_strided( |
| 7434 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7435 | TensorOptions options, DimnameList names |
| 7436 | ) override { |
| 7437 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7438 | if (!names.empty()) { |
| 7439 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7440 | } |
| 7441 | // super must happen after, so that downstream can use maybe_get_output |
| 7442 | // to retrieve the output |
| 7443 | at::native::structured_logaddexp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7444 | } |
| 7445 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7446 | return *outputs_[output_idx]; |
| 7447 | } |
| 7448 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7449 | }; |
| 7450 | at::Tensor wrapper_CPU_logaddexp(const at::Tensor & self, const at::Tensor & other) { |
| 7451 | structured_logaddexp_out_functional op; |
| 7452 | op.meta(self, other); |
| 7453 | op.impl(self, other, *op.outputs_[0]); |
| 7454 | return std::move(op.outputs_[0]).take(); |
| 7455 | } |
| 7456 | struct structured_logaddexp_out_out final : public at::native::structured_logaddexp_out { |
| 7457 | structured_logaddexp_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7458 | void set_output_strided( |
| 7459 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7460 | TensorOptions options, DimnameList names |
| 7461 | ) override { |
| 7462 | const auto& out = outputs_[output_idx].get(); |
| 7463 | resize_out(out, sizes, strides, options); |
| 7464 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7465 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7466 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7467 | } |
| 7468 | if (!names.empty()) { |
| 7469 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7470 | } |
| 7471 | // super must happen after, so that downstream can use maybe_get_output |
| 7472 | // to retrieve the output |
| 7473 | at::native::structured_logaddexp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7474 | } |
| 7475 | void set_output_raw_strided( |
| 7476 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7477 | TensorOptions options, DimnameList names |
| 7478 | ) override { |
| 7479 | const auto& out = outputs_[output_idx].get(); |
| 7480 | resize_out(out, sizes, strides, options); |
| 7481 | if (!names.empty()) { |
| 7482 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7483 | } |
| 7484 | // super must happen after, so that downstream can use maybe_get_output |
| 7485 | // to retrieve the output |
| 7486 | at::native::structured_logaddexp_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7487 | } |
| 7488 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7489 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7490 | } |
| 7491 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7492 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7493 | }; |
| 7494 | at::Tensor & wrapper_CPU_logaddexp_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7495 | structured_logaddexp_out_out op(out); |
| 7496 | op.meta(self, other); |
| 7497 | op.impl(self, other, op.maybe_get_output(0)); |
| 7498 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7499 | return out; |
| 7500 | } |
| 7501 | struct structured_logaddexp2_out_functional final : public at::native::structured_logaddexp2_out { |
| 7502 | void set_output_strided( |
| 7503 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7504 | TensorOptions options, DimnameList names |
| 7505 | ) override { |
| 7506 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7507 | if (!names.empty()) { |
| 7508 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7509 | } |
| 7510 | // super must happen after, so that downstream can use maybe_get_output |
| 7511 | // to retrieve the output |
| 7512 | at::native::structured_logaddexp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7513 | } |
| 7514 | void set_output_raw_strided( |
| 7515 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7516 | TensorOptions options, DimnameList names |
| 7517 | ) override { |
| 7518 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7519 | if (!names.empty()) { |
| 7520 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7521 | } |
| 7522 | // super must happen after, so that downstream can use maybe_get_output |
| 7523 | // to retrieve the output |
| 7524 | at::native::structured_logaddexp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7525 | } |
| 7526 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7527 | return *outputs_[output_idx]; |
| 7528 | } |
| 7529 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7530 | }; |
| 7531 | at::Tensor wrapper_CPU_logaddexp2(const at::Tensor & self, const at::Tensor & other) { |
| 7532 | structured_logaddexp2_out_functional op; |
| 7533 | op.meta(self, other); |
| 7534 | op.impl(self, other, *op.outputs_[0]); |
| 7535 | return std::move(op.outputs_[0]).take(); |
| 7536 | } |
| 7537 | struct structured_logaddexp2_out_out final : public at::native::structured_logaddexp2_out { |
| 7538 | structured_logaddexp2_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7539 | void set_output_strided( |
| 7540 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7541 | TensorOptions options, DimnameList names |
| 7542 | ) override { |
| 7543 | const auto& out = outputs_[output_idx].get(); |
| 7544 | resize_out(out, sizes, strides, options); |
| 7545 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7546 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7547 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7548 | } |
| 7549 | if (!names.empty()) { |
| 7550 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7551 | } |
| 7552 | // super must happen after, so that downstream can use maybe_get_output |
| 7553 | // to retrieve the output |
| 7554 | at::native::structured_logaddexp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7555 | } |
| 7556 | void set_output_raw_strided( |
| 7557 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7558 | TensorOptions options, DimnameList names |
| 7559 | ) override { |
| 7560 | const auto& out = outputs_[output_idx].get(); |
| 7561 | resize_out(out, sizes, strides, options); |
| 7562 | if (!names.empty()) { |
| 7563 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7564 | } |
| 7565 | // super must happen after, so that downstream can use maybe_get_output |
| 7566 | // to retrieve the output |
| 7567 | at::native::structured_logaddexp2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7568 | } |
| 7569 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7570 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7571 | } |
| 7572 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7573 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7574 | }; |
| 7575 | at::Tensor & wrapper_CPU_logaddexp2_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7576 | structured_logaddexp2_out_out op(out); |
| 7577 | op.meta(self, other); |
| 7578 | op.impl(self, other, op.maybe_get_output(0)); |
| 7579 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7580 | return out; |
| 7581 | } |
| 7582 | struct structured_xlogy_out_functional final : public at::native::structured_xlogy_out { |
| 7583 | void set_output_strided( |
| 7584 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7585 | TensorOptions options, DimnameList names |
| 7586 | ) override { |
| 7587 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7588 | if (!names.empty()) { |
| 7589 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7590 | } |
| 7591 | // super must happen after, so that downstream can use maybe_get_output |
| 7592 | // to retrieve the output |
| 7593 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7594 | } |
| 7595 | void set_output_raw_strided( |
| 7596 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7597 | TensorOptions options, DimnameList names |
| 7598 | ) override { |
| 7599 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7600 | if (!names.empty()) { |
| 7601 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7602 | } |
| 7603 | // super must happen after, so that downstream can use maybe_get_output |
| 7604 | // to retrieve the output |
| 7605 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7606 | } |
| 7607 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7608 | return *outputs_[output_idx]; |
| 7609 | } |
| 7610 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7611 | }; |
| 7612 | at::Tensor wrapper_CPU_xlogy_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 7613 | structured_xlogy_out_functional op; |
| 7614 | op.meta(self, other); |
| 7615 | op.impl(self, other, *op.outputs_[0]); |
| 7616 | return std::move(op.outputs_[0]).take(); |
| 7617 | } |
| 7618 | struct structured_xlogy_out_out final : public at::native::structured_xlogy_out { |
| 7619 | structured_xlogy_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7620 | void set_output_strided( |
| 7621 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7622 | TensorOptions options, DimnameList names |
| 7623 | ) override { |
| 7624 | const auto& out = outputs_[output_idx].get(); |
| 7625 | resize_out(out, sizes, strides, options); |
| 7626 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7627 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7628 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7629 | } |
| 7630 | if (!names.empty()) { |
| 7631 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7632 | } |
| 7633 | // super must happen after, so that downstream can use maybe_get_output |
| 7634 | // to retrieve the output |
| 7635 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7636 | } |
| 7637 | void set_output_raw_strided( |
| 7638 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7639 | TensorOptions options, DimnameList names |
| 7640 | ) override { |
| 7641 | const auto& out = outputs_[output_idx].get(); |
| 7642 | resize_out(out, sizes, strides, options); |
| 7643 | if (!names.empty()) { |
| 7644 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7645 | } |
| 7646 | // super must happen after, so that downstream can use maybe_get_output |
| 7647 | // to retrieve the output |
| 7648 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7649 | } |
| 7650 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7651 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7652 | } |
| 7653 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7654 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7655 | }; |
| 7656 | at::Tensor & wrapper_CPU_xlogy_out_OutTensor(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7657 | structured_xlogy_out_out op(out); |
| 7658 | op.meta(self, other); |
| 7659 | op.impl(self, other, op.maybe_get_output(0)); |
| 7660 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7661 | return out; |
| 7662 | } |
| 7663 | struct structured_xlogy_out_inplace final : public at::native::structured_xlogy_out { |
| 7664 | structured_xlogy_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7665 | void set_output_strided( |
| 7666 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7667 | TensorOptions options, DimnameList names |
| 7668 | ) override { |
| 7669 | const auto& out = outputs_[output_idx].get(); |
| 7670 | check_inplace(out, sizes, options); |
| 7671 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7672 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7673 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7674 | } |
| 7675 | if (!names.empty()) { |
| 7676 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7677 | } |
| 7678 | // super must happen after, so that downstream can use maybe_get_output |
| 7679 | // to retrieve the output |
| 7680 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7681 | } |
| 7682 | void set_output_raw_strided( |
| 7683 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7684 | TensorOptions options, DimnameList names |
| 7685 | ) override { |
| 7686 | const auto& out = outputs_[output_idx].get(); |
| 7687 | check_inplace(out, sizes, options); |
| 7688 | if (!names.empty()) { |
| 7689 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7690 | } |
| 7691 | // super must happen after, so that downstream can use maybe_get_output |
| 7692 | // to retrieve the output |
| 7693 | at::native::structured_xlogy_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7694 | } |
| 7695 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7696 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7697 | } |
| 7698 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7699 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7700 | }; |
| 7701 | at::Tensor & wrapper_CPU_xlogy__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 7702 | structured_xlogy_out_inplace op(self); |
| 7703 | op.meta(self, other); |
| 7704 | op.impl(self, other, op.outputs_[0]); |
| 7705 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7706 | return self; |
| 7707 | } |
| 7708 | namespace { |
| 7709 | at::Tensor & wrapper_CPU_out_logspace_out(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) { |
| 7710 | // No device check |
| 7711 | // DeviceGuard omitted |
| 7712 | return at::native::logspace_out(start, end, steps, base, out); |
| 7713 | } |
| 7714 | } // anonymous namespace |
| 7715 | struct structured_log_softmax_cpu_out_functional final : public at::native::structured_log_softmax_cpu_out { |
| 7716 | void set_output_strided( |
| 7717 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7718 | TensorOptions options, DimnameList names |
| 7719 | ) override { |
| 7720 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7721 | if (!names.empty()) { |
| 7722 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7723 | } |
| 7724 | // super must happen after, so that downstream can use maybe_get_output |
| 7725 | // to retrieve the output |
| 7726 | } |
| 7727 | void set_output_raw_strided( |
| 7728 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7729 | TensorOptions options, DimnameList names |
| 7730 | ) override { |
| 7731 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7732 | if (!names.empty()) { |
| 7733 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7734 | } |
| 7735 | // super must happen after, so that downstream can use maybe_get_output |
| 7736 | // to retrieve the output |
| 7737 | } |
| 7738 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7739 | return *outputs_[output_idx]; |
| 7740 | } |
| 7741 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7742 | }; |
| 7743 | at::Tensor wrapper_CPU__log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 7744 | structured_log_softmax_cpu_out_functional op; |
| 7745 | op.meta(self, dim, half_to_float); |
| 7746 | op.impl(self, dim, half_to_float, *op.outputs_[0]); |
| 7747 | return std::move(op.outputs_[0]).take(); |
| 7748 | } |
| 7749 | struct structured_log_softmax_cpu_out_out final : public at::native::structured_log_softmax_cpu_out { |
| 7750 | structured_log_softmax_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7751 | void set_output_strided( |
| 7752 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7753 | TensorOptions options, DimnameList names |
| 7754 | ) override { |
| 7755 | const auto& out = outputs_[output_idx].get(); |
| 7756 | resize_out(out, sizes, strides, options); |
| 7757 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7758 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7759 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7760 | } |
| 7761 | if (!names.empty()) { |
| 7762 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7763 | } |
| 7764 | // super must happen after, so that downstream can use maybe_get_output |
| 7765 | // to retrieve the output |
| 7766 | } |
| 7767 | void set_output_raw_strided( |
| 7768 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7769 | TensorOptions options, DimnameList names |
| 7770 | ) override { |
| 7771 | const auto& out = outputs_[output_idx].get(); |
| 7772 | resize_out(out, sizes, strides, options); |
| 7773 | if (!names.empty()) { |
| 7774 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7775 | } |
| 7776 | // super must happen after, so that downstream can use maybe_get_output |
| 7777 | // to retrieve the output |
| 7778 | } |
| 7779 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7780 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7781 | } |
| 7782 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7783 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7784 | }; |
| 7785 | at::Tensor & wrapper_CPU__log_softmax_out_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 7786 | structured_log_softmax_cpu_out_out op(out); |
| 7787 | op.meta(self, dim, half_to_float); |
| 7788 | op.impl(self, dim, half_to_float, op.maybe_get_output(0)); |
| 7789 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7790 | return out; |
| 7791 | } |
| 7792 | struct structured_log_softmax_backward_cpu_out_functional final : public at::native::structured_log_softmax_backward_cpu_out { |
| 7793 | void set_output_strided( |
| 7794 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7795 | TensorOptions options, DimnameList names |
| 7796 | ) override { |
| 7797 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7798 | if (!names.empty()) { |
| 7799 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7800 | } |
| 7801 | // super must happen after, so that downstream can use maybe_get_output |
| 7802 | // to retrieve the output |
| 7803 | } |
| 7804 | void set_output_raw_strided( |
| 7805 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7806 | TensorOptions options, DimnameList names |
| 7807 | ) override { |
| 7808 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7809 | if (!names.empty()) { |
| 7810 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7811 | } |
| 7812 | // super must happen after, so that downstream can use maybe_get_output |
| 7813 | // to retrieve the output |
| 7814 | } |
| 7815 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7816 | return *outputs_[output_idx]; |
| 7817 | } |
| 7818 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7819 | }; |
| 7820 | at::Tensor wrapper_CPU__log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 7821 | structured_log_softmax_backward_cpu_out_functional op; |
| 7822 | op.meta(grad_output, output, dim, input_dtype); |
| 7823 | op.impl(grad_output, output, dim, input_dtype, *op.outputs_[0]); |
| 7824 | return std::move(op.outputs_[0]).take(); |
| 7825 | } |
| 7826 | struct structured_log_softmax_backward_cpu_out_out final : public at::native::structured_log_softmax_backward_cpu_out { |
| 7827 | structured_log_softmax_backward_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 7828 | void set_output_strided( |
| 7829 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7830 | TensorOptions options, DimnameList names |
| 7831 | ) override { |
| 7832 | const auto& out = outputs_[output_idx].get(); |
| 7833 | resize_out(out, sizes, strides, options); |
| 7834 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7835 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7836 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7837 | } |
| 7838 | if (!names.empty()) { |
| 7839 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7840 | } |
| 7841 | // super must happen after, so that downstream can use maybe_get_output |
| 7842 | // to retrieve the output |
| 7843 | } |
| 7844 | void set_output_raw_strided( |
| 7845 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7846 | TensorOptions options, DimnameList names |
| 7847 | ) override { |
| 7848 | const auto& out = outputs_[output_idx].get(); |
| 7849 | resize_out(out, sizes, strides, options); |
| 7850 | if (!names.empty()) { |
| 7851 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7852 | } |
| 7853 | // super must happen after, so that downstream can use maybe_get_output |
| 7854 | // to retrieve the output |
| 7855 | } |
| 7856 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7857 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7858 | } |
| 7859 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7860 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7861 | }; |
| 7862 | at::Tensor & wrapper_CPU__log_softmax_backward_data_out_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { |
| 7863 | structured_log_softmax_backward_cpu_out_out op(out); |
| 7864 | op.meta(grad_output, output, dim, input_dtype); |
| 7865 | op.impl(grad_output, output, dim, input_dtype, op.maybe_get_output(0)); |
| 7866 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7867 | return out; |
| 7868 | } |
| 7869 | namespace { |
| 7870 | at::Tensor wrapper_CPU___logcumsumexp(const at::Tensor & self, int64_t dim) { |
| 7871 | // No device check |
| 7872 | // DeviceGuard omitted |
| 7873 | return at::native::_logcumsumexp_cpu(self, dim); |
| 7874 | } |
| 7875 | } // anonymous namespace |
| 7876 | namespace { |
| 7877 | at::Tensor & wrapper_CPU_out__logcumsumexp_out(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 7878 | // No device check |
| 7879 | // DeviceGuard omitted |
| 7880 | return at::native::_logcumsumexp_out_cpu(self, dim, out); |
| 7881 | } |
| 7882 | } // anonymous namespace |
| 7883 | namespace { |
| 7884 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___aminmax(const at::Tensor & self) { |
| 7885 | // No device check |
| 7886 | // DeviceGuard omitted |
| 7887 | return at::native::_aminmax_all(self); |
| 7888 | } |
| 7889 | } // anonymous namespace |
| 7890 | namespace { |
| 7891 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_dim__aminmax(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 7892 | // No device check |
| 7893 | // DeviceGuard omitted |
| 7894 | return at::native::_aminmax(self, dim, keepdim); |
| 7895 | } |
| 7896 | } // anonymous namespace |
| 7897 | struct structured_aminmax_out_functional final : public at::native::structured_aminmax_out { |
| 7898 | void set_output_strided( |
| 7899 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7900 | TensorOptions options, DimnameList names |
| 7901 | ) override { |
| 7902 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7903 | if (!names.empty()) { |
| 7904 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7905 | } |
| 7906 | // super must happen after, so that downstream can use maybe_get_output |
| 7907 | // to retrieve the output |
| 7908 | } |
| 7909 | void set_output_raw_strided( |
| 7910 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7911 | TensorOptions options, DimnameList names |
| 7912 | ) override { |
| 7913 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7914 | if (!names.empty()) { |
| 7915 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7916 | } |
| 7917 | // super must happen after, so that downstream can use maybe_get_output |
| 7918 | // to retrieve the output |
| 7919 | } |
| 7920 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7921 | return *outputs_[output_idx]; |
| 7922 | } |
| 7923 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 7924 | }; |
| 7925 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_aminmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 7926 | structured_aminmax_out_functional op; |
| 7927 | op.meta(self, dim, keepdim); |
| 7928 | op.impl(self, dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 7929 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 7930 | } |
| 7931 | struct structured_aminmax_out_out final : public at::native::structured_aminmax_out { |
| 7932 | structured_aminmax_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 7933 | void set_output_strided( |
| 7934 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7935 | TensorOptions options, DimnameList names |
| 7936 | ) override { |
| 7937 | const auto& out = outputs_[output_idx].get(); |
| 7938 | resize_out(out, sizes, strides, options); |
| 7939 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7940 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7941 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7942 | } |
| 7943 | if (!names.empty()) { |
| 7944 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7945 | } |
| 7946 | // super must happen after, so that downstream can use maybe_get_output |
| 7947 | // to retrieve the output |
| 7948 | } |
| 7949 | void set_output_raw_strided( |
| 7950 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7951 | TensorOptions options, DimnameList names |
| 7952 | ) override { |
| 7953 | const auto& out = outputs_[output_idx].get(); |
| 7954 | resize_out(out, sizes, strides, options); |
| 7955 | if (!names.empty()) { |
| 7956 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7957 | } |
| 7958 | // super must happen after, so that downstream can use maybe_get_output |
| 7959 | // to retrieve the output |
| 7960 | } |
| 7961 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7962 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7963 | } |
| 7964 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 7965 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 7966 | }; |
| 7967 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_aminmax_out_out(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & min, at::Tensor & max) { |
| 7968 | structured_aminmax_out_out op(min, max); |
| 7969 | op.meta(self, dim, keepdim); |
| 7970 | op.impl(self, dim, keepdim, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 7971 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7972 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 7973 | return std::forward_as_tuple(min, max); |
| 7974 | } |
| 7975 | namespace { |
| 7976 | at::Tensor wrapper_CPU___compute_linear_combination(const at::Tensor & input, const at::Tensor & coefficients) { |
| 7977 | // No device check |
| 7978 | // DeviceGuard omitted |
| 7979 | return at::native::_compute_linear_combination(input, coefficients); |
| 7980 | } |
| 7981 | } // anonymous namespace |
| 7982 | namespace { |
| 7983 | at::Tensor & wrapper_CPU_out__compute_linear_combination_out(const at::Tensor & input, const at::Tensor & coefficients, at::Tensor & out) { |
| 7984 | // No device check |
| 7985 | // DeviceGuard omitted |
| 7986 | return at::native::_compute_linear_combination_out(input, coefficients, out); |
| 7987 | } |
| 7988 | } // anonymous namespace |
| 7989 | struct structured_max_out_functional final : public at::native::structured_max_out { |
| 7990 | void set_output_strided( |
| 7991 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7992 | TensorOptions options, DimnameList names |
| 7993 | ) override { |
| 7994 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7995 | if (!names.empty()) { |
| 7996 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7997 | } |
| 7998 | // super must happen after, so that downstream can use maybe_get_output |
| 7999 | // to retrieve the output |
| 8000 | } |
| 8001 | void set_output_raw_strided( |
| 8002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8003 | TensorOptions options, DimnameList names |
| 8004 | ) override { |
| 8005 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8006 | if (!names.empty()) { |
| 8007 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8008 | } |
| 8009 | // super must happen after, so that downstream can use maybe_get_output |
| 8010 | // to retrieve the output |
| 8011 | } |
| 8012 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8013 | return *outputs_[output_idx]; |
| 8014 | } |
| 8015 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 8016 | }; |
| 8017 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_max_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 8018 | structured_max_out_functional op; |
| 8019 | auto precompute = op.meta(self, dim, keepdim); |
| 8020 | (void)precompute; |
| 8021 | op.impl(self, precompute.dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 8022 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 8023 | } |
| 8024 | struct structured_max_out_out final : public at::native::structured_max_out { |
| 8025 | structured_max_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 8026 | void set_output_strided( |
| 8027 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8028 | TensorOptions options, DimnameList names |
| 8029 | ) override { |
| 8030 | const auto& out = outputs_[output_idx].get(); |
| 8031 | resize_out(out, sizes, strides, options); |
| 8032 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8033 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8034 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8035 | } |
| 8036 | if (!names.empty()) { |
| 8037 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8038 | } |
| 8039 | // super must happen after, so that downstream can use maybe_get_output |
| 8040 | // to retrieve the output |
| 8041 | } |
| 8042 | void set_output_raw_strided( |
| 8043 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8044 | TensorOptions options, DimnameList names |
| 8045 | ) override { |
| 8046 | const auto& out = outputs_[output_idx].get(); |
| 8047 | resize_out(out, sizes, strides, options); |
| 8048 | if (!names.empty()) { |
| 8049 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8050 | } |
| 8051 | // super must happen after, so that downstream can use maybe_get_output |
| 8052 | // to retrieve the output |
| 8053 | } |
| 8054 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8055 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8056 | } |
| 8057 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 8058 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 8059 | }; |
| 8060 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_max_out_dim_max(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { |
| 8061 | structured_max_out_out op(max, max_values); |
| 8062 | auto precompute = op.meta(self, dim, keepdim); |
| 8063 | (void)precompute; |
| 8064 | op.impl(self, precompute.dim, keepdim, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 8065 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8066 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 8067 | return std::forward_as_tuple(max, max_values); |
| 8068 | } |
| 8069 | struct structured_amax_out_functional final : public at::native::structured_amax_out { |
| 8070 | void set_output_strided( |
| 8071 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8072 | TensorOptions options, DimnameList names |
| 8073 | ) override { |
| 8074 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8075 | if (!names.empty()) { |
| 8076 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8077 | } |
| 8078 | // super must happen after, so that downstream can use maybe_get_output |
| 8079 | // to retrieve the output |
| 8080 | } |
| 8081 | void set_output_raw_strided( |
| 8082 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8083 | TensorOptions options, DimnameList names |
| 8084 | ) override { |
| 8085 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8086 | if (!names.empty()) { |
| 8087 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8088 | } |
| 8089 | // super must happen after, so that downstream can use maybe_get_output |
| 8090 | // to retrieve the output |
| 8091 | } |
| 8092 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8093 | return *outputs_[output_idx]; |
| 8094 | } |
| 8095 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8096 | }; |
| 8097 | at::Tensor wrapper_CPU_amax(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 8098 | structured_amax_out_functional op; |
| 8099 | op.meta(self, dim, keepdim); |
| 8100 | op.impl(self, dim, keepdim, *op.outputs_[0]); |
| 8101 | return std::move(op.outputs_[0]).take(); |
| 8102 | } |
| 8103 | struct structured_amax_out_out final : public at::native::structured_amax_out { |
| 8104 | structured_amax_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8105 | void set_output_strided( |
| 8106 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8107 | TensorOptions options, DimnameList names |
| 8108 | ) override { |
| 8109 | const auto& out = outputs_[output_idx].get(); |
| 8110 | resize_out(out, sizes, strides, options); |
| 8111 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8112 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8113 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8114 | } |
| 8115 | if (!names.empty()) { |
| 8116 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8117 | } |
| 8118 | // super must happen after, so that downstream can use maybe_get_output |
| 8119 | // to retrieve the output |
| 8120 | } |
| 8121 | void set_output_raw_strided( |
| 8122 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8123 | TensorOptions options, DimnameList names |
| 8124 | ) override { |
| 8125 | const auto& out = outputs_[output_idx].get(); |
| 8126 | resize_out(out, sizes, strides, options); |
| 8127 | if (!names.empty()) { |
| 8128 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8129 | } |
| 8130 | // super must happen after, so that downstream can use maybe_get_output |
| 8131 | // to retrieve the output |
| 8132 | } |
| 8133 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8134 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8135 | } |
| 8136 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8137 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8138 | }; |
| 8139 | at::Tensor & wrapper_CPU_amax_out_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 8140 | structured_amax_out_out op(out); |
| 8141 | op.meta(self, dim, keepdim); |
| 8142 | op.impl(self, dim, keepdim, op.maybe_get_output(0)); |
| 8143 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8144 | return out; |
| 8145 | } |
| 8146 | struct structured_mean_out_functional final : public at::native::structured_mean_out { |
| 8147 | void set_output_strided( |
| 8148 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8149 | TensorOptions options, DimnameList names |
| 8150 | ) override { |
| 8151 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8152 | if (!names.empty()) { |
| 8153 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8154 | } |
| 8155 | // super must happen after, so that downstream can use maybe_get_output |
| 8156 | // to retrieve the output |
| 8157 | } |
| 8158 | void set_output_raw_strided( |
| 8159 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8160 | TensorOptions options, DimnameList names |
| 8161 | ) override { |
| 8162 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8163 | if (!names.empty()) { |
| 8164 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8165 | } |
| 8166 | // super must happen after, so that downstream can use maybe_get_output |
| 8167 | // to retrieve the output |
| 8168 | } |
| 8169 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8170 | return *outputs_[output_idx]; |
| 8171 | } |
| 8172 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8173 | }; |
| 8174 | at::Tensor wrapper_CPU_mean_dim(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 8175 | structured_mean_out_functional op; |
| 8176 | op.meta(self, dim, keepdim, dtype); |
| 8177 | op.impl(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 8178 | return std::move(op.outputs_[0]).take(); |
| 8179 | } |
| 8180 | struct structured_mean_out_out final : public at::native::structured_mean_out { |
| 8181 | structured_mean_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8182 | void set_output_strided( |
| 8183 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8184 | TensorOptions options, DimnameList names |
| 8185 | ) override { |
| 8186 | const auto& out = outputs_[output_idx].get(); |
| 8187 | resize_out(out, sizes, strides, options); |
| 8188 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8189 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8190 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8191 | } |
| 8192 | if (!names.empty()) { |
| 8193 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8194 | } |
| 8195 | // super must happen after, so that downstream can use maybe_get_output |
| 8196 | // to retrieve the output |
| 8197 | } |
| 8198 | void set_output_raw_strided( |
| 8199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8200 | TensorOptions options, DimnameList names |
| 8201 | ) override { |
| 8202 | const auto& out = outputs_[output_idx].get(); |
| 8203 | resize_out(out, sizes, strides, options); |
| 8204 | if (!names.empty()) { |
| 8205 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8206 | } |
| 8207 | // super must happen after, so that downstream can use maybe_get_output |
| 8208 | // to retrieve the output |
| 8209 | } |
| 8210 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8211 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8212 | } |
| 8213 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8214 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8215 | }; |
| 8216 | at::Tensor & wrapper_CPU_mean_out_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 8217 | structured_mean_out_out op(out); |
| 8218 | op.meta(self, dim, keepdim, dtype); |
| 8219 | op.impl(self, dim, keepdim, dtype, op.maybe_get_output(0)); |
| 8220 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8221 | return out; |
| 8222 | } |
| 8223 | namespace { |
| 8224 | at::Tensor wrapper_CPU__median(const at::Tensor & self) { |
| 8225 | // No device check |
| 8226 | // DeviceGuard omitted |
| 8227 | return at::native::median_cpu(self); |
| 8228 | } |
| 8229 | } // anonymous namespace |
| 8230 | namespace { |
| 8231 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_dim_values_median_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 8232 | // No device check |
| 8233 | // DeviceGuard omitted |
| 8234 | return at::native::median_out_cpu(self, dim, keepdim, values, indices); |
| 8235 | } |
| 8236 | } // anonymous namespace |
| 8237 | namespace { |
| 8238 | at::Tensor wrapper_CPU__nanmedian(const at::Tensor & self) { |
| 8239 | // No device check |
| 8240 | // DeviceGuard omitted |
| 8241 | return at::native::nanmedian_cpu(self); |
| 8242 | } |
| 8243 | } // anonymous namespace |
| 8244 | namespace { |
| 8245 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_dim_values_nanmedian_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 8246 | // No device check |
| 8247 | // DeviceGuard omitted |
| 8248 | return at::native::nanmedian_out_cpu(self, dim, keepdim, values, indices); |
| 8249 | } |
| 8250 | } // anonymous namespace |
| 8251 | struct structured_min_out_functional final : public at::native::structured_min_out { |
| 8252 | void set_output_strided( |
| 8253 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8254 | TensorOptions options, DimnameList names |
| 8255 | ) override { |
| 8256 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8257 | if (!names.empty()) { |
| 8258 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8259 | } |
| 8260 | // super must happen after, so that downstream can use maybe_get_output |
| 8261 | // to retrieve the output |
| 8262 | } |
| 8263 | void set_output_raw_strided( |
| 8264 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8265 | TensorOptions options, DimnameList names |
| 8266 | ) override { |
| 8267 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8268 | if (!names.empty()) { |
| 8269 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8270 | } |
| 8271 | // super must happen after, so that downstream can use maybe_get_output |
| 8272 | // to retrieve the output |
| 8273 | } |
| 8274 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8275 | return *outputs_[output_idx]; |
| 8276 | } |
| 8277 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 8278 | }; |
| 8279 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_min_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 8280 | structured_min_out_functional op; |
| 8281 | auto precompute = op.meta(self, dim, keepdim); |
| 8282 | (void)precompute; |
| 8283 | op.impl(self, precompute.dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 8284 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 8285 | } |
| 8286 | struct structured_min_out_out final : public at::native::structured_min_out { |
| 8287 | structured_min_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 8288 | void set_output_strided( |
| 8289 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8290 | TensorOptions options, DimnameList names |
| 8291 | ) override { |
| 8292 | const auto& out = outputs_[output_idx].get(); |
| 8293 | resize_out(out, sizes, strides, options); |
| 8294 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8295 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8296 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8297 | } |
| 8298 | if (!names.empty()) { |
| 8299 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8300 | } |
| 8301 | // super must happen after, so that downstream can use maybe_get_output |
| 8302 | // to retrieve the output |
| 8303 | } |
| 8304 | void set_output_raw_strided( |
| 8305 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8306 | TensorOptions options, DimnameList names |
| 8307 | ) override { |
| 8308 | const auto& out = outputs_[output_idx].get(); |
| 8309 | resize_out(out, sizes, strides, options); |
| 8310 | if (!names.empty()) { |
| 8311 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8312 | } |
| 8313 | // super must happen after, so that downstream can use maybe_get_output |
| 8314 | // to retrieve the output |
| 8315 | } |
| 8316 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8317 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8318 | } |
| 8319 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 8320 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 8321 | }; |
| 8322 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_min_out_dim_min(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 8323 | structured_min_out_out op(min, min_indices); |
| 8324 | auto precompute = op.meta(self, dim, keepdim); |
| 8325 | (void)precompute; |
| 8326 | op.impl(self, precompute.dim, keepdim, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 8327 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8328 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 8329 | return std::forward_as_tuple(min, min_indices); |
| 8330 | } |
| 8331 | struct structured_amin_out_functional final : public at::native::structured_amin_out { |
| 8332 | void set_output_strided( |
| 8333 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8334 | TensorOptions options, DimnameList names |
| 8335 | ) override { |
| 8336 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8337 | if (!names.empty()) { |
| 8338 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8339 | } |
| 8340 | // super must happen after, so that downstream can use maybe_get_output |
| 8341 | // to retrieve the output |
| 8342 | } |
| 8343 | void set_output_raw_strided( |
| 8344 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8345 | TensorOptions options, DimnameList names |
| 8346 | ) override { |
| 8347 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8348 | if (!names.empty()) { |
| 8349 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8350 | } |
| 8351 | // super must happen after, so that downstream can use maybe_get_output |
| 8352 | // to retrieve the output |
| 8353 | } |
| 8354 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8355 | return *outputs_[output_idx]; |
| 8356 | } |
| 8357 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8358 | }; |
| 8359 | at::Tensor wrapper_CPU_amin(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 8360 | structured_amin_out_functional op; |
| 8361 | op.meta(self, dim, keepdim); |
| 8362 | op.impl(self, dim, keepdim, *op.outputs_[0]); |
| 8363 | return std::move(op.outputs_[0]).take(); |
| 8364 | } |
| 8365 | struct structured_amin_out_out final : public at::native::structured_amin_out { |
| 8366 | structured_amin_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8367 | void set_output_strided( |
| 8368 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8369 | TensorOptions options, DimnameList names |
| 8370 | ) override { |
| 8371 | const auto& out = outputs_[output_idx].get(); |
| 8372 | resize_out(out, sizes, strides, options); |
| 8373 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8374 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8375 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8376 | } |
| 8377 | if (!names.empty()) { |
| 8378 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8379 | } |
| 8380 | // super must happen after, so that downstream can use maybe_get_output |
| 8381 | // to retrieve the output |
| 8382 | } |
| 8383 | void set_output_raw_strided( |
| 8384 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8385 | TensorOptions options, DimnameList names |
| 8386 | ) override { |
| 8387 | const auto& out = outputs_[output_idx].get(); |
| 8388 | resize_out(out, sizes, strides, options); |
| 8389 | if (!names.empty()) { |
| 8390 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8391 | } |
| 8392 | // super must happen after, so that downstream can use maybe_get_output |
| 8393 | // to retrieve the output |
| 8394 | } |
| 8395 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8396 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8397 | } |
| 8398 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8399 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8400 | }; |
| 8401 | at::Tensor & wrapper_CPU_amin_out_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 8402 | structured_amin_out_out op(out); |
| 8403 | op.meta(self, dim, keepdim); |
| 8404 | op.impl(self, dim, keepdim, op.maybe_get_output(0)); |
| 8405 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8406 | return out; |
| 8407 | } |
| 8408 | namespace { |
| 8409 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__mkldnn_rnn_layer(const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) { |
| 8410 | // No device check |
| 8411 | // DeviceGuard omitted |
| 8412 | return at::native::mkldnn_rnn_layer(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); |
| 8413 | } |
| 8414 | } // anonymous namespace |
| 8415 | namespace { |
| 8416 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__mkldnn_rnn_layer_backward(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { |
| 8417 | // No device check |
| 8418 | // DeviceGuard omitted |
| 8419 | return at::native::mkldnn_rnn_layer_backward(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); |
| 8420 | } |
| 8421 | } // anonymous namespace |
| 8422 | struct structured_mm_out_cpu_functional final : public at::native::structured_mm_out_cpu { |
| 8423 | void set_output_strided( |
| 8424 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8425 | TensorOptions options, DimnameList names |
| 8426 | ) override { |
| 8427 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8428 | if (!names.empty()) { |
| 8429 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8430 | } |
| 8431 | // super must happen after, so that downstream can use maybe_get_output |
| 8432 | // to retrieve the output |
| 8433 | } |
| 8434 | void set_output_raw_strided( |
| 8435 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8436 | TensorOptions options, DimnameList names |
| 8437 | ) override { |
| 8438 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8439 | if (!names.empty()) { |
| 8440 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8441 | } |
| 8442 | // super must happen after, so that downstream can use maybe_get_output |
| 8443 | // to retrieve the output |
| 8444 | } |
| 8445 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8446 | return *outputs_[output_idx]; |
| 8447 | } |
| 8448 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8449 | }; |
| 8450 | at::Tensor wrapper_CPU_mm(const at::Tensor & self, const at::Tensor & mat2) { |
| 8451 | structured_mm_out_cpu_functional op; |
| 8452 | op.meta(self, mat2); |
| 8453 | op.impl(self, mat2, *op.outputs_[0]); |
| 8454 | return std::move(op.outputs_[0]).take(); |
| 8455 | } |
| 8456 | struct structured_mm_out_cpu_out final : public at::native::structured_mm_out_cpu { |
| 8457 | structured_mm_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8458 | void set_output_strided( |
| 8459 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8460 | TensorOptions options, DimnameList names |
| 8461 | ) override { |
| 8462 | const auto& out = outputs_[output_idx].get(); |
| 8463 | resize_out(out, sizes, strides, options); |
| 8464 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8465 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8466 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8467 | } |
| 8468 | if (!names.empty()) { |
| 8469 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8470 | } |
| 8471 | // super must happen after, so that downstream can use maybe_get_output |
| 8472 | // to retrieve the output |
| 8473 | } |
| 8474 | void set_output_raw_strided( |
| 8475 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8476 | TensorOptions options, DimnameList names |
| 8477 | ) override { |
| 8478 | const auto& out = outputs_[output_idx].get(); |
| 8479 | resize_out(out, sizes, strides, options); |
| 8480 | if (!names.empty()) { |
| 8481 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8482 | } |
| 8483 | // super must happen after, so that downstream can use maybe_get_output |
| 8484 | // to retrieve the output |
| 8485 | } |
| 8486 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8487 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8488 | } |
| 8489 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8490 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8491 | }; |
| 8492 | at::Tensor & wrapper_CPU_mm_out_out(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 8493 | structured_mm_out_cpu_out op(out); |
| 8494 | op.meta(self, mat2); |
| 8495 | op.impl(self, mat2, op.maybe_get_output(0)); |
| 8496 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8497 | return out; |
| 8498 | } |
| 8499 | namespace { |
| 8500 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__mode(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 8501 | // No device check |
| 8502 | // DeviceGuard omitted |
| 8503 | return at::native::mode(self, dim, keepdim); |
| 8504 | } |
| 8505 | } // anonymous namespace |
| 8506 | struct structured_mul_out_functional final : public at::native::structured_mul_out { |
| 8507 | void set_output_strided( |
| 8508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8509 | TensorOptions options, DimnameList names |
| 8510 | ) override { |
| 8511 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8512 | if (!names.empty()) { |
| 8513 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8514 | } |
| 8515 | // super must happen after, so that downstream can use maybe_get_output |
| 8516 | // to retrieve the output |
| 8517 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8518 | } |
| 8519 | void set_output_raw_strided( |
| 8520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8521 | TensorOptions options, DimnameList names |
| 8522 | ) override { |
| 8523 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8524 | if (!names.empty()) { |
| 8525 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8526 | } |
| 8527 | // super must happen after, so that downstream can use maybe_get_output |
| 8528 | // to retrieve the output |
| 8529 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8530 | } |
| 8531 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8532 | return *outputs_[output_idx]; |
| 8533 | } |
| 8534 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8535 | }; |
| 8536 | at::Tensor wrapper_CPU_mul_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 8537 | structured_mul_out_functional op; |
| 8538 | op.meta(self, other); |
| 8539 | op.impl(self, other, *op.outputs_[0]); |
| 8540 | return std::move(op.outputs_[0]).take(); |
| 8541 | } |
| 8542 | struct structured_mul_out_out final : public at::native::structured_mul_out { |
| 8543 | structured_mul_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8544 | void set_output_strided( |
| 8545 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8546 | TensorOptions options, DimnameList names |
| 8547 | ) override { |
| 8548 | const auto& out = outputs_[output_idx].get(); |
| 8549 | resize_out(out, sizes, strides, options); |
| 8550 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8551 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8552 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8553 | } |
| 8554 | if (!names.empty()) { |
| 8555 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8556 | } |
| 8557 | // super must happen after, so that downstream can use maybe_get_output |
| 8558 | // to retrieve the output |
| 8559 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8560 | } |
| 8561 | void set_output_raw_strided( |
| 8562 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8563 | TensorOptions options, DimnameList names |
| 8564 | ) override { |
| 8565 | const auto& out = outputs_[output_idx].get(); |
| 8566 | resize_out(out, sizes, strides, options); |
| 8567 | if (!names.empty()) { |
| 8568 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8569 | } |
| 8570 | // super must happen after, so that downstream can use maybe_get_output |
| 8571 | // to retrieve the output |
| 8572 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8573 | } |
| 8574 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8575 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8576 | } |
| 8577 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8578 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8579 | }; |
| 8580 | at::Tensor & wrapper_CPU_mul_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8581 | structured_mul_out_out op(out); |
| 8582 | op.meta(self, other); |
| 8583 | op.impl(self, other, op.maybe_get_output(0)); |
| 8584 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8585 | return out; |
| 8586 | } |
| 8587 | struct structured_mul_out_inplace final : public at::native::structured_mul_out { |
| 8588 | structured_mul_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8589 | void set_output_strided( |
| 8590 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8591 | TensorOptions options, DimnameList names |
| 8592 | ) override { |
| 8593 | const auto& out = outputs_[output_idx].get(); |
| 8594 | check_inplace(out, sizes, options); |
| 8595 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8596 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8597 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8598 | } |
| 8599 | if (!names.empty()) { |
| 8600 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8601 | } |
| 8602 | // super must happen after, so that downstream can use maybe_get_output |
| 8603 | // to retrieve the output |
| 8604 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8605 | } |
| 8606 | void set_output_raw_strided( |
| 8607 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8608 | TensorOptions options, DimnameList names |
| 8609 | ) override { |
| 8610 | const auto& out = outputs_[output_idx].get(); |
| 8611 | check_inplace(out, sizes, options); |
| 8612 | if (!names.empty()) { |
| 8613 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8614 | } |
| 8615 | // super must happen after, so that downstream can use maybe_get_output |
| 8616 | // to retrieve the output |
| 8617 | at::native::structured_mul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8618 | } |
| 8619 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8620 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8621 | } |
| 8622 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8623 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8624 | }; |
| 8625 | at::Tensor & wrapper_CPU_mul__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 8626 | structured_mul_out_inplace op(self); |
| 8627 | op.meta(self, other); |
| 8628 | op.impl(self, other, op.outputs_[0]); |
| 8629 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8630 | return self; |
| 8631 | } |
| 8632 | namespace { |
| 8633 | at::Tensor & wrapper_CPU_out_mvlgamma_out(const at::Tensor & self, int64_t p, at::Tensor & out) { |
| 8634 | // No device check |
| 8635 | // DeviceGuard omitted |
| 8636 | return at::native::mvlgamma_out(self, p, out); |
| 8637 | } |
| 8638 | } // anonymous namespace |
| 8639 | namespace { |
| 8640 | at::Tensor wrapper_CPU__narrow_copy(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 8641 | // No device check |
| 8642 | // DeviceGuard omitted |
| 8643 | return at::native::narrow_copy_dense_cpu(self, dim, start.expect_int(), length.expect_int()); |
| 8644 | } |
| 8645 | } // anonymous namespace |
| 8646 | namespace { |
| 8647 | at::Tensor & wrapper_CPU_out_narrow_copy_out(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { |
| 8648 | // No device check |
| 8649 | // DeviceGuard omitted |
| 8650 | return at::native::narrow_copy_dense_cpu_out(self, dim, start.expect_int(), length.expect_int(), out); |
| 8651 | } |
| 8652 | } // anonymous namespace |
| 8653 | namespace { |
| 8654 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_batch_norm(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps) { |
| 8655 | // No device check |
| 8656 | // DeviceGuard omitted |
| 8657 | return at::native::batch_norm_cpu(input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 8658 | } |
| 8659 | } // anonymous namespace |
| 8660 | namespace { |
| 8661 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_out_native_batch_norm_out(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 8662 | // No device check |
| 8663 | // DeviceGuard omitted |
| 8664 | return at::native::batch_norm_cpu_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 8665 | } |
| 8666 | } // anonymous namespace |
| 8667 | namespace { |
| 8668 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_out__native_batch_norm_legit_out(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 8669 | // No device check |
| 8670 | // DeviceGuard omitted |
| 8671 | return at::native::_batch_norm_legit_cpu_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 8672 | } |
| 8673 | } // anonymous namespace |
| 8674 | namespace { |
| 8675 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___native_batch_norm_legit(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) { |
| 8676 | // No device check |
| 8677 | // DeviceGuard omitted |
| 8678 | return at::native::_batch_norm_legit_cpu(input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 8679 | } |
| 8680 | } // anonymous namespace |
| 8681 | namespace { |
| 8682 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_no_stats__native_batch_norm_legit(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, bool training, double momentum, double eps) { |
| 8683 | // No device check |
| 8684 | // DeviceGuard omitted |
| 8685 | return at::native::_batch_norm_legit_no_stats_cpu(input, weight, bias, training, momentum, eps); |
| 8686 | } |
| 8687 | } // anonymous namespace |
| 8688 | namespace { |
| 8689 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_no_stats_out__native_batch_norm_legit_out(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 8690 | // No device check |
| 8691 | // DeviceGuard omitted |
| 8692 | return at::native::_batch_norm_legit_no_stats_cpu_out(input, weight, bias, training, momentum, eps, out, save_mean, save_invstd); |
| 8693 | } |
| 8694 | } // anonymous namespace |
| 8695 | namespace { |
| 8696 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) { |
| 8697 | // No device check |
| 8698 | // DeviceGuard omitted |
| 8699 | return at::native::batch_norm_backward_cpu(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); |
| 8700 | } |
| 8701 | } // anonymous namespace |
| 8702 | namespace { |
| 8703 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__batch_norm_update_stats(const at::Tensor & input, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum) { |
| 8704 | // No device check |
| 8705 | // DeviceGuard omitted |
| 8706 | return at::native::batch_norm_update_stats_cpu(input, running_mean, running_var, momentum); |
| 8707 | } |
| 8708 | } // anonymous namespace |
| 8709 | namespace { |
| 8710 | at::Tensor wrapper_CPU___cdist_forward(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode) { |
| 8711 | // No device check |
| 8712 | // DeviceGuard omitted |
| 8713 | return at::native::_cdist_forward(x1, x2, p, compute_mode); |
| 8714 | } |
| 8715 | } // anonymous namespace |
| 8716 | namespace { |
| 8717 | at::Tensor wrapper_CPU___cdist_backward(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) { |
| 8718 | // No device check |
| 8719 | // DeviceGuard omitted |
| 8720 | return at::native::_cdist_backward(grad, x1, x2, p, cdist); |
| 8721 | } |
| 8722 | } // anonymous namespace |
| 8723 | namespace { |
| 8724 | at::Tensor wrapper_CPU___pdist_forward(const at::Tensor & self, double p) { |
| 8725 | // No device check |
| 8726 | // DeviceGuard omitted |
| 8727 | return at::native::_pdist_forward(self, p); |
| 8728 | } |
| 8729 | } // anonymous namespace |
| 8730 | namespace { |
| 8731 | at::Tensor wrapper_CPU___pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { |
| 8732 | // No device check |
| 8733 | // DeviceGuard omitted |
| 8734 | return at::native::_pdist_backward(grad, self, p, pdist); |
| 8735 | } |
| 8736 | } // anonymous namespace |
| 8737 | namespace { |
| 8738 | at::Tensor wrapper_CPU__pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) { |
| 8739 | // No device check |
| 8740 | // DeviceGuard omitted |
| 8741 | return at::native::pixel_shuffle_cpu(self, upscale_factor); |
| 8742 | } |
| 8743 | } // anonymous namespace |
| 8744 | namespace { |
| 8745 | at::Tensor wrapper_CPU__pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor) { |
| 8746 | // No device check |
| 8747 | // DeviceGuard omitted |
| 8748 | return at::native::pixel_unshuffle_cpu(self, downscale_factor); |
| 8749 | } |
| 8750 | } // anonymous namespace |
| 8751 | namespace { |
| 8752 | at::Tensor wrapper_CPU__channel_shuffle(const at::Tensor & self, int64_t groups) { |
| 8753 | // No device check |
| 8754 | // DeviceGuard omitted |
| 8755 | return at::native::channel_shuffle(self, groups); |
| 8756 | } |
| 8757 | } // anonymous namespace |
| 8758 | namespace { |
| 8759 | at::Tensor wrapper_CPU__native_channel_shuffle(const at::Tensor & self, int64_t groups) { |
| 8760 | // No device check |
| 8761 | // DeviceGuard omitted |
| 8762 | return at::native::channel_shuffle_cpu(self, groups); |
| 8763 | } |
| 8764 | } // anonymous namespace |
| 8765 | namespace { |
| 8766 | at::Tensor & wrapper_CPU_generator_out_randperm_out(int64_t n, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8767 | // No device check |
| 8768 | // DeviceGuard omitted |
| 8769 | return at::native::randperm_out_cpu(n, generator, out); |
| 8770 | } |
| 8771 | } // anonymous namespace |
| 8772 | namespace { |
| 8773 | at::Tensor & wrapper_CPU_out_range_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 8774 | // No device check |
| 8775 | // DeviceGuard omitted |
| 8776 | return at::native::range_out(start, end, step, out); |
| 8777 | } |
| 8778 | } // anonymous namespace |
| 8779 | struct structured_reciprocal_out_functional final : public at::native::structured_reciprocal_out { |
| 8780 | void set_output_strided( |
| 8781 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8782 | TensorOptions options, DimnameList names |
| 8783 | ) override { |
| 8784 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8785 | if (!names.empty()) { |
| 8786 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8787 | } |
| 8788 | // super must happen after, so that downstream can use maybe_get_output |
| 8789 | // to retrieve the output |
| 8790 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8791 | } |
| 8792 | void set_output_raw_strided( |
| 8793 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8794 | TensorOptions options, DimnameList names |
| 8795 | ) override { |
| 8796 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8797 | if (!names.empty()) { |
| 8798 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8799 | } |
| 8800 | // super must happen after, so that downstream can use maybe_get_output |
| 8801 | // to retrieve the output |
| 8802 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8803 | } |
| 8804 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8805 | return *outputs_[output_idx]; |
| 8806 | } |
| 8807 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8808 | }; |
| 8809 | at::Tensor wrapper_CPU_reciprocal(const at::Tensor & self) { |
| 8810 | structured_reciprocal_out_functional op; |
| 8811 | op.meta(self); |
| 8812 | op.impl(self, *op.outputs_[0]); |
| 8813 | return std::move(op.outputs_[0]).take(); |
| 8814 | } |
| 8815 | struct structured_reciprocal_out_out final : public at::native::structured_reciprocal_out { |
| 8816 | structured_reciprocal_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8817 | void set_output_strided( |
| 8818 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8819 | TensorOptions options, DimnameList names |
| 8820 | ) override { |
| 8821 | const auto& out = outputs_[output_idx].get(); |
| 8822 | resize_out(out, sizes, strides, options); |
| 8823 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8824 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8825 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8826 | } |
| 8827 | if (!names.empty()) { |
| 8828 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8829 | } |
| 8830 | // super must happen after, so that downstream can use maybe_get_output |
| 8831 | // to retrieve the output |
| 8832 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8833 | } |
| 8834 | void set_output_raw_strided( |
| 8835 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8836 | TensorOptions options, DimnameList names |
| 8837 | ) override { |
| 8838 | const auto& out = outputs_[output_idx].get(); |
| 8839 | resize_out(out, sizes, strides, options); |
| 8840 | if (!names.empty()) { |
| 8841 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8842 | } |
| 8843 | // super must happen after, so that downstream can use maybe_get_output |
| 8844 | // to retrieve the output |
| 8845 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8846 | } |
| 8847 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8848 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8849 | } |
| 8850 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8851 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8852 | }; |
| 8853 | at::Tensor & wrapper_CPU_reciprocal_out_out(const at::Tensor & self, at::Tensor & out) { |
| 8854 | structured_reciprocal_out_out op(out); |
| 8855 | op.meta(self); |
| 8856 | op.impl(self, op.maybe_get_output(0)); |
| 8857 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8858 | return out; |
| 8859 | } |
| 8860 | struct structured_reciprocal_out_inplace final : public at::native::structured_reciprocal_out { |
| 8861 | structured_reciprocal_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8862 | void set_output_strided( |
| 8863 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8864 | TensorOptions options, DimnameList names |
| 8865 | ) override { |
| 8866 | const auto& out = outputs_[output_idx].get(); |
| 8867 | check_inplace(out, sizes, options); |
| 8868 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8869 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8870 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8871 | } |
| 8872 | if (!names.empty()) { |
| 8873 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8874 | } |
| 8875 | // super must happen after, so that downstream can use maybe_get_output |
| 8876 | // to retrieve the output |
| 8877 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8878 | } |
| 8879 | void set_output_raw_strided( |
| 8880 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8881 | TensorOptions options, DimnameList names |
| 8882 | ) override { |
| 8883 | const auto& out = outputs_[output_idx].get(); |
| 8884 | check_inplace(out, sizes, options); |
| 8885 | if (!names.empty()) { |
| 8886 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8887 | } |
| 8888 | // super must happen after, so that downstream can use maybe_get_output |
| 8889 | // to retrieve the output |
| 8890 | at::native::structured_reciprocal_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8891 | } |
| 8892 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8893 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8894 | } |
| 8895 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8896 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8897 | }; |
| 8898 | at::Tensor & wrapper_CPU_reciprocal_(at::Tensor & self) { |
| 8899 | structured_reciprocal_out_inplace op(self); |
| 8900 | op.meta(self); |
| 8901 | op.impl(self, op.outputs_[0]); |
| 8902 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8903 | return self; |
| 8904 | } |
| 8905 | struct structured_neg_out_functional final : public at::native::structured_neg_out { |
| 8906 | void set_output_strided( |
| 8907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8908 | TensorOptions options, DimnameList names |
| 8909 | ) override { |
| 8910 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8911 | if (!names.empty()) { |
| 8912 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8913 | } |
| 8914 | // super must happen after, so that downstream can use maybe_get_output |
| 8915 | // to retrieve the output |
| 8916 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8917 | } |
| 8918 | void set_output_raw_strided( |
| 8919 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8920 | TensorOptions options, DimnameList names |
| 8921 | ) override { |
| 8922 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8923 | if (!names.empty()) { |
| 8924 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8925 | } |
| 8926 | // super must happen after, so that downstream can use maybe_get_output |
| 8927 | // to retrieve the output |
| 8928 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8929 | } |
| 8930 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8931 | return *outputs_[output_idx]; |
| 8932 | } |
| 8933 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8934 | }; |
| 8935 | at::Tensor wrapper_CPU_neg(const at::Tensor & self) { |
| 8936 | structured_neg_out_functional op; |
| 8937 | op.meta(self); |
| 8938 | op.impl(self, *op.outputs_[0]); |
| 8939 | return std::move(op.outputs_[0]).take(); |
| 8940 | } |
| 8941 | struct structured_neg_out_out final : public at::native::structured_neg_out { |
| 8942 | structured_neg_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 8943 | void set_output_strided( |
| 8944 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8945 | TensorOptions options, DimnameList names |
| 8946 | ) override { |
| 8947 | const auto& out = outputs_[output_idx].get(); |
| 8948 | resize_out(out, sizes, strides, options); |
| 8949 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8950 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8951 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8952 | } |
| 8953 | if (!names.empty()) { |
| 8954 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8955 | } |
| 8956 | // super must happen after, so that downstream can use maybe_get_output |
| 8957 | // to retrieve the output |
| 8958 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8959 | } |
| 8960 | void set_output_raw_strided( |
| 8961 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8962 | TensorOptions options, DimnameList names |
| 8963 | ) override { |
| 8964 | const auto& out = outputs_[output_idx].get(); |
| 8965 | resize_out(out, sizes, strides, options); |
| 8966 | if (!names.empty()) { |
| 8967 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8968 | } |
| 8969 | // super must happen after, so that downstream can use maybe_get_output |
| 8970 | // to retrieve the output |
| 8971 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8972 | } |
| 8973 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8974 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8975 | } |
| 8976 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8977 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8978 | }; |
| 8979 | at::Tensor & wrapper_CPU_neg_out_out(const at::Tensor & self, at::Tensor & out) { |
| 8980 | structured_neg_out_out op(out); |
| 8981 | op.meta(self); |
| 8982 | op.impl(self, op.maybe_get_output(0)); |
| 8983 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8984 | return out; |
| 8985 | } |
| 8986 | struct structured_neg_out_inplace final : public at::native::structured_neg_out { |
| 8987 | structured_neg_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8988 | void set_output_strided( |
| 8989 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8990 | TensorOptions options, DimnameList names |
| 8991 | ) override { |
| 8992 | const auto& out = outputs_[output_idx].get(); |
| 8993 | check_inplace(out, sizes, options); |
| 8994 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8995 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8996 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8997 | } |
| 8998 | if (!names.empty()) { |
| 8999 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9000 | } |
| 9001 | // super must happen after, so that downstream can use maybe_get_output |
| 9002 | // to retrieve the output |
| 9003 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9004 | } |
| 9005 | void set_output_raw_strided( |
| 9006 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9007 | TensorOptions options, DimnameList names |
| 9008 | ) override { |
| 9009 | const auto& out = outputs_[output_idx].get(); |
| 9010 | check_inplace(out, sizes, options); |
| 9011 | if (!names.empty()) { |
| 9012 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9013 | } |
| 9014 | // super must happen after, so that downstream can use maybe_get_output |
| 9015 | // to retrieve the output |
| 9016 | at::native::structured_neg_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9017 | } |
| 9018 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9019 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9020 | } |
| 9021 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9022 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9023 | }; |
| 9024 | at::Tensor & wrapper_CPU_neg_(at::Tensor & self) { |
| 9025 | structured_neg_out_inplace op(self); |
| 9026 | op.meta(self); |
| 9027 | op.impl(self, op.outputs_[0]); |
| 9028 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9029 | return self; |
| 9030 | } |
| 9031 | namespace { |
| 9032 | at::Tensor wrapper_CPU_Tensor_repeat_interleave(const at::Tensor & repeats, c10::optional<int64_t> output_size) { |
| 9033 | // No device check |
| 9034 | // DeviceGuard omitted |
| 9035 | return at::native::repeat_interleave_cpu(repeats, output_size); |
| 9036 | } |
| 9037 | } // anonymous namespace |
| 9038 | namespace { |
| 9039 | at::Tensor wrapper_CPU___reshape_alias(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 9040 | // No device check |
| 9041 | // DeviceGuard omitted |
| 9042 | return at::native::_reshape_alias(self, C10_AS_INTARRAYREF_SLOW(size), C10_AS_INTARRAYREF_SLOW(stride)); |
| 9043 | } |
| 9044 | } // anonymous namespace |
| 9045 | struct structured_round_out_functional final : public at::native::structured_round_out { |
| 9046 | void set_output_strided( |
| 9047 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9048 | TensorOptions options, DimnameList names |
| 9049 | ) override { |
| 9050 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9051 | if (!names.empty()) { |
| 9052 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9053 | } |
| 9054 | // super must happen after, so that downstream can use maybe_get_output |
| 9055 | // to retrieve the output |
| 9056 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9057 | } |
| 9058 | void set_output_raw_strided( |
| 9059 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9060 | TensorOptions options, DimnameList names |
| 9061 | ) override { |
| 9062 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9063 | if (!names.empty()) { |
| 9064 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9065 | } |
| 9066 | // super must happen after, so that downstream can use maybe_get_output |
| 9067 | // to retrieve the output |
| 9068 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9069 | } |
| 9070 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9071 | return *outputs_[output_idx]; |
| 9072 | } |
| 9073 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9074 | }; |
| 9075 | at::Tensor wrapper_CPU_round(const at::Tensor & self) { |
| 9076 | structured_round_out_functional op; |
| 9077 | op.meta(self); |
| 9078 | op.impl(self, *op.outputs_[0]); |
| 9079 | return std::move(op.outputs_[0]).take(); |
| 9080 | } |
| 9081 | struct structured_round_out_out final : public at::native::structured_round_out { |
| 9082 | structured_round_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9083 | void set_output_strided( |
| 9084 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9085 | TensorOptions options, DimnameList names |
| 9086 | ) override { |
| 9087 | const auto& out = outputs_[output_idx].get(); |
| 9088 | resize_out(out, sizes, strides, options); |
| 9089 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9090 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9091 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9092 | } |
| 9093 | if (!names.empty()) { |
| 9094 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9095 | } |
| 9096 | // super must happen after, so that downstream can use maybe_get_output |
| 9097 | // to retrieve the output |
| 9098 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9099 | } |
| 9100 | void set_output_raw_strided( |
| 9101 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9102 | TensorOptions options, DimnameList names |
| 9103 | ) override { |
| 9104 | const auto& out = outputs_[output_idx].get(); |
| 9105 | resize_out(out, sizes, strides, options); |
| 9106 | if (!names.empty()) { |
| 9107 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9108 | } |
| 9109 | // super must happen after, so that downstream can use maybe_get_output |
| 9110 | // to retrieve the output |
| 9111 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9112 | } |
| 9113 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9114 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9115 | } |
| 9116 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9117 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9118 | }; |
| 9119 | at::Tensor & wrapper_CPU_round_out_out(const at::Tensor & self, at::Tensor & out) { |
| 9120 | structured_round_out_out op(out); |
| 9121 | op.meta(self); |
| 9122 | op.impl(self, op.maybe_get_output(0)); |
| 9123 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9124 | return out; |
| 9125 | } |
| 9126 | struct structured_round_out_inplace final : public at::native::structured_round_out { |
| 9127 | structured_round_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9128 | void set_output_strided( |
| 9129 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9130 | TensorOptions options, DimnameList names |
| 9131 | ) override { |
| 9132 | const auto& out = outputs_[output_idx].get(); |
| 9133 | check_inplace(out, sizes, options); |
| 9134 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9135 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9136 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9137 | } |
| 9138 | if (!names.empty()) { |
| 9139 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9140 | } |
| 9141 | // super must happen after, so that downstream can use maybe_get_output |
| 9142 | // to retrieve the output |
| 9143 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9144 | } |
| 9145 | void set_output_raw_strided( |
| 9146 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9147 | TensorOptions options, DimnameList names |
| 9148 | ) override { |
| 9149 | const auto& out = outputs_[output_idx].get(); |
| 9150 | check_inplace(out, sizes, options); |
| 9151 | if (!names.empty()) { |
| 9152 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9153 | } |
| 9154 | // super must happen after, so that downstream can use maybe_get_output |
| 9155 | // to retrieve the output |
| 9156 | at::native::structured_round_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9157 | } |
| 9158 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9159 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9160 | } |
| 9161 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9162 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9163 | }; |
| 9164 | at::Tensor & wrapper_CPU_round_(at::Tensor & self) { |
| 9165 | structured_round_out_inplace op(self); |
| 9166 | op.meta(self); |
| 9167 | op.impl(self, op.outputs_[0]); |
| 9168 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9169 | return self; |
| 9170 | } |
| 9171 | struct structured_round_decimals_out_functional final : public at::native::structured_round_decimals_out { |
| 9172 | void set_output_strided( |
| 9173 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9174 | TensorOptions options, DimnameList names |
| 9175 | ) override { |
| 9176 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9177 | if (!names.empty()) { |
| 9178 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9179 | } |
| 9180 | // super must happen after, so that downstream can use maybe_get_output |
| 9181 | // to retrieve the output |
| 9182 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9183 | } |
| 9184 | void set_output_raw_strided( |
| 9185 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9186 | TensorOptions options, DimnameList names |
| 9187 | ) override { |
| 9188 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9189 | if (!names.empty()) { |
| 9190 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9191 | } |
| 9192 | // super must happen after, so that downstream can use maybe_get_output |
| 9193 | // to retrieve the output |
| 9194 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9195 | } |
| 9196 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9197 | return *outputs_[output_idx]; |
| 9198 | } |
| 9199 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9200 | }; |
| 9201 | at::Tensor wrapper_CPU_round_decimals(const at::Tensor & self, int64_t decimals) { |
| 9202 | structured_round_decimals_out_functional op; |
| 9203 | op.meta(self, decimals); |
| 9204 | op.impl(self, decimals, *op.outputs_[0]); |
| 9205 | return std::move(op.outputs_[0]).take(); |
| 9206 | } |
| 9207 | struct structured_round_decimals_out_out final : public at::native::structured_round_decimals_out { |
| 9208 | structured_round_decimals_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9209 | void set_output_strided( |
| 9210 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9211 | TensorOptions options, DimnameList names |
| 9212 | ) override { |
| 9213 | const auto& out = outputs_[output_idx].get(); |
| 9214 | resize_out(out, sizes, strides, options); |
| 9215 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9216 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9217 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9218 | } |
| 9219 | if (!names.empty()) { |
| 9220 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9221 | } |
| 9222 | // super must happen after, so that downstream can use maybe_get_output |
| 9223 | // to retrieve the output |
| 9224 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9225 | } |
| 9226 | void set_output_raw_strided( |
| 9227 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9228 | TensorOptions options, DimnameList names |
| 9229 | ) override { |
| 9230 | const auto& out = outputs_[output_idx].get(); |
| 9231 | resize_out(out, sizes, strides, options); |
| 9232 | if (!names.empty()) { |
| 9233 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9234 | } |
| 9235 | // super must happen after, so that downstream can use maybe_get_output |
| 9236 | // to retrieve the output |
| 9237 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9238 | } |
| 9239 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9240 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9241 | } |
| 9242 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9243 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9244 | }; |
| 9245 | at::Tensor & wrapper_CPU_round_out_decimals_out(const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 9246 | structured_round_decimals_out_out op(out); |
| 9247 | op.meta(self, decimals); |
| 9248 | op.impl(self, decimals, op.maybe_get_output(0)); |
| 9249 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9250 | return out; |
| 9251 | } |
| 9252 | struct structured_round_decimals_out_inplace final : public at::native::structured_round_decimals_out { |
| 9253 | structured_round_decimals_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9254 | void set_output_strided( |
| 9255 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9256 | TensorOptions options, DimnameList names |
| 9257 | ) override { |
| 9258 | const auto& out = outputs_[output_idx].get(); |
| 9259 | check_inplace(out, sizes, options); |
| 9260 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9261 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9262 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9263 | } |
| 9264 | if (!names.empty()) { |
| 9265 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9266 | } |
| 9267 | // super must happen after, so that downstream can use maybe_get_output |
| 9268 | // to retrieve the output |
| 9269 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9270 | } |
| 9271 | void set_output_raw_strided( |
| 9272 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9273 | TensorOptions options, DimnameList names |
| 9274 | ) override { |
| 9275 | const auto& out = outputs_[output_idx].get(); |
| 9276 | check_inplace(out, sizes, options); |
| 9277 | if (!names.empty()) { |
| 9278 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9279 | } |
| 9280 | // super must happen after, so that downstream can use maybe_get_output |
| 9281 | // to retrieve the output |
| 9282 | at::native::structured_round_decimals_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9283 | } |
| 9284 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9285 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9286 | } |
| 9287 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9288 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9289 | }; |
| 9290 | at::Tensor & wrapper_CPU_round__decimals(at::Tensor & self, int64_t decimals) { |
| 9291 | structured_round_decimals_out_inplace op(self); |
| 9292 | op.meta(self, decimals); |
| 9293 | op.impl(self, decimals, op.outputs_[0]); |
| 9294 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9295 | return self; |
| 9296 | } |
| 9297 | namespace { |
| 9298 | at::Tensor wrapper_CPU__relu(const at::Tensor & self) { |
| 9299 | // No device check |
| 9300 | // DeviceGuard omitted |
| 9301 | return at::native::relu(self); |
| 9302 | } |
| 9303 | } // anonymous namespace |
| 9304 | namespace { |
| 9305 | at::Tensor & wrapper_CPU__relu_(at::Tensor & self) { |
| 9306 | // No device check |
| 9307 | // DeviceGuard omitted |
| 9308 | return at::native::relu_(self); |
| 9309 | } |
| 9310 | } // anonymous namespace |
| 9311 | namespace { |
| 9312 | at::Tensor wrapper_CPU___prelu_kernel(const at::Tensor & self, const at::Tensor & weight) { |
| 9313 | // No device check |
| 9314 | // DeviceGuard omitted |
| 9315 | return at::native::_prelu_kernel(self, weight); |
| 9316 | } |
| 9317 | } // anonymous namespace |
| 9318 | namespace { |
| 9319 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___prelu_kernel_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) { |
| 9320 | // No device check |
| 9321 | // DeviceGuard omitted |
| 9322 | return at::native::_prelu_kernel_backward(grad_output, self, weight); |
| 9323 | } |
| 9324 | } // anonymous namespace |
| 9325 | struct structured_gelu_out_cpu_functional final : public at::native::structured_gelu_out_cpu { |
| 9326 | void set_output_strided( |
| 9327 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9328 | TensorOptions options, DimnameList names |
| 9329 | ) override { |
| 9330 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9331 | if (!names.empty()) { |
| 9332 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9333 | } |
| 9334 | // super must happen after, so that downstream can use maybe_get_output |
| 9335 | // to retrieve the output |
| 9336 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9337 | } |
| 9338 | void set_output_raw_strided( |
| 9339 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9340 | TensorOptions options, DimnameList names |
| 9341 | ) override { |
| 9342 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9343 | if (!names.empty()) { |
| 9344 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9345 | } |
| 9346 | // super must happen after, so that downstream can use maybe_get_output |
| 9347 | // to retrieve the output |
| 9348 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9349 | } |
| 9350 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9351 | return *outputs_[output_idx]; |
| 9352 | } |
| 9353 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9354 | }; |
| 9355 | at::Tensor wrapper_CPU_gelu(const at::Tensor & self, c10::string_view approximate) { |
| 9356 | structured_gelu_out_cpu_functional op; |
| 9357 | op.meta(self, approximate); |
| 9358 | op.impl(self, approximate, *op.outputs_[0]); |
| 9359 | return std::move(op.outputs_[0]).take(); |
| 9360 | } |
| 9361 | struct structured_gelu_out_cpu_out final : public at::native::structured_gelu_out_cpu { |
| 9362 | structured_gelu_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9363 | void set_output_strided( |
| 9364 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9365 | TensorOptions options, DimnameList names |
| 9366 | ) override { |
| 9367 | const auto& out = outputs_[output_idx].get(); |
| 9368 | resize_out(out, sizes, strides, options); |
| 9369 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9370 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9371 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9372 | } |
| 9373 | if (!names.empty()) { |
| 9374 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9375 | } |
| 9376 | // super must happen after, so that downstream can use maybe_get_output |
| 9377 | // to retrieve the output |
| 9378 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9379 | } |
| 9380 | void set_output_raw_strided( |
| 9381 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9382 | TensorOptions options, DimnameList names |
| 9383 | ) override { |
| 9384 | const auto& out = outputs_[output_idx].get(); |
| 9385 | resize_out(out, sizes, strides, options); |
| 9386 | if (!names.empty()) { |
| 9387 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9388 | } |
| 9389 | // super must happen after, so that downstream can use maybe_get_output |
| 9390 | // to retrieve the output |
| 9391 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9392 | } |
| 9393 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9394 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9395 | } |
| 9396 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9397 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9398 | }; |
| 9399 | at::Tensor & wrapper_CPU_gelu_out_out(const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { |
| 9400 | structured_gelu_out_cpu_out op(out); |
| 9401 | op.meta(self, approximate); |
| 9402 | op.impl(self, approximate, op.maybe_get_output(0)); |
| 9403 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9404 | return out; |
| 9405 | } |
| 9406 | struct structured_gelu_out_cpu_inplace final : public at::native::structured_gelu_out_cpu { |
| 9407 | structured_gelu_out_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9408 | void set_output_strided( |
| 9409 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9410 | TensorOptions options, DimnameList names |
| 9411 | ) override { |
| 9412 | const auto& out = outputs_[output_idx].get(); |
| 9413 | check_inplace(out, sizes, options); |
| 9414 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9415 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9416 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9417 | } |
| 9418 | if (!names.empty()) { |
| 9419 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9420 | } |
| 9421 | // super must happen after, so that downstream can use maybe_get_output |
| 9422 | // to retrieve the output |
| 9423 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9424 | } |
| 9425 | void set_output_raw_strided( |
| 9426 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9427 | TensorOptions options, DimnameList names |
| 9428 | ) override { |
| 9429 | const auto& out = outputs_[output_idx].get(); |
| 9430 | check_inplace(out, sizes, options); |
| 9431 | if (!names.empty()) { |
| 9432 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9433 | } |
| 9434 | // super must happen after, so that downstream can use maybe_get_output |
| 9435 | // to retrieve the output |
| 9436 | at::native::structured_gelu_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9437 | } |
| 9438 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9439 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9440 | } |
| 9441 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9442 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9443 | }; |
| 9444 | at::Tensor & wrapper_CPU_gelu_(at::Tensor & self, c10::string_view approximate) { |
| 9445 | structured_gelu_out_cpu_inplace op(self); |
| 9446 | op.meta(self, approximate); |
| 9447 | op.impl(self, approximate, op.outputs_[0]); |
| 9448 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9449 | return self; |
| 9450 | } |
| 9451 | struct structured_gelu_backward_out_cpu_functional final : public at::native::structured_gelu_backward_out_cpu { |
| 9452 | void set_output_strided( |
| 9453 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9454 | TensorOptions options, DimnameList names |
| 9455 | ) override { |
| 9456 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9457 | if (!names.empty()) { |
| 9458 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9459 | } |
| 9460 | // super must happen after, so that downstream can use maybe_get_output |
| 9461 | // to retrieve the output |
| 9462 | at::native::structured_gelu_backward_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9463 | } |
| 9464 | void set_output_raw_strided( |
| 9465 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9466 | TensorOptions options, DimnameList names |
| 9467 | ) override { |
| 9468 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9469 | if (!names.empty()) { |
| 9470 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9471 | } |
| 9472 | // super must happen after, so that downstream can use maybe_get_output |
| 9473 | // to retrieve the output |
| 9474 | at::native::structured_gelu_backward_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9475 | } |
| 9476 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9477 | return *outputs_[output_idx]; |
| 9478 | } |
| 9479 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9480 | }; |
| 9481 | at::Tensor wrapper_CPU_gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { |
| 9482 | structured_gelu_backward_out_cpu_functional op; |
| 9483 | op.meta(grad_output, self, approximate); |
| 9484 | op.impl(grad_output, self, approximate, *op.outputs_[0]); |
| 9485 | return std::move(op.outputs_[0]).take(); |
| 9486 | } |
| 9487 | struct structured_gelu_backward_out_cpu_out final : public at::native::structured_gelu_backward_out_cpu { |
| 9488 | structured_gelu_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9489 | void set_output_strided( |
| 9490 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9491 | TensorOptions options, DimnameList names |
| 9492 | ) override { |
| 9493 | const auto& out = outputs_[output_idx].get(); |
| 9494 | resize_out(out, sizes, strides, options); |
| 9495 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9496 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9497 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9498 | } |
| 9499 | if (!names.empty()) { |
| 9500 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9501 | } |
| 9502 | // super must happen after, so that downstream can use maybe_get_output |
| 9503 | // to retrieve the output |
| 9504 | at::native::structured_gelu_backward_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9505 | } |
| 9506 | void set_output_raw_strided( |
| 9507 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9508 | TensorOptions options, DimnameList names |
| 9509 | ) override { |
| 9510 | const auto& out = outputs_[output_idx].get(); |
| 9511 | resize_out(out, sizes, strides, options); |
| 9512 | if (!names.empty()) { |
| 9513 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9514 | } |
| 9515 | // super must happen after, so that downstream can use maybe_get_output |
| 9516 | // to retrieve the output |
| 9517 | at::native::structured_gelu_backward_out_cpu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9518 | } |
| 9519 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9520 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9521 | } |
| 9522 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9523 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9524 | }; |
| 9525 | at::Tensor & wrapper_CPU_gelu_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input) { |
| 9526 | structured_gelu_backward_out_cpu_out op(grad_input); |
| 9527 | op.meta(grad_output, self, approximate); |
| 9528 | op.impl(grad_output, self, approximate, op.maybe_get_output(0)); |
| 9529 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9530 | return grad_input; |
| 9531 | } |
| 9532 | struct structured_hardshrink_out_functional final : public at::native::structured_hardshrink_out { |
| 9533 | void set_output_strided( |
| 9534 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9535 | TensorOptions options, DimnameList names |
| 9536 | ) override { |
| 9537 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9538 | if (!names.empty()) { |
| 9539 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9540 | } |
| 9541 | // super must happen after, so that downstream can use maybe_get_output |
| 9542 | // to retrieve the output |
| 9543 | at::native::structured_hardshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9544 | } |
| 9545 | void set_output_raw_strided( |
| 9546 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9547 | TensorOptions options, DimnameList names |
| 9548 | ) override { |
| 9549 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9550 | if (!names.empty()) { |
| 9551 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9552 | } |
| 9553 | // super must happen after, so that downstream can use maybe_get_output |
| 9554 | // to retrieve the output |
| 9555 | at::native::structured_hardshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9556 | } |
| 9557 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9558 | return *outputs_[output_idx]; |
| 9559 | } |
| 9560 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9561 | }; |
| 9562 | at::Tensor wrapper_CPU_hardshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 9563 | structured_hardshrink_out_functional op; |
| 9564 | op.meta(self, lambd); |
| 9565 | op.impl(self, lambd, *op.outputs_[0]); |
| 9566 | return std::move(op.outputs_[0]).take(); |
| 9567 | } |
| 9568 | struct structured_hardshrink_out_out final : public at::native::structured_hardshrink_out { |
| 9569 | structured_hardshrink_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9570 | void set_output_strided( |
| 9571 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9572 | TensorOptions options, DimnameList names |
| 9573 | ) override { |
| 9574 | const auto& out = outputs_[output_idx].get(); |
| 9575 | resize_out(out, sizes, strides, options); |
| 9576 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9577 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9578 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9579 | } |
| 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 | at::native::structured_hardshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9586 | } |
| 9587 | void set_output_raw_strided( |
| 9588 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9589 | TensorOptions options, DimnameList names |
| 9590 | ) override { |
| 9591 | const auto& out = outputs_[output_idx].get(); |
| 9592 | resize_out(out, sizes, strides, options); |
| 9593 | if (!names.empty()) { |
| 9594 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9595 | } |
| 9596 | // super must happen after, so that downstream can use maybe_get_output |
| 9597 | // to retrieve the output |
| 9598 | at::native::structured_hardshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9599 | } |
| 9600 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9601 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9602 | } |
| 9603 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9604 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9605 | }; |
| 9606 | at::Tensor & wrapper_CPU_hardshrink_out_out(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 9607 | structured_hardshrink_out_out op(out); |
| 9608 | op.meta(self, lambd); |
| 9609 | op.impl(self, lambd, op.maybe_get_output(0)); |
| 9610 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9611 | return out; |
| 9612 | } |
| 9613 | struct structured_hardshrink_backward_out_functional final : public at::native::structured_hardshrink_backward_out { |
| 9614 | void set_output_strided( |
| 9615 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9616 | TensorOptions options, DimnameList names |
| 9617 | ) override { |
| 9618 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9619 | if (!names.empty()) { |
| 9620 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9621 | } |
| 9622 | // super must happen after, so that downstream can use maybe_get_output |
| 9623 | // to retrieve the output |
| 9624 | at::native::structured_hardshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9625 | } |
| 9626 | void set_output_raw_strided( |
| 9627 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9628 | TensorOptions options, DimnameList names |
| 9629 | ) override { |
| 9630 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9631 | if (!names.empty()) { |
| 9632 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9633 | } |
| 9634 | // super must happen after, so that downstream can use maybe_get_output |
| 9635 | // to retrieve the output |
| 9636 | at::native::structured_hardshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9637 | } |
| 9638 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9639 | return *outputs_[output_idx]; |
| 9640 | } |
| 9641 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9642 | }; |
| 9643 | at::Tensor wrapper_CPU_hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { |
| 9644 | structured_hardshrink_backward_out_functional op; |
| 9645 | op.meta(grad_out, self, lambd); |
| 9646 | op.impl(grad_out, self, lambd, *op.outputs_[0]); |
| 9647 | return std::move(op.outputs_[0]).take(); |
| 9648 | } |
| 9649 | struct structured_hardshrink_backward_out_out final : public at::native::structured_hardshrink_backward_out { |
| 9650 | structured_hardshrink_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9651 | void set_output_strided( |
| 9652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9653 | TensorOptions options, DimnameList names |
| 9654 | ) override { |
| 9655 | const auto& out = outputs_[output_idx].get(); |
| 9656 | resize_out(out, sizes, strides, options); |
| 9657 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9658 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9659 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9660 | } |
| 9661 | if (!names.empty()) { |
| 9662 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9663 | } |
| 9664 | // super must happen after, so that downstream can use maybe_get_output |
| 9665 | // to retrieve the output |
| 9666 | at::native::structured_hardshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9667 | } |
| 9668 | void set_output_raw_strided( |
| 9669 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9670 | TensorOptions options, DimnameList names |
| 9671 | ) override { |
| 9672 | const auto& out = outputs_[output_idx].get(); |
| 9673 | resize_out(out, sizes, strides, options); |
| 9674 | if (!names.empty()) { |
| 9675 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9676 | } |
| 9677 | // super must happen after, so that downstream can use maybe_get_output |
| 9678 | // to retrieve the output |
| 9679 | at::native::structured_hardshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9680 | } |
| 9681 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9682 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9683 | } |
| 9684 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9685 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9686 | }; |
| 9687 | at::Tensor & wrapper_CPU_hardshrink_backward_out_grad_input(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 9688 | structured_hardshrink_backward_out_out op(grad_input); |
| 9689 | op.meta(grad_out, self, lambd); |
| 9690 | op.impl(grad_out, self, lambd, op.maybe_get_output(0)); |
| 9691 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9692 | return grad_input; |
| 9693 | } |
| 9694 | struct structured_rsqrt_out_functional final : public at::native::structured_rsqrt_out { |
| 9695 | void set_output_strided( |
| 9696 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9697 | TensorOptions options, DimnameList names |
| 9698 | ) override { |
| 9699 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9700 | if (!names.empty()) { |
| 9701 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9702 | } |
| 9703 | // super must happen after, so that downstream can use maybe_get_output |
| 9704 | // to retrieve the output |
| 9705 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9706 | } |
| 9707 | void set_output_raw_strided( |
| 9708 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9709 | TensorOptions options, DimnameList names |
| 9710 | ) override { |
| 9711 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9712 | if (!names.empty()) { |
| 9713 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9714 | } |
| 9715 | // super must happen after, so that downstream can use maybe_get_output |
| 9716 | // to retrieve the output |
| 9717 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9718 | } |
| 9719 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9720 | return *outputs_[output_idx]; |
| 9721 | } |
| 9722 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9723 | }; |
| 9724 | at::Tensor wrapper_CPU_rsqrt(const at::Tensor & self) { |
| 9725 | structured_rsqrt_out_functional op; |
| 9726 | op.meta(self); |
| 9727 | op.impl(self, *op.outputs_[0]); |
| 9728 | return std::move(op.outputs_[0]).take(); |
| 9729 | } |
| 9730 | struct structured_rsqrt_out_out final : public at::native::structured_rsqrt_out { |
| 9731 | structured_rsqrt_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9732 | void set_output_strided( |
| 9733 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9734 | TensorOptions options, DimnameList names |
| 9735 | ) override { |
| 9736 | const auto& out = outputs_[output_idx].get(); |
| 9737 | resize_out(out, sizes, strides, options); |
| 9738 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9739 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9740 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9741 | } |
| 9742 | if (!names.empty()) { |
| 9743 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9744 | } |
| 9745 | // super must happen after, so that downstream can use maybe_get_output |
| 9746 | // to retrieve the output |
| 9747 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9748 | } |
| 9749 | void set_output_raw_strided( |
| 9750 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9751 | TensorOptions options, DimnameList names |
| 9752 | ) override { |
| 9753 | const auto& out = outputs_[output_idx].get(); |
| 9754 | resize_out(out, sizes, strides, options); |
| 9755 | if (!names.empty()) { |
| 9756 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9757 | } |
| 9758 | // super must happen after, so that downstream can use maybe_get_output |
| 9759 | // to retrieve the output |
| 9760 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9761 | } |
| 9762 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9763 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9764 | } |
| 9765 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9766 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9767 | }; |
| 9768 | at::Tensor & wrapper_CPU_rsqrt_out_out(const at::Tensor & self, at::Tensor & out) { |
| 9769 | structured_rsqrt_out_out op(out); |
| 9770 | op.meta(self); |
| 9771 | op.impl(self, op.maybe_get_output(0)); |
| 9772 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9773 | return out; |
| 9774 | } |
| 9775 | struct structured_rsqrt_out_inplace final : public at::native::structured_rsqrt_out { |
| 9776 | structured_rsqrt_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9777 | void set_output_strided( |
| 9778 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9779 | TensorOptions options, DimnameList names |
| 9780 | ) override { |
| 9781 | const auto& out = outputs_[output_idx].get(); |
| 9782 | check_inplace(out, sizes, options); |
| 9783 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9784 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9785 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9786 | } |
| 9787 | if (!names.empty()) { |
| 9788 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9789 | } |
| 9790 | // super must happen after, so that downstream can use maybe_get_output |
| 9791 | // to retrieve the output |
| 9792 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9793 | } |
| 9794 | void set_output_raw_strided( |
| 9795 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9796 | TensorOptions options, DimnameList names |
| 9797 | ) override { |
| 9798 | const auto& out = outputs_[output_idx].get(); |
| 9799 | check_inplace(out, sizes, options); |
| 9800 | if (!names.empty()) { |
| 9801 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9802 | } |
| 9803 | // super must happen after, so that downstream can use maybe_get_output |
| 9804 | // to retrieve the output |
| 9805 | at::native::structured_rsqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9806 | } |
| 9807 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9808 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9809 | } |
| 9810 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9811 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9812 | }; |
| 9813 | at::Tensor & wrapper_CPU_rsqrt_(at::Tensor & self) { |
| 9814 | structured_rsqrt_out_inplace op(self); |
| 9815 | op.meta(self); |
| 9816 | op.impl(self, op.outputs_[0]); |
| 9817 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9818 | return self; |
| 9819 | } |
| 9820 | struct structured_silu_out_functional final : public at::native::structured_silu_out { |
| 9821 | void set_output_strided( |
| 9822 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9823 | TensorOptions options, DimnameList names |
| 9824 | ) override { |
| 9825 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9826 | if (!names.empty()) { |
| 9827 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9828 | } |
| 9829 | // super must happen after, so that downstream can use maybe_get_output |
| 9830 | // to retrieve the output |
| 9831 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9832 | } |
| 9833 | void set_output_raw_strided( |
| 9834 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9835 | TensorOptions options, DimnameList names |
| 9836 | ) override { |
| 9837 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9838 | if (!names.empty()) { |
| 9839 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9840 | } |
| 9841 | // super must happen after, so that downstream can use maybe_get_output |
| 9842 | // to retrieve the output |
| 9843 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9844 | } |
| 9845 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9846 | return *outputs_[output_idx]; |
| 9847 | } |
| 9848 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9849 | }; |
| 9850 | at::Tensor wrapper_CPU_silu(const at::Tensor & self) { |
| 9851 | structured_silu_out_functional op; |
| 9852 | op.meta(self); |
| 9853 | op.impl(self, *op.outputs_[0]); |
| 9854 | return std::move(op.outputs_[0]).take(); |
| 9855 | } |
| 9856 | struct structured_silu_out_out final : public at::native::structured_silu_out { |
| 9857 | structured_silu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9858 | void set_output_strided( |
| 9859 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9860 | TensorOptions options, DimnameList names |
| 9861 | ) override { |
| 9862 | const auto& out = outputs_[output_idx].get(); |
| 9863 | resize_out(out, sizes, strides, options); |
| 9864 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9865 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9866 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9867 | } |
| 9868 | if (!names.empty()) { |
| 9869 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9870 | } |
| 9871 | // super must happen after, so that downstream can use maybe_get_output |
| 9872 | // to retrieve the output |
| 9873 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9874 | } |
| 9875 | void set_output_raw_strided( |
| 9876 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9877 | TensorOptions options, DimnameList names |
| 9878 | ) override { |
| 9879 | const auto& out = outputs_[output_idx].get(); |
| 9880 | resize_out(out, sizes, strides, options); |
| 9881 | if (!names.empty()) { |
| 9882 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9883 | } |
| 9884 | // super must happen after, so that downstream can use maybe_get_output |
| 9885 | // to retrieve the output |
| 9886 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9887 | } |
| 9888 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9889 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9890 | } |
| 9891 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9892 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9893 | }; |
| 9894 | at::Tensor & wrapper_CPU_silu_out_out(const at::Tensor & self, at::Tensor & out) { |
| 9895 | structured_silu_out_out op(out); |
| 9896 | op.meta(self); |
| 9897 | op.impl(self, op.maybe_get_output(0)); |
| 9898 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9899 | return out; |
| 9900 | } |
| 9901 | struct structured_silu_out_inplace final : public at::native::structured_silu_out { |
| 9902 | structured_silu_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9903 | void set_output_strided( |
| 9904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9905 | TensorOptions options, DimnameList names |
| 9906 | ) override { |
| 9907 | const auto& out = outputs_[output_idx].get(); |
| 9908 | check_inplace(out, sizes, options); |
| 9909 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9910 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9911 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9912 | } |
| 9913 | if (!names.empty()) { |
| 9914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9915 | } |
| 9916 | // super must happen after, so that downstream can use maybe_get_output |
| 9917 | // to retrieve the output |
| 9918 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9919 | } |
| 9920 | void set_output_raw_strided( |
| 9921 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9922 | TensorOptions options, DimnameList names |
| 9923 | ) override { |
| 9924 | const auto& out = outputs_[output_idx].get(); |
| 9925 | check_inplace(out, sizes, options); |
| 9926 | if (!names.empty()) { |
| 9927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9928 | } |
| 9929 | // super must happen after, so that downstream can use maybe_get_output |
| 9930 | // to retrieve the output |
| 9931 | at::native::structured_silu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9932 | } |
| 9933 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9934 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9935 | } |
| 9936 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9937 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9938 | }; |
| 9939 | at::Tensor & wrapper_CPU_silu_(at::Tensor & self) { |
| 9940 | structured_silu_out_inplace op(self); |
| 9941 | op.meta(self); |
| 9942 | op.impl(self, op.outputs_[0]); |
| 9943 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9944 | return self; |
| 9945 | } |
| 9946 | struct structured_silu_backward_out_functional final : public at::native::structured_silu_backward_out { |
| 9947 | void set_output_strided( |
| 9948 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9949 | TensorOptions options, DimnameList names |
| 9950 | ) override { |
| 9951 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9952 | if (!names.empty()) { |
| 9953 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9954 | } |
| 9955 | // super must happen after, so that downstream can use maybe_get_output |
| 9956 | // to retrieve the output |
| 9957 | at::native::structured_silu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9958 | } |
| 9959 | void set_output_raw_strided( |
| 9960 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9961 | TensorOptions options, DimnameList names |
| 9962 | ) override { |
| 9963 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9964 | if (!names.empty()) { |
| 9965 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9966 | } |
| 9967 | // super must happen after, so that downstream can use maybe_get_output |
| 9968 | // to retrieve the output |
| 9969 | at::native::structured_silu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9970 | } |
| 9971 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9972 | return *outputs_[output_idx]; |
| 9973 | } |
| 9974 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9975 | }; |
| 9976 | at::Tensor wrapper_CPU_silu_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 9977 | structured_silu_backward_out_functional op; |
| 9978 | op.meta(grad_output, self); |
| 9979 | op.impl(grad_output, self, *op.outputs_[0]); |
| 9980 | return std::move(op.outputs_[0]).take(); |
| 9981 | } |
| 9982 | struct structured_silu_backward_out_out final : public at::native::structured_silu_backward_out { |
| 9983 | structured_silu_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 9984 | void set_output_strided( |
| 9985 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9986 | TensorOptions options, DimnameList names |
| 9987 | ) override { |
| 9988 | const auto& out = outputs_[output_idx].get(); |
| 9989 | resize_out(out, sizes, strides, options); |
| 9990 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9991 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9992 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9993 | } |
| 9994 | if (!names.empty()) { |
| 9995 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9996 | } |
| 9997 | // super must happen after, so that downstream can use maybe_get_output |
| 9998 | // to retrieve the output |
| 9999 | at::native::structured_silu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10000 | } |
| 10001 | void set_output_raw_strided( |
| 10002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10003 | TensorOptions options, DimnameList names |
| 10004 | ) override { |
| 10005 | const auto& out = outputs_[output_idx].get(); |
| 10006 | resize_out(out, sizes, strides, options); |
| 10007 | if (!names.empty()) { |
| 10008 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10009 | } |
| 10010 | // super must happen after, so that downstream can use maybe_get_output |
| 10011 | // to retrieve the output |
| 10012 | at::native::structured_silu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10013 | } |
| 10014 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10015 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10016 | } |
| 10017 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10018 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10019 | }; |
| 10020 | at::Tensor & wrapper_CPU_silu_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 10021 | structured_silu_backward_out_out op(grad_input); |
| 10022 | op.meta(grad_output, self); |
| 10023 | op.impl(grad_output, self, op.maybe_get_output(0)); |
| 10024 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10025 | return grad_input; |
| 10026 | } |
| 10027 | struct structured_mish_out_functional final : public at::native::structured_mish_out { |
| 10028 | void set_output_strided( |
| 10029 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10030 | TensorOptions options, DimnameList names |
| 10031 | ) override { |
| 10032 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10033 | if (!names.empty()) { |
| 10034 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10035 | } |
| 10036 | // super must happen after, so that downstream can use maybe_get_output |
| 10037 | // to retrieve the output |
| 10038 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10039 | } |
| 10040 | void set_output_raw_strided( |
| 10041 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10042 | TensorOptions options, DimnameList names |
| 10043 | ) override { |
| 10044 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10045 | if (!names.empty()) { |
| 10046 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10047 | } |
| 10048 | // super must happen after, so that downstream can use maybe_get_output |
| 10049 | // to retrieve the output |
| 10050 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10051 | } |
| 10052 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10053 | return *outputs_[output_idx]; |
| 10054 | } |
| 10055 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10056 | }; |
| 10057 | at::Tensor wrapper_CPU_mish(const at::Tensor & self) { |
| 10058 | structured_mish_out_functional op; |
| 10059 | op.meta(self); |
| 10060 | op.impl(self, *op.outputs_[0]); |
| 10061 | return std::move(op.outputs_[0]).take(); |
| 10062 | } |
| 10063 | struct structured_mish_out_out final : public at::native::structured_mish_out { |
| 10064 | structured_mish_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10065 | void set_output_strided( |
| 10066 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10067 | TensorOptions options, DimnameList names |
| 10068 | ) override { |
| 10069 | const auto& out = outputs_[output_idx].get(); |
| 10070 | resize_out(out, sizes, strides, options); |
| 10071 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10072 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10073 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10074 | } |
| 10075 | if (!names.empty()) { |
| 10076 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10077 | } |
| 10078 | // super must happen after, so that downstream can use maybe_get_output |
| 10079 | // to retrieve the output |
| 10080 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10081 | } |
| 10082 | void set_output_raw_strided( |
| 10083 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10084 | TensorOptions options, DimnameList names |
| 10085 | ) override { |
| 10086 | const auto& out = outputs_[output_idx].get(); |
| 10087 | resize_out(out, sizes, strides, options); |
| 10088 | if (!names.empty()) { |
| 10089 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10090 | } |
| 10091 | // super must happen after, so that downstream can use maybe_get_output |
| 10092 | // to retrieve the output |
| 10093 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10094 | } |
| 10095 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10096 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10097 | } |
| 10098 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10099 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10100 | }; |
| 10101 | at::Tensor & wrapper_CPU_mish_out_out(const at::Tensor & self, at::Tensor & out) { |
| 10102 | structured_mish_out_out op(out); |
| 10103 | op.meta(self); |
| 10104 | op.impl(self, op.maybe_get_output(0)); |
| 10105 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10106 | return out; |
| 10107 | } |
| 10108 | struct structured_mish_out_inplace final : public at::native::structured_mish_out { |
| 10109 | structured_mish_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10110 | void set_output_strided( |
| 10111 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10112 | TensorOptions options, DimnameList names |
| 10113 | ) override { |
| 10114 | const auto& out = outputs_[output_idx].get(); |
| 10115 | check_inplace(out, sizes, options); |
| 10116 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10117 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10118 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10119 | } |
| 10120 | if (!names.empty()) { |
| 10121 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10122 | } |
| 10123 | // super must happen after, so that downstream can use maybe_get_output |
| 10124 | // to retrieve the output |
| 10125 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10126 | } |
| 10127 | void set_output_raw_strided( |
| 10128 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10129 | TensorOptions options, DimnameList names |
| 10130 | ) override { |
| 10131 | const auto& out = outputs_[output_idx].get(); |
| 10132 | check_inplace(out, sizes, options); |
| 10133 | if (!names.empty()) { |
| 10134 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10135 | } |
| 10136 | // super must happen after, so that downstream can use maybe_get_output |
| 10137 | // to retrieve the output |
| 10138 | at::native::structured_mish_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10139 | } |
| 10140 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10141 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10142 | } |
| 10143 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10144 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10145 | }; |
| 10146 | at::Tensor & wrapper_CPU_mish_(at::Tensor & self) { |
| 10147 | structured_mish_out_inplace op(self); |
| 10148 | op.meta(self); |
| 10149 | op.impl(self, op.outputs_[0]); |
| 10150 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10151 | return self; |
| 10152 | } |
| 10153 | namespace { |
| 10154 | at::Tensor wrapper_CPU__mish_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 10155 | // No device check |
| 10156 | // DeviceGuard omitted |
| 10157 | return at::native::mish_backward(grad_output, self); |
| 10158 | } |
| 10159 | } // anonymous namespace |
| 10160 | struct structured_sigmoid_out_functional final : public at::native::structured_sigmoid_out { |
| 10161 | void set_output_strided( |
| 10162 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10163 | TensorOptions options, DimnameList names |
| 10164 | ) override { |
| 10165 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10166 | if (!names.empty()) { |
| 10167 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10168 | } |
| 10169 | // super must happen after, so that downstream can use maybe_get_output |
| 10170 | // to retrieve the output |
| 10171 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10172 | } |
| 10173 | void set_output_raw_strided( |
| 10174 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10175 | TensorOptions options, DimnameList names |
| 10176 | ) override { |
| 10177 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10178 | if (!names.empty()) { |
| 10179 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10180 | } |
| 10181 | // super must happen after, so that downstream can use maybe_get_output |
| 10182 | // to retrieve the output |
| 10183 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10184 | } |
| 10185 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10186 | return *outputs_[output_idx]; |
| 10187 | } |
| 10188 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10189 | }; |
| 10190 | at::Tensor wrapper_CPU_sigmoid(const at::Tensor & self) { |
| 10191 | structured_sigmoid_out_functional op; |
| 10192 | op.meta(self); |
| 10193 | op.impl(self, *op.outputs_[0]); |
| 10194 | return std::move(op.outputs_[0]).take(); |
| 10195 | } |
| 10196 | struct structured_sigmoid_out_out final : public at::native::structured_sigmoid_out { |
| 10197 | structured_sigmoid_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10198 | void set_output_strided( |
| 10199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10200 | TensorOptions options, DimnameList names |
| 10201 | ) override { |
| 10202 | const auto& out = outputs_[output_idx].get(); |
| 10203 | resize_out(out, sizes, strides, options); |
| 10204 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10205 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10206 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10207 | } |
| 10208 | if (!names.empty()) { |
| 10209 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10210 | } |
| 10211 | // super must happen after, so that downstream can use maybe_get_output |
| 10212 | // to retrieve the output |
| 10213 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10214 | } |
| 10215 | void set_output_raw_strided( |
| 10216 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10217 | TensorOptions options, DimnameList names |
| 10218 | ) override { |
| 10219 | const auto& out = outputs_[output_idx].get(); |
| 10220 | resize_out(out, sizes, strides, options); |
| 10221 | if (!names.empty()) { |
| 10222 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10223 | } |
| 10224 | // super must happen after, so that downstream can use maybe_get_output |
| 10225 | // to retrieve the output |
| 10226 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10227 | } |
| 10228 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10229 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10230 | } |
| 10231 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10232 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10233 | }; |
| 10234 | at::Tensor & wrapper_CPU_sigmoid_out_out(const at::Tensor & self, at::Tensor & out) { |
| 10235 | structured_sigmoid_out_out op(out); |
| 10236 | op.meta(self); |
| 10237 | op.impl(self, op.maybe_get_output(0)); |
| 10238 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10239 | return out; |
| 10240 | } |
| 10241 | struct structured_sigmoid_out_inplace final : public at::native::structured_sigmoid_out { |
| 10242 | structured_sigmoid_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10243 | void set_output_strided( |
| 10244 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10245 | TensorOptions options, DimnameList names |
| 10246 | ) override { |
| 10247 | const auto& out = outputs_[output_idx].get(); |
| 10248 | check_inplace(out, sizes, options); |
| 10249 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10250 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10251 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10252 | } |
| 10253 | if (!names.empty()) { |
| 10254 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10255 | } |
| 10256 | // super must happen after, so that downstream can use maybe_get_output |
| 10257 | // to retrieve the output |
| 10258 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10259 | } |
| 10260 | void set_output_raw_strided( |
| 10261 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10262 | TensorOptions options, DimnameList names |
| 10263 | ) override { |
| 10264 | const auto& out = outputs_[output_idx].get(); |
| 10265 | check_inplace(out, sizes, options); |
| 10266 | if (!names.empty()) { |
| 10267 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10268 | } |
| 10269 | // super must happen after, so that downstream can use maybe_get_output |
| 10270 | // to retrieve the output |
| 10271 | at::native::structured_sigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10272 | } |
| 10273 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10274 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10275 | } |
| 10276 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10277 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10278 | }; |
| 10279 | at::Tensor & wrapper_CPU_sigmoid_(at::Tensor & self) { |
| 10280 | structured_sigmoid_out_inplace op(self); |
| 10281 | op.meta(self); |
| 10282 | op.impl(self, op.outputs_[0]); |
| 10283 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10284 | return self; |
| 10285 | } |
| 10286 | namespace { |
| 10287 | at::Tensor wrapper_CPU__logit(const at::Tensor & self, c10::optional<double> eps) { |
| 10288 | // No device check |
| 10289 | // DeviceGuard omitted |
| 10290 | return at::native::logit(self, eps); |
| 10291 | } |
| 10292 | } // anonymous namespace |
| 10293 | namespace { |
| 10294 | at::Tensor & wrapper_CPU_out_logit_out(const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) { |
| 10295 | // No device check |
| 10296 | // DeviceGuard omitted |
| 10297 | return at::native::logit_out(self, eps, out); |
| 10298 | } |
| 10299 | } // anonymous namespace |
| 10300 | namespace { |
| 10301 | at::Tensor & wrapper_CPU__logit_(at::Tensor & self, c10::optional<double> eps) { |
| 10302 | // No device check |
| 10303 | // DeviceGuard omitted |
| 10304 | return at::native::logit_(self, eps); |
| 10305 | } |
| 10306 | } // anonymous namespace |
| 10307 | struct structured_sin_out_functional final : public at::native::structured_sin_out { |
| 10308 | void set_output_strided( |
| 10309 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10310 | TensorOptions options, DimnameList names |
| 10311 | ) override { |
| 10312 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10313 | if (!names.empty()) { |
| 10314 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10315 | } |
| 10316 | // super must happen after, so that downstream can use maybe_get_output |
| 10317 | // to retrieve the output |
| 10318 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10319 | } |
| 10320 | void set_output_raw_strided( |
| 10321 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10322 | TensorOptions options, DimnameList names |
| 10323 | ) override { |
| 10324 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10325 | if (!names.empty()) { |
| 10326 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10327 | } |
| 10328 | // super must happen after, so that downstream can use maybe_get_output |
| 10329 | // to retrieve the output |
| 10330 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10331 | } |
| 10332 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10333 | return *outputs_[output_idx]; |
| 10334 | } |
| 10335 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10336 | }; |
| 10337 | at::Tensor wrapper_CPU_sin(const at::Tensor & self) { |
| 10338 | structured_sin_out_functional op; |
| 10339 | op.meta(self); |
| 10340 | op.impl(self, *op.outputs_[0]); |
| 10341 | return std::move(op.outputs_[0]).take(); |
| 10342 | } |
| 10343 | struct structured_sin_out_out final : public at::native::structured_sin_out { |
| 10344 | structured_sin_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10345 | void set_output_strided( |
| 10346 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10347 | TensorOptions options, DimnameList names |
| 10348 | ) override { |
| 10349 | const auto& out = outputs_[output_idx].get(); |
| 10350 | resize_out(out, sizes, strides, options); |
| 10351 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10352 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10353 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10354 | } |
| 10355 | if (!names.empty()) { |
| 10356 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10357 | } |
| 10358 | // super must happen after, so that downstream can use maybe_get_output |
| 10359 | // to retrieve the output |
| 10360 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10361 | } |
| 10362 | void set_output_raw_strided( |
| 10363 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10364 | TensorOptions options, DimnameList names |
| 10365 | ) override { |
| 10366 | const auto& out = outputs_[output_idx].get(); |
| 10367 | resize_out(out, sizes, strides, options); |
| 10368 | if (!names.empty()) { |
| 10369 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10370 | } |
| 10371 | // super must happen after, so that downstream can use maybe_get_output |
| 10372 | // to retrieve the output |
| 10373 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10374 | } |
| 10375 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10376 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10377 | } |
| 10378 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10379 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10380 | }; |
| 10381 | at::Tensor & wrapper_CPU_sin_out_out(const at::Tensor & self, at::Tensor & out) { |
| 10382 | structured_sin_out_out op(out); |
| 10383 | op.meta(self); |
| 10384 | op.impl(self, op.maybe_get_output(0)); |
| 10385 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10386 | return out; |
| 10387 | } |
| 10388 | struct structured_sin_out_inplace final : public at::native::structured_sin_out { |
| 10389 | structured_sin_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10390 | void set_output_strided( |
| 10391 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10392 | TensorOptions options, DimnameList names |
| 10393 | ) override { |
| 10394 | const auto& out = outputs_[output_idx].get(); |
| 10395 | check_inplace(out, sizes, options); |
| 10396 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10397 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10398 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10399 | } |
| 10400 | if (!names.empty()) { |
| 10401 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10402 | } |
| 10403 | // super must happen after, so that downstream can use maybe_get_output |
| 10404 | // to retrieve the output |
| 10405 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10406 | } |
| 10407 | void set_output_raw_strided( |
| 10408 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10409 | TensorOptions options, DimnameList names |
| 10410 | ) override { |
| 10411 | const auto& out = outputs_[output_idx].get(); |
| 10412 | check_inplace(out, sizes, options); |
| 10413 | if (!names.empty()) { |
| 10414 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10415 | } |
| 10416 | // super must happen after, so that downstream can use maybe_get_output |
| 10417 | // to retrieve the output |
| 10418 | at::native::structured_sin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10419 | } |
| 10420 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10421 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10422 | } |
| 10423 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10424 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10425 | }; |
| 10426 | at::Tensor & wrapper_CPU_sin_(at::Tensor & self) { |
| 10427 | structured_sin_out_inplace op(self); |
| 10428 | op.meta(self); |
| 10429 | op.impl(self, op.outputs_[0]); |
| 10430 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10431 | return self; |
| 10432 | } |
| 10433 | struct structured_sinc_out_functional final : public at::native::structured_sinc_out { |
| 10434 | void set_output_strided( |
| 10435 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10436 | TensorOptions options, DimnameList names |
| 10437 | ) override { |
| 10438 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10439 | if (!names.empty()) { |
| 10440 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10441 | } |
| 10442 | // super must happen after, so that downstream can use maybe_get_output |
| 10443 | // to retrieve the output |
| 10444 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10445 | } |
| 10446 | void set_output_raw_strided( |
| 10447 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10448 | TensorOptions options, DimnameList names |
| 10449 | ) override { |
| 10450 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10451 | if (!names.empty()) { |
| 10452 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10453 | } |
| 10454 | // super must happen after, so that downstream can use maybe_get_output |
| 10455 | // to retrieve the output |
| 10456 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10457 | } |
| 10458 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10459 | return *outputs_[output_idx]; |
| 10460 | } |
| 10461 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10462 | }; |
| 10463 | at::Tensor wrapper_CPU_sinc(const at::Tensor & self) { |
| 10464 | structured_sinc_out_functional op; |
| 10465 | op.meta(self); |
| 10466 | op.impl(self, *op.outputs_[0]); |
| 10467 | return std::move(op.outputs_[0]).take(); |
| 10468 | } |
| 10469 | struct structured_sinc_out_out final : public at::native::structured_sinc_out { |
| 10470 | structured_sinc_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10471 | void set_output_strided( |
| 10472 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10473 | TensorOptions options, DimnameList names |
| 10474 | ) override { |
| 10475 | const auto& out = outputs_[output_idx].get(); |
| 10476 | resize_out(out, sizes, strides, options); |
| 10477 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10478 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10479 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10480 | } |
| 10481 | if (!names.empty()) { |
| 10482 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10483 | } |
| 10484 | // super must happen after, so that downstream can use maybe_get_output |
| 10485 | // to retrieve the output |
| 10486 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10487 | } |
| 10488 | void set_output_raw_strided( |
| 10489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10490 | TensorOptions options, DimnameList names |
| 10491 | ) override { |
| 10492 | const auto& out = outputs_[output_idx].get(); |
| 10493 | resize_out(out, sizes, strides, options); |
| 10494 | if (!names.empty()) { |
| 10495 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10496 | } |
| 10497 | // super must happen after, so that downstream can use maybe_get_output |
| 10498 | // to retrieve the output |
| 10499 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10500 | } |
| 10501 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10502 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10503 | } |
| 10504 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10505 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10506 | }; |
| 10507 | at::Tensor & wrapper_CPU_sinc_out_out(const at::Tensor & self, at::Tensor & out) { |
| 10508 | structured_sinc_out_out op(out); |
| 10509 | op.meta(self); |
| 10510 | op.impl(self, op.maybe_get_output(0)); |
| 10511 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10512 | return out; |
| 10513 | } |
| 10514 | struct structured_sinc_out_inplace final : public at::native::structured_sinc_out { |
| 10515 | structured_sinc_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10516 | void set_output_strided( |
| 10517 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10518 | TensorOptions options, DimnameList names |
| 10519 | ) override { |
| 10520 | const auto& out = outputs_[output_idx].get(); |
| 10521 | check_inplace(out, sizes, options); |
| 10522 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10523 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10524 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10525 | } |
| 10526 | if (!names.empty()) { |
| 10527 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10528 | } |
| 10529 | // super must happen after, so that downstream can use maybe_get_output |
| 10530 | // to retrieve the output |
| 10531 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10532 | } |
| 10533 | void set_output_raw_strided( |
| 10534 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10535 | TensorOptions options, DimnameList names |
| 10536 | ) override { |
| 10537 | const auto& out = outputs_[output_idx].get(); |
| 10538 | check_inplace(out, sizes, options); |
| 10539 | if (!names.empty()) { |
| 10540 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10541 | } |
| 10542 | // super must happen after, so that downstream can use maybe_get_output |
| 10543 | // to retrieve the output |
| 10544 | at::native::structured_sinc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10545 | } |
| 10546 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10547 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10548 | } |
| 10549 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10550 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10551 | }; |
| 10552 | at::Tensor & wrapper_CPU_sinc_(at::Tensor & self) { |
| 10553 | structured_sinc_out_inplace op(self); |
| 10554 | op.meta(self); |
| 10555 | op.impl(self, op.outputs_[0]); |
| 10556 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10557 | return self; |
| 10558 | } |
| 10559 | struct structured_sinh_out_functional final : public at::native::structured_sinh_out { |
| 10560 | void set_output_strided( |
| 10561 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10562 | TensorOptions options, DimnameList names |
| 10563 | ) override { |
| 10564 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10565 | if (!names.empty()) { |
| 10566 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10567 | } |
| 10568 | // super must happen after, so that downstream can use maybe_get_output |
| 10569 | // to retrieve the output |
| 10570 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10571 | } |
| 10572 | void set_output_raw_strided( |
| 10573 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10574 | TensorOptions options, DimnameList names |
| 10575 | ) override { |
| 10576 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10577 | if (!names.empty()) { |
| 10578 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10579 | } |
| 10580 | // super must happen after, so that downstream can use maybe_get_output |
| 10581 | // to retrieve the output |
| 10582 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10583 | } |
| 10584 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10585 | return *outputs_[output_idx]; |
| 10586 | } |
| 10587 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10588 | }; |
| 10589 | at::Tensor wrapper_CPU_sinh(const at::Tensor & self) { |
| 10590 | structured_sinh_out_functional op; |
| 10591 | op.meta(self); |
| 10592 | op.impl(self, *op.outputs_[0]); |
| 10593 | return std::move(op.outputs_[0]).take(); |
| 10594 | } |
| 10595 | struct structured_sinh_out_out final : public at::native::structured_sinh_out { |
| 10596 | structured_sinh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10597 | void set_output_strided( |
| 10598 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10599 | TensorOptions options, DimnameList names |
| 10600 | ) override { |
| 10601 | const auto& out = outputs_[output_idx].get(); |
| 10602 | resize_out(out, sizes, strides, options); |
| 10603 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10604 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10605 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10606 | } |
| 10607 | if (!names.empty()) { |
| 10608 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10609 | } |
| 10610 | // super must happen after, so that downstream can use maybe_get_output |
| 10611 | // to retrieve the output |
| 10612 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10613 | } |
| 10614 | void set_output_raw_strided( |
| 10615 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10616 | TensorOptions options, DimnameList names |
| 10617 | ) override { |
| 10618 | const auto& out = outputs_[output_idx].get(); |
| 10619 | resize_out(out, sizes, strides, options); |
| 10620 | if (!names.empty()) { |
| 10621 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10622 | } |
| 10623 | // super must happen after, so that downstream can use maybe_get_output |
| 10624 | // to retrieve the output |
| 10625 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10626 | } |
| 10627 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10628 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10629 | } |
| 10630 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10631 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10632 | }; |
| 10633 | at::Tensor & wrapper_CPU_sinh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 10634 | structured_sinh_out_out op(out); |
| 10635 | op.meta(self); |
| 10636 | op.impl(self, op.maybe_get_output(0)); |
| 10637 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10638 | return out; |
| 10639 | } |
| 10640 | struct structured_sinh_out_inplace final : public at::native::structured_sinh_out { |
| 10641 | structured_sinh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10642 | void set_output_strided( |
| 10643 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10644 | TensorOptions options, DimnameList names |
| 10645 | ) override { |
| 10646 | const auto& out = outputs_[output_idx].get(); |
| 10647 | check_inplace(out, sizes, options); |
| 10648 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10649 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10650 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10651 | } |
| 10652 | if (!names.empty()) { |
| 10653 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10654 | } |
| 10655 | // super must happen after, so that downstream can use maybe_get_output |
| 10656 | // to retrieve the output |
| 10657 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10658 | } |
| 10659 | void set_output_raw_strided( |
| 10660 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10661 | TensorOptions options, DimnameList names |
| 10662 | ) override { |
| 10663 | const auto& out = outputs_[output_idx].get(); |
| 10664 | check_inplace(out, sizes, options); |
| 10665 | if (!names.empty()) { |
| 10666 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10667 | } |
| 10668 | // super must happen after, so that downstream can use maybe_get_output |
| 10669 | // to retrieve the output |
| 10670 | at::native::structured_sinh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10671 | } |
| 10672 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10673 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10674 | } |
| 10675 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10676 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10677 | }; |
| 10678 | at::Tensor & wrapper_CPU_sinh_(at::Tensor & self) { |
| 10679 | structured_sinh_out_inplace op(self); |
| 10680 | op.meta(self); |
| 10681 | op.impl(self, op.outputs_[0]); |
| 10682 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10683 | return self; |
| 10684 | } |
| 10685 | struct structured_softmax_cpu_out_functional final : public at::native::structured_softmax_cpu_out { |
| 10686 | void set_output_strided( |
| 10687 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10688 | TensorOptions options, DimnameList names |
| 10689 | ) override { |
| 10690 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10691 | if (!names.empty()) { |
| 10692 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10693 | } |
| 10694 | // super must happen after, so that downstream can use maybe_get_output |
| 10695 | // to retrieve the output |
| 10696 | } |
| 10697 | void set_output_raw_strided( |
| 10698 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10699 | TensorOptions options, DimnameList names |
| 10700 | ) override { |
| 10701 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10702 | if (!names.empty()) { |
| 10703 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10704 | } |
| 10705 | // super must happen after, so that downstream can use maybe_get_output |
| 10706 | // to retrieve the output |
| 10707 | } |
| 10708 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10709 | return *outputs_[output_idx]; |
| 10710 | } |
| 10711 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10712 | }; |
| 10713 | at::Tensor wrapper_CPU__softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 10714 | structured_softmax_cpu_out_functional op; |
| 10715 | op.meta(self, dim, half_to_float); |
| 10716 | op.impl(self, dim, half_to_float, *op.outputs_[0]); |
| 10717 | return std::move(op.outputs_[0]).take(); |
| 10718 | } |
| 10719 | struct structured_softmax_cpu_out_out final : public at::native::structured_softmax_cpu_out { |
| 10720 | structured_softmax_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10721 | void set_output_strided( |
| 10722 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10723 | TensorOptions options, DimnameList names |
| 10724 | ) override { |
| 10725 | const auto& out = outputs_[output_idx].get(); |
| 10726 | resize_out(out, sizes, strides, options); |
| 10727 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10728 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10729 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10730 | } |
| 10731 | if (!names.empty()) { |
| 10732 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10733 | } |
| 10734 | // super must happen after, so that downstream can use maybe_get_output |
| 10735 | // to retrieve the output |
| 10736 | } |
| 10737 | void set_output_raw_strided( |
| 10738 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10739 | TensorOptions options, DimnameList names |
| 10740 | ) override { |
| 10741 | const auto& out = outputs_[output_idx].get(); |
| 10742 | resize_out(out, sizes, strides, options); |
| 10743 | if (!names.empty()) { |
| 10744 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10745 | } |
| 10746 | // super must happen after, so that downstream can use maybe_get_output |
| 10747 | // to retrieve the output |
| 10748 | } |
| 10749 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10750 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10751 | } |
| 10752 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10753 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10754 | }; |
| 10755 | at::Tensor & wrapper_CPU__softmax_out_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 10756 | structured_softmax_cpu_out_out op(out); |
| 10757 | op.meta(self, dim, half_to_float); |
| 10758 | op.impl(self, dim, half_to_float, op.maybe_get_output(0)); |
| 10759 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10760 | return out; |
| 10761 | } |
| 10762 | struct structured_softmax_backward_cpu_out_functional final : public at::native::structured_softmax_backward_cpu_out { |
| 10763 | void set_output_strided( |
| 10764 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10765 | TensorOptions options, DimnameList names |
| 10766 | ) override { |
| 10767 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10768 | if (!names.empty()) { |
| 10769 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10770 | } |
| 10771 | // super must happen after, so that downstream can use maybe_get_output |
| 10772 | // to retrieve the output |
| 10773 | } |
| 10774 | void set_output_raw_strided( |
| 10775 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10776 | TensorOptions options, DimnameList names |
| 10777 | ) override { |
| 10778 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10779 | if (!names.empty()) { |
| 10780 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10781 | } |
| 10782 | // super must happen after, so that downstream can use maybe_get_output |
| 10783 | // to retrieve the output |
| 10784 | } |
| 10785 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10786 | return *outputs_[output_idx]; |
| 10787 | } |
| 10788 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10789 | }; |
| 10790 | at::Tensor wrapper_CPU__softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 10791 | structured_softmax_backward_cpu_out_functional op; |
| 10792 | op.meta(grad_output, output, dim, input_dtype); |
| 10793 | op.impl(grad_output, output, dim, input_dtype, *op.outputs_[0]); |
| 10794 | return std::move(op.outputs_[0]).take(); |
| 10795 | } |
| 10796 | struct structured_softmax_backward_cpu_out_out final : public at::native::structured_softmax_backward_cpu_out { |
| 10797 | structured_softmax_backward_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10798 | void set_output_strided( |
| 10799 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10800 | TensorOptions options, DimnameList names |
| 10801 | ) override { |
| 10802 | const auto& out = outputs_[output_idx].get(); |
| 10803 | resize_out(out, sizes, strides, options); |
| 10804 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10805 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10806 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10807 | } |
| 10808 | if (!names.empty()) { |
| 10809 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10810 | } |
| 10811 | // super must happen after, so that downstream can use maybe_get_output |
| 10812 | // to retrieve the output |
| 10813 | } |
| 10814 | void set_output_raw_strided( |
| 10815 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10816 | TensorOptions options, DimnameList names |
| 10817 | ) override { |
| 10818 | const auto& out = outputs_[output_idx].get(); |
| 10819 | resize_out(out, sizes, strides, options); |
| 10820 | if (!names.empty()) { |
| 10821 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10822 | } |
| 10823 | // super must happen after, so that downstream can use maybe_get_output |
| 10824 | // to retrieve the output |
| 10825 | } |
| 10826 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10827 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10828 | } |
| 10829 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10830 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10831 | }; |
| 10832 | at::Tensor & wrapper_CPU__softmax_backward_data_out_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { |
| 10833 | structured_softmax_backward_cpu_out_out op(grad_input); |
| 10834 | op.meta(grad_output, output, dim, input_dtype); |
| 10835 | op.impl(grad_output, output, dim, input_dtype, op.maybe_get_output(0)); |
| 10836 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10837 | return grad_input; |
| 10838 | } |
| 10839 | namespace { |
| 10840 | at::Tensor & wrapper_CPU_out_sspaddmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 10841 | // No device check |
| 10842 | // DeviceGuard omitted |
| 10843 | return at::native::_sspaddmm_out_only_sparse(self, mat1, mat2, beta, alpha, out); |
| 10844 | } |
| 10845 | } // anonymous namespace |
| 10846 | namespace { |
| 10847 | at::Tensor wrapper_CPU___stack(at::TensorList tensors, int64_t dim) { |
| 10848 | // No device check |
| 10849 | // DeviceGuard omitted |
| 10850 | return at::native::_stack_cpu(tensors, dim); |
| 10851 | } |
| 10852 | } // anonymous namespace |
| 10853 | namespace { |
| 10854 | at::Tensor & wrapper_CPU_out__stack_out(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 10855 | // No device check |
| 10856 | // DeviceGuard omitted |
| 10857 | return at::native::_stack_out_cpu(tensors, dim, out); |
| 10858 | } |
| 10859 | } // anonymous namespace |
| 10860 | struct structured_sum_out_functional final : public at::native::structured_sum_out { |
| 10861 | void set_output_strided( |
| 10862 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10863 | TensorOptions options, DimnameList names |
| 10864 | ) override { |
| 10865 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10866 | if (!names.empty()) { |
| 10867 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10868 | } |
| 10869 | // super must happen after, so that downstream can use maybe_get_output |
| 10870 | // to retrieve the output |
| 10871 | } |
| 10872 | void set_output_raw_strided( |
| 10873 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10874 | TensorOptions options, DimnameList names |
| 10875 | ) override { |
| 10876 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10877 | if (!names.empty()) { |
| 10878 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10879 | } |
| 10880 | // super must happen after, so that downstream can use maybe_get_output |
| 10881 | // to retrieve the output |
| 10882 | } |
| 10883 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10884 | return *outputs_[output_idx]; |
| 10885 | } |
| 10886 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10887 | }; |
| 10888 | at::Tensor wrapper_CPU_sum_dim_IntList(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 10889 | structured_sum_out_functional op; |
| 10890 | op.meta(self, dim, keepdim, dtype); |
| 10891 | op.impl(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 10892 | return std::move(op.outputs_[0]).take(); |
| 10893 | } |
| 10894 | struct structured_sum_out_out final : public at::native::structured_sum_out { |
| 10895 | structured_sum_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10896 | void set_output_strided( |
| 10897 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10898 | TensorOptions options, DimnameList names |
| 10899 | ) override { |
| 10900 | const auto& out = outputs_[output_idx].get(); |
| 10901 | resize_out(out, sizes, strides, options); |
| 10902 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10903 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10904 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10905 | } |
| 10906 | if (!names.empty()) { |
| 10907 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10908 | } |
| 10909 | // super must happen after, so that downstream can use maybe_get_output |
| 10910 | // to retrieve the output |
| 10911 | } |
| 10912 | void set_output_raw_strided( |
| 10913 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10914 | TensorOptions options, DimnameList names |
| 10915 | ) override { |
| 10916 | const auto& out = outputs_[output_idx].get(); |
| 10917 | resize_out(out, sizes, strides, options); |
| 10918 | if (!names.empty()) { |
| 10919 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10920 | } |
| 10921 | // super must happen after, so that downstream can use maybe_get_output |
| 10922 | // to retrieve the output |
| 10923 | } |
| 10924 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10925 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10926 | } |
| 10927 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10928 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10929 | }; |
| 10930 | at::Tensor & wrapper_CPU_sum_out_IntList_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 10931 | structured_sum_out_out op(out); |
| 10932 | op.meta(self, dim, keepdim, dtype); |
| 10933 | op.impl(self, dim, keepdim, dtype, op.maybe_get_output(0)); |
| 10934 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10935 | return out; |
| 10936 | } |
| 10937 | namespace { |
| 10938 | at::Tensor wrapper_CPU__nansum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 10939 | // No device check |
| 10940 | // DeviceGuard omitted |
| 10941 | return at::native::nansum(self, dim, keepdim, dtype); |
| 10942 | } |
| 10943 | } // anonymous namespace |
| 10944 | namespace { |
| 10945 | at::Tensor & wrapper_CPU_out_nansum_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 10946 | // No device check |
| 10947 | // DeviceGuard omitted |
| 10948 | return at::native::nansum_out(self, dim, keepdim, dtype, out); |
| 10949 | } |
| 10950 | } // anonymous namespace |
| 10951 | struct structured_sqrt_out_functional final : public at::native::structured_sqrt_out { |
| 10952 | void set_output_strided( |
| 10953 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10954 | TensorOptions options, DimnameList names |
| 10955 | ) override { |
| 10956 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10957 | if (!names.empty()) { |
| 10958 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10959 | } |
| 10960 | // super must happen after, so that downstream can use maybe_get_output |
| 10961 | // to retrieve the output |
| 10962 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10963 | } |
| 10964 | void set_output_raw_strided( |
| 10965 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10966 | TensorOptions options, DimnameList names |
| 10967 | ) override { |
| 10968 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10969 | if (!names.empty()) { |
| 10970 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10971 | } |
| 10972 | // super must happen after, so that downstream can use maybe_get_output |
| 10973 | // to retrieve the output |
| 10974 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10975 | } |
| 10976 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10977 | return *outputs_[output_idx]; |
| 10978 | } |
| 10979 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10980 | }; |
| 10981 | at::Tensor wrapper_CPU_sqrt(const at::Tensor & self) { |
| 10982 | structured_sqrt_out_functional op; |
| 10983 | op.meta(self); |
| 10984 | op.impl(self, *op.outputs_[0]); |
| 10985 | return std::move(op.outputs_[0]).take(); |
| 10986 | } |
| 10987 | struct structured_sqrt_out_out final : public at::native::structured_sqrt_out { |
| 10988 | structured_sqrt_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 10989 | void set_output_strided( |
| 10990 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10991 | TensorOptions options, DimnameList names |
| 10992 | ) override { |
| 10993 | const auto& out = outputs_[output_idx].get(); |
| 10994 | resize_out(out, sizes, strides, options); |
| 10995 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10996 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10997 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10998 | } |
| 10999 | if (!names.empty()) { |
| 11000 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11001 | } |
| 11002 | // super must happen after, so that downstream can use maybe_get_output |
| 11003 | // to retrieve the output |
| 11004 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11005 | } |
| 11006 | void set_output_raw_strided( |
| 11007 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11008 | TensorOptions options, DimnameList names |
| 11009 | ) override { |
| 11010 | const auto& out = outputs_[output_idx].get(); |
| 11011 | resize_out(out, sizes, strides, options); |
| 11012 | if (!names.empty()) { |
| 11013 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11014 | } |
| 11015 | // super must happen after, so that downstream can use maybe_get_output |
| 11016 | // to retrieve the output |
| 11017 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11018 | } |
| 11019 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11020 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11021 | } |
| 11022 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11023 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11024 | }; |
| 11025 | at::Tensor & wrapper_CPU_sqrt_out_out(const at::Tensor & self, at::Tensor & out) { |
| 11026 | structured_sqrt_out_out op(out); |
| 11027 | op.meta(self); |
| 11028 | op.impl(self, op.maybe_get_output(0)); |
| 11029 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11030 | return out; |
| 11031 | } |
| 11032 | struct structured_sqrt_out_inplace final : public at::native::structured_sqrt_out { |
| 11033 | structured_sqrt_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11034 | void set_output_strided( |
| 11035 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11036 | TensorOptions options, DimnameList names |
| 11037 | ) override { |
| 11038 | const auto& out = outputs_[output_idx].get(); |
| 11039 | check_inplace(out, sizes, options); |
| 11040 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11041 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11042 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11043 | } |
| 11044 | if (!names.empty()) { |
| 11045 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11046 | } |
| 11047 | // super must happen after, so that downstream can use maybe_get_output |
| 11048 | // to retrieve the output |
| 11049 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11050 | } |
| 11051 | void set_output_raw_strided( |
| 11052 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11053 | TensorOptions options, DimnameList names |
| 11054 | ) override { |
| 11055 | const auto& out = outputs_[output_idx].get(); |
| 11056 | check_inplace(out, sizes, options); |
| 11057 | if (!names.empty()) { |
| 11058 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11059 | } |
| 11060 | // super must happen after, so that downstream can use maybe_get_output |
| 11061 | // to retrieve the output |
| 11062 | at::native::structured_sqrt_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11063 | } |
| 11064 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11065 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11066 | } |
| 11067 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11068 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11069 | }; |
| 11070 | at::Tensor & wrapper_CPU_sqrt_(at::Tensor & self) { |
| 11071 | structured_sqrt_out_inplace op(self); |
| 11072 | op.meta(self); |
| 11073 | op.impl(self, op.outputs_[0]); |
| 11074 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11075 | return self; |
| 11076 | } |
| 11077 | namespace { |
| 11078 | at::Tensor wrapper_CPU_correction_std(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 11079 | // No device check |
| 11080 | // DeviceGuard omitted |
| 11081 | return at::native::std(self, dim, correction, keepdim); |
| 11082 | } |
| 11083 | } // anonymous namespace |
| 11084 | namespace { |
| 11085 | at::Tensor & wrapper_CPU_correction_out_std_out(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 11086 | // No device check |
| 11087 | // DeviceGuard omitted |
| 11088 | return at::native::std_out(self, dim, correction, keepdim, out); |
| 11089 | } |
| 11090 | } // anonymous namespace |
| 11091 | namespace { |
| 11092 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_correction_std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 11093 | // No device check |
| 11094 | // DeviceGuard omitted |
| 11095 | return at::native::std_mean(self, dim, correction, keepdim); |
| 11096 | } |
| 11097 | } // anonymous namespace |
| 11098 | namespace { |
| 11099 | at::Tensor wrapper_CPU__prod(const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 11100 | // No device check |
| 11101 | // DeviceGuard omitted |
| 11102 | return at::native::prod(self, dtype); |
| 11103 | } |
| 11104 | } // anonymous namespace |
| 11105 | struct structured_prod_out_functional final : public at::native::structured_prod_out { |
| 11106 | void set_output_strided( |
| 11107 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11108 | TensorOptions options, DimnameList names |
| 11109 | ) override { |
| 11110 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11111 | if (!names.empty()) { |
| 11112 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11113 | } |
| 11114 | // super must happen after, so that downstream can use maybe_get_output |
| 11115 | // to retrieve the output |
| 11116 | } |
| 11117 | void set_output_raw_strided( |
| 11118 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11119 | TensorOptions options, DimnameList names |
| 11120 | ) override { |
| 11121 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11122 | if (!names.empty()) { |
| 11123 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11124 | } |
| 11125 | // super must happen after, so that downstream can use maybe_get_output |
| 11126 | // to retrieve the output |
| 11127 | } |
| 11128 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11129 | return *outputs_[output_idx]; |
| 11130 | } |
| 11131 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11132 | }; |
| 11133 | at::Tensor wrapper_CPU_prod_dim_int(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 11134 | structured_prod_out_functional op; |
| 11135 | op.meta(self, dim, keepdim, dtype); |
| 11136 | op.impl(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 11137 | return std::move(op.outputs_[0]).take(); |
| 11138 | } |
| 11139 | struct structured_prod_out_out final : public at::native::structured_prod_out { |
| 11140 | structured_prod_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11141 | void set_output_strided( |
| 11142 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11143 | TensorOptions options, DimnameList names |
| 11144 | ) override { |
| 11145 | const auto& out = outputs_[output_idx].get(); |
| 11146 | resize_out(out, sizes, strides, options); |
| 11147 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11148 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11149 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11150 | } |
| 11151 | if (!names.empty()) { |
| 11152 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11153 | } |
| 11154 | // super must happen after, so that downstream can use maybe_get_output |
| 11155 | // to retrieve the output |
| 11156 | } |
| 11157 | void set_output_raw_strided( |
| 11158 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11159 | TensorOptions options, DimnameList names |
| 11160 | ) override { |
| 11161 | const auto& out = outputs_[output_idx].get(); |
| 11162 | resize_out(out, sizes, strides, options); |
| 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 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11170 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11171 | } |
| 11172 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11173 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11174 | }; |
| 11175 | at::Tensor & wrapper_CPU_prod_out_int_out(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 11176 | structured_prod_out_out op(out); |
| 11177 | op.meta(self, dim, keepdim, dtype); |
| 11178 | op.impl(self, dim, keepdim, dtype, op.maybe_get_output(0)); |
| 11179 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11180 | return out; |
| 11181 | } |
| 11182 | struct structured_tan_out_functional final : public at::native::structured_tan_out { |
| 11183 | void set_output_strided( |
| 11184 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11185 | TensorOptions options, DimnameList names |
| 11186 | ) override { |
| 11187 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11188 | if (!names.empty()) { |
| 11189 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11190 | } |
| 11191 | // super must happen after, so that downstream can use maybe_get_output |
| 11192 | // to retrieve the output |
| 11193 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11194 | } |
| 11195 | void set_output_raw_strided( |
| 11196 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11197 | TensorOptions options, DimnameList names |
| 11198 | ) override { |
| 11199 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11200 | if (!names.empty()) { |
| 11201 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11202 | } |
| 11203 | // super must happen after, so that downstream can use maybe_get_output |
| 11204 | // to retrieve the output |
| 11205 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11206 | } |
| 11207 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11208 | return *outputs_[output_idx]; |
| 11209 | } |
| 11210 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11211 | }; |
| 11212 | at::Tensor wrapper_CPU_tan(const at::Tensor & self) { |
| 11213 | structured_tan_out_functional op; |
| 11214 | op.meta(self); |
| 11215 | op.impl(self, *op.outputs_[0]); |
| 11216 | return std::move(op.outputs_[0]).take(); |
| 11217 | } |
| 11218 | struct structured_tan_out_out final : public at::native::structured_tan_out { |
| 11219 | structured_tan_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11220 | void set_output_strided( |
| 11221 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11222 | TensorOptions options, DimnameList names |
| 11223 | ) override { |
| 11224 | const auto& out = outputs_[output_idx].get(); |
| 11225 | resize_out(out, sizes, strides, options); |
| 11226 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11227 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11228 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11229 | } |
| 11230 | if (!names.empty()) { |
| 11231 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11232 | } |
| 11233 | // super must happen after, so that downstream can use maybe_get_output |
| 11234 | // to retrieve the output |
| 11235 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11236 | } |
| 11237 | void set_output_raw_strided( |
| 11238 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11239 | TensorOptions options, DimnameList names |
| 11240 | ) override { |
| 11241 | const auto& out = outputs_[output_idx].get(); |
| 11242 | resize_out(out, sizes, strides, options); |
| 11243 | if (!names.empty()) { |
| 11244 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11245 | } |
| 11246 | // super must happen after, so that downstream can use maybe_get_output |
| 11247 | // to retrieve the output |
| 11248 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11249 | } |
| 11250 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11251 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11252 | } |
| 11253 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11254 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11255 | }; |
| 11256 | at::Tensor & wrapper_CPU_tan_out_out(const at::Tensor & self, at::Tensor & out) { |
| 11257 | structured_tan_out_out op(out); |
| 11258 | op.meta(self); |
| 11259 | op.impl(self, op.maybe_get_output(0)); |
| 11260 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11261 | return out; |
| 11262 | } |
| 11263 | struct structured_tan_out_inplace final : public at::native::structured_tan_out { |
| 11264 | structured_tan_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11265 | void set_output_strided( |
| 11266 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11267 | TensorOptions options, DimnameList names |
| 11268 | ) override { |
| 11269 | const auto& out = outputs_[output_idx].get(); |
| 11270 | check_inplace(out, sizes, options); |
| 11271 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11272 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11273 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11274 | } |
| 11275 | if (!names.empty()) { |
| 11276 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11277 | } |
| 11278 | // super must happen after, so that downstream can use maybe_get_output |
| 11279 | // to retrieve the output |
| 11280 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11281 | } |
| 11282 | void set_output_raw_strided( |
| 11283 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11284 | TensorOptions options, DimnameList names |
| 11285 | ) override { |
| 11286 | const auto& out = outputs_[output_idx].get(); |
| 11287 | check_inplace(out, sizes, options); |
| 11288 | if (!names.empty()) { |
| 11289 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11290 | } |
| 11291 | // super must happen after, so that downstream can use maybe_get_output |
| 11292 | // to retrieve the output |
| 11293 | at::native::structured_tan_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 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 | }; |
| 11301 | at::Tensor & wrapper_CPU_tan_(at::Tensor & self) { |
| 11302 | structured_tan_out_inplace op(self); |
| 11303 | op.meta(self); |
| 11304 | op.impl(self, op.outputs_[0]); |
| 11305 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11306 | return self; |
| 11307 | } |
| 11308 | struct structured_tanh_out_functional final : public at::native::structured_tanh_out { |
| 11309 | void set_output_strided( |
| 11310 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11311 | TensorOptions options, DimnameList names |
| 11312 | ) override { |
| 11313 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11314 | if (!names.empty()) { |
| 11315 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11316 | } |
| 11317 | // super must happen after, so that downstream can use maybe_get_output |
| 11318 | // to retrieve the output |
| 11319 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11320 | } |
| 11321 | void set_output_raw_strided( |
| 11322 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11323 | TensorOptions options, DimnameList names |
| 11324 | ) override { |
| 11325 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11326 | if (!names.empty()) { |
| 11327 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11328 | } |
| 11329 | // super must happen after, so that downstream can use maybe_get_output |
| 11330 | // to retrieve the output |
| 11331 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11332 | } |
| 11333 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11334 | return *outputs_[output_idx]; |
| 11335 | } |
| 11336 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11337 | }; |
| 11338 | at::Tensor wrapper_CPU_tanh(const at::Tensor & self) { |
| 11339 | structured_tanh_out_functional op; |
| 11340 | op.meta(self); |
| 11341 | op.impl(self, *op.outputs_[0]); |
| 11342 | return std::move(op.outputs_[0]).take(); |
| 11343 | } |
| 11344 | struct structured_tanh_out_out final : public at::native::structured_tanh_out { |
| 11345 | structured_tanh_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11346 | void set_output_strided( |
| 11347 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11348 | TensorOptions options, DimnameList names |
| 11349 | ) override { |
| 11350 | const auto& out = outputs_[output_idx].get(); |
| 11351 | resize_out(out, sizes, strides, options); |
| 11352 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11353 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11354 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11355 | } |
| 11356 | if (!names.empty()) { |
| 11357 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11358 | } |
| 11359 | // super must happen after, so that downstream can use maybe_get_output |
| 11360 | // to retrieve the output |
| 11361 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11362 | } |
| 11363 | void set_output_raw_strided( |
| 11364 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11365 | TensorOptions options, DimnameList names |
| 11366 | ) override { |
| 11367 | const auto& out = outputs_[output_idx].get(); |
| 11368 | resize_out(out, sizes, strides, options); |
| 11369 | if (!names.empty()) { |
| 11370 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11371 | } |
| 11372 | // super must happen after, so that downstream can use maybe_get_output |
| 11373 | // to retrieve the output |
| 11374 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11375 | } |
| 11376 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11377 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11378 | } |
| 11379 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11380 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11381 | }; |
| 11382 | at::Tensor & wrapper_CPU_tanh_out_out(const at::Tensor & self, at::Tensor & out) { |
| 11383 | structured_tanh_out_out op(out); |
| 11384 | op.meta(self); |
| 11385 | op.impl(self, op.maybe_get_output(0)); |
| 11386 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11387 | return out; |
| 11388 | } |
| 11389 | struct structured_tanh_out_inplace final : public at::native::structured_tanh_out { |
| 11390 | structured_tanh_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11391 | void set_output_strided( |
| 11392 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11393 | TensorOptions options, DimnameList names |
| 11394 | ) override { |
| 11395 | const auto& out = outputs_[output_idx].get(); |
| 11396 | check_inplace(out, sizes, options); |
| 11397 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11398 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11399 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11400 | } |
| 11401 | if (!names.empty()) { |
| 11402 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11403 | } |
| 11404 | // super must happen after, so that downstream can use maybe_get_output |
| 11405 | // to retrieve the output |
| 11406 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11407 | } |
| 11408 | void set_output_raw_strided( |
| 11409 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11410 | TensorOptions options, DimnameList names |
| 11411 | ) override { |
| 11412 | const auto& out = outputs_[output_idx].get(); |
| 11413 | check_inplace(out, sizes, options); |
| 11414 | if (!names.empty()) { |
| 11415 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11416 | } |
| 11417 | // super must happen after, so that downstream can use maybe_get_output |
| 11418 | // to retrieve the output |
| 11419 | at::native::structured_tanh_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11420 | } |
| 11421 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11422 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11423 | } |
| 11424 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11425 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11426 | }; |
| 11427 | at::Tensor & wrapper_CPU_tanh_(at::Tensor & self) { |
| 11428 | structured_tanh_out_inplace op(self); |
| 11429 | op.meta(self); |
| 11430 | op.impl(self, op.outputs_[0]); |
| 11431 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11432 | return self; |
| 11433 | } |
| 11434 | namespace { |
| 11435 | at::Tensor & wrapper_CPU_out_tensordot_out(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { |
| 11436 | // No device check |
| 11437 | // DeviceGuard omitted |
| 11438 | return at::native::tensordot_out(self, other, dims_self, dims_other, out); |
| 11439 | } |
| 11440 | } // anonymous namespace |
| 11441 | struct structured_threshold_out_functional final : public at::native::structured_threshold_out { |
| 11442 | void set_output_strided( |
| 11443 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11444 | TensorOptions options, DimnameList names |
| 11445 | ) override { |
| 11446 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11447 | if (!names.empty()) { |
| 11448 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11449 | } |
| 11450 | // super must happen after, so that downstream can use maybe_get_output |
| 11451 | // to retrieve the output |
| 11452 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11453 | } |
| 11454 | void set_output_raw_strided( |
| 11455 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11456 | TensorOptions options, DimnameList names |
| 11457 | ) override { |
| 11458 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11459 | if (!names.empty()) { |
| 11460 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11461 | } |
| 11462 | // super must happen after, so that downstream can use maybe_get_output |
| 11463 | // to retrieve the output |
| 11464 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11465 | } |
| 11466 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11467 | return *outputs_[output_idx]; |
| 11468 | } |
| 11469 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11470 | }; |
| 11471 | at::Tensor wrapper_CPU_threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 11472 | structured_threshold_out_functional op; |
| 11473 | op.meta(self, threshold, value); |
| 11474 | op.impl(self, threshold, value, *op.outputs_[0]); |
| 11475 | return std::move(op.outputs_[0]).take(); |
| 11476 | } |
| 11477 | struct structured_threshold_out_out final : public at::native::structured_threshold_out { |
| 11478 | structured_threshold_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11479 | void set_output_strided( |
| 11480 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11481 | TensorOptions options, DimnameList names |
| 11482 | ) override { |
| 11483 | const auto& out = outputs_[output_idx].get(); |
| 11484 | resize_out(out, sizes, strides, options); |
| 11485 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11486 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11487 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11488 | } |
| 11489 | if (!names.empty()) { |
| 11490 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11491 | } |
| 11492 | // super must happen after, so that downstream can use maybe_get_output |
| 11493 | // to retrieve the output |
| 11494 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11495 | } |
| 11496 | void set_output_raw_strided( |
| 11497 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11498 | TensorOptions options, DimnameList names |
| 11499 | ) override { |
| 11500 | const auto& out = outputs_[output_idx].get(); |
| 11501 | resize_out(out, sizes, strides, options); |
| 11502 | if (!names.empty()) { |
| 11503 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11504 | } |
| 11505 | // super must happen after, so that downstream can use maybe_get_output |
| 11506 | // to retrieve the output |
| 11507 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11508 | } |
| 11509 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11510 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11511 | } |
| 11512 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11513 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11514 | }; |
| 11515 | at::Tensor & wrapper_CPU_threshold_out_out(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { |
| 11516 | structured_threshold_out_out op(out); |
| 11517 | op.meta(self, threshold, value); |
| 11518 | op.impl(self, threshold, value, op.maybe_get_output(0)); |
| 11519 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11520 | return out; |
| 11521 | } |
| 11522 | struct structured_threshold_out_inplace final : public at::native::structured_threshold_out { |
| 11523 | structured_threshold_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11524 | void set_output_strided( |
| 11525 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11526 | TensorOptions options, DimnameList names |
| 11527 | ) override { |
| 11528 | const auto& out = outputs_[output_idx].get(); |
| 11529 | check_inplace(out, sizes, options); |
| 11530 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11531 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11532 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11533 | } |
| 11534 | if (!names.empty()) { |
| 11535 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11536 | } |
| 11537 | // super must happen after, so that downstream can use maybe_get_output |
| 11538 | // to retrieve the output |
| 11539 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11540 | } |
| 11541 | void set_output_raw_strided( |
| 11542 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11543 | TensorOptions options, DimnameList names |
| 11544 | ) override { |
| 11545 | const auto& out = outputs_[output_idx].get(); |
| 11546 | check_inplace(out, sizes, options); |
| 11547 | if (!names.empty()) { |
| 11548 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11549 | } |
| 11550 | // super must happen after, so that downstream can use maybe_get_output |
| 11551 | // to retrieve the output |
| 11552 | at::native::structured_threshold_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11553 | } |
| 11554 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11555 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11556 | } |
| 11557 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11558 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11559 | }; |
| 11560 | at::Tensor & wrapper_CPU_threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 11561 | structured_threshold_out_inplace op(self); |
| 11562 | op.meta(self, threshold, value); |
| 11563 | op.impl(self, threshold, value, op.outputs_[0]); |
| 11564 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11565 | return self; |
| 11566 | } |
| 11567 | struct structured_threshold_backward_out_functional final : public at::native::structured_threshold_backward_out { |
| 11568 | void set_output_strided( |
| 11569 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11570 | TensorOptions options, DimnameList names |
| 11571 | ) override { |
| 11572 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11573 | if (!names.empty()) { |
| 11574 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11575 | } |
| 11576 | // super must happen after, so that downstream can use maybe_get_output |
| 11577 | // to retrieve the output |
| 11578 | at::native::structured_threshold_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11579 | } |
| 11580 | void set_output_raw_strided( |
| 11581 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11582 | TensorOptions options, DimnameList names |
| 11583 | ) override { |
| 11584 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11585 | if (!names.empty()) { |
| 11586 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11587 | } |
| 11588 | // super must happen after, so that downstream can use maybe_get_output |
| 11589 | // to retrieve the output |
| 11590 | at::native::structured_threshold_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11591 | } |
| 11592 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11593 | return *outputs_[output_idx]; |
| 11594 | } |
| 11595 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11596 | }; |
| 11597 | at::Tensor wrapper_CPU_threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { |
| 11598 | structured_threshold_backward_out_functional op; |
| 11599 | op.meta(grad_output, self, threshold); |
| 11600 | op.impl(grad_output, self, threshold, *op.outputs_[0]); |
| 11601 | return std::move(op.outputs_[0]).take(); |
| 11602 | } |
| 11603 | struct structured_threshold_backward_out_out final : public at::native::structured_threshold_backward_out { |
| 11604 | structured_threshold_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11605 | void set_output_strided( |
| 11606 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11607 | TensorOptions options, DimnameList names |
| 11608 | ) override { |
| 11609 | const auto& out = outputs_[output_idx].get(); |
| 11610 | resize_out(out, sizes, strides, options); |
| 11611 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11612 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11613 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11614 | } |
| 11615 | if (!names.empty()) { |
| 11616 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11617 | } |
| 11618 | // super must happen after, so that downstream can use maybe_get_output |
| 11619 | // to retrieve the output |
| 11620 | at::native::structured_threshold_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11621 | } |
| 11622 | void set_output_raw_strided( |
| 11623 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11624 | TensorOptions options, DimnameList names |
| 11625 | ) override { |
| 11626 | const auto& out = outputs_[output_idx].get(); |
| 11627 | resize_out(out, sizes, strides, options); |
| 11628 | if (!names.empty()) { |
| 11629 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11630 | } |
| 11631 | // super must happen after, so that downstream can use maybe_get_output |
| 11632 | // to retrieve the output |
| 11633 | at::native::structured_threshold_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11634 | } |
| 11635 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11636 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11637 | } |
| 11638 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11639 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11640 | }; |
| 11641 | at::Tensor & wrapper_CPU_threshold_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input) { |
| 11642 | structured_threshold_backward_out_out op(grad_input); |
| 11643 | op.meta(grad_output, self, threshold); |
| 11644 | op.impl(grad_output, self, threshold, op.maybe_get_output(0)); |
| 11645 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11646 | return grad_input; |
| 11647 | } |
| 11648 | namespace { |
| 11649 | at::Tensor wrapper_CPU__flip(const at::Tensor & self, at::IntArrayRef dims) { |
| 11650 | // No device check |
| 11651 | // DeviceGuard omitted |
| 11652 | return at::native::flip(self, dims); |
| 11653 | } |
| 11654 | } // anonymous namespace |
| 11655 | namespace { |
| 11656 | at::Tensor wrapper_CPU__roll(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims) { |
| 11657 | // No device check |
| 11658 | // DeviceGuard omitted |
| 11659 | return at::native::roll_cpu(self, shifts, dims); |
| 11660 | } |
| 11661 | } // anonymous namespace |
| 11662 | namespace { |
| 11663 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___transform_bias_rescale_qkv(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) { |
| 11664 | // No device check |
| 11665 | // DeviceGuard omitted |
| 11666 | return at::native::transform_bias_rescale_qkv_cpu(qkv, qkv_bias, num_heads); |
| 11667 | } |
| 11668 | } // anonymous namespace |
| 11669 | namespace { |
| 11670 | at::Tensor wrapper_CPU___nested_tensor_from_mask(const at::Tensor & t, const at::Tensor & mask, bool mask_check) { |
| 11671 | // No device check |
| 11672 | // DeviceGuard omitted |
| 11673 | return at::native::NestedTensor_nested_tensor_from_mask(t, mask, mask_check); |
| 11674 | } |
| 11675 | } // anonymous namespace |
| 11676 | namespace { |
| 11677 | bool wrapper_CPU___nested_tensor_from_mask_left_aligned(const at::Tensor & t, const at::Tensor & mask) { |
| 11678 | // No device check |
| 11679 | // DeviceGuard omitted |
| 11680 | return at::native::NestedTensor_nested_tensor_from_mask_left_aligned(t, mask); |
| 11681 | } |
| 11682 | } // anonymous namespace |
| 11683 | namespace { |
| 11684 | at::Tensor wrapper_CPU___nested_from_padded(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) { |
| 11685 | // No device check |
| 11686 | // DeviceGuard omitted |
| 11687 | return at::native::nested_from_padded_generic(padded, cpu_nested_shape_example, fuse_transform_0213); |
| 11688 | } |
| 11689 | } // anonymous namespace |
| 11690 | namespace { |
| 11691 | at::Tensor wrapper_CPU___nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 11692 | // No device check |
| 11693 | // DeviceGuard omitted |
| 11694 | return at::native::_nested_view_from_buffer(self, nested_size, nested_strides, offsets); |
| 11695 | } |
| 11696 | } // anonymous namespace |
| 11697 | struct structured_trunc_out_functional final : public at::native::structured_trunc_out { |
| 11698 | void set_output_strided( |
| 11699 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11700 | TensorOptions options, DimnameList names |
| 11701 | ) override { |
| 11702 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11703 | if (!names.empty()) { |
| 11704 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11705 | } |
| 11706 | // super must happen after, so that downstream can use maybe_get_output |
| 11707 | // to retrieve the output |
| 11708 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11709 | } |
| 11710 | void set_output_raw_strided( |
| 11711 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11712 | TensorOptions options, DimnameList names |
| 11713 | ) override { |
| 11714 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11715 | if (!names.empty()) { |
| 11716 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11717 | } |
| 11718 | // super must happen after, so that downstream can use maybe_get_output |
| 11719 | // to retrieve the output |
| 11720 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11721 | } |
| 11722 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11723 | return *outputs_[output_idx]; |
| 11724 | } |
| 11725 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11726 | }; |
| 11727 | at::Tensor wrapper_CPU_trunc(const at::Tensor & self) { |
| 11728 | structured_trunc_out_functional op; |
| 11729 | op.meta(self); |
| 11730 | op.impl(self, *op.outputs_[0]); |
| 11731 | return std::move(op.outputs_[0]).take(); |
| 11732 | } |
| 11733 | struct structured_trunc_out_out final : public at::native::structured_trunc_out { |
| 11734 | structured_trunc_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11735 | void set_output_strided( |
| 11736 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11737 | TensorOptions options, DimnameList names |
| 11738 | ) override { |
| 11739 | const auto& out = outputs_[output_idx].get(); |
| 11740 | resize_out(out, sizes, strides, options); |
| 11741 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11742 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11743 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11744 | } |
| 11745 | if (!names.empty()) { |
| 11746 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11747 | } |
| 11748 | // super must happen after, so that downstream can use maybe_get_output |
| 11749 | // to retrieve the output |
| 11750 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11751 | } |
| 11752 | void set_output_raw_strided( |
| 11753 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11754 | TensorOptions options, DimnameList names |
| 11755 | ) override { |
| 11756 | const auto& out = outputs_[output_idx].get(); |
| 11757 | resize_out(out, sizes, strides, options); |
| 11758 | if (!names.empty()) { |
| 11759 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11760 | } |
| 11761 | // super must happen after, so that downstream can use maybe_get_output |
| 11762 | // to retrieve the output |
| 11763 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11764 | } |
| 11765 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11766 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11767 | } |
| 11768 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11769 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11770 | }; |
| 11771 | at::Tensor & wrapper_CPU_trunc_out_out(const at::Tensor & self, at::Tensor & out) { |
| 11772 | structured_trunc_out_out op(out); |
| 11773 | op.meta(self); |
| 11774 | op.impl(self, op.maybe_get_output(0)); |
| 11775 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11776 | return out; |
| 11777 | } |
| 11778 | struct structured_trunc_out_inplace final : public at::native::structured_trunc_out { |
| 11779 | structured_trunc_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11780 | void set_output_strided( |
| 11781 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11782 | TensorOptions options, DimnameList names |
| 11783 | ) override { |
| 11784 | const auto& out = outputs_[output_idx].get(); |
| 11785 | check_inplace(out, sizes, options); |
| 11786 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11787 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11788 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11789 | } |
| 11790 | if (!names.empty()) { |
| 11791 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11792 | } |
| 11793 | // super must happen after, so that downstream can use maybe_get_output |
| 11794 | // to retrieve the output |
| 11795 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11796 | } |
| 11797 | void set_output_raw_strided( |
| 11798 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11799 | TensorOptions options, DimnameList names |
| 11800 | ) override { |
| 11801 | const auto& out = outputs_[output_idx].get(); |
| 11802 | check_inplace(out, sizes, options); |
| 11803 | if (!names.empty()) { |
| 11804 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11805 | } |
| 11806 | // super must happen after, so that downstream can use maybe_get_output |
| 11807 | // to retrieve the output |
| 11808 | at::native::structured_trunc_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11809 | } |
| 11810 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11811 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11812 | } |
| 11813 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11814 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11815 | }; |
| 11816 | at::Tensor & wrapper_CPU_trunc_(at::Tensor & self) { |
| 11817 | structured_trunc_out_inplace op(self); |
| 11818 | op.meta(self); |
| 11819 | op.impl(self, op.outputs_[0]); |
| 11820 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11821 | return self; |
| 11822 | } |
| 11823 | namespace { |
| 11824 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___unique(const at::Tensor & self, bool sorted, bool return_inverse) { |
| 11825 | // No device check |
| 11826 | // DeviceGuard omitted |
| 11827 | return at::native::_unique_cpu(self, sorted, return_inverse); |
| 11828 | } |
| 11829 | } // anonymous namespace |
| 11830 | namespace { |
| 11831 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__unique_dim(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { |
| 11832 | // No device check |
| 11833 | // DeviceGuard omitted |
| 11834 | return at::native::unique_dim_cpu(self, dim, sorted, return_inverse, return_counts); |
| 11835 | } |
| 11836 | } // anonymous namespace |
| 11837 | namespace { |
| 11838 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__unique_consecutive(const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim) { |
| 11839 | // No device check |
| 11840 | // DeviceGuard omitted |
| 11841 | return at::native::unique_consecutive_cpu(self, return_inverse, return_counts, dim); |
| 11842 | } |
| 11843 | } // anonymous namespace |
| 11844 | namespace { |
| 11845 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts) { |
| 11846 | // No device check |
| 11847 | // DeviceGuard omitted |
| 11848 | return at::native::unique_dim_consecutive_cpu(self, dim, return_inverse, return_counts); |
| 11849 | } |
| 11850 | } // anonymous namespace |
| 11851 | namespace { |
| 11852 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___unique2(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) { |
| 11853 | // No device check |
| 11854 | // DeviceGuard omitted |
| 11855 | return at::native::_unique2_cpu(self, sorted, return_inverse, return_counts); |
| 11856 | } |
| 11857 | } // anonymous namespace |
| 11858 | namespace { |
| 11859 | at::Tensor wrapper_CPU_correction_var(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 11860 | // No device check |
| 11861 | // DeviceGuard omitted |
| 11862 | return at::native::var(self, dim, correction, keepdim); |
| 11863 | } |
| 11864 | } // anonymous namespace |
| 11865 | namespace { |
| 11866 | at::Tensor & wrapper_CPU_correction_out_var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 11867 | // No device check |
| 11868 | // DeviceGuard omitted |
| 11869 | return at::native::var_out(self, dim, correction, keepdim, out); |
| 11870 | } |
| 11871 | } // anonymous namespace |
| 11872 | namespace { |
| 11873 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_correction_var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 11874 | // No device check |
| 11875 | // DeviceGuard omitted |
| 11876 | return at::native::var_mean(self, dim, correction, keepdim); |
| 11877 | } |
| 11878 | } // anonymous namespace |
| 11879 | namespace { |
| 11880 | at::Tensor wrapper_CPU_self_where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { |
| 11881 | // No device check |
| 11882 | // DeviceGuard omitted |
| 11883 | return at::native::where(condition, self, other); |
| 11884 | } |
| 11885 | } // anonymous namespace |
| 11886 | namespace { |
| 11887 | at::Tensor & wrapper_CPU_self_out_where_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11888 | // No device check |
| 11889 | // DeviceGuard omitted |
| 11890 | return at::native::where_self_out(condition, self, other, out); |
| 11891 | } |
| 11892 | } // anonymous namespace |
| 11893 | namespace { |
| 11894 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___weight_norm_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim) { |
| 11895 | // No device check |
| 11896 | // DeviceGuard omitted |
| 11897 | return at::native::weight_norm_cpu(v, g, dim); |
| 11898 | } |
| 11899 | } // anonymous namespace |
| 11900 | namespace { |
| 11901 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___weight_norm_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { |
| 11902 | // No device check |
| 11903 | // DeviceGuard omitted |
| 11904 | return at::native::weight_norm_backward_cpu(grad_w, saved_v, saved_g, saved_norms, dim); |
| 11905 | } |
| 11906 | } // anonymous namespace |
| 11907 | namespace { |
| 11908 | at::Tensor wrapper_CPU___efficientzerotensor(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 11909 | // No device check |
| 11910 | // DeviceGuard omitted |
| 11911 | return at::native::_efficientzerotensor(size, dtype, layout, device, pin_memory); |
| 11912 | } |
| 11913 | } // anonymous namespace |
| 11914 | namespace { |
| 11915 | at::Tensor wrapper_CPU___standard_gamma_grad(const at::Tensor & self, const at::Tensor & output) { |
| 11916 | // No device check |
| 11917 | // DeviceGuard omitted |
| 11918 | return at::native::_standard_gamma_grad_cpu(self, output); |
| 11919 | } |
| 11920 | } // anonymous namespace |
| 11921 | namespace { |
| 11922 | at::Tensor wrapper_CPU___standard_gamma(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 11923 | // No device check |
| 11924 | // DeviceGuard omitted |
| 11925 | return at::native::_s_gamma_cpu(self, generator); |
| 11926 | } |
| 11927 | } // anonymous namespace |
| 11928 | namespace { |
| 11929 | at::Tensor wrapper_CPU___dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { |
| 11930 | // No device check |
| 11931 | // DeviceGuard omitted |
| 11932 | return at::native::_dirichlet_grad_cpu(x, alpha, total); |
| 11933 | } |
| 11934 | } // anonymous namespace |
| 11935 | namespace { |
| 11936 | at::Tensor wrapper_CPU___sample_dirichlet(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 11937 | // No device check |
| 11938 | // DeviceGuard omitted |
| 11939 | return at::native::_s_dirichlet_cpu(self, generator); |
| 11940 | } |
| 11941 | } // anonymous namespace |
| 11942 | namespace { |
| 11943 | at::Tensor wrapper_CPU__poisson(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 11944 | // No device check |
| 11945 | // DeviceGuard omitted |
| 11946 | return at::native::_s_poisson_cpu(self, generator); |
| 11947 | } |
| 11948 | } // anonymous namespace |
| 11949 | namespace { |
| 11950 | at::Tensor wrapper_CPU__binomial(const at::Tensor & count, const at::Tensor & prob, c10::optional<at::Generator> generator) { |
| 11951 | // No device check |
| 11952 | // DeviceGuard omitted |
| 11953 | return at::native::_s_binomial_cpu(count, prob, generator); |
| 11954 | } |
| 11955 | } // anonymous namespace |
| 11956 | namespace { |
| 11957 | at::Tensor wrapper_CPU___spdiags(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout) { |
| 11958 | // No device check |
| 11959 | // DeviceGuard omitted |
| 11960 | return at::native::spdiags(diagonals, offsets, shape, layout); |
| 11961 | } |
| 11962 | } // anonymous namespace |
| 11963 | struct structured_norm_dtype_out_functional final : public at::native::structured_norm_dtype_out { |
| 11964 | void set_output_strided( |
| 11965 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11966 | TensorOptions options, DimnameList names |
| 11967 | ) override { |
| 11968 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11969 | if (!names.empty()) { |
| 11970 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11971 | } |
| 11972 | // super must happen after, so that downstream can use maybe_get_output |
| 11973 | // to retrieve the output |
| 11974 | } |
| 11975 | void set_output_raw_strided( |
| 11976 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11977 | TensorOptions options, DimnameList names |
| 11978 | ) override { |
| 11979 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11980 | if (!names.empty()) { |
| 11981 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11982 | } |
| 11983 | // super must happen after, so that downstream can use maybe_get_output |
| 11984 | // to retrieve the output |
| 11985 | } |
| 11986 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11987 | return *outputs_[output_idx]; |
| 11988 | } |
| 11989 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11990 | }; |
| 11991 | at::Tensor wrapper_CPU_norm_ScalarOpt_dim_dtype(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { |
| 11992 | structured_norm_dtype_out_functional op; |
| 11993 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, dtype); |
| 11994 | op.impl(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, dtype, *op.outputs_[0]); |
| 11995 | return std::move(op.outputs_[0]).take(); |
| 11996 | } |
| 11997 | struct structured_norm_dtype_out_out final : public at::native::structured_norm_dtype_out { |
| 11998 | structured_norm_dtype_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 11999 | void set_output_strided( |
| 12000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12001 | TensorOptions options, DimnameList names |
| 12002 | ) override { |
| 12003 | const auto& out = outputs_[output_idx].get(); |
| 12004 | resize_out(out, sizes, strides, options); |
| 12005 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12006 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12007 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12008 | } |
| 12009 | if (!names.empty()) { |
| 12010 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12011 | } |
| 12012 | // super must happen after, so that downstream can use maybe_get_output |
| 12013 | // to retrieve the output |
| 12014 | } |
| 12015 | void set_output_raw_strided( |
| 12016 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12017 | TensorOptions options, DimnameList names |
| 12018 | ) override { |
| 12019 | const auto& out = outputs_[output_idx].get(); |
| 12020 | resize_out(out, sizes, strides, options); |
| 12021 | if (!names.empty()) { |
| 12022 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12023 | } |
| 12024 | // super must happen after, so that downstream can use maybe_get_output |
| 12025 | // to retrieve the output |
| 12026 | } |
| 12027 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12028 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12029 | } |
| 12030 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12031 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12032 | }; |
| 12033 | at::Tensor & wrapper_CPU_norm_out_dtype_out(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { |
| 12034 | structured_norm_dtype_out_out op(out); |
| 12035 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, dtype); |
| 12036 | op.impl(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, dtype, op.maybe_get_output(0)); |
| 12037 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12038 | return out; |
| 12039 | } |
| 12040 | struct structured_norm_out_functional final : public at::native::structured_norm_out { |
| 12041 | void set_output_strided( |
| 12042 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12043 | TensorOptions options, DimnameList names |
| 12044 | ) override { |
| 12045 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12046 | if (!names.empty()) { |
| 12047 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12048 | } |
| 12049 | // super must happen after, so that downstream can use maybe_get_output |
| 12050 | // to retrieve the output |
| 12051 | } |
| 12052 | void set_output_raw_strided( |
| 12053 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12054 | TensorOptions options, DimnameList names |
| 12055 | ) override { |
| 12056 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12057 | if (!names.empty()) { |
| 12058 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12059 | } |
| 12060 | // super must happen after, so that downstream can use maybe_get_output |
| 12061 | // to retrieve the output |
| 12062 | } |
| 12063 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12064 | return *outputs_[output_idx]; |
| 12065 | } |
| 12066 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12067 | }; |
| 12068 | at::Tensor wrapper_CPU_norm_ScalarOpt_dim(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim) { |
| 12069 | structured_norm_out_functional op; |
| 12070 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim); |
| 12071 | op.impl(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, *op.outputs_[0]); |
| 12072 | return std::move(op.outputs_[0]).take(); |
| 12073 | } |
| 12074 | struct structured_norm_out_out final : public at::native::structured_norm_out { |
| 12075 | structured_norm_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12076 | void set_output_strided( |
| 12077 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12078 | TensorOptions options, DimnameList names |
| 12079 | ) override { |
| 12080 | const auto& out = outputs_[output_idx].get(); |
| 12081 | resize_out(out, sizes, strides, options); |
| 12082 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12083 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12084 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12085 | } |
| 12086 | if (!names.empty()) { |
| 12087 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12088 | } |
| 12089 | // super must happen after, so that downstream can use maybe_get_output |
| 12090 | // to retrieve the output |
| 12091 | } |
| 12092 | void set_output_raw_strided( |
| 12093 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12094 | TensorOptions options, DimnameList names |
| 12095 | ) override { |
| 12096 | const auto& out = outputs_[output_idx].get(); |
| 12097 | resize_out(out, sizes, strides, options); |
| 12098 | if (!names.empty()) { |
| 12099 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12100 | } |
| 12101 | // super must happen after, so that downstream can use maybe_get_output |
| 12102 | // to retrieve the output |
| 12103 | } |
| 12104 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12105 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12106 | } |
| 12107 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12108 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12109 | }; |
| 12110 | at::Tensor & wrapper_CPU_norm_out_out(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 12111 | structured_norm_out_out op(out); |
| 12112 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim); |
| 12113 | op.impl(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, op.maybe_get_output(0)); |
| 12114 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12115 | return out; |
| 12116 | } |
| 12117 | namespace { |
| 12118 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_Tensor_out_frexp_out(const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { |
| 12119 | // No device check |
| 12120 | // DeviceGuard omitted |
| 12121 | return at::native::frexp_out(self, mantissa, exponent); |
| 12122 | } |
| 12123 | } // anonymous namespace |
| 12124 | namespace { |
| 12125 | at::Tensor & wrapper_CPU__zero_(at::Tensor & self) { |
| 12126 | // No device check |
| 12127 | // DeviceGuard omitted |
| 12128 | return at::native::zero_(self); |
| 12129 | } |
| 12130 | } // anonymous namespace |
| 12131 | struct structured_sub_out_functional final : public at::native::structured_sub_out { |
| 12132 | void set_output_strided( |
| 12133 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12134 | TensorOptions options, DimnameList names |
| 12135 | ) override { |
| 12136 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12137 | if (!names.empty()) { |
| 12138 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12139 | } |
| 12140 | // super must happen after, so that downstream can use maybe_get_output |
| 12141 | // to retrieve the output |
| 12142 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12143 | } |
| 12144 | void set_output_raw_strided( |
| 12145 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12146 | TensorOptions options, DimnameList names |
| 12147 | ) override { |
| 12148 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12149 | if (!names.empty()) { |
| 12150 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12151 | } |
| 12152 | // super must happen after, so that downstream can use maybe_get_output |
| 12153 | // to retrieve the output |
| 12154 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12155 | } |
| 12156 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12157 | return *outputs_[output_idx]; |
| 12158 | } |
| 12159 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12160 | }; |
| 12161 | at::Tensor wrapper_CPU_sub_Tensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 12162 | structured_sub_out_functional op; |
| 12163 | op.meta(self, other, alpha); |
| 12164 | op.impl(self, other, alpha, *op.outputs_[0]); |
| 12165 | return std::move(op.outputs_[0]).take(); |
| 12166 | } |
| 12167 | struct structured_sub_out_out final : public at::native::structured_sub_out { |
| 12168 | structured_sub_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12169 | void set_output_strided( |
| 12170 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12171 | TensorOptions options, DimnameList names |
| 12172 | ) override { |
| 12173 | const auto& out = outputs_[output_idx].get(); |
| 12174 | resize_out(out, sizes, strides, options); |
| 12175 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12176 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12177 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12178 | } |
| 12179 | if (!names.empty()) { |
| 12180 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12181 | } |
| 12182 | // super must happen after, so that downstream can use maybe_get_output |
| 12183 | // to retrieve the output |
| 12184 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12185 | } |
| 12186 | void set_output_raw_strided( |
| 12187 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12188 | TensorOptions options, DimnameList names |
| 12189 | ) override { |
| 12190 | const auto& out = outputs_[output_idx].get(); |
| 12191 | resize_out(out, sizes, strides, options); |
| 12192 | if (!names.empty()) { |
| 12193 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12194 | } |
| 12195 | // super must happen after, so that downstream can use maybe_get_output |
| 12196 | // to retrieve the output |
| 12197 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12198 | } |
| 12199 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12200 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12201 | } |
| 12202 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12203 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12204 | }; |
| 12205 | at::Tensor & wrapper_CPU_sub_out_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 12206 | structured_sub_out_out op(out); |
| 12207 | op.meta(self, other, alpha); |
| 12208 | op.impl(self, other, alpha, op.maybe_get_output(0)); |
| 12209 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12210 | return out; |
| 12211 | } |
| 12212 | struct structured_sub_out_inplace final : public at::native::structured_sub_out { |
| 12213 | structured_sub_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12214 | void set_output_strided( |
| 12215 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12216 | TensorOptions options, DimnameList names |
| 12217 | ) override { |
| 12218 | const auto& out = outputs_[output_idx].get(); |
| 12219 | check_inplace(out, sizes, options); |
| 12220 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12221 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12222 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12223 | } |
| 12224 | if (!names.empty()) { |
| 12225 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12226 | } |
| 12227 | // super must happen after, so that downstream can use maybe_get_output |
| 12228 | // to retrieve the output |
| 12229 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12230 | } |
| 12231 | void set_output_raw_strided( |
| 12232 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12233 | TensorOptions options, DimnameList names |
| 12234 | ) override { |
| 12235 | const auto& out = outputs_[output_idx].get(); |
| 12236 | check_inplace(out, sizes, options); |
| 12237 | if (!names.empty()) { |
| 12238 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12239 | } |
| 12240 | // super must happen after, so that downstream can use maybe_get_output |
| 12241 | // to retrieve the output |
| 12242 | at::native::structured_sub_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12243 | } |
| 12244 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12245 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12246 | } |
| 12247 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12248 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12249 | }; |
| 12250 | at::Tensor & wrapper_CPU_sub__Tensor(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 12251 | structured_sub_out_inplace op(self); |
| 12252 | op.meta(self, other, alpha); |
| 12253 | op.impl(self, other, alpha, op.outputs_[0]); |
| 12254 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12255 | return self; |
| 12256 | } |
| 12257 | namespace { |
| 12258 | at::Tensor wrapper_CPU_Tensor_rsub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 12259 | // No device check |
| 12260 | // DeviceGuard omitted |
| 12261 | return at::native::rsub(self, other, alpha); |
| 12262 | } |
| 12263 | } // anonymous namespace |
| 12264 | struct structured_heaviside_out_functional final : public at::native::structured_heaviside_out { |
| 12265 | void set_output_strided( |
| 12266 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12267 | TensorOptions options, DimnameList names |
| 12268 | ) override { |
| 12269 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12270 | if (!names.empty()) { |
| 12271 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12272 | } |
| 12273 | // super must happen after, so that downstream can use maybe_get_output |
| 12274 | // to retrieve the output |
| 12275 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12276 | } |
| 12277 | void set_output_raw_strided( |
| 12278 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12279 | TensorOptions options, DimnameList names |
| 12280 | ) override { |
| 12281 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12282 | if (!names.empty()) { |
| 12283 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12284 | } |
| 12285 | // super must happen after, so that downstream can use maybe_get_output |
| 12286 | // to retrieve the output |
| 12287 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12288 | } |
| 12289 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12290 | return *outputs_[output_idx]; |
| 12291 | } |
| 12292 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12293 | }; |
| 12294 | at::Tensor wrapper_CPU_heaviside(const at::Tensor & self, const at::Tensor & values) { |
| 12295 | structured_heaviside_out_functional op; |
| 12296 | op.meta(self, values); |
| 12297 | op.impl(self, values, *op.outputs_[0]); |
| 12298 | return std::move(op.outputs_[0]).take(); |
| 12299 | } |
| 12300 | struct structured_heaviside_out_out final : public at::native::structured_heaviside_out { |
| 12301 | structured_heaviside_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12302 | void set_output_strided( |
| 12303 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12304 | TensorOptions options, DimnameList names |
| 12305 | ) override { |
| 12306 | const auto& out = outputs_[output_idx].get(); |
| 12307 | resize_out(out, sizes, strides, options); |
| 12308 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12309 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12310 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12311 | } |
| 12312 | if (!names.empty()) { |
| 12313 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12314 | } |
| 12315 | // super must happen after, so that downstream can use maybe_get_output |
| 12316 | // to retrieve the output |
| 12317 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12318 | } |
| 12319 | void set_output_raw_strided( |
| 12320 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12321 | TensorOptions options, DimnameList names |
| 12322 | ) override { |
| 12323 | const auto& out = outputs_[output_idx].get(); |
| 12324 | resize_out(out, sizes, strides, options); |
| 12325 | if (!names.empty()) { |
| 12326 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12327 | } |
| 12328 | // super must happen after, so that downstream can use maybe_get_output |
| 12329 | // to retrieve the output |
| 12330 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12331 | } |
| 12332 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12333 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12334 | } |
| 12335 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12336 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12337 | }; |
| 12338 | at::Tensor & wrapper_CPU_heaviside_out_out(const at::Tensor & self, const at::Tensor & values, at::Tensor & out) { |
| 12339 | structured_heaviside_out_out op(out); |
| 12340 | op.meta(self, values); |
| 12341 | op.impl(self, values, op.maybe_get_output(0)); |
| 12342 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12343 | return out; |
| 12344 | } |
| 12345 | struct structured_heaviside_out_inplace final : public at::native::structured_heaviside_out { |
| 12346 | structured_heaviside_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12347 | void set_output_strided( |
| 12348 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12349 | TensorOptions options, DimnameList names |
| 12350 | ) override { |
| 12351 | const auto& out = outputs_[output_idx].get(); |
| 12352 | check_inplace(out, sizes, options); |
| 12353 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12354 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12355 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12356 | } |
| 12357 | if (!names.empty()) { |
| 12358 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12359 | } |
| 12360 | // super must happen after, so that downstream can use maybe_get_output |
| 12361 | // to retrieve the output |
| 12362 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12363 | } |
| 12364 | void set_output_raw_strided( |
| 12365 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12366 | TensorOptions options, DimnameList names |
| 12367 | ) override { |
| 12368 | const auto& out = outputs_[output_idx].get(); |
| 12369 | check_inplace(out, sizes, options); |
| 12370 | if (!names.empty()) { |
| 12371 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12372 | } |
| 12373 | // super must happen after, so that downstream can use maybe_get_output |
| 12374 | // to retrieve the output |
| 12375 | at::native::structured_heaviside_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12376 | } |
| 12377 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12378 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12379 | } |
| 12380 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12381 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12382 | }; |
| 12383 | at::Tensor & wrapper_CPU_heaviside_(at::Tensor & self, const at::Tensor & values) { |
| 12384 | structured_heaviside_out_inplace op(self); |
| 12385 | op.meta(self, values); |
| 12386 | op.impl(self, values, op.outputs_[0]); |
| 12387 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12388 | return self; |
| 12389 | } |
| 12390 | struct structured_addmm_out_cpu_functional final : public at::native::structured_addmm_out_cpu { |
| 12391 | void set_output_strided( |
| 12392 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12393 | TensorOptions options, DimnameList names |
| 12394 | ) override { |
| 12395 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12396 | if (!names.empty()) { |
| 12397 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12398 | } |
| 12399 | // super must happen after, so that downstream can use maybe_get_output |
| 12400 | // to retrieve the output |
| 12401 | } |
| 12402 | void set_output_raw_strided( |
| 12403 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12404 | TensorOptions options, DimnameList names |
| 12405 | ) override { |
| 12406 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12407 | if (!names.empty()) { |
| 12408 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12409 | } |
| 12410 | // super must happen after, so that downstream can use maybe_get_output |
| 12411 | // to retrieve the output |
| 12412 | } |
| 12413 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12414 | return *outputs_[output_idx]; |
| 12415 | } |
| 12416 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12417 | }; |
| 12418 | at::Tensor wrapper_CPU_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 12419 | structured_addmm_out_cpu_functional op; |
| 12420 | op.meta(self, mat1, mat2, beta, alpha); |
| 12421 | op.impl(self, mat1, mat2, beta, alpha, *op.outputs_[0]); |
| 12422 | return std::move(op.outputs_[0]).take(); |
| 12423 | } |
| 12424 | struct structured_addmm_out_cpu_out final : public at::native::structured_addmm_out_cpu { |
| 12425 | structured_addmm_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12426 | void set_output_strided( |
| 12427 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12428 | TensorOptions options, DimnameList names |
| 12429 | ) override { |
| 12430 | const auto& out = outputs_[output_idx].get(); |
| 12431 | resize_out(out, sizes, strides, options); |
| 12432 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12433 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12434 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12435 | } |
| 12436 | if (!names.empty()) { |
| 12437 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12438 | } |
| 12439 | // super must happen after, so that downstream can use maybe_get_output |
| 12440 | // to retrieve the output |
| 12441 | } |
| 12442 | void set_output_raw_strided( |
| 12443 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12444 | TensorOptions options, DimnameList names |
| 12445 | ) override { |
| 12446 | const auto& out = outputs_[output_idx].get(); |
| 12447 | resize_out(out, sizes, strides, options); |
| 12448 | if (!names.empty()) { |
| 12449 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12450 | } |
| 12451 | // super must happen after, so that downstream can use maybe_get_output |
| 12452 | // to retrieve the output |
| 12453 | } |
| 12454 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12455 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12456 | } |
| 12457 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12458 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12459 | }; |
| 12460 | at::Tensor & wrapper_CPU_addmm_out_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 12461 | structured_addmm_out_cpu_out op(out); |
| 12462 | op.meta(self, mat1, mat2, beta, alpha); |
| 12463 | op.impl(self, mat1, mat2, beta, alpha, op.maybe_get_output(0)); |
| 12464 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12465 | return out; |
| 12466 | } |
| 12467 | struct structured_addmm_out_cpu_inplace final : public at::native::structured_addmm_out_cpu { |
| 12468 | structured_addmm_out_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12469 | void set_output_strided( |
| 12470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12471 | TensorOptions options, DimnameList names |
| 12472 | ) override { |
| 12473 | const auto& out = outputs_[output_idx].get(); |
| 12474 | check_inplace(out, sizes, options); |
| 12475 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12476 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12477 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12478 | } |
| 12479 | if (!names.empty()) { |
| 12480 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12481 | } |
| 12482 | // super must happen after, so that downstream can use maybe_get_output |
| 12483 | // to retrieve the output |
| 12484 | } |
| 12485 | void set_output_raw_strided( |
| 12486 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12487 | TensorOptions options, DimnameList names |
| 12488 | ) override { |
| 12489 | const auto& out = outputs_[output_idx].get(); |
| 12490 | check_inplace(out, sizes, options); |
| 12491 | if (!names.empty()) { |
| 12492 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12493 | } |
| 12494 | // super must happen after, so that downstream can use maybe_get_output |
| 12495 | // to retrieve the output |
| 12496 | } |
| 12497 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12498 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12499 | } |
| 12500 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12501 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12502 | }; |
| 12503 | at::Tensor & wrapper_CPU_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 12504 | structured_addmm_out_cpu_inplace op(self); |
| 12505 | op.meta(self, mat1, mat2, beta, alpha); |
| 12506 | op.impl(self, mat1, mat2, beta, alpha, op.outputs_[0]); |
| 12507 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12508 | return self; |
| 12509 | } |
| 12510 | struct structured_addmm_activation_out_cpu_functional final : public at::native::structured_addmm_activation_out_cpu { |
| 12511 | void set_output_strided( |
| 12512 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12513 | TensorOptions options, DimnameList names |
| 12514 | ) override { |
| 12515 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12516 | if (!names.empty()) { |
| 12517 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12518 | } |
| 12519 | // super must happen after, so that downstream can use maybe_get_output |
| 12520 | // to retrieve the output |
| 12521 | } |
| 12522 | void set_output_raw_strided( |
| 12523 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12524 | TensorOptions options, DimnameList names |
| 12525 | ) override { |
| 12526 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12527 | if (!names.empty()) { |
| 12528 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12529 | } |
| 12530 | // super must happen after, so that downstream can use maybe_get_output |
| 12531 | // to retrieve the output |
| 12532 | } |
| 12533 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12534 | return *outputs_[output_idx]; |
| 12535 | } |
| 12536 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12537 | }; |
| 12538 | at::Tensor wrapper_CPU__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) { |
| 12539 | structured_addmm_activation_out_cpu_functional op; |
| 12540 | op.meta(self, mat1, mat2, beta, alpha, use_gelu); |
| 12541 | op.impl(self, mat1, mat2, beta, alpha, use_gelu, *op.outputs_[0]); |
| 12542 | return std::move(op.outputs_[0]).take(); |
| 12543 | } |
| 12544 | struct structured_addmm_activation_out_cpu_out final : public at::native::structured_addmm_activation_out_cpu { |
| 12545 | structured_addmm_activation_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12546 | void set_output_strided( |
| 12547 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12548 | TensorOptions options, DimnameList names |
| 12549 | ) override { |
| 12550 | const auto& out = outputs_[output_idx].get(); |
| 12551 | resize_out(out, sizes, strides, options); |
| 12552 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12553 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12554 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12555 | } |
| 12556 | if (!names.empty()) { |
| 12557 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12558 | } |
| 12559 | // super must happen after, so that downstream can use maybe_get_output |
| 12560 | // to retrieve the output |
| 12561 | } |
| 12562 | void set_output_raw_strided( |
| 12563 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12564 | TensorOptions options, DimnameList names |
| 12565 | ) override { |
| 12566 | const auto& out = outputs_[output_idx].get(); |
| 12567 | resize_out(out, sizes, strides, options); |
| 12568 | if (!names.empty()) { |
| 12569 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12570 | } |
| 12571 | // super must happen after, so that downstream can use maybe_get_output |
| 12572 | // to retrieve the output |
| 12573 | } |
| 12574 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12575 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12576 | } |
| 12577 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12578 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12579 | }; |
| 12580 | at::Tensor & wrapper_CPU__addmm_activation_out_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu, at::Tensor & out) { |
| 12581 | structured_addmm_activation_out_cpu_out op(out); |
| 12582 | op.meta(self, mat1, mat2, beta, alpha, use_gelu); |
| 12583 | op.impl(self, mat1, mat2, beta, alpha, use_gelu, op.maybe_get_output(0)); |
| 12584 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12585 | return out; |
| 12586 | } |
| 12587 | namespace { |
| 12588 | int64_t wrapper_CPU__sparse_dim(const at::Tensor & self) { |
| 12589 | // No device check |
| 12590 | // DeviceGuard omitted |
| 12591 | return at::native::sparse_dim_strided(self); |
| 12592 | } |
| 12593 | } // anonymous namespace |
| 12594 | namespace { |
| 12595 | int64_t wrapper_CPU__dense_dim(const at::Tensor & self) { |
| 12596 | // No device check |
| 12597 | // DeviceGuard omitted |
| 12598 | return at::native::dense_dim_strided(self); |
| 12599 | } |
| 12600 | } // anonymous namespace |
| 12601 | namespace { |
| 12602 | at::Tensor wrapper_CPU_sparse_dim_to_sparse(const at::Tensor & self, int64_t sparse_dim) { |
| 12603 | // No device check |
| 12604 | // DeviceGuard omitted |
| 12605 | return at::native::dense_to_sparse(self, sparse_dim); |
| 12606 | } |
| 12607 | } // anonymous namespace |
| 12608 | namespace { |
| 12609 | at::Tensor wrapper_CPU__to_sparse(const at::Tensor & self, c10::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 12610 | // No device check |
| 12611 | // DeviceGuard omitted |
| 12612 | return at::native::dense_to_sparse(self, layout, blocksize, dense_dim); |
| 12613 | } |
| 12614 | } // anonymous namespace |
| 12615 | namespace { |
| 12616 | at::Tensor wrapper_CPU__to_sparse_csr(const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 12617 | // No device check |
| 12618 | // DeviceGuard omitted |
| 12619 | return at::native::dense_to_sparse_csr(self, dense_dim); |
| 12620 | } |
| 12621 | } // anonymous namespace |
| 12622 | namespace { |
| 12623 | at::Tensor wrapper_CPU__to_sparse_csc(const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 12624 | // No device check |
| 12625 | // DeviceGuard omitted |
| 12626 | return at::native::dense_to_sparse_csc(self, dense_dim); |
| 12627 | } |
| 12628 | } // anonymous namespace |
| 12629 | namespace { |
| 12630 | at::Tensor wrapper_CPU__to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 12631 | // No device check |
| 12632 | // DeviceGuard omitted |
| 12633 | return at::native::dense_to_sparse_bsr(self, blocksize, dense_dim); |
| 12634 | } |
| 12635 | } // anonymous namespace |
| 12636 | namespace { |
| 12637 | at::Tensor wrapper_CPU__to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 12638 | // No device check |
| 12639 | // DeviceGuard omitted |
| 12640 | return at::native::dense_to_sparse_bsc(self, blocksize, dense_dim); |
| 12641 | } |
| 12642 | } // anonymous namespace |
| 12643 | namespace { |
| 12644 | at::Tensor wrapper_CPU__to_mkldnn(const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 12645 | // No device check |
| 12646 | // DeviceGuard omitted |
| 12647 | return at::native::dense_to_mkldnn(self, dtype); |
| 12648 | } |
| 12649 | } // anonymous namespace |
| 12650 | namespace { |
| 12651 | at::Tensor wrapper_CPU__quantize_per_tensor_dynamic(const at::Tensor & self, at::ScalarType dtype, bool reduce_range) { |
| 12652 | // No device check |
| 12653 | // DeviceGuard omitted |
| 12654 | return at::native::quantize_per_tensor_dynamic(self, dtype, reduce_range); |
| 12655 | } |
| 12656 | } // anonymous namespace |
| 12657 | namespace { |
| 12658 | at::Tensor wrapper_CPU__quantize_per_tensor(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) { |
| 12659 | // No device check |
| 12660 | // DeviceGuard omitted |
| 12661 | return at::native::quantize_per_tensor(self, scale, zero_point, dtype); |
| 12662 | } |
| 12663 | } // anonymous namespace |
| 12664 | namespace { |
| 12665 | at::Tensor wrapper_CPU_tensor_qparams_quantize_per_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) { |
| 12666 | // No device check |
| 12667 | // DeviceGuard omitted |
| 12668 | return at::native::quantize_per_tensor_tensor_qparams(self, scale, zero_point, dtype); |
| 12669 | } |
| 12670 | } // anonymous namespace |
| 12671 | namespace { |
| 12672 | ::std::vector<at::Tensor> wrapper_CPU_tensors_quantize_per_tensor(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) { |
| 12673 | // No device check |
| 12674 | // DeviceGuard omitted |
| 12675 | return at::native::quantize_per_tensor_list_cpu(tensors, scales, zero_points, dtype); |
| 12676 | } |
| 12677 | } // anonymous namespace |
| 12678 | namespace { |
| 12679 | at::Tensor wrapper_CPU__quantize_per_channel(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { |
| 12680 | // No device check |
| 12681 | // DeviceGuard omitted |
| 12682 | return at::native::quantize_per_channel(self, scales, zero_points, axis, dtype); |
| 12683 | } |
| 12684 | } // anonymous namespace |
| 12685 | namespace { |
| 12686 | at::Tensor wrapper_CPU_self_dequantize(const at::Tensor & self) { |
| 12687 | // No device check |
| 12688 | // DeviceGuard omitted |
| 12689 | return at::native::dequantize_cpu_or_cuda(self); |
| 12690 | } |
| 12691 | } // anonymous namespace |
| 12692 | namespace { |
| 12693 | at::Tensor wrapper_CPU___make_per_tensor_quantized_tensor(const at::Tensor & self, double scale, int64_t zero_point) { |
| 12694 | // No device check |
| 12695 | // DeviceGuard omitted |
| 12696 | return at::native::make_per_tensor_quantized_tensor_cpu(self, scale, zero_point); |
| 12697 | } |
| 12698 | } // anonymous namespace |
| 12699 | namespace { |
| 12700 | at::Tensor wrapper_CPU___make_per_channel_quantized_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) { |
| 12701 | // No device check |
| 12702 | // DeviceGuard omitted |
| 12703 | return at::native::make_per_channel_quantized_tensor_cpu(self, scale, zero_point, axis); |
| 12704 | } |
| 12705 | } // anonymous namespace |
| 12706 | namespace { |
| 12707 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__fake_quantize_per_tensor_affine_cachemask(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { |
| 12708 | // No device check |
| 12709 | // DeviceGuard omitted |
| 12710 | return at::native::fake_quantize_per_tensor_affine_cachemask(self, scale, zero_point, quant_min, quant_max); |
| 12711 | } |
| 12712 | } // anonymous namespace |
| 12713 | namespace { |
| 12714 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___fake_quantize_per_tensor_affine_cachemask_tensor_qparams(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) { |
| 12715 | // No device check |
| 12716 | // DeviceGuard omitted |
| 12717 | return at::native::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max); |
| 12718 | } |
| 12719 | } // anonymous namespace |
| 12720 | namespace { |
| 12721 | at::Tensor wrapper_CPU___fake_quantize_learnable_per_tensor_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 12722 | // No device check |
| 12723 | // DeviceGuard omitted |
| 12724 | return at::native::_fake_quantize_learnable_per_tensor_affine(self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 12725 | } |
| 12726 | } // anonymous namespace |
| 12727 | namespace { |
| 12728 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___fake_quantize_learnable_per_tensor_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 12729 | // No device check |
| 12730 | // DeviceGuard omitted |
| 12731 | return at::native::_fake_quantize_learnable_per_tensor_affine_backward(grad, self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 12732 | } |
| 12733 | } // anonymous namespace |
| 12734 | namespace { |
| 12735 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__fake_quantize_per_channel_affine_cachemask(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { |
| 12736 | // No device check |
| 12737 | // DeviceGuard omitted |
| 12738 | return at::native::fake_quantize_per_channel_affine_cachemask(self, scale, zero_point, axis, quant_min, quant_max); |
| 12739 | } |
| 12740 | } // anonymous namespace |
| 12741 | namespace { |
| 12742 | at::Tensor wrapper_CPU___fake_quantize_learnable_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 12743 | // No device check |
| 12744 | // DeviceGuard omitted |
| 12745 | return at::native::_fake_quantize_learnable_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 12746 | } |
| 12747 | } // anonymous namespace |
| 12748 | namespace { |
| 12749 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___fake_quantize_learnable_per_channel_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 12750 | // No device check |
| 12751 | // DeviceGuard omitted |
| 12752 | return at::native::_fake_quantize_learnable_per_channel_affine_backward(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 12753 | } |
| 12754 | } // anonymous namespace |
| 12755 | namespace { |
| 12756 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___fused_moving_avg_obs_fq_helper(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) { |
| 12757 | // No device check |
| 12758 | // DeviceGuard omitted |
| 12759 | return at::native::fused_moving_avg_obs_fake_quant_cpu(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 12760 | } |
| 12761 | } // anonymous namespace |
| 12762 | namespace { |
| 12763 | at::Scalar wrapper_CPU___local_scalar_dense(const at::Tensor & self) { |
| 12764 | // No device check |
| 12765 | // DeviceGuard omitted |
| 12766 | return at::native::_local_scalar_dense_cpu(self); |
| 12767 | } |
| 12768 | } // anonymous namespace |
| 12769 | namespace { |
| 12770 | at::Tensor & wrapper_CPU_source_Storage_set_(at::Tensor & self, at::Storage source) { |
| 12771 | // No device check |
| 12772 | // DeviceGuard omitted |
| 12773 | return at::native::set_(self, source); |
| 12774 | } |
| 12775 | } // anonymous namespace |
| 12776 | namespace { |
| 12777 | at::Tensor & wrapper_CPU_source_Storage_storage_offset_set_(at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 12778 | // No device check |
| 12779 | // DeviceGuard omitted |
| 12780 | return at::native::set_storage_cpu_(self, source, storage_offset.expect_int(), C10_AS_INTARRAYREF_SLOW(size), C10_AS_INTARRAYREF_SLOW(stride)); |
| 12781 | } |
| 12782 | } // anonymous namespace |
| 12783 | namespace { |
| 12784 | at::Tensor & wrapper_CPU_source_Tensor_set_(at::Tensor & self, const at::Tensor & source) { |
| 12785 | // No device check |
| 12786 | // DeviceGuard omitted |
| 12787 | return at::native::set_tensor_(self, source); |
| 12788 | } |
| 12789 | } // anonymous namespace |
| 12790 | namespace { |
| 12791 | at::Tensor & wrapper_CPU__set_(at::Tensor & self) { |
| 12792 | // No device check |
| 12793 | // DeviceGuard omitted |
| 12794 | return at::native::set_cpu_(self); |
| 12795 | } |
| 12796 | } // anonymous namespace |
| 12797 | namespace { |
| 12798 | bool wrapper_CPU__is_set_to(const at::Tensor & self, const at::Tensor & tensor) { |
| 12799 | // No device check |
| 12800 | // DeviceGuard omitted |
| 12801 | return at::native::is_set_to(self, tensor); |
| 12802 | } |
| 12803 | } // anonymous namespace |
| 12804 | namespace { |
| 12805 | at::Tensor & wrapper_CPU_Scalar_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { |
| 12806 | // No device check |
| 12807 | // DeviceGuard omitted |
| 12808 | return at::native::masked_fill__cpu(self, mask, value); |
| 12809 | } |
| 12810 | } // anonymous namespace |
| 12811 | namespace { |
| 12812 | at::Tensor & wrapper_CPU_Tensor_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { |
| 12813 | // No device check |
| 12814 | // DeviceGuard omitted |
| 12815 | return at::native::masked_fill__cpu(self, mask, value); |
| 12816 | } |
| 12817 | } // anonymous namespace |
| 12818 | namespace { |
| 12819 | at::Tensor & wrapper_CPU__masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { |
| 12820 | // No device check |
| 12821 | // DeviceGuard omitted |
| 12822 | return at::native::masked_scatter__cpu(self, mask, source); |
| 12823 | } |
| 12824 | } // anonymous namespace |
| 12825 | namespace { |
| 12826 | at::Tensor wrapper_CPU___masked_softmax(const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type) { |
| 12827 | // No device check |
| 12828 | // DeviceGuard omitted |
| 12829 | return at::native::masked_softmax_cpu(self, mask, dim, mask_type); |
| 12830 | } |
| 12831 | } // anonymous namespace |
| 12832 | namespace { |
| 12833 | at::Tensor wrapper_CPU___masked_softmax_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional<int64_t> dim) { |
| 12834 | // No device check |
| 12835 | // DeviceGuard omitted |
| 12836 | return at::native::masked_softmax_backward_cpu(grad_output, output, mask, dim); |
| 12837 | } |
| 12838 | } // anonymous namespace |
| 12839 | namespace { |
| 12840 | at::Tensor wrapper_CPU__view(const at::Tensor & self, c10::SymIntArrayRef size) { |
| 12841 | // No device check |
| 12842 | // DeviceGuard omitted |
| 12843 | return at::native::view(self, C10_AS_INTARRAYREF_SLOW(size)); |
| 12844 | } |
| 12845 | } // anonymous namespace |
| 12846 | namespace { |
| 12847 | at::Tensor & wrapper_CPU__put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) { |
| 12848 | // No device check |
| 12849 | // DeviceGuard omitted |
| 12850 | return at::native::put_(self, index, source, accumulate); |
| 12851 | } |
| 12852 | } // anonymous namespace |
| 12853 | struct structured_index_add_cpu_out_functional final : public at::native::structured_index_add_cpu_out { |
| 12854 | void set_output_strided( |
| 12855 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12856 | TensorOptions options, DimnameList names |
| 12857 | ) override { |
| 12858 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12859 | if (!names.empty()) { |
| 12860 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12861 | } |
| 12862 | // super must happen after, so that downstream can use maybe_get_output |
| 12863 | // to retrieve the output |
| 12864 | } |
| 12865 | void set_output_raw_strided( |
| 12866 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12867 | TensorOptions options, DimnameList names |
| 12868 | ) override { |
| 12869 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12870 | if (!names.empty()) { |
| 12871 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12872 | } |
| 12873 | // super must happen after, so that downstream can use maybe_get_output |
| 12874 | // to retrieve the output |
| 12875 | } |
| 12876 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12877 | return *outputs_[output_idx]; |
| 12878 | } |
| 12879 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12880 | }; |
| 12881 | at::Tensor wrapper_CPU_index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 12882 | structured_index_add_cpu_out_functional op; |
| 12883 | auto precompute = op.meta(self, dim, index, source, alpha); |
| 12884 | (void)precompute; |
| 12885 | op.impl(self, precompute.dim, index, source, alpha, *op.outputs_[0]); |
| 12886 | return std::move(op.outputs_[0]).take(); |
| 12887 | } |
| 12888 | struct structured_index_add_cpu_out_out final : public at::native::structured_index_add_cpu_out { |
| 12889 | structured_index_add_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 12890 | void set_output_strided( |
| 12891 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12892 | TensorOptions options, DimnameList names |
| 12893 | ) override { |
| 12894 | const auto& out = outputs_[output_idx].get(); |
| 12895 | resize_out(out, sizes, strides, options); |
| 12896 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12897 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12898 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12899 | } |
| 12900 | if (!names.empty()) { |
| 12901 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12902 | } |
| 12903 | // super must happen after, so that downstream can use maybe_get_output |
| 12904 | // to retrieve the output |
| 12905 | } |
| 12906 | void set_output_raw_strided( |
| 12907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12908 | TensorOptions options, DimnameList names |
| 12909 | ) override { |
| 12910 | const auto& out = outputs_[output_idx].get(); |
| 12911 | resize_out(out, sizes, strides, options); |
| 12912 | if (!names.empty()) { |
| 12913 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12914 | } |
| 12915 | // super must happen after, so that downstream can use maybe_get_output |
| 12916 | // to retrieve the output |
| 12917 | } |
| 12918 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12919 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12920 | } |
| 12921 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12922 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12923 | }; |
| 12924 | at::Tensor & wrapper_CPU_index_add_out_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out) { |
| 12925 | structured_index_add_cpu_out_out op(out); |
| 12926 | auto precompute = op.meta(self, dim, index, source, alpha); |
| 12927 | (void)precompute; |
| 12928 | op.impl(self, precompute.dim, index, source, alpha, op.maybe_get_output(0)); |
| 12929 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12930 | return out; |
| 12931 | } |
| 12932 | struct structured_index_add_cpu_out_inplace final : public at::native::structured_index_add_cpu_out { |
| 12933 | structured_index_add_cpu_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12934 | void set_output_strided( |
| 12935 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12936 | TensorOptions options, DimnameList names |
| 12937 | ) override { |
| 12938 | const auto& out = outputs_[output_idx].get(); |
| 12939 | check_inplace(out, sizes, options); |
| 12940 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12941 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12942 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12943 | } |
| 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 | } |
| 12950 | void set_output_raw_strided( |
| 12951 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12952 | TensorOptions options, DimnameList names |
| 12953 | ) override { |
| 12954 | const auto& out = outputs_[output_idx].get(); |
| 12955 | check_inplace(out, sizes, options); |
| 12956 | if (!names.empty()) { |
| 12957 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12958 | } |
| 12959 | // super must happen after, so that downstream can use maybe_get_output |
| 12960 | // to retrieve the output |
| 12961 | } |
| 12962 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12963 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12964 | } |
| 12965 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12966 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12967 | }; |
| 12968 | at::Tensor & wrapper_CPU_index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 12969 | structured_index_add_cpu_out_inplace op(self); |
| 12970 | auto precompute = op.meta(self, dim, index, source, alpha); |
| 12971 | (void)precompute; |
| 12972 | op.impl(self, precompute.dim, index, source, alpha, op.outputs_[0]); |
| 12973 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12974 | return self; |
| 12975 | } |
| 12976 | struct structured_index_reduce_cpu_out_functional final : public at::native::structured_index_reduce_cpu_out { |
| 12977 | void set_output_strided( |
| 12978 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12979 | TensorOptions options, DimnameList names |
| 12980 | ) override { |
| 12981 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 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 | } |
| 12988 | void set_output_raw_strided( |
| 12989 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12990 | TensorOptions options, DimnameList names |
| 12991 | ) override { |
| 12992 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12993 | if (!names.empty()) { |
| 12994 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12995 | } |
| 12996 | // super must happen after, so that downstream can use maybe_get_output |
| 12997 | // to retrieve the output |
| 12998 | } |
| 12999 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13000 | return *outputs_[output_idx]; |
| 13001 | } |
| 13002 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13003 | }; |
| 13004 | at::Tensor wrapper_CPU_index_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 13005 | structured_index_reduce_cpu_out_functional op; |
| 13006 | auto precompute = op.meta(self, dim, index, source, reduce, include_self); |
| 13007 | (void)precompute; |
| 13008 | op.impl(self, precompute.dim, index, source, reduce, include_self, *op.outputs_[0]); |
| 13009 | return std::move(op.outputs_[0]).take(); |
| 13010 | } |
| 13011 | struct structured_index_reduce_cpu_out_out final : public at::native::structured_index_reduce_cpu_out { |
| 13012 | structured_index_reduce_cpu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13013 | void set_output_strided( |
| 13014 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13015 | TensorOptions options, DimnameList names |
| 13016 | ) override { |
| 13017 | const auto& out = outputs_[output_idx].get(); |
| 13018 | resize_out(out, sizes, strides, options); |
| 13019 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13020 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13021 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13022 | } |
| 13023 | if (!names.empty()) { |
| 13024 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13025 | } |
| 13026 | // super must happen after, so that downstream can use maybe_get_output |
| 13027 | // to retrieve the output |
| 13028 | } |
| 13029 | void set_output_raw_strided( |
| 13030 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13031 | TensorOptions options, DimnameList names |
| 13032 | ) override { |
| 13033 | const auto& out = outputs_[output_idx].get(); |
| 13034 | resize_out(out, sizes, strides, options); |
| 13035 | if (!names.empty()) { |
| 13036 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13037 | } |
| 13038 | // super must happen after, so that downstream can use maybe_get_output |
| 13039 | // to retrieve the output |
| 13040 | } |
| 13041 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13042 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13043 | } |
| 13044 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13045 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13046 | }; |
| 13047 | at::Tensor & wrapper_CPU_index_reduce_out_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 13048 | structured_index_reduce_cpu_out_out op(out); |
| 13049 | auto precompute = op.meta(self, dim, index, source, reduce, include_self); |
| 13050 | (void)precompute; |
| 13051 | op.impl(self, precompute.dim, index, source, reduce, include_self, op.maybe_get_output(0)); |
| 13052 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13053 | return out; |
| 13054 | } |
| 13055 | struct structured_index_reduce_cpu_out_inplace final : public at::native::structured_index_reduce_cpu_out { |
| 13056 | structured_index_reduce_cpu_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13057 | void set_output_strided( |
| 13058 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13059 | TensorOptions options, DimnameList names |
| 13060 | ) override { |
| 13061 | const auto& out = outputs_[output_idx].get(); |
| 13062 | check_inplace(out, sizes, options); |
| 13063 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13064 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13065 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13066 | } |
| 13067 | if (!names.empty()) { |
| 13068 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13069 | } |
| 13070 | // super must happen after, so that downstream can use maybe_get_output |
| 13071 | // to retrieve the output |
| 13072 | } |
| 13073 | void set_output_raw_strided( |
| 13074 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13075 | TensorOptions options, DimnameList names |
| 13076 | ) override { |
| 13077 | const auto& out = outputs_[output_idx].get(); |
| 13078 | check_inplace(out, sizes, options); |
| 13079 | if (!names.empty()) { |
| 13080 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13081 | } |
| 13082 | // super must happen after, so that downstream can use maybe_get_output |
| 13083 | // to retrieve the output |
| 13084 | } |
| 13085 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13086 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13087 | } |
| 13088 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13089 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13090 | }; |
| 13091 | at::Tensor & wrapper_CPU_index_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 13092 | structured_index_reduce_cpu_out_inplace op(self); |
| 13093 | auto precompute = op.meta(self, dim, index, source, reduce, include_self); |
| 13094 | (void)precompute; |
| 13095 | op.impl(self, precompute.dim, index, source, reduce, include_self, op.outputs_[0]); |
| 13096 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13097 | return self; |
| 13098 | } |
| 13099 | namespace { |
| 13100 | at::Tensor & wrapper_CPU_int_Scalar_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 13101 | // No device check |
| 13102 | // DeviceGuard omitted |
| 13103 | return at::native::index_fill_(self, dim, index, value); |
| 13104 | } |
| 13105 | } // anonymous namespace |
| 13106 | namespace { |
| 13107 | at::Tensor & wrapper_CPU_int_Tensor_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 13108 | // No device check |
| 13109 | // DeviceGuard omitted |
| 13110 | return at::native::index_fill_(self, dim, index, value); |
| 13111 | } |
| 13112 | } // anonymous namespace |
| 13113 | struct structured_scatter_src_out_functional final : public at::native::structured_scatter_src_out { |
| 13114 | void set_output_strided( |
| 13115 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13116 | TensorOptions options, DimnameList names |
| 13117 | ) override { |
| 13118 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13119 | if (!names.empty()) { |
| 13120 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13121 | } |
| 13122 | // super must happen after, so that downstream can use maybe_get_output |
| 13123 | // to retrieve the output |
| 13124 | } |
| 13125 | void set_output_raw_strided( |
| 13126 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13127 | TensorOptions options, DimnameList names |
| 13128 | ) override { |
| 13129 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13130 | if (!names.empty()) { |
| 13131 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13132 | } |
| 13133 | // super must happen after, so that downstream can use maybe_get_output |
| 13134 | // to retrieve the output |
| 13135 | } |
| 13136 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13137 | return *outputs_[output_idx]; |
| 13138 | } |
| 13139 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13140 | }; |
| 13141 | at::Tensor wrapper_CPU_scatter_src(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 13142 | structured_scatter_src_out_functional op; |
| 13143 | op.meta(self, dim, index, src); |
| 13144 | op.impl(self, dim, index, src, *op.outputs_[0]); |
| 13145 | return std::move(op.outputs_[0]).take(); |
| 13146 | } |
| 13147 | struct structured_scatter_src_out_out final : public at::native::structured_scatter_src_out { |
| 13148 | structured_scatter_src_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13149 | void set_output_strided( |
| 13150 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13151 | TensorOptions options, DimnameList names |
| 13152 | ) override { |
| 13153 | const auto& out = outputs_[output_idx].get(); |
| 13154 | resize_out(out, sizes, strides, options); |
| 13155 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13156 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13157 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13158 | } |
| 13159 | if (!names.empty()) { |
| 13160 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13161 | } |
| 13162 | // super must happen after, so that downstream can use maybe_get_output |
| 13163 | // to retrieve the output |
| 13164 | } |
| 13165 | void set_output_raw_strided( |
| 13166 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13167 | TensorOptions options, DimnameList names |
| 13168 | ) override { |
| 13169 | const auto& out = outputs_[output_idx].get(); |
| 13170 | resize_out(out, sizes, strides, options); |
| 13171 | if (!names.empty()) { |
| 13172 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13173 | } |
| 13174 | // super must happen after, so that downstream can use maybe_get_output |
| 13175 | // to retrieve the output |
| 13176 | } |
| 13177 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13178 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13179 | } |
| 13180 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13181 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13182 | }; |
| 13183 | at::Tensor & wrapper_CPU_scatter_out_src_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 13184 | structured_scatter_src_out_out op(out); |
| 13185 | op.meta(self, dim, index, src); |
| 13186 | op.impl(self, dim, index, src, op.maybe_get_output(0)); |
| 13187 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13188 | return out; |
| 13189 | } |
| 13190 | struct structured_scatter_src_out_inplace final : public at::native::structured_scatter_src_out { |
| 13191 | structured_scatter_src_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13192 | void set_output_strided( |
| 13193 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13194 | TensorOptions options, DimnameList names |
| 13195 | ) override { |
| 13196 | const auto& out = outputs_[output_idx].get(); |
| 13197 | check_inplace(out, sizes, options); |
| 13198 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13199 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13200 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13201 | } |
| 13202 | if (!names.empty()) { |
| 13203 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13204 | } |
| 13205 | // super must happen after, so that downstream can use maybe_get_output |
| 13206 | // to retrieve the output |
| 13207 | } |
| 13208 | void set_output_raw_strided( |
| 13209 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13210 | TensorOptions options, DimnameList names |
| 13211 | ) override { |
| 13212 | const auto& out = outputs_[output_idx].get(); |
| 13213 | check_inplace(out, sizes, options); |
| 13214 | if (!names.empty()) { |
| 13215 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13216 | } |
| 13217 | // super must happen after, so that downstream can use maybe_get_output |
| 13218 | // to retrieve the output |
| 13219 | } |
| 13220 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13221 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13222 | } |
| 13223 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13224 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13225 | }; |
| 13226 | at::Tensor & wrapper_CPU_scatter__src(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 13227 | structured_scatter_src_out_inplace op(self); |
| 13228 | op.meta(self, dim, index, src); |
| 13229 | op.impl(self, dim, index, src, op.outputs_[0]); |
| 13230 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13231 | return self; |
| 13232 | } |
| 13233 | struct structured_scatter_value_out_functional final : public at::native::structured_scatter_value_out { |
| 13234 | void set_output_strided( |
| 13235 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13236 | TensorOptions options, DimnameList names |
| 13237 | ) override { |
| 13238 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13239 | if (!names.empty()) { |
| 13240 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13241 | } |
| 13242 | // super must happen after, so that downstream can use maybe_get_output |
| 13243 | // to retrieve the output |
| 13244 | } |
| 13245 | void set_output_raw_strided( |
| 13246 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13247 | TensorOptions options, DimnameList names |
| 13248 | ) override { |
| 13249 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13250 | if (!names.empty()) { |
| 13251 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13252 | } |
| 13253 | // super must happen after, so that downstream can use maybe_get_output |
| 13254 | // to retrieve the output |
| 13255 | } |
| 13256 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13257 | return *outputs_[output_idx]; |
| 13258 | } |
| 13259 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13260 | }; |
| 13261 | at::Tensor wrapper_CPU_scatter_value(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 13262 | structured_scatter_value_out_functional op; |
| 13263 | op.meta(self, dim, index, value); |
| 13264 | op.impl(self, dim, index, value, *op.outputs_[0]); |
| 13265 | return std::move(op.outputs_[0]).take(); |
| 13266 | } |
| 13267 | struct structured_scatter_value_out_out final : public at::native::structured_scatter_value_out { |
| 13268 | structured_scatter_value_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13269 | void set_output_strided( |
| 13270 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13271 | TensorOptions options, DimnameList names |
| 13272 | ) override { |
| 13273 | const auto& out = outputs_[output_idx].get(); |
| 13274 | resize_out(out, sizes, strides, options); |
| 13275 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13276 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13277 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13278 | } |
| 13279 | if (!names.empty()) { |
| 13280 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13281 | } |
| 13282 | // super must happen after, so that downstream can use maybe_get_output |
| 13283 | // to retrieve the output |
| 13284 | } |
| 13285 | void set_output_raw_strided( |
| 13286 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13287 | TensorOptions options, DimnameList names |
| 13288 | ) override { |
| 13289 | const auto& out = outputs_[output_idx].get(); |
| 13290 | resize_out(out, sizes, strides, options); |
| 13291 | if (!names.empty()) { |
| 13292 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13293 | } |
| 13294 | // super must happen after, so that downstream can use maybe_get_output |
| 13295 | // to retrieve the output |
| 13296 | } |
| 13297 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13298 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13299 | } |
| 13300 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13301 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13302 | }; |
| 13303 | at::Tensor & wrapper_CPU_scatter_out_value_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 13304 | structured_scatter_value_out_out op(out); |
| 13305 | op.meta(self, dim, index, value); |
| 13306 | op.impl(self, dim, index, value, op.maybe_get_output(0)); |
| 13307 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13308 | return out; |
| 13309 | } |
| 13310 | struct structured_scatter_value_out_inplace final : public at::native::structured_scatter_value_out { |
| 13311 | structured_scatter_value_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13312 | void set_output_strided( |
| 13313 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13314 | TensorOptions options, DimnameList names |
| 13315 | ) override { |
| 13316 | const auto& out = outputs_[output_idx].get(); |
| 13317 | check_inplace(out, sizes, options); |
| 13318 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13319 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13320 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13321 | } |
| 13322 | if (!names.empty()) { |
| 13323 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13324 | } |
| 13325 | // super must happen after, so that downstream can use maybe_get_output |
| 13326 | // to retrieve the output |
| 13327 | } |
| 13328 | void set_output_raw_strided( |
| 13329 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13330 | TensorOptions options, DimnameList names |
| 13331 | ) override { |
| 13332 | const auto& out = outputs_[output_idx].get(); |
| 13333 | check_inplace(out, sizes, options); |
| 13334 | if (!names.empty()) { |
| 13335 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13336 | } |
| 13337 | // super must happen after, so that downstream can use maybe_get_output |
| 13338 | // to retrieve the output |
| 13339 | } |
| 13340 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13341 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13342 | } |
| 13343 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13344 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13345 | }; |
| 13346 | at::Tensor & wrapper_CPU_scatter__value(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 13347 | structured_scatter_value_out_inplace op(self); |
| 13348 | op.meta(self, dim, index, value); |
| 13349 | op.impl(self, dim, index, value, op.outputs_[0]); |
| 13350 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13351 | return self; |
| 13352 | } |
| 13353 | struct structured_scatter_reduce_out_functional final : public at::native::structured_scatter_reduce_out { |
| 13354 | void set_output_strided( |
| 13355 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13356 | TensorOptions options, DimnameList names |
| 13357 | ) override { |
| 13358 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13359 | if (!names.empty()) { |
| 13360 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13361 | } |
| 13362 | // super must happen after, so that downstream can use maybe_get_output |
| 13363 | // to retrieve the output |
| 13364 | } |
| 13365 | void set_output_raw_strided( |
| 13366 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13367 | TensorOptions options, DimnameList names |
| 13368 | ) override { |
| 13369 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13370 | if (!names.empty()) { |
| 13371 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13372 | } |
| 13373 | // super must happen after, so that downstream can use maybe_get_output |
| 13374 | // to retrieve the output |
| 13375 | } |
| 13376 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13377 | return *outputs_[output_idx]; |
| 13378 | } |
| 13379 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13380 | }; |
| 13381 | at::Tensor wrapper_CPU_scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 13382 | structured_scatter_reduce_out_functional op; |
| 13383 | op.meta(self, dim, index, src, reduce); |
| 13384 | op.impl(self, dim, index, src, reduce, *op.outputs_[0]); |
| 13385 | return std::move(op.outputs_[0]).take(); |
| 13386 | } |
| 13387 | struct structured_scatter_reduce_out_out final : public at::native::structured_scatter_reduce_out { |
| 13388 | structured_scatter_reduce_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13389 | void set_output_strided( |
| 13390 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13391 | TensorOptions options, DimnameList names |
| 13392 | ) override { |
| 13393 | const auto& out = outputs_[output_idx].get(); |
| 13394 | resize_out(out, sizes, strides, options); |
| 13395 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13396 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13397 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13398 | } |
| 13399 | if (!names.empty()) { |
| 13400 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13401 | } |
| 13402 | // super must happen after, so that downstream can use maybe_get_output |
| 13403 | // to retrieve the output |
| 13404 | } |
| 13405 | void set_output_raw_strided( |
| 13406 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13407 | TensorOptions options, DimnameList names |
| 13408 | ) override { |
| 13409 | const auto& out = outputs_[output_idx].get(); |
| 13410 | resize_out(out, sizes, strides, options); |
| 13411 | if (!names.empty()) { |
| 13412 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13413 | } |
| 13414 | // super must happen after, so that downstream can use maybe_get_output |
| 13415 | // to retrieve the output |
| 13416 | } |
| 13417 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13418 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13419 | } |
| 13420 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13421 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13422 | }; |
| 13423 | at::Tensor & wrapper_CPU_scatter_out_reduce_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) { |
| 13424 | structured_scatter_reduce_out_out op(out); |
| 13425 | op.meta(self, dim, index, src, reduce); |
| 13426 | op.impl(self, dim, index, src, reduce, op.maybe_get_output(0)); |
| 13427 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13428 | return out; |
| 13429 | } |
| 13430 | struct structured_scatter_reduce_out_inplace final : public at::native::structured_scatter_reduce_out { |
| 13431 | structured_scatter_reduce_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13432 | void set_output_strided( |
| 13433 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13434 | TensorOptions options, DimnameList names |
| 13435 | ) override { |
| 13436 | const auto& out = outputs_[output_idx].get(); |
| 13437 | check_inplace(out, sizes, options); |
| 13438 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13439 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13440 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13441 | } |
| 13442 | if (!names.empty()) { |
| 13443 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13444 | } |
| 13445 | // super must happen after, so that downstream can use maybe_get_output |
| 13446 | // to retrieve the output |
| 13447 | } |
| 13448 | void set_output_raw_strided( |
| 13449 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13450 | TensorOptions options, DimnameList names |
| 13451 | ) override { |
| 13452 | const auto& out = outputs_[output_idx].get(); |
| 13453 | check_inplace(out, sizes, options); |
| 13454 | if (!names.empty()) { |
| 13455 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13456 | } |
| 13457 | // super must happen after, so that downstream can use maybe_get_output |
| 13458 | // to retrieve the output |
| 13459 | } |
| 13460 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13461 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13462 | } |
| 13463 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13464 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13465 | }; |
| 13466 | at::Tensor & wrapper_CPU_scatter__reduce(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 13467 | structured_scatter_reduce_out_inplace op(self); |
| 13468 | op.meta(self, dim, index, src, reduce); |
| 13469 | op.impl(self, dim, index, src, reduce, op.outputs_[0]); |
| 13470 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13471 | return self; |
| 13472 | } |
| 13473 | struct structured_scatter_value_reduce_out_functional final : public at::native::structured_scatter_value_reduce_out { |
| 13474 | void set_output_strided( |
| 13475 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13476 | TensorOptions options, DimnameList names |
| 13477 | ) override { |
| 13478 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13479 | if (!names.empty()) { |
| 13480 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13481 | } |
| 13482 | // super must happen after, so that downstream can use maybe_get_output |
| 13483 | // to retrieve the output |
| 13484 | } |
| 13485 | void set_output_raw_strided( |
| 13486 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13487 | TensorOptions options, DimnameList names |
| 13488 | ) override { |
| 13489 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13490 | if (!names.empty()) { |
| 13491 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13492 | } |
| 13493 | // super must happen after, so that downstream can use maybe_get_output |
| 13494 | // to retrieve the output |
| 13495 | } |
| 13496 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13497 | return *outputs_[output_idx]; |
| 13498 | } |
| 13499 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13500 | }; |
| 13501 | at::Tensor wrapper_CPU_scatter_value_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 13502 | structured_scatter_value_reduce_out_functional op; |
| 13503 | op.meta(self, dim, index, value, reduce); |
| 13504 | op.impl(self, dim, index, value, reduce, *op.outputs_[0]); |
| 13505 | return std::move(op.outputs_[0]).take(); |
| 13506 | } |
| 13507 | struct structured_scatter_value_reduce_out_out final : public at::native::structured_scatter_value_reduce_out { |
| 13508 | structured_scatter_value_reduce_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13509 | void set_output_strided( |
| 13510 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13511 | TensorOptions options, DimnameList names |
| 13512 | ) override { |
| 13513 | const auto& out = outputs_[output_idx].get(); |
| 13514 | resize_out(out, sizes, strides, options); |
| 13515 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13516 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13517 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13518 | } |
| 13519 | if (!names.empty()) { |
| 13520 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13521 | } |
| 13522 | // super must happen after, so that downstream can use maybe_get_output |
| 13523 | // to retrieve the output |
| 13524 | } |
| 13525 | void set_output_raw_strided( |
| 13526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13527 | TensorOptions options, DimnameList names |
| 13528 | ) override { |
| 13529 | const auto& out = outputs_[output_idx].get(); |
| 13530 | resize_out(out, sizes, strides, options); |
| 13531 | if (!names.empty()) { |
| 13532 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13533 | } |
| 13534 | // super must happen after, so that downstream can use maybe_get_output |
| 13535 | // to retrieve the output |
| 13536 | } |
| 13537 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13538 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13539 | } |
| 13540 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13541 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13542 | }; |
| 13543 | at::Tensor & wrapper_CPU_scatter_out_value_reduce_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out) { |
| 13544 | structured_scatter_value_reduce_out_out op(out); |
| 13545 | op.meta(self, dim, index, value, reduce); |
| 13546 | op.impl(self, dim, index, value, reduce, op.maybe_get_output(0)); |
| 13547 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13548 | return out; |
| 13549 | } |
| 13550 | struct structured_scatter_value_reduce_out_inplace final : public at::native::structured_scatter_value_reduce_out { |
| 13551 | structured_scatter_value_reduce_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13552 | void set_output_strided( |
| 13553 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13554 | TensorOptions options, DimnameList names |
| 13555 | ) override { |
| 13556 | const auto& out = outputs_[output_idx].get(); |
| 13557 | check_inplace(out, sizes, options); |
| 13558 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13559 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13560 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13561 | } |
| 13562 | if (!names.empty()) { |
| 13563 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13564 | } |
| 13565 | // super must happen after, so that downstream can use maybe_get_output |
| 13566 | // to retrieve the output |
| 13567 | } |
| 13568 | void set_output_raw_strided( |
| 13569 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13570 | TensorOptions options, DimnameList names |
| 13571 | ) override { |
| 13572 | const auto& out = outputs_[output_idx].get(); |
| 13573 | check_inplace(out, sizes, options); |
| 13574 | if (!names.empty()) { |
| 13575 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13576 | } |
| 13577 | // super must happen after, so that downstream can use maybe_get_output |
| 13578 | // to retrieve the output |
| 13579 | } |
| 13580 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13581 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13582 | } |
| 13583 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13584 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13585 | }; |
| 13586 | at::Tensor & wrapper_CPU_scatter__value_reduce(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 13587 | structured_scatter_value_reduce_out_inplace op(self); |
| 13588 | op.meta(self, dim, index, value, reduce); |
| 13589 | op.impl(self, dim, index, value, reduce, op.outputs_[0]); |
| 13590 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13591 | return self; |
| 13592 | } |
| 13593 | struct structured_scatter_add_functional final : public at::native::structured_scatter_add { |
| 13594 | void set_output_strided( |
| 13595 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13596 | TensorOptions options, DimnameList names |
| 13597 | ) override { |
| 13598 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13599 | if (!names.empty()) { |
| 13600 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13601 | } |
| 13602 | // super must happen after, so that downstream can use maybe_get_output |
| 13603 | // to retrieve the output |
| 13604 | } |
| 13605 | void set_output_raw_strided( |
| 13606 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13607 | TensorOptions options, DimnameList names |
| 13608 | ) override { |
| 13609 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13610 | if (!names.empty()) { |
| 13611 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13612 | } |
| 13613 | // super must happen after, so that downstream can use maybe_get_output |
| 13614 | // to retrieve the output |
| 13615 | } |
| 13616 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13617 | return *outputs_[output_idx]; |
| 13618 | } |
| 13619 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13620 | }; |
| 13621 | at::Tensor wrapper_CPU_scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 13622 | structured_scatter_add_functional op; |
| 13623 | op.meta(self, dim, index, src); |
| 13624 | op.impl(self, dim, index, src, *op.outputs_[0]); |
| 13625 | return std::move(op.outputs_[0]).take(); |
| 13626 | } |
| 13627 | struct structured_scatter_add_out final : public at::native::structured_scatter_add { |
| 13628 | structured_scatter_add_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13629 | void set_output_strided( |
| 13630 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13631 | TensorOptions options, DimnameList names |
| 13632 | ) override { |
| 13633 | const auto& out = outputs_[output_idx].get(); |
| 13634 | resize_out(out, sizes, strides, options); |
| 13635 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13636 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13637 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13638 | } |
| 13639 | if (!names.empty()) { |
| 13640 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13641 | } |
| 13642 | // super must happen after, so that downstream can use maybe_get_output |
| 13643 | // to retrieve the output |
| 13644 | } |
| 13645 | void set_output_raw_strided( |
| 13646 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13647 | TensorOptions options, DimnameList names |
| 13648 | ) override { |
| 13649 | const auto& out = outputs_[output_idx].get(); |
| 13650 | resize_out(out, sizes, strides, options); |
| 13651 | if (!names.empty()) { |
| 13652 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13653 | } |
| 13654 | // super must happen after, so that downstream can use maybe_get_output |
| 13655 | // to retrieve the output |
| 13656 | } |
| 13657 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13658 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13659 | } |
| 13660 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13661 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13662 | }; |
| 13663 | at::Tensor & wrapper_CPU_scatter_add_out_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 13664 | structured_scatter_add_out op(out); |
| 13665 | op.meta(self, dim, index, src); |
| 13666 | op.impl(self, dim, index, src, op.maybe_get_output(0)); |
| 13667 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13668 | return out; |
| 13669 | } |
| 13670 | struct structured_scatter_add_inplace final : public at::native::structured_scatter_add { |
| 13671 | structured_scatter_add_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13672 | void set_output_strided( |
| 13673 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13674 | TensorOptions options, DimnameList names |
| 13675 | ) override { |
| 13676 | const auto& out = outputs_[output_idx].get(); |
| 13677 | check_inplace(out, sizes, options); |
| 13678 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13679 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13680 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13681 | } |
| 13682 | if (!names.empty()) { |
| 13683 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13684 | } |
| 13685 | // super must happen after, so that downstream can use maybe_get_output |
| 13686 | // to retrieve the output |
| 13687 | } |
| 13688 | void set_output_raw_strided( |
| 13689 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13690 | TensorOptions options, DimnameList names |
| 13691 | ) override { |
| 13692 | const auto& out = outputs_[output_idx].get(); |
| 13693 | check_inplace(out, sizes, options); |
| 13694 | if (!names.empty()) { |
| 13695 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13696 | } |
| 13697 | // super must happen after, so that downstream can use maybe_get_output |
| 13698 | // to retrieve the output |
| 13699 | } |
| 13700 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13701 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13702 | } |
| 13703 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13704 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13705 | }; |
| 13706 | at::Tensor & wrapper_CPU_scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 13707 | structured_scatter_add_inplace op(self); |
| 13708 | op.meta(self, dim, index, src); |
| 13709 | op.impl(self, dim, index, src, op.outputs_[0]); |
| 13710 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13711 | return self; |
| 13712 | } |
| 13713 | struct structured_scatter_reduce_two_functional final : public at::native::structured_scatter_reduce_two { |
| 13714 | void set_output_strided( |
| 13715 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13716 | TensorOptions options, DimnameList names |
| 13717 | ) override { |
| 13718 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13719 | if (!names.empty()) { |
| 13720 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13721 | } |
| 13722 | // super must happen after, so that downstream can use maybe_get_output |
| 13723 | // to retrieve the output |
| 13724 | } |
| 13725 | void set_output_raw_strided( |
| 13726 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13727 | TensorOptions options, DimnameList names |
| 13728 | ) override { |
| 13729 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13730 | if (!names.empty()) { |
| 13731 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13732 | } |
| 13733 | // super must happen after, so that downstream can use maybe_get_output |
| 13734 | // to retrieve the output |
| 13735 | } |
| 13736 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13737 | return *outputs_[output_idx]; |
| 13738 | } |
| 13739 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13740 | }; |
| 13741 | at::Tensor wrapper_CPU_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) { |
| 13742 | structured_scatter_reduce_two_functional op; |
| 13743 | op.meta(self, dim, index, src, reduce, include_self); |
| 13744 | op.impl(self, dim, index, src, reduce, include_self, *op.outputs_[0]); |
| 13745 | return std::move(op.outputs_[0]).take(); |
| 13746 | } |
| 13747 | struct structured_scatter_reduce_two_out final : public at::native::structured_scatter_reduce_two { |
| 13748 | structured_scatter_reduce_two_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13749 | void set_output_strided( |
| 13750 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13751 | TensorOptions options, DimnameList names |
| 13752 | ) override { |
| 13753 | const auto& out = outputs_[output_idx].get(); |
| 13754 | resize_out(out, sizes, strides, options); |
| 13755 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13756 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13757 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13758 | } |
| 13759 | if (!names.empty()) { |
| 13760 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13761 | } |
| 13762 | // super must happen after, so that downstream can use maybe_get_output |
| 13763 | // to retrieve the output |
| 13764 | } |
| 13765 | void set_output_raw_strided( |
| 13766 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13767 | TensorOptions options, DimnameList names |
| 13768 | ) override { |
| 13769 | const auto& out = outputs_[output_idx].get(); |
| 13770 | resize_out(out, sizes, strides, options); |
| 13771 | if (!names.empty()) { |
| 13772 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13773 | } |
| 13774 | // super must happen after, so that downstream can use maybe_get_output |
| 13775 | // to retrieve the output |
| 13776 | } |
| 13777 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13778 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13779 | } |
| 13780 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13781 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13782 | }; |
| 13783 | at::Tensor & wrapper_CPU_scatter_reduce_out_two_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 13784 | structured_scatter_reduce_two_out op(out); |
| 13785 | op.meta(self, dim, index, src, reduce, include_self); |
| 13786 | op.impl(self, dim, index, src, reduce, include_self, op.maybe_get_output(0)); |
| 13787 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13788 | return out; |
| 13789 | } |
| 13790 | struct structured_scatter_reduce_two_inplace final : public at::native::structured_scatter_reduce_two { |
| 13791 | structured_scatter_reduce_two_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13792 | void set_output_strided( |
| 13793 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13794 | TensorOptions options, DimnameList names |
| 13795 | ) override { |
| 13796 | const auto& out = outputs_[output_idx].get(); |
| 13797 | check_inplace(out, sizes, options); |
| 13798 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13799 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13800 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13801 | } |
| 13802 | if (!names.empty()) { |
| 13803 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13804 | } |
| 13805 | // super must happen after, so that downstream can use maybe_get_output |
| 13806 | // to retrieve the output |
| 13807 | } |
| 13808 | void set_output_raw_strided( |
| 13809 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13810 | TensorOptions options, DimnameList names |
| 13811 | ) override { |
| 13812 | const auto& out = outputs_[output_idx].get(); |
| 13813 | check_inplace(out, sizes, options); |
| 13814 | if (!names.empty()) { |
| 13815 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13816 | } |
| 13817 | // super must happen after, so that downstream can use maybe_get_output |
| 13818 | // to retrieve the output |
| 13819 | } |
| 13820 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13821 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13822 | } |
| 13823 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13824 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13825 | }; |
| 13826 | at::Tensor & wrapper_CPU_scatter_reduce__two(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 13827 | structured_scatter_reduce_two_inplace op(self); |
| 13828 | op.meta(self, dim, index, src, reduce, include_self); |
| 13829 | op.impl(self, dim, index, src, reduce, include_self, op.outputs_[0]); |
| 13830 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13831 | return self; |
| 13832 | } |
| 13833 | struct structured_eq_Scalar_out_functional final : public at::native::structured_eq_Scalar_out { |
| 13834 | void set_output_strided( |
| 13835 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13836 | TensorOptions options, DimnameList names |
| 13837 | ) override { |
| 13838 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13839 | if (!names.empty()) { |
| 13840 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13841 | } |
| 13842 | // super must happen after, so that downstream can use maybe_get_output |
| 13843 | // to retrieve the output |
| 13844 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13845 | } |
| 13846 | void set_output_raw_strided( |
| 13847 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13848 | TensorOptions options, DimnameList names |
| 13849 | ) override { |
| 13850 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13851 | if (!names.empty()) { |
| 13852 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13853 | } |
| 13854 | // super must happen after, so that downstream can use maybe_get_output |
| 13855 | // to retrieve the output |
| 13856 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13857 | } |
| 13858 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13859 | return *outputs_[output_idx]; |
| 13860 | } |
| 13861 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13862 | }; |
| 13863 | at::Tensor wrapper_CPU_eq_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 13864 | structured_eq_Scalar_out_functional op; |
| 13865 | op.meta(self, other); |
| 13866 | op.impl(self, other, *op.outputs_[0]); |
| 13867 | return std::move(op.outputs_[0]).take(); |
| 13868 | } |
| 13869 | struct structured_eq_Scalar_out_out final : public at::native::structured_eq_Scalar_out { |
| 13870 | structured_eq_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13871 | void set_output_strided( |
| 13872 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13873 | TensorOptions options, DimnameList names |
| 13874 | ) override { |
| 13875 | const auto& out = outputs_[output_idx].get(); |
| 13876 | resize_out(out, sizes, strides, options); |
| 13877 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13878 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13879 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13880 | } |
| 13881 | if (!names.empty()) { |
| 13882 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13883 | } |
| 13884 | // super must happen after, so that downstream can use maybe_get_output |
| 13885 | // to retrieve the output |
| 13886 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13887 | } |
| 13888 | void set_output_raw_strided( |
| 13889 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13890 | TensorOptions options, DimnameList names |
| 13891 | ) override { |
| 13892 | const auto& out = outputs_[output_idx].get(); |
| 13893 | resize_out(out, sizes, strides, options); |
| 13894 | if (!names.empty()) { |
| 13895 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13896 | } |
| 13897 | // super must happen after, so that downstream can use maybe_get_output |
| 13898 | // to retrieve the output |
| 13899 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13900 | } |
| 13901 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13902 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13903 | } |
| 13904 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13905 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13906 | }; |
| 13907 | at::Tensor & wrapper_CPU_eq_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13908 | structured_eq_Scalar_out_out op(out); |
| 13909 | op.meta(self, other); |
| 13910 | op.impl(self, other, op.maybe_get_output(0)); |
| 13911 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13912 | return out; |
| 13913 | } |
| 13914 | struct structured_eq_Scalar_out_inplace final : public at::native::structured_eq_Scalar_out { |
| 13915 | structured_eq_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13916 | void set_output_strided( |
| 13917 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13918 | TensorOptions options, DimnameList names |
| 13919 | ) override { |
| 13920 | const auto& out = outputs_[output_idx].get(); |
| 13921 | check_inplace(out, sizes, options); |
| 13922 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13923 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13924 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13925 | } |
| 13926 | if (!names.empty()) { |
| 13927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13928 | } |
| 13929 | // super must happen after, so that downstream can use maybe_get_output |
| 13930 | // to retrieve the output |
| 13931 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13932 | } |
| 13933 | void set_output_raw_strided( |
| 13934 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13935 | TensorOptions options, DimnameList names |
| 13936 | ) override { |
| 13937 | const auto& out = outputs_[output_idx].get(); |
| 13938 | check_inplace(out, sizes, options); |
| 13939 | if (!names.empty()) { |
| 13940 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13941 | } |
| 13942 | // super must happen after, so that downstream can use maybe_get_output |
| 13943 | // to retrieve the output |
| 13944 | at::native::structured_eq_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13945 | } |
| 13946 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13947 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13948 | } |
| 13949 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13950 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13951 | }; |
| 13952 | at::Tensor & wrapper_CPU_eq__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 13953 | structured_eq_Scalar_out_inplace op(self); |
| 13954 | op.meta(self, other); |
| 13955 | op.impl(self, other, op.outputs_[0]); |
| 13956 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13957 | return self; |
| 13958 | } |
| 13959 | struct structured_eq_Tensor_out_functional final : public at::native::structured_eq_Tensor_out { |
| 13960 | void set_output_strided( |
| 13961 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13962 | TensorOptions options, DimnameList names |
| 13963 | ) override { |
| 13964 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13965 | if (!names.empty()) { |
| 13966 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13967 | } |
| 13968 | // super must happen after, so that downstream can use maybe_get_output |
| 13969 | // to retrieve the output |
| 13970 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13971 | } |
| 13972 | void set_output_raw_strided( |
| 13973 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13974 | TensorOptions options, DimnameList names |
| 13975 | ) override { |
| 13976 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13977 | if (!names.empty()) { |
| 13978 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13979 | } |
| 13980 | // super must happen after, so that downstream can use maybe_get_output |
| 13981 | // to retrieve the output |
| 13982 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13983 | } |
| 13984 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13985 | return *outputs_[output_idx]; |
| 13986 | } |
| 13987 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13988 | }; |
| 13989 | at::Tensor wrapper_CPU_eq_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 13990 | structured_eq_Tensor_out_functional op; |
| 13991 | op.meta(self, other); |
| 13992 | op.impl(self, other, *op.outputs_[0]); |
| 13993 | return std::move(op.outputs_[0]).take(); |
| 13994 | } |
| 13995 | struct structured_eq_Tensor_out_out final : public at::native::structured_eq_Tensor_out { |
| 13996 | structured_eq_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 13997 | void set_output_strided( |
| 13998 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13999 | TensorOptions options, DimnameList names |
| 14000 | ) override { |
| 14001 | const auto& out = outputs_[output_idx].get(); |
| 14002 | resize_out(out, sizes, strides, options); |
| 14003 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14004 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14005 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14006 | } |
| 14007 | if (!names.empty()) { |
| 14008 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14009 | } |
| 14010 | // super must happen after, so that downstream can use maybe_get_output |
| 14011 | // to retrieve the output |
| 14012 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14013 | } |
| 14014 | void set_output_raw_strided( |
| 14015 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14016 | TensorOptions options, DimnameList names |
| 14017 | ) override { |
| 14018 | const auto& out = outputs_[output_idx].get(); |
| 14019 | resize_out(out, sizes, strides, options); |
| 14020 | if (!names.empty()) { |
| 14021 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14022 | } |
| 14023 | // super must happen after, so that downstream can use maybe_get_output |
| 14024 | // to retrieve the output |
| 14025 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14026 | } |
| 14027 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14028 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14029 | } |
| 14030 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14031 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14032 | }; |
| 14033 | at::Tensor & wrapper_CPU_eq_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14034 | structured_eq_Tensor_out_out op(out); |
| 14035 | op.meta(self, other); |
| 14036 | op.impl(self, other, op.maybe_get_output(0)); |
| 14037 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14038 | return out; |
| 14039 | } |
| 14040 | struct structured_eq_Tensor_out_inplace final : public at::native::structured_eq_Tensor_out { |
| 14041 | structured_eq_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14042 | void set_output_strided( |
| 14043 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14044 | TensorOptions options, DimnameList names |
| 14045 | ) override { |
| 14046 | const auto& out = outputs_[output_idx].get(); |
| 14047 | check_inplace(out, sizes, options); |
| 14048 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14049 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14050 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14051 | } |
| 14052 | if (!names.empty()) { |
| 14053 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14054 | } |
| 14055 | // super must happen after, so that downstream can use maybe_get_output |
| 14056 | // to retrieve the output |
| 14057 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14058 | } |
| 14059 | void set_output_raw_strided( |
| 14060 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14061 | TensorOptions options, DimnameList names |
| 14062 | ) override { |
| 14063 | const auto& out = outputs_[output_idx].get(); |
| 14064 | check_inplace(out, sizes, options); |
| 14065 | if (!names.empty()) { |
| 14066 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14067 | } |
| 14068 | // super must happen after, so that downstream can use maybe_get_output |
| 14069 | // to retrieve the output |
| 14070 | at::native::structured_eq_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14071 | } |
| 14072 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14073 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14074 | } |
| 14075 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14076 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14077 | }; |
| 14078 | at::Tensor & wrapper_CPU_eq__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14079 | structured_eq_Tensor_out_inplace op(self); |
| 14080 | op.meta(self, other); |
| 14081 | op.impl(self, other, op.outputs_[0]); |
| 14082 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14083 | return self; |
| 14084 | } |
| 14085 | struct structured_bitwise_and_out_functional final : public at::native::structured_bitwise_and_out { |
| 14086 | void set_output_strided( |
| 14087 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14088 | TensorOptions options, DimnameList names |
| 14089 | ) override { |
| 14090 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14091 | if (!names.empty()) { |
| 14092 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14093 | } |
| 14094 | // super must happen after, so that downstream can use maybe_get_output |
| 14095 | // to retrieve the output |
| 14096 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14097 | } |
| 14098 | void set_output_raw_strided( |
| 14099 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14100 | TensorOptions options, DimnameList names |
| 14101 | ) override { |
| 14102 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14103 | if (!names.empty()) { |
| 14104 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14105 | } |
| 14106 | // super must happen after, so that downstream can use maybe_get_output |
| 14107 | // to retrieve the output |
| 14108 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14109 | } |
| 14110 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14111 | return *outputs_[output_idx]; |
| 14112 | } |
| 14113 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14114 | }; |
| 14115 | at::Tensor wrapper_CPU_bitwise_and_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14116 | structured_bitwise_and_out_functional op; |
| 14117 | op.meta(self, other); |
| 14118 | op.impl(self, other, *op.outputs_[0]); |
| 14119 | return std::move(op.outputs_[0]).take(); |
| 14120 | } |
| 14121 | struct structured_bitwise_and_out_out final : public at::native::structured_bitwise_and_out { |
| 14122 | structured_bitwise_and_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14123 | void set_output_strided( |
| 14124 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14125 | TensorOptions options, DimnameList names |
| 14126 | ) override { |
| 14127 | const auto& out = outputs_[output_idx].get(); |
| 14128 | resize_out(out, sizes, strides, options); |
| 14129 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14130 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14131 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14132 | } |
| 14133 | if (!names.empty()) { |
| 14134 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14135 | } |
| 14136 | // super must happen after, so that downstream can use maybe_get_output |
| 14137 | // to retrieve the output |
| 14138 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14139 | } |
| 14140 | void set_output_raw_strided( |
| 14141 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14142 | TensorOptions options, DimnameList names |
| 14143 | ) override { |
| 14144 | const auto& out = outputs_[output_idx].get(); |
| 14145 | resize_out(out, sizes, strides, options); |
| 14146 | if (!names.empty()) { |
| 14147 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14148 | } |
| 14149 | // super must happen after, so that downstream can use maybe_get_output |
| 14150 | // to retrieve the output |
| 14151 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14152 | } |
| 14153 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14154 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14155 | } |
| 14156 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14157 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14158 | }; |
| 14159 | at::Tensor & wrapper_CPU_bitwise_and_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14160 | structured_bitwise_and_out_out op(out); |
| 14161 | op.meta(self, other); |
| 14162 | op.impl(self, other, op.maybe_get_output(0)); |
| 14163 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14164 | return out; |
| 14165 | } |
| 14166 | struct structured_bitwise_and_out_inplace final : public at::native::structured_bitwise_and_out { |
| 14167 | structured_bitwise_and_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14168 | void set_output_strided( |
| 14169 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14170 | TensorOptions options, DimnameList names |
| 14171 | ) override { |
| 14172 | const auto& out = outputs_[output_idx].get(); |
| 14173 | check_inplace(out, sizes, options); |
| 14174 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14175 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14176 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14177 | } |
| 14178 | if (!names.empty()) { |
| 14179 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14180 | } |
| 14181 | // super must happen after, so that downstream can use maybe_get_output |
| 14182 | // to retrieve the output |
| 14183 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14184 | } |
| 14185 | void set_output_raw_strided( |
| 14186 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14187 | TensorOptions options, DimnameList names |
| 14188 | ) override { |
| 14189 | const auto& out = outputs_[output_idx].get(); |
| 14190 | check_inplace(out, sizes, options); |
| 14191 | if (!names.empty()) { |
| 14192 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14193 | } |
| 14194 | // super must happen after, so that downstream can use maybe_get_output |
| 14195 | // to retrieve the output |
| 14196 | at::native::structured_bitwise_and_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14197 | } |
| 14198 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14199 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14200 | } |
| 14201 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14202 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14203 | }; |
| 14204 | at::Tensor & wrapper_CPU_bitwise_and__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14205 | structured_bitwise_and_out_inplace op(self); |
| 14206 | op.meta(self, other); |
| 14207 | op.impl(self, other, op.outputs_[0]); |
| 14208 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14209 | return self; |
| 14210 | } |
| 14211 | struct structured_bitwise_or_out_functional final : public at::native::structured_bitwise_or_out { |
| 14212 | void set_output_strided( |
| 14213 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14214 | TensorOptions options, DimnameList names |
| 14215 | ) override { |
| 14216 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14217 | if (!names.empty()) { |
| 14218 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14219 | } |
| 14220 | // super must happen after, so that downstream can use maybe_get_output |
| 14221 | // to retrieve the output |
| 14222 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14223 | } |
| 14224 | void set_output_raw_strided( |
| 14225 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14226 | TensorOptions options, DimnameList names |
| 14227 | ) override { |
| 14228 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14229 | if (!names.empty()) { |
| 14230 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14231 | } |
| 14232 | // super must happen after, so that downstream can use maybe_get_output |
| 14233 | // to retrieve the output |
| 14234 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14235 | } |
| 14236 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14237 | return *outputs_[output_idx]; |
| 14238 | } |
| 14239 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14240 | }; |
| 14241 | at::Tensor wrapper_CPU_bitwise_or_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14242 | structured_bitwise_or_out_functional op; |
| 14243 | op.meta(self, other); |
| 14244 | op.impl(self, other, *op.outputs_[0]); |
| 14245 | return std::move(op.outputs_[0]).take(); |
| 14246 | } |
| 14247 | struct structured_bitwise_or_out_out final : public at::native::structured_bitwise_or_out { |
| 14248 | structured_bitwise_or_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14249 | void set_output_strided( |
| 14250 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14251 | TensorOptions options, DimnameList names |
| 14252 | ) override { |
| 14253 | const auto& out = outputs_[output_idx].get(); |
| 14254 | resize_out(out, sizes, strides, options); |
| 14255 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14256 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14257 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14258 | } |
| 14259 | if (!names.empty()) { |
| 14260 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14261 | } |
| 14262 | // super must happen after, so that downstream can use maybe_get_output |
| 14263 | // to retrieve the output |
| 14264 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14265 | } |
| 14266 | void set_output_raw_strided( |
| 14267 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14268 | TensorOptions options, DimnameList names |
| 14269 | ) override { |
| 14270 | const auto& out = outputs_[output_idx].get(); |
| 14271 | resize_out(out, sizes, strides, options); |
| 14272 | if (!names.empty()) { |
| 14273 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14274 | } |
| 14275 | // super must happen after, so that downstream can use maybe_get_output |
| 14276 | // to retrieve the output |
| 14277 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14278 | } |
| 14279 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14280 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14281 | } |
| 14282 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14283 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14284 | }; |
| 14285 | at::Tensor & wrapper_CPU_bitwise_or_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14286 | structured_bitwise_or_out_out op(out); |
| 14287 | op.meta(self, other); |
| 14288 | op.impl(self, other, op.maybe_get_output(0)); |
| 14289 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14290 | return out; |
| 14291 | } |
| 14292 | struct structured_bitwise_or_out_inplace final : public at::native::structured_bitwise_or_out { |
| 14293 | structured_bitwise_or_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14294 | void set_output_strided( |
| 14295 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14296 | TensorOptions options, DimnameList names |
| 14297 | ) override { |
| 14298 | const auto& out = outputs_[output_idx].get(); |
| 14299 | check_inplace(out, sizes, options); |
| 14300 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14301 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14302 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14303 | } |
| 14304 | if (!names.empty()) { |
| 14305 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14306 | } |
| 14307 | // super must happen after, so that downstream can use maybe_get_output |
| 14308 | // to retrieve the output |
| 14309 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14310 | } |
| 14311 | void set_output_raw_strided( |
| 14312 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14313 | TensorOptions options, DimnameList names |
| 14314 | ) override { |
| 14315 | const auto& out = outputs_[output_idx].get(); |
| 14316 | check_inplace(out, sizes, options); |
| 14317 | if (!names.empty()) { |
| 14318 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14319 | } |
| 14320 | // super must happen after, so that downstream can use maybe_get_output |
| 14321 | // to retrieve the output |
| 14322 | at::native::structured_bitwise_or_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14323 | } |
| 14324 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14325 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14326 | } |
| 14327 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14328 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14329 | }; |
| 14330 | at::Tensor & wrapper_CPU_bitwise_or__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14331 | structured_bitwise_or_out_inplace op(self); |
| 14332 | op.meta(self, other); |
| 14333 | op.impl(self, other, op.outputs_[0]); |
| 14334 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14335 | return self; |
| 14336 | } |
| 14337 | struct structured_bitwise_xor_out_functional final : public at::native::structured_bitwise_xor_out { |
| 14338 | void set_output_strided( |
| 14339 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14340 | TensorOptions options, DimnameList names |
| 14341 | ) override { |
| 14342 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14343 | if (!names.empty()) { |
| 14344 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14345 | } |
| 14346 | // super must happen after, so that downstream can use maybe_get_output |
| 14347 | // to retrieve the output |
| 14348 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14349 | } |
| 14350 | void set_output_raw_strided( |
| 14351 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14352 | TensorOptions options, DimnameList names |
| 14353 | ) override { |
| 14354 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14355 | if (!names.empty()) { |
| 14356 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14357 | } |
| 14358 | // super must happen after, so that downstream can use maybe_get_output |
| 14359 | // to retrieve the output |
| 14360 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14361 | } |
| 14362 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14363 | return *outputs_[output_idx]; |
| 14364 | } |
| 14365 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14366 | }; |
| 14367 | at::Tensor wrapper_CPU_bitwise_xor_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14368 | structured_bitwise_xor_out_functional op; |
| 14369 | op.meta(self, other); |
| 14370 | op.impl(self, other, *op.outputs_[0]); |
| 14371 | return std::move(op.outputs_[0]).take(); |
| 14372 | } |
| 14373 | struct structured_bitwise_xor_out_out final : public at::native::structured_bitwise_xor_out { |
| 14374 | structured_bitwise_xor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14375 | void set_output_strided( |
| 14376 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14377 | TensorOptions options, DimnameList names |
| 14378 | ) override { |
| 14379 | const auto& out = outputs_[output_idx].get(); |
| 14380 | resize_out(out, sizes, strides, options); |
| 14381 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14382 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14383 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14384 | } |
| 14385 | if (!names.empty()) { |
| 14386 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14387 | } |
| 14388 | // super must happen after, so that downstream can use maybe_get_output |
| 14389 | // to retrieve the output |
| 14390 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14391 | } |
| 14392 | void set_output_raw_strided( |
| 14393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14394 | TensorOptions options, DimnameList names |
| 14395 | ) override { |
| 14396 | const auto& out = outputs_[output_idx].get(); |
| 14397 | resize_out(out, sizes, strides, options); |
| 14398 | if (!names.empty()) { |
| 14399 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14400 | } |
| 14401 | // super must happen after, so that downstream can use maybe_get_output |
| 14402 | // to retrieve the output |
| 14403 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14404 | } |
| 14405 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14406 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14407 | } |
| 14408 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14409 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14410 | }; |
| 14411 | at::Tensor & wrapper_CPU_bitwise_xor_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14412 | structured_bitwise_xor_out_out op(out); |
| 14413 | op.meta(self, other); |
| 14414 | op.impl(self, other, op.maybe_get_output(0)); |
| 14415 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14416 | return out; |
| 14417 | } |
| 14418 | struct structured_bitwise_xor_out_inplace final : public at::native::structured_bitwise_xor_out { |
| 14419 | structured_bitwise_xor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14420 | void set_output_strided( |
| 14421 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14422 | TensorOptions options, DimnameList names |
| 14423 | ) override { |
| 14424 | const auto& out = outputs_[output_idx].get(); |
| 14425 | check_inplace(out, sizes, options); |
| 14426 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14427 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14428 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14429 | } |
| 14430 | if (!names.empty()) { |
| 14431 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14432 | } |
| 14433 | // super must happen after, so that downstream can use maybe_get_output |
| 14434 | // to retrieve the output |
| 14435 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14436 | } |
| 14437 | void set_output_raw_strided( |
| 14438 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14439 | TensorOptions options, DimnameList names |
| 14440 | ) override { |
| 14441 | const auto& out = outputs_[output_idx].get(); |
| 14442 | check_inplace(out, sizes, options); |
| 14443 | if (!names.empty()) { |
| 14444 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14445 | } |
| 14446 | // super must happen after, so that downstream can use maybe_get_output |
| 14447 | // to retrieve the output |
| 14448 | at::native::structured_bitwise_xor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14449 | } |
| 14450 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14451 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14452 | } |
| 14453 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14454 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14455 | }; |
| 14456 | at::Tensor & wrapper_CPU_bitwise_xor__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14457 | structured_bitwise_xor_out_inplace op(self); |
| 14458 | op.meta(self, other); |
| 14459 | op.impl(self, other, op.outputs_[0]); |
| 14460 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14461 | return self; |
| 14462 | } |
| 14463 | namespace { |
| 14464 | at::Tensor wrapper_CPU_Scalar___lshift__(const at::Tensor & self, const at::Scalar & other) { |
| 14465 | // No device check |
| 14466 | // DeviceGuard omitted |
| 14467 | return at::native::__lshift__(self, other); |
| 14468 | } |
| 14469 | } // anonymous namespace |
| 14470 | namespace { |
| 14471 | at::Tensor & wrapper_CPU_Scalar___ilshift__(at::Tensor & self, const at::Scalar & other) { |
| 14472 | // No device check |
| 14473 | // DeviceGuard omitted |
| 14474 | return at::native::__ilshift__(self, other); |
| 14475 | } |
| 14476 | } // anonymous namespace |
| 14477 | namespace { |
| 14478 | at::Tensor wrapper_CPU_Tensor___lshift__(const at::Tensor & self, const at::Tensor & other) { |
| 14479 | // No device check |
| 14480 | // DeviceGuard omitted |
| 14481 | return at::native::__lshift__(self, other); |
| 14482 | } |
| 14483 | } // anonymous namespace |
| 14484 | namespace { |
| 14485 | at::Tensor & wrapper_CPU_Tensor___ilshift__(at::Tensor & self, const at::Tensor & other) { |
| 14486 | // No device check |
| 14487 | // DeviceGuard omitted |
| 14488 | return at::native::__ilshift__(self, other); |
| 14489 | } |
| 14490 | } // anonymous namespace |
| 14491 | struct structured_bitwise_left_shift_out_functional final : public at::native::structured_bitwise_left_shift_out { |
| 14492 | void set_output_strided( |
| 14493 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14494 | TensorOptions options, DimnameList names |
| 14495 | ) override { |
| 14496 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14497 | if (!names.empty()) { |
| 14498 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14499 | } |
| 14500 | // super must happen after, so that downstream can use maybe_get_output |
| 14501 | // to retrieve the output |
| 14502 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14503 | } |
| 14504 | void set_output_raw_strided( |
| 14505 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14506 | TensorOptions options, DimnameList names |
| 14507 | ) override { |
| 14508 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14509 | if (!names.empty()) { |
| 14510 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14511 | } |
| 14512 | // super must happen after, so that downstream can use maybe_get_output |
| 14513 | // to retrieve the output |
| 14514 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14515 | } |
| 14516 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14517 | return *outputs_[output_idx]; |
| 14518 | } |
| 14519 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14520 | }; |
| 14521 | at::Tensor wrapper_CPU_bitwise_left_shift_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14522 | structured_bitwise_left_shift_out_functional op; |
| 14523 | op.meta(self, other); |
| 14524 | op.impl(self, other, *op.outputs_[0]); |
| 14525 | return std::move(op.outputs_[0]).take(); |
| 14526 | } |
| 14527 | struct structured_bitwise_left_shift_out_out final : public at::native::structured_bitwise_left_shift_out { |
| 14528 | structured_bitwise_left_shift_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14529 | void set_output_strided( |
| 14530 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14531 | TensorOptions options, DimnameList names |
| 14532 | ) override { |
| 14533 | const auto& out = outputs_[output_idx].get(); |
| 14534 | resize_out(out, sizes, strides, options); |
| 14535 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14536 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14537 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14538 | } |
| 14539 | if (!names.empty()) { |
| 14540 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14541 | } |
| 14542 | // super must happen after, so that downstream can use maybe_get_output |
| 14543 | // to retrieve the output |
| 14544 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14545 | } |
| 14546 | void set_output_raw_strided( |
| 14547 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14548 | TensorOptions options, DimnameList names |
| 14549 | ) override { |
| 14550 | const auto& out = outputs_[output_idx].get(); |
| 14551 | resize_out(out, sizes, strides, options); |
| 14552 | if (!names.empty()) { |
| 14553 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14554 | } |
| 14555 | // super must happen after, so that downstream can use maybe_get_output |
| 14556 | // to retrieve the output |
| 14557 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14558 | } |
| 14559 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14560 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14561 | } |
| 14562 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14563 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14564 | }; |
| 14565 | at::Tensor & wrapper_CPU_bitwise_left_shift_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14566 | structured_bitwise_left_shift_out_out op(out); |
| 14567 | op.meta(self, other); |
| 14568 | op.impl(self, other, op.maybe_get_output(0)); |
| 14569 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14570 | return out; |
| 14571 | } |
| 14572 | struct structured_bitwise_left_shift_out_inplace final : public at::native::structured_bitwise_left_shift_out { |
| 14573 | structured_bitwise_left_shift_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14574 | void set_output_strided( |
| 14575 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14576 | TensorOptions options, DimnameList names |
| 14577 | ) override { |
| 14578 | const auto& out = outputs_[output_idx].get(); |
| 14579 | check_inplace(out, sizes, options); |
| 14580 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14581 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14582 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14583 | } |
| 14584 | if (!names.empty()) { |
| 14585 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14586 | } |
| 14587 | // super must happen after, so that downstream can use maybe_get_output |
| 14588 | // to retrieve the output |
| 14589 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14590 | } |
| 14591 | void set_output_raw_strided( |
| 14592 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14593 | TensorOptions options, DimnameList names |
| 14594 | ) override { |
| 14595 | const auto& out = outputs_[output_idx].get(); |
| 14596 | check_inplace(out, sizes, options); |
| 14597 | if (!names.empty()) { |
| 14598 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14599 | } |
| 14600 | // super must happen after, so that downstream can use maybe_get_output |
| 14601 | // to retrieve the output |
| 14602 | at::native::structured_bitwise_left_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14603 | } |
| 14604 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14605 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14606 | } |
| 14607 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14608 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14609 | }; |
| 14610 | at::Tensor & wrapper_CPU_bitwise_left_shift__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14611 | structured_bitwise_left_shift_out_inplace op(self); |
| 14612 | op.meta(self, other); |
| 14613 | op.impl(self, other, op.outputs_[0]); |
| 14614 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14615 | return self; |
| 14616 | } |
| 14617 | namespace { |
| 14618 | at::Tensor wrapper_CPU_Scalar___rshift__(const at::Tensor & self, const at::Scalar & other) { |
| 14619 | // No device check |
| 14620 | // DeviceGuard omitted |
| 14621 | return at::native::__rshift__(self, other); |
| 14622 | } |
| 14623 | } // anonymous namespace |
| 14624 | namespace { |
| 14625 | at::Tensor & wrapper_CPU_Scalar___irshift__(at::Tensor & self, const at::Scalar & other) { |
| 14626 | // No device check |
| 14627 | // DeviceGuard omitted |
| 14628 | return at::native::__irshift__(self, other); |
| 14629 | } |
| 14630 | } // anonymous namespace |
| 14631 | namespace { |
| 14632 | at::Tensor wrapper_CPU_Tensor___rshift__(const at::Tensor & self, const at::Tensor & other) { |
| 14633 | // No device check |
| 14634 | // DeviceGuard omitted |
| 14635 | return at::native::__rshift__(self, other); |
| 14636 | } |
| 14637 | } // anonymous namespace |
| 14638 | namespace { |
| 14639 | at::Tensor & wrapper_CPU_Tensor___irshift__(at::Tensor & self, const at::Tensor & other) { |
| 14640 | // No device check |
| 14641 | // DeviceGuard omitted |
| 14642 | return at::native::__irshift__(self, other); |
| 14643 | } |
| 14644 | } // anonymous namespace |
| 14645 | struct structured_bitwise_right_shift_out_functional final : public at::native::structured_bitwise_right_shift_out { |
| 14646 | void set_output_strided( |
| 14647 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14648 | TensorOptions options, DimnameList names |
| 14649 | ) override { |
| 14650 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14651 | if (!names.empty()) { |
| 14652 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14653 | } |
| 14654 | // super must happen after, so that downstream can use maybe_get_output |
| 14655 | // to retrieve the output |
| 14656 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14657 | } |
| 14658 | void set_output_raw_strided( |
| 14659 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14660 | TensorOptions options, DimnameList names |
| 14661 | ) override { |
| 14662 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14663 | if (!names.empty()) { |
| 14664 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14665 | } |
| 14666 | // super must happen after, so that downstream can use maybe_get_output |
| 14667 | // to retrieve the output |
| 14668 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14669 | } |
| 14670 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14671 | return *outputs_[output_idx]; |
| 14672 | } |
| 14673 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14674 | }; |
| 14675 | at::Tensor wrapper_CPU_bitwise_right_shift_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14676 | structured_bitwise_right_shift_out_functional op; |
| 14677 | op.meta(self, other); |
| 14678 | op.impl(self, other, *op.outputs_[0]); |
| 14679 | return std::move(op.outputs_[0]).take(); |
| 14680 | } |
| 14681 | struct structured_bitwise_right_shift_out_out final : public at::native::structured_bitwise_right_shift_out { |
| 14682 | structured_bitwise_right_shift_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14683 | void set_output_strided( |
| 14684 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14685 | TensorOptions options, DimnameList names |
| 14686 | ) override { |
| 14687 | const auto& out = outputs_[output_idx].get(); |
| 14688 | resize_out(out, sizes, strides, options); |
| 14689 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14690 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14691 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14692 | } |
| 14693 | if (!names.empty()) { |
| 14694 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14695 | } |
| 14696 | // super must happen after, so that downstream can use maybe_get_output |
| 14697 | // to retrieve the output |
| 14698 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14699 | } |
| 14700 | void set_output_raw_strided( |
| 14701 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14702 | TensorOptions options, DimnameList names |
| 14703 | ) override { |
| 14704 | const auto& out = outputs_[output_idx].get(); |
| 14705 | resize_out(out, sizes, strides, options); |
| 14706 | if (!names.empty()) { |
| 14707 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14708 | } |
| 14709 | // super must happen after, so that downstream can use maybe_get_output |
| 14710 | // to retrieve the output |
| 14711 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14712 | } |
| 14713 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14714 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14715 | } |
| 14716 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14717 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14718 | }; |
| 14719 | at::Tensor & wrapper_CPU_bitwise_right_shift_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14720 | structured_bitwise_right_shift_out_out op(out); |
| 14721 | op.meta(self, other); |
| 14722 | op.impl(self, other, op.maybe_get_output(0)); |
| 14723 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14724 | return out; |
| 14725 | } |
| 14726 | struct structured_bitwise_right_shift_out_inplace final : public at::native::structured_bitwise_right_shift_out { |
| 14727 | structured_bitwise_right_shift_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14728 | void set_output_strided( |
| 14729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14730 | TensorOptions options, DimnameList names |
| 14731 | ) override { |
| 14732 | const auto& out = outputs_[output_idx].get(); |
| 14733 | check_inplace(out, sizes, options); |
| 14734 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14735 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14736 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14737 | } |
| 14738 | if (!names.empty()) { |
| 14739 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14740 | } |
| 14741 | // super must happen after, so that downstream can use maybe_get_output |
| 14742 | // to retrieve the output |
| 14743 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14744 | } |
| 14745 | void set_output_raw_strided( |
| 14746 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14747 | TensorOptions options, DimnameList names |
| 14748 | ) override { |
| 14749 | const auto& out = outputs_[output_idx].get(); |
| 14750 | check_inplace(out, sizes, options); |
| 14751 | if (!names.empty()) { |
| 14752 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14753 | } |
| 14754 | // super must happen after, so that downstream can use maybe_get_output |
| 14755 | // to retrieve the output |
| 14756 | at::native::structured_bitwise_right_shift_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14757 | } |
| 14758 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14759 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14760 | } |
| 14761 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14762 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14763 | }; |
| 14764 | at::Tensor & wrapper_CPU_bitwise_right_shift__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14765 | structured_bitwise_right_shift_out_inplace op(self); |
| 14766 | op.meta(self, other); |
| 14767 | op.impl(self, other, op.outputs_[0]); |
| 14768 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14769 | return self; |
| 14770 | } |
| 14771 | struct structured_tril_cpu_functional final : public at::native::structured_tril_cpu { |
| 14772 | void set_output_strided( |
| 14773 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14774 | TensorOptions options, DimnameList names |
| 14775 | ) override { |
| 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 | void set_output_raw_strided( |
| 14784 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14785 | TensorOptions options, DimnameList names |
| 14786 | ) override { |
| 14787 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14788 | if (!names.empty()) { |
| 14789 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14790 | } |
| 14791 | // super must happen after, so that downstream can use maybe_get_output |
| 14792 | // to retrieve the output |
| 14793 | } |
| 14794 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14795 | return *outputs_[output_idx]; |
| 14796 | } |
| 14797 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14798 | }; |
| 14799 | at::Tensor wrapper_CPU_tril(const at::Tensor & self, int64_t diagonal) { |
| 14800 | structured_tril_cpu_functional op; |
| 14801 | op.meta(self, diagonal); |
| 14802 | op.impl(self, diagonal, *op.outputs_[0]); |
| 14803 | return std::move(op.outputs_[0]).take(); |
| 14804 | } |
| 14805 | struct structured_tril_cpu_out final : public at::native::structured_tril_cpu { |
| 14806 | structured_tril_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14807 | void set_output_strided( |
| 14808 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14809 | TensorOptions options, DimnameList names |
| 14810 | ) override { |
| 14811 | const auto& out = outputs_[output_idx].get(); |
| 14812 | resize_out(out, sizes, strides, options); |
| 14813 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14814 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14815 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14816 | } |
| 14817 | if (!names.empty()) { |
| 14818 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14819 | } |
| 14820 | // super must happen after, so that downstream can use maybe_get_output |
| 14821 | // to retrieve the output |
| 14822 | } |
| 14823 | void set_output_raw_strided( |
| 14824 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14825 | TensorOptions options, DimnameList names |
| 14826 | ) override { |
| 14827 | const auto& out = outputs_[output_idx].get(); |
| 14828 | resize_out(out, sizes, strides, options); |
| 14829 | if (!names.empty()) { |
| 14830 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14831 | } |
| 14832 | // super must happen after, so that downstream can use maybe_get_output |
| 14833 | // to retrieve the output |
| 14834 | } |
| 14835 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14836 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14837 | } |
| 14838 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14839 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14840 | }; |
| 14841 | at::Tensor & wrapper_CPU_tril_out_out(const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 14842 | structured_tril_cpu_out op(out); |
| 14843 | op.meta(self, diagonal); |
| 14844 | op.impl(self, diagonal, op.maybe_get_output(0)); |
| 14845 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14846 | return out; |
| 14847 | } |
| 14848 | struct structured_tril_cpu_inplace final : public at::native::structured_tril_cpu { |
| 14849 | structured_tril_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14850 | void set_output_strided( |
| 14851 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14852 | TensorOptions options, DimnameList names |
| 14853 | ) override { |
| 14854 | const auto& out = outputs_[output_idx].get(); |
| 14855 | check_inplace(out, sizes, options); |
| 14856 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14857 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14858 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14859 | } |
| 14860 | if (!names.empty()) { |
| 14861 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14862 | } |
| 14863 | // super must happen after, so that downstream can use maybe_get_output |
| 14864 | // to retrieve the output |
| 14865 | } |
| 14866 | void set_output_raw_strided( |
| 14867 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14868 | TensorOptions options, DimnameList names |
| 14869 | ) override { |
| 14870 | const auto& out = outputs_[output_idx].get(); |
| 14871 | check_inplace(out, sizes, options); |
| 14872 | if (!names.empty()) { |
| 14873 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14874 | } |
| 14875 | // super must happen after, so that downstream can use maybe_get_output |
| 14876 | // to retrieve the output |
| 14877 | } |
| 14878 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14879 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14880 | } |
| 14881 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14882 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14883 | }; |
| 14884 | at::Tensor & wrapper_CPU_tril_(at::Tensor & self, int64_t diagonal) { |
| 14885 | structured_tril_cpu_inplace op(self); |
| 14886 | op.meta(self, diagonal); |
| 14887 | op.impl(self, diagonal, op.outputs_[0]); |
| 14888 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14889 | return self; |
| 14890 | } |
| 14891 | struct structured_triu_cpu_functional final : public at::native::structured_triu_cpu { |
| 14892 | void set_output_strided( |
| 14893 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14894 | TensorOptions options, DimnameList names |
| 14895 | ) override { |
| 14896 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14897 | if (!names.empty()) { |
| 14898 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14899 | } |
| 14900 | // super must happen after, so that downstream can use maybe_get_output |
| 14901 | // to retrieve the output |
| 14902 | } |
| 14903 | void set_output_raw_strided( |
| 14904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14905 | TensorOptions options, DimnameList names |
| 14906 | ) override { |
| 14907 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14908 | if (!names.empty()) { |
| 14909 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14910 | } |
| 14911 | // super must happen after, so that downstream can use maybe_get_output |
| 14912 | // to retrieve the output |
| 14913 | } |
| 14914 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14915 | return *outputs_[output_idx]; |
| 14916 | } |
| 14917 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14918 | }; |
| 14919 | at::Tensor wrapper_CPU_triu(const at::Tensor & self, int64_t diagonal) { |
| 14920 | structured_triu_cpu_functional op; |
| 14921 | op.meta(self, diagonal); |
| 14922 | op.impl(self, diagonal, *op.outputs_[0]); |
| 14923 | return std::move(op.outputs_[0]).take(); |
| 14924 | } |
| 14925 | struct structured_triu_cpu_out final : public at::native::structured_triu_cpu { |
| 14926 | structured_triu_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 14927 | void set_output_strided( |
| 14928 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14929 | TensorOptions options, DimnameList names |
| 14930 | ) override { |
| 14931 | const auto& out = outputs_[output_idx].get(); |
| 14932 | resize_out(out, sizes, strides, options); |
| 14933 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14934 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14935 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14936 | } |
| 14937 | if (!names.empty()) { |
| 14938 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14939 | } |
| 14940 | // super must happen after, so that downstream can use maybe_get_output |
| 14941 | // to retrieve the output |
| 14942 | } |
| 14943 | void set_output_raw_strided( |
| 14944 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14945 | TensorOptions options, DimnameList names |
| 14946 | ) override { |
| 14947 | const auto& out = outputs_[output_idx].get(); |
| 14948 | resize_out(out, sizes, strides, options); |
| 14949 | if (!names.empty()) { |
| 14950 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14951 | } |
| 14952 | // super must happen after, so that downstream can use maybe_get_output |
| 14953 | // to retrieve the output |
| 14954 | } |
| 14955 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14956 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14957 | } |
| 14958 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14959 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14960 | }; |
| 14961 | at::Tensor & wrapper_CPU_triu_out_out(const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 14962 | structured_triu_cpu_out op(out); |
| 14963 | op.meta(self, diagonal); |
| 14964 | op.impl(self, diagonal, op.maybe_get_output(0)); |
| 14965 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14966 | return out; |
| 14967 | } |
| 14968 | struct structured_triu_cpu_inplace final : public at::native::structured_triu_cpu { |
| 14969 | structured_triu_cpu_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14970 | void set_output_strided( |
| 14971 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14972 | TensorOptions options, DimnameList names |
| 14973 | ) override { |
| 14974 | const auto& out = outputs_[output_idx].get(); |
| 14975 | check_inplace(out, sizes, options); |
| 14976 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14977 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14978 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14979 | } |
| 14980 | if (!names.empty()) { |
| 14981 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14982 | } |
| 14983 | // super must happen after, so that downstream can use maybe_get_output |
| 14984 | // to retrieve the output |
| 14985 | } |
| 14986 | void set_output_raw_strided( |
| 14987 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14988 | TensorOptions options, DimnameList names |
| 14989 | ) override { |
| 14990 | const auto& out = outputs_[output_idx].get(); |
| 14991 | check_inplace(out, sizes, options); |
| 14992 | if (!names.empty()) { |
| 14993 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14994 | } |
| 14995 | // super must happen after, so that downstream can use maybe_get_output |
| 14996 | // to retrieve the output |
| 14997 | } |
| 14998 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14999 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15000 | } |
| 15001 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15002 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15003 | }; |
| 15004 | at::Tensor & wrapper_CPU_triu_(at::Tensor & self, int64_t diagonal) { |
| 15005 | structured_triu_cpu_inplace op(self); |
| 15006 | op.meta(self, diagonal); |
| 15007 | op.impl(self, diagonal, op.outputs_[0]); |
| 15008 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15009 | return self; |
| 15010 | } |
| 15011 | struct structured_digamma_out_functional final : public at::native::structured_digamma_out { |
| 15012 | void set_output_strided( |
| 15013 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15014 | TensorOptions options, DimnameList names |
| 15015 | ) override { |
| 15016 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15017 | if (!names.empty()) { |
| 15018 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15019 | } |
| 15020 | // super must happen after, so that downstream can use maybe_get_output |
| 15021 | // to retrieve the output |
| 15022 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15023 | } |
| 15024 | void set_output_raw_strided( |
| 15025 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15026 | TensorOptions options, DimnameList names |
| 15027 | ) override { |
| 15028 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15029 | if (!names.empty()) { |
| 15030 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15031 | } |
| 15032 | // super must happen after, so that downstream can use maybe_get_output |
| 15033 | // to retrieve the output |
| 15034 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15035 | } |
| 15036 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15037 | return *outputs_[output_idx]; |
| 15038 | } |
| 15039 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15040 | }; |
| 15041 | at::Tensor wrapper_CPU_digamma(const at::Tensor & self) { |
| 15042 | structured_digamma_out_functional op; |
| 15043 | op.meta(self); |
| 15044 | op.impl(self, *op.outputs_[0]); |
| 15045 | return std::move(op.outputs_[0]).take(); |
| 15046 | } |
| 15047 | struct structured_digamma_out_out final : public at::native::structured_digamma_out { |
| 15048 | structured_digamma_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15049 | void set_output_strided( |
| 15050 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15051 | TensorOptions options, DimnameList names |
| 15052 | ) override { |
| 15053 | const auto& out = outputs_[output_idx].get(); |
| 15054 | resize_out(out, sizes, strides, options); |
| 15055 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15056 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15057 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15058 | } |
| 15059 | if (!names.empty()) { |
| 15060 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15061 | } |
| 15062 | // super must happen after, so that downstream can use maybe_get_output |
| 15063 | // to retrieve the output |
| 15064 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15065 | } |
| 15066 | void set_output_raw_strided( |
| 15067 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15068 | TensorOptions options, DimnameList names |
| 15069 | ) override { |
| 15070 | const auto& out = outputs_[output_idx].get(); |
| 15071 | resize_out(out, sizes, strides, options); |
| 15072 | if (!names.empty()) { |
| 15073 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15074 | } |
| 15075 | // super must happen after, so that downstream can use maybe_get_output |
| 15076 | // to retrieve the output |
| 15077 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15078 | } |
| 15079 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15080 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15081 | } |
| 15082 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15083 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15084 | }; |
| 15085 | at::Tensor & wrapper_CPU_digamma_out_out(const at::Tensor & self, at::Tensor & out) { |
| 15086 | structured_digamma_out_out op(out); |
| 15087 | op.meta(self); |
| 15088 | op.impl(self, op.maybe_get_output(0)); |
| 15089 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15090 | return out; |
| 15091 | } |
| 15092 | struct structured_digamma_out_inplace final : public at::native::structured_digamma_out { |
| 15093 | structured_digamma_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15094 | void set_output_strided( |
| 15095 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15096 | TensorOptions options, DimnameList names |
| 15097 | ) override { |
| 15098 | const auto& out = outputs_[output_idx].get(); |
| 15099 | check_inplace(out, sizes, options); |
| 15100 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15101 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15102 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15103 | } |
| 15104 | if (!names.empty()) { |
| 15105 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15106 | } |
| 15107 | // super must happen after, so that downstream can use maybe_get_output |
| 15108 | // to retrieve the output |
| 15109 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15110 | } |
| 15111 | void set_output_raw_strided( |
| 15112 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15113 | TensorOptions options, DimnameList names |
| 15114 | ) override { |
| 15115 | const auto& out = outputs_[output_idx].get(); |
| 15116 | check_inplace(out, sizes, options); |
| 15117 | if (!names.empty()) { |
| 15118 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15119 | } |
| 15120 | // super must happen after, so that downstream can use maybe_get_output |
| 15121 | // to retrieve the output |
| 15122 | at::native::structured_digamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15123 | } |
| 15124 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15125 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15126 | } |
| 15127 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15128 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15129 | }; |
| 15130 | at::Tensor & wrapper_CPU_digamma_(at::Tensor & self) { |
| 15131 | structured_digamma_out_inplace op(self); |
| 15132 | op.meta(self); |
| 15133 | op.impl(self, op.outputs_[0]); |
| 15134 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15135 | return self; |
| 15136 | } |
| 15137 | struct structured_lerp_Scalar_functional final : public at::native::structured_lerp_Scalar { |
| 15138 | void set_output_strided( |
| 15139 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15140 | TensorOptions options, DimnameList names |
| 15141 | ) override { |
| 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::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15149 | } |
| 15150 | void set_output_raw_strided( |
| 15151 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15152 | TensorOptions options, DimnameList names |
| 15153 | ) override { |
| 15154 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15155 | if (!names.empty()) { |
| 15156 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15157 | } |
| 15158 | // super must happen after, so that downstream can use maybe_get_output |
| 15159 | // to retrieve the output |
| 15160 | at::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15161 | } |
| 15162 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15163 | return *outputs_[output_idx]; |
| 15164 | } |
| 15165 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15166 | }; |
| 15167 | at::Tensor wrapper_CPU_lerp_Scalar(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 15168 | structured_lerp_Scalar_functional op; |
| 15169 | op.meta(self, end, weight); |
| 15170 | op.impl(self, end, weight, *op.outputs_[0]); |
| 15171 | return std::move(op.outputs_[0]).take(); |
| 15172 | } |
| 15173 | struct structured_lerp_Scalar_out final : public at::native::structured_lerp_Scalar { |
| 15174 | structured_lerp_Scalar_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15175 | void set_output_strided( |
| 15176 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15177 | TensorOptions options, DimnameList names |
| 15178 | ) override { |
| 15179 | const auto& out = outputs_[output_idx].get(); |
| 15180 | resize_out(out, sizes, strides, options); |
| 15181 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15182 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15183 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15184 | } |
| 15185 | if (!names.empty()) { |
| 15186 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15187 | } |
| 15188 | // super must happen after, so that downstream can use maybe_get_output |
| 15189 | // to retrieve the output |
| 15190 | at::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15191 | } |
| 15192 | void set_output_raw_strided( |
| 15193 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15194 | TensorOptions options, DimnameList names |
| 15195 | ) override { |
| 15196 | const auto& out = outputs_[output_idx].get(); |
| 15197 | resize_out(out, sizes, strides, options); |
| 15198 | if (!names.empty()) { |
| 15199 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15200 | } |
| 15201 | // super must happen after, so that downstream can use maybe_get_output |
| 15202 | // to retrieve the output |
| 15203 | at::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15204 | } |
| 15205 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15206 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15207 | } |
| 15208 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15209 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15210 | }; |
| 15211 | at::Tensor & wrapper_CPU_lerp_out_Scalar_out(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { |
| 15212 | structured_lerp_Scalar_out op(out); |
| 15213 | op.meta(self, end, weight); |
| 15214 | op.impl(self, end, weight, op.maybe_get_output(0)); |
| 15215 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15216 | return out; |
| 15217 | } |
| 15218 | struct structured_lerp_Scalar_inplace final : public at::native::structured_lerp_Scalar { |
| 15219 | structured_lerp_Scalar_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15220 | void set_output_strided( |
| 15221 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15222 | TensorOptions options, DimnameList names |
| 15223 | ) override { |
| 15224 | const auto& out = outputs_[output_idx].get(); |
| 15225 | check_inplace(out, sizes, options); |
| 15226 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15227 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15228 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15229 | } |
| 15230 | if (!names.empty()) { |
| 15231 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15232 | } |
| 15233 | // super must happen after, so that downstream can use maybe_get_output |
| 15234 | // to retrieve the output |
| 15235 | at::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15236 | } |
| 15237 | void set_output_raw_strided( |
| 15238 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15239 | TensorOptions options, DimnameList names |
| 15240 | ) override { |
| 15241 | const auto& out = outputs_[output_idx].get(); |
| 15242 | check_inplace(out, sizes, options); |
| 15243 | if (!names.empty()) { |
| 15244 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15245 | } |
| 15246 | // super must happen after, so that downstream can use maybe_get_output |
| 15247 | // to retrieve the output |
| 15248 | at::native::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15249 | } |
| 15250 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15251 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15252 | } |
| 15253 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15254 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15255 | }; |
| 15256 | at::Tensor & wrapper_CPU_lerp__Scalar(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 15257 | structured_lerp_Scalar_inplace op(self); |
| 15258 | op.meta(self, end, weight); |
| 15259 | op.impl(self, end, weight, op.outputs_[0]); |
| 15260 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15261 | return self; |
| 15262 | } |
| 15263 | struct structured_lerp_Tensor_functional final : public at::native::structured_lerp_Tensor { |
| 15264 | void set_output_strided( |
| 15265 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15266 | TensorOptions options, DimnameList names |
| 15267 | ) override { |
| 15268 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15269 | if (!names.empty()) { |
| 15270 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15271 | } |
| 15272 | // super must happen after, so that downstream can use maybe_get_output |
| 15273 | // to retrieve the output |
| 15274 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15275 | } |
| 15276 | void set_output_raw_strided( |
| 15277 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15278 | TensorOptions options, DimnameList names |
| 15279 | ) override { |
| 15280 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15281 | if (!names.empty()) { |
| 15282 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15283 | } |
| 15284 | // super must happen after, so that downstream can use maybe_get_output |
| 15285 | // to retrieve the output |
| 15286 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15287 | } |
| 15288 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15289 | return *outputs_[output_idx]; |
| 15290 | } |
| 15291 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15292 | }; |
| 15293 | at::Tensor wrapper_CPU_lerp_Tensor(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 15294 | structured_lerp_Tensor_functional op; |
| 15295 | op.meta(self, end, weight); |
| 15296 | op.impl(self, end, weight, *op.outputs_[0]); |
| 15297 | return std::move(op.outputs_[0]).take(); |
| 15298 | } |
| 15299 | struct structured_lerp_Tensor_out final : public at::native::structured_lerp_Tensor { |
| 15300 | structured_lerp_Tensor_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15301 | void set_output_strided( |
| 15302 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15303 | TensorOptions options, DimnameList names |
| 15304 | ) override { |
| 15305 | const auto& out = outputs_[output_idx].get(); |
| 15306 | resize_out(out, sizes, strides, options); |
| 15307 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15308 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15309 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15310 | } |
| 15311 | if (!names.empty()) { |
| 15312 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15313 | } |
| 15314 | // super must happen after, so that downstream can use maybe_get_output |
| 15315 | // to retrieve the output |
| 15316 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15317 | } |
| 15318 | void set_output_raw_strided( |
| 15319 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15320 | TensorOptions options, DimnameList names |
| 15321 | ) override { |
| 15322 | const auto& out = outputs_[output_idx].get(); |
| 15323 | resize_out(out, sizes, strides, options); |
| 15324 | if (!names.empty()) { |
| 15325 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15326 | } |
| 15327 | // super must happen after, so that downstream can use maybe_get_output |
| 15328 | // to retrieve the output |
| 15329 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15330 | } |
| 15331 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15332 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15333 | } |
| 15334 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15335 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15336 | }; |
| 15337 | at::Tensor & wrapper_CPU_lerp_out_Tensor_out(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { |
| 15338 | structured_lerp_Tensor_out op(out); |
| 15339 | op.meta(self, end, weight); |
| 15340 | op.impl(self, end, weight, op.maybe_get_output(0)); |
| 15341 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15342 | return out; |
| 15343 | } |
| 15344 | struct structured_lerp_Tensor_inplace final : public at::native::structured_lerp_Tensor { |
| 15345 | structured_lerp_Tensor_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15346 | void set_output_strided( |
| 15347 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15348 | TensorOptions options, DimnameList names |
| 15349 | ) override { |
| 15350 | const auto& out = outputs_[output_idx].get(); |
| 15351 | check_inplace(out, sizes, options); |
| 15352 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15353 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15354 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15355 | } |
| 15356 | if (!names.empty()) { |
| 15357 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15358 | } |
| 15359 | // super must happen after, so that downstream can use maybe_get_output |
| 15360 | // to retrieve the output |
| 15361 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15362 | } |
| 15363 | void set_output_raw_strided( |
| 15364 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15365 | TensorOptions options, DimnameList names |
| 15366 | ) override { |
| 15367 | const auto& out = outputs_[output_idx].get(); |
| 15368 | check_inplace(out, sizes, options); |
| 15369 | if (!names.empty()) { |
| 15370 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15371 | } |
| 15372 | // super must happen after, so that downstream can use maybe_get_output |
| 15373 | // to retrieve the output |
| 15374 | at::native::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15375 | } |
| 15376 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15377 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15378 | } |
| 15379 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15380 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15381 | }; |
| 15382 | at::Tensor & wrapper_CPU_lerp__Tensor(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 15383 | structured_lerp_Tensor_inplace op(self); |
| 15384 | op.meta(self, end, weight); |
| 15385 | op.impl(self, end, weight, op.outputs_[0]); |
| 15386 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15387 | return self; |
| 15388 | } |
| 15389 | namespace { |
| 15390 | at::Tensor wrapper_CPU__addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 15391 | // No device check |
| 15392 | // DeviceGuard omitted |
| 15393 | return at::native::addbmm(self, batch1, batch2, beta, alpha); |
| 15394 | } |
| 15395 | } // anonymous namespace |
| 15396 | namespace { |
| 15397 | at::Tensor & wrapper_CPU_out_addbmm_out(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 15398 | // No device check |
| 15399 | // DeviceGuard omitted |
| 15400 | return at::native::addbmm_out(self, batch1, batch2, beta, alpha, out); |
| 15401 | } |
| 15402 | } // anonymous namespace |
| 15403 | namespace { |
| 15404 | at::Tensor & wrapper_CPU__addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 15405 | // No device check |
| 15406 | // DeviceGuard omitted |
| 15407 | return at::native::addbmm_(self, batch1, batch2, beta, alpha); |
| 15408 | } |
| 15409 | } // anonymous namespace |
| 15410 | namespace { |
| 15411 | at::Tensor & wrapper_CPU_from_random_(at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 15412 | // No device check |
| 15413 | // DeviceGuard omitted |
| 15414 | return at::native::random_(self, from, to, generator); |
| 15415 | } |
| 15416 | } // anonymous namespace |
| 15417 | namespace { |
| 15418 | at::Tensor & wrapper_CPU_to_random_(at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 15419 | // No device check |
| 15420 | // DeviceGuard omitted |
| 15421 | return at::native::random_(self, to, generator); |
| 15422 | } |
| 15423 | } // anonymous namespace |
| 15424 | namespace { |
| 15425 | at::Tensor & wrapper_CPU__random_(at::Tensor & self, c10::optional<at::Generator> generator) { |
| 15426 | // No device check |
| 15427 | // DeviceGuard omitted |
| 15428 | return at::native::random_(self, generator); |
| 15429 | } |
| 15430 | } // anonymous namespace |
| 15431 | namespace { |
| 15432 | at::Tensor & wrapper_CPU__uniform_(at::Tensor & self, double from, double to, c10::optional<at::Generator> generator) { |
| 15433 | // No device check |
| 15434 | // DeviceGuard omitted |
| 15435 | return at::native::uniform_(self, from, to, generator); |
| 15436 | } |
| 15437 | } // anonymous namespace |
| 15438 | namespace { |
| 15439 | at::Tensor & wrapper_CPU__cauchy_(at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 15440 | // No device check |
| 15441 | // DeviceGuard omitted |
| 15442 | return at::native::cauchy_(self, median, sigma, generator); |
| 15443 | } |
| 15444 | } // anonymous namespace |
| 15445 | namespace { |
| 15446 | at::Tensor & wrapper_CPU__log_normal_(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 15447 | // No device check |
| 15448 | // DeviceGuard omitted |
| 15449 | return at::native::log_normal_(self, mean, std, generator); |
| 15450 | } |
| 15451 | } // anonymous namespace |
| 15452 | namespace { |
| 15453 | at::Tensor & wrapper_CPU__exponential_(at::Tensor & self, double lambd, c10::optional<at::Generator> generator) { |
| 15454 | // No device check |
| 15455 | // DeviceGuard omitted |
| 15456 | return at::native::exponential_(self, lambd, generator); |
| 15457 | } |
| 15458 | } // anonymous namespace |
| 15459 | namespace { |
| 15460 | at::Tensor & wrapper_CPU__geometric_(at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 15461 | // No device check |
| 15462 | // DeviceGuard omitted |
| 15463 | return at::native::geometric_(self, p, generator); |
| 15464 | } |
| 15465 | } // anonymous namespace |
| 15466 | namespace { |
| 15467 | at::Tensor wrapper_CPU__tril_indices(int64_t row, int64_t col, int64_t offset, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 15468 | // No device check |
| 15469 | // DeviceGuard omitted |
| 15470 | return at::native::tril_indices_cpu(row, col, offset, dtype, layout, device, pin_memory); |
| 15471 | } |
| 15472 | } // anonymous namespace |
| 15473 | namespace { |
| 15474 | at::Tensor wrapper_CPU__triu_indices(int64_t row, int64_t col, int64_t offset, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 15475 | // No device check |
| 15476 | // DeviceGuard omitted |
| 15477 | return at::native::triu_indices_cpu(row, col, offset, dtype, layout, device, pin_memory); |
| 15478 | } |
| 15479 | } // anonymous namespace |
| 15480 | namespace { |
| 15481 | at::Tensor wrapper_CPU__trace(const at::Tensor & self) { |
| 15482 | // No device check |
| 15483 | // DeviceGuard omitted |
| 15484 | return at::native::trace_cpu(self); |
| 15485 | } |
| 15486 | } // anonymous namespace |
| 15487 | struct structured_ne_Scalar_out_functional final : public at::native::structured_ne_Scalar_out { |
| 15488 | void set_output_strided( |
| 15489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15490 | TensorOptions options, DimnameList names |
| 15491 | ) override { |
| 15492 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15493 | if (!names.empty()) { |
| 15494 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15495 | } |
| 15496 | // super must happen after, so that downstream can use maybe_get_output |
| 15497 | // to retrieve the output |
| 15498 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15499 | } |
| 15500 | void set_output_raw_strided( |
| 15501 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15502 | TensorOptions options, DimnameList names |
| 15503 | ) override { |
| 15504 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15505 | if (!names.empty()) { |
| 15506 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15507 | } |
| 15508 | // super must happen after, so that downstream can use maybe_get_output |
| 15509 | // to retrieve the output |
| 15510 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15511 | } |
| 15512 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15513 | return *outputs_[output_idx]; |
| 15514 | } |
| 15515 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15516 | }; |
| 15517 | at::Tensor wrapper_CPU_ne_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 15518 | structured_ne_Scalar_out_functional op; |
| 15519 | op.meta(self, other); |
| 15520 | op.impl(self, other, *op.outputs_[0]); |
| 15521 | return std::move(op.outputs_[0]).take(); |
| 15522 | } |
| 15523 | struct structured_ne_Scalar_out_out final : public at::native::structured_ne_Scalar_out { |
| 15524 | structured_ne_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15525 | void set_output_strided( |
| 15526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15527 | TensorOptions options, DimnameList names |
| 15528 | ) override { |
| 15529 | const auto& out = outputs_[output_idx].get(); |
| 15530 | resize_out(out, sizes, strides, options); |
| 15531 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15532 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15533 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15534 | } |
| 15535 | if (!names.empty()) { |
| 15536 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15537 | } |
| 15538 | // super must happen after, so that downstream can use maybe_get_output |
| 15539 | // to retrieve the output |
| 15540 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15541 | } |
| 15542 | void set_output_raw_strided( |
| 15543 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15544 | TensorOptions options, DimnameList names |
| 15545 | ) override { |
| 15546 | const auto& out = outputs_[output_idx].get(); |
| 15547 | resize_out(out, sizes, strides, options); |
| 15548 | if (!names.empty()) { |
| 15549 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15550 | } |
| 15551 | // super must happen after, so that downstream can use maybe_get_output |
| 15552 | // to retrieve the output |
| 15553 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15554 | } |
| 15555 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15556 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15557 | } |
| 15558 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15559 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15560 | }; |
| 15561 | at::Tensor & wrapper_CPU_ne_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 15562 | structured_ne_Scalar_out_out op(out); |
| 15563 | op.meta(self, other); |
| 15564 | op.impl(self, other, op.maybe_get_output(0)); |
| 15565 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15566 | return out; |
| 15567 | } |
| 15568 | struct structured_ne_Scalar_out_inplace final : public at::native::structured_ne_Scalar_out { |
| 15569 | structured_ne_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15570 | void set_output_strided( |
| 15571 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15572 | TensorOptions options, DimnameList names |
| 15573 | ) override { |
| 15574 | const auto& out = outputs_[output_idx].get(); |
| 15575 | check_inplace(out, sizes, options); |
| 15576 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15577 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15578 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15579 | } |
| 15580 | if (!names.empty()) { |
| 15581 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15582 | } |
| 15583 | // super must happen after, so that downstream can use maybe_get_output |
| 15584 | // to retrieve the output |
| 15585 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15586 | } |
| 15587 | void set_output_raw_strided( |
| 15588 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15589 | TensorOptions options, DimnameList names |
| 15590 | ) override { |
| 15591 | const auto& out = outputs_[output_idx].get(); |
| 15592 | check_inplace(out, sizes, options); |
| 15593 | if (!names.empty()) { |
| 15594 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15595 | } |
| 15596 | // super must happen after, so that downstream can use maybe_get_output |
| 15597 | // to retrieve the output |
| 15598 | at::native::structured_ne_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15599 | } |
| 15600 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15601 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15602 | } |
| 15603 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15604 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15605 | }; |
| 15606 | at::Tensor & wrapper_CPU_ne__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 15607 | structured_ne_Scalar_out_inplace op(self); |
| 15608 | op.meta(self, other); |
| 15609 | op.impl(self, other, op.outputs_[0]); |
| 15610 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15611 | return self; |
| 15612 | } |
| 15613 | struct structured_ne_Tensor_out_functional final : public at::native::structured_ne_Tensor_out { |
| 15614 | void set_output_strided( |
| 15615 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15616 | TensorOptions options, DimnameList names |
| 15617 | ) override { |
| 15618 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15619 | if (!names.empty()) { |
| 15620 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15621 | } |
| 15622 | // super must happen after, so that downstream can use maybe_get_output |
| 15623 | // to retrieve the output |
| 15624 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15625 | } |
| 15626 | void set_output_raw_strided( |
| 15627 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15628 | TensorOptions options, DimnameList names |
| 15629 | ) override { |
| 15630 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15631 | if (!names.empty()) { |
| 15632 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15633 | } |
| 15634 | // super must happen after, so that downstream can use maybe_get_output |
| 15635 | // to retrieve the output |
| 15636 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15637 | } |
| 15638 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15639 | return *outputs_[output_idx]; |
| 15640 | } |
| 15641 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15642 | }; |
| 15643 | at::Tensor wrapper_CPU_ne_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 15644 | structured_ne_Tensor_out_functional op; |
| 15645 | op.meta(self, other); |
| 15646 | op.impl(self, other, *op.outputs_[0]); |
| 15647 | return std::move(op.outputs_[0]).take(); |
| 15648 | } |
| 15649 | struct structured_ne_Tensor_out_out final : public at::native::structured_ne_Tensor_out { |
| 15650 | structured_ne_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15651 | void set_output_strided( |
| 15652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15653 | TensorOptions options, DimnameList names |
| 15654 | ) override { |
| 15655 | const auto& out = outputs_[output_idx].get(); |
| 15656 | resize_out(out, sizes, strides, options); |
| 15657 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15658 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15659 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15660 | } |
| 15661 | if (!names.empty()) { |
| 15662 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15663 | } |
| 15664 | // super must happen after, so that downstream can use maybe_get_output |
| 15665 | // to retrieve the output |
| 15666 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15667 | } |
| 15668 | void set_output_raw_strided( |
| 15669 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15670 | TensorOptions options, DimnameList names |
| 15671 | ) override { |
| 15672 | const auto& out = outputs_[output_idx].get(); |
| 15673 | resize_out(out, sizes, strides, options); |
| 15674 | if (!names.empty()) { |
| 15675 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15676 | } |
| 15677 | // super must happen after, so that downstream can use maybe_get_output |
| 15678 | // to retrieve the output |
| 15679 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15680 | } |
| 15681 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15682 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15683 | } |
| 15684 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15685 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15686 | }; |
| 15687 | at::Tensor & wrapper_CPU_ne_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 15688 | structured_ne_Tensor_out_out op(out); |
| 15689 | op.meta(self, other); |
| 15690 | op.impl(self, other, op.maybe_get_output(0)); |
| 15691 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15692 | return out; |
| 15693 | } |
| 15694 | struct structured_ne_Tensor_out_inplace final : public at::native::structured_ne_Tensor_out { |
| 15695 | structured_ne_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15696 | void set_output_strided( |
| 15697 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15698 | TensorOptions options, DimnameList names |
| 15699 | ) override { |
| 15700 | const auto& out = outputs_[output_idx].get(); |
| 15701 | check_inplace(out, sizes, options); |
| 15702 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15703 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15704 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15705 | } |
| 15706 | if (!names.empty()) { |
| 15707 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15708 | } |
| 15709 | // super must happen after, so that downstream can use maybe_get_output |
| 15710 | // to retrieve the output |
| 15711 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15712 | } |
| 15713 | void set_output_raw_strided( |
| 15714 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15715 | TensorOptions options, DimnameList names |
| 15716 | ) override { |
| 15717 | const auto& out = outputs_[output_idx].get(); |
| 15718 | check_inplace(out, sizes, options); |
| 15719 | if (!names.empty()) { |
| 15720 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15721 | } |
| 15722 | // super must happen after, so that downstream can use maybe_get_output |
| 15723 | // to retrieve the output |
| 15724 | at::native::structured_ne_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15725 | } |
| 15726 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15727 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15728 | } |
| 15729 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15730 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15731 | }; |
| 15732 | at::Tensor & wrapper_CPU_ne__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 15733 | structured_ne_Tensor_out_inplace op(self); |
| 15734 | op.meta(self, other); |
| 15735 | op.impl(self, other, op.outputs_[0]); |
| 15736 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15737 | return self; |
| 15738 | } |
| 15739 | struct structured_ge_Scalar_out_functional final : public at::native::structured_ge_Scalar_out { |
| 15740 | void set_output_strided( |
| 15741 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15742 | TensorOptions options, DimnameList names |
| 15743 | ) override { |
| 15744 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15745 | if (!names.empty()) { |
| 15746 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15747 | } |
| 15748 | // super must happen after, so that downstream can use maybe_get_output |
| 15749 | // to retrieve the output |
| 15750 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15751 | } |
| 15752 | void set_output_raw_strided( |
| 15753 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15754 | TensorOptions options, DimnameList names |
| 15755 | ) override { |
| 15756 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15757 | if (!names.empty()) { |
| 15758 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15759 | } |
| 15760 | // super must happen after, so that downstream can use maybe_get_output |
| 15761 | // to retrieve the output |
| 15762 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15763 | } |
| 15764 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15765 | return *outputs_[output_idx]; |
| 15766 | } |
| 15767 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15768 | }; |
| 15769 | at::Tensor wrapper_CPU_ge_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 15770 | structured_ge_Scalar_out_functional op; |
| 15771 | op.meta(self, other); |
| 15772 | op.impl(self, other, *op.outputs_[0]); |
| 15773 | return std::move(op.outputs_[0]).take(); |
| 15774 | } |
| 15775 | struct structured_ge_Scalar_out_out final : public at::native::structured_ge_Scalar_out { |
| 15776 | structured_ge_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15777 | void set_output_strided( |
| 15778 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15779 | TensorOptions options, DimnameList names |
| 15780 | ) override { |
| 15781 | const auto& out = outputs_[output_idx].get(); |
| 15782 | resize_out(out, sizes, strides, options); |
| 15783 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15784 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15785 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15786 | } |
| 15787 | if (!names.empty()) { |
| 15788 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15789 | } |
| 15790 | // super must happen after, so that downstream can use maybe_get_output |
| 15791 | // to retrieve the output |
| 15792 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15793 | } |
| 15794 | void set_output_raw_strided( |
| 15795 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15796 | TensorOptions options, DimnameList names |
| 15797 | ) override { |
| 15798 | const auto& out = outputs_[output_idx].get(); |
| 15799 | resize_out(out, sizes, strides, options); |
| 15800 | if (!names.empty()) { |
| 15801 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15802 | } |
| 15803 | // super must happen after, so that downstream can use maybe_get_output |
| 15804 | // to retrieve the output |
| 15805 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15806 | } |
| 15807 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15808 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15809 | } |
| 15810 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15811 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15812 | }; |
| 15813 | at::Tensor & wrapper_CPU_ge_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 15814 | structured_ge_Scalar_out_out op(out); |
| 15815 | op.meta(self, other); |
| 15816 | op.impl(self, other, op.maybe_get_output(0)); |
| 15817 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15818 | return out; |
| 15819 | } |
| 15820 | struct structured_ge_Scalar_out_inplace final : public at::native::structured_ge_Scalar_out { |
| 15821 | structured_ge_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15822 | void set_output_strided( |
| 15823 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15824 | TensorOptions options, DimnameList names |
| 15825 | ) override { |
| 15826 | const auto& out = outputs_[output_idx].get(); |
| 15827 | check_inplace(out, sizes, options); |
| 15828 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15829 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15830 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15831 | } |
| 15832 | if (!names.empty()) { |
| 15833 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15834 | } |
| 15835 | // super must happen after, so that downstream can use maybe_get_output |
| 15836 | // to retrieve the output |
| 15837 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15838 | } |
| 15839 | void set_output_raw_strided( |
| 15840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15841 | TensorOptions options, DimnameList names |
| 15842 | ) override { |
| 15843 | const auto& out = outputs_[output_idx].get(); |
| 15844 | check_inplace(out, sizes, options); |
| 15845 | if (!names.empty()) { |
| 15846 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15847 | } |
| 15848 | // super must happen after, so that downstream can use maybe_get_output |
| 15849 | // to retrieve the output |
| 15850 | at::native::structured_ge_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15851 | } |
| 15852 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15853 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15854 | } |
| 15855 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15856 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15857 | }; |
| 15858 | at::Tensor & wrapper_CPU_ge__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 15859 | structured_ge_Scalar_out_inplace op(self); |
| 15860 | op.meta(self, other); |
| 15861 | op.impl(self, other, op.outputs_[0]); |
| 15862 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15863 | return self; |
| 15864 | } |
| 15865 | struct structured_ge_Tensor_out_functional final : public at::native::structured_ge_Tensor_out { |
| 15866 | void set_output_strided( |
| 15867 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15868 | TensorOptions options, DimnameList names |
| 15869 | ) override { |
| 15870 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15871 | if (!names.empty()) { |
| 15872 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15873 | } |
| 15874 | // super must happen after, so that downstream can use maybe_get_output |
| 15875 | // to retrieve the output |
| 15876 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15877 | } |
| 15878 | void set_output_raw_strided( |
| 15879 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15880 | TensorOptions options, DimnameList names |
| 15881 | ) override { |
| 15882 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15883 | if (!names.empty()) { |
| 15884 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15885 | } |
| 15886 | // super must happen after, so that downstream can use maybe_get_output |
| 15887 | // to retrieve the output |
| 15888 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15889 | } |
| 15890 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15891 | return *outputs_[output_idx]; |
| 15892 | } |
| 15893 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15894 | }; |
| 15895 | at::Tensor wrapper_CPU_ge_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 15896 | structured_ge_Tensor_out_functional op; |
| 15897 | op.meta(self, other); |
| 15898 | op.impl(self, other, *op.outputs_[0]); |
| 15899 | return std::move(op.outputs_[0]).take(); |
| 15900 | } |
| 15901 | struct structured_ge_Tensor_out_out final : public at::native::structured_ge_Tensor_out { |
| 15902 | structured_ge_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 15903 | void set_output_strided( |
| 15904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15905 | TensorOptions options, DimnameList names |
| 15906 | ) override { |
| 15907 | const auto& out = outputs_[output_idx].get(); |
| 15908 | resize_out(out, sizes, strides, options); |
| 15909 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15910 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15911 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15912 | } |
| 15913 | if (!names.empty()) { |
| 15914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15915 | } |
| 15916 | // super must happen after, so that downstream can use maybe_get_output |
| 15917 | // to retrieve the output |
| 15918 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15919 | } |
| 15920 | void set_output_raw_strided( |
| 15921 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15922 | TensorOptions options, DimnameList names |
| 15923 | ) override { |
| 15924 | const auto& out = outputs_[output_idx].get(); |
| 15925 | resize_out(out, sizes, strides, options); |
| 15926 | if (!names.empty()) { |
| 15927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15928 | } |
| 15929 | // super must happen after, so that downstream can use maybe_get_output |
| 15930 | // to retrieve the output |
| 15931 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15932 | } |
| 15933 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15934 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15935 | } |
| 15936 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15937 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15938 | }; |
| 15939 | at::Tensor & wrapper_CPU_ge_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 15940 | structured_ge_Tensor_out_out op(out); |
| 15941 | op.meta(self, other); |
| 15942 | op.impl(self, other, op.maybe_get_output(0)); |
| 15943 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15944 | return out; |
| 15945 | } |
| 15946 | struct structured_ge_Tensor_out_inplace final : public at::native::structured_ge_Tensor_out { |
| 15947 | structured_ge_Tensor_out_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 | const auto& out = outputs_[output_idx].get(); |
| 15953 | check_inplace(out, sizes, options); |
| 15954 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15955 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15956 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15957 | } |
| 15958 | if (!names.empty()) { |
| 15959 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15960 | } |
| 15961 | // super must happen after, so that downstream can use maybe_get_output |
| 15962 | // to retrieve the output |
| 15963 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15964 | } |
| 15965 | void set_output_raw_strided( |
| 15966 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15967 | TensorOptions options, DimnameList names |
| 15968 | ) override { |
| 15969 | const auto& out = outputs_[output_idx].get(); |
| 15970 | check_inplace(out, sizes, options); |
| 15971 | if (!names.empty()) { |
| 15972 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15973 | } |
| 15974 | // super must happen after, so that downstream can use maybe_get_output |
| 15975 | // to retrieve the output |
| 15976 | at::native::structured_ge_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15977 | } |
| 15978 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15979 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15980 | } |
| 15981 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15982 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15983 | }; |
| 15984 | at::Tensor & wrapper_CPU_ge__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 15985 | structured_ge_Tensor_out_inplace op(self); |
| 15986 | op.meta(self, other); |
| 15987 | op.impl(self, other, op.outputs_[0]); |
| 15988 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15989 | return self; |
| 15990 | } |
| 15991 | struct structured_le_Scalar_out_functional final : public at::native::structured_le_Scalar_out { |
| 15992 | void set_output_strided( |
| 15993 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15994 | TensorOptions options, DimnameList names |
| 15995 | ) override { |
| 15996 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15997 | if (!names.empty()) { |
| 15998 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15999 | } |
| 16000 | // super must happen after, so that downstream can use maybe_get_output |
| 16001 | // to retrieve the output |
| 16002 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16003 | } |
| 16004 | void set_output_raw_strided( |
| 16005 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16006 | TensorOptions options, DimnameList names |
| 16007 | ) override { |
| 16008 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16009 | if (!names.empty()) { |
| 16010 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16011 | } |
| 16012 | // super must happen after, so that downstream can use maybe_get_output |
| 16013 | // to retrieve the output |
| 16014 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16015 | } |
| 16016 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16017 | return *outputs_[output_idx]; |
| 16018 | } |
| 16019 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16020 | }; |
| 16021 | at::Tensor wrapper_CPU_le_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 16022 | structured_le_Scalar_out_functional op; |
| 16023 | op.meta(self, other); |
| 16024 | op.impl(self, other, *op.outputs_[0]); |
| 16025 | return std::move(op.outputs_[0]).take(); |
| 16026 | } |
| 16027 | struct structured_le_Scalar_out_out final : public at::native::structured_le_Scalar_out { |
| 16028 | structured_le_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16029 | void set_output_strided( |
| 16030 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16031 | TensorOptions options, DimnameList names |
| 16032 | ) override { |
| 16033 | const auto& out = outputs_[output_idx].get(); |
| 16034 | resize_out(out, sizes, strides, options); |
| 16035 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16036 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16037 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16038 | } |
| 16039 | if (!names.empty()) { |
| 16040 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16041 | } |
| 16042 | // super must happen after, so that downstream can use maybe_get_output |
| 16043 | // to retrieve the output |
| 16044 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16045 | } |
| 16046 | void set_output_raw_strided( |
| 16047 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16048 | TensorOptions options, DimnameList names |
| 16049 | ) override { |
| 16050 | const auto& out = outputs_[output_idx].get(); |
| 16051 | resize_out(out, sizes, strides, options); |
| 16052 | if (!names.empty()) { |
| 16053 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16054 | } |
| 16055 | // super must happen after, so that downstream can use maybe_get_output |
| 16056 | // to retrieve the output |
| 16057 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16058 | } |
| 16059 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16060 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16061 | } |
| 16062 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16063 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16064 | }; |
| 16065 | at::Tensor & wrapper_CPU_le_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 16066 | structured_le_Scalar_out_out op(out); |
| 16067 | op.meta(self, other); |
| 16068 | op.impl(self, other, op.maybe_get_output(0)); |
| 16069 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16070 | return out; |
| 16071 | } |
| 16072 | struct structured_le_Scalar_out_inplace final : public at::native::structured_le_Scalar_out { |
| 16073 | structured_le_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16074 | void set_output_strided( |
| 16075 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16076 | TensorOptions options, DimnameList names |
| 16077 | ) override { |
| 16078 | const auto& out = outputs_[output_idx].get(); |
| 16079 | check_inplace(out, sizes, options); |
| 16080 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16081 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16082 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16083 | } |
| 16084 | if (!names.empty()) { |
| 16085 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16086 | } |
| 16087 | // super must happen after, so that downstream can use maybe_get_output |
| 16088 | // to retrieve the output |
| 16089 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16090 | } |
| 16091 | void set_output_raw_strided( |
| 16092 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16093 | TensorOptions options, DimnameList names |
| 16094 | ) override { |
| 16095 | const auto& out = outputs_[output_idx].get(); |
| 16096 | check_inplace(out, sizes, options); |
| 16097 | if (!names.empty()) { |
| 16098 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16099 | } |
| 16100 | // super must happen after, so that downstream can use maybe_get_output |
| 16101 | // to retrieve the output |
| 16102 | at::native::structured_le_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16103 | } |
| 16104 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16105 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16106 | } |
| 16107 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16108 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16109 | }; |
| 16110 | at::Tensor & wrapper_CPU_le__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 16111 | structured_le_Scalar_out_inplace op(self); |
| 16112 | op.meta(self, other); |
| 16113 | op.impl(self, other, op.outputs_[0]); |
| 16114 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16115 | return self; |
| 16116 | } |
| 16117 | struct structured_le_Tensor_out_functional final : public at::native::structured_le_Tensor_out { |
| 16118 | void set_output_strided( |
| 16119 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16120 | TensorOptions options, DimnameList names |
| 16121 | ) override { |
| 16122 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16123 | if (!names.empty()) { |
| 16124 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16125 | } |
| 16126 | // super must happen after, so that downstream can use maybe_get_output |
| 16127 | // to retrieve the output |
| 16128 | at::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16129 | } |
| 16130 | void set_output_raw_strided( |
| 16131 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16132 | TensorOptions options, DimnameList names |
| 16133 | ) override { |
| 16134 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16135 | if (!names.empty()) { |
| 16136 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16137 | } |
| 16138 | // super must happen after, so that downstream can use maybe_get_output |
| 16139 | // to retrieve the output |
| 16140 | at::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16141 | } |
| 16142 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16143 | return *outputs_[output_idx]; |
| 16144 | } |
| 16145 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16146 | }; |
| 16147 | at::Tensor wrapper_CPU_le_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 16148 | structured_le_Tensor_out_functional op; |
| 16149 | op.meta(self, other); |
| 16150 | op.impl(self, other, *op.outputs_[0]); |
| 16151 | return std::move(op.outputs_[0]).take(); |
| 16152 | } |
| 16153 | struct structured_le_Tensor_out_out final : public at::native::structured_le_Tensor_out { |
| 16154 | structured_le_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16155 | void set_output_strided( |
| 16156 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16157 | TensorOptions options, DimnameList names |
| 16158 | ) override { |
| 16159 | const auto& out = outputs_[output_idx].get(); |
| 16160 | resize_out(out, sizes, strides, options); |
| 16161 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16162 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16163 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16164 | } |
| 16165 | if (!names.empty()) { |
| 16166 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16167 | } |
| 16168 | // super must happen after, so that downstream can use maybe_get_output |
| 16169 | // to retrieve the output |
| 16170 | at::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16171 | } |
| 16172 | void set_output_raw_strided( |
| 16173 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16174 | TensorOptions options, DimnameList names |
| 16175 | ) override { |
| 16176 | const auto& out = outputs_[output_idx].get(); |
| 16177 | resize_out(out, sizes, strides, options); |
| 16178 | if (!names.empty()) { |
| 16179 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16180 | } |
| 16181 | // super must happen after, so that downstream can use maybe_get_output |
| 16182 | // to retrieve the output |
| 16183 | at::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16184 | } |
| 16185 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16186 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16187 | } |
| 16188 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16189 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16190 | }; |
| 16191 | at::Tensor & wrapper_CPU_le_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 16192 | structured_le_Tensor_out_out op(out); |
| 16193 | op.meta(self, other); |
| 16194 | op.impl(self, other, op.maybe_get_output(0)); |
| 16195 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16196 | return out; |
| 16197 | } |
| 16198 | struct structured_le_Tensor_out_inplace final : public at::native::structured_le_Tensor_out { |
| 16199 | structured_le_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16200 | void set_output_strided( |
| 16201 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16202 | TensorOptions options, DimnameList names |
| 16203 | ) override { |
| 16204 | const auto& out = outputs_[output_idx].get(); |
| 16205 | check_inplace(out, sizes, options); |
| 16206 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16207 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16208 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16209 | } |
| 16210 | if (!names.empty()) { |
| 16211 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16212 | } |
| 16213 | // super must happen after, so that downstream can use maybe_get_output |
| 16214 | // to retrieve the output |
| 16215 | at::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16216 | } |
| 16217 | void set_output_raw_strided( |
| 16218 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16219 | TensorOptions options, DimnameList names |
| 16220 | ) override { |
| 16221 | const auto& out = outputs_[output_idx].get(); |
| 16222 | check_inplace(out, sizes, 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::native::structured_le_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16229 | } |
| 16230 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16231 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16232 | } |
| 16233 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16234 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16235 | }; |
| 16236 | at::Tensor & wrapper_CPU_le__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 16237 | structured_le_Tensor_out_inplace op(self); |
| 16238 | op.meta(self, other); |
| 16239 | op.impl(self, other, op.outputs_[0]); |
| 16240 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16241 | return self; |
| 16242 | } |
| 16243 | struct structured_gt_Scalar_out_functional final : public at::native::structured_gt_Scalar_out { |
| 16244 | void set_output_strided( |
| 16245 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16246 | TensorOptions options, DimnameList names |
| 16247 | ) override { |
| 16248 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16249 | if (!names.empty()) { |
| 16250 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16251 | } |
| 16252 | // super must happen after, so that downstream can use maybe_get_output |
| 16253 | // to retrieve the output |
| 16254 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16255 | } |
| 16256 | void set_output_raw_strided( |
| 16257 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16258 | TensorOptions options, DimnameList names |
| 16259 | ) override { |
| 16260 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16261 | if (!names.empty()) { |
| 16262 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16263 | } |
| 16264 | // super must happen after, so that downstream can use maybe_get_output |
| 16265 | // to retrieve the output |
| 16266 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16267 | } |
| 16268 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16269 | return *outputs_[output_idx]; |
| 16270 | } |
| 16271 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16272 | }; |
| 16273 | at::Tensor wrapper_CPU_gt_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 16274 | structured_gt_Scalar_out_functional op; |
| 16275 | op.meta(self, other); |
| 16276 | op.impl(self, other, *op.outputs_[0]); |
| 16277 | return std::move(op.outputs_[0]).take(); |
| 16278 | } |
| 16279 | struct structured_gt_Scalar_out_out final : public at::native::structured_gt_Scalar_out { |
| 16280 | structured_gt_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16281 | void set_output_strided( |
| 16282 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16283 | TensorOptions options, DimnameList names |
| 16284 | ) override { |
| 16285 | const auto& out = outputs_[output_idx].get(); |
| 16286 | resize_out(out, sizes, strides, options); |
| 16287 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16288 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16289 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16290 | } |
| 16291 | if (!names.empty()) { |
| 16292 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16293 | } |
| 16294 | // super must happen after, so that downstream can use maybe_get_output |
| 16295 | // to retrieve the output |
| 16296 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16297 | } |
| 16298 | void set_output_raw_strided( |
| 16299 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16300 | TensorOptions options, DimnameList names |
| 16301 | ) override { |
| 16302 | const auto& out = outputs_[output_idx].get(); |
| 16303 | resize_out(out, sizes, strides, options); |
| 16304 | if (!names.empty()) { |
| 16305 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16306 | } |
| 16307 | // super must happen after, so that downstream can use maybe_get_output |
| 16308 | // to retrieve the output |
| 16309 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16310 | } |
| 16311 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16312 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16313 | } |
| 16314 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16315 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16316 | }; |
| 16317 | at::Tensor & wrapper_CPU_gt_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 16318 | structured_gt_Scalar_out_out op(out); |
| 16319 | op.meta(self, other); |
| 16320 | op.impl(self, other, op.maybe_get_output(0)); |
| 16321 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16322 | return out; |
| 16323 | } |
| 16324 | struct structured_gt_Scalar_out_inplace final : public at::native::structured_gt_Scalar_out { |
| 16325 | structured_gt_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16326 | void set_output_strided( |
| 16327 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16328 | TensorOptions options, DimnameList names |
| 16329 | ) override { |
| 16330 | const auto& out = outputs_[output_idx].get(); |
| 16331 | check_inplace(out, sizes, options); |
| 16332 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16333 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16334 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16335 | } |
| 16336 | if (!names.empty()) { |
| 16337 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16338 | } |
| 16339 | // super must happen after, so that downstream can use maybe_get_output |
| 16340 | // to retrieve the output |
| 16341 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16342 | } |
| 16343 | void set_output_raw_strided( |
| 16344 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16345 | TensorOptions options, DimnameList names |
| 16346 | ) override { |
| 16347 | const auto& out = outputs_[output_idx].get(); |
| 16348 | check_inplace(out, sizes, options); |
| 16349 | if (!names.empty()) { |
| 16350 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16351 | } |
| 16352 | // super must happen after, so that downstream can use maybe_get_output |
| 16353 | // to retrieve the output |
| 16354 | at::native::structured_gt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16355 | } |
| 16356 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16357 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16358 | } |
| 16359 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16360 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16361 | }; |
| 16362 | at::Tensor & wrapper_CPU_gt__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 16363 | structured_gt_Scalar_out_inplace op(self); |
| 16364 | op.meta(self, other); |
| 16365 | op.impl(self, other, op.outputs_[0]); |
| 16366 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16367 | return self; |
| 16368 | } |
| 16369 | struct structured_gt_Tensor_out_functional final : public at::native::structured_gt_Tensor_out { |
| 16370 | void set_output_strided( |
| 16371 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16372 | TensorOptions options, DimnameList names |
| 16373 | ) override { |
| 16374 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16375 | if (!names.empty()) { |
| 16376 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16377 | } |
| 16378 | // super must happen after, so that downstream can use maybe_get_output |
| 16379 | // to retrieve the output |
| 16380 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16381 | } |
| 16382 | void set_output_raw_strided( |
| 16383 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16384 | TensorOptions options, DimnameList names |
| 16385 | ) override { |
| 16386 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16387 | if (!names.empty()) { |
| 16388 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16389 | } |
| 16390 | // super must happen after, so that downstream can use maybe_get_output |
| 16391 | // to retrieve the output |
| 16392 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16393 | } |
| 16394 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16395 | return *outputs_[output_idx]; |
| 16396 | } |
| 16397 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16398 | }; |
| 16399 | at::Tensor wrapper_CPU_gt_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 16400 | structured_gt_Tensor_out_functional op; |
| 16401 | op.meta(self, other); |
| 16402 | op.impl(self, other, *op.outputs_[0]); |
| 16403 | return std::move(op.outputs_[0]).take(); |
| 16404 | } |
| 16405 | struct structured_gt_Tensor_out_out final : public at::native::structured_gt_Tensor_out { |
| 16406 | structured_gt_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16407 | void set_output_strided( |
| 16408 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16409 | TensorOptions options, DimnameList names |
| 16410 | ) override { |
| 16411 | const auto& out = outputs_[output_idx].get(); |
| 16412 | resize_out(out, sizes, strides, options); |
| 16413 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16414 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16415 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16416 | } |
| 16417 | if (!names.empty()) { |
| 16418 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16419 | } |
| 16420 | // super must happen after, so that downstream can use maybe_get_output |
| 16421 | // to retrieve the output |
| 16422 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16423 | } |
| 16424 | void set_output_raw_strided( |
| 16425 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16426 | TensorOptions options, DimnameList names |
| 16427 | ) override { |
| 16428 | const auto& out = outputs_[output_idx].get(); |
| 16429 | resize_out(out, sizes, strides, options); |
| 16430 | if (!names.empty()) { |
| 16431 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16432 | } |
| 16433 | // super must happen after, so that downstream can use maybe_get_output |
| 16434 | // to retrieve the output |
| 16435 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16436 | } |
| 16437 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16438 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16439 | } |
| 16440 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16441 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16442 | }; |
| 16443 | at::Tensor & wrapper_CPU_gt_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 16444 | structured_gt_Tensor_out_out op(out); |
| 16445 | op.meta(self, other); |
| 16446 | op.impl(self, other, op.maybe_get_output(0)); |
| 16447 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16448 | return out; |
| 16449 | } |
| 16450 | struct structured_gt_Tensor_out_inplace final : public at::native::structured_gt_Tensor_out { |
| 16451 | structured_gt_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16452 | void set_output_strided( |
| 16453 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16454 | TensorOptions options, DimnameList names |
| 16455 | ) override { |
| 16456 | const auto& out = outputs_[output_idx].get(); |
| 16457 | check_inplace(out, sizes, options); |
| 16458 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16459 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16460 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16461 | } |
| 16462 | if (!names.empty()) { |
| 16463 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16464 | } |
| 16465 | // super must happen after, so that downstream can use maybe_get_output |
| 16466 | // to retrieve the output |
| 16467 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16468 | } |
| 16469 | void set_output_raw_strided( |
| 16470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16471 | TensorOptions options, DimnameList names |
| 16472 | ) override { |
| 16473 | const auto& out = outputs_[output_idx].get(); |
| 16474 | check_inplace(out, sizes, options); |
| 16475 | if (!names.empty()) { |
| 16476 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16477 | } |
| 16478 | // super must happen after, so that downstream can use maybe_get_output |
| 16479 | // to retrieve the output |
| 16480 | at::native::structured_gt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16481 | } |
| 16482 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16483 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16484 | } |
| 16485 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16486 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16487 | }; |
| 16488 | at::Tensor & wrapper_CPU_gt__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 16489 | structured_gt_Tensor_out_inplace op(self); |
| 16490 | op.meta(self, other); |
| 16491 | op.impl(self, other, op.outputs_[0]); |
| 16492 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16493 | return self; |
| 16494 | } |
| 16495 | struct structured_lt_Scalar_out_functional final : public at::native::structured_lt_Scalar_out { |
| 16496 | void set_output_strided( |
| 16497 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16498 | TensorOptions options, DimnameList names |
| 16499 | ) override { |
| 16500 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16501 | if (!names.empty()) { |
| 16502 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16503 | } |
| 16504 | // super must happen after, so that downstream can use maybe_get_output |
| 16505 | // to retrieve the output |
| 16506 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16507 | } |
| 16508 | void set_output_raw_strided( |
| 16509 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16510 | TensorOptions options, DimnameList names |
| 16511 | ) override { |
| 16512 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16513 | if (!names.empty()) { |
| 16514 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16515 | } |
| 16516 | // super must happen after, so that downstream can use maybe_get_output |
| 16517 | // to retrieve the output |
| 16518 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16519 | } |
| 16520 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16521 | return *outputs_[output_idx]; |
| 16522 | } |
| 16523 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16524 | }; |
| 16525 | at::Tensor wrapper_CPU_lt_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 16526 | structured_lt_Scalar_out_functional op; |
| 16527 | op.meta(self, other); |
| 16528 | op.impl(self, other, *op.outputs_[0]); |
| 16529 | return std::move(op.outputs_[0]).take(); |
| 16530 | } |
| 16531 | struct structured_lt_Scalar_out_out final : public at::native::structured_lt_Scalar_out { |
| 16532 | structured_lt_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16533 | void set_output_strided( |
| 16534 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16535 | TensorOptions options, DimnameList names |
| 16536 | ) override { |
| 16537 | const auto& out = outputs_[output_idx].get(); |
| 16538 | resize_out(out, sizes, strides, options); |
| 16539 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16540 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16541 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16542 | } |
| 16543 | if (!names.empty()) { |
| 16544 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16545 | } |
| 16546 | // super must happen after, so that downstream can use maybe_get_output |
| 16547 | // to retrieve the output |
| 16548 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16549 | } |
| 16550 | void set_output_raw_strided( |
| 16551 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16552 | TensorOptions options, DimnameList names |
| 16553 | ) override { |
| 16554 | const auto& out = outputs_[output_idx].get(); |
| 16555 | resize_out(out, sizes, strides, options); |
| 16556 | if (!names.empty()) { |
| 16557 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16558 | } |
| 16559 | // super must happen after, so that downstream can use maybe_get_output |
| 16560 | // to retrieve the output |
| 16561 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16562 | } |
| 16563 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16564 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16565 | } |
| 16566 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16567 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16568 | }; |
| 16569 | at::Tensor & wrapper_CPU_lt_out_Scalar_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 16570 | structured_lt_Scalar_out_out op(out); |
| 16571 | op.meta(self, other); |
| 16572 | op.impl(self, other, op.maybe_get_output(0)); |
| 16573 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16574 | return out; |
| 16575 | } |
| 16576 | struct structured_lt_Scalar_out_inplace final : public at::native::structured_lt_Scalar_out { |
| 16577 | structured_lt_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16578 | void set_output_strided( |
| 16579 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16580 | TensorOptions options, DimnameList names |
| 16581 | ) override { |
| 16582 | const auto& out = outputs_[output_idx].get(); |
| 16583 | check_inplace(out, sizes, options); |
| 16584 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16585 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16586 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16587 | } |
| 16588 | if (!names.empty()) { |
| 16589 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16590 | } |
| 16591 | // super must happen after, so that downstream can use maybe_get_output |
| 16592 | // to retrieve the output |
| 16593 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16594 | } |
| 16595 | void set_output_raw_strided( |
| 16596 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16597 | TensorOptions options, DimnameList names |
| 16598 | ) override { |
| 16599 | const auto& out = outputs_[output_idx].get(); |
| 16600 | check_inplace(out, sizes, options); |
| 16601 | if (!names.empty()) { |
| 16602 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16603 | } |
| 16604 | // super must happen after, so that downstream can use maybe_get_output |
| 16605 | // to retrieve the output |
| 16606 | at::native::structured_lt_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16607 | } |
| 16608 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16609 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16610 | } |
| 16611 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16612 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16613 | }; |
| 16614 | at::Tensor & wrapper_CPU_lt__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 16615 | structured_lt_Scalar_out_inplace op(self); |
| 16616 | op.meta(self, other); |
| 16617 | op.impl(self, other, op.outputs_[0]); |
| 16618 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16619 | return self; |
| 16620 | } |
| 16621 | struct structured_lt_Tensor_out_functional final : public at::native::structured_lt_Tensor_out { |
| 16622 | void set_output_strided( |
| 16623 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16624 | TensorOptions options, DimnameList names |
| 16625 | ) override { |
| 16626 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16627 | if (!names.empty()) { |
| 16628 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16629 | } |
| 16630 | // super must happen after, so that downstream can use maybe_get_output |
| 16631 | // to retrieve the output |
| 16632 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16633 | } |
| 16634 | void set_output_raw_strided( |
| 16635 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16636 | TensorOptions options, DimnameList names |
| 16637 | ) override { |
| 16638 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16639 | if (!names.empty()) { |
| 16640 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16641 | } |
| 16642 | // super must happen after, so that downstream can use maybe_get_output |
| 16643 | // to retrieve the output |
| 16644 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16645 | } |
| 16646 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16647 | return *outputs_[output_idx]; |
| 16648 | } |
| 16649 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16650 | }; |
| 16651 | at::Tensor wrapper_CPU_lt_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 16652 | structured_lt_Tensor_out_functional op; |
| 16653 | op.meta(self, other); |
| 16654 | op.impl(self, other, *op.outputs_[0]); |
| 16655 | return std::move(op.outputs_[0]).take(); |
| 16656 | } |
| 16657 | struct structured_lt_Tensor_out_out final : public at::native::structured_lt_Tensor_out { |
| 16658 | structured_lt_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16659 | void set_output_strided( |
| 16660 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16661 | TensorOptions options, DimnameList names |
| 16662 | ) override { |
| 16663 | const auto& out = outputs_[output_idx].get(); |
| 16664 | resize_out(out, sizes, strides, options); |
| 16665 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16666 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16667 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16668 | } |
| 16669 | if (!names.empty()) { |
| 16670 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16671 | } |
| 16672 | // super must happen after, so that downstream can use maybe_get_output |
| 16673 | // to retrieve the output |
| 16674 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16675 | } |
| 16676 | void set_output_raw_strided( |
| 16677 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16678 | TensorOptions options, DimnameList names |
| 16679 | ) override { |
| 16680 | const auto& out = outputs_[output_idx].get(); |
| 16681 | resize_out(out, sizes, strides, options); |
| 16682 | if (!names.empty()) { |
| 16683 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16684 | } |
| 16685 | // super must happen after, so that downstream can use maybe_get_output |
| 16686 | // to retrieve the output |
| 16687 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16688 | } |
| 16689 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16690 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16691 | } |
| 16692 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16693 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16694 | }; |
| 16695 | at::Tensor & wrapper_CPU_lt_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 16696 | structured_lt_Tensor_out_out op(out); |
| 16697 | op.meta(self, other); |
| 16698 | op.impl(self, other, op.maybe_get_output(0)); |
| 16699 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16700 | return out; |
| 16701 | } |
| 16702 | struct structured_lt_Tensor_out_inplace final : public at::native::structured_lt_Tensor_out { |
| 16703 | structured_lt_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16704 | void set_output_strided( |
| 16705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16706 | TensorOptions options, DimnameList names |
| 16707 | ) override { |
| 16708 | const auto& out = outputs_[output_idx].get(); |
| 16709 | check_inplace(out, sizes, options); |
| 16710 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16711 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16712 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16713 | } |
| 16714 | if (!names.empty()) { |
| 16715 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16716 | } |
| 16717 | // super must happen after, so that downstream can use maybe_get_output |
| 16718 | // to retrieve the output |
| 16719 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16720 | } |
| 16721 | void set_output_raw_strided( |
| 16722 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16723 | TensorOptions options, DimnameList names |
| 16724 | ) override { |
| 16725 | const auto& out = outputs_[output_idx].get(); |
| 16726 | check_inplace(out, sizes, options); |
| 16727 | if (!names.empty()) { |
| 16728 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16729 | } |
| 16730 | // super must happen after, so that downstream can use maybe_get_output |
| 16731 | // to retrieve the output |
| 16732 | at::native::structured_lt_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16733 | } |
| 16734 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16735 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16736 | } |
| 16737 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16738 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16739 | }; |
| 16740 | at::Tensor & wrapper_CPU_lt__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 16741 | structured_lt_Tensor_out_inplace op(self); |
| 16742 | op.meta(self, other); |
| 16743 | op.impl(self, other, op.outputs_[0]); |
| 16744 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16745 | return self; |
| 16746 | } |
| 16747 | namespace { |
| 16748 | at::Tensor wrapper_CPU__take(const at::Tensor & self, const at::Tensor & index) { |
| 16749 | // No device check |
| 16750 | // DeviceGuard omitted |
| 16751 | return at::native::take(self, index); |
| 16752 | } |
| 16753 | } // anonymous namespace |
| 16754 | namespace { |
| 16755 | at::Tensor & wrapper_CPU_out_take_out(const at::Tensor & self, const at::Tensor & index, at::Tensor & out) { |
| 16756 | // No device check |
| 16757 | // DeviceGuard omitted |
| 16758 | return at::native::take_out(self, index, out); |
| 16759 | } |
| 16760 | } // anonymous namespace |
| 16761 | namespace { |
| 16762 | at::Tensor wrapper_CPU__index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) { |
| 16763 | // No device check |
| 16764 | // DeviceGuard omitted |
| 16765 | return at::native::index_select_cpu_(self, dim, index); |
| 16766 | } |
| 16767 | } // anonymous namespace |
| 16768 | namespace { |
| 16769 | at::Tensor & wrapper_CPU_out_index_select_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out) { |
| 16770 | // No device check |
| 16771 | // DeviceGuard omitted |
| 16772 | return at::native::index_select_out_cpu_(self, dim, index, out); |
| 16773 | } |
| 16774 | } // anonymous namespace |
| 16775 | namespace { |
| 16776 | at::Tensor wrapper_CPU__masked_select(const at::Tensor & self, const at::Tensor & mask) { |
| 16777 | // No device check |
| 16778 | // DeviceGuard omitted |
| 16779 | return at::native::masked_select_cpu(self, mask); |
| 16780 | } |
| 16781 | } // anonymous namespace |
| 16782 | namespace { |
| 16783 | at::Tensor & wrapper_CPU_out_masked_select_out(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { |
| 16784 | // No device check |
| 16785 | // DeviceGuard omitted |
| 16786 | return at::native::masked_select_out_cpu(self, mask, out); |
| 16787 | } |
| 16788 | } // anonymous namespace |
| 16789 | namespace { |
| 16790 | at::Tensor wrapper_CPU__nonzero(const at::Tensor & self) { |
| 16791 | // No device check |
| 16792 | // DeviceGuard omitted |
| 16793 | return at::native::nonzero_cpu(self); |
| 16794 | } |
| 16795 | } // anonymous namespace |
| 16796 | namespace { |
| 16797 | at::Tensor & wrapper_CPU_out_nonzero_out(const at::Tensor & self, at::Tensor & out) { |
| 16798 | // No device check |
| 16799 | // DeviceGuard omitted |
| 16800 | return at::native::nonzero_out_cpu(self, out); |
| 16801 | } |
| 16802 | } // anonymous namespace |
| 16803 | struct structured_gather_out_functional final : public at::native::structured_gather_out { |
| 16804 | void set_output_strided( |
| 16805 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16806 | TensorOptions options, DimnameList names |
| 16807 | ) override { |
| 16808 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16809 | if (!names.empty()) { |
| 16810 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16811 | } |
| 16812 | // super must happen after, so that downstream can use maybe_get_output |
| 16813 | // to retrieve the output |
| 16814 | } |
| 16815 | void set_output_raw_strided( |
| 16816 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16817 | TensorOptions options, DimnameList names |
| 16818 | ) override { |
| 16819 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16820 | if (!names.empty()) { |
| 16821 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16822 | } |
| 16823 | // super must happen after, so that downstream can use maybe_get_output |
| 16824 | // to retrieve the output |
| 16825 | } |
| 16826 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16827 | return *outputs_[output_idx]; |
| 16828 | } |
| 16829 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16830 | }; |
| 16831 | at::Tensor wrapper_CPU_gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 16832 | structured_gather_out_functional op; |
| 16833 | op.meta(self, dim, index, sparse_grad); |
| 16834 | op.impl(self, dim, index, sparse_grad, *op.outputs_[0]); |
| 16835 | return std::move(op.outputs_[0]).take(); |
| 16836 | } |
| 16837 | struct structured_gather_out_out final : public at::native::structured_gather_out { |
| 16838 | structured_gather_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16839 | void set_output_strided( |
| 16840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16841 | TensorOptions options, DimnameList names |
| 16842 | ) override { |
| 16843 | const auto& out = outputs_[output_idx].get(); |
| 16844 | resize_out(out, sizes, strides, options); |
| 16845 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16846 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16847 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16848 | } |
| 16849 | if (!names.empty()) { |
| 16850 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16851 | } |
| 16852 | // super must happen after, so that downstream can use maybe_get_output |
| 16853 | // to retrieve the output |
| 16854 | } |
| 16855 | void set_output_raw_strided( |
| 16856 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16857 | TensorOptions options, DimnameList names |
| 16858 | ) override { |
| 16859 | const auto& out = outputs_[output_idx].get(); |
| 16860 | resize_out(out, sizes, strides, options); |
| 16861 | if (!names.empty()) { |
| 16862 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16863 | } |
| 16864 | // super must happen after, so that downstream can use maybe_get_output |
| 16865 | // to retrieve the output |
| 16866 | } |
| 16867 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16868 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16869 | } |
| 16870 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16871 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16872 | }; |
| 16873 | at::Tensor & wrapper_CPU_gather_out_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 16874 | structured_gather_out_out op(out); |
| 16875 | op.meta(self, dim, index, sparse_grad); |
| 16876 | op.impl(self, dim, index, sparse_grad, op.maybe_get_output(0)); |
| 16877 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16878 | return out; |
| 16879 | } |
| 16880 | struct structured_addcmul_out_functional final : public at::native::structured_addcmul_out { |
| 16881 | void set_output_strided( |
| 16882 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16883 | TensorOptions options, DimnameList names |
| 16884 | ) override { |
| 16885 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16886 | if (!names.empty()) { |
| 16887 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16888 | } |
| 16889 | // super must happen after, so that downstream can use maybe_get_output |
| 16890 | // to retrieve the output |
| 16891 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16892 | } |
| 16893 | void set_output_raw_strided( |
| 16894 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16895 | TensorOptions options, DimnameList names |
| 16896 | ) override { |
| 16897 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16898 | if (!names.empty()) { |
| 16899 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16900 | } |
| 16901 | // super must happen after, so that downstream can use maybe_get_output |
| 16902 | // to retrieve the output |
| 16903 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16904 | } |
| 16905 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16906 | return *outputs_[output_idx]; |
| 16907 | } |
| 16908 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16909 | }; |
| 16910 | at::Tensor wrapper_CPU_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 16911 | structured_addcmul_out_functional op; |
| 16912 | op.meta(self, tensor1, tensor2, value); |
| 16913 | op.impl(self, tensor1, tensor2, value, *op.outputs_[0]); |
| 16914 | return std::move(op.outputs_[0]).take(); |
| 16915 | } |
| 16916 | struct structured_addcmul_out_out final : public at::native::structured_addcmul_out { |
| 16917 | structured_addcmul_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 16918 | void set_output_strided( |
| 16919 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16920 | TensorOptions options, DimnameList names |
| 16921 | ) override { |
| 16922 | const auto& out = outputs_[output_idx].get(); |
| 16923 | resize_out(out, sizes, strides, options); |
| 16924 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16925 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16926 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16927 | } |
| 16928 | if (!names.empty()) { |
| 16929 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16930 | } |
| 16931 | // super must happen after, so that downstream can use maybe_get_output |
| 16932 | // to retrieve the output |
| 16933 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16934 | } |
| 16935 | void set_output_raw_strided( |
| 16936 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16937 | TensorOptions options, DimnameList names |
| 16938 | ) override { |
| 16939 | const auto& out = outputs_[output_idx].get(); |
| 16940 | resize_out(out, sizes, strides, options); |
| 16941 | if (!names.empty()) { |
| 16942 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16943 | } |
| 16944 | // super must happen after, so that downstream can use maybe_get_output |
| 16945 | // to retrieve the output |
| 16946 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16947 | } |
| 16948 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16949 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16950 | } |
| 16951 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16952 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16953 | }; |
| 16954 | at::Tensor & wrapper_CPU_addcmul_out_out(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { |
| 16955 | structured_addcmul_out_out op(out); |
| 16956 | op.meta(self, tensor1, tensor2, value); |
| 16957 | op.impl(self, tensor1, tensor2, value, op.maybe_get_output(0)); |
| 16958 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16959 | return out; |
| 16960 | } |
| 16961 | struct structured_addcmul_out_inplace final : public at::native::structured_addcmul_out { |
| 16962 | structured_addcmul_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 16963 | void set_output_strided( |
| 16964 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16965 | TensorOptions options, DimnameList names |
| 16966 | ) override { |
| 16967 | const auto& out = outputs_[output_idx].get(); |
| 16968 | check_inplace(out, sizes, options); |
| 16969 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 16970 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 16971 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 16972 | } |
| 16973 | if (!names.empty()) { |
| 16974 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16975 | } |
| 16976 | // super must happen after, so that downstream can use maybe_get_output |
| 16977 | // to retrieve the output |
| 16978 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16979 | } |
| 16980 | void set_output_raw_strided( |
| 16981 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16982 | TensorOptions options, DimnameList names |
| 16983 | ) override { |
| 16984 | const auto& out = outputs_[output_idx].get(); |
| 16985 | check_inplace(out, sizes, options); |
| 16986 | if (!names.empty()) { |
| 16987 | namedinference::propagate_names(outputs_[output_idx], names); |
| 16988 | } |
| 16989 | // super must happen after, so that downstream can use maybe_get_output |
| 16990 | // to retrieve the output |
| 16991 | at::native::structured_addcmul_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16992 | } |
| 16993 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16994 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 16995 | } |
| 16996 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 16997 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 16998 | }; |
| 16999 | at::Tensor & wrapper_CPU_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 17000 | structured_addcmul_out_inplace op(self); |
| 17001 | op.meta(self, tensor1, tensor2, value); |
| 17002 | op.impl(self, tensor1, tensor2, value, op.outputs_[0]); |
| 17003 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17004 | return self; |
| 17005 | } |
| 17006 | struct structured_addcdiv_out_functional final : public at::native::structured_addcdiv_out { |
| 17007 | void set_output_strided( |
| 17008 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17009 | TensorOptions options, DimnameList names |
| 17010 | ) override { |
| 17011 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17012 | if (!names.empty()) { |
| 17013 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17014 | } |
| 17015 | // super must happen after, so that downstream can use maybe_get_output |
| 17016 | // to retrieve the output |
| 17017 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17018 | } |
| 17019 | void set_output_raw_strided( |
| 17020 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17021 | TensorOptions options, DimnameList names |
| 17022 | ) override { |
| 17023 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17024 | if (!names.empty()) { |
| 17025 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17026 | } |
| 17027 | // super must happen after, so that downstream can use maybe_get_output |
| 17028 | // to retrieve the output |
| 17029 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17030 | } |
| 17031 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17032 | return *outputs_[output_idx]; |
| 17033 | } |
| 17034 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17035 | }; |
| 17036 | at::Tensor wrapper_CPU_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 17037 | structured_addcdiv_out_functional op; |
| 17038 | op.meta(self, tensor1, tensor2, value); |
| 17039 | op.impl(self, tensor1, tensor2, value, *op.outputs_[0]); |
| 17040 | return std::move(op.outputs_[0]).take(); |
| 17041 | } |
| 17042 | struct structured_addcdiv_out_out final : public at::native::structured_addcdiv_out { |
| 17043 | structured_addcdiv_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17044 | void set_output_strided( |
| 17045 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17046 | TensorOptions options, DimnameList names |
| 17047 | ) override { |
| 17048 | const auto& out = outputs_[output_idx].get(); |
| 17049 | resize_out(out, sizes, strides, options); |
| 17050 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17051 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17052 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17053 | } |
| 17054 | if (!names.empty()) { |
| 17055 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17056 | } |
| 17057 | // super must happen after, so that downstream can use maybe_get_output |
| 17058 | // to retrieve the output |
| 17059 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17060 | } |
| 17061 | void set_output_raw_strided( |
| 17062 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17063 | TensorOptions options, DimnameList names |
| 17064 | ) override { |
| 17065 | const auto& out = outputs_[output_idx].get(); |
| 17066 | resize_out(out, sizes, strides, options); |
| 17067 | if (!names.empty()) { |
| 17068 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17069 | } |
| 17070 | // super must happen after, so that downstream can use maybe_get_output |
| 17071 | // to retrieve the output |
| 17072 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17073 | } |
| 17074 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17075 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17076 | } |
| 17077 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17078 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17079 | }; |
| 17080 | at::Tensor & wrapper_CPU_addcdiv_out_out(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { |
| 17081 | structured_addcdiv_out_out op(out); |
| 17082 | op.meta(self, tensor1, tensor2, value); |
| 17083 | op.impl(self, tensor1, tensor2, value, op.maybe_get_output(0)); |
| 17084 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17085 | return out; |
| 17086 | } |
| 17087 | struct structured_addcdiv_out_inplace final : public at::native::structured_addcdiv_out { |
| 17088 | structured_addcdiv_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 17089 | void set_output_strided( |
| 17090 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17091 | TensorOptions options, DimnameList names |
| 17092 | ) override { |
| 17093 | const auto& out = outputs_[output_idx].get(); |
| 17094 | check_inplace(out, sizes, options); |
| 17095 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17096 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17097 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17098 | } |
| 17099 | if (!names.empty()) { |
| 17100 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17101 | } |
| 17102 | // super must happen after, so that downstream can use maybe_get_output |
| 17103 | // to retrieve the output |
| 17104 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17105 | } |
| 17106 | void set_output_raw_strided( |
| 17107 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17108 | TensorOptions options, DimnameList names |
| 17109 | ) override { |
| 17110 | const auto& out = outputs_[output_idx].get(); |
| 17111 | check_inplace(out, sizes, options); |
| 17112 | if (!names.empty()) { |
| 17113 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17114 | } |
| 17115 | // super must happen after, so that downstream can use maybe_get_output |
| 17116 | // to retrieve the output |
| 17117 | at::native::structured_addcdiv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17118 | } |
| 17119 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17120 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17121 | } |
| 17122 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17123 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17124 | }; |
| 17125 | at::Tensor & wrapper_CPU_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 17126 | structured_addcdiv_out_inplace op(self); |
| 17127 | op.meta(self, tensor1, tensor2, value); |
| 17128 | op.impl(self, tensor1, tensor2, value, op.outputs_[0]); |
| 17129 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17130 | return self; |
| 17131 | } |
| 17132 | struct structured_triangular_solve_out_functional final : public at::native::structured_triangular_solve_out { |
| 17133 | void set_output_strided( |
| 17134 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17135 | TensorOptions options, DimnameList names |
| 17136 | ) override { |
| 17137 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17138 | if (!names.empty()) { |
| 17139 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17140 | } |
| 17141 | // super must happen after, so that downstream can use maybe_get_output |
| 17142 | // to retrieve the output |
| 17143 | } |
| 17144 | void set_output_raw_strided( |
| 17145 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17146 | TensorOptions options, DimnameList names |
| 17147 | ) override { |
| 17148 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17149 | if (!names.empty()) { |
| 17150 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17151 | } |
| 17152 | // super must happen after, so that downstream can use maybe_get_output |
| 17153 | // to retrieve the output |
| 17154 | } |
| 17155 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17156 | return *outputs_[output_idx]; |
| 17157 | } |
| 17158 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 17159 | }; |
| 17160 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 17161 | structured_triangular_solve_out_functional op; |
| 17162 | op.meta(self, A, upper, transpose, unitriangular); |
| 17163 | op.impl(self, A, upper, transpose, unitriangular, *op.outputs_[0], *op.outputs_[1]); |
| 17164 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 17165 | } |
| 17166 | struct structured_triangular_solve_out_out final : public at::native::structured_triangular_solve_out { |
| 17167 | structured_triangular_solve_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 17168 | void set_output_strided( |
| 17169 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17170 | TensorOptions options, DimnameList names |
| 17171 | ) override { |
| 17172 | const auto& out = outputs_[output_idx].get(); |
| 17173 | resize_out(out, sizes, strides, options); |
| 17174 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17175 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17176 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17177 | } |
| 17178 | if (!names.empty()) { |
| 17179 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17180 | } |
| 17181 | // super must happen after, so that downstream can use maybe_get_output |
| 17182 | // to retrieve the output |
| 17183 | } |
| 17184 | void set_output_raw_strided( |
| 17185 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17186 | TensorOptions options, DimnameList names |
| 17187 | ) override { |
| 17188 | const auto& out = outputs_[output_idx].get(); |
| 17189 | resize_out(out, sizes, strides, options); |
| 17190 | if (!names.empty()) { |
| 17191 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17192 | } |
| 17193 | // super must happen after, so that downstream can use maybe_get_output |
| 17194 | // to retrieve the output |
| 17195 | } |
| 17196 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17197 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17198 | } |
| 17199 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 17200 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 17201 | }; |
| 17202 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_triangular_solve_out_X(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { |
| 17203 | structured_triangular_solve_out_out op(X, M); |
| 17204 | op.meta(self, A, upper, transpose, unitriangular); |
| 17205 | op.impl(self, A, upper, transpose, unitriangular, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 17206 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17207 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 17208 | return std::forward_as_tuple(X, M); |
| 17209 | } |
| 17210 | namespace { |
| 17211 | at::Tensor wrapper_CPU__linalg_solve_triangular(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { |
| 17212 | // No device check |
| 17213 | // DeviceGuard omitted |
| 17214 | return at::native::linalg_solve_triangular(self, B, upper, left, unitriangular); |
| 17215 | } |
| 17216 | } // anonymous namespace |
| 17217 | namespace { |
| 17218 | at::Tensor & wrapper_CPU_out_linalg_solve_triangular_out(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { |
| 17219 | // No device check |
| 17220 | // DeviceGuard omitted |
| 17221 | return at::native::linalg_solve_triangular_out(self, B, upper, left, unitriangular, out); |
| 17222 | } |
| 17223 | } // anonymous namespace |
| 17224 | namespace { |
| 17225 | at::Tensor wrapper_CPU__cholesky(const at::Tensor & self, bool upper) { |
| 17226 | // No device check |
| 17227 | // DeviceGuard omitted |
| 17228 | return at::native::cholesky(self, upper); |
| 17229 | } |
| 17230 | } // anonymous namespace |
| 17231 | namespace { |
| 17232 | at::Tensor & wrapper_CPU_out_cholesky_out(const at::Tensor & self, bool upper, at::Tensor & out) { |
| 17233 | // No device check |
| 17234 | // DeviceGuard omitted |
| 17235 | return at::native::cholesky_out(self, upper, out); |
| 17236 | } |
| 17237 | } // anonymous namespace |
| 17238 | namespace { |
| 17239 | at::Tensor wrapper_CPU___cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper) { |
| 17240 | // No device check |
| 17241 | // DeviceGuard omitted |
| 17242 | return at::native::_cholesky_solve_helper_cpu(self, A, upper); |
| 17243 | } |
| 17244 | } // anonymous namespace |
| 17245 | namespace { |
| 17246 | at::Tensor wrapper_CPU__cholesky_inverse(const at::Tensor & self, bool upper) { |
| 17247 | // No device check |
| 17248 | // DeviceGuard omitted |
| 17249 | return at::native::cholesky_inverse(self, upper); |
| 17250 | } |
| 17251 | } // anonymous namespace |
| 17252 | namespace { |
| 17253 | at::Tensor & wrapper_CPU_out_cholesky_inverse_out(const at::Tensor & self, bool upper, at::Tensor & out) { |
| 17254 | // No device check |
| 17255 | // DeviceGuard omitted |
| 17256 | return at::native::cholesky_inverse_out(self, upper, out); |
| 17257 | } |
| 17258 | } // anonymous namespace |
| 17259 | namespace { |
| 17260 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__geqrf(const at::Tensor & self) { |
| 17261 | // No device check |
| 17262 | // DeviceGuard omitted |
| 17263 | return at::native::geqrf(self); |
| 17264 | } |
| 17265 | } // anonymous namespace |
| 17266 | namespace { |
| 17267 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_a_geqrf_out(const at::Tensor & self, at::Tensor & a, at::Tensor & tau) { |
| 17268 | // No device check |
| 17269 | // DeviceGuard omitted |
| 17270 | return at::native::geqrf_out(self, a, tau); |
| 17271 | } |
| 17272 | } // anonymous namespace |
| 17273 | namespace { |
| 17274 | at::Tensor wrapper_CPU__ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { |
| 17275 | // No device check |
| 17276 | // DeviceGuard omitted |
| 17277 | return at::native::ormqr(self, input2, input3, left, transpose); |
| 17278 | } |
| 17279 | } // anonymous namespace |
| 17280 | namespace { |
| 17281 | at::Tensor & wrapper_CPU_out_ormqr_out(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) { |
| 17282 | // No device check |
| 17283 | // DeviceGuard omitted |
| 17284 | return at::native::ormqr_out(self, input2, input3, left, transpose, out); |
| 17285 | } |
| 17286 | } // anonymous namespace |
| 17287 | struct structured_lu_unpack_out_functional final : public at::native::structured_lu_unpack_out { |
| 17288 | void set_output_strided( |
| 17289 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17290 | TensorOptions options, DimnameList names |
| 17291 | ) override { |
| 17292 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17293 | if (!names.empty()) { |
| 17294 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17295 | } |
| 17296 | // super must happen after, so that downstream can use maybe_get_output |
| 17297 | // to retrieve the output |
| 17298 | } |
| 17299 | void set_output_raw_strided( |
| 17300 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17301 | TensorOptions options, DimnameList names |
| 17302 | ) override { |
| 17303 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17304 | if (!names.empty()) { |
| 17305 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17306 | } |
| 17307 | // super must happen after, so that downstream can use maybe_get_output |
| 17308 | // to retrieve the output |
| 17309 | } |
| 17310 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17311 | return *outputs_[output_idx]; |
| 17312 | } |
| 17313 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 17314 | }; |
| 17315 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) { |
| 17316 | structured_lu_unpack_out_functional op; |
| 17317 | op.meta(LU_data, LU_pivots, unpack_data, unpack_pivots); |
| 17318 | op.impl(LU_data, LU_pivots, unpack_data, unpack_pivots, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 17319 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 17320 | } |
| 17321 | struct structured_lu_unpack_out_out final : public at::native::structured_lu_unpack_out { |
| 17322 | structured_lu_unpack_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 17323 | void set_output_strided( |
| 17324 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17325 | TensorOptions options, DimnameList names |
| 17326 | ) override { |
| 17327 | const auto& out = outputs_[output_idx].get(); |
| 17328 | resize_out(out, sizes, strides, options); |
| 17329 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17330 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17331 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17332 | } |
| 17333 | if (!names.empty()) { |
| 17334 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17335 | } |
| 17336 | // super must happen after, so that downstream can use maybe_get_output |
| 17337 | // to retrieve the output |
| 17338 | } |
| 17339 | void set_output_raw_strided( |
| 17340 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17341 | TensorOptions options, DimnameList names |
| 17342 | ) override { |
| 17343 | const auto& out = outputs_[output_idx].get(); |
| 17344 | resize_out(out, sizes, strides, options); |
| 17345 | if (!names.empty()) { |
| 17346 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17347 | } |
| 17348 | // super must happen after, so that downstream can use maybe_get_output |
| 17349 | // to retrieve the output |
| 17350 | } |
| 17351 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17352 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17353 | } |
| 17354 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 17355 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 17356 | }; |
| 17357 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_lu_unpack_out_out(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U) { |
| 17358 | structured_lu_unpack_out_out op(P, L, U); |
| 17359 | op.meta(LU_data, LU_pivots, unpack_data, unpack_pivots); |
| 17360 | op.impl(LU_data, LU_pivots, unpack_data, unpack_pivots, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 17361 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17362 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 17363 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 17364 | return std::forward_as_tuple(P, L, U); |
| 17365 | } |
| 17366 | namespace { |
| 17367 | at::Tensor wrapper_CPU__multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator) { |
| 17368 | // No device check |
| 17369 | // DeviceGuard omitted |
| 17370 | return at::native::multinomial(self, num_samples, replacement, generator); |
| 17371 | } |
| 17372 | } // anonymous namespace |
| 17373 | namespace { |
| 17374 | at::Tensor & wrapper_CPU_out_multinomial_out(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 17375 | // No device check |
| 17376 | // DeviceGuard omitted |
| 17377 | return at::native::multinomial_out(self, num_samples, replacement, generator, out); |
| 17378 | } |
| 17379 | } // anonymous namespace |
| 17380 | struct structured_lgamma_out_functional final : public at::native::structured_lgamma_out { |
| 17381 | void set_output_strided( |
| 17382 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17383 | TensorOptions options, DimnameList names |
| 17384 | ) override { |
| 17385 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17386 | if (!names.empty()) { |
| 17387 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17388 | } |
| 17389 | // super must happen after, so that downstream can use maybe_get_output |
| 17390 | // to retrieve the output |
| 17391 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17392 | } |
| 17393 | void set_output_raw_strided( |
| 17394 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17395 | TensorOptions options, DimnameList names |
| 17396 | ) override { |
| 17397 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17398 | if (!names.empty()) { |
| 17399 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17400 | } |
| 17401 | // super must happen after, so that downstream can use maybe_get_output |
| 17402 | // to retrieve the output |
| 17403 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17404 | } |
| 17405 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17406 | return *outputs_[output_idx]; |
| 17407 | } |
| 17408 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17409 | }; |
| 17410 | at::Tensor wrapper_CPU_lgamma(const at::Tensor & self) { |
| 17411 | structured_lgamma_out_functional op; |
| 17412 | op.meta(self); |
| 17413 | op.impl(self, *op.outputs_[0]); |
| 17414 | return std::move(op.outputs_[0]).take(); |
| 17415 | } |
| 17416 | struct structured_lgamma_out_out final : public at::native::structured_lgamma_out { |
| 17417 | structured_lgamma_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17418 | void set_output_strided( |
| 17419 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17420 | TensorOptions options, DimnameList names |
| 17421 | ) override { |
| 17422 | const auto& out = outputs_[output_idx].get(); |
| 17423 | resize_out(out, sizes, strides, options); |
| 17424 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17425 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17426 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17427 | } |
| 17428 | if (!names.empty()) { |
| 17429 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17430 | } |
| 17431 | // super must happen after, so that downstream can use maybe_get_output |
| 17432 | // to retrieve the output |
| 17433 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17434 | } |
| 17435 | void set_output_raw_strided( |
| 17436 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17437 | TensorOptions options, DimnameList names |
| 17438 | ) override { |
| 17439 | const auto& out = outputs_[output_idx].get(); |
| 17440 | resize_out(out, sizes, strides, options); |
| 17441 | if (!names.empty()) { |
| 17442 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17443 | } |
| 17444 | // super must happen after, so that downstream can use maybe_get_output |
| 17445 | // to retrieve the output |
| 17446 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17447 | } |
| 17448 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17449 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17450 | } |
| 17451 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17452 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17453 | }; |
| 17454 | at::Tensor & wrapper_CPU_lgamma_out_out(const at::Tensor & self, at::Tensor & out) { |
| 17455 | structured_lgamma_out_out op(out); |
| 17456 | op.meta(self); |
| 17457 | op.impl(self, op.maybe_get_output(0)); |
| 17458 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17459 | return out; |
| 17460 | } |
| 17461 | struct structured_lgamma_out_inplace final : public at::native::structured_lgamma_out { |
| 17462 | structured_lgamma_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 17463 | void set_output_strided( |
| 17464 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17465 | TensorOptions options, DimnameList names |
| 17466 | ) override { |
| 17467 | const auto& out = outputs_[output_idx].get(); |
| 17468 | check_inplace(out, sizes, options); |
| 17469 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17470 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17471 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17472 | } |
| 17473 | if (!names.empty()) { |
| 17474 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17475 | } |
| 17476 | // super must happen after, so that downstream can use maybe_get_output |
| 17477 | // to retrieve the output |
| 17478 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17479 | } |
| 17480 | void set_output_raw_strided( |
| 17481 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17482 | TensorOptions options, DimnameList names |
| 17483 | ) override { |
| 17484 | const auto& out = outputs_[output_idx].get(); |
| 17485 | check_inplace(out, sizes, options); |
| 17486 | if (!names.empty()) { |
| 17487 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17488 | } |
| 17489 | // super must happen after, so that downstream can use maybe_get_output |
| 17490 | // to retrieve the output |
| 17491 | at::native::structured_lgamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17492 | } |
| 17493 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17494 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17495 | } |
| 17496 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17497 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17498 | }; |
| 17499 | at::Tensor & wrapper_CPU_lgamma_(at::Tensor & self) { |
| 17500 | structured_lgamma_out_inplace op(self); |
| 17501 | op.meta(self); |
| 17502 | op.impl(self, op.outputs_[0]); |
| 17503 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17504 | return self; |
| 17505 | } |
| 17506 | struct structured_polygamma_out_functional final : public at::native::structured_polygamma_out { |
| 17507 | void set_output_strided( |
| 17508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17509 | TensorOptions options, DimnameList names |
| 17510 | ) override { |
| 17511 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17512 | if (!names.empty()) { |
| 17513 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17514 | } |
| 17515 | // super must happen after, so that downstream can use maybe_get_output |
| 17516 | // to retrieve the output |
| 17517 | at::native::structured_polygamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17518 | } |
| 17519 | void set_output_raw_strided( |
| 17520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17521 | TensorOptions options, DimnameList names |
| 17522 | ) override { |
| 17523 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17524 | if (!names.empty()) { |
| 17525 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17526 | } |
| 17527 | // super must happen after, so that downstream can use maybe_get_output |
| 17528 | // to retrieve the output |
| 17529 | at::native::structured_polygamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17530 | } |
| 17531 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17532 | return *outputs_[output_idx]; |
| 17533 | } |
| 17534 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17535 | }; |
| 17536 | at::Tensor wrapper_CPU_polygamma(int64_t n, const at::Tensor & self) { |
| 17537 | structured_polygamma_out_functional op; |
| 17538 | op.meta(n, self); |
| 17539 | op.impl(n, self, *op.outputs_[0]); |
| 17540 | return std::move(op.outputs_[0]).take(); |
| 17541 | } |
| 17542 | struct structured_polygamma_out_out final : public at::native::structured_polygamma_out { |
| 17543 | structured_polygamma_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17544 | void set_output_strided( |
| 17545 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17546 | TensorOptions options, DimnameList names |
| 17547 | ) override { |
| 17548 | const auto& out = outputs_[output_idx].get(); |
| 17549 | resize_out(out, sizes, strides, options); |
| 17550 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17551 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17552 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17553 | } |
| 17554 | if (!names.empty()) { |
| 17555 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17556 | } |
| 17557 | // super must happen after, so that downstream can use maybe_get_output |
| 17558 | // to retrieve the output |
| 17559 | at::native::structured_polygamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17560 | } |
| 17561 | void set_output_raw_strided( |
| 17562 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17563 | TensorOptions options, DimnameList names |
| 17564 | ) override { |
| 17565 | const auto& out = outputs_[output_idx].get(); |
| 17566 | resize_out(out, sizes, strides, options); |
| 17567 | if (!names.empty()) { |
| 17568 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17569 | } |
| 17570 | // super must happen after, so that downstream can use maybe_get_output |
| 17571 | // to retrieve the output |
| 17572 | at::native::structured_polygamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17573 | } |
| 17574 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17575 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17576 | } |
| 17577 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17578 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17579 | }; |
| 17580 | at::Tensor & wrapper_CPU_polygamma_out_out(int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 17581 | structured_polygamma_out_out op(out); |
| 17582 | op.meta(n, self); |
| 17583 | op.impl(n, self, op.maybe_get_output(0)); |
| 17584 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17585 | return out; |
| 17586 | } |
| 17587 | struct structured_erfinv_out_functional final : public at::native::structured_erfinv_out { |
| 17588 | void set_output_strided( |
| 17589 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17590 | TensorOptions options, DimnameList names |
| 17591 | ) override { |
| 17592 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17593 | if (!names.empty()) { |
| 17594 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17595 | } |
| 17596 | // super must happen after, so that downstream can use maybe_get_output |
| 17597 | // to retrieve the output |
| 17598 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17599 | } |
| 17600 | void set_output_raw_strided( |
| 17601 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17602 | TensorOptions options, DimnameList names |
| 17603 | ) override { |
| 17604 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17605 | if (!names.empty()) { |
| 17606 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17607 | } |
| 17608 | // super must happen after, so that downstream can use maybe_get_output |
| 17609 | // to retrieve the output |
| 17610 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17611 | } |
| 17612 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17613 | return *outputs_[output_idx]; |
| 17614 | } |
| 17615 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17616 | }; |
| 17617 | at::Tensor wrapper_CPU_erfinv(const at::Tensor & self) { |
| 17618 | structured_erfinv_out_functional op; |
| 17619 | op.meta(self); |
| 17620 | op.impl(self, *op.outputs_[0]); |
| 17621 | return std::move(op.outputs_[0]).take(); |
| 17622 | } |
| 17623 | struct structured_erfinv_out_out final : public at::native::structured_erfinv_out { |
| 17624 | structured_erfinv_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17625 | void set_output_strided( |
| 17626 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17627 | TensorOptions options, DimnameList names |
| 17628 | ) override { |
| 17629 | const auto& out = outputs_[output_idx].get(); |
| 17630 | resize_out(out, sizes, strides, options); |
| 17631 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17632 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17633 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17634 | } |
| 17635 | if (!names.empty()) { |
| 17636 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17637 | } |
| 17638 | // super must happen after, so that downstream can use maybe_get_output |
| 17639 | // to retrieve the output |
| 17640 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17641 | } |
| 17642 | void set_output_raw_strided( |
| 17643 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17644 | TensorOptions options, DimnameList names |
| 17645 | ) override { |
| 17646 | const auto& out = outputs_[output_idx].get(); |
| 17647 | resize_out(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 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17654 | } |
| 17655 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17656 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17657 | } |
| 17658 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17659 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17660 | }; |
| 17661 | at::Tensor & wrapper_CPU_erfinv_out_out(const at::Tensor & self, at::Tensor & out) { |
| 17662 | structured_erfinv_out_out op(out); |
| 17663 | op.meta(self); |
| 17664 | op.impl(self, op.maybe_get_output(0)); |
| 17665 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17666 | return out; |
| 17667 | } |
| 17668 | struct structured_erfinv_out_inplace final : public at::native::structured_erfinv_out { |
| 17669 | structured_erfinv_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 17670 | void set_output_strided( |
| 17671 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17672 | TensorOptions options, DimnameList names |
| 17673 | ) override { |
| 17674 | const auto& out = outputs_[output_idx].get(); |
| 17675 | check_inplace(out, sizes, options); |
| 17676 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17677 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17678 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17679 | } |
| 17680 | if (!names.empty()) { |
| 17681 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17682 | } |
| 17683 | // super must happen after, so that downstream can use maybe_get_output |
| 17684 | // to retrieve the output |
| 17685 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17686 | } |
| 17687 | void set_output_raw_strided( |
| 17688 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17689 | TensorOptions options, DimnameList names |
| 17690 | ) override { |
| 17691 | const auto& out = outputs_[output_idx].get(); |
| 17692 | check_inplace(out, sizes, options); |
| 17693 | if (!names.empty()) { |
| 17694 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17695 | } |
| 17696 | // super must happen after, so that downstream can use maybe_get_output |
| 17697 | // to retrieve the output |
| 17698 | at::native::structured_erfinv_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17699 | } |
| 17700 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17701 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17702 | } |
| 17703 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17704 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17705 | }; |
| 17706 | at::Tensor & wrapper_CPU_erfinv_(at::Tensor & self) { |
| 17707 | structured_erfinv_out_inplace op(self); |
| 17708 | op.meta(self); |
| 17709 | op.impl(self, op.outputs_[0]); |
| 17710 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17711 | return self; |
| 17712 | } |
| 17713 | struct structured_i0_out_functional final : public at::native::structured_i0_out { |
| 17714 | void set_output_strided( |
| 17715 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17716 | TensorOptions options, DimnameList names |
| 17717 | ) override { |
| 17718 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17719 | if (!names.empty()) { |
| 17720 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17721 | } |
| 17722 | // super must happen after, so that downstream can use maybe_get_output |
| 17723 | // to retrieve the output |
| 17724 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17725 | } |
| 17726 | void set_output_raw_strided( |
| 17727 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17728 | TensorOptions options, DimnameList names |
| 17729 | ) override { |
| 17730 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17731 | if (!names.empty()) { |
| 17732 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17733 | } |
| 17734 | // super must happen after, so that downstream can use maybe_get_output |
| 17735 | // to retrieve the output |
| 17736 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17737 | } |
| 17738 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17739 | return *outputs_[output_idx]; |
| 17740 | } |
| 17741 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17742 | }; |
| 17743 | at::Tensor wrapper_CPU_i0(const at::Tensor & self) { |
| 17744 | structured_i0_out_functional op; |
| 17745 | op.meta(self); |
| 17746 | op.impl(self, *op.outputs_[0]); |
| 17747 | return std::move(op.outputs_[0]).take(); |
| 17748 | } |
| 17749 | struct structured_i0_out_out final : public at::native::structured_i0_out { |
| 17750 | structured_i0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17751 | void set_output_strided( |
| 17752 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17753 | TensorOptions options, DimnameList names |
| 17754 | ) override { |
| 17755 | const auto& out = outputs_[output_idx].get(); |
| 17756 | resize_out(out, sizes, strides, options); |
| 17757 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17758 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17759 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17760 | } |
| 17761 | if (!names.empty()) { |
| 17762 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17763 | } |
| 17764 | // super must happen after, so that downstream can use maybe_get_output |
| 17765 | // to retrieve the output |
| 17766 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17767 | } |
| 17768 | void set_output_raw_strided( |
| 17769 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17770 | TensorOptions options, DimnameList names |
| 17771 | ) override { |
| 17772 | const auto& out = outputs_[output_idx].get(); |
| 17773 | resize_out(out, sizes, strides, options); |
| 17774 | if (!names.empty()) { |
| 17775 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17776 | } |
| 17777 | // super must happen after, so that downstream can use maybe_get_output |
| 17778 | // to retrieve the output |
| 17779 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17780 | } |
| 17781 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17782 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17783 | } |
| 17784 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17785 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17786 | }; |
| 17787 | at::Tensor & wrapper_CPU_i0_out_out(const at::Tensor & self, at::Tensor & out) { |
| 17788 | structured_i0_out_out op(out); |
| 17789 | op.meta(self); |
| 17790 | op.impl(self, op.maybe_get_output(0)); |
| 17791 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17792 | return out; |
| 17793 | } |
| 17794 | struct structured_i0_out_inplace final : public at::native::structured_i0_out { |
| 17795 | structured_i0_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 17796 | void set_output_strided( |
| 17797 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17798 | TensorOptions options, DimnameList names |
| 17799 | ) override { |
| 17800 | const auto& out = outputs_[output_idx].get(); |
| 17801 | check_inplace(out, sizes, options); |
| 17802 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17803 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17804 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17805 | } |
| 17806 | if (!names.empty()) { |
| 17807 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17808 | } |
| 17809 | // super must happen after, so that downstream can use maybe_get_output |
| 17810 | // to retrieve the output |
| 17811 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17812 | } |
| 17813 | void set_output_raw_strided( |
| 17814 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17815 | TensorOptions options, DimnameList names |
| 17816 | ) override { |
| 17817 | const auto& out = outputs_[output_idx].get(); |
| 17818 | check_inplace(out, sizes, options); |
| 17819 | if (!names.empty()) { |
| 17820 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17821 | } |
| 17822 | // super must happen after, so that downstream can use maybe_get_output |
| 17823 | // to retrieve the output |
| 17824 | at::native::structured_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17825 | } |
| 17826 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17827 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17828 | } |
| 17829 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17830 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17831 | }; |
| 17832 | at::Tensor & wrapper_CPU_i0_(at::Tensor & self) { |
| 17833 | structured_i0_out_inplace op(self); |
| 17834 | op.meta(self); |
| 17835 | op.impl(self, op.outputs_[0]); |
| 17836 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17837 | return self; |
| 17838 | } |
| 17839 | struct structured_sign_out_functional final : public at::native::structured_sign_out { |
| 17840 | void set_output_strided( |
| 17841 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17842 | TensorOptions options, DimnameList names |
| 17843 | ) override { |
| 17844 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17845 | if (!names.empty()) { |
| 17846 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17847 | } |
| 17848 | // super must happen after, so that downstream can use maybe_get_output |
| 17849 | // to retrieve the output |
| 17850 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17851 | } |
| 17852 | void set_output_raw_strided( |
| 17853 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17854 | TensorOptions options, DimnameList names |
| 17855 | ) override { |
| 17856 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17857 | if (!names.empty()) { |
| 17858 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17859 | } |
| 17860 | // super must happen after, so that downstream can use maybe_get_output |
| 17861 | // to retrieve the output |
| 17862 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17863 | } |
| 17864 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17865 | return *outputs_[output_idx]; |
| 17866 | } |
| 17867 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17868 | }; |
| 17869 | at::Tensor wrapper_CPU_sign(const at::Tensor & self) { |
| 17870 | structured_sign_out_functional op; |
| 17871 | op.meta(self); |
| 17872 | op.impl(self, *op.outputs_[0]); |
| 17873 | return std::move(op.outputs_[0]).take(); |
| 17874 | } |
| 17875 | struct structured_sign_out_out final : public at::native::structured_sign_out { |
| 17876 | structured_sign_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 17877 | void set_output_strided( |
| 17878 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17879 | TensorOptions options, DimnameList names |
| 17880 | ) override { |
| 17881 | const auto& out = outputs_[output_idx].get(); |
| 17882 | resize_out(out, sizes, strides, options); |
| 17883 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17884 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17885 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17886 | } |
| 17887 | if (!names.empty()) { |
| 17888 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17889 | } |
| 17890 | // super must happen after, so that downstream can use maybe_get_output |
| 17891 | // to retrieve the output |
| 17892 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17893 | } |
| 17894 | void set_output_raw_strided( |
| 17895 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17896 | TensorOptions options, DimnameList names |
| 17897 | ) override { |
| 17898 | const auto& out = outputs_[output_idx].get(); |
| 17899 | resize_out(out, sizes, strides, options); |
| 17900 | if (!names.empty()) { |
| 17901 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17902 | } |
| 17903 | // super must happen after, so that downstream can use maybe_get_output |
| 17904 | // to retrieve the output |
| 17905 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17906 | } |
| 17907 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17908 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17909 | } |
| 17910 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17911 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17912 | }; |
| 17913 | at::Tensor & wrapper_CPU_sign_out_out(const at::Tensor & self, at::Tensor & out) { |
| 17914 | structured_sign_out_out op(out); |
| 17915 | op.meta(self); |
| 17916 | op.impl(self, op.maybe_get_output(0)); |
| 17917 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17918 | return out; |
| 17919 | } |
| 17920 | struct structured_sign_out_inplace final : public at::native::structured_sign_out { |
| 17921 | structured_sign_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 17922 | void set_output_strided( |
| 17923 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17924 | TensorOptions options, DimnameList names |
| 17925 | ) override { |
| 17926 | const auto& out = outputs_[output_idx].get(); |
| 17927 | check_inplace(out, sizes, options); |
| 17928 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 17929 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 17930 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 17931 | } |
| 17932 | if (!names.empty()) { |
| 17933 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17934 | } |
| 17935 | // super must happen after, so that downstream can use maybe_get_output |
| 17936 | // to retrieve the output |
| 17937 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17938 | } |
| 17939 | void set_output_raw_strided( |
| 17940 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17941 | TensorOptions options, DimnameList names |
| 17942 | ) override { |
| 17943 | const auto& out = outputs_[output_idx].get(); |
| 17944 | check_inplace(out, sizes, options); |
| 17945 | if (!names.empty()) { |
| 17946 | namedinference::propagate_names(outputs_[output_idx], names); |
| 17947 | } |
| 17948 | // super must happen after, so that downstream can use maybe_get_output |
| 17949 | // to retrieve the output |
| 17950 | at::native::structured_sign_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17951 | } |
| 17952 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17953 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 17954 | } |
| 17955 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 17956 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 17957 | }; |
| 17958 | at::Tensor & wrapper_CPU_sign_(at::Tensor & self) { |
| 17959 | structured_sign_out_inplace op(self); |
| 17960 | op.meta(self); |
| 17961 | op.impl(self, op.outputs_[0]); |
| 17962 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 17963 | return self; |
| 17964 | } |
| 17965 | struct structured_signbit_out_functional final : public at::native::structured_signbit_out { |
| 17966 | void set_output_strided( |
| 17967 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17968 | TensorOptions options, DimnameList names |
| 17969 | ) override { |
| 17970 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17971 | if (!names.empty()) { |
| 17972 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17973 | } |
| 17974 | // super must happen after, so that downstream can use maybe_get_output |
| 17975 | // to retrieve the output |
| 17976 | at::native::structured_signbit_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17977 | } |
| 17978 | void set_output_raw_strided( |
| 17979 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17980 | TensorOptions options, DimnameList names |
| 17981 | ) override { |
| 17982 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17983 | if (!names.empty()) { |
| 17984 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17985 | } |
| 17986 | // super must happen after, so that downstream can use maybe_get_output |
| 17987 | // to retrieve the output |
| 17988 | at::native::structured_signbit_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 17989 | } |
| 17990 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17991 | return *outputs_[output_idx]; |
| 17992 | } |
| 17993 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17994 | }; |
| 17995 | at::Tensor wrapper_CPU_signbit(const at::Tensor & self) { |
| 17996 | structured_signbit_out_functional op; |
| 17997 | op.meta(self); |
| 17998 | op.impl(self, *op.outputs_[0]); |
| 17999 | return std::move(op.outputs_[0]).take(); |
| 18000 | } |
| 18001 | struct structured_signbit_out_out final : public at::native::structured_signbit_out { |
| 18002 | structured_signbit_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18003 | void set_output_strided( |
| 18004 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18005 | TensorOptions options, DimnameList names |
| 18006 | ) override { |
| 18007 | const auto& out = outputs_[output_idx].get(); |
| 18008 | resize_out(out, sizes, strides, options); |
| 18009 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18010 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18011 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18012 | } |
| 18013 | if (!names.empty()) { |
| 18014 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18015 | } |
| 18016 | // super must happen after, so that downstream can use maybe_get_output |
| 18017 | // to retrieve the output |
| 18018 | at::native::structured_signbit_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18019 | } |
| 18020 | void set_output_raw_strided( |
| 18021 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18022 | TensorOptions options, DimnameList names |
| 18023 | ) override { |
| 18024 | const auto& out = outputs_[output_idx].get(); |
| 18025 | resize_out(out, sizes, strides, options); |
| 18026 | if (!names.empty()) { |
| 18027 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18028 | } |
| 18029 | // super must happen after, so that downstream can use maybe_get_output |
| 18030 | // to retrieve the output |
| 18031 | at::native::structured_signbit_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18032 | } |
| 18033 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18034 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18035 | } |
| 18036 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18037 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18038 | }; |
| 18039 | at::Tensor & wrapper_CPU_signbit_out_out(const at::Tensor & self, at::Tensor & out) { |
| 18040 | structured_signbit_out_out op(out); |
| 18041 | op.meta(self); |
| 18042 | op.impl(self, op.maybe_get_output(0)); |
| 18043 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18044 | return out; |
| 18045 | } |
| 18046 | struct structured_atan2_out_functional final : public at::native::structured_atan2_out { |
| 18047 | void set_output_strided( |
| 18048 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18049 | TensorOptions options, DimnameList names |
| 18050 | ) override { |
| 18051 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18052 | if (!names.empty()) { |
| 18053 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18054 | } |
| 18055 | // super must happen after, so that downstream can use maybe_get_output |
| 18056 | // to retrieve the output |
| 18057 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18058 | } |
| 18059 | void set_output_raw_strided( |
| 18060 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18061 | TensorOptions options, DimnameList names |
| 18062 | ) override { |
| 18063 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18064 | if (!names.empty()) { |
| 18065 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18066 | } |
| 18067 | // super must happen after, so that downstream can use maybe_get_output |
| 18068 | // to retrieve the output |
| 18069 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18070 | } |
| 18071 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18072 | return *outputs_[output_idx]; |
| 18073 | } |
| 18074 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18075 | }; |
| 18076 | at::Tensor wrapper_CPU_atan2(const at::Tensor & self, const at::Tensor & other) { |
| 18077 | structured_atan2_out_functional op; |
| 18078 | op.meta(self, other); |
| 18079 | op.impl(self, other, *op.outputs_[0]); |
| 18080 | return std::move(op.outputs_[0]).take(); |
| 18081 | } |
| 18082 | struct structured_atan2_out_out final : public at::native::structured_atan2_out { |
| 18083 | structured_atan2_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18084 | void set_output_strided( |
| 18085 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18086 | TensorOptions options, DimnameList names |
| 18087 | ) override { |
| 18088 | const auto& out = outputs_[output_idx].get(); |
| 18089 | resize_out(out, sizes, strides, options); |
| 18090 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18091 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18092 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18093 | } |
| 18094 | if (!names.empty()) { |
| 18095 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18096 | } |
| 18097 | // super must happen after, so that downstream can use maybe_get_output |
| 18098 | // to retrieve the output |
| 18099 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18100 | } |
| 18101 | void set_output_raw_strided( |
| 18102 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18103 | TensorOptions options, DimnameList names |
| 18104 | ) override { |
| 18105 | const auto& out = outputs_[output_idx].get(); |
| 18106 | resize_out(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 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18113 | } |
| 18114 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18115 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18116 | } |
| 18117 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18118 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18119 | }; |
| 18120 | at::Tensor & wrapper_CPU_atan2_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18121 | structured_atan2_out_out op(out); |
| 18122 | op.meta(self, other); |
| 18123 | op.impl(self, other, op.maybe_get_output(0)); |
| 18124 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18125 | return out; |
| 18126 | } |
| 18127 | struct structured_atan2_out_inplace final : public at::native::structured_atan2_out { |
| 18128 | structured_atan2_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18129 | void set_output_strided( |
| 18130 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18131 | TensorOptions options, DimnameList names |
| 18132 | ) override { |
| 18133 | const auto& out = outputs_[output_idx].get(); |
| 18134 | check_inplace(out, sizes, options); |
| 18135 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18136 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18137 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18138 | } |
| 18139 | if (!names.empty()) { |
| 18140 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18141 | } |
| 18142 | // super must happen after, so that downstream can use maybe_get_output |
| 18143 | // to retrieve the output |
| 18144 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18145 | } |
| 18146 | void set_output_raw_strided( |
| 18147 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18148 | TensorOptions options, DimnameList names |
| 18149 | ) override { |
| 18150 | const auto& out = outputs_[output_idx].get(); |
| 18151 | check_inplace(out, sizes, options); |
| 18152 | if (!names.empty()) { |
| 18153 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18154 | } |
| 18155 | // super must happen after, so that downstream can use maybe_get_output |
| 18156 | // to retrieve the output |
| 18157 | at::native::structured_atan2_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18158 | } |
| 18159 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18160 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18161 | } |
| 18162 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18163 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18164 | }; |
| 18165 | at::Tensor & wrapper_CPU_atan2_(at::Tensor & self, const at::Tensor & other) { |
| 18166 | structured_atan2_out_inplace op(self); |
| 18167 | op.meta(self, other); |
| 18168 | op.impl(self, other, op.outputs_[0]); |
| 18169 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18170 | return self; |
| 18171 | } |
| 18172 | namespace { |
| 18173 | at::Tensor wrapper_CPU__histc(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { |
| 18174 | // No device check |
| 18175 | // DeviceGuard omitted |
| 18176 | return at::native::histogram_histc_cpu(self, bins, min, max); |
| 18177 | } |
| 18178 | } // anonymous namespace |
| 18179 | namespace { |
| 18180 | at::Tensor & wrapper_CPU_out_histc_out(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) { |
| 18181 | // No device check |
| 18182 | // DeviceGuard omitted |
| 18183 | return at::native::histogram_histc_cpu_out(self, bins, min, max, out); |
| 18184 | } |
| 18185 | } // anonymous namespace |
| 18186 | namespace { |
| 18187 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_bins_tensor_histogram(const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 18188 | // No device check |
| 18189 | // DeviceGuard omitted |
| 18190 | return at::native::histogram_cpu(self, bins, weight, density); |
| 18191 | } |
| 18192 | } // anonymous namespace |
| 18193 | namespace { |
| 18194 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_bins_tensor_out_histogram_out(const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 18195 | // No device check |
| 18196 | // DeviceGuard omitted |
| 18197 | return at::native::histogram_out_cpu(self, bins, weight, density, hist, bin_edges); |
| 18198 | } |
| 18199 | } // anonymous namespace |
| 18200 | namespace { |
| 18201 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_bin_ct_histogram(const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 18202 | // No device check |
| 18203 | // DeviceGuard omitted |
| 18204 | return at::native::histogram_cpu(self, bins, range, weight, density); |
| 18205 | } |
| 18206 | } // anonymous namespace |
| 18207 | namespace { |
| 18208 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_bin_ct_out_histogram_out(const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 18209 | // No device check |
| 18210 | // DeviceGuard omitted |
| 18211 | return at::native::histogram_out_cpu(self, bins, range, weight, density, hist, bin_edges); |
| 18212 | } |
| 18213 | } // anonymous namespace |
| 18214 | namespace { |
| 18215 | ::std::vector<at::Tensor> wrapper_CPU___histogramdd_bin_edges(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 18216 | // No device check |
| 18217 | // DeviceGuard omitted |
| 18218 | return at::native::histogramdd_bin_edges_cpu(self, bins, range, weight, density); |
| 18219 | } |
| 18220 | } // anonymous namespace |
| 18221 | namespace { |
| 18222 | at::Tensor wrapper_CPU___histogramdd_from_bin_cts(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 18223 | // No device check |
| 18224 | // DeviceGuard omitted |
| 18225 | return at::native::histogramdd_cpu(self, bins, range, weight, density); |
| 18226 | } |
| 18227 | } // anonymous namespace |
| 18228 | namespace { |
| 18229 | at::Tensor wrapper_CPU___histogramdd_from_bin_tensors(const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 18230 | // No device check |
| 18231 | // DeviceGuard omitted |
| 18232 | return at::native::histogramdd_cpu(self, bins, weight, density); |
| 18233 | } |
| 18234 | } // anonymous namespace |
| 18235 | struct structured_fmod_out_functional final : public at::native::structured_fmod_out { |
| 18236 | void set_output_strided( |
| 18237 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18238 | TensorOptions options, DimnameList names |
| 18239 | ) override { |
| 18240 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18241 | if (!names.empty()) { |
| 18242 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18243 | } |
| 18244 | // super must happen after, so that downstream can use maybe_get_output |
| 18245 | // to retrieve the output |
| 18246 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18247 | } |
| 18248 | void set_output_raw_strided( |
| 18249 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18250 | TensorOptions options, DimnameList names |
| 18251 | ) override { |
| 18252 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18253 | if (!names.empty()) { |
| 18254 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18255 | } |
| 18256 | // super must happen after, so that downstream can use maybe_get_output |
| 18257 | // to retrieve the output |
| 18258 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 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 | }; |
| 18265 | at::Tensor wrapper_CPU_fmod_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 18266 | structured_fmod_out_functional op; |
| 18267 | op.meta(self, other); |
| 18268 | op.impl(self, other, *op.outputs_[0]); |
| 18269 | return std::move(op.outputs_[0]).take(); |
| 18270 | } |
| 18271 | struct structured_fmod_out_out final : public at::native::structured_fmod_out { |
| 18272 | structured_fmod_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18273 | void set_output_strided( |
| 18274 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18275 | TensorOptions options, DimnameList names |
| 18276 | ) override { |
| 18277 | const auto& out = outputs_[output_idx].get(); |
| 18278 | resize_out(out, sizes, strides, options); |
| 18279 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18280 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18281 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18282 | } |
| 18283 | if (!names.empty()) { |
| 18284 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18285 | } |
| 18286 | // super must happen after, so that downstream can use maybe_get_output |
| 18287 | // to retrieve the output |
| 18288 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18289 | } |
| 18290 | void set_output_raw_strided( |
| 18291 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18292 | TensorOptions options, DimnameList names |
| 18293 | ) override { |
| 18294 | const auto& out = outputs_[output_idx].get(); |
| 18295 | resize_out(out, sizes, strides, options); |
| 18296 | if (!names.empty()) { |
| 18297 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18298 | } |
| 18299 | // super must happen after, so that downstream can use maybe_get_output |
| 18300 | // to retrieve the output |
| 18301 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18302 | } |
| 18303 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18304 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18305 | } |
| 18306 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18307 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18308 | }; |
| 18309 | at::Tensor & wrapper_CPU_fmod_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18310 | structured_fmod_out_out op(out); |
| 18311 | op.meta(self, other); |
| 18312 | op.impl(self, other, op.maybe_get_output(0)); |
| 18313 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18314 | return out; |
| 18315 | } |
| 18316 | struct structured_fmod_out_inplace final : public at::native::structured_fmod_out { |
| 18317 | structured_fmod_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18318 | void set_output_strided( |
| 18319 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18320 | TensorOptions options, DimnameList names |
| 18321 | ) override { |
| 18322 | const auto& out = outputs_[output_idx].get(); |
| 18323 | check_inplace(out, sizes, options); |
| 18324 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18325 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18326 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18327 | } |
| 18328 | if (!names.empty()) { |
| 18329 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18330 | } |
| 18331 | // super must happen after, so that downstream can use maybe_get_output |
| 18332 | // to retrieve the output |
| 18333 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18334 | } |
| 18335 | void set_output_raw_strided( |
| 18336 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18337 | TensorOptions options, DimnameList names |
| 18338 | ) override { |
| 18339 | const auto& out = outputs_[output_idx].get(); |
| 18340 | check_inplace(out, sizes, options); |
| 18341 | if (!names.empty()) { |
| 18342 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18343 | } |
| 18344 | // super must happen after, so that downstream can use maybe_get_output |
| 18345 | // to retrieve the output |
| 18346 | at::native::structured_fmod_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18347 | } |
| 18348 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18349 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18350 | } |
| 18351 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18352 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18353 | }; |
| 18354 | at::Tensor & wrapper_CPU_fmod__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 18355 | structured_fmod_out_inplace op(self); |
| 18356 | op.meta(self, other); |
| 18357 | op.impl(self, other, op.outputs_[0]); |
| 18358 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18359 | return self; |
| 18360 | } |
| 18361 | struct structured_hypot_out_functional final : public at::native::structured_hypot_out { |
| 18362 | void set_output_strided( |
| 18363 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18364 | TensorOptions options, DimnameList names |
| 18365 | ) override { |
| 18366 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18367 | if (!names.empty()) { |
| 18368 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18369 | } |
| 18370 | // super must happen after, so that downstream can use maybe_get_output |
| 18371 | // to retrieve the output |
| 18372 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18373 | } |
| 18374 | void set_output_raw_strided( |
| 18375 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18376 | TensorOptions options, DimnameList names |
| 18377 | ) override { |
| 18378 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18379 | if (!names.empty()) { |
| 18380 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18381 | } |
| 18382 | // super must happen after, so that downstream can use maybe_get_output |
| 18383 | // to retrieve the output |
| 18384 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18385 | } |
| 18386 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18387 | return *outputs_[output_idx]; |
| 18388 | } |
| 18389 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18390 | }; |
| 18391 | at::Tensor wrapper_CPU_hypot(const at::Tensor & self, const at::Tensor & other) { |
| 18392 | structured_hypot_out_functional op; |
| 18393 | op.meta(self, other); |
| 18394 | op.impl(self, other, *op.outputs_[0]); |
| 18395 | return std::move(op.outputs_[0]).take(); |
| 18396 | } |
| 18397 | struct structured_hypot_out_out final : public at::native::structured_hypot_out { |
| 18398 | structured_hypot_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18399 | void set_output_strided( |
| 18400 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18401 | TensorOptions options, DimnameList names |
| 18402 | ) override { |
| 18403 | const auto& out = outputs_[output_idx].get(); |
| 18404 | resize_out(out, sizes, strides, options); |
| 18405 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18406 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18407 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18408 | } |
| 18409 | if (!names.empty()) { |
| 18410 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18411 | } |
| 18412 | // super must happen after, so that downstream can use maybe_get_output |
| 18413 | // to retrieve the output |
| 18414 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18415 | } |
| 18416 | void set_output_raw_strided( |
| 18417 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18418 | TensorOptions options, DimnameList names |
| 18419 | ) override { |
| 18420 | const auto& out = outputs_[output_idx].get(); |
| 18421 | resize_out(out, sizes, strides, options); |
| 18422 | if (!names.empty()) { |
| 18423 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18424 | } |
| 18425 | // super must happen after, so that downstream can use maybe_get_output |
| 18426 | // to retrieve the output |
| 18427 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18428 | } |
| 18429 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18430 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18431 | } |
| 18432 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18433 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18434 | }; |
| 18435 | at::Tensor & wrapper_CPU_hypot_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18436 | structured_hypot_out_out op(out); |
| 18437 | op.meta(self, other); |
| 18438 | op.impl(self, other, op.maybe_get_output(0)); |
| 18439 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18440 | return out; |
| 18441 | } |
| 18442 | struct structured_hypot_out_inplace final : public at::native::structured_hypot_out { |
| 18443 | structured_hypot_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18444 | void set_output_strided( |
| 18445 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18446 | TensorOptions options, DimnameList names |
| 18447 | ) override { |
| 18448 | const auto& out = outputs_[output_idx].get(); |
| 18449 | check_inplace(out, sizes, options); |
| 18450 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18451 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18452 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18453 | } |
| 18454 | if (!names.empty()) { |
| 18455 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18456 | } |
| 18457 | // super must happen after, so that downstream can use maybe_get_output |
| 18458 | // to retrieve the output |
| 18459 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18460 | } |
| 18461 | void set_output_raw_strided( |
| 18462 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18463 | TensorOptions options, DimnameList names |
| 18464 | ) override { |
| 18465 | const auto& out = outputs_[output_idx].get(); |
| 18466 | check_inplace(out, sizes, options); |
| 18467 | if (!names.empty()) { |
| 18468 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18469 | } |
| 18470 | // super must happen after, so that downstream can use maybe_get_output |
| 18471 | // to retrieve the output |
| 18472 | at::native::structured_hypot_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18473 | } |
| 18474 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18475 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18476 | } |
| 18477 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18478 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18479 | }; |
| 18480 | at::Tensor & wrapper_CPU_hypot_(at::Tensor & self, const at::Tensor & other) { |
| 18481 | structured_hypot_out_inplace op(self); |
| 18482 | op.meta(self, other); |
| 18483 | op.impl(self, other, op.outputs_[0]); |
| 18484 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18485 | return self; |
| 18486 | } |
| 18487 | struct structured_igamma_out_functional final : public at::native::structured_igamma_out { |
| 18488 | void set_output_strided( |
| 18489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18490 | TensorOptions options, DimnameList names |
| 18491 | ) override { |
| 18492 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18493 | if (!names.empty()) { |
| 18494 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18495 | } |
| 18496 | // super must happen after, so that downstream can use maybe_get_output |
| 18497 | // to retrieve the output |
| 18498 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18499 | } |
| 18500 | void set_output_raw_strided( |
| 18501 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18502 | TensorOptions options, DimnameList names |
| 18503 | ) override { |
| 18504 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18505 | if (!names.empty()) { |
| 18506 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18507 | } |
| 18508 | // super must happen after, so that downstream can use maybe_get_output |
| 18509 | // to retrieve the output |
| 18510 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18511 | } |
| 18512 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18513 | return *outputs_[output_idx]; |
| 18514 | } |
| 18515 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18516 | }; |
| 18517 | at::Tensor wrapper_CPU_igamma(const at::Tensor & self, const at::Tensor & other) { |
| 18518 | structured_igamma_out_functional op; |
| 18519 | op.meta(self, other); |
| 18520 | op.impl(self, other, *op.outputs_[0]); |
| 18521 | return std::move(op.outputs_[0]).take(); |
| 18522 | } |
| 18523 | struct structured_igamma_out_out final : public at::native::structured_igamma_out { |
| 18524 | structured_igamma_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18525 | void set_output_strided( |
| 18526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18527 | TensorOptions options, DimnameList names |
| 18528 | ) override { |
| 18529 | const auto& out = outputs_[output_idx].get(); |
| 18530 | resize_out(out, sizes, strides, options); |
| 18531 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18532 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18533 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18534 | } |
| 18535 | if (!names.empty()) { |
| 18536 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18537 | } |
| 18538 | // super must happen after, so that downstream can use maybe_get_output |
| 18539 | // to retrieve the output |
| 18540 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18541 | } |
| 18542 | void set_output_raw_strided( |
| 18543 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18544 | TensorOptions options, DimnameList names |
| 18545 | ) override { |
| 18546 | const auto& out = outputs_[output_idx].get(); |
| 18547 | resize_out(out, sizes, strides, options); |
| 18548 | if (!names.empty()) { |
| 18549 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18550 | } |
| 18551 | // super must happen after, so that downstream can use maybe_get_output |
| 18552 | // to retrieve the output |
| 18553 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18554 | } |
| 18555 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18556 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18557 | } |
| 18558 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18559 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18560 | }; |
| 18561 | at::Tensor & wrapper_CPU_igamma_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18562 | structured_igamma_out_out op(out); |
| 18563 | op.meta(self, other); |
| 18564 | op.impl(self, other, op.maybe_get_output(0)); |
| 18565 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18566 | return out; |
| 18567 | } |
| 18568 | struct structured_igamma_out_inplace final : public at::native::structured_igamma_out { |
| 18569 | structured_igamma_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18570 | void set_output_strided( |
| 18571 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18572 | TensorOptions options, DimnameList names |
| 18573 | ) override { |
| 18574 | const auto& out = outputs_[output_idx].get(); |
| 18575 | check_inplace(out, sizes, options); |
| 18576 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18577 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18578 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18579 | } |
| 18580 | if (!names.empty()) { |
| 18581 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18582 | } |
| 18583 | // super must happen after, so that downstream can use maybe_get_output |
| 18584 | // to retrieve the output |
| 18585 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18586 | } |
| 18587 | void set_output_raw_strided( |
| 18588 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18589 | TensorOptions options, DimnameList names |
| 18590 | ) override { |
| 18591 | const auto& out = outputs_[output_idx].get(); |
| 18592 | check_inplace(out, sizes, options); |
| 18593 | if (!names.empty()) { |
| 18594 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18595 | } |
| 18596 | // super must happen after, so that downstream can use maybe_get_output |
| 18597 | // to retrieve the output |
| 18598 | at::native::structured_igamma_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18599 | } |
| 18600 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18601 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18602 | } |
| 18603 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18604 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18605 | }; |
| 18606 | at::Tensor & wrapper_CPU_igamma_(at::Tensor & self, const at::Tensor & other) { |
| 18607 | structured_igamma_out_inplace op(self); |
| 18608 | op.meta(self, other); |
| 18609 | op.impl(self, other, op.outputs_[0]); |
| 18610 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18611 | return self; |
| 18612 | } |
| 18613 | struct structured_igammac_out_functional final : public at::native::structured_igammac_out { |
| 18614 | void set_output_strided( |
| 18615 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18616 | TensorOptions options, DimnameList names |
| 18617 | ) override { |
| 18618 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18619 | if (!names.empty()) { |
| 18620 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18621 | } |
| 18622 | // super must happen after, so that downstream can use maybe_get_output |
| 18623 | // to retrieve the output |
| 18624 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18625 | } |
| 18626 | void set_output_raw_strided( |
| 18627 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18628 | TensorOptions options, DimnameList names |
| 18629 | ) override { |
| 18630 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18631 | if (!names.empty()) { |
| 18632 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18633 | } |
| 18634 | // super must happen after, so that downstream can use maybe_get_output |
| 18635 | // to retrieve the output |
| 18636 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18637 | } |
| 18638 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18639 | return *outputs_[output_idx]; |
| 18640 | } |
| 18641 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18642 | }; |
| 18643 | at::Tensor wrapper_CPU_igammac(const at::Tensor & self, const at::Tensor & other) { |
| 18644 | structured_igammac_out_functional op; |
| 18645 | op.meta(self, other); |
| 18646 | op.impl(self, other, *op.outputs_[0]); |
| 18647 | return std::move(op.outputs_[0]).take(); |
| 18648 | } |
| 18649 | struct structured_igammac_out_out final : public at::native::structured_igammac_out { |
| 18650 | structured_igammac_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18651 | void set_output_strided( |
| 18652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18653 | TensorOptions options, DimnameList names |
| 18654 | ) override { |
| 18655 | const auto& out = outputs_[output_idx].get(); |
| 18656 | resize_out(out, sizes, strides, options); |
| 18657 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18658 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18659 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18660 | } |
| 18661 | if (!names.empty()) { |
| 18662 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18663 | } |
| 18664 | // super must happen after, so that downstream can use maybe_get_output |
| 18665 | // to retrieve the output |
| 18666 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18667 | } |
| 18668 | void set_output_raw_strided( |
| 18669 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18670 | TensorOptions options, DimnameList names |
| 18671 | ) override { |
| 18672 | const auto& out = outputs_[output_idx].get(); |
| 18673 | resize_out(out, sizes, strides, options); |
| 18674 | if (!names.empty()) { |
| 18675 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18676 | } |
| 18677 | // super must happen after, so that downstream can use maybe_get_output |
| 18678 | // to retrieve the output |
| 18679 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18680 | } |
| 18681 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18682 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18683 | } |
| 18684 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18685 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18686 | }; |
| 18687 | at::Tensor & wrapper_CPU_igammac_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18688 | structured_igammac_out_out op(out); |
| 18689 | op.meta(self, other); |
| 18690 | op.impl(self, other, op.maybe_get_output(0)); |
| 18691 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18692 | return out; |
| 18693 | } |
| 18694 | struct structured_igammac_out_inplace final : public at::native::structured_igammac_out { |
| 18695 | structured_igammac_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18696 | void set_output_strided( |
| 18697 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18698 | TensorOptions options, DimnameList names |
| 18699 | ) override { |
| 18700 | const auto& out = outputs_[output_idx].get(); |
| 18701 | check_inplace(out, sizes, options); |
| 18702 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18703 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18704 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18705 | } |
| 18706 | if (!names.empty()) { |
| 18707 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18708 | } |
| 18709 | // super must happen after, so that downstream can use maybe_get_output |
| 18710 | // to retrieve the output |
| 18711 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18712 | } |
| 18713 | void set_output_raw_strided( |
| 18714 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18715 | TensorOptions options, DimnameList names |
| 18716 | ) override { |
| 18717 | const auto& out = outputs_[output_idx].get(); |
| 18718 | check_inplace(out, sizes, options); |
| 18719 | if (!names.empty()) { |
| 18720 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18721 | } |
| 18722 | // super must happen after, so that downstream can use maybe_get_output |
| 18723 | // to retrieve the output |
| 18724 | at::native::structured_igammac_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18725 | } |
| 18726 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18727 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18728 | } |
| 18729 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18730 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18731 | }; |
| 18732 | at::Tensor & wrapper_CPU_igammac_(at::Tensor & self, const at::Tensor & other) { |
| 18733 | structured_igammac_out_inplace op(self); |
| 18734 | op.meta(self, other); |
| 18735 | op.impl(self, other, op.outputs_[0]); |
| 18736 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18737 | return self; |
| 18738 | } |
| 18739 | struct structured_nextafter_out_functional final : public at::native::structured_nextafter_out { |
| 18740 | void set_output_strided( |
| 18741 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18742 | TensorOptions options, DimnameList names |
| 18743 | ) override { |
| 18744 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18745 | if (!names.empty()) { |
| 18746 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18747 | } |
| 18748 | // super must happen after, so that downstream can use maybe_get_output |
| 18749 | // to retrieve the output |
| 18750 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18751 | } |
| 18752 | void set_output_raw_strided( |
| 18753 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18754 | TensorOptions options, DimnameList names |
| 18755 | ) override { |
| 18756 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18757 | if (!names.empty()) { |
| 18758 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18759 | } |
| 18760 | // super must happen after, so that downstream can use maybe_get_output |
| 18761 | // to retrieve the output |
| 18762 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18763 | } |
| 18764 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18765 | return *outputs_[output_idx]; |
| 18766 | } |
| 18767 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18768 | }; |
| 18769 | at::Tensor wrapper_CPU_nextafter(const at::Tensor & self, const at::Tensor & other) { |
| 18770 | structured_nextafter_out_functional op; |
| 18771 | op.meta(self, other); |
| 18772 | op.impl(self, other, *op.outputs_[0]); |
| 18773 | return std::move(op.outputs_[0]).take(); |
| 18774 | } |
| 18775 | struct structured_nextafter_out_out final : public at::native::structured_nextafter_out { |
| 18776 | structured_nextafter_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18777 | void set_output_strided( |
| 18778 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18779 | TensorOptions options, DimnameList names |
| 18780 | ) override { |
| 18781 | const auto& out = outputs_[output_idx].get(); |
| 18782 | resize_out(out, sizes, strides, options); |
| 18783 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18784 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18785 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18786 | } |
| 18787 | if (!names.empty()) { |
| 18788 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18789 | } |
| 18790 | // super must happen after, so that downstream can use maybe_get_output |
| 18791 | // to retrieve the output |
| 18792 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18793 | } |
| 18794 | void set_output_raw_strided( |
| 18795 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18796 | TensorOptions options, DimnameList names |
| 18797 | ) override { |
| 18798 | const auto& out = outputs_[output_idx].get(); |
| 18799 | resize_out(out, sizes, strides, options); |
| 18800 | if (!names.empty()) { |
| 18801 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18802 | } |
| 18803 | // super must happen after, so that downstream can use maybe_get_output |
| 18804 | // to retrieve the output |
| 18805 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18806 | } |
| 18807 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18808 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18809 | } |
| 18810 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18811 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18812 | }; |
| 18813 | at::Tensor & wrapper_CPU_nextafter_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18814 | structured_nextafter_out_out op(out); |
| 18815 | op.meta(self, other); |
| 18816 | op.impl(self, other, op.maybe_get_output(0)); |
| 18817 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18818 | return out; |
| 18819 | } |
| 18820 | struct structured_nextafter_out_inplace final : public at::native::structured_nextafter_out { |
| 18821 | structured_nextafter_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18822 | void set_output_strided( |
| 18823 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18824 | TensorOptions options, DimnameList names |
| 18825 | ) override { |
| 18826 | const auto& out = outputs_[output_idx].get(); |
| 18827 | check_inplace(out, sizes, options); |
| 18828 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18829 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18830 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18831 | } |
| 18832 | if (!names.empty()) { |
| 18833 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18834 | } |
| 18835 | // super must happen after, so that downstream can use maybe_get_output |
| 18836 | // to retrieve the output |
| 18837 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18838 | } |
| 18839 | void set_output_raw_strided( |
| 18840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18841 | TensorOptions options, DimnameList names |
| 18842 | ) override { |
| 18843 | const auto& out = outputs_[output_idx].get(); |
| 18844 | check_inplace(out, sizes, options); |
| 18845 | if (!names.empty()) { |
| 18846 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18847 | } |
| 18848 | // super must happen after, so that downstream can use maybe_get_output |
| 18849 | // to retrieve the output |
| 18850 | at::native::structured_nextafter_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18851 | } |
| 18852 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18853 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18854 | } |
| 18855 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18856 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18857 | }; |
| 18858 | at::Tensor & wrapper_CPU_nextafter_(at::Tensor & self, const at::Tensor & other) { |
| 18859 | structured_nextafter_out_inplace op(self); |
| 18860 | op.meta(self, other); |
| 18861 | op.impl(self, other, op.outputs_[0]); |
| 18862 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18863 | return self; |
| 18864 | } |
| 18865 | struct structured_remainder_out_functional final : public at::native::structured_remainder_out { |
| 18866 | void set_output_strided( |
| 18867 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18868 | TensorOptions options, DimnameList names |
| 18869 | ) override { |
| 18870 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18871 | if (!names.empty()) { |
| 18872 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18873 | } |
| 18874 | // super must happen after, so that downstream can use maybe_get_output |
| 18875 | // to retrieve the output |
| 18876 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18877 | } |
| 18878 | void set_output_raw_strided( |
| 18879 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18880 | TensorOptions options, DimnameList names |
| 18881 | ) override { |
| 18882 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18883 | if (!names.empty()) { |
| 18884 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18885 | } |
| 18886 | // super must happen after, so that downstream can use maybe_get_output |
| 18887 | // to retrieve the output |
| 18888 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18889 | } |
| 18890 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18891 | return *outputs_[output_idx]; |
| 18892 | } |
| 18893 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18894 | }; |
| 18895 | at::Tensor wrapper_CPU_remainder_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 18896 | structured_remainder_out_functional op; |
| 18897 | op.meta(self, other); |
| 18898 | op.impl(self, other, *op.outputs_[0]); |
| 18899 | return std::move(op.outputs_[0]).take(); |
| 18900 | } |
| 18901 | struct structured_remainder_out_out final : public at::native::structured_remainder_out { |
| 18902 | structured_remainder_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 18903 | void set_output_strided( |
| 18904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18905 | TensorOptions options, DimnameList names |
| 18906 | ) override { |
| 18907 | const auto& out = outputs_[output_idx].get(); |
| 18908 | resize_out(out, sizes, strides, options); |
| 18909 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18910 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18911 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18912 | } |
| 18913 | if (!names.empty()) { |
| 18914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18915 | } |
| 18916 | // super must happen after, so that downstream can use maybe_get_output |
| 18917 | // to retrieve the output |
| 18918 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18919 | } |
| 18920 | void set_output_raw_strided( |
| 18921 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18922 | TensorOptions options, DimnameList names |
| 18923 | ) override { |
| 18924 | const auto& out = outputs_[output_idx].get(); |
| 18925 | resize_out(out, sizes, strides, options); |
| 18926 | if (!names.empty()) { |
| 18927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18928 | } |
| 18929 | // super must happen after, so that downstream can use maybe_get_output |
| 18930 | // to retrieve the output |
| 18931 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18932 | } |
| 18933 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18934 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18935 | } |
| 18936 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18937 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18938 | }; |
| 18939 | at::Tensor & wrapper_CPU_remainder_out_Tensor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18940 | structured_remainder_out_out op(out); |
| 18941 | op.meta(self, other); |
| 18942 | op.impl(self, other, op.maybe_get_output(0)); |
| 18943 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18944 | return out; |
| 18945 | } |
| 18946 | struct structured_remainder_out_inplace final : public at::native::structured_remainder_out { |
| 18947 | structured_remainder_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 18948 | void set_output_strided( |
| 18949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18950 | TensorOptions options, DimnameList names |
| 18951 | ) override { |
| 18952 | const auto& out = outputs_[output_idx].get(); |
| 18953 | check_inplace(out, sizes, options); |
| 18954 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 18955 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 18956 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 18957 | } |
| 18958 | if (!names.empty()) { |
| 18959 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18960 | } |
| 18961 | // super must happen after, so that downstream can use maybe_get_output |
| 18962 | // to retrieve the output |
| 18963 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18964 | } |
| 18965 | void set_output_raw_strided( |
| 18966 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18967 | TensorOptions options, DimnameList names |
| 18968 | ) override { |
| 18969 | const auto& out = outputs_[output_idx].get(); |
| 18970 | check_inplace(out, sizes, options); |
| 18971 | if (!names.empty()) { |
| 18972 | namedinference::propagate_names(outputs_[output_idx], names); |
| 18973 | } |
| 18974 | // super must happen after, so that downstream can use maybe_get_output |
| 18975 | // to retrieve the output |
| 18976 | at::native::structured_remainder_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18977 | } |
| 18978 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18979 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 18980 | } |
| 18981 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 18982 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 18983 | }; |
| 18984 | at::Tensor & wrapper_CPU_remainder__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 18985 | structured_remainder_out_inplace op(self); |
| 18986 | op.meta(self, other); |
| 18987 | op.impl(self, other, op.outputs_[0]); |
| 18988 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 18989 | return self; |
| 18990 | } |
| 18991 | namespace { |
| 18992 | at::Tensor wrapper_CPU_Scalar_Tensor_remainder(const at::Scalar & self, const at::Tensor & other) { |
| 18993 | // No device check |
| 18994 | // DeviceGuard omitted |
| 18995 | return at::native::remainder(self, other); |
| 18996 | } |
| 18997 | } // anonymous namespace |
| 18998 | namespace { |
| 18999 | at::Tensor wrapper_CPU__min(const at::Tensor & self) { |
| 19000 | // No device check |
| 19001 | // DeviceGuard omitted |
| 19002 | return at::native::min(self); |
| 19003 | } |
| 19004 | } // anonymous namespace |
| 19005 | struct structured_fmin_out_functional final : public at::native::structured_fmin_out { |
| 19006 | void set_output_strided( |
| 19007 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19008 | TensorOptions options, DimnameList names |
| 19009 | ) override { |
| 19010 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19011 | if (!names.empty()) { |
| 19012 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19013 | } |
| 19014 | // super must happen after, so that downstream can use maybe_get_output |
| 19015 | // to retrieve the output |
| 19016 | at::native::structured_fmin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19017 | } |
| 19018 | void set_output_raw_strided( |
| 19019 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19020 | TensorOptions options, DimnameList names |
| 19021 | ) override { |
| 19022 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19023 | if (!names.empty()) { |
| 19024 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19025 | } |
| 19026 | // super must happen after, so that downstream can use maybe_get_output |
| 19027 | // to retrieve the output |
| 19028 | at::native::structured_fmin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19029 | } |
| 19030 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19031 | return *outputs_[output_idx]; |
| 19032 | } |
| 19033 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19034 | }; |
| 19035 | at::Tensor wrapper_CPU_fmin(const at::Tensor & self, const at::Tensor & other) { |
| 19036 | structured_fmin_out_functional op; |
| 19037 | op.meta(self, other); |
| 19038 | op.impl(self, other, *op.outputs_[0]); |
| 19039 | return std::move(op.outputs_[0]).take(); |
| 19040 | } |
| 19041 | struct structured_fmin_out_out final : public at::native::structured_fmin_out { |
| 19042 | structured_fmin_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19043 | void set_output_strided( |
| 19044 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19045 | TensorOptions options, DimnameList names |
| 19046 | ) override { |
| 19047 | const auto& out = outputs_[output_idx].get(); |
| 19048 | resize_out(out, sizes, strides, options); |
| 19049 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19050 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19051 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19052 | } |
| 19053 | if (!names.empty()) { |
| 19054 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19055 | } |
| 19056 | // super must happen after, so that downstream can use maybe_get_output |
| 19057 | // to retrieve the output |
| 19058 | at::native::structured_fmin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19059 | } |
| 19060 | void set_output_raw_strided( |
| 19061 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19062 | TensorOptions options, DimnameList names |
| 19063 | ) override { |
| 19064 | const auto& out = outputs_[output_idx].get(); |
| 19065 | resize_out(out, sizes, strides, options); |
| 19066 | if (!names.empty()) { |
| 19067 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19068 | } |
| 19069 | // super must happen after, so that downstream can use maybe_get_output |
| 19070 | // to retrieve the output |
| 19071 | at::native::structured_fmin_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19072 | } |
| 19073 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19074 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19075 | } |
| 19076 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19077 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19078 | }; |
| 19079 | at::Tensor & wrapper_CPU_fmin_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 19080 | structured_fmin_out_out op(out); |
| 19081 | op.meta(self, other); |
| 19082 | op.impl(self, other, op.maybe_get_output(0)); |
| 19083 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19084 | return out; |
| 19085 | } |
| 19086 | namespace { |
| 19087 | at::Tensor wrapper_CPU__max(const at::Tensor & self) { |
| 19088 | // No device check |
| 19089 | // DeviceGuard omitted |
| 19090 | return at::native::max(self); |
| 19091 | } |
| 19092 | } // anonymous namespace |
| 19093 | namespace { |
| 19094 | at::Tensor & wrapper_CPU_unary_out_max_out(const at::Tensor & self, at::Tensor & out) { |
| 19095 | // No device check |
| 19096 | // DeviceGuard omitted |
| 19097 | return at::native::max_unary_out(self, out); |
| 19098 | } |
| 19099 | } // anonymous namespace |
| 19100 | struct structured_fmax_out_functional final : public at::native::structured_fmax_out { |
| 19101 | void set_output_strided( |
| 19102 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19103 | TensorOptions options, DimnameList names |
| 19104 | ) override { |
| 19105 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19106 | if (!names.empty()) { |
| 19107 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19108 | } |
| 19109 | // super must happen after, so that downstream can use maybe_get_output |
| 19110 | // to retrieve the output |
| 19111 | at::native::structured_fmax_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19112 | } |
| 19113 | void set_output_raw_strided( |
| 19114 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19115 | TensorOptions options, DimnameList names |
| 19116 | ) override { |
| 19117 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19118 | if (!names.empty()) { |
| 19119 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19120 | } |
| 19121 | // super must happen after, so that downstream can use maybe_get_output |
| 19122 | // to retrieve the output |
| 19123 | at::native::structured_fmax_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19124 | } |
| 19125 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19126 | return *outputs_[output_idx]; |
| 19127 | } |
| 19128 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19129 | }; |
| 19130 | at::Tensor wrapper_CPU_fmax(const at::Tensor & self, const at::Tensor & other) { |
| 19131 | structured_fmax_out_functional op; |
| 19132 | op.meta(self, other); |
| 19133 | op.impl(self, other, *op.outputs_[0]); |
| 19134 | return std::move(op.outputs_[0]).take(); |
| 19135 | } |
| 19136 | struct structured_fmax_out_out final : public at::native::structured_fmax_out { |
| 19137 | structured_fmax_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19138 | void set_output_strided( |
| 19139 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19140 | TensorOptions options, DimnameList names |
| 19141 | ) override { |
| 19142 | const auto& out = outputs_[output_idx].get(); |
| 19143 | resize_out(out, sizes, strides, options); |
| 19144 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19145 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19146 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19147 | } |
| 19148 | if (!names.empty()) { |
| 19149 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19150 | } |
| 19151 | // super must happen after, so that downstream can use maybe_get_output |
| 19152 | // to retrieve the output |
| 19153 | at::native::structured_fmax_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19154 | } |
| 19155 | void set_output_raw_strided( |
| 19156 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19157 | TensorOptions options, DimnameList names |
| 19158 | ) override { |
| 19159 | const auto& out = outputs_[output_idx].get(); |
| 19160 | resize_out(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::native::structured_fmax_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19167 | } |
| 19168 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19169 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19170 | } |
| 19171 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19172 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19173 | }; |
| 19174 | at::Tensor & wrapper_CPU_fmax_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 19175 | structured_fmax_out_out op(out); |
| 19176 | op.meta(self, other); |
| 19177 | op.impl(self, other, op.maybe_get_output(0)); |
| 19178 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19179 | return out; |
| 19180 | } |
| 19181 | struct structured_maximum_out_functional final : public at::native::structured_maximum_out { |
| 19182 | void set_output_strided( |
| 19183 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19184 | TensorOptions options, DimnameList names |
| 19185 | ) override { |
| 19186 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19187 | if (!names.empty()) { |
| 19188 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19189 | } |
| 19190 | // super must happen after, so that downstream can use maybe_get_output |
| 19191 | // to retrieve the output |
| 19192 | at::native::structured_maximum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19193 | } |
| 19194 | void set_output_raw_strided( |
| 19195 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19196 | TensorOptions options, DimnameList names |
| 19197 | ) override { |
| 19198 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19199 | if (!names.empty()) { |
| 19200 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19201 | } |
| 19202 | // super must happen after, so that downstream can use maybe_get_output |
| 19203 | // to retrieve the output |
| 19204 | at::native::structured_maximum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19205 | } |
| 19206 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19207 | return *outputs_[output_idx]; |
| 19208 | } |
| 19209 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19210 | }; |
| 19211 | at::Tensor wrapper_CPU_maximum(const at::Tensor & self, const at::Tensor & other) { |
| 19212 | structured_maximum_out_functional op; |
| 19213 | op.meta(self, other); |
| 19214 | op.impl(self, other, *op.outputs_[0]); |
| 19215 | return std::move(op.outputs_[0]).take(); |
| 19216 | } |
| 19217 | struct structured_maximum_out_out final : public at::native::structured_maximum_out { |
| 19218 | structured_maximum_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19219 | void set_output_strided( |
| 19220 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19221 | TensorOptions options, DimnameList names |
| 19222 | ) override { |
| 19223 | const auto& out = outputs_[output_idx].get(); |
| 19224 | resize_out(out, sizes, strides, options); |
| 19225 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19226 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19227 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19228 | } |
| 19229 | if (!names.empty()) { |
| 19230 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19231 | } |
| 19232 | // super must happen after, so that downstream can use maybe_get_output |
| 19233 | // to retrieve the output |
| 19234 | at::native::structured_maximum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19235 | } |
| 19236 | void set_output_raw_strided( |
| 19237 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19238 | TensorOptions options, DimnameList names |
| 19239 | ) override { |
| 19240 | const auto& out = outputs_[output_idx].get(); |
| 19241 | resize_out(out, sizes, strides, options); |
| 19242 | if (!names.empty()) { |
| 19243 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19244 | } |
| 19245 | // super must happen after, so that downstream can use maybe_get_output |
| 19246 | // to retrieve the output |
| 19247 | at::native::structured_maximum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19248 | } |
| 19249 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19250 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19251 | } |
| 19252 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19253 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19254 | }; |
| 19255 | at::Tensor & wrapper_CPU_maximum_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 19256 | structured_maximum_out_out op(out); |
| 19257 | op.meta(self, other); |
| 19258 | op.impl(self, other, op.maybe_get_output(0)); |
| 19259 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19260 | return out; |
| 19261 | } |
| 19262 | struct structured_minimum_out_functional final : public at::native::structured_minimum_out { |
| 19263 | void set_output_strided( |
| 19264 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19265 | TensorOptions options, DimnameList names |
| 19266 | ) override { |
| 19267 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19268 | if (!names.empty()) { |
| 19269 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19270 | } |
| 19271 | // super must happen after, so that downstream can use maybe_get_output |
| 19272 | // to retrieve the output |
| 19273 | at::native::structured_minimum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19274 | } |
| 19275 | void set_output_raw_strided( |
| 19276 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19277 | TensorOptions options, DimnameList names |
| 19278 | ) override { |
| 19279 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19280 | if (!names.empty()) { |
| 19281 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19282 | } |
| 19283 | // super must happen after, so that downstream can use maybe_get_output |
| 19284 | // to retrieve the output |
| 19285 | at::native::structured_minimum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19286 | } |
| 19287 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19288 | return *outputs_[output_idx]; |
| 19289 | } |
| 19290 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19291 | }; |
| 19292 | at::Tensor wrapper_CPU_minimum(const at::Tensor & self, const at::Tensor & other) { |
| 19293 | structured_minimum_out_functional op; |
| 19294 | op.meta(self, other); |
| 19295 | op.impl(self, other, *op.outputs_[0]); |
| 19296 | return std::move(op.outputs_[0]).take(); |
| 19297 | } |
| 19298 | struct structured_minimum_out_out final : public at::native::structured_minimum_out { |
| 19299 | structured_minimum_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19300 | void set_output_strided( |
| 19301 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19302 | TensorOptions options, DimnameList names |
| 19303 | ) override { |
| 19304 | const auto& out = outputs_[output_idx].get(); |
| 19305 | resize_out(out, sizes, strides, options); |
| 19306 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19307 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19308 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19309 | } |
| 19310 | if (!names.empty()) { |
| 19311 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19312 | } |
| 19313 | // super must happen after, so that downstream can use maybe_get_output |
| 19314 | // to retrieve the output |
| 19315 | at::native::structured_minimum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19316 | } |
| 19317 | void set_output_raw_strided( |
| 19318 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19319 | TensorOptions options, DimnameList names |
| 19320 | ) override { |
| 19321 | const auto& out = outputs_[output_idx].get(); |
| 19322 | resize_out(out, sizes, strides, options); |
| 19323 | if (!names.empty()) { |
| 19324 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19325 | } |
| 19326 | // super must happen after, so that downstream can use maybe_get_output |
| 19327 | // to retrieve the output |
| 19328 | at::native::structured_minimum_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19329 | } |
| 19330 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19331 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19332 | } |
| 19333 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19334 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19335 | }; |
| 19336 | at::Tensor & wrapper_CPU_minimum_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 19337 | structured_minimum_out_out op(out); |
| 19338 | op.meta(self, other); |
| 19339 | op.impl(self, other, op.maybe_get_output(0)); |
| 19340 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19341 | return out; |
| 19342 | } |
| 19343 | struct structured_sort_stable_out_functional final : public at::native::structured_sort_stable_out { |
| 19344 | void set_output_strided( |
| 19345 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19346 | TensorOptions options, DimnameList names |
| 19347 | ) override { |
| 19348 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19349 | if (!names.empty()) { |
| 19350 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19351 | } |
| 19352 | // super must happen after, so that downstream can use maybe_get_output |
| 19353 | // to retrieve the output |
| 19354 | } |
| 19355 | void set_output_raw_strided( |
| 19356 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19357 | TensorOptions options, DimnameList names |
| 19358 | ) override { |
| 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>, 2> outputs_; |
| 19370 | }; |
| 19371 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_sort_stable(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending) { |
| 19372 | structured_sort_stable_out_functional op; |
| 19373 | op.meta(self, stable, dim, descending); |
| 19374 | op.impl(self, stable, dim, descending, *op.outputs_[0], *op.outputs_[1]); |
| 19375 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19376 | } |
| 19377 | struct structured_sort_stable_out_out final : public at::native::structured_sort_stable_out { |
| 19378 | structured_sort_stable_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 19379 | void set_output_strided( |
| 19380 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19381 | TensorOptions options, DimnameList names |
| 19382 | ) override { |
| 19383 | const auto& out = outputs_[output_idx].get(); |
| 19384 | resize_out(out, sizes, strides, options); |
| 19385 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19386 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19387 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19388 | } |
| 19389 | if (!names.empty()) { |
| 19390 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19391 | } |
| 19392 | // super must happen after, so that downstream can use maybe_get_output |
| 19393 | // to retrieve the output |
| 19394 | } |
| 19395 | void set_output_raw_strided( |
| 19396 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19397 | TensorOptions options, DimnameList names |
| 19398 | ) override { |
| 19399 | const auto& out = outputs_[output_idx].get(); |
| 19400 | resize_out(out, sizes, strides, options); |
| 19401 | if (!names.empty()) { |
| 19402 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19403 | } |
| 19404 | // super must happen after, so that downstream can use maybe_get_output |
| 19405 | // to retrieve the output |
| 19406 | } |
| 19407 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19408 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19409 | } |
| 19410 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 19411 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 19412 | }; |
| 19413 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_sort_out_values_stable(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 19414 | structured_sort_stable_out_out op(values, indices); |
| 19415 | op.meta(self, stable, dim, descending); |
| 19416 | op.impl(self, stable, dim, descending, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 19417 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19418 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 19419 | return std::forward_as_tuple(values, indices); |
| 19420 | } |
| 19421 | namespace { |
| 19422 | at::Tensor wrapper_CPU_stable_argsort(const at::Tensor & self, bool stable, int64_t dim, bool descending) { |
| 19423 | // No device check |
| 19424 | // DeviceGuard omitted |
| 19425 | return at::native::argsort_stable(self, stable, dim, descending); |
| 19426 | } |
| 19427 | } // anonymous namespace |
| 19428 | struct structured_topk_out_cpu_functional final : public at::native::structured_topk_out_cpu { |
| 19429 | void set_output_strided( |
| 19430 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19431 | TensorOptions options, DimnameList names |
| 19432 | ) override { |
| 19433 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19434 | if (!names.empty()) { |
| 19435 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19436 | } |
| 19437 | // super must happen after, so that downstream can use maybe_get_output |
| 19438 | // to retrieve the output |
| 19439 | } |
| 19440 | void set_output_raw_strided( |
| 19441 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19442 | TensorOptions options, DimnameList names |
| 19443 | ) override { |
| 19444 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19445 | if (!names.empty()) { |
| 19446 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19447 | } |
| 19448 | // super must happen after, so that downstream can use maybe_get_output |
| 19449 | // to retrieve the output |
| 19450 | } |
| 19451 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19452 | return *outputs_[output_idx]; |
| 19453 | } |
| 19454 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 19455 | }; |
| 19456 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 19457 | structured_topk_out_cpu_functional op; |
| 19458 | op.meta(self, k, dim, largest, sorted); |
| 19459 | op.impl(self, k, dim, largest, sorted, *op.outputs_[0], *op.outputs_[1]); |
| 19460 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19461 | } |
| 19462 | struct structured_topk_out_cpu_out final : public at::native::structured_topk_out_cpu { |
| 19463 | structured_topk_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 19464 | void set_output_strided( |
| 19465 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19466 | TensorOptions options, DimnameList names |
| 19467 | ) override { |
| 19468 | const auto& out = outputs_[output_idx].get(); |
| 19469 | resize_out(out, sizes, strides, options); |
| 19470 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19471 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19472 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19473 | } |
| 19474 | if (!names.empty()) { |
| 19475 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19476 | } |
| 19477 | // super must happen after, so that downstream can use maybe_get_output |
| 19478 | // to retrieve the output |
| 19479 | } |
| 19480 | void set_output_raw_strided( |
| 19481 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19482 | TensorOptions options, DimnameList names |
| 19483 | ) override { |
| 19484 | const auto& out = outputs_[output_idx].get(); |
| 19485 | resize_out(out, sizes, strides, options); |
| 19486 | if (!names.empty()) { |
| 19487 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19488 | } |
| 19489 | // super must happen after, so that downstream can use maybe_get_output |
| 19490 | // to retrieve the output |
| 19491 | } |
| 19492 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19493 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19494 | } |
| 19495 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 19496 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 19497 | }; |
| 19498 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_topk_out_values(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { |
| 19499 | structured_topk_out_cpu_out op(values, indices); |
| 19500 | op.meta(self, k, dim, largest, sorted); |
| 19501 | op.impl(self, k, dim, largest, sorted, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 19502 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19503 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 19504 | return std::forward_as_tuple(values, indices); |
| 19505 | } |
| 19506 | struct structured_all_all_out_functional final : public at::native::structured_all_all_out { |
| 19507 | void set_output_strided( |
| 19508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19509 | TensorOptions options, DimnameList names |
| 19510 | ) override { |
| 19511 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19512 | if (!names.empty()) { |
| 19513 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19514 | } |
| 19515 | // super must happen after, so that downstream can use maybe_get_output |
| 19516 | // to retrieve the output |
| 19517 | } |
| 19518 | void set_output_raw_strided( |
| 19519 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19520 | TensorOptions options, DimnameList names |
| 19521 | ) override { |
| 19522 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19523 | if (!names.empty()) { |
| 19524 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19525 | } |
| 19526 | // super must happen after, so that downstream can use maybe_get_output |
| 19527 | // to retrieve the output |
| 19528 | } |
| 19529 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19530 | return *outputs_[output_idx]; |
| 19531 | } |
| 19532 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19533 | }; |
| 19534 | at::Tensor wrapper_CPU_all(const at::Tensor & self) { |
| 19535 | structured_all_all_out_functional op; |
| 19536 | op.meta(self); |
| 19537 | op.impl(self, *op.outputs_[0]); |
| 19538 | return std::move(op.outputs_[0]).take(); |
| 19539 | } |
| 19540 | struct structured_all_all_out_out final : public at::native::structured_all_all_out { |
| 19541 | structured_all_all_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19542 | void set_output_strided( |
| 19543 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19544 | TensorOptions options, DimnameList names |
| 19545 | ) override { |
| 19546 | const auto& out = outputs_[output_idx].get(); |
| 19547 | resize_out(out, sizes, strides, options); |
| 19548 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19549 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19550 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19551 | } |
| 19552 | if (!names.empty()) { |
| 19553 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19554 | } |
| 19555 | // super must happen after, so that downstream can use maybe_get_output |
| 19556 | // to retrieve the output |
| 19557 | } |
| 19558 | void set_output_raw_strided( |
| 19559 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19560 | TensorOptions options, DimnameList names |
| 19561 | ) override { |
| 19562 | const auto& out = outputs_[output_idx].get(); |
| 19563 | resize_out(out, sizes, strides, options); |
| 19564 | if (!names.empty()) { |
| 19565 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19566 | } |
| 19567 | // super must happen after, so that downstream can use maybe_get_output |
| 19568 | // to retrieve the output |
| 19569 | } |
| 19570 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19571 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19572 | } |
| 19573 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19574 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19575 | }; |
| 19576 | at::Tensor & wrapper_CPU_all_out_all_out(const at::Tensor & self, at::Tensor & out) { |
| 19577 | structured_all_all_out_out op(out); |
| 19578 | op.meta(self); |
| 19579 | op.impl(self, op.maybe_get_output(0)); |
| 19580 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19581 | return out; |
| 19582 | } |
| 19583 | struct structured_any_all_out_functional final : public at::native::structured_any_all_out { |
| 19584 | void set_output_strided( |
| 19585 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19586 | TensorOptions options, DimnameList names |
| 19587 | ) override { |
| 19588 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19589 | if (!names.empty()) { |
| 19590 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19591 | } |
| 19592 | // super must happen after, so that downstream can use maybe_get_output |
| 19593 | // to retrieve the output |
| 19594 | } |
| 19595 | void set_output_raw_strided( |
| 19596 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19597 | TensorOptions options, DimnameList names |
| 19598 | ) override { |
| 19599 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19600 | if (!names.empty()) { |
| 19601 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19602 | } |
| 19603 | // super must happen after, so that downstream can use maybe_get_output |
| 19604 | // to retrieve the output |
| 19605 | } |
| 19606 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19607 | return *outputs_[output_idx]; |
| 19608 | } |
| 19609 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19610 | }; |
| 19611 | at::Tensor wrapper_CPU_any(const at::Tensor & self) { |
| 19612 | structured_any_all_out_functional op; |
| 19613 | op.meta(self); |
| 19614 | op.impl(self, *op.outputs_[0]); |
| 19615 | return std::move(op.outputs_[0]).take(); |
| 19616 | } |
| 19617 | struct structured_any_all_out_out final : public at::native::structured_any_all_out { |
| 19618 | structured_any_all_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19619 | void set_output_strided( |
| 19620 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19621 | TensorOptions options, DimnameList names |
| 19622 | ) override { |
| 19623 | const auto& out = outputs_[output_idx].get(); |
| 19624 | resize_out(out, sizes, strides, options); |
| 19625 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19626 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19627 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19628 | } |
| 19629 | if (!names.empty()) { |
| 19630 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19631 | } |
| 19632 | // super must happen after, so that downstream can use maybe_get_output |
| 19633 | // to retrieve the output |
| 19634 | } |
| 19635 | void set_output_raw_strided( |
| 19636 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19637 | TensorOptions options, DimnameList names |
| 19638 | ) override { |
| 19639 | const auto& out = outputs_[output_idx].get(); |
| 19640 | resize_out(out, sizes, strides, options); |
| 19641 | if (!names.empty()) { |
| 19642 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19643 | } |
| 19644 | // super must happen after, so that downstream can use maybe_get_output |
| 19645 | // to retrieve the output |
| 19646 | } |
| 19647 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19648 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19649 | } |
| 19650 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19651 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19652 | }; |
| 19653 | at::Tensor & wrapper_CPU_any_out_all_out(const at::Tensor & self, at::Tensor & out) { |
| 19654 | structured_any_all_out_out op(out); |
| 19655 | op.meta(self); |
| 19656 | op.impl(self, op.maybe_get_output(0)); |
| 19657 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19658 | return out; |
| 19659 | } |
| 19660 | struct structured_renorm_out_functional final : public at::native::structured_renorm_out { |
| 19661 | void set_output_strided( |
| 19662 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19663 | TensorOptions options, DimnameList names |
| 19664 | ) override { |
| 19665 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19666 | if (!names.empty()) { |
| 19667 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19668 | } |
| 19669 | // super must happen after, so that downstream can use maybe_get_output |
| 19670 | // to retrieve the output |
| 19671 | } |
| 19672 | void set_output_raw_strided( |
| 19673 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19674 | TensorOptions options, DimnameList names |
| 19675 | ) override { |
| 19676 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19677 | if (!names.empty()) { |
| 19678 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19679 | } |
| 19680 | // super must happen after, so that downstream can use maybe_get_output |
| 19681 | // to retrieve the output |
| 19682 | } |
| 19683 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19684 | return *outputs_[output_idx]; |
| 19685 | } |
| 19686 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19687 | }; |
| 19688 | at::Tensor wrapper_CPU_renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 19689 | structured_renorm_out_functional op; |
| 19690 | op.meta(self, p, dim, maxnorm); |
| 19691 | op.impl(self, p, dim, maxnorm, *op.outputs_[0]); |
| 19692 | return std::move(op.outputs_[0]).take(); |
| 19693 | } |
| 19694 | struct structured_renorm_out_out final : public at::native::structured_renorm_out { |
| 19695 | structured_renorm_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19696 | void set_output_strided( |
| 19697 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19698 | TensorOptions options, DimnameList names |
| 19699 | ) override { |
| 19700 | const auto& out = outputs_[output_idx].get(); |
| 19701 | resize_out(out, sizes, strides, options); |
| 19702 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19703 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19704 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19705 | } |
| 19706 | if (!names.empty()) { |
| 19707 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19708 | } |
| 19709 | // super must happen after, so that downstream can use maybe_get_output |
| 19710 | // to retrieve the output |
| 19711 | } |
| 19712 | void set_output_raw_strided( |
| 19713 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19714 | TensorOptions options, DimnameList names |
| 19715 | ) override { |
| 19716 | const auto& out = outputs_[output_idx].get(); |
| 19717 | resize_out(out, sizes, strides, options); |
| 19718 | if (!names.empty()) { |
| 19719 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19720 | } |
| 19721 | // super must happen after, so that downstream can use maybe_get_output |
| 19722 | // to retrieve the output |
| 19723 | } |
| 19724 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19725 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19726 | } |
| 19727 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19728 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19729 | }; |
| 19730 | at::Tensor & wrapper_CPU_renorm_out_out(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out) { |
| 19731 | structured_renorm_out_out op(out); |
| 19732 | op.meta(self, p, dim, maxnorm); |
| 19733 | op.impl(self, p, dim, maxnorm, op.maybe_get_output(0)); |
| 19734 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19735 | return out; |
| 19736 | } |
| 19737 | struct structured_renorm_out_inplace final : public at::native::structured_renorm_out { |
| 19738 | structured_renorm_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 19739 | void set_output_strided( |
| 19740 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19741 | TensorOptions options, DimnameList names |
| 19742 | ) override { |
| 19743 | const auto& out = outputs_[output_idx].get(); |
| 19744 | check_inplace(out, sizes, options); |
| 19745 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19746 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19747 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19748 | } |
| 19749 | if (!names.empty()) { |
| 19750 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19751 | } |
| 19752 | // super must happen after, so that downstream can use maybe_get_output |
| 19753 | // to retrieve the output |
| 19754 | } |
| 19755 | void set_output_raw_strided( |
| 19756 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19757 | TensorOptions options, DimnameList names |
| 19758 | ) override { |
| 19759 | const auto& out = outputs_[output_idx].get(); |
| 19760 | check_inplace(out, sizes, options); |
| 19761 | if (!names.empty()) { |
| 19762 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19763 | } |
| 19764 | // super must happen after, so that downstream can use maybe_get_output |
| 19765 | // to retrieve the output |
| 19766 | } |
| 19767 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19768 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19769 | } |
| 19770 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19771 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19772 | }; |
| 19773 | at::Tensor & wrapper_CPU_renorm_(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 19774 | structured_renorm_out_inplace op(self); |
| 19775 | op.meta(self, p, dim, maxnorm); |
| 19776 | op.impl(self, p, dim, maxnorm, op.outputs_[0]); |
| 19777 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19778 | return self; |
| 19779 | } |
| 19780 | namespace { |
| 19781 | at::Tensor wrapper_CPU__unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { |
| 19782 | // No device check |
| 19783 | // DeviceGuard omitted |
| 19784 | return at::native::unfold(self, dimension, size, step); |
| 19785 | } |
| 19786 | } // anonymous namespace |
| 19787 | namespace { |
| 19788 | at::Tensor wrapper_CPU__unfold_backward(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { |
| 19789 | // No device check |
| 19790 | // DeviceGuard omitted |
| 19791 | return at::native::unfold_backward(grad_in, C10_AS_INTARRAYREF_SLOW(input_sizes), dim, size, step); |
| 19792 | } |
| 19793 | } // anonymous namespace |
| 19794 | namespace { |
| 19795 | bool wrapper_CPU__equal(const at::Tensor & self, const at::Tensor & other) { |
| 19796 | // No device check |
| 19797 | // DeviceGuard omitted |
| 19798 | return at::native::cpu_equal(self, other); |
| 19799 | } |
| 19800 | } // anonymous namespace |
| 19801 | struct structured_pow_Tensor_Tensor_out_functional final : public at::native::structured_pow_Tensor_Tensor_out { |
| 19802 | void set_output_strided( |
| 19803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19804 | TensorOptions options, DimnameList names |
| 19805 | ) override { |
| 19806 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19807 | if (!names.empty()) { |
| 19808 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19809 | } |
| 19810 | // super must happen after, so that downstream can use maybe_get_output |
| 19811 | // to retrieve the output |
| 19812 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19813 | } |
| 19814 | void set_output_raw_strided( |
| 19815 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19816 | TensorOptions options, DimnameList names |
| 19817 | ) override { |
| 19818 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19819 | if (!names.empty()) { |
| 19820 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19821 | } |
| 19822 | // super must happen after, so that downstream can use maybe_get_output |
| 19823 | // to retrieve the output |
| 19824 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19825 | } |
| 19826 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19827 | return *outputs_[output_idx]; |
| 19828 | } |
| 19829 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19830 | }; |
| 19831 | at::Tensor wrapper_CPU_pow_Tensor_Tensor(const at::Tensor & self, const at::Tensor & exponent) { |
| 19832 | structured_pow_Tensor_Tensor_out_functional op; |
| 19833 | op.meta(self, exponent); |
| 19834 | op.impl(self, exponent, *op.outputs_[0]); |
| 19835 | return std::move(op.outputs_[0]).take(); |
| 19836 | } |
| 19837 | struct structured_pow_Tensor_Tensor_out_out final : public at::native::structured_pow_Tensor_Tensor_out { |
| 19838 | structured_pow_Tensor_Tensor_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19839 | void set_output_strided( |
| 19840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19841 | TensorOptions options, DimnameList names |
| 19842 | ) override { |
| 19843 | const auto& out = outputs_[output_idx].get(); |
| 19844 | resize_out(out, sizes, strides, options); |
| 19845 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19846 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19847 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19848 | } |
| 19849 | if (!names.empty()) { |
| 19850 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19851 | } |
| 19852 | // super must happen after, so that downstream can use maybe_get_output |
| 19853 | // to retrieve the output |
| 19854 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19855 | } |
| 19856 | void set_output_raw_strided( |
| 19857 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19858 | TensorOptions options, DimnameList names |
| 19859 | ) override { |
| 19860 | const auto& out = outputs_[output_idx].get(); |
| 19861 | resize_out(out, sizes, strides, options); |
| 19862 | if (!names.empty()) { |
| 19863 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19864 | } |
| 19865 | // super must happen after, so that downstream can use maybe_get_output |
| 19866 | // to retrieve the output |
| 19867 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19868 | } |
| 19869 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19870 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19871 | } |
| 19872 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19873 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19874 | }; |
| 19875 | at::Tensor & wrapper_CPU_pow_out_Tensor_Tensor_out(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { |
| 19876 | structured_pow_Tensor_Tensor_out_out op(out); |
| 19877 | op.meta(self, exponent); |
| 19878 | op.impl(self, exponent, op.maybe_get_output(0)); |
| 19879 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19880 | return out; |
| 19881 | } |
| 19882 | struct structured_pow_Tensor_Tensor_out_inplace final : public at::native::structured_pow_Tensor_Tensor_out { |
| 19883 | structured_pow_Tensor_Tensor_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 19884 | void set_output_strided( |
| 19885 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19886 | TensorOptions options, DimnameList names |
| 19887 | ) override { |
| 19888 | const auto& out = outputs_[output_idx].get(); |
| 19889 | check_inplace(out, sizes, options); |
| 19890 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19891 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19892 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19893 | } |
| 19894 | if (!names.empty()) { |
| 19895 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19896 | } |
| 19897 | // super must happen after, so that downstream can use maybe_get_output |
| 19898 | // to retrieve the output |
| 19899 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19900 | } |
| 19901 | void set_output_raw_strided( |
| 19902 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19903 | TensorOptions options, DimnameList names |
| 19904 | ) override { |
| 19905 | const auto& out = outputs_[output_idx].get(); |
| 19906 | check_inplace(out, sizes, options); |
| 19907 | if (!names.empty()) { |
| 19908 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19909 | } |
| 19910 | // super must happen after, so that downstream can use maybe_get_output |
| 19911 | // to retrieve the output |
| 19912 | at::native::structured_pow_Tensor_Tensor_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19913 | } |
| 19914 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19915 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19916 | } |
| 19917 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19918 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19919 | }; |
| 19920 | at::Tensor & wrapper_CPU_pow__Tensor(at::Tensor & self, const at::Tensor & exponent) { |
| 19921 | structured_pow_Tensor_Tensor_out_inplace op(self); |
| 19922 | op.meta(self, exponent); |
| 19923 | op.impl(self, exponent, op.outputs_[0]); |
| 19924 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 19925 | return self; |
| 19926 | } |
| 19927 | struct structured_pow_Scalar_out_functional final : public at::native::structured_pow_Scalar_out { |
| 19928 | void set_output_strided( |
| 19929 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19930 | TensorOptions options, DimnameList names |
| 19931 | ) override { |
| 19932 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19933 | if (!names.empty()) { |
| 19934 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19935 | } |
| 19936 | // super must happen after, so that downstream can use maybe_get_output |
| 19937 | // to retrieve the output |
| 19938 | } |
| 19939 | void set_output_raw_strided( |
| 19940 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19941 | TensorOptions options, DimnameList names |
| 19942 | ) override { |
| 19943 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19944 | if (!names.empty()) { |
| 19945 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19946 | } |
| 19947 | // super must happen after, so that downstream can use maybe_get_output |
| 19948 | // to retrieve the output |
| 19949 | } |
| 19950 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19951 | return *outputs_[output_idx]; |
| 19952 | } |
| 19953 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19954 | }; |
| 19955 | at::Tensor wrapper_CPU_pow_Scalar(const at::Scalar & self, const at::Tensor & exponent) { |
| 19956 | structured_pow_Scalar_out_functional op; |
| 19957 | op.meta(self, exponent); |
| 19958 | op.impl(self, exponent, *op.outputs_[0]); |
| 19959 | return std::move(op.outputs_[0]).take(); |
| 19960 | } |
| 19961 | struct structured_pow_Scalar_out_out final : public at::native::structured_pow_Scalar_out { |
| 19962 | structured_pow_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 19963 | void set_output_strided( |
| 19964 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19965 | TensorOptions options, DimnameList names |
| 19966 | ) override { |
| 19967 | const auto& out = outputs_[output_idx].get(); |
| 19968 | resize_out(out, sizes, strides, options); |
| 19969 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 19970 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 19971 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 19972 | } |
| 19973 | if (!names.empty()) { |
| 19974 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19975 | } |
| 19976 | // super must happen after, so that downstream can use maybe_get_output |
| 19977 | // to retrieve the output |
| 19978 | } |
| 19979 | void set_output_raw_strided( |
| 19980 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19981 | TensorOptions options, DimnameList names |
| 19982 | ) override { |
| 19983 | const auto& out = outputs_[output_idx].get(); |
| 19984 | resize_out(out, sizes, strides, options); |
| 19985 | if (!names.empty()) { |
| 19986 | namedinference::propagate_names(outputs_[output_idx], names); |
| 19987 | } |
| 19988 | // super must happen after, so that downstream can use maybe_get_output |
| 19989 | // to retrieve the output |
| 19990 | } |
| 19991 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19992 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 19993 | } |
| 19994 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 19995 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 19996 | }; |
| 19997 | at::Tensor & wrapper_CPU_pow_out_Scalar_out(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { |
| 19998 | structured_pow_Scalar_out_out op(out); |
| 19999 | op.meta(self, exponent); |
| 20000 | op.impl(self, exponent, op.maybe_get_output(0)); |
| 20001 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 20002 | return out; |
| 20003 | } |
| 20004 | struct structured_pow_Tensor_Scalar_out_functional final : public at::native::structured_pow_Tensor_Scalar_out { |
| 20005 | void set_output_strided( |
| 20006 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20007 | TensorOptions options, DimnameList names |
| 20008 | ) override { |
| 20009 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20010 | if (!names.empty()) { |
| 20011 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20012 | } |
| 20013 | // super must happen after, so that downstream can use maybe_get_output |
| 20014 | // to retrieve the output |
| 20015 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20016 | } |
| 20017 | void set_output_raw_strided( |
| 20018 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20019 | TensorOptions options, DimnameList names |
| 20020 | ) override { |
| 20021 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20022 | if (!names.empty()) { |
| 20023 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20024 | } |
| 20025 | // super must happen after, so that downstream can use maybe_get_output |
| 20026 | // to retrieve the output |
| 20027 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20028 | } |
| 20029 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20030 | return *outputs_[output_idx]; |
| 20031 | } |
| 20032 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20033 | }; |
| 20034 | at::Tensor wrapper_CPU_pow_Tensor_Scalar(const at::Tensor & self, const at::Scalar & exponent) { |
| 20035 | structured_pow_Tensor_Scalar_out_functional op; |
| 20036 | op.meta(self, exponent); |
| 20037 | op.impl(self, exponent, *op.outputs_[0]); |
| 20038 | return std::move(op.outputs_[0]).take(); |
| 20039 | } |
| 20040 | struct structured_pow_Tensor_Scalar_out_out final : public at::native::structured_pow_Tensor_Scalar_out { |
| 20041 | structured_pow_Tensor_Scalar_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 20042 | void set_output_strided( |
| 20043 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20044 | TensorOptions options, DimnameList names |
| 20045 | ) override { |
| 20046 | const auto& out = outputs_[output_idx].get(); |
| 20047 | resize_out(out, sizes, strides, options); |
| 20048 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 20049 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 20050 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 20051 | } |
| 20052 | if (!names.empty()) { |
| 20053 | namedinference::propagate_names(outputs_[output_idx], names); |
| 20054 | } |
| 20055 | // super must happen after, so that downstream can use maybe_get_output |
| 20056 | // to retrieve the output |
| 20057 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20058 | } |
| 20059 | void set_output_raw_strided( |
| 20060 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20061 | TensorOptions options, DimnameList names |
| 20062 | ) override { |
| 20063 | const auto& out = outputs_[output_idx].get(); |
| 20064 | resize_out(out, sizes, strides, options); |
| 20065 | if (!names.empty()) { |
| 20066 | namedinference::propagate_names(outputs_[output_idx], names); |
| 20067 | } |
| 20068 | // super must happen after, so that downstream can use maybe_get_output |
| 20069 | // to retrieve the output |
| 20070 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20071 | } |
| 20072 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20073 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 20074 | } |
| 20075 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 20076 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 20077 | }; |
| 20078 | at::Tensor & wrapper_CPU_pow_out_Tensor_Scalar_out(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { |
| 20079 | structured_pow_Tensor_Scalar_out_out op(out); |
| 20080 | op.meta(self, exponent); |
| 20081 | op.impl(self, exponent, op.maybe_get_output(0)); |
| 20082 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 20083 | return out; |
| 20084 | } |
| 20085 | struct structured_pow_Tensor_Scalar_out_inplace final : public at::native::structured_pow_Tensor_Scalar_out { |
| 20086 | structured_pow_Tensor_Scalar_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 20087 | void set_output_strided( |
| 20088 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20089 | TensorOptions options, DimnameList names |
| 20090 | ) override { |
| 20091 | const auto& out = outputs_[output_idx].get(); |
| 20092 | check_inplace(out, sizes, options); |
| 20093 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 20094 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 20095 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 20096 | } |
| 20097 | if (!names.empty()) { |
| 20098 | namedinference::propagate_names(outputs_[output_idx], names); |
| 20099 | } |
| 20100 | // super must happen after, so that downstream can use maybe_get_output |
| 20101 | // to retrieve the output |
| 20102 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20103 | } |
| 20104 | void set_output_raw_strided( |
| 20105 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20106 | TensorOptions options, DimnameList names |
| 20107 | ) override { |
| 20108 | const auto& out = outputs_[output_idx].get(); |
| 20109 | check_inplace(out, sizes, options); |
| 20110 | if (!names.empty()) { |
| 20111 | namedinference::propagate_names(outputs_[output_idx], names); |
| 20112 | } |
| 20113 | // super must happen after, so that downstream can use maybe_get_output |
| 20114 | // to retrieve the output |
| 20115 | at::native::structured_pow_Tensor_Scalar_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20116 | } |
| 20117 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20118 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 20119 | } |
| 20120 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 20121 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 20122 | }; |
| 20123 | at::Tensor & wrapper_CPU_pow__Scalar(at::Tensor & self, const at::Scalar & exponent) { |
| 20124 | structured_pow_Tensor_Scalar_out_inplace op(self); |
| 20125 | op.meta(self, exponent); |
| 20126 | op.impl(self, exponent, op.outputs_[0]); |
| 20127 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 20128 | return self; |
| 20129 | } |
| 20130 | namespace { |
| 20131 | at::Tensor & wrapper_CPU__normal_(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 20132 | // No device check |
| 20133 | // DeviceGuard omitted |
| 20134 | return at::native::normal_(self, mean, std, generator); |
| 20135 | } |
| 20136 | } // anonymous namespace |
| 20137 | namespace { |
| 20138 | at::Tensor wrapper_CPU_Tensor_float_normal(const at::Tensor & mean, double std, c10::optional<at::Generator> generator) { |
| 20139 | // No device check |
| 20140 | // DeviceGuard omitted |
| 20141 | return at::native::normal(mean, std, generator); |
| 20142 | } |
| 20143 | } // anonymous namespace |
| 20144 | namespace { |
| 20145 | at::Tensor & wrapper_CPU_Tensor_float_out_normal_out(const at::Tensor & mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 20146 | // No device check |
| 20147 | // DeviceGuard omitted |
| 20148 | return at::native::normal_out(mean, std, generator, out); |
| 20149 | } |
| 20150 | } // anonymous namespace |
| 20151 | namespace { |
| 20152 | at::Tensor wrapper_CPU_float_Tensor_normal(double mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 20153 | // No device check |
| 20154 | // DeviceGuard omitted |
| 20155 | return at::native::normal(mean, std, generator); |
| 20156 | } |
| 20157 | } // anonymous namespace |
| 20158 | namespace { |
| 20159 | at::Tensor & wrapper_CPU_float_Tensor_out_normal_out(double mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 20160 | // No device check |
| 20161 | // DeviceGuard omitted |
| 20162 | return at::native::normal_out(mean, std, generator, out); |
| 20163 | } |
| 20164 | } // anonymous namespace |
| 20165 | namespace { |
| 20166 | at::Tensor wrapper_CPU_Tensor_Tensor_normal(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 20167 | // No device check |
| 20168 | // DeviceGuard omitted |
| 20169 | return at::native::normal(mean, std, generator); |
| 20170 | } |
| 20171 | } // anonymous namespace |
| 20172 | namespace { |
| 20173 | at::Tensor & wrapper_CPU_Tensor_Tensor_out_normal_out(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 20174 | // No device check |
| 20175 | // DeviceGuard omitted |
| 20176 | return at::native::normal_out(mean, std, generator, out); |
| 20177 | } |
| 20178 | } // anonymous namespace |
| 20179 | namespace { |
| 20180 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_add(at::TensorList self, const at::Scalar & scalar) { |
| 20181 | // No device check |
| 20182 | // DeviceGuard omitted |
| 20183 | return at::native::foreach_tensor_add_scalar_kernel_slow(self, scalar); |
| 20184 | } |
| 20185 | } // anonymous namespace |
| 20186 | namespace { |
| 20187 | void wrapper_CPU_Scalar__foreach_add_(at::TensorList self, const at::Scalar & scalar) { |
| 20188 | // No device check |
| 20189 | // DeviceGuard omitted |
| 20190 | return at::native::foreach_tensor_add_scalar_kernel_slow_(self, scalar); |
| 20191 | } |
| 20192 | } // anonymous namespace |
| 20193 | namespace { |
| 20194 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_sub(at::TensorList self, const at::Scalar & scalar) { |
| 20195 | // No device check |
| 20196 | // DeviceGuard omitted |
| 20197 | return at::native::foreach_tensor_sub_scalar_kernel_slow(self, scalar); |
| 20198 | } |
| 20199 | } // anonymous namespace |
| 20200 | namespace { |
| 20201 | void wrapper_CPU_Scalar__foreach_sub_(at::TensorList self, const at::Scalar & scalar) { |
| 20202 | // No device check |
| 20203 | // DeviceGuard omitted |
| 20204 | return at::native::foreach_tensor_sub_scalar_kernel_slow_(self, scalar); |
| 20205 | } |
| 20206 | } // anonymous namespace |
| 20207 | namespace { |
| 20208 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_mul(at::TensorList self, const at::Scalar & scalar) { |
| 20209 | // No device check |
| 20210 | // DeviceGuard omitted |
| 20211 | return at::native::foreach_tensor_mul_scalar_kernel_slow(self, scalar); |
| 20212 | } |
| 20213 | } // anonymous namespace |
| 20214 | namespace { |
| 20215 | void wrapper_CPU_Scalar__foreach_mul_(at::TensorList self, const at::Scalar & scalar) { |
| 20216 | // No device check |
| 20217 | // DeviceGuard omitted |
| 20218 | return at::native::foreach_tensor_mul_scalar_kernel_slow_(self, scalar); |
| 20219 | } |
| 20220 | } // anonymous namespace |
| 20221 | namespace { |
| 20222 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_div(at::TensorList self, const at::Scalar & scalar) { |
| 20223 | // No device check |
| 20224 | // DeviceGuard omitted |
| 20225 | return at::native::foreach_tensor_div_scalar_kernel_slow(self, scalar); |
| 20226 | } |
| 20227 | } // anonymous namespace |
| 20228 | namespace { |
| 20229 | void wrapper_CPU_Scalar__foreach_div_(at::TensorList self, const at::Scalar & scalar) { |
| 20230 | // No device check |
| 20231 | // DeviceGuard omitted |
| 20232 | return at::native::foreach_tensor_div_scalar_kernel_slow_(self, scalar); |
| 20233 | } |
| 20234 | } // anonymous namespace |
| 20235 | namespace { |
| 20236 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_clamp_min(at::TensorList self, const at::Scalar & scalar) { |
| 20237 | // No device check |
| 20238 | // DeviceGuard omitted |
| 20239 | return at::native::foreach_tensor_clamp_min_scalar_kernel_slow(self, scalar); |
| 20240 | } |
| 20241 | } // anonymous namespace |
| 20242 | namespace { |
| 20243 | void wrapper_CPU_Scalar__foreach_clamp_min_(at::TensorList self, const at::Scalar & scalar) { |
| 20244 | // No device check |
| 20245 | // DeviceGuard omitted |
| 20246 | return at::native::foreach_tensor_clamp_min_scalar_kernel_slow_(self, scalar); |
| 20247 | } |
| 20248 | } // anonymous namespace |
| 20249 | namespace { |
| 20250 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_clamp_max(at::TensorList self, const at::Scalar & scalar) { |
| 20251 | // No device check |
| 20252 | // DeviceGuard omitted |
| 20253 | return at::native::foreach_tensor_clamp_max_scalar_kernel_slow(self, scalar); |
| 20254 | } |
| 20255 | } // anonymous namespace |
| 20256 | namespace { |
| 20257 | void wrapper_CPU_Scalar__foreach_clamp_max_(at::TensorList self, const at::Scalar & scalar) { |
| 20258 | // No device check |
| 20259 | // DeviceGuard omitted |
| 20260 | return at::native::foreach_tensor_clamp_max_scalar_kernel_slow_(self, scalar); |
| 20261 | } |
| 20262 | } // anonymous namespace |
| 20263 | namespace { |
| 20264 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_maximum(at::TensorList self, const at::Scalar & scalar) { |
| 20265 | // No device check |
| 20266 | // DeviceGuard omitted |
| 20267 | return at::native::foreach_tensor_clamp_min_scalar_kernel_slow(self, scalar); |
| 20268 | } |
| 20269 | } // anonymous namespace |
| 20270 | namespace { |
| 20271 | void wrapper_CPU_Scalar__foreach_maximum_(at::TensorList self, const at::Scalar & scalar) { |
| 20272 | // No device check |
| 20273 | // DeviceGuard omitted |
| 20274 | return at::native::foreach_tensor_clamp_min_scalar_kernel_slow_(self, scalar); |
| 20275 | } |
| 20276 | } // anonymous namespace |
| 20277 | namespace { |
| 20278 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_minimum(at::TensorList self, const at::Scalar & scalar) { |
| 20279 | // No device check |
| 20280 | // DeviceGuard omitted |
| 20281 | return at::native::foreach_tensor_clamp_max_scalar_kernel_slow(self, scalar); |
| 20282 | } |
| 20283 | } // anonymous namespace |
| 20284 | namespace { |
| 20285 | void wrapper_CPU_Scalar__foreach_minimum_(at::TensorList self, const at::Scalar & scalar) { |
| 20286 | // No device check |
| 20287 | // DeviceGuard omitted |
| 20288 | return at::native::foreach_tensor_clamp_max_scalar_kernel_slow_(self, scalar); |
| 20289 | } |
| 20290 | } // anonymous namespace |
| 20291 | namespace { |
| 20292 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 20293 | // No device check |
| 20294 | // DeviceGuard omitted |
| 20295 | return at::native::foreach_tensor_add_list_kernel_slow(self, other, alpha); |
| 20296 | } |
| 20297 | } // anonymous namespace |
| 20298 | namespace { |
| 20299 | void wrapper_CPU_List__foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 20300 | // No device check |
| 20301 | // DeviceGuard omitted |
| 20302 | return at::native::foreach_tensor_add_list_kernel_slow_(self, other, alpha); |
| 20303 | } |
| 20304 | } // anonymous namespace |
| 20305 | namespace { |
| 20306 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_sub(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 20307 | // No device check |
| 20308 | // DeviceGuard omitted |
| 20309 | return at::native::foreach_tensor_sub_list_kernel_slow(self, other, alpha); |
| 20310 | } |
| 20311 | } // anonymous namespace |
| 20312 | namespace { |
| 20313 | void wrapper_CPU_List__foreach_sub_(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 20314 | // No device check |
| 20315 | // DeviceGuard omitted |
| 20316 | return at::native::foreach_tensor_sub_list_kernel_slow_(self, other, alpha); |
| 20317 | } |
| 20318 | } // anonymous namespace |
| 20319 | namespace { |
| 20320 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_mul(at::TensorList self, at::TensorList other) { |
| 20321 | // No device check |
| 20322 | // DeviceGuard omitted |
| 20323 | return at::native::foreach_tensor_mul_list_kernel_slow(self, other); |
| 20324 | } |
| 20325 | } // anonymous namespace |
| 20326 | namespace { |
| 20327 | void wrapper_CPU_List__foreach_mul_(at::TensorList self, at::TensorList other) { |
| 20328 | // No device check |
| 20329 | // DeviceGuard omitted |
| 20330 | return at::native::foreach_tensor_mul_list_kernel_slow_(self, other); |
| 20331 | } |
| 20332 | } // anonymous namespace |
| 20333 | namespace { |
| 20334 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_div(at::TensorList self, at::TensorList other) { |
| 20335 | // No device check |
| 20336 | // DeviceGuard omitted |
| 20337 | return at::native::foreach_tensor_div_list_kernel_slow(self, other); |
| 20338 | } |
| 20339 | } // anonymous namespace |
| 20340 | namespace { |
| 20341 | void wrapper_CPU_List__foreach_div_(at::TensorList self, at::TensorList other) { |
| 20342 | // No device check |
| 20343 | // DeviceGuard omitted |
| 20344 | return at::native::foreach_tensor_div_list_kernel_slow_(self, other); |
| 20345 | } |
| 20346 | } // anonymous namespace |
| 20347 | namespace { |
| 20348 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_clamp_min(at::TensorList self, at::TensorList other) { |
| 20349 | // No device check |
| 20350 | // DeviceGuard omitted |
| 20351 | return at::native::foreach_tensor_clamp_min_list_kernel_slow(self, other); |
| 20352 | } |
| 20353 | } // anonymous namespace |
| 20354 | namespace { |
| 20355 | void wrapper_CPU_List__foreach_clamp_min_(at::TensorList self, at::TensorList other) { |
| 20356 | // No device check |
| 20357 | // DeviceGuard omitted |
| 20358 | return at::native::foreach_tensor_clamp_min_list_kernel_slow_(self, other); |
| 20359 | } |
| 20360 | } // anonymous namespace |
| 20361 | namespace { |
| 20362 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_clamp_max(at::TensorList self, at::TensorList other) { |
| 20363 | // No device check |
| 20364 | // DeviceGuard omitted |
| 20365 | return at::native::foreach_tensor_clamp_max_list_kernel_slow(self, other); |
| 20366 | } |
| 20367 | } // anonymous namespace |
| 20368 | namespace { |
| 20369 | void wrapper_CPU_List__foreach_clamp_max_(at::TensorList self, at::TensorList other) { |
| 20370 | // No device check |
| 20371 | // DeviceGuard omitted |
| 20372 | return at::native::foreach_tensor_clamp_max_list_kernel_slow_(self, other); |
| 20373 | } |
| 20374 | } // anonymous namespace |
| 20375 | namespace { |
| 20376 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_maximum(at::TensorList self, at::TensorList other) { |
| 20377 | // No device check |
| 20378 | // DeviceGuard omitted |
| 20379 | return at::native::foreach_tensor_clamp_min_list_kernel_slow(self, other); |
| 20380 | } |
| 20381 | } // anonymous namespace |
| 20382 | namespace { |
| 20383 | void wrapper_CPU_List__foreach_maximum_(at::TensorList self, at::TensorList other) { |
| 20384 | // No device check |
| 20385 | // DeviceGuard omitted |
| 20386 | return at::native::foreach_tensor_clamp_min_list_kernel_slow_(self, other); |
| 20387 | } |
| 20388 | } // anonymous namespace |
| 20389 | namespace { |
| 20390 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_minimum(at::TensorList self, at::TensorList other) { |
| 20391 | // No device check |
| 20392 | // DeviceGuard omitted |
| 20393 | return at::native::foreach_tensor_clamp_max_list_kernel_slow(self, other); |
| 20394 | } |
| 20395 | } // anonymous namespace |
| 20396 | namespace { |
| 20397 | void wrapper_CPU_List__foreach_minimum_(at::TensorList self, at::TensorList other) { |
| 20398 | // No device check |
| 20399 | // DeviceGuard omitted |
| 20400 | return at::native::foreach_tensor_clamp_max_list_kernel_slow_(self, other); |
| 20401 | } |
| 20402 | } // anonymous namespace |
| 20403 | namespace { |
| 20404 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20405 | // No device check |
| 20406 | // DeviceGuard omitted |
| 20407 | return at::native::foreach_tensor_add_scalarlist_kernel_slow(self, scalars); |
| 20408 | } |
| 20409 | } // anonymous namespace |
| 20410 | namespace { |
| 20411 | void wrapper_CPU_ScalarList__foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20412 | // No device check |
| 20413 | // DeviceGuard omitted |
| 20414 | return at::native::foreach_tensor_add_scalarlist_kernel_slow_(self, scalars); |
| 20415 | } |
| 20416 | } // anonymous namespace |
| 20417 | namespace { |
| 20418 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_sub(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20419 | // No device check |
| 20420 | // DeviceGuard omitted |
| 20421 | return at::native::foreach_tensor_sub_scalarlist_kernel_slow(self, scalars); |
| 20422 | } |
| 20423 | } // anonymous namespace |
| 20424 | namespace { |
| 20425 | void wrapper_CPU_ScalarList__foreach_sub_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20426 | // No device check |
| 20427 | // DeviceGuard omitted |
| 20428 | return at::native::foreach_tensor_sub_scalarlist_kernel_slow_(self, scalars); |
| 20429 | } |
| 20430 | } // anonymous namespace |
| 20431 | namespace { |
| 20432 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_div(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20433 | // No device check |
| 20434 | // DeviceGuard omitted |
| 20435 | return at::native::foreach_tensor_div_scalarlist_kernel_slow(self, scalars); |
| 20436 | } |
| 20437 | } // anonymous namespace |
| 20438 | namespace { |
| 20439 | void wrapper_CPU_ScalarList__foreach_div_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20440 | // No device check |
| 20441 | // DeviceGuard omitted |
| 20442 | return at::native::foreach_tensor_div_scalarlist_kernel_slow_(self, scalars); |
| 20443 | } |
| 20444 | } // anonymous namespace |
| 20445 | namespace { |
| 20446 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_mul(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20447 | // No device check |
| 20448 | // DeviceGuard omitted |
| 20449 | return at::native::foreach_tensor_mul_scalarlist_kernel_slow(self, scalars); |
| 20450 | } |
| 20451 | } // anonymous namespace |
| 20452 | namespace { |
| 20453 | void wrapper_CPU_ScalarList__foreach_mul_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20454 | // No device check |
| 20455 | // DeviceGuard omitted |
| 20456 | return at::native::foreach_tensor_mul_scalarlist_kernel_slow_(self, scalars); |
| 20457 | } |
| 20458 | } // anonymous namespace |
| 20459 | namespace { |
| 20460 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_clamp_min(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20461 | // No device check |
| 20462 | // DeviceGuard omitted |
| 20463 | return at::native::foreach_tensor_clamp_min_scalarlist_kernel_slow(self, scalars); |
| 20464 | } |
| 20465 | } // anonymous namespace |
| 20466 | namespace { |
| 20467 | void wrapper_CPU_ScalarList__foreach_clamp_min_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20468 | // No device check |
| 20469 | // DeviceGuard omitted |
| 20470 | return at::native::foreach_tensor_clamp_min_scalarlist_kernel_slow_(self, scalars); |
| 20471 | } |
| 20472 | } // anonymous namespace |
| 20473 | namespace { |
| 20474 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_clamp_max(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20475 | // No device check |
| 20476 | // DeviceGuard omitted |
| 20477 | return at::native::foreach_tensor_clamp_max_scalarlist_kernel_slow(self, scalars); |
| 20478 | } |
| 20479 | } // anonymous namespace |
| 20480 | namespace { |
| 20481 | void wrapper_CPU_ScalarList__foreach_clamp_max_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20482 | // No device check |
| 20483 | // DeviceGuard omitted |
| 20484 | return at::native::foreach_tensor_clamp_max_scalarlist_kernel_slow_(self, scalars); |
| 20485 | } |
| 20486 | } // anonymous namespace |
| 20487 | namespace { |
| 20488 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_maximum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20489 | // No device check |
| 20490 | // DeviceGuard omitted |
| 20491 | return at::native::foreach_tensor_clamp_min_scalarlist_kernel_slow(self, scalars); |
| 20492 | } |
| 20493 | } // anonymous namespace |
| 20494 | namespace { |
| 20495 | void wrapper_CPU_ScalarList__foreach_maximum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20496 | // No device check |
| 20497 | // DeviceGuard omitted |
| 20498 | return at::native::foreach_tensor_clamp_min_scalarlist_kernel_slow_(self, scalars); |
| 20499 | } |
| 20500 | } // anonymous namespace |
| 20501 | namespace { |
| 20502 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_minimum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20503 | // No device check |
| 20504 | // DeviceGuard omitted |
| 20505 | return at::native::foreach_tensor_clamp_max_scalarlist_kernel_slow(self, scalars); |
| 20506 | } |
| 20507 | } // anonymous namespace |
| 20508 | namespace { |
| 20509 | void wrapper_CPU_ScalarList__foreach_minimum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 20510 | // No device check |
| 20511 | // DeviceGuard omitted |
| 20512 | return at::native::foreach_tensor_clamp_max_scalarlist_kernel_slow_(self, scalars); |
| 20513 | } |
| 20514 | } // anonymous namespace |
| 20515 | namespace { |
| 20516 | ::std::vector<at::Tensor> wrapper_CPU___foreach_exp(at::TensorList self) { |
| 20517 | // No device check |
| 20518 | // DeviceGuard omitted |
| 20519 | return at::native::foreach_tensor_exp_slow(self); |
| 20520 | } |
| 20521 | } // anonymous namespace |
| 20522 | namespace { |
| 20523 | void wrapper_CPU___foreach_exp_(at::TensorList self) { |
| 20524 | // No device check |
| 20525 | // DeviceGuard omitted |
| 20526 | return at::native::foreach_tensor_exp_slow_(self); |
| 20527 | } |
| 20528 | } // anonymous namespace |
| 20529 | namespace { |
| 20530 | void wrapper_CPU___foreach_zero_(at::TensorList self) { |
| 20531 | // No device check |
| 20532 | // DeviceGuard omitted |
| 20533 | return at::native::foreach_tensor_zero_slow_(self); |
| 20534 | } |
| 20535 | } // anonymous namespace |
| 20536 | namespace { |
| 20537 | ::std::vector<at::Tensor> wrapper_CPU___foreach_sqrt(at::TensorList self) { |
| 20538 | // No device check |
| 20539 | // DeviceGuard omitted |
| 20540 | return at::native::foreach_tensor_sqrt_slow(self); |
| 20541 | } |
| 20542 | } // anonymous namespace |
| 20543 | namespace { |
| 20544 | void wrapper_CPU___foreach_sqrt_(at::TensorList self) { |
| 20545 | // No device check |
| 20546 | // DeviceGuard omitted |
| 20547 | return at::native::foreach_tensor_sqrt_slow_(self); |
| 20548 | } |
| 20549 | } // anonymous namespace |
| 20550 | namespace { |
| 20551 | ::std::vector<at::Tensor> wrapper_CPU___foreach_abs(at::TensorList self) { |
| 20552 | // No device check |
| 20553 | // DeviceGuard omitted |
| 20554 | return at::native::foreach_tensor_abs_slow(self); |
| 20555 | } |
| 20556 | } // anonymous namespace |
| 20557 | namespace { |
| 20558 | void wrapper_CPU___foreach_abs_(at::TensorList self) { |
| 20559 | // No device check |
| 20560 | // DeviceGuard omitted |
| 20561 | return at::native::foreach_tensor_abs_slow_(self); |
| 20562 | } |
| 20563 | } // anonymous namespace |
| 20564 | namespace { |
| 20565 | ::std::vector<at::Tensor> wrapper_CPU___foreach_acos(at::TensorList self) { |
| 20566 | // No device check |
| 20567 | // DeviceGuard omitted |
| 20568 | return at::native::foreach_tensor_acos_slow(self); |
| 20569 | } |
| 20570 | } // anonymous namespace |
| 20571 | namespace { |
| 20572 | void wrapper_CPU___foreach_acos_(at::TensorList self) { |
| 20573 | // No device check |
| 20574 | // DeviceGuard omitted |
| 20575 | return at::native::foreach_tensor_acos_slow_(self); |
| 20576 | } |
| 20577 | } // anonymous namespace |
| 20578 | namespace { |
| 20579 | ::std::vector<at::Tensor> wrapper_CPU___foreach_asin(at::TensorList self) { |
| 20580 | // No device check |
| 20581 | // DeviceGuard omitted |
| 20582 | return at::native::foreach_tensor_asin_slow(self); |
| 20583 | } |
| 20584 | } // anonymous namespace |
| 20585 | namespace { |
| 20586 | void wrapper_CPU___foreach_asin_(at::TensorList self) { |
| 20587 | // No device check |
| 20588 | // DeviceGuard omitted |
| 20589 | return at::native::foreach_tensor_asin_slow_(self); |
| 20590 | } |
| 20591 | } // anonymous namespace |
| 20592 | namespace { |
| 20593 | ::std::vector<at::Tensor> wrapper_CPU___foreach_atan(at::TensorList self) { |
| 20594 | // No device check |
| 20595 | // DeviceGuard omitted |
| 20596 | return at::native::foreach_tensor_atan_slow(self); |
| 20597 | } |
| 20598 | } // anonymous namespace |
| 20599 | namespace { |
| 20600 | void wrapper_CPU___foreach_atan_(at::TensorList self) { |
| 20601 | // No device check |
| 20602 | // DeviceGuard omitted |
| 20603 | return at::native::foreach_tensor_atan_slow_(self); |
| 20604 | } |
| 20605 | } // anonymous namespace |
| 20606 | namespace { |
| 20607 | ::std::vector<at::Tensor> wrapper_CPU___foreach_ceil(at::TensorList self) { |
| 20608 | // No device check |
| 20609 | // DeviceGuard omitted |
| 20610 | return at::native::foreach_tensor_ceil_slow(self); |
| 20611 | } |
| 20612 | } // anonymous namespace |
| 20613 | namespace { |
| 20614 | void wrapper_CPU___foreach_ceil_(at::TensorList self) { |
| 20615 | // No device check |
| 20616 | // DeviceGuard omitted |
| 20617 | return at::native::foreach_tensor_ceil_slow_(self); |
| 20618 | } |
| 20619 | } // anonymous namespace |
| 20620 | namespace { |
| 20621 | ::std::vector<at::Tensor> wrapper_CPU___foreach_cos(at::TensorList self) { |
| 20622 | // No device check |
| 20623 | // DeviceGuard omitted |
| 20624 | return at::native::foreach_tensor_cos_slow(self); |
| 20625 | } |
| 20626 | } // anonymous namespace |
| 20627 | namespace { |
| 20628 | void wrapper_CPU___foreach_cos_(at::TensorList self) { |
| 20629 | // No device check |
| 20630 | // DeviceGuard omitted |
| 20631 | return at::native::foreach_tensor_cos_slow_(self); |
| 20632 | } |
| 20633 | } // anonymous namespace |
| 20634 | namespace { |
| 20635 | ::std::vector<at::Tensor> wrapper_CPU___foreach_cosh(at::TensorList self) { |
| 20636 | // No device check |
| 20637 | // DeviceGuard omitted |
| 20638 | return at::native::foreach_tensor_cosh_slow(self); |
| 20639 | } |
| 20640 | } // anonymous namespace |
| 20641 | namespace { |
| 20642 | void wrapper_CPU___foreach_cosh_(at::TensorList self) { |
| 20643 | // No device check |
| 20644 | // DeviceGuard omitted |
| 20645 | return at::native::foreach_tensor_cosh_slow_(self); |
| 20646 | } |
| 20647 | } // anonymous namespace |
| 20648 | namespace { |
| 20649 | ::std::vector<at::Tensor> wrapper_CPU___foreach_erf(at::TensorList self) { |
| 20650 | // No device check |
| 20651 | // DeviceGuard omitted |
| 20652 | return at::native::foreach_tensor_erf_slow(self); |
| 20653 | } |
| 20654 | } // anonymous namespace |
| 20655 | namespace { |
| 20656 | void wrapper_CPU___foreach_erf_(at::TensorList self) { |
| 20657 | // No device check |
| 20658 | // DeviceGuard omitted |
| 20659 | return at::native::foreach_tensor_erf_slow_(self); |
| 20660 | } |
| 20661 | } // anonymous namespace |
| 20662 | namespace { |
| 20663 | ::std::vector<at::Tensor> wrapper_CPU___foreach_erfc(at::TensorList self) { |
| 20664 | // No device check |
| 20665 | // DeviceGuard omitted |
| 20666 | return at::native::foreach_tensor_erfc_slow(self); |
| 20667 | } |
| 20668 | } // anonymous namespace |
| 20669 | namespace { |
| 20670 | void wrapper_CPU___foreach_erfc_(at::TensorList self) { |
| 20671 | // No device check |
| 20672 | // DeviceGuard omitted |
| 20673 | return at::native::foreach_tensor_erfc_slow_(self); |
| 20674 | } |
| 20675 | } // anonymous namespace |
| 20676 | namespace { |
| 20677 | ::std::vector<at::Tensor> wrapper_CPU___foreach_expm1(at::TensorList self) { |
| 20678 | // No device check |
| 20679 | // DeviceGuard omitted |
| 20680 | return at::native::foreach_tensor_expm1_slow(self); |
| 20681 | } |
| 20682 | } // anonymous namespace |
| 20683 | namespace { |
| 20684 | void wrapper_CPU___foreach_expm1_(at::TensorList self) { |
| 20685 | // No device check |
| 20686 | // DeviceGuard omitted |
| 20687 | return at::native::foreach_tensor_expm1_slow_(self); |
| 20688 | } |
| 20689 | } // anonymous namespace |
| 20690 | namespace { |
| 20691 | ::std::vector<at::Tensor> wrapper_CPU___foreach_floor(at::TensorList self) { |
| 20692 | // No device check |
| 20693 | // DeviceGuard omitted |
| 20694 | return at::native::foreach_tensor_floor_slow(self); |
| 20695 | } |
| 20696 | } // anonymous namespace |
| 20697 | namespace { |
| 20698 | void wrapper_CPU___foreach_floor_(at::TensorList self) { |
| 20699 | // No device check |
| 20700 | // DeviceGuard omitted |
| 20701 | return at::native::foreach_tensor_floor_slow_(self); |
| 20702 | } |
| 20703 | } // anonymous namespace |
| 20704 | namespace { |
| 20705 | ::std::vector<at::Tensor> wrapper_CPU___foreach_log(at::TensorList self) { |
| 20706 | // No device check |
| 20707 | // DeviceGuard omitted |
| 20708 | return at::native::foreach_tensor_log_slow(self); |
| 20709 | } |
| 20710 | } // anonymous namespace |
| 20711 | namespace { |
| 20712 | void wrapper_CPU___foreach_log_(at::TensorList self) { |
| 20713 | // No device check |
| 20714 | // DeviceGuard omitted |
| 20715 | return at::native::foreach_tensor_log_slow_(self); |
| 20716 | } |
| 20717 | } // anonymous namespace |
| 20718 | namespace { |
| 20719 | ::std::vector<at::Tensor> wrapper_CPU___foreach_log10(at::TensorList self) { |
| 20720 | // No device check |
| 20721 | // DeviceGuard omitted |
| 20722 | return at::native::foreach_tensor_log10_slow(self); |
| 20723 | } |
| 20724 | } // anonymous namespace |
| 20725 | namespace { |
| 20726 | void wrapper_CPU___foreach_log10_(at::TensorList self) { |
| 20727 | // No device check |
| 20728 | // DeviceGuard omitted |
| 20729 | return at::native::foreach_tensor_log10_slow_(self); |
| 20730 | } |
| 20731 | } // anonymous namespace |
| 20732 | namespace { |
| 20733 | ::std::vector<at::Tensor> wrapper_CPU___foreach_log1p(at::TensorList self) { |
| 20734 | // No device check |
| 20735 | // DeviceGuard omitted |
| 20736 | return at::native::foreach_tensor_log1p_slow(self); |
| 20737 | } |
| 20738 | } // anonymous namespace |
| 20739 | namespace { |
| 20740 | void wrapper_CPU___foreach_log1p_(at::TensorList self) { |
| 20741 | // No device check |
| 20742 | // DeviceGuard omitted |
| 20743 | return at::native::foreach_tensor_log1p_slow_(self); |
| 20744 | } |
| 20745 | } // anonymous namespace |
| 20746 | namespace { |
| 20747 | ::std::vector<at::Tensor> wrapper_CPU___foreach_log2(at::TensorList self) { |
| 20748 | // No device check |
| 20749 | // DeviceGuard omitted |
| 20750 | return at::native::foreach_tensor_log2_slow(self); |
| 20751 | } |
| 20752 | } // anonymous namespace |
| 20753 | namespace { |
| 20754 | void wrapper_CPU___foreach_log2_(at::TensorList self) { |
| 20755 | // No device check |
| 20756 | // DeviceGuard omitted |
| 20757 | return at::native::foreach_tensor_log2_slow_(self); |
| 20758 | } |
| 20759 | } // anonymous namespace |
| 20760 | namespace { |
| 20761 | ::std::vector<at::Tensor> wrapper_CPU___foreach_neg(at::TensorList self) { |
| 20762 | // No device check |
| 20763 | // DeviceGuard omitted |
| 20764 | return at::native::foreach_tensor_neg_slow(self); |
| 20765 | } |
| 20766 | } // anonymous namespace |
| 20767 | namespace { |
| 20768 | void wrapper_CPU___foreach_neg_(at::TensorList self) { |
| 20769 | // No device check |
| 20770 | // DeviceGuard omitted |
| 20771 | return at::native::foreach_tensor_neg_slow_(self); |
| 20772 | } |
| 20773 | } // anonymous namespace |
| 20774 | namespace { |
| 20775 | ::std::vector<at::Tensor> wrapper_CPU___foreach_tan(at::TensorList self) { |
| 20776 | // No device check |
| 20777 | // DeviceGuard omitted |
| 20778 | return at::native::foreach_tensor_tan_slow(self); |
| 20779 | } |
| 20780 | } // anonymous namespace |
| 20781 | namespace { |
| 20782 | void wrapper_CPU___foreach_tan_(at::TensorList self) { |
| 20783 | // No device check |
| 20784 | // DeviceGuard omitted |
| 20785 | return at::native::foreach_tensor_tan_slow_(self); |
| 20786 | } |
| 20787 | } // anonymous namespace |
| 20788 | namespace { |
| 20789 | ::std::vector<at::Tensor> wrapper_CPU___foreach_tanh(at::TensorList self) { |
| 20790 | // No device check |
| 20791 | // DeviceGuard omitted |
| 20792 | return at::native::foreach_tensor_tanh_slow(self); |
| 20793 | } |
| 20794 | } // anonymous namespace |
| 20795 | namespace { |
| 20796 | void wrapper_CPU___foreach_tanh_(at::TensorList self) { |
| 20797 | // No device check |
| 20798 | // DeviceGuard omitted |
| 20799 | return at::native::foreach_tensor_tanh_slow_(self); |
| 20800 | } |
| 20801 | } // anonymous namespace |
| 20802 | namespace { |
| 20803 | ::std::vector<at::Tensor> wrapper_CPU___foreach_sin(at::TensorList self) { |
| 20804 | // No device check |
| 20805 | // DeviceGuard omitted |
| 20806 | return at::native::foreach_tensor_sin_slow(self); |
| 20807 | } |
| 20808 | } // anonymous namespace |
| 20809 | namespace { |
| 20810 | void wrapper_CPU___foreach_sin_(at::TensorList self) { |
| 20811 | // No device check |
| 20812 | // DeviceGuard omitted |
| 20813 | return at::native::foreach_tensor_sin_slow_(self); |
| 20814 | } |
| 20815 | } // anonymous namespace |
| 20816 | namespace { |
| 20817 | ::std::vector<at::Tensor> wrapper_CPU___foreach_sinh(at::TensorList self) { |
| 20818 | // No device check |
| 20819 | // DeviceGuard omitted |
| 20820 | return at::native::foreach_tensor_sinh_slow(self); |
| 20821 | } |
| 20822 | } // anonymous namespace |
| 20823 | namespace { |
| 20824 | void wrapper_CPU___foreach_sinh_(at::TensorList self) { |
| 20825 | // No device check |
| 20826 | // DeviceGuard omitted |
| 20827 | return at::native::foreach_tensor_sinh_slow_(self); |
| 20828 | } |
| 20829 | } // anonymous namespace |
| 20830 | namespace { |
| 20831 | ::std::vector<at::Tensor> wrapper_CPU___foreach_round(at::TensorList self) { |
| 20832 | // No device check |
| 20833 | // DeviceGuard omitted |
| 20834 | return at::native::foreach_tensor_round_slow(self); |
| 20835 | } |
| 20836 | } // anonymous namespace |
| 20837 | namespace { |
| 20838 | void wrapper_CPU___foreach_round_(at::TensorList self) { |
| 20839 | // No device check |
| 20840 | // DeviceGuard omitted |
| 20841 | return at::native::foreach_tensor_round_slow_(self); |
| 20842 | } |
| 20843 | } // anonymous namespace |
| 20844 | namespace { |
| 20845 | ::std::vector<at::Tensor> wrapper_CPU___foreach_lgamma(at::TensorList self) { |
| 20846 | // No device check |
| 20847 | // DeviceGuard omitted |
| 20848 | return at::native::foreach_tensor_lgamma_slow(self); |
| 20849 | } |
| 20850 | } // anonymous namespace |
| 20851 | namespace { |
| 20852 | void wrapper_CPU___foreach_lgamma_(at::TensorList self) { |
| 20853 | // No device check |
| 20854 | // DeviceGuard omitted |
| 20855 | return at::native::foreach_tensor_lgamma_slow_(self); |
| 20856 | } |
| 20857 | } // anonymous namespace |
| 20858 | namespace { |
| 20859 | ::std::vector<at::Tensor> wrapper_CPU___foreach_frac(at::TensorList self) { |
| 20860 | // No device check |
| 20861 | // DeviceGuard omitted |
| 20862 | return at::native::foreach_tensor_frac_slow(self); |
| 20863 | } |
| 20864 | } // anonymous namespace |
| 20865 | namespace { |
| 20866 | void wrapper_CPU___foreach_frac_(at::TensorList self) { |
| 20867 | // No device check |
| 20868 | // DeviceGuard omitted |
| 20869 | return at::native::foreach_tensor_frac_slow_(self); |
| 20870 | } |
| 20871 | } // anonymous namespace |
| 20872 | namespace { |
| 20873 | ::std::vector<at::Tensor> wrapper_CPU___foreach_reciprocal(at::TensorList self) { |
| 20874 | // No device check |
| 20875 | // DeviceGuard omitted |
| 20876 | return at::native::foreach_tensor_reciprocal_slow(self); |
| 20877 | } |
| 20878 | } // anonymous namespace |
| 20879 | namespace { |
| 20880 | void wrapper_CPU___foreach_reciprocal_(at::TensorList self) { |
| 20881 | // No device check |
| 20882 | // DeviceGuard omitted |
| 20883 | return at::native::foreach_tensor_reciprocal_slow_(self); |
| 20884 | } |
| 20885 | } // anonymous namespace |
| 20886 | namespace { |
| 20887 | ::std::vector<at::Tensor> wrapper_CPU___foreach_sigmoid(at::TensorList self) { |
| 20888 | // No device check |
| 20889 | // DeviceGuard omitted |
| 20890 | return at::native::foreach_tensor_sigmoid_slow(self); |
| 20891 | } |
| 20892 | } // anonymous namespace |
| 20893 | namespace { |
| 20894 | void wrapper_CPU___foreach_sigmoid_(at::TensorList self) { |
| 20895 | // No device check |
| 20896 | // DeviceGuard omitted |
| 20897 | return at::native::foreach_tensor_sigmoid_slow_(self); |
| 20898 | } |
| 20899 | } // anonymous namespace |
| 20900 | namespace { |
| 20901 | ::std::vector<at::Tensor> wrapper_CPU___foreach_trunc(at::TensorList self) { |
| 20902 | // No device check |
| 20903 | // DeviceGuard omitted |
| 20904 | return at::native::foreach_tensor_trunc_slow(self); |
| 20905 | } |
| 20906 | } // anonymous namespace |
| 20907 | namespace { |
| 20908 | void wrapper_CPU___foreach_trunc_(at::TensorList self) { |
| 20909 | // No device check |
| 20910 | // DeviceGuard omitted |
| 20911 | return at::native::foreach_tensor_trunc_slow_(self); |
| 20912 | } |
| 20913 | } // anonymous namespace |
| 20914 | namespace { |
| 20915 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 20916 | // No device check |
| 20917 | // DeviceGuard omitted |
| 20918 | return at::native::foreach_tensor_addcdiv_scalar_slow(self, tensor1, tensor2, value); |
| 20919 | } |
| 20920 | } // anonymous namespace |
| 20921 | namespace { |
| 20922 | void wrapper_CPU_Scalar__foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 20923 | // No device check |
| 20924 | // DeviceGuard omitted |
| 20925 | return at::native::foreach_tensor_addcdiv_scalar_slow_(self, tensor1, tensor2, value); |
| 20926 | } |
| 20927 | } // anonymous namespace |
| 20928 | namespace { |
| 20929 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 20930 | // No device check |
| 20931 | // DeviceGuard omitted |
| 20932 | return at::native::foreach_tensor_addcmul_scalar_slow(self, tensor1, tensor2, value); |
| 20933 | } |
| 20934 | } // anonymous namespace |
| 20935 | namespace { |
| 20936 | void wrapper_CPU_Scalar__foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 20937 | // No device check |
| 20938 | // DeviceGuard omitted |
| 20939 | return at::native::foreach_tensor_addcmul_scalar_slow_(self, tensor1, tensor2, value); |
| 20940 | } |
| 20941 | } // anonymous namespace |
| 20942 | namespace { |
| 20943 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 20944 | // No device check |
| 20945 | // DeviceGuard omitted |
| 20946 | return at::native::foreach_tensor_addcdiv_scalarlist_slow(self, tensor1, tensor2, scalars); |
| 20947 | } |
| 20948 | } // anonymous namespace |
| 20949 | namespace { |
| 20950 | void wrapper_CPU_ScalarList__foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 20951 | // No device check |
| 20952 | // DeviceGuard omitted |
| 20953 | return at::native::foreach_tensor_addcdiv_scalarlist_slow_(self, tensor1, tensor2, scalars); |
| 20954 | } |
| 20955 | } // anonymous namespace |
| 20956 | namespace { |
| 20957 | ::std::vector<at::Tensor> wrapper_CPU_Tensor__foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 20958 | // No device check |
| 20959 | // DeviceGuard omitted |
| 20960 | return at::native::foreach_tensor_addcdiv_tensor_slow(self, tensor1, tensor2, scalars); |
| 20961 | } |
| 20962 | } // anonymous namespace |
| 20963 | namespace { |
| 20964 | void wrapper_CPU_Tensor__foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 20965 | // No device check |
| 20966 | // DeviceGuard omitted |
| 20967 | return at::native::foreach_tensor_addcdiv_tensor_slow_(self, tensor1, tensor2, scalars); |
| 20968 | } |
| 20969 | } // anonymous namespace |
| 20970 | namespace { |
| 20971 | ::std::vector<at::Tensor> wrapper_CPU_ScalarList__foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 20972 | // No device check |
| 20973 | // DeviceGuard omitted |
| 20974 | return at::native::foreach_tensor_addcmul_scalarlist_slow(self, tensor1, tensor2, scalars); |
| 20975 | } |
| 20976 | } // anonymous namespace |
| 20977 | namespace { |
| 20978 | void wrapper_CPU_ScalarList__foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 20979 | // No device check |
| 20980 | // DeviceGuard omitted |
| 20981 | return at::native::foreach_tensor_addcmul_scalarlist_slow_(self, tensor1, tensor2, scalars); |
| 20982 | } |
| 20983 | } // anonymous namespace |
| 20984 | namespace { |
| 20985 | ::std::vector<at::Tensor> wrapper_CPU_Tensor__foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 20986 | // No device check |
| 20987 | // DeviceGuard omitted |
| 20988 | return at::native::foreach_tensor_addcmul_tensor_slow(self, tensor1, tensor2, scalars); |
| 20989 | } |
| 20990 | } // anonymous namespace |
| 20991 | namespace { |
| 20992 | void wrapper_CPU_Tensor__foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 20993 | // No device check |
| 20994 | // DeviceGuard omitted |
| 20995 | return at::native::foreach_tensor_addcmul_tensor_slow_(self, tensor1, tensor2, scalars); |
| 20996 | } |
| 20997 | } // anonymous namespace |
| 20998 | namespace { |
| 20999 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_norm(at::TensorList self, const at::Scalar & ord) { |
| 21000 | // No device check |
| 21001 | // DeviceGuard omitted |
| 21002 | return at::native::foreach_tensor_norm_slow(self, ord); |
| 21003 | } |
| 21004 | } // anonymous namespace |
| 21005 | namespace { |
| 21006 | ::std::vector<at::Tensor> wrapper_CPU_List__foreach_lerp(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { |
| 21007 | // No device check |
| 21008 | // DeviceGuard omitted |
| 21009 | return at::native::foreach_tensor_ternary_lerp_slow(self, tensors1, weights); |
| 21010 | } |
| 21011 | } // anonymous namespace |
| 21012 | namespace { |
| 21013 | void wrapper_CPU_List__foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { |
| 21014 | // No device check |
| 21015 | // DeviceGuard omitted |
| 21016 | return at::native::foreach_tensor_ternary_lerp_slow_(self, tensors1, weights); |
| 21017 | } |
| 21018 | } // anonymous namespace |
| 21019 | namespace { |
| 21020 | ::std::vector<at::Tensor> wrapper_CPU_Scalar__foreach_lerp(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { |
| 21021 | // No device check |
| 21022 | // DeviceGuard omitted |
| 21023 | return at::native::foreach_tensor_lerp_list_kernel_slow(self, tensors1, weight); |
| 21024 | } |
| 21025 | } // anonymous namespace |
| 21026 | namespace { |
| 21027 | void wrapper_CPU_Scalar__foreach_lerp_(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { |
| 21028 | // No device check |
| 21029 | // DeviceGuard omitted |
| 21030 | return at::native::foreach_tensor_lerp_list_kernel_slow_(self, tensors1, weight); |
| 21031 | } |
| 21032 | } // anonymous namespace |
| 21033 | namespace { |
| 21034 | at::Tensor wrapper_CPU_Tensor_bucketize(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 21035 | // No device check |
| 21036 | // DeviceGuard omitted |
| 21037 | return at::native::bucketize_cpu(self, boundaries, out_int32, right); |
| 21038 | } |
| 21039 | } // anonymous namespace |
| 21040 | namespace { |
| 21041 | at::Tensor & wrapper_CPU_Tensor_out_bucketize_out(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 21042 | // No device check |
| 21043 | // DeviceGuard omitted |
| 21044 | return at::native::bucketize_out_cpu(self, boundaries, out_int32, right, out); |
| 21045 | } |
| 21046 | } // anonymous namespace |
| 21047 | namespace { |
| 21048 | at::Tensor wrapper_CPU_Scalar_bucketize(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 21049 | // No device check |
| 21050 | // DeviceGuard omitted |
| 21051 | return at::native::bucketize_cpu(self, boundaries, out_int32, right); |
| 21052 | } |
| 21053 | } // anonymous namespace |
| 21054 | namespace { |
| 21055 | at::Tensor wrapper_CPU_Tensor_searchsorted(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter) { |
| 21056 | // No device check |
| 21057 | // DeviceGuard omitted |
| 21058 | return at::native::searchsorted_cpu(sorted_sequence, self, out_int32, right, side, sorter); |
| 21059 | } |
| 21060 | } // anonymous namespace |
| 21061 | namespace { |
| 21062 | at::Tensor & wrapper_CPU_Tensor_out_searchsorted_out(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter, at::Tensor & out) { |
| 21063 | // No device check |
| 21064 | // DeviceGuard omitted |
| 21065 | return at::native::searchsorted_out_cpu(sorted_sequence, self, out_int32, right, side, sorter, out); |
| 21066 | } |
| 21067 | } // anonymous namespace |
| 21068 | namespace { |
| 21069 | at::Tensor wrapper_CPU_Scalar_searchsorted(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter) { |
| 21070 | // No device check |
| 21071 | // DeviceGuard omitted |
| 21072 | return at::native::searchsorted_cpu(sorted_sequence, self, out_int32, right, side, sorter); |
| 21073 | } |
| 21074 | } // anonymous namespace |
| 21075 | struct structured__convert_indices_from_coo_to_csr_structured_cpu_functional final : public at::native::structured__convert_indices_from_coo_to_csr_structured_cpu { |
| 21076 | void set_output_strided( |
| 21077 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21078 | TensorOptions options, DimnameList names |
| 21079 | ) override { |
| 21080 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21081 | if (!names.empty()) { |
| 21082 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21083 | } |
| 21084 | // super must happen after, so that downstream can use maybe_get_output |
| 21085 | // to retrieve the output |
| 21086 | } |
| 21087 | void set_output_raw_strided( |
| 21088 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21089 | TensorOptions options, DimnameList names |
| 21090 | ) override { |
| 21091 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21092 | if (!names.empty()) { |
| 21093 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21094 | } |
| 21095 | // super must happen after, so that downstream can use maybe_get_output |
| 21096 | // to retrieve the output |
| 21097 | } |
| 21098 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21099 | return *outputs_[output_idx]; |
| 21100 | } |
| 21101 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21102 | }; |
| 21103 | at::Tensor wrapper_CPU__convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32) { |
| 21104 | structured__convert_indices_from_coo_to_csr_structured_cpu_functional op; |
| 21105 | op.meta(self, size, out_int32); |
| 21106 | op.impl(self, size, out_int32, *op.outputs_[0]); |
| 21107 | return std::move(op.outputs_[0]).take(); |
| 21108 | } |
| 21109 | struct structured__convert_indices_from_coo_to_csr_structured_cpu_out final : public at::native::structured__convert_indices_from_coo_to_csr_structured_cpu { |
| 21110 | structured__convert_indices_from_coo_to_csr_structured_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21111 | void set_output_strided( |
| 21112 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21113 | TensorOptions options, DimnameList names |
| 21114 | ) override { |
| 21115 | const auto& out = outputs_[output_idx].get(); |
| 21116 | resize_out(out, sizes, strides, options); |
| 21117 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21118 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21119 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21120 | } |
| 21121 | if (!names.empty()) { |
| 21122 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21123 | } |
| 21124 | // super must happen after, so that downstream can use maybe_get_output |
| 21125 | // to retrieve the output |
| 21126 | } |
| 21127 | void set_output_raw_strided( |
| 21128 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21129 | TensorOptions options, DimnameList names |
| 21130 | ) override { |
| 21131 | const auto& out = outputs_[output_idx].get(); |
| 21132 | resize_out(out, sizes, strides, options); |
| 21133 | if (!names.empty()) { |
| 21134 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21135 | } |
| 21136 | // super must happen after, so that downstream can use maybe_get_output |
| 21137 | // to retrieve the output |
| 21138 | } |
| 21139 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21140 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21141 | } |
| 21142 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21143 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21144 | }; |
| 21145 | at::Tensor & wrapper_CPU__convert_indices_from_coo_to_csr_out_out(const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out) { |
| 21146 | structured__convert_indices_from_coo_to_csr_structured_cpu_out op(out); |
| 21147 | op.meta(self, size, out_int32); |
| 21148 | op.impl(self, size, out_int32, op.maybe_get_output(0)); |
| 21149 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21150 | return out; |
| 21151 | } |
| 21152 | struct structured__convert_indices_from_csr_to_coo_structured_cpu_functional final : public at::native::structured__convert_indices_from_csr_to_coo_structured_cpu { |
| 21153 | void set_output_strided( |
| 21154 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21155 | TensorOptions options, DimnameList names |
| 21156 | ) override { |
| 21157 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21158 | if (!names.empty()) { |
| 21159 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21160 | } |
| 21161 | // super must happen after, so that downstream can use maybe_get_output |
| 21162 | // to retrieve the output |
| 21163 | } |
| 21164 | void set_output_raw_strided( |
| 21165 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21166 | TensorOptions options, DimnameList names |
| 21167 | ) override { |
| 21168 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21169 | if (!names.empty()) { |
| 21170 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21171 | } |
| 21172 | // super must happen after, so that downstream can use maybe_get_output |
| 21173 | // to retrieve the output |
| 21174 | } |
| 21175 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21176 | return *outputs_[output_idx]; |
| 21177 | } |
| 21178 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21179 | }; |
| 21180 | at::Tensor wrapper_CPU__convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 21181 | structured__convert_indices_from_csr_to_coo_structured_cpu_functional op; |
| 21182 | op.meta(crow_indices, col_indices, out_int32, transpose); |
| 21183 | op.impl(crow_indices, col_indices, out_int32, transpose, *op.outputs_[0]); |
| 21184 | return std::move(op.outputs_[0]).take(); |
| 21185 | } |
| 21186 | struct structured__convert_indices_from_csr_to_coo_structured_cpu_out final : public at::native::structured__convert_indices_from_csr_to_coo_structured_cpu { |
| 21187 | structured__convert_indices_from_csr_to_coo_structured_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21188 | void set_output_strided( |
| 21189 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21190 | TensorOptions options, DimnameList names |
| 21191 | ) override { |
| 21192 | const auto& out = outputs_[output_idx].get(); |
| 21193 | resize_out(out, sizes, strides, options); |
| 21194 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21195 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21196 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21197 | } |
| 21198 | if (!names.empty()) { |
| 21199 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21200 | } |
| 21201 | // super must happen after, so that downstream can use maybe_get_output |
| 21202 | // to retrieve the output |
| 21203 | } |
| 21204 | void set_output_raw_strided( |
| 21205 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21206 | TensorOptions options, DimnameList names |
| 21207 | ) override { |
| 21208 | const auto& out = outputs_[output_idx].get(); |
| 21209 | resize_out(out, sizes, strides, options); |
| 21210 | if (!names.empty()) { |
| 21211 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21212 | } |
| 21213 | // super must happen after, so that downstream can use maybe_get_output |
| 21214 | // to retrieve the output |
| 21215 | } |
| 21216 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21217 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21218 | } |
| 21219 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21220 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21221 | }; |
| 21222 | at::Tensor & wrapper_CPU__convert_indices_from_csr_to_coo_out_out(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { |
| 21223 | structured__convert_indices_from_csr_to_coo_structured_cpu_out op(out); |
| 21224 | op.meta(crow_indices, col_indices, out_int32, transpose); |
| 21225 | op.impl(crow_indices, col_indices, out_int32, transpose, op.maybe_get_output(0)); |
| 21226 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21227 | return out; |
| 21228 | } |
| 21229 | struct structured_mse_loss_out_functional final : public at::native::structured_mse_loss_out { |
| 21230 | void set_output_strided( |
| 21231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21232 | TensorOptions options, DimnameList names |
| 21233 | ) override { |
| 21234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21235 | if (!names.empty()) { |
| 21236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21237 | } |
| 21238 | // super must happen after, so that downstream can use maybe_get_output |
| 21239 | // to retrieve the output |
| 21240 | at::native::structured_mse_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21241 | } |
| 21242 | void set_output_raw_strided( |
| 21243 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21244 | TensorOptions options, DimnameList names |
| 21245 | ) override { |
| 21246 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21247 | if (!names.empty()) { |
| 21248 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21249 | } |
| 21250 | // super must happen after, so that downstream can use maybe_get_output |
| 21251 | // to retrieve the output |
| 21252 | at::native::structured_mse_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21253 | } |
| 21254 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21255 | return *outputs_[output_idx]; |
| 21256 | } |
| 21257 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21258 | }; |
| 21259 | at::Tensor wrapper_CPU_mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 21260 | structured_mse_loss_out_functional op; |
| 21261 | op.meta(self, target, reduction); |
| 21262 | op.impl(self, target, reduction, *op.outputs_[0]); |
| 21263 | return std::move(op.outputs_[0]).take(); |
| 21264 | } |
| 21265 | struct structured_mse_loss_out_out final : public at::native::structured_mse_loss_out { |
| 21266 | structured_mse_loss_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21267 | void set_output_strided( |
| 21268 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21269 | TensorOptions options, DimnameList names |
| 21270 | ) override { |
| 21271 | const auto& out = outputs_[output_idx].get(); |
| 21272 | resize_out(out, sizes, strides, options); |
| 21273 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21274 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21275 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21276 | } |
| 21277 | if (!names.empty()) { |
| 21278 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21279 | } |
| 21280 | // super must happen after, so that downstream can use maybe_get_output |
| 21281 | // to retrieve the output |
| 21282 | at::native::structured_mse_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21283 | } |
| 21284 | void set_output_raw_strided( |
| 21285 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21286 | TensorOptions options, DimnameList names |
| 21287 | ) override { |
| 21288 | const auto& out = outputs_[output_idx].get(); |
| 21289 | resize_out(out, sizes, strides, options); |
| 21290 | if (!names.empty()) { |
| 21291 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21292 | } |
| 21293 | // super must happen after, so that downstream can use maybe_get_output |
| 21294 | // to retrieve the output |
| 21295 | at::native::structured_mse_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21296 | } |
| 21297 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21298 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21299 | } |
| 21300 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21301 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21302 | }; |
| 21303 | at::Tensor & wrapper_CPU_mse_loss_out_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 21304 | structured_mse_loss_out_out op(out); |
| 21305 | op.meta(self, target, reduction); |
| 21306 | op.impl(self, target, reduction, op.maybe_get_output(0)); |
| 21307 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21308 | return out; |
| 21309 | } |
| 21310 | namespace { |
| 21311 | at::Tensor wrapper_CPU__mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 21312 | // No device check |
| 21313 | // DeviceGuard omitted |
| 21314 | return at::native::mse_loss_backward(grad_output, self, target, reduction); |
| 21315 | } |
| 21316 | } // anonymous namespace |
| 21317 | namespace { |
| 21318 | at::Tensor & wrapper_CPU_grad_input_mse_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) { |
| 21319 | // No device check |
| 21320 | // DeviceGuard omitted |
| 21321 | return at::native::mse_loss_backward_out(grad_output, self, target, reduction, grad_input); |
| 21322 | } |
| 21323 | } // anonymous namespace |
| 21324 | namespace { |
| 21325 | at::Tensor wrapper_CPU__multi_margin_loss(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 21326 | // No device check |
| 21327 | // DeviceGuard omitted |
| 21328 | return at::native::multi_margin_loss_cpu(self, target, p, margin, weight, reduction); |
| 21329 | } |
| 21330 | } // anonymous namespace |
| 21331 | namespace { |
| 21332 | at::Tensor & wrapper_CPU_out_multi_margin_loss_out(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 21333 | // No device check |
| 21334 | // DeviceGuard omitted |
| 21335 | return at::native::multi_margin_loss_cpu_out(self, target, p, margin, weight, reduction, out); |
| 21336 | } |
| 21337 | } // anonymous namespace |
| 21338 | namespace { |
| 21339 | at::Tensor wrapper_CPU__multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 21340 | // No device check |
| 21341 | // DeviceGuard omitted |
| 21342 | return at::native::multi_margin_loss_cpu_backward(grad_output, self, target, p, margin, weight, reduction); |
| 21343 | } |
| 21344 | } // anonymous namespace |
| 21345 | namespace { |
| 21346 | at::Tensor & wrapper_CPU_grad_input_multi_margin_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) { |
| 21347 | // No device check |
| 21348 | // DeviceGuard omitted |
| 21349 | return at::native::multi_margin_loss_cpu_backward_out(grad_output, self, target, p, margin, weight, reduction, grad_input); |
| 21350 | } |
| 21351 | } // anonymous namespace |
| 21352 | namespace { |
| 21353 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 21354 | // No device check |
| 21355 | // DeviceGuard omitted |
| 21356 | return at::native::multilabel_margin_loss_forward_cpu(self, target, reduction); |
| 21357 | } |
| 21358 | } // anonymous namespace |
| 21359 | namespace { |
| 21360 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_output_multilabel_margin_loss_forward_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & output, at::Tensor & is_target) { |
| 21361 | // No device check |
| 21362 | // DeviceGuard omitted |
| 21363 | return at::native::multilabel_margin_loss_forward_out_cpu(self, target, reduction, output, is_target); |
| 21364 | } |
| 21365 | } // anonymous namespace |
| 21366 | namespace { |
| 21367 | at::Tensor wrapper_CPU__multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { |
| 21368 | // No device check |
| 21369 | // DeviceGuard omitted |
| 21370 | return at::native::multilabel_margin_loss_backward_cpu(grad_output, self, target, reduction, is_target); |
| 21371 | } |
| 21372 | } // anonymous namespace |
| 21373 | namespace { |
| 21374 | at::Tensor & wrapper_CPU_grad_input_multilabel_margin_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input) { |
| 21375 | // No device check |
| 21376 | // DeviceGuard omitted |
| 21377 | return at::native::multilabel_margin_loss_backward_cpu_out(grad_output, self, target, reduction, is_target, grad_input); |
| 21378 | } |
| 21379 | } // anonymous namespace |
| 21380 | struct structured_nll_loss_forward_out_cpu_functional final : public at::native::structured_nll_loss_forward_out_cpu { |
| 21381 | void set_output_strided( |
| 21382 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21383 | TensorOptions options, DimnameList names |
| 21384 | ) override { |
| 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 | } |
| 21392 | void set_output_raw_strided( |
| 21393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21394 | TensorOptions options, DimnameList names |
| 21395 | ) override { |
| 21396 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21397 | if (!names.empty()) { |
| 21398 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21399 | } |
| 21400 | // super must happen after, so that downstream can use maybe_get_output |
| 21401 | // to retrieve the output |
| 21402 | } |
| 21403 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21404 | return *outputs_[output_idx]; |
| 21405 | } |
| 21406 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 21407 | }; |
| 21408 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 21409 | structured_nll_loss_forward_out_cpu_functional op; |
| 21410 | op.meta(self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index); |
| 21411 | op.impl(self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, *op.outputs_[0], *op.outputs_[1]); |
| 21412 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 21413 | } |
| 21414 | struct structured_nll_loss_forward_out_cpu_out final : public at::native::structured_nll_loss_forward_out_cpu { |
| 21415 | structured_nll_loss_forward_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 21416 | void set_output_strided( |
| 21417 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21418 | TensorOptions options, DimnameList names |
| 21419 | ) override { |
| 21420 | const auto& out = outputs_[output_idx].get(); |
| 21421 | resize_out(out, sizes, strides, options); |
| 21422 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21423 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21424 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21425 | } |
| 21426 | if (!names.empty()) { |
| 21427 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21428 | } |
| 21429 | // super must happen after, so that downstream can use maybe_get_output |
| 21430 | // to retrieve the output |
| 21431 | } |
| 21432 | void set_output_raw_strided( |
| 21433 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21434 | TensorOptions options, DimnameList names |
| 21435 | ) override { |
| 21436 | const auto& out = outputs_[output_idx].get(); |
| 21437 | resize_out(out, sizes, strides, options); |
| 21438 | if (!names.empty()) { |
| 21439 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21440 | } |
| 21441 | // super must happen after, so that downstream can use maybe_get_output |
| 21442 | // to retrieve the output |
| 21443 | } |
| 21444 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21445 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21446 | } |
| 21447 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 21448 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 21449 | }; |
| 21450 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_nll_loss_forward_out_output(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 21451 | structured_nll_loss_forward_out_cpu_out op(output, total_weight); |
| 21452 | op.meta(self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index); |
| 21453 | op.impl(self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 21454 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21455 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 21456 | return std::forward_as_tuple(output, total_weight); |
| 21457 | } |
| 21458 | struct structured_nll_loss_backward_out_cpu_functional final : public at::native::structured_nll_loss_backward_out_cpu { |
| 21459 | void set_output_strided( |
| 21460 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21461 | TensorOptions options, DimnameList names |
| 21462 | ) override { |
| 21463 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21464 | if (!names.empty()) { |
| 21465 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21466 | } |
| 21467 | // super must happen after, so that downstream can use maybe_get_output |
| 21468 | // to retrieve the output |
| 21469 | } |
| 21470 | void set_output_raw_strided( |
| 21471 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21472 | TensorOptions options, DimnameList names |
| 21473 | ) override { |
| 21474 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21475 | if (!names.empty()) { |
| 21476 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21477 | } |
| 21478 | // super must happen after, so that downstream can use maybe_get_output |
| 21479 | // to retrieve the output |
| 21480 | } |
| 21481 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21482 | return *outputs_[output_idx]; |
| 21483 | } |
| 21484 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21485 | }; |
| 21486 | at::Tensor wrapper_CPU_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) { |
| 21487 | structured_nll_loss_backward_out_cpu_functional op; |
| 21488 | op.meta(grad_output, self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, total_weight); |
| 21489 | op.impl(grad_output, self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, total_weight, *op.outputs_[0]); |
| 21490 | return std::move(op.outputs_[0]).take(); |
| 21491 | } |
| 21492 | struct structured_nll_loss_backward_out_cpu_out final : public at::native::structured_nll_loss_backward_out_cpu { |
| 21493 | structured_nll_loss_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21494 | void set_output_strided( |
| 21495 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21496 | TensorOptions options, DimnameList names |
| 21497 | ) override { |
| 21498 | const auto& out = outputs_[output_idx].get(); |
| 21499 | resize_out(out, sizes, strides, options); |
| 21500 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21501 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21502 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21503 | } |
| 21504 | if (!names.empty()) { |
| 21505 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21506 | } |
| 21507 | // super must happen after, so that downstream can use maybe_get_output |
| 21508 | // to retrieve the output |
| 21509 | } |
| 21510 | void set_output_raw_strided( |
| 21511 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21512 | TensorOptions options, DimnameList names |
| 21513 | ) override { |
| 21514 | const auto& out = outputs_[output_idx].get(); |
| 21515 | resize_out(out, sizes, strides, options); |
| 21516 | if (!names.empty()) { |
| 21517 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21518 | } |
| 21519 | // super must happen after, so that downstream can use maybe_get_output |
| 21520 | // to retrieve the output |
| 21521 | } |
| 21522 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21523 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21524 | } |
| 21525 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21526 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21527 | }; |
| 21528 | at::Tensor & wrapper_CPU_nll_loss_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 21529 | structured_nll_loss_backward_out_cpu_out op(grad_input); |
| 21530 | op.meta(grad_output, self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, total_weight); |
| 21531 | op.impl(grad_output, self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, total_weight, op.maybe_get_output(0)); |
| 21532 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21533 | return grad_input; |
| 21534 | } |
| 21535 | namespace { |
| 21536 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 21537 | // No device check |
| 21538 | // DeviceGuard omitted |
| 21539 | return at::native::nll_loss2d_forward_cpu(self, target, weight, reduction, ignore_index.expect_int()); |
| 21540 | } |
| 21541 | } // anonymous namespace |
| 21542 | namespace { |
| 21543 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_output_nll_loss2d_forward_out(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 21544 | // No device check |
| 21545 | // DeviceGuard omitted |
| 21546 | return at::native::nll_loss2d_forward_out_cpu(self, target, weight, reduction, ignore_index.expect_int(), output, total_weight); |
| 21547 | } |
| 21548 | } // anonymous namespace |
| 21549 | namespace { |
| 21550 | at::Tensor wrapper_CPU__nll_loss2d_backward(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) { |
| 21551 | // No device check |
| 21552 | // DeviceGuard omitted |
| 21553 | return at::native::nll_loss2d_backward_cpu(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight); |
| 21554 | } |
| 21555 | } // anonymous namespace |
| 21556 | namespace { |
| 21557 | at::Tensor & wrapper_CPU_grad_input_nll_loss2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 21558 | // No device check |
| 21559 | // DeviceGuard omitted |
| 21560 | return at::native::nll_loss2d_backward_out_cpu(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight, grad_input); |
| 21561 | } |
| 21562 | } // anonymous namespace |
| 21563 | struct structured_smooth_l1_loss_out_functional final : public at::native::structured_smooth_l1_loss_out { |
| 21564 | void set_output_strided( |
| 21565 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21566 | TensorOptions options, DimnameList names |
| 21567 | ) override { |
| 21568 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21569 | if (!names.empty()) { |
| 21570 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21571 | } |
| 21572 | // super must happen after, so that downstream can use maybe_get_output |
| 21573 | // to retrieve the output |
| 21574 | at::native::structured_smooth_l1_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21575 | } |
| 21576 | void set_output_raw_strided( |
| 21577 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21578 | TensorOptions options, DimnameList names |
| 21579 | ) override { |
| 21580 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21581 | if (!names.empty()) { |
| 21582 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21583 | } |
| 21584 | // super must happen after, so that downstream can use maybe_get_output |
| 21585 | // to retrieve the output |
| 21586 | at::native::structured_smooth_l1_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21587 | } |
| 21588 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21589 | return *outputs_[output_idx]; |
| 21590 | } |
| 21591 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21592 | }; |
| 21593 | at::Tensor wrapper_CPU_smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 21594 | structured_smooth_l1_loss_out_functional op; |
| 21595 | op.meta(self, target, reduction, beta); |
| 21596 | op.impl(self, target, reduction, beta, *op.outputs_[0]); |
| 21597 | return std::move(op.outputs_[0]).take(); |
| 21598 | } |
| 21599 | struct structured_smooth_l1_loss_out_out final : public at::native::structured_smooth_l1_loss_out { |
| 21600 | structured_smooth_l1_loss_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21601 | void set_output_strided( |
| 21602 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21603 | TensorOptions options, DimnameList names |
| 21604 | ) override { |
| 21605 | const auto& out = outputs_[output_idx].get(); |
| 21606 | resize_out(out, sizes, strides, options); |
| 21607 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21608 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21609 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21610 | } |
| 21611 | if (!names.empty()) { |
| 21612 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21613 | } |
| 21614 | // super must happen after, so that downstream can use maybe_get_output |
| 21615 | // to retrieve the output |
| 21616 | at::native::structured_smooth_l1_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21617 | } |
| 21618 | void set_output_raw_strided( |
| 21619 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21620 | TensorOptions options, DimnameList names |
| 21621 | ) override { |
| 21622 | const auto& out = outputs_[output_idx].get(); |
| 21623 | resize_out(out, sizes, strides, options); |
| 21624 | if (!names.empty()) { |
| 21625 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21626 | } |
| 21627 | // super must happen after, so that downstream can use maybe_get_output |
| 21628 | // to retrieve the output |
| 21629 | at::native::structured_smooth_l1_loss_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21630 | } |
| 21631 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21632 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21633 | } |
| 21634 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21635 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21636 | }; |
| 21637 | at::Tensor & wrapper_CPU_smooth_l1_loss_out_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out) { |
| 21638 | structured_smooth_l1_loss_out_out op(out); |
| 21639 | op.meta(self, target, reduction, beta); |
| 21640 | op.impl(self, target, reduction, beta, op.maybe_get_output(0)); |
| 21641 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21642 | return out; |
| 21643 | } |
| 21644 | namespace { |
| 21645 | at::Tensor & wrapper_CPU_grad_input_smooth_l1_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { |
| 21646 | // No device check |
| 21647 | // DeviceGuard omitted |
| 21648 | return at::native::smooth_l1_loss_backward_out(grad_output, self, target, reduction, beta, grad_input); |
| 21649 | } |
| 21650 | } // anonymous namespace |
| 21651 | namespace { |
| 21652 | at::Tensor wrapper_CPU__huber_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 21653 | // No device check |
| 21654 | // DeviceGuard omitted |
| 21655 | return at::native::huber_loss(self, target, reduction, delta); |
| 21656 | } |
| 21657 | } // anonymous namespace |
| 21658 | namespace { |
| 21659 | at::Tensor & wrapper_CPU_out_huber_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { |
| 21660 | // No device check |
| 21661 | // DeviceGuard omitted |
| 21662 | return at::native::huber_loss_out(self, target, reduction, delta, out); |
| 21663 | } |
| 21664 | } // anonymous namespace |
| 21665 | namespace { |
| 21666 | at::Tensor & wrapper_CPU_out_huber_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { |
| 21667 | // No device check |
| 21668 | // DeviceGuard omitted |
| 21669 | return at::native::huber_loss_backward_out(grad_output, self, target, reduction, delta, grad_input); |
| 21670 | } |
| 21671 | } // anonymous namespace |
| 21672 | struct structured_elu_out_functional final : public at::native::structured_elu_out { |
| 21673 | void set_output_strided( |
| 21674 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21675 | TensorOptions options, DimnameList names |
| 21676 | ) override { |
| 21677 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21678 | if (!names.empty()) { |
| 21679 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21680 | } |
| 21681 | // super must happen after, so that downstream can use maybe_get_output |
| 21682 | // to retrieve the output |
| 21683 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21684 | } |
| 21685 | void set_output_raw_strided( |
| 21686 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21687 | TensorOptions options, DimnameList names |
| 21688 | ) override { |
| 21689 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21690 | if (!names.empty()) { |
| 21691 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21692 | } |
| 21693 | // super must happen after, so that downstream can use maybe_get_output |
| 21694 | // to retrieve the output |
| 21695 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21696 | } |
| 21697 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21698 | return *outputs_[output_idx]; |
| 21699 | } |
| 21700 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21701 | }; |
| 21702 | at::Tensor wrapper_CPU_elu(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 21703 | structured_elu_out_functional op; |
| 21704 | op.meta(self, alpha, scale, input_scale); |
| 21705 | op.impl(self, alpha, scale, input_scale, *op.outputs_[0]); |
| 21706 | return std::move(op.outputs_[0]).take(); |
| 21707 | } |
| 21708 | struct structured_elu_out_out final : public at::native::structured_elu_out { |
| 21709 | structured_elu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21710 | void set_output_strided( |
| 21711 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21712 | TensorOptions options, DimnameList names |
| 21713 | ) override { |
| 21714 | const auto& out = outputs_[output_idx].get(); |
| 21715 | resize_out(out, sizes, strides, options); |
| 21716 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21717 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21718 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21719 | } |
| 21720 | if (!names.empty()) { |
| 21721 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21722 | } |
| 21723 | // super must happen after, so that downstream can use maybe_get_output |
| 21724 | // to retrieve the output |
| 21725 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21726 | } |
| 21727 | void set_output_raw_strided( |
| 21728 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21729 | TensorOptions options, DimnameList names |
| 21730 | ) override { |
| 21731 | const auto& out = outputs_[output_idx].get(); |
| 21732 | resize_out(out, sizes, strides, options); |
| 21733 | if (!names.empty()) { |
| 21734 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21735 | } |
| 21736 | // super must happen after, so that downstream can use maybe_get_output |
| 21737 | // to retrieve the output |
| 21738 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21739 | } |
| 21740 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21741 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21742 | } |
| 21743 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21744 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21745 | }; |
| 21746 | at::Tensor & wrapper_CPU_elu_out_out(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out) { |
| 21747 | structured_elu_out_out op(out); |
| 21748 | op.meta(self, alpha, scale, input_scale); |
| 21749 | op.impl(self, alpha, scale, input_scale, op.maybe_get_output(0)); |
| 21750 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21751 | return out; |
| 21752 | } |
| 21753 | struct structured_elu_out_inplace final : public at::native::structured_elu_out { |
| 21754 | structured_elu_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 21755 | void set_output_strided( |
| 21756 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21757 | TensorOptions options, DimnameList names |
| 21758 | ) override { |
| 21759 | const auto& out = outputs_[output_idx].get(); |
| 21760 | check_inplace(out, sizes, options); |
| 21761 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21762 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21763 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21764 | } |
| 21765 | if (!names.empty()) { |
| 21766 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21767 | } |
| 21768 | // super must happen after, so that downstream can use maybe_get_output |
| 21769 | // to retrieve the output |
| 21770 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21771 | } |
| 21772 | void set_output_raw_strided( |
| 21773 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21774 | TensorOptions options, DimnameList names |
| 21775 | ) override { |
| 21776 | const auto& out = outputs_[output_idx].get(); |
| 21777 | check_inplace(out, sizes, options); |
| 21778 | if (!names.empty()) { |
| 21779 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21780 | } |
| 21781 | // super must happen after, so that downstream can use maybe_get_output |
| 21782 | // to retrieve the output |
| 21783 | at::native::structured_elu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21784 | } |
| 21785 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21786 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21787 | } |
| 21788 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21789 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21790 | }; |
| 21791 | at::Tensor & wrapper_CPU_elu_(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 21792 | structured_elu_out_inplace op(self); |
| 21793 | op.meta(self, alpha, scale, input_scale); |
| 21794 | op.impl(self, alpha, scale, input_scale, op.outputs_[0]); |
| 21795 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21796 | return self; |
| 21797 | } |
| 21798 | struct structured_elu_backward_out_functional final : public at::native::structured_elu_backward_out { |
| 21799 | void set_output_strided( |
| 21800 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21801 | TensorOptions options, DimnameList names |
| 21802 | ) override { |
| 21803 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21804 | if (!names.empty()) { |
| 21805 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21806 | } |
| 21807 | // super must happen after, so that downstream can use maybe_get_output |
| 21808 | // to retrieve the output |
| 21809 | at::native::structured_elu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21810 | } |
| 21811 | void set_output_raw_strided( |
| 21812 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21813 | TensorOptions options, DimnameList names |
| 21814 | ) override { |
| 21815 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21816 | if (!names.empty()) { |
| 21817 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21818 | } |
| 21819 | // super must happen after, so that downstream can use maybe_get_output |
| 21820 | // to retrieve the output |
| 21821 | at::native::structured_elu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21822 | } |
| 21823 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21824 | return *outputs_[output_idx]; |
| 21825 | } |
| 21826 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21827 | }; |
| 21828 | at::Tensor wrapper_CPU_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) { |
| 21829 | structured_elu_backward_out_functional op; |
| 21830 | op.meta(grad_output, alpha, scale, input_scale, is_result, self_or_result); |
| 21831 | op.impl(grad_output, alpha, scale, input_scale, is_result, self_or_result, *op.outputs_[0]); |
| 21832 | return std::move(op.outputs_[0]).take(); |
| 21833 | } |
| 21834 | struct structured_elu_backward_out_out final : public at::native::structured_elu_backward_out { |
| 21835 | structured_elu_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21836 | void set_output_strided( |
| 21837 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21838 | TensorOptions options, DimnameList names |
| 21839 | ) override { |
| 21840 | const auto& out = outputs_[output_idx].get(); |
| 21841 | resize_out(out, sizes, strides, options); |
| 21842 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21843 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21844 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21845 | } |
| 21846 | if (!names.empty()) { |
| 21847 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21848 | } |
| 21849 | // super must happen after, so that downstream can use maybe_get_output |
| 21850 | // to retrieve the output |
| 21851 | at::native::structured_elu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21852 | } |
| 21853 | void set_output_raw_strided( |
| 21854 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21855 | TensorOptions options, DimnameList names |
| 21856 | ) override { |
| 21857 | const auto& out = outputs_[output_idx].get(); |
| 21858 | resize_out(out, sizes, strides, options); |
| 21859 | if (!names.empty()) { |
| 21860 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21861 | } |
| 21862 | // super must happen after, so that downstream can use maybe_get_output |
| 21863 | // to retrieve the output |
| 21864 | at::native::structured_elu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21865 | } |
| 21866 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21867 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21868 | } |
| 21869 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21870 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21871 | }; |
| 21872 | at::Tensor & wrapper_CPU_elu_backward_out_grad_input(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input) { |
| 21873 | structured_elu_backward_out_out op(grad_input); |
| 21874 | op.meta(grad_output, alpha, scale, input_scale, is_result, self_or_result); |
| 21875 | op.impl(grad_output, alpha, scale, input_scale, is_result, self_or_result, op.maybe_get_output(0)); |
| 21876 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21877 | return grad_input; |
| 21878 | } |
| 21879 | struct structured_glu_out_functional final : public at::native::structured_glu_out { |
| 21880 | void set_output_strided( |
| 21881 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21882 | TensorOptions options, DimnameList names |
| 21883 | ) override { |
| 21884 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21885 | if (!names.empty()) { |
| 21886 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21887 | } |
| 21888 | // super must happen after, so that downstream can use maybe_get_output |
| 21889 | // to retrieve the output |
| 21890 | at::native::structured_glu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21891 | } |
| 21892 | void set_output_raw_strided( |
| 21893 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21894 | TensorOptions options, DimnameList names |
| 21895 | ) override { |
| 21896 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21897 | if (!names.empty()) { |
| 21898 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21899 | } |
| 21900 | // super must happen after, so that downstream can use maybe_get_output |
| 21901 | // to retrieve the output |
| 21902 | at::native::structured_glu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21903 | } |
| 21904 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21905 | return *outputs_[output_idx]; |
| 21906 | } |
| 21907 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21908 | }; |
| 21909 | at::Tensor wrapper_CPU_glu(const at::Tensor & self, int64_t dim) { |
| 21910 | structured_glu_out_functional op; |
| 21911 | op.meta(self, dim); |
| 21912 | op.impl(self, dim, *op.outputs_[0]); |
| 21913 | return std::move(op.outputs_[0]).take(); |
| 21914 | } |
| 21915 | struct structured_glu_out_out final : public at::native::structured_glu_out { |
| 21916 | structured_glu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 21917 | void set_output_strided( |
| 21918 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21919 | TensorOptions options, DimnameList names |
| 21920 | ) override { |
| 21921 | const auto& out = outputs_[output_idx].get(); |
| 21922 | resize_out(out, sizes, strides, options); |
| 21923 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 21924 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 21925 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 21926 | } |
| 21927 | if (!names.empty()) { |
| 21928 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21929 | } |
| 21930 | // super must happen after, so that downstream can use maybe_get_output |
| 21931 | // to retrieve the output |
| 21932 | at::native::structured_glu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21933 | } |
| 21934 | void set_output_raw_strided( |
| 21935 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21936 | TensorOptions options, DimnameList names |
| 21937 | ) override { |
| 21938 | const auto& out = outputs_[output_idx].get(); |
| 21939 | resize_out(out, sizes, strides, options); |
| 21940 | if (!names.empty()) { |
| 21941 | namedinference::propagate_names(outputs_[output_idx], names); |
| 21942 | } |
| 21943 | // super must happen after, so that downstream can use maybe_get_output |
| 21944 | // to retrieve the output |
| 21945 | at::native::structured_glu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21946 | } |
| 21947 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21948 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 21949 | } |
| 21950 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 21951 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 21952 | }; |
| 21953 | at::Tensor & wrapper_CPU_glu_out_out(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 21954 | structured_glu_out_out op(out); |
| 21955 | op.meta(self, dim); |
| 21956 | op.impl(self, dim, op.maybe_get_output(0)); |
| 21957 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 21958 | return out; |
| 21959 | } |
| 21960 | namespace { |
| 21961 | at::Tensor wrapper_CPU__glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { |
| 21962 | // No device check |
| 21963 | // DeviceGuard omitted |
| 21964 | return at::native::glu_backward_cpu(grad_output, self, dim); |
| 21965 | } |
| 21966 | } // anonymous namespace |
| 21967 | namespace { |
| 21968 | at::Tensor & wrapper_CPU_grad_input_glu_backward_out(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, at::Tensor & grad_input) { |
| 21969 | // No device check |
| 21970 | // DeviceGuard omitted |
| 21971 | return at::native::glu_backward_cpu_out(grad_output, self, dim, grad_input); |
| 21972 | } |
| 21973 | } // anonymous namespace |
| 21974 | namespace { |
| 21975 | at::Tensor wrapper_CPU__glu_jvp(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) { |
| 21976 | // No device check |
| 21977 | // DeviceGuard omitted |
| 21978 | return at::native::glu_jvp(glu, x, dx, dim); |
| 21979 | } |
| 21980 | } // anonymous namespace |
| 21981 | namespace { |
| 21982 | at::Tensor wrapper_CPU__glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { |
| 21983 | // No device check |
| 21984 | // DeviceGuard omitted |
| 21985 | return at::native::glu_backward_jvp(grad_x, grad_glu, x, dgrad_glu, dx, dim); |
| 21986 | } |
| 21987 | } // anonymous namespace |
| 21988 | struct structured_hardsigmoid_out_functional final : public at::native::structured_hardsigmoid_out { |
| 21989 | void set_output_strided( |
| 21990 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21991 | TensorOptions options, DimnameList names |
| 21992 | ) override { |
| 21993 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21994 | if (!names.empty()) { |
| 21995 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21996 | } |
| 21997 | // super must happen after, so that downstream can use maybe_get_output |
| 21998 | // to retrieve the output |
| 21999 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22000 | } |
| 22001 | void set_output_raw_strided( |
| 22002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22003 | TensorOptions options, DimnameList names |
| 22004 | ) override { |
| 22005 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22006 | if (!names.empty()) { |
| 22007 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22008 | } |
| 22009 | // super must happen after, so that downstream can use maybe_get_output |
| 22010 | // to retrieve the output |
| 22011 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22012 | } |
| 22013 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22014 | return *outputs_[output_idx]; |
| 22015 | } |
| 22016 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22017 | }; |
| 22018 | at::Tensor wrapper_CPU_hardsigmoid(const at::Tensor & self) { |
| 22019 | structured_hardsigmoid_out_functional op; |
| 22020 | op.meta(self); |
| 22021 | op.impl(self, *op.outputs_[0]); |
| 22022 | return std::move(op.outputs_[0]).take(); |
| 22023 | } |
| 22024 | struct structured_hardsigmoid_out_out final : public at::native::structured_hardsigmoid_out { |
| 22025 | structured_hardsigmoid_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22026 | void set_output_strided( |
| 22027 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22028 | TensorOptions options, DimnameList names |
| 22029 | ) override { |
| 22030 | const auto& out = outputs_[output_idx].get(); |
| 22031 | resize_out(out, sizes, strides, options); |
| 22032 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22033 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22034 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22035 | } |
| 22036 | if (!names.empty()) { |
| 22037 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22038 | } |
| 22039 | // super must happen after, so that downstream can use maybe_get_output |
| 22040 | // to retrieve the output |
| 22041 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22042 | } |
| 22043 | void set_output_raw_strided( |
| 22044 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22045 | TensorOptions options, DimnameList names |
| 22046 | ) override { |
| 22047 | const auto& out = outputs_[output_idx].get(); |
| 22048 | resize_out(out, sizes, strides, options); |
| 22049 | if (!names.empty()) { |
| 22050 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22051 | } |
| 22052 | // super must happen after, so that downstream can use maybe_get_output |
| 22053 | // to retrieve the output |
| 22054 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22055 | } |
| 22056 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22057 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22058 | } |
| 22059 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22060 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22061 | }; |
| 22062 | at::Tensor & wrapper_CPU_hardsigmoid_out_out(const at::Tensor & self, at::Tensor & out) { |
| 22063 | structured_hardsigmoid_out_out op(out); |
| 22064 | op.meta(self); |
| 22065 | op.impl(self, op.maybe_get_output(0)); |
| 22066 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22067 | return out; |
| 22068 | } |
| 22069 | struct structured_hardsigmoid_out_inplace final : public at::native::structured_hardsigmoid_out { |
| 22070 | structured_hardsigmoid_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 22071 | void set_output_strided( |
| 22072 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22073 | TensorOptions options, DimnameList names |
| 22074 | ) override { |
| 22075 | const auto& out = outputs_[output_idx].get(); |
| 22076 | check_inplace(out, sizes, options); |
| 22077 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22078 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22079 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22080 | } |
| 22081 | if (!names.empty()) { |
| 22082 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22083 | } |
| 22084 | // super must happen after, so that downstream can use maybe_get_output |
| 22085 | // to retrieve the output |
| 22086 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22087 | } |
| 22088 | void set_output_raw_strided( |
| 22089 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22090 | TensorOptions options, DimnameList names |
| 22091 | ) override { |
| 22092 | const auto& out = outputs_[output_idx].get(); |
| 22093 | check_inplace(out, sizes, options); |
| 22094 | if (!names.empty()) { |
| 22095 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22096 | } |
| 22097 | // super must happen after, so that downstream can use maybe_get_output |
| 22098 | // to retrieve the output |
| 22099 | at::native::structured_hardsigmoid_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22100 | } |
| 22101 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22102 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22103 | } |
| 22104 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22105 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22106 | }; |
| 22107 | at::Tensor & wrapper_CPU_hardsigmoid_(at::Tensor & self) { |
| 22108 | structured_hardsigmoid_out_inplace op(self); |
| 22109 | op.meta(self); |
| 22110 | op.impl(self, op.outputs_[0]); |
| 22111 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22112 | return self; |
| 22113 | } |
| 22114 | struct structured_hardsigmoid_backward_out_functional final : public at::native::structured_hardsigmoid_backward_out { |
| 22115 | void set_output_strided( |
| 22116 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22117 | TensorOptions options, DimnameList names |
| 22118 | ) override { |
| 22119 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22120 | if (!names.empty()) { |
| 22121 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22122 | } |
| 22123 | // super must happen after, so that downstream can use maybe_get_output |
| 22124 | // to retrieve the output |
| 22125 | at::native::structured_hardsigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22126 | } |
| 22127 | void set_output_raw_strided( |
| 22128 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22129 | TensorOptions options, DimnameList names |
| 22130 | ) override { |
| 22131 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22132 | if (!names.empty()) { |
| 22133 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22134 | } |
| 22135 | // super must happen after, so that downstream can use maybe_get_output |
| 22136 | // to retrieve the output |
| 22137 | at::native::structured_hardsigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22138 | } |
| 22139 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22140 | return *outputs_[output_idx]; |
| 22141 | } |
| 22142 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22143 | }; |
| 22144 | at::Tensor wrapper_CPU_hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22145 | structured_hardsigmoid_backward_out_functional op; |
| 22146 | op.meta(grad_output, self); |
| 22147 | op.impl(grad_output, self, *op.outputs_[0]); |
| 22148 | return std::move(op.outputs_[0]).take(); |
| 22149 | } |
| 22150 | struct structured_hardsigmoid_backward_out_out final : public at::native::structured_hardsigmoid_backward_out { |
| 22151 | structured_hardsigmoid_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22152 | void set_output_strided( |
| 22153 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22154 | TensorOptions options, DimnameList names |
| 22155 | ) override { |
| 22156 | const auto& out = outputs_[output_idx].get(); |
| 22157 | resize_out(out, sizes, strides, options); |
| 22158 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22159 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22160 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22161 | } |
| 22162 | if (!names.empty()) { |
| 22163 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22164 | } |
| 22165 | // super must happen after, so that downstream can use maybe_get_output |
| 22166 | // to retrieve the output |
| 22167 | at::native::structured_hardsigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22168 | } |
| 22169 | void set_output_raw_strided( |
| 22170 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22171 | TensorOptions options, DimnameList names |
| 22172 | ) override { |
| 22173 | const auto& out = outputs_[output_idx].get(); |
| 22174 | resize_out(out, sizes, strides, options); |
| 22175 | if (!names.empty()) { |
| 22176 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22177 | } |
| 22178 | // super must happen after, so that downstream can use maybe_get_output |
| 22179 | // to retrieve the output |
| 22180 | at::native::structured_hardsigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22181 | } |
| 22182 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22183 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22184 | } |
| 22185 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22186 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22187 | }; |
| 22188 | at::Tensor & wrapper_CPU_hardsigmoid_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 22189 | structured_hardsigmoid_backward_out_out op(grad_input); |
| 22190 | op.meta(grad_output, self); |
| 22191 | op.impl(grad_output, self, op.maybe_get_output(0)); |
| 22192 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22193 | return grad_input; |
| 22194 | } |
| 22195 | namespace { |
| 22196 | at::Tensor wrapper_CPU__hardtanh(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 22197 | // No device check |
| 22198 | // DeviceGuard omitted |
| 22199 | return at::native::hardtanh(self, min_val, max_val); |
| 22200 | } |
| 22201 | } // anonymous namespace |
| 22202 | namespace { |
| 22203 | at::Tensor & wrapper_CPU_out_hardtanh_out(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) { |
| 22204 | // No device check |
| 22205 | // DeviceGuard omitted |
| 22206 | return at::native::hardtanh_out(self, min_val, max_val, out); |
| 22207 | } |
| 22208 | } // anonymous namespace |
| 22209 | namespace { |
| 22210 | at::Tensor & wrapper_CPU__hardtanh_(at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 22211 | // No device check |
| 22212 | // DeviceGuard omitted |
| 22213 | return at::native::hardtanh_(self, min_val, max_val); |
| 22214 | } |
| 22215 | } // anonymous namespace |
| 22216 | namespace { |
| 22217 | at::Tensor wrapper_CPU__hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 22218 | // No device check |
| 22219 | // DeviceGuard omitted |
| 22220 | return at::native::hardtanh_backward(grad_output, self, min_val, max_val); |
| 22221 | } |
| 22222 | } // anonymous namespace |
| 22223 | namespace { |
| 22224 | at::Tensor & wrapper_CPU_grad_input_hardtanh_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & grad_input) { |
| 22225 | // No device check |
| 22226 | // DeviceGuard omitted |
| 22227 | return at::native::hardtanh_backward_out(grad_output, self, min_val, max_val, grad_input); |
| 22228 | } |
| 22229 | } // anonymous namespace |
| 22230 | namespace { |
| 22231 | at::Tensor wrapper_CPU__hardswish(const at::Tensor & self) { |
| 22232 | // No device check |
| 22233 | // DeviceGuard omitted |
| 22234 | return at::native::hardswish(self); |
| 22235 | } |
| 22236 | } // anonymous namespace |
| 22237 | namespace { |
| 22238 | at::Tensor & wrapper_CPU_out_hardswish_out(const at::Tensor & self, at::Tensor & out) { |
| 22239 | // No device check |
| 22240 | // DeviceGuard omitted |
| 22241 | return at::native::hardswish_out(self, out); |
| 22242 | } |
| 22243 | } // anonymous namespace |
| 22244 | namespace { |
| 22245 | at::Tensor & wrapper_CPU__hardswish_(at::Tensor & self) { |
| 22246 | // No device check |
| 22247 | // DeviceGuard omitted |
| 22248 | return at::native::hardswish_(self); |
| 22249 | } |
| 22250 | } // anonymous namespace |
| 22251 | namespace { |
| 22252 | at::Tensor wrapper_CPU__hardswish_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22253 | // No device check |
| 22254 | // DeviceGuard omitted |
| 22255 | return at::native::hardswish_backward(grad_output, self); |
| 22256 | } |
| 22257 | } // anonymous namespace |
| 22258 | struct structured_leaky_relu_out_functional final : public at::native::structured_leaky_relu_out { |
| 22259 | void set_output_strided( |
| 22260 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22261 | TensorOptions options, DimnameList names |
| 22262 | ) override { |
| 22263 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22264 | if (!names.empty()) { |
| 22265 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22266 | } |
| 22267 | // super must happen after, so that downstream can use maybe_get_output |
| 22268 | // to retrieve the output |
| 22269 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22270 | } |
| 22271 | void set_output_raw_strided( |
| 22272 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22273 | TensorOptions options, DimnameList names |
| 22274 | ) override { |
| 22275 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22276 | if (!names.empty()) { |
| 22277 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22278 | } |
| 22279 | // super must happen after, so that downstream can use maybe_get_output |
| 22280 | // to retrieve the output |
| 22281 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22282 | } |
| 22283 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22284 | return *outputs_[output_idx]; |
| 22285 | } |
| 22286 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22287 | }; |
| 22288 | at::Tensor wrapper_CPU_leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope) { |
| 22289 | structured_leaky_relu_out_functional op; |
| 22290 | op.meta(self, negative_slope); |
| 22291 | op.impl(self, negative_slope, *op.outputs_[0]); |
| 22292 | return std::move(op.outputs_[0]).take(); |
| 22293 | } |
| 22294 | struct structured_leaky_relu_out_out final : public at::native::structured_leaky_relu_out { |
| 22295 | structured_leaky_relu_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22296 | void set_output_strided( |
| 22297 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22298 | TensorOptions options, DimnameList names |
| 22299 | ) override { |
| 22300 | const auto& out = outputs_[output_idx].get(); |
| 22301 | resize_out(out, sizes, strides, options); |
| 22302 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22303 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22304 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22305 | } |
| 22306 | if (!names.empty()) { |
| 22307 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22308 | } |
| 22309 | // super must happen after, so that downstream can use maybe_get_output |
| 22310 | // to retrieve the output |
| 22311 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22312 | } |
| 22313 | void set_output_raw_strided( |
| 22314 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22315 | TensorOptions options, DimnameList names |
| 22316 | ) override { |
| 22317 | const auto& out = outputs_[output_idx].get(); |
| 22318 | resize_out(out, sizes, strides, options); |
| 22319 | if (!names.empty()) { |
| 22320 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22321 | } |
| 22322 | // super must happen after, so that downstream can use maybe_get_output |
| 22323 | // to retrieve the output |
| 22324 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22325 | } |
| 22326 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22327 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22328 | } |
| 22329 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22330 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22331 | }; |
| 22332 | at::Tensor & wrapper_CPU_leaky_relu_out_out(const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) { |
| 22333 | structured_leaky_relu_out_out op(out); |
| 22334 | op.meta(self, negative_slope); |
| 22335 | op.impl(self, negative_slope, op.maybe_get_output(0)); |
| 22336 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22337 | return out; |
| 22338 | } |
| 22339 | struct structured_leaky_relu_out_inplace final : public at::native::structured_leaky_relu_out { |
| 22340 | structured_leaky_relu_out_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 22341 | void set_output_strided( |
| 22342 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22343 | TensorOptions options, DimnameList names |
| 22344 | ) override { |
| 22345 | const auto& out = outputs_[output_idx].get(); |
| 22346 | check_inplace(out, sizes, options); |
| 22347 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22348 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22349 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22350 | } |
| 22351 | if (!names.empty()) { |
| 22352 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22353 | } |
| 22354 | // super must happen after, so that downstream can use maybe_get_output |
| 22355 | // to retrieve the output |
| 22356 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22357 | } |
| 22358 | void set_output_raw_strided( |
| 22359 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22360 | TensorOptions options, DimnameList names |
| 22361 | ) override { |
| 22362 | const auto& out = outputs_[output_idx].get(); |
| 22363 | check_inplace(out, sizes, options); |
| 22364 | if (!names.empty()) { |
| 22365 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22366 | } |
| 22367 | // super must happen after, so that downstream can use maybe_get_output |
| 22368 | // to retrieve the output |
| 22369 | at::native::structured_leaky_relu_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22370 | } |
| 22371 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22372 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22373 | } |
| 22374 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22375 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22376 | }; |
| 22377 | at::Tensor & wrapper_CPU_leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope) { |
| 22378 | structured_leaky_relu_out_inplace op(self); |
| 22379 | op.meta(self, negative_slope); |
| 22380 | op.impl(self, negative_slope, op.outputs_[0]); |
| 22381 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22382 | return self; |
| 22383 | } |
| 22384 | struct structured_leaky_relu_backward_out_functional final : public at::native::structured_leaky_relu_backward_out { |
| 22385 | void set_output_strided( |
| 22386 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22387 | TensorOptions options, DimnameList names |
| 22388 | ) override { |
| 22389 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22390 | if (!names.empty()) { |
| 22391 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22392 | } |
| 22393 | // super must happen after, so that downstream can use maybe_get_output |
| 22394 | // to retrieve the output |
| 22395 | at::native::structured_leaky_relu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22396 | } |
| 22397 | void set_output_raw_strided( |
| 22398 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22399 | TensorOptions options, DimnameList names |
| 22400 | ) override { |
| 22401 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22402 | if (!names.empty()) { |
| 22403 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22404 | } |
| 22405 | // super must happen after, so that downstream can use maybe_get_output |
| 22406 | // to retrieve the output |
| 22407 | at::native::structured_leaky_relu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22408 | } |
| 22409 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22410 | return *outputs_[output_idx]; |
| 22411 | } |
| 22412 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22413 | }; |
| 22414 | at::Tensor wrapper_CPU_leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { |
| 22415 | structured_leaky_relu_backward_out_functional op; |
| 22416 | op.meta(grad_output, self, negative_slope, self_is_result); |
| 22417 | op.impl(grad_output, self, negative_slope, self_is_result, *op.outputs_[0]); |
| 22418 | return std::move(op.outputs_[0]).take(); |
| 22419 | } |
| 22420 | struct structured_leaky_relu_backward_out_out final : public at::native::structured_leaky_relu_backward_out { |
| 22421 | structured_leaky_relu_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22422 | void set_output_strided( |
| 22423 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22424 | TensorOptions options, DimnameList names |
| 22425 | ) override { |
| 22426 | const auto& out = outputs_[output_idx].get(); |
| 22427 | resize_out(out, sizes, strides, options); |
| 22428 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22429 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22430 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22431 | } |
| 22432 | if (!names.empty()) { |
| 22433 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22434 | } |
| 22435 | // super must happen after, so that downstream can use maybe_get_output |
| 22436 | // to retrieve the output |
| 22437 | at::native::structured_leaky_relu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22438 | } |
| 22439 | void set_output_raw_strided( |
| 22440 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22441 | TensorOptions options, DimnameList names |
| 22442 | ) override { |
| 22443 | const auto& out = outputs_[output_idx].get(); |
| 22444 | resize_out(out, sizes, strides, options); |
| 22445 | if (!names.empty()) { |
| 22446 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22447 | } |
| 22448 | // super must happen after, so that downstream can use maybe_get_output |
| 22449 | // to retrieve the output |
| 22450 | at::native::structured_leaky_relu_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22451 | } |
| 22452 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22453 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22454 | } |
| 22455 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22456 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22457 | }; |
| 22458 | at::Tensor & wrapper_CPU_leaky_relu_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input) { |
| 22459 | structured_leaky_relu_backward_out_out op(grad_input); |
| 22460 | op.meta(grad_output, self, negative_slope, self_is_result); |
| 22461 | op.impl(grad_output, self, negative_slope, self_is_result, op.maybe_get_output(0)); |
| 22462 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22463 | return grad_input; |
| 22464 | } |
| 22465 | namespace { |
| 22466 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__log_sigmoid_forward(const at::Tensor & self) { |
| 22467 | // No device check |
| 22468 | // DeviceGuard omitted |
| 22469 | return at::native::log_sigmoid_forward_cpu(self); |
| 22470 | } |
| 22471 | } // anonymous namespace |
| 22472 | namespace { |
| 22473 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_output_log_sigmoid_forward_out(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { |
| 22474 | // No device check |
| 22475 | // DeviceGuard omitted |
| 22476 | return at::native::log_sigmoid_forward_out_cpu(self, output, buffer); |
| 22477 | } |
| 22478 | } // anonymous namespace |
| 22479 | namespace { |
| 22480 | at::Tensor wrapper_CPU__log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { |
| 22481 | // No device check |
| 22482 | // DeviceGuard omitted |
| 22483 | return at::native::log_sigmoid_backward_cpu(grad_output, self, buffer); |
| 22484 | } |
| 22485 | } // anonymous namespace |
| 22486 | namespace { |
| 22487 | at::Tensor & wrapper_CPU_grad_input_log_sigmoid_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) { |
| 22488 | // No device check |
| 22489 | // DeviceGuard omitted |
| 22490 | return at::native::log_sigmoid_backward_cpu_out(grad_output, self, buffer, grad_input); |
| 22491 | } |
| 22492 | } // anonymous namespace |
| 22493 | namespace { |
| 22494 | at::Tensor wrapper_CPU__rrelu_with_noise(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 22495 | // No device check |
| 22496 | // DeviceGuard omitted |
| 22497 | return at::native::rrelu_with_noise_cpu(self, noise, lower, upper, training, generator); |
| 22498 | } |
| 22499 | } // anonymous namespace |
| 22500 | namespace { |
| 22501 | at::Tensor & wrapper_CPU_out_rrelu_with_noise_out(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 22502 | // No device check |
| 22503 | // DeviceGuard omitted |
| 22504 | return at::native::rrelu_with_noise_out_cpu(self, noise, lower, upper, training, generator, out); |
| 22505 | } |
| 22506 | } // anonymous namespace |
| 22507 | namespace { |
| 22508 | at::Tensor & wrapper_CPU__rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 22509 | // No device check |
| 22510 | // DeviceGuard omitted |
| 22511 | return at::native::rrelu_with_noise_cpu_(self, noise, lower, upper, training, generator); |
| 22512 | } |
| 22513 | } // anonymous namespace |
| 22514 | struct structured_softplus_out_functional final : public at::native::structured_softplus_out { |
| 22515 | void set_output_strided( |
| 22516 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22517 | TensorOptions options, DimnameList names |
| 22518 | ) override { |
| 22519 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22520 | if (!names.empty()) { |
| 22521 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22522 | } |
| 22523 | // super must happen after, so that downstream can use maybe_get_output |
| 22524 | // to retrieve the output |
| 22525 | at::native::structured_softplus_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22526 | } |
| 22527 | void set_output_raw_strided( |
| 22528 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22529 | TensorOptions options, DimnameList names |
| 22530 | ) override { |
| 22531 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22532 | if (!names.empty()) { |
| 22533 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22534 | } |
| 22535 | // super must happen after, so that downstream can use maybe_get_output |
| 22536 | // to retrieve the output |
| 22537 | at::native::structured_softplus_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22538 | } |
| 22539 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22540 | return *outputs_[output_idx]; |
| 22541 | } |
| 22542 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22543 | }; |
| 22544 | at::Tensor wrapper_CPU_softplus(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 22545 | structured_softplus_out_functional op; |
| 22546 | op.meta(self, beta, threshold); |
| 22547 | op.impl(self, beta, threshold, *op.outputs_[0]); |
| 22548 | return std::move(op.outputs_[0]).take(); |
| 22549 | } |
| 22550 | struct structured_softplus_out_out final : public at::native::structured_softplus_out { |
| 22551 | structured_softplus_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22552 | void set_output_strided( |
| 22553 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22554 | TensorOptions options, DimnameList names |
| 22555 | ) override { |
| 22556 | const auto& out = outputs_[output_idx].get(); |
| 22557 | resize_out(out, sizes, strides, options); |
| 22558 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22559 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22560 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22561 | } |
| 22562 | if (!names.empty()) { |
| 22563 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22564 | } |
| 22565 | // super must happen after, so that downstream can use maybe_get_output |
| 22566 | // to retrieve the output |
| 22567 | at::native::structured_softplus_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22568 | } |
| 22569 | void set_output_raw_strided( |
| 22570 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22571 | TensorOptions options, DimnameList names |
| 22572 | ) override { |
| 22573 | const auto& out = outputs_[output_idx].get(); |
| 22574 | resize_out(out, sizes, strides, options); |
| 22575 | if (!names.empty()) { |
| 22576 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22577 | } |
| 22578 | // super must happen after, so that downstream can use maybe_get_output |
| 22579 | // to retrieve the output |
| 22580 | at::native::structured_softplus_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22581 | } |
| 22582 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22583 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22584 | } |
| 22585 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22586 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22587 | }; |
| 22588 | at::Tensor & wrapper_CPU_softplus_out_out(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out) { |
| 22589 | structured_softplus_out_out op(out); |
| 22590 | op.meta(self, beta, threshold); |
| 22591 | op.impl(self, beta, threshold, op.maybe_get_output(0)); |
| 22592 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22593 | return out; |
| 22594 | } |
| 22595 | struct structured_softplus_backward_out_functional final : public at::native::structured_softplus_backward_out { |
| 22596 | void set_output_strided( |
| 22597 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22598 | TensorOptions options, DimnameList names |
| 22599 | ) override { |
| 22600 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22601 | if (!names.empty()) { |
| 22602 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22603 | } |
| 22604 | // super must happen after, so that downstream can use maybe_get_output |
| 22605 | // to retrieve the output |
| 22606 | at::native::structured_softplus_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22607 | } |
| 22608 | void set_output_raw_strided( |
| 22609 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22610 | TensorOptions options, DimnameList names |
| 22611 | ) override { |
| 22612 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22613 | if (!names.empty()) { |
| 22614 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22615 | } |
| 22616 | // super must happen after, so that downstream can use maybe_get_output |
| 22617 | // to retrieve the output |
| 22618 | at::native::structured_softplus_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22619 | } |
| 22620 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22621 | return *outputs_[output_idx]; |
| 22622 | } |
| 22623 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22624 | }; |
| 22625 | at::Tensor wrapper_CPU_softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 22626 | structured_softplus_backward_out_functional op; |
| 22627 | op.meta(grad_output, self, beta, threshold); |
| 22628 | op.impl(grad_output, self, beta, threshold, *op.outputs_[0]); |
| 22629 | return std::move(op.outputs_[0]).take(); |
| 22630 | } |
| 22631 | struct structured_softplus_backward_out_out final : public at::native::structured_softplus_backward_out { |
| 22632 | structured_softplus_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22633 | void set_output_strided( |
| 22634 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22635 | TensorOptions options, DimnameList names |
| 22636 | ) override { |
| 22637 | const auto& out = outputs_[output_idx].get(); |
| 22638 | resize_out(out, sizes, strides, options); |
| 22639 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22640 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22641 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22642 | } |
| 22643 | if (!names.empty()) { |
| 22644 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22645 | } |
| 22646 | // super must happen after, so that downstream can use maybe_get_output |
| 22647 | // to retrieve the output |
| 22648 | at::native::structured_softplus_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22649 | } |
| 22650 | void set_output_raw_strided( |
| 22651 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22652 | TensorOptions options, DimnameList names |
| 22653 | ) override { |
| 22654 | const auto& out = outputs_[output_idx].get(); |
| 22655 | resize_out(out, sizes, strides, options); |
| 22656 | if (!names.empty()) { |
| 22657 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22658 | } |
| 22659 | // super must happen after, so that downstream can use maybe_get_output |
| 22660 | // to retrieve the output |
| 22661 | at::native::structured_softplus_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22662 | } |
| 22663 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22664 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22665 | } |
| 22666 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22667 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22668 | }; |
| 22669 | at::Tensor & wrapper_CPU_softplus_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) { |
| 22670 | structured_softplus_backward_out_out op(grad_input); |
| 22671 | op.meta(grad_output, self, beta, threshold); |
| 22672 | op.impl(grad_output, self, beta, threshold, op.maybe_get_output(0)); |
| 22673 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22674 | return grad_input; |
| 22675 | } |
| 22676 | struct structured_softshrink_out_functional final : public at::native::structured_softshrink_out { |
| 22677 | void set_output_strided( |
| 22678 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22679 | TensorOptions options, DimnameList names |
| 22680 | ) override { |
| 22681 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22682 | if (!names.empty()) { |
| 22683 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22684 | } |
| 22685 | // super must happen after, so that downstream can use maybe_get_output |
| 22686 | // to retrieve the output |
| 22687 | at::native::structured_softshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22688 | } |
| 22689 | void set_output_raw_strided( |
| 22690 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22691 | TensorOptions options, DimnameList names |
| 22692 | ) override { |
| 22693 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22694 | if (!names.empty()) { |
| 22695 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22696 | } |
| 22697 | // super must happen after, so that downstream can use maybe_get_output |
| 22698 | // to retrieve the output |
| 22699 | at::native::structured_softshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22700 | } |
| 22701 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22702 | return *outputs_[output_idx]; |
| 22703 | } |
| 22704 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22705 | }; |
| 22706 | at::Tensor wrapper_CPU_softshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 22707 | structured_softshrink_out_functional op; |
| 22708 | op.meta(self, lambd); |
| 22709 | op.impl(self, lambd, *op.outputs_[0]); |
| 22710 | return std::move(op.outputs_[0]).take(); |
| 22711 | } |
| 22712 | struct structured_softshrink_out_out final : public at::native::structured_softshrink_out { |
| 22713 | structured_softshrink_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22714 | void set_output_strided( |
| 22715 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22716 | TensorOptions options, DimnameList names |
| 22717 | ) override { |
| 22718 | const auto& out = outputs_[output_idx].get(); |
| 22719 | resize_out(out, sizes, strides, options); |
| 22720 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22721 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22722 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22723 | } |
| 22724 | if (!names.empty()) { |
| 22725 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22726 | } |
| 22727 | // super must happen after, so that downstream can use maybe_get_output |
| 22728 | // to retrieve the output |
| 22729 | at::native::structured_softshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22730 | } |
| 22731 | void set_output_raw_strided( |
| 22732 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22733 | TensorOptions options, DimnameList names |
| 22734 | ) override { |
| 22735 | const auto& out = outputs_[output_idx].get(); |
| 22736 | resize_out(out, sizes, strides, options); |
| 22737 | if (!names.empty()) { |
| 22738 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22739 | } |
| 22740 | // super must happen after, so that downstream can use maybe_get_output |
| 22741 | // to retrieve the output |
| 22742 | at::native::structured_softshrink_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22743 | } |
| 22744 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22745 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22746 | } |
| 22747 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22748 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22749 | }; |
| 22750 | at::Tensor & wrapper_CPU_softshrink_out_out(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 22751 | structured_softshrink_out_out op(out); |
| 22752 | op.meta(self, lambd); |
| 22753 | op.impl(self, lambd, op.maybe_get_output(0)); |
| 22754 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22755 | return out; |
| 22756 | } |
| 22757 | struct structured_softshrink_backward_out_functional final : public at::native::structured_softshrink_backward_out { |
| 22758 | void set_output_strided( |
| 22759 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22760 | TensorOptions options, DimnameList names |
| 22761 | ) override { |
| 22762 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22763 | if (!names.empty()) { |
| 22764 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22765 | } |
| 22766 | // super must happen after, so that downstream can use maybe_get_output |
| 22767 | // to retrieve the output |
| 22768 | at::native::structured_softshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22769 | } |
| 22770 | void set_output_raw_strided( |
| 22771 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22772 | TensorOptions options, DimnameList names |
| 22773 | ) override { |
| 22774 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22775 | if (!names.empty()) { |
| 22776 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22777 | } |
| 22778 | // super must happen after, so that downstream can use maybe_get_output |
| 22779 | // to retrieve the output |
| 22780 | at::native::structured_softshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22781 | } |
| 22782 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22783 | return *outputs_[output_idx]; |
| 22784 | } |
| 22785 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22786 | }; |
| 22787 | at::Tensor wrapper_CPU_softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { |
| 22788 | structured_softshrink_backward_out_functional op; |
| 22789 | op.meta(grad_output, self, lambd); |
| 22790 | op.impl(grad_output, self, lambd, *op.outputs_[0]); |
| 22791 | return std::move(op.outputs_[0]).take(); |
| 22792 | } |
| 22793 | struct structured_softshrink_backward_out_out final : public at::native::structured_softshrink_backward_out { |
| 22794 | structured_softshrink_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 22795 | void set_output_strided( |
| 22796 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22797 | TensorOptions options, DimnameList names |
| 22798 | ) override { |
| 22799 | const auto& out = outputs_[output_idx].get(); |
| 22800 | resize_out(out, sizes, strides, options); |
| 22801 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22802 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22803 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22804 | } |
| 22805 | if (!names.empty()) { |
| 22806 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22807 | } |
| 22808 | // super must happen after, so that downstream can use maybe_get_output |
| 22809 | // to retrieve the output |
| 22810 | at::native::structured_softshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22811 | } |
| 22812 | void set_output_raw_strided( |
| 22813 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22814 | TensorOptions options, DimnameList names |
| 22815 | ) override { |
| 22816 | const auto& out = outputs_[output_idx].get(); |
| 22817 | resize_out(out, sizes, strides, options); |
| 22818 | if (!names.empty()) { |
| 22819 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22820 | } |
| 22821 | // super must happen after, so that downstream can use maybe_get_output |
| 22822 | // to retrieve the output |
| 22823 | at::native::structured_softshrink_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 22824 | } |
| 22825 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22826 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22827 | } |
| 22828 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 22829 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 22830 | }; |
| 22831 | at::Tensor & wrapper_CPU_softshrink_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 22832 | structured_softshrink_backward_out_out op(grad_input); |
| 22833 | op.meta(grad_output, self, lambd); |
| 22834 | op.impl(grad_output, self, lambd, op.maybe_get_output(0)); |
| 22835 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22836 | return grad_input; |
| 22837 | } |
| 22838 | namespace { |
| 22839 | at::Tensor & wrapper_CPU_out_adaptive_avg_pool2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 22840 | // No device check |
| 22841 | // DeviceGuard omitted |
| 22842 | return at::native::adaptive_avg_pool2d_out_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size), out); |
| 22843 | } |
| 22844 | } // anonymous namespace |
| 22845 | namespace { |
| 22846 | at::Tensor wrapper_CPU___adaptive_avg_pool2d(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 22847 | // No device check |
| 22848 | // DeviceGuard omitted |
| 22849 | return at::native::adaptive_avg_pool2d_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size)); |
| 22850 | } |
| 22851 | } // anonymous namespace |
| 22852 | namespace { |
| 22853 | at::Tensor wrapper_CPU___adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22854 | // No device check |
| 22855 | // DeviceGuard omitted |
| 22856 | return at::native::adaptive_avg_pool2d_backward_cpu(grad_output, self); |
| 22857 | } |
| 22858 | } // anonymous namespace |
| 22859 | namespace { |
| 22860 | at::Tensor & wrapper_CPU_out_adaptive_avg_pool3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 22861 | // No device check |
| 22862 | // DeviceGuard omitted |
| 22863 | return at::native::adaptive_avg_pool3d_out_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size), out); |
| 22864 | } |
| 22865 | } // anonymous namespace |
| 22866 | namespace { |
| 22867 | at::Tensor wrapper_CPU___adaptive_avg_pool3d(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 22868 | // No device check |
| 22869 | // DeviceGuard omitted |
| 22870 | return at::native::adaptive_avg_pool3d_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size)); |
| 22871 | } |
| 22872 | } // anonymous namespace |
| 22873 | namespace { |
| 22874 | at::Tensor & wrapper_CPU_grad_input_adaptive_avg_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 22875 | // No device check |
| 22876 | // DeviceGuard omitted |
| 22877 | return at::native::adaptive_avg_pool3d_backward_out_cpu(grad_output, self, grad_input); |
| 22878 | } |
| 22879 | } // anonymous namespace |
| 22880 | namespace { |
| 22881 | at::Tensor wrapper_CPU___adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22882 | // No device check |
| 22883 | // DeviceGuard omitted |
| 22884 | return at::native::adaptive_avg_pool3d_backward_cpu(grad_output, self); |
| 22885 | } |
| 22886 | } // anonymous namespace |
| 22887 | struct structured_adaptive_max_pool2d_out_cpu_functional final : public at::native::structured_adaptive_max_pool2d_out_cpu { |
| 22888 | void set_output_strided( |
| 22889 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22890 | TensorOptions options, DimnameList names |
| 22891 | ) override { |
| 22892 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22893 | if (!names.empty()) { |
| 22894 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22895 | } |
| 22896 | // super must happen after, so that downstream can use maybe_get_output |
| 22897 | // to retrieve the output |
| 22898 | } |
| 22899 | void set_output_raw_strided( |
| 22900 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22901 | TensorOptions options, DimnameList names |
| 22902 | ) override { |
| 22903 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22904 | if (!names.empty()) { |
| 22905 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22906 | } |
| 22907 | // super must happen after, so that downstream can use maybe_get_output |
| 22908 | // to retrieve the output |
| 22909 | } |
| 22910 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22911 | return *outputs_[output_idx]; |
| 22912 | } |
| 22913 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 22914 | }; |
| 22915 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 22916 | structured_adaptive_max_pool2d_out_cpu_functional op; |
| 22917 | op.meta(self, output_size); |
| 22918 | op.impl(self, output_size, *op.outputs_[0], *op.outputs_[1]); |
| 22919 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 22920 | } |
| 22921 | struct structured_adaptive_max_pool2d_out_cpu_out final : public at::native::structured_adaptive_max_pool2d_out_cpu { |
| 22922 | structured_adaptive_max_pool2d_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 22923 | void set_output_strided( |
| 22924 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22925 | TensorOptions options, DimnameList names |
| 22926 | ) override { |
| 22927 | const auto& out = outputs_[output_idx].get(); |
| 22928 | resize_out(out, sizes, strides, options); |
| 22929 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 22930 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 22931 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 22932 | } |
| 22933 | if (!names.empty()) { |
| 22934 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22935 | } |
| 22936 | // super must happen after, so that downstream can use maybe_get_output |
| 22937 | // to retrieve the output |
| 22938 | } |
| 22939 | void set_output_raw_strided( |
| 22940 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22941 | TensorOptions options, DimnameList names |
| 22942 | ) override { |
| 22943 | const auto& out = outputs_[output_idx].get(); |
| 22944 | resize_out(out, sizes, strides, options); |
| 22945 | if (!names.empty()) { |
| 22946 | namedinference::propagate_names(outputs_[output_idx], names); |
| 22947 | } |
| 22948 | // super must happen after, so that downstream can use maybe_get_output |
| 22949 | // to retrieve the output |
| 22950 | } |
| 22951 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22952 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 22953 | } |
| 22954 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 22955 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 22956 | }; |
| 22957 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_adaptive_max_pool2d_out_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 22958 | structured_adaptive_max_pool2d_out_cpu_out op(out, indices); |
| 22959 | op.meta(self, output_size); |
| 22960 | op.impl(self, output_size, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 22961 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 22962 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 22963 | return std::forward_as_tuple(out, indices); |
| 22964 | } |
| 22965 | struct structured_adaptive_max_pool2d_backward_out_cpu_functional final : public at::native::structured_adaptive_max_pool2d_backward_out_cpu { |
| 22966 | void set_output_strided( |
| 22967 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22968 | TensorOptions options, DimnameList names |
| 22969 | ) override { |
| 22970 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22971 | if (!names.empty()) { |
| 22972 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22973 | } |
| 22974 | // super must happen after, so that downstream can use maybe_get_output |
| 22975 | // to retrieve the output |
| 22976 | } |
| 22977 | void set_output_raw_strided( |
| 22978 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 22979 | TensorOptions options, DimnameList names |
| 22980 | ) override { |
| 22981 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 22982 | if (!names.empty()) { |
| 22983 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 22984 | } |
| 22985 | // super must happen after, so that downstream can use maybe_get_output |
| 22986 | // to retrieve the output |
| 22987 | } |
| 22988 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 22989 | return *outputs_[output_idx]; |
| 22990 | } |
| 22991 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 22992 | }; |
| 22993 | at::Tensor wrapper_CPU_adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 22994 | structured_adaptive_max_pool2d_backward_out_cpu_functional op; |
| 22995 | op.meta(grad_output, self, indices); |
| 22996 | op.impl(grad_output, self, indices, *op.outputs_[0]); |
| 22997 | return std::move(op.outputs_[0]).take(); |
| 22998 | } |
| 22999 | struct structured_adaptive_max_pool2d_backward_out_cpu_out final : public at::native::structured_adaptive_max_pool2d_backward_out_cpu { |
| 23000 | structured_adaptive_max_pool2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23001 | void set_output_strided( |
| 23002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23003 | TensorOptions options, DimnameList names |
| 23004 | ) override { |
| 23005 | const auto& out = outputs_[output_idx].get(); |
| 23006 | resize_out(out, sizes, strides, options); |
| 23007 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23008 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23009 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23010 | } |
| 23011 | if (!names.empty()) { |
| 23012 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23013 | } |
| 23014 | // super must happen after, so that downstream can use maybe_get_output |
| 23015 | // to retrieve the output |
| 23016 | } |
| 23017 | void set_output_raw_strided( |
| 23018 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23019 | TensorOptions options, DimnameList names |
| 23020 | ) override { |
| 23021 | const auto& out = outputs_[output_idx].get(); |
| 23022 | resize_out(out, sizes, strides, options); |
| 23023 | if (!names.empty()) { |
| 23024 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23025 | } |
| 23026 | // super must happen after, so that downstream can use maybe_get_output |
| 23027 | // to retrieve the output |
| 23028 | } |
| 23029 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23030 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23031 | } |
| 23032 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23033 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23034 | }; |
| 23035 | at::Tensor & wrapper_CPU_adaptive_max_pool2d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 23036 | structured_adaptive_max_pool2d_backward_out_cpu_out op(grad_input); |
| 23037 | op.meta(grad_output, self, indices); |
| 23038 | op.impl(grad_output, self, indices, op.maybe_get_output(0)); |
| 23039 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23040 | return grad_input; |
| 23041 | } |
| 23042 | struct structured_adaptive_max_pool3d_out_cpu_functional final : public at::native::structured_adaptive_max_pool3d_out_cpu { |
| 23043 | void set_output_strided( |
| 23044 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23045 | TensorOptions options, DimnameList names |
| 23046 | ) override { |
| 23047 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23048 | if (!names.empty()) { |
| 23049 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23050 | } |
| 23051 | // super must happen after, so that downstream can use maybe_get_output |
| 23052 | // to retrieve the output |
| 23053 | } |
| 23054 | void set_output_raw_strided( |
| 23055 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23056 | TensorOptions options, DimnameList names |
| 23057 | ) override { |
| 23058 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23059 | if (!names.empty()) { |
| 23060 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23061 | } |
| 23062 | // super must happen after, so that downstream can use maybe_get_output |
| 23063 | // to retrieve the output |
| 23064 | } |
| 23065 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23066 | return *outputs_[output_idx]; |
| 23067 | } |
| 23068 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 23069 | }; |
| 23070 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 23071 | structured_adaptive_max_pool3d_out_cpu_functional op; |
| 23072 | op.meta(self, output_size); |
| 23073 | op.impl(self, output_size, *op.outputs_[0], *op.outputs_[1]); |
| 23074 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 23075 | } |
| 23076 | struct structured_adaptive_max_pool3d_out_cpu_out final : public at::native::structured_adaptive_max_pool3d_out_cpu { |
| 23077 | structured_adaptive_max_pool3d_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 23078 | void set_output_strided( |
| 23079 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23080 | TensorOptions options, DimnameList names |
| 23081 | ) override { |
| 23082 | const auto& out = outputs_[output_idx].get(); |
| 23083 | resize_out(out, sizes, strides, options); |
| 23084 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23085 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23086 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23087 | } |
| 23088 | if (!names.empty()) { |
| 23089 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23090 | } |
| 23091 | // super must happen after, so that downstream can use maybe_get_output |
| 23092 | // to retrieve the output |
| 23093 | } |
| 23094 | void set_output_raw_strided( |
| 23095 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23096 | TensorOptions options, DimnameList names |
| 23097 | ) override { |
| 23098 | const auto& out = outputs_[output_idx].get(); |
| 23099 | resize_out(out, sizes, strides, options); |
| 23100 | if (!names.empty()) { |
| 23101 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23102 | } |
| 23103 | // super must happen after, so that downstream can use maybe_get_output |
| 23104 | // to retrieve the output |
| 23105 | } |
| 23106 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23107 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23108 | } |
| 23109 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 23110 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 23111 | }; |
| 23112 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_adaptive_max_pool3d_out_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 23113 | structured_adaptive_max_pool3d_out_cpu_out op(out, indices); |
| 23114 | op.meta(self, output_size); |
| 23115 | op.impl(self, output_size, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 23116 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23117 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 23118 | return std::forward_as_tuple(out, indices); |
| 23119 | } |
| 23120 | struct structured_adaptive_max_pool3d_backward_out_cpu_functional final : public at::native::structured_adaptive_max_pool3d_backward_out_cpu { |
| 23121 | void set_output_strided( |
| 23122 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23123 | TensorOptions options, DimnameList names |
| 23124 | ) override { |
| 23125 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23126 | if (!names.empty()) { |
| 23127 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23128 | } |
| 23129 | // super must happen after, so that downstream can use maybe_get_output |
| 23130 | // to retrieve the output |
| 23131 | } |
| 23132 | void set_output_raw_strided( |
| 23133 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23134 | TensorOptions options, DimnameList names |
| 23135 | ) override { |
| 23136 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23137 | if (!names.empty()) { |
| 23138 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23139 | } |
| 23140 | // super must happen after, so that downstream can use maybe_get_output |
| 23141 | // to retrieve the output |
| 23142 | } |
| 23143 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23144 | return *outputs_[output_idx]; |
| 23145 | } |
| 23146 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23147 | }; |
| 23148 | at::Tensor wrapper_CPU_adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 23149 | structured_adaptive_max_pool3d_backward_out_cpu_functional op; |
| 23150 | op.meta(grad_output, self, indices); |
| 23151 | op.impl(grad_output, self, indices, *op.outputs_[0]); |
| 23152 | return std::move(op.outputs_[0]).take(); |
| 23153 | } |
| 23154 | struct structured_adaptive_max_pool3d_backward_out_cpu_out final : public at::native::structured_adaptive_max_pool3d_backward_out_cpu { |
| 23155 | structured_adaptive_max_pool3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23156 | void set_output_strided( |
| 23157 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23158 | TensorOptions options, DimnameList names |
| 23159 | ) override { |
| 23160 | const auto& out = outputs_[output_idx].get(); |
| 23161 | resize_out(out, sizes, strides, options); |
| 23162 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23163 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23164 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23165 | } |
| 23166 | if (!names.empty()) { |
| 23167 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23168 | } |
| 23169 | // super must happen after, so that downstream can use maybe_get_output |
| 23170 | // to retrieve the output |
| 23171 | } |
| 23172 | void set_output_raw_strided( |
| 23173 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23174 | TensorOptions options, DimnameList names |
| 23175 | ) override { |
| 23176 | const auto& out = outputs_[output_idx].get(); |
| 23177 | resize_out(out, sizes, strides, options); |
| 23178 | if (!names.empty()) { |
| 23179 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23180 | } |
| 23181 | // super must happen after, so that downstream can use maybe_get_output |
| 23182 | // to retrieve the output |
| 23183 | } |
| 23184 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23185 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23186 | } |
| 23187 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23188 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23189 | }; |
| 23190 | at::Tensor & wrapper_CPU_adaptive_max_pool3d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 23191 | structured_adaptive_max_pool3d_backward_out_cpu_out op(grad_input); |
| 23192 | op.meta(grad_output, self, indices); |
| 23193 | op.impl(grad_output, self, indices, op.maybe_get_output(0)); |
| 23194 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23195 | return grad_input; |
| 23196 | } |
| 23197 | struct structured_avg_pool2d_out_cpu_functional final : public at::native::structured_avg_pool2d_out_cpu { |
| 23198 | void set_output_strided( |
| 23199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23200 | TensorOptions options, DimnameList names |
| 23201 | ) override { |
| 23202 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23203 | if (!names.empty()) { |
| 23204 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23205 | } |
| 23206 | // super must happen after, so that downstream can use maybe_get_output |
| 23207 | // to retrieve the output |
| 23208 | } |
| 23209 | void set_output_raw_strided( |
| 23210 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23211 | TensorOptions options, DimnameList names |
| 23212 | ) override { |
| 23213 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23214 | if (!names.empty()) { |
| 23215 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23216 | } |
| 23217 | // super must happen after, so that downstream can use maybe_get_output |
| 23218 | // to retrieve the output |
| 23219 | } |
| 23220 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23221 | return *outputs_[output_idx]; |
| 23222 | } |
| 23223 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23224 | }; |
| 23225 | at::Tensor wrapper_CPU_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) { |
| 23226 | structured_avg_pool2d_out_cpu_functional op; |
| 23227 | auto precompute = op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23228 | (void)precompute; |
| 23229 | op.impl(self, precompute.kH, precompute.kW, precompute.dH, precompute.dW, precompute.padH, precompute.padW, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 23230 | return std::move(op.outputs_[0]).take(); |
| 23231 | } |
| 23232 | struct structured_avg_pool2d_out_cpu_out final : public at::native::structured_avg_pool2d_out_cpu { |
| 23233 | structured_avg_pool2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23234 | void set_output_strided( |
| 23235 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23236 | TensorOptions options, DimnameList names |
| 23237 | ) override { |
| 23238 | const auto& out = outputs_[output_idx].get(); |
| 23239 | resize_out(out, sizes, strides, options); |
| 23240 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23241 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23242 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23243 | } |
| 23244 | if (!names.empty()) { |
| 23245 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23246 | } |
| 23247 | // super must happen after, so that downstream can use maybe_get_output |
| 23248 | // to retrieve the output |
| 23249 | } |
| 23250 | void set_output_raw_strided( |
| 23251 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23252 | TensorOptions options, DimnameList names |
| 23253 | ) override { |
| 23254 | const auto& out = outputs_[output_idx].get(); |
| 23255 | resize_out(out, sizes, strides, options); |
| 23256 | if (!names.empty()) { |
| 23257 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23258 | } |
| 23259 | // super must happen after, so that downstream can use maybe_get_output |
| 23260 | // to retrieve the output |
| 23261 | } |
| 23262 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23263 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23264 | } |
| 23265 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23266 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23267 | }; |
| 23268 | at::Tensor & wrapper_CPU_avg_pool2d_out_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 23269 | structured_avg_pool2d_out_cpu_out op(out); |
| 23270 | auto precompute = op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23271 | (void)precompute; |
| 23272 | op.impl(self, precompute.kH, precompute.kW, precompute.dH, precompute.dW, precompute.padH, precompute.padW, ceil_mode, count_include_pad, divisor_override, op.maybe_get_output(0)); |
| 23273 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23274 | return out; |
| 23275 | } |
| 23276 | struct structured_avg_pool2d_backward_out_cpu_functional final : public at::native::structured_avg_pool2d_backward_out_cpu { |
| 23277 | void set_output_strided( |
| 23278 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23279 | TensorOptions options, DimnameList names |
| 23280 | ) override { |
| 23281 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23282 | if (!names.empty()) { |
| 23283 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23284 | } |
| 23285 | // super must happen after, so that downstream can use maybe_get_output |
| 23286 | // to retrieve the output |
| 23287 | } |
| 23288 | void set_output_raw_strided( |
| 23289 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23290 | TensorOptions options, DimnameList names |
| 23291 | ) override { |
| 23292 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23293 | if (!names.empty()) { |
| 23294 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23295 | } |
| 23296 | // super must happen after, so that downstream can use maybe_get_output |
| 23297 | // to retrieve the output |
| 23298 | } |
| 23299 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23300 | return *outputs_[output_idx]; |
| 23301 | } |
| 23302 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23303 | }; |
| 23304 | at::Tensor wrapper_CPU_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) { |
| 23305 | structured_avg_pool2d_backward_out_cpu_functional op; |
| 23306 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23307 | op.impl(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 23308 | return std::move(op.outputs_[0]).take(); |
| 23309 | } |
| 23310 | struct structured_avg_pool2d_backward_out_cpu_out final : public at::native::structured_avg_pool2d_backward_out_cpu { |
| 23311 | structured_avg_pool2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23312 | void set_output_strided( |
| 23313 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23314 | TensorOptions options, DimnameList names |
| 23315 | ) override { |
| 23316 | const auto& out = outputs_[output_idx].get(); |
| 23317 | resize_out(out, sizes, strides, options); |
| 23318 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23319 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23320 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23321 | } |
| 23322 | if (!names.empty()) { |
| 23323 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23324 | } |
| 23325 | // super must happen after, so that downstream can use maybe_get_output |
| 23326 | // to retrieve the output |
| 23327 | } |
| 23328 | void set_output_raw_strided( |
| 23329 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23330 | TensorOptions options, DimnameList names |
| 23331 | ) override { |
| 23332 | const auto& out = outputs_[output_idx].get(); |
| 23333 | resize_out(out, sizes, strides, options); |
| 23334 | if (!names.empty()) { |
| 23335 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23336 | } |
| 23337 | // super must happen after, so that downstream can use maybe_get_output |
| 23338 | // to retrieve the output |
| 23339 | } |
| 23340 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23341 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23342 | } |
| 23343 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23344 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23345 | }; |
| 23346 | at::Tensor & wrapper_CPU_avg_pool2d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & grad_input) { |
| 23347 | structured_avg_pool2d_backward_out_cpu_out op(grad_input); |
| 23348 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23349 | op.impl(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, op.maybe_get_output(0)); |
| 23350 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23351 | return grad_input; |
| 23352 | } |
| 23353 | struct structured_avg_pool3d_out_cpu_functional final : public at::native::structured_avg_pool3d_out_cpu { |
| 23354 | void set_output_strided( |
| 23355 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23356 | TensorOptions options, DimnameList names |
| 23357 | ) override { |
| 23358 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23359 | if (!names.empty()) { |
| 23360 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23361 | } |
| 23362 | // super must happen after, so that downstream can use maybe_get_output |
| 23363 | // to retrieve the output |
| 23364 | } |
| 23365 | void set_output_raw_strided( |
| 23366 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23367 | TensorOptions options, DimnameList names |
| 23368 | ) override { |
| 23369 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23370 | if (!names.empty()) { |
| 23371 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23372 | } |
| 23373 | // super must happen after, so that downstream can use maybe_get_output |
| 23374 | // to retrieve the output |
| 23375 | } |
| 23376 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23377 | return *outputs_[output_idx]; |
| 23378 | } |
| 23379 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23380 | }; |
| 23381 | at::Tensor wrapper_CPU_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) { |
| 23382 | structured_avg_pool3d_out_cpu_functional op; |
| 23383 | op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23384 | op.impl(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 23385 | return std::move(op.outputs_[0]).take(); |
| 23386 | } |
| 23387 | struct structured_avg_pool3d_out_cpu_out final : public at::native::structured_avg_pool3d_out_cpu { |
| 23388 | structured_avg_pool3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23389 | void set_output_strided( |
| 23390 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23391 | TensorOptions options, DimnameList names |
| 23392 | ) override { |
| 23393 | const auto& out = outputs_[output_idx].get(); |
| 23394 | resize_out(out, sizes, strides, options); |
| 23395 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23396 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23397 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23398 | } |
| 23399 | if (!names.empty()) { |
| 23400 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23401 | } |
| 23402 | // super must happen after, so that downstream can use maybe_get_output |
| 23403 | // to retrieve the output |
| 23404 | } |
| 23405 | void set_output_raw_strided( |
| 23406 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23407 | TensorOptions options, DimnameList names |
| 23408 | ) override { |
| 23409 | const auto& out = outputs_[output_idx].get(); |
| 23410 | resize_out(out, sizes, strides, options); |
| 23411 | if (!names.empty()) { |
| 23412 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23413 | } |
| 23414 | // super must happen after, so that downstream can use maybe_get_output |
| 23415 | // to retrieve the output |
| 23416 | } |
| 23417 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23418 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23419 | } |
| 23420 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23421 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23422 | }; |
| 23423 | at::Tensor & wrapper_CPU_avg_pool3d_out_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 23424 | structured_avg_pool3d_out_cpu_out op(out); |
| 23425 | op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23426 | op.impl(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, op.maybe_get_output(0)); |
| 23427 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23428 | return out; |
| 23429 | } |
| 23430 | struct structured_avg_pool3d_backward_out_cpu_functional final : public at::native::structured_avg_pool3d_backward_out_cpu { |
| 23431 | void set_output_strided( |
| 23432 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23433 | TensorOptions options, DimnameList names |
| 23434 | ) override { |
| 23435 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23436 | if (!names.empty()) { |
| 23437 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23438 | } |
| 23439 | // super must happen after, so that downstream can use maybe_get_output |
| 23440 | // to retrieve the output |
| 23441 | } |
| 23442 | void set_output_raw_strided( |
| 23443 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23444 | TensorOptions options, DimnameList names |
| 23445 | ) override { |
| 23446 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23447 | if (!names.empty()) { |
| 23448 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23449 | } |
| 23450 | // super must happen after, so that downstream can use maybe_get_output |
| 23451 | // to retrieve the output |
| 23452 | } |
| 23453 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23454 | return *outputs_[output_idx]; |
| 23455 | } |
| 23456 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23457 | }; |
| 23458 | at::Tensor wrapper_CPU_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) { |
| 23459 | structured_avg_pool3d_backward_out_cpu_functional op; |
| 23460 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23461 | op.impl(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 23462 | return std::move(op.outputs_[0]).take(); |
| 23463 | } |
| 23464 | struct structured_avg_pool3d_backward_out_cpu_out final : public at::native::structured_avg_pool3d_backward_out_cpu { |
| 23465 | structured_avg_pool3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23466 | void set_output_strided( |
| 23467 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23468 | TensorOptions options, DimnameList names |
| 23469 | ) override { |
| 23470 | const auto& out = outputs_[output_idx].get(); |
| 23471 | resize_out(out, sizes, strides, options); |
| 23472 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23473 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23474 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23475 | } |
| 23476 | if (!names.empty()) { |
| 23477 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23478 | } |
| 23479 | // super must happen after, so that downstream can use maybe_get_output |
| 23480 | // to retrieve the output |
| 23481 | } |
| 23482 | void set_output_raw_strided( |
| 23483 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23484 | TensorOptions options, DimnameList names |
| 23485 | ) override { |
| 23486 | const auto& out = outputs_[output_idx].get(); |
| 23487 | resize_out(out, sizes, strides, options); |
| 23488 | if (!names.empty()) { |
| 23489 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23490 | } |
| 23491 | // super must happen after, so that downstream can use maybe_get_output |
| 23492 | // to retrieve the output |
| 23493 | } |
| 23494 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23495 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23496 | } |
| 23497 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23498 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23499 | }; |
| 23500 | at::Tensor & wrapper_CPU_avg_pool3d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & grad_input) { |
| 23501 | structured_avg_pool3d_backward_out_cpu_out op(grad_input); |
| 23502 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 23503 | op.impl(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, op.maybe_get_output(0)); |
| 23504 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23505 | return grad_input; |
| 23506 | } |
| 23507 | struct structured_fractional_max_pool2d_out_cpu_functional final : public at::native::structured_fractional_max_pool2d_out_cpu { |
| 23508 | void set_output_strided( |
| 23509 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23510 | TensorOptions options, DimnameList names |
| 23511 | ) override { |
| 23512 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23513 | if (!names.empty()) { |
| 23514 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23515 | } |
| 23516 | // super must happen after, so that downstream can use maybe_get_output |
| 23517 | // to retrieve the output |
| 23518 | } |
| 23519 | void set_output_raw_strided( |
| 23520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23521 | TensorOptions options, DimnameList names |
| 23522 | ) override { |
| 23523 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23524 | if (!names.empty()) { |
| 23525 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23526 | } |
| 23527 | // super must happen after, so that downstream can use maybe_get_output |
| 23528 | // to retrieve the output |
| 23529 | } |
| 23530 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23531 | return *outputs_[output_idx]; |
| 23532 | } |
| 23533 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 23534 | }; |
| 23535 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_fractional_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 23536 | structured_fractional_max_pool2d_out_cpu_functional op; |
| 23537 | op.meta(self, kernel_size, output_size, random_samples); |
| 23538 | op.impl(self, kernel_size, output_size, random_samples, *op.outputs_[0], *op.outputs_[1]); |
| 23539 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 23540 | } |
| 23541 | struct structured_fractional_max_pool2d_out_cpu_out final : public at::native::structured_fractional_max_pool2d_out_cpu { |
| 23542 | structured_fractional_max_pool2d_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 23543 | void set_output_strided( |
| 23544 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23545 | TensorOptions options, DimnameList names |
| 23546 | ) override { |
| 23547 | const auto& out = outputs_[output_idx].get(); |
| 23548 | resize_out(out, sizes, strides, options); |
| 23549 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23550 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23551 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23552 | } |
| 23553 | if (!names.empty()) { |
| 23554 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23555 | } |
| 23556 | // super must happen after, so that downstream can use maybe_get_output |
| 23557 | // to retrieve the output |
| 23558 | } |
| 23559 | void set_output_raw_strided( |
| 23560 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23561 | TensorOptions options, DimnameList names |
| 23562 | ) override { |
| 23563 | const auto& out = outputs_[output_idx].get(); |
| 23564 | resize_out(out, sizes, strides, options); |
| 23565 | if (!names.empty()) { |
| 23566 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23567 | } |
| 23568 | // super must happen after, so that downstream can use maybe_get_output |
| 23569 | // to retrieve the output |
| 23570 | } |
| 23571 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23572 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23573 | } |
| 23574 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 23575 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 23576 | }; |
| 23577 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_fractional_max_pool2d_out_output(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 23578 | structured_fractional_max_pool2d_out_cpu_out op(output, indices); |
| 23579 | op.meta(self, kernel_size, output_size, random_samples); |
| 23580 | op.impl(self, kernel_size, output_size, random_samples, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 23581 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23582 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 23583 | return std::forward_as_tuple(output, indices); |
| 23584 | } |
| 23585 | struct structured_fractional_max_pool2d_backward_cpu_functional final : public at::native::structured_fractional_max_pool2d_backward_cpu { |
| 23586 | void set_output_strided( |
| 23587 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23588 | TensorOptions options, DimnameList names |
| 23589 | ) override { |
| 23590 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23591 | if (!names.empty()) { |
| 23592 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23593 | } |
| 23594 | // super must happen after, so that downstream can use maybe_get_output |
| 23595 | // to retrieve the output |
| 23596 | } |
| 23597 | void set_output_raw_strided( |
| 23598 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23599 | TensorOptions options, DimnameList names |
| 23600 | ) override { |
| 23601 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23602 | if (!names.empty()) { |
| 23603 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23604 | } |
| 23605 | // super must happen after, so that downstream can use maybe_get_output |
| 23606 | // to retrieve the output |
| 23607 | } |
| 23608 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23609 | return *outputs_[output_idx]; |
| 23610 | } |
| 23611 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23612 | }; |
| 23613 | at::Tensor wrapper_CPU_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) { |
| 23614 | structured_fractional_max_pool2d_backward_cpu_functional op; |
| 23615 | op.meta(grad_output, self, kernel_size, output_size, indices); |
| 23616 | op.impl(grad_output, self, kernel_size, output_size, indices, *op.outputs_[0]); |
| 23617 | return std::move(op.outputs_[0]).take(); |
| 23618 | } |
| 23619 | struct structured_fractional_max_pool2d_backward_cpu_out final : public at::native::structured_fractional_max_pool2d_backward_cpu { |
| 23620 | structured_fractional_max_pool2d_backward_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23621 | void set_output_strided( |
| 23622 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23623 | TensorOptions options, DimnameList names |
| 23624 | ) override { |
| 23625 | const auto& out = outputs_[output_idx].get(); |
| 23626 | resize_out(out, sizes, strides, options); |
| 23627 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23628 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23629 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23630 | } |
| 23631 | if (!names.empty()) { |
| 23632 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23633 | } |
| 23634 | // super must happen after, so that downstream can use maybe_get_output |
| 23635 | // to retrieve the output |
| 23636 | } |
| 23637 | void set_output_raw_strided( |
| 23638 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23639 | TensorOptions options, DimnameList names |
| 23640 | ) override { |
| 23641 | const auto& out = outputs_[output_idx].get(); |
| 23642 | resize_out(out, sizes, strides, options); |
| 23643 | if (!names.empty()) { |
| 23644 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23645 | } |
| 23646 | // super must happen after, so that downstream can use maybe_get_output |
| 23647 | // to retrieve the output |
| 23648 | } |
| 23649 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23650 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23651 | } |
| 23652 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23653 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23654 | }; |
| 23655 | at::Tensor & wrapper_CPU_fractional_max_pool2d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { |
| 23656 | structured_fractional_max_pool2d_backward_cpu_out op(grad_input); |
| 23657 | op.meta(grad_output, self, kernel_size, output_size, indices); |
| 23658 | op.impl(grad_output, self, kernel_size, output_size, indices, op.maybe_get_output(0)); |
| 23659 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23660 | return grad_input; |
| 23661 | } |
| 23662 | struct structured_fractional_max_pool3d_out_cpu_functional final : public at::native::structured_fractional_max_pool3d_out_cpu { |
| 23663 | void set_output_strided( |
| 23664 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23665 | TensorOptions options, DimnameList names |
| 23666 | ) override { |
| 23667 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23668 | if (!names.empty()) { |
| 23669 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23670 | } |
| 23671 | // super must happen after, so that downstream can use maybe_get_output |
| 23672 | // to retrieve the output |
| 23673 | } |
| 23674 | void set_output_raw_strided( |
| 23675 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23676 | TensorOptions options, DimnameList names |
| 23677 | ) override { |
| 23678 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23679 | if (!names.empty()) { |
| 23680 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23681 | } |
| 23682 | // super must happen after, so that downstream can use maybe_get_output |
| 23683 | // to retrieve the output |
| 23684 | } |
| 23685 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23686 | return *outputs_[output_idx]; |
| 23687 | } |
| 23688 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 23689 | }; |
| 23690 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_fractional_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 23691 | structured_fractional_max_pool3d_out_cpu_functional op; |
| 23692 | auto precompute = op.meta(self, kernel_size, output_size, random_samples); |
| 23693 | (void)precompute; |
| 23694 | op.impl(self, precompute.poolSizeT, precompute.poolSizeH, precompute.poolSizeW, precompute.outputT, precompute.outputH, precompute.outputW, random_samples, precompute.numBatch, precompute.numPlanes, precompute.inputT, precompute.inputH, precompute.inputW, *op.outputs_[0], *op.outputs_[1]); |
| 23695 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 23696 | } |
| 23697 | struct structured_fractional_max_pool3d_out_cpu_out final : public at::native::structured_fractional_max_pool3d_out_cpu { |
| 23698 | structured_fractional_max_pool3d_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 23699 | void set_output_strided( |
| 23700 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23701 | TensorOptions options, DimnameList names |
| 23702 | ) override { |
| 23703 | const auto& out = outputs_[output_idx].get(); |
| 23704 | resize_out(out, sizes, strides, options); |
| 23705 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23706 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23707 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23708 | } |
| 23709 | if (!names.empty()) { |
| 23710 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23711 | } |
| 23712 | // super must happen after, so that downstream can use maybe_get_output |
| 23713 | // to retrieve the output |
| 23714 | } |
| 23715 | void set_output_raw_strided( |
| 23716 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23717 | TensorOptions options, DimnameList names |
| 23718 | ) override { |
| 23719 | const auto& out = outputs_[output_idx].get(); |
| 23720 | resize_out(out, sizes, strides, options); |
| 23721 | if (!names.empty()) { |
| 23722 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23723 | } |
| 23724 | // super must happen after, so that downstream can use maybe_get_output |
| 23725 | // to retrieve the output |
| 23726 | } |
| 23727 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23728 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23729 | } |
| 23730 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 23731 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 23732 | }; |
| 23733 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_fractional_max_pool3d_out_output(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 23734 | structured_fractional_max_pool3d_out_cpu_out op(output, indices); |
| 23735 | auto precompute = op.meta(self, kernel_size, output_size, random_samples); |
| 23736 | (void)precompute; |
| 23737 | op.impl(self, precompute.poolSizeT, precompute.poolSizeH, precompute.poolSizeW, precompute.outputT, precompute.outputH, precompute.outputW, random_samples, precompute.numBatch, precompute.numPlanes, precompute.inputT, precompute.inputH, precompute.inputW, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 23738 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23739 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 23740 | return std::forward_as_tuple(output, indices); |
| 23741 | } |
| 23742 | namespace { |
| 23743 | at::Tensor wrapper_CPU__fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 23744 | // No device check |
| 23745 | // DeviceGuard omitted |
| 23746 | return at::native::fractional_max_pool3d_backward_cpu(grad_output, self, kernel_size, output_size, indices); |
| 23747 | } |
| 23748 | } // anonymous namespace |
| 23749 | namespace { |
| 23750 | at::Tensor & wrapper_CPU_grad_input_fractional_max_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { |
| 23751 | // No device check |
| 23752 | // DeviceGuard omitted |
| 23753 | return at::native::fractional_max_pool3d_backward_out_cpu(grad_output, self, kernel_size, output_size, indices, grad_input); |
| 23754 | } |
| 23755 | } // anonymous namespace |
| 23756 | struct structured_max_pool2d_with_indices_out_cpu_functional final : public at::native::structured_max_pool2d_with_indices_out_cpu { |
| 23757 | void set_output_strided( |
| 23758 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23759 | TensorOptions options, DimnameList names |
| 23760 | ) override { |
| 23761 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23762 | if (!names.empty()) { |
| 23763 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23764 | } |
| 23765 | // super must happen after, so that downstream can use maybe_get_output |
| 23766 | // to retrieve the output |
| 23767 | } |
| 23768 | void set_output_raw_strided( |
| 23769 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23770 | TensorOptions options, DimnameList names |
| 23771 | ) override { |
| 23772 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23773 | if (!names.empty()) { |
| 23774 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23775 | } |
| 23776 | // super must happen after, so that downstream can use maybe_get_output |
| 23777 | // to retrieve the output |
| 23778 | } |
| 23779 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23780 | return *outputs_[output_idx]; |
| 23781 | } |
| 23782 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 23783 | }; |
| 23784 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_max_pool2d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 23785 | structured_max_pool2d_with_indices_out_cpu_functional op; |
| 23786 | op.meta(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 23787 | op.impl(self, kernel_size, stride, padding, dilation, ceil_mode, *op.outputs_[0], *op.outputs_[1]); |
| 23788 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 23789 | } |
| 23790 | struct structured_max_pool2d_with_indices_out_cpu_out final : public at::native::structured_max_pool2d_with_indices_out_cpu { |
| 23791 | structured_max_pool2d_with_indices_out_cpu_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 23792 | void set_output_strided( |
| 23793 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23794 | TensorOptions options, DimnameList names |
| 23795 | ) override { |
| 23796 | const auto& out = outputs_[output_idx].get(); |
| 23797 | resize_out(out, sizes, strides, options); |
| 23798 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23799 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23800 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23801 | } |
| 23802 | if (!names.empty()) { |
| 23803 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23804 | } |
| 23805 | // super must happen after, so that downstream can use maybe_get_output |
| 23806 | // to retrieve the output |
| 23807 | } |
| 23808 | void set_output_raw_strided( |
| 23809 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23810 | TensorOptions options, DimnameList names |
| 23811 | ) override { |
| 23812 | const auto& out = outputs_[output_idx].get(); |
| 23813 | resize_out(out, sizes, strides, options); |
| 23814 | if (!names.empty()) { |
| 23815 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23816 | } |
| 23817 | // super must happen after, so that downstream can use maybe_get_output |
| 23818 | // to retrieve the output |
| 23819 | } |
| 23820 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23821 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23822 | } |
| 23823 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 23824 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 23825 | }; |
| 23826 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_max_pool2d_with_indices_out_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { |
| 23827 | structured_max_pool2d_with_indices_out_cpu_out op(out, indices); |
| 23828 | op.meta(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 23829 | op.impl(self, kernel_size, stride, padding, dilation, ceil_mode, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 23830 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23831 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 23832 | return std::forward_as_tuple(out, indices); |
| 23833 | } |
| 23834 | struct structured_max_pool2d_with_indices_backward_out_cpu_functional final : public at::native::structured_max_pool2d_with_indices_backward_out_cpu { |
| 23835 | void set_output_strided( |
| 23836 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23837 | TensorOptions options, DimnameList names |
| 23838 | ) override { |
| 23839 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23840 | if (!names.empty()) { |
| 23841 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23842 | } |
| 23843 | // super must happen after, so that downstream can use maybe_get_output |
| 23844 | // to retrieve the output |
| 23845 | } |
| 23846 | void set_output_raw_strided( |
| 23847 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23848 | TensorOptions options, DimnameList names |
| 23849 | ) override { |
| 23850 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23851 | if (!names.empty()) { |
| 23852 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23853 | } |
| 23854 | // super must happen after, so that downstream can use maybe_get_output |
| 23855 | // to retrieve the output |
| 23856 | } |
| 23857 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23858 | return *outputs_[output_idx]; |
| 23859 | } |
| 23860 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23861 | }; |
| 23862 | at::Tensor wrapper_CPU_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) { |
| 23863 | structured_max_pool2d_with_indices_backward_out_cpu_functional op; |
| 23864 | op.meta(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 23865 | op.impl(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, *op.outputs_[0]); |
| 23866 | return std::move(op.outputs_[0]).take(); |
| 23867 | } |
| 23868 | struct structured_max_pool2d_with_indices_backward_out_cpu_out final : public at::native::structured_max_pool2d_with_indices_backward_out_cpu { |
| 23869 | structured_max_pool2d_with_indices_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 23870 | void set_output_strided( |
| 23871 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23872 | TensorOptions options, DimnameList names |
| 23873 | ) override { |
| 23874 | const auto& out = outputs_[output_idx].get(); |
| 23875 | resize_out(out, sizes, strides, options); |
| 23876 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 23877 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 23878 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 23879 | } |
| 23880 | if (!names.empty()) { |
| 23881 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23882 | } |
| 23883 | // super must happen after, so that downstream can use maybe_get_output |
| 23884 | // to retrieve the output |
| 23885 | } |
| 23886 | void set_output_raw_strided( |
| 23887 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23888 | TensorOptions options, DimnameList names |
| 23889 | ) override { |
| 23890 | const auto& out = outputs_[output_idx].get(); |
| 23891 | resize_out(out, sizes, strides, options); |
| 23892 | if (!names.empty()) { |
| 23893 | namedinference::propagate_names(outputs_[output_idx], names); |
| 23894 | } |
| 23895 | // super must happen after, so that downstream can use maybe_get_output |
| 23896 | // to retrieve the output |
| 23897 | } |
| 23898 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23899 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 23900 | } |
| 23901 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 23902 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 23903 | }; |
| 23904 | at::Tensor & wrapper_CPU_max_pool2d_with_indices_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 23905 | structured_max_pool2d_with_indices_backward_out_cpu_out op(grad_input); |
| 23906 | op.meta(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 23907 | op.impl(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, op.maybe_get_output(0)); |
| 23908 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 23909 | return grad_input; |
| 23910 | } |
| 23911 | namespace { |
| 23912 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__max_pool3d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 23913 | // No device check |
| 23914 | // DeviceGuard omitted |
| 23915 | return at::native::max_pool3d_with_indices_cpu(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 23916 | } |
| 23917 | } // anonymous namespace |
| 23918 | namespace { |
| 23919 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_out_max_pool3d_with_indices_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { |
| 23920 | // No device check |
| 23921 | // DeviceGuard omitted |
| 23922 | return at::native::max_pool3d_with_indices_out_cpu(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); |
| 23923 | } |
| 23924 | } // anonymous namespace |
| 23925 | namespace { |
| 23926 | at::Tensor wrapper_CPU__max_pool3d_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) { |
| 23927 | // No device check |
| 23928 | // DeviceGuard omitted |
| 23929 | return at::native::max_pool3d_with_indices_backward_cpu(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 23930 | } |
| 23931 | } // anonymous namespace |
| 23932 | namespace { |
| 23933 | at::Tensor & wrapper_CPU_grad_input_max_pool3d_with_indices_backward_out(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, at::Tensor & grad_input) { |
| 23934 | // No device check |
| 23935 | // DeviceGuard omitted |
| 23936 | return at::native::max_pool3d_with_indices_backward_out_cpu(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); |
| 23937 | } |
| 23938 | } // anonymous namespace |
| 23939 | namespace { |
| 23940 | at::Tensor wrapper_CPU__max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { |
| 23941 | // No device check |
| 23942 | // DeviceGuard omitted |
| 23943 | return at::native::max_unpooling2d_forward_cpu(self, indices, output_size); |
| 23944 | } |
| 23945 | } // anonymous namespace |
| 23946 | namespace { |
| 23947 | at::Tensor & wrapper_CPU_out_max_unpool2d_out(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) { |
| 23948 | // No device check |
| 23949 | // DeviceGuard omitted |
| 23950 | return at::native::max_unpooling2d_forward_out_cpu(self, indices, output_size, out); |
| 23951 | } |
| 23952 | } // anonymous namespace |
| 23953 | namespace { |
| 23954 | at::Tensor wrapper_CPU__max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 23955 | // No device check |
| 23956 | // DeviceGuard omitted |
| 23957 | return at::native::max_unpooling3d_forward_cpu(self, indices, output_size, stride, padding); |
| 23958 | } |
| 23959 | } // anonymous namespace |
| 23960 | namespace { |
| 23961 | at::Tensor & wrapper_CPU_out_max_unpool3d_out(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { |
| 23962 | // No device check |
| 23963 | // DeviceGuard omitted |
| 23964 | return at::native::max_unpooling3d_forward_out_cpu(self, indices, output_size, stride, padding, out); |
| 23965 | } |
| 23966 | } // anonymous namespace |
| 23967 | struct structured_reflection_pad1d_out_cpu_functional final : public at::native::structured_reflection_pad1d_out_cpu { |
| 23968 | void set_output_strided( |
| 23969 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23970 | TensorOptions options, DimnameList names |
| 23971 | ) override { |
| 23972 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23973 | if (!names.empty()) { |
| 23974 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23975 | } |
| 23976 | // super must happen after, so that downstream can use maybe_get_output |
| 23977 | // to retrieve the output |
| 23978 | } |
| 23979 | void set_output_raw_strided( |
| 23980 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 23981 | TensorOptions options, DimnameList names |
| 23982 | ) override { |
| 23983 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 23984 | if (!names.empty()) { |
| 23985 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 23986 | } |
| 23987 | // super must happen after, so that downstream can use maybe_get_output |
| 23988 | // to retrieve the output |
| 23989 | } |
| 23990 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 23991 | return *outputs_[output_idx]; |
| 23992 | } |
| 23993 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 23994 | }; |
| 23995 | at::Tensor wrapper_CPU_reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 23996 | structured_reflection_pad1d_out_cpu_functional op; |
| 23997 | op.meta(self, padding); |
| 23998 | op.impl(self, padding, *op.outputs_[0]); |
| 23999 | return std::move(op.outputs_[0]).take(); |
| 24000 | } |
| 24001 | struct structured_reflection_pad1d_out_cpu_out final : public at::native::structured_reflection_pad1d_out_cpu { |
| 24002 | structured_reflection_pad1d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24003 | void set_output_strided( |
| 24004 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24005 | TensorOptions options, DimnameList names |
| 24006 | ) override { |
| 24007 | const auto& out = outputs_[output_idx].get(); |
| 24008 | resize_out(out, sizes, strides, options); |
| 24009 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24010 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24011 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24012 | } |
| 24013 | if (!names.empty()) { |
| 24014 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24015 | } |
| 24016 | // super must happen after, so that downstream can use maybe_get_output |
| 24017 | // to retrieve the output |
| 24018 | } |
| 24019 | void set_output_raw_strided( |
| 24020 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24021 | TensorOptions options, DimnameList names |
| 24022 | ) override { |
| 24023 | const auto& out = outputs_[output_idx].get(); |
| 24024 | resize_out(out, sizes, strides, options); |
| 24025 | if (!names.empty()) { |
| 24026 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24027 | } |
| 24028 | // super must happen after, so that downstream can use maybe_get_output |
| 24029 | // to retrieve the output |
| 24030 | } |
| 24031 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24032 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24033 | } |
| 24034 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24035 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24036 | }; |
| 24037 | at::Tensor & wrapper_CPU_reflection_pad1d_out_out(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 24038 | structured_reflection_pad1d_out_cpu_out op(out); |
| 24039 | op.meta(self, padding); |
| 24040 | op.impl(self, padding, op.maybe_get_output(0)); |
| 24041 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24042 | return out; |
| 24043 | } |
| 24044 | struct structured_reflection_pad1d_backward_out_cpu_functional final : public at::native::structured_reflection_pad1d_backward_out_cpu { |
| 24045 | void set_output_strided( |
| 24046 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24047 | TensorOptions options, DimnameList names |
| 24048 | ) override { |
| 24049 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24050 | if (!names.empty()) { |
| 24051 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24052 | } |
| 24053 | // super must happen after, so that downstream can use maybe_get_output |
| 24054 | // to retrieve the output |
| 24055 | } |
| 24056 | void set_output_raw_strided( |
| 24057 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24058 | TensorOptions options, DimnameList names |
| 24059 | ) override { |
| 24060 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24061 | if (!names.empty()) { |
| 24062 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24063 | } |
| 24064 | // super must happen after, so that downstream can use maybe_get_output |
| 24065 | // to retrieve the output |
| 24066 | } |
| 24067 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24068 | return *outputs_[output_idx]; |
| 24069 | } |
| 24070 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24071 | }; |
| 24072 | at::Tensor wrapper_CPU_reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 24073 | structured_reflection_pad1d_backward_out_cpu_functional op; |
| 24074 | op.meta(grad_output, self, padding); |
| 24075 | op.impl(grad_output, self, padding, *op.outputs_[0]); |
| 24076 | return std::move(op.outputs_[0]).take(); |
| 24077 | } |
| 24078 | struct structured_reflection_pad1d_backward_out_cpu_out final : public at::native::structured_reflection_pad1d_backward_out_cpu { |
| 24079 | structured_reflection_pad1d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24080 | void set_output_strided( |
| 24081 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24082 | TensorOptions options, DimnameList names |
| 24083 | ) override { |
| 24084 | const auto& out = outputs_[output_idx].get(); |
| 24085 | resize_out(out, sizes, strides, options); |
| 24086 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24087 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24088 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24089 | } |
| 24090 | if (!names.empty()) { |
| 24091 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24092 | } |
| 24093 | // super must happen after, so that downstream can use maybe_get_output |
| 24094 | // to retrieve the output |
| 24095 | } |
| 24096 | void set_output_raw_strided( |
| 24097 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24098 | TensorOptions options, DimnameList names |
| 24099 | ) override { |
| 24100 | const auto& out = outputs_[output_idx].get(); |
| 24101 | resize_out(out, sizes, strides, options); |
| 24102 | if (!names.empty()) { |
| 24103 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24104 | } |
| 24105 | // super must happen after, so that downstream can use maybe_get_output |
| 24106 | // to retrieve the output |
| 24107 | } |
| 24108 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24109 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24110 | } |
| 24111 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24112 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24113 | }; |
| 24114 | at::Tensor & wrapper_CPU_reflection_pad1d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 24115 | structured_reflection_pad1d_backward_out_cpu_out op(grad_input); |
| 24116 | op.meta(grad_output, self, padding); |
| 24117 | op.impl(grad_output, self, padding, op.maybe_get_output(0)); |
| 24118 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24119 | return grad_input; |
| 24120 | } |
| 24121 | namespace { |
| 24122 | at::Tensor wrapper_CPU__reflection_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 24123 | // No device check |
| 24124 | // DeviceGuard omitted |
| 24125 | return at::native::reflection_pad2d_cpu(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 24126 | } |
| 24127 | } // anonymous namespace |
| 24128 | namespace { |
| 24129 | at::Tensor & wrapper_CPU_out_reflection_pad2d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 24130 | // No device check |
| 24131 | // DeviceGuard omitted |
| 24132 | return at::native::reflection_pad2d_out_cpu(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 24133 | } |
| 24134 | } // anonymous namespace |
| 24135 | namespace { |
| 24136 | at::Tensor wrapper_CPU__reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 24137 | // No device check |
| 24138 | // DeviceGuard omitted |
| 24139 | return at::native::reflection_pad2d_backward_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 24140 | } |
| 24141 | } // anonymous namespace |
| 24142 | namespace { |
| 24143 | at::Tensor & wrapper_CPU_grad_input_reflection_pad2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 24144 | // No device check |
| 24145 | // DeviceGuard omitted |
| 24146 | return at::native::reflection_pad2d_backward_out_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 24147 | } |
| 24148 | } // anonymous namespace |
| 24149 | struct structured_reflection_pad3d_out_cpu_functional final : public at::native::structured_reflection_pad3d_out_cpu { |
| 24150 | void set_output_strided( |
| 24151 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24152 | TensorOptions options, DimnameList names |
| 24153 | ) override { |
| 24154 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24155 | if (!names.empty()) { |
| 24156 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24157 | } |
| 24158 | // super must happen after, so that downstream can use maybe_get_output |
| 24159 | // to retrieve the output |
| 24160 | } |
| 24161 | void set_output_raw_strided( |
| 24162 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24163 | TensorOptions options, DimnameList names |
| 24164 | ) override { |
| 24165 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24166 | if (!names.empty()) { |
| 24167 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24168 | } |
| 24169 | // super must happen after, so that downstream can use maybe_get_output |
| 24170 | // to retrieve the output |
| 24171 | } |
| 24172 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24173 | return *outputs_[output_idx]; |
| 24174 | } |
| 24175 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24176 | }; |
| 24177 | at::Tensor wrapper_CPU_reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 24178 | structured_reflection_pad3d_out_cpu_functional op; |
| 24179 | op.meta(self, padding); |
| 24180 | op.impl(self, padding, *op.outputs_[0]); |
| 24181 | return std::move(op.outputs_[0]).take(); |
| 24182 | } |
| 24183 | struct structured_reflection_pad3d_out_cpu_out final : public at::native::structured_reflection_pad3d_out_cpu { |
| 24184 | structured_reflection_pad3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24185 | void set_output_strided( |
| 24186 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24187 | TensorOptions options, DimnameList names |
| 24188 | ) override { |
| 24189 | const auto& out = outputs_[output_idx].get(); |
| 24190 | resize_out(out, sizes, strides, options); |
| 24191 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24192 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24193 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24194 | } |
| 24195 | if (!names.empty()) { |
| 24196 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24197 | } |
| 24198 | // super must happen after, so that downstream can use maybe_get_output |
| 24199 | // to retrieve the output |
| 24200 | } |
| 24201 | void set_output_raw_strided( |
| 24202 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24203 | TensorOptions options, DimnameList names |
| 24204 | ) override { |
| 24205 | const auto& out = outputs_[output_idx].get(); |
| 24206 | resize_out(out, sizes, strides, options); |
| 24207 | if (!names.empty()) { |
| 24208 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24209 | } |
| 24210 | // super must happen after, so that downstream can use maybe_get_output |
| 24211 | // to retrieve the output |
| 24212 | } |
| 24213 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24214 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24215 | } |
| 24216 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24217 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24218 | }; |
| 24219 | at::Tensor & wrapper_CPU_reflection_pad3d_out_out(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 24220 | structured_reflection_pad3d_out_cpu_out op(out); |
| 24221 | op.meta(self, padding); |
| 24222 | op.impl(self, padding, op.maybe_get_output(0)); |
| 24223 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24224 | return out; |
| 24225 | } |
| 24226 | struct structured_reflection_pad3d_backward_out_cpu_functional final : public at::native::structured_reflection_pad3d_backward_out_cpu { |
| 24227 | void set_output_strided( |
| 24228 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24229 | TensorOptions options, DimnameList names |
| 24230 | ) override { |
| 24231 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24232 | if (!names.empty()) { |
| 24233 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24234 | } |
| 24235 | // super must happen after, so that downstream can use maybe_get_output |
| 24236 | // to retrieve the output |
| 24237 | } |
| 24238 | void set_output_raw_strided( |
| 24239 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24240 | TensorOptions options, DimnameList names |
| 24241 | ) override { |
| 24242 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24243 | if (!names.empty()) { |
| 24244 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24245 | } |
| 24246 | // super must happen after, so that downstream can use maybe_get_output |
| 24247 | // to retrieve the output |
| 24248 | } |
| 24249 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24250 | return *outputs_[output_idx]; |
| 24251 | } |
| 24252 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24253 | }; |
| 24254 | at::Tensor wrapper_CPU_reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 24255 | structured_reflection_pad3d_backward_out_cpu_functional op; |
| 24256 | op.meta(grad_output, self, padding); |
| 24257 | op.impl(grad_output, self, padding, *op.outputs_[0]); |
| 24258 | return std::move(op.outputs_[0]).take(); |
| 24259 | } |
| 24260 | struct structured_reflection_pad3d_backward_out_cpu_out final : public at::native::structured_reflection_pad3d_backward_out_cpu { |
| 24261 | structured_reflection_pad3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24262 | void set_output_strided( |
| 24263 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24264 | TensorOptions options, DimnameList names |
| 24265 | ) override { |
| 24266 | const auto& out = outputs_[output_idx].get(); |
| 24267 | resize_out(out, sizes, strides, options); |
| 24268 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24269 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24270 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24271 | } |
| 24272 | if (!names.empty()) { |
| 24273 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24274 | } |
| 24275 | // super must happen after, so that downstream can use maybe_get_output |
| 24276 | // to retrieve the output |
| 24277 | } |
| 24278 | void set_output_raw_strided( |
| 24279 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24280 | TensorOptions options, DimnameList names |
| 24281 | ) override { |
| 24282 | const auto& out = outputs_[output_idx].get(); |
| 24283 | resize_out(out, sizes, strides, options); |
| 24284 | if (!names.empty()) { |
| 24285 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24286 | } |
| 24287 | // super must happen after, so that downstream can use maybe_get_output |
| 24288 | // to retrieve the output |
| 24289 | } |
| 24290 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24291 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24292 | } |
| 24293 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24294 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24295 | }; |
| 24296 | at::Tensor & wrapper_CPU_reflection_pad3d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 24297 | structured_reflection_pad3d_backward_out_cpu_out op(grad_input); |
| 24298 | op.meta(grad_output, self, padding); |
| 24299 | op.impl(grad_output, self, padding, op.maybe_get_output(0)); |
| 24300 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24301 | return grad_input; |
| 24302 | } |
| 24303 | struct structured_replication_pad1d_out_cpu_functional final : public at::native::structured_replication_pad1d_out_cpu { |
| 24304 | void set_output_strided( |
| 24305 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24306 | TensorOptions options, DimnameList names |
| 24307 | ) override { |
| 24308 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24309 | if (!names.empty()) { |
| 24310 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24311 | } |
| 24312 | // super must happen after, so that downstream can use maybe_get_output |
| 24313 | // to retrieve the output |
| 24314 | } |
| 24315 | void set_output_raw_strided( |
| 24316 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24317 | TensorOptions options, DimnameList names |
| 24318 | ) override { |
| 24319 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24320 | if (!names.empty()) { |
| 24321 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24322 | } |
| 24323 | // super must happen after, so that downstream can use maybe_get_output |
| 24324 | // to retrieve the output |
| 24325 | } |
| 24326 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24327 | return *outputs_[output_idx]; |
| 24328 | } |
| 24329 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24330 | }; |
| 24331 | at::Tensor wrapper_CPU_replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 24332 | structured_replication_pad1d_out_cpu_functional op; |
| 24333 | op.meta(self, padding); |
| 24334 | op.impl(self, padding, *op.outputs_[0]); |
| 24335 | return std::move(op.outputs_[0]).take(); |
| 24336 | } |
| 24337 | struct structured_replication_pad1d_out_cpu_out final : public at::native::structured_replication_pad1d_out_cpu { |
| 24338 | structured_replication_pad1d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24339 | void set_output_strided( |
| 24340 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24341 | TensorOptions options, DimnameList names |
| 24342 | ) override { |
| 24343 | const auto& out = outputs_[output_idx].get(); |
| 24344 | resize_out(out, sizes, strides, options); |
| 24345 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24346 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24347 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24348 | } |
| 24349 | if (!names.empty()) { |
| 24350 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24351 | } |
| 24352 | // super must happen after, so that downstream can use maybe_get_output |
| 24353 | // to retrieve the output |
| 24354 | } |
| 24355 | void set_output_raw_strided( |
| 24356 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24357 | TensorOptions options, DimnameList names |
| 24358 | ) override { |
| 24359 | const auto& out = outputs_[output_idx].get(); |
| 24360 | resize_out(out, sizes, strides, options); |
| 24361 | if (!names.empty()) { |
| 24362 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24363 | } |
| 24364 | // super must happen after, so that downstream can use maybe_get_output |
| 24365 | // to retrieve the output |
| 24366 | } |
| 24367 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24368 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24369 | } |
| 24370 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24371 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24372 | }; |
| 24373 | at::Tensor & wrapper_CPU_replication_pad1d_out_out(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 24374 | structured_replication_pad1d_out_cpu_out op(out); |
| 24375 | op.meta(self, padding); |
| 24376 | op.impl(self, padding, op.maybe_get_output(0)); |
| 24377 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24378 | return out; |
| 24379 | } |
| 24380 | struct structured_replication_pad1d_backward_out_cpu_functional final : public at::native::structured_replication_pad1d_backward_out_cpu { |
| 24381 | void set_output_strided( |
| 24382 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24383 | TensorOptions options, DimnameList names |
| 24384 | ) override { |
| 24385 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24386 | if (!names.empty()) { |
| 24387 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24388 | } |
| 24389 | // super must happen after, so that downstream can use maybe_get_output |
| 24390 | // to retrieve the output |
| 24391 | } |
| 24392 | void set_output_raw_strided( |
| 24393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24394 | TensorOptions options, DimnameList names |
| 24395 | ) override { |
| 24396 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24397 | if (!names.empty()) { |
| 24398 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24399 | } |
| 24400 | // super must happen after, so that downstream can use maybe_get_output |
| 24401 | // to retrieve the output |
| 24402 | } |
| 24403 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24404 | return *outputs_[output_idx]; |
| 24405 | } |
| 24406 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24407 | }; |
| 24408 | at::Tensor wrapper_CPU_replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 24409 | structured_replication_pad1d_backward_out_cpu_functional op; |
| 24410 | op.meta(grad_output, self, padding); |
| 24411 | op.impl(grad_output, self, padding, *op.outputs_[0]); |
| 24412 | return std::move(op.outputs_[0]).take(); |
| 24413 | } |
| 24414 | struct structured_replication_pad1d_backward_out_cpu_out final : public at::native::structured_replication_pad1d_backward_out_cpu { |
| 24415 | structured_replication_pad1d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24416 | void set_output_strided( |
| 24417 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24418 | TensorOptions options, DimnameList names |
| 24419 | ) override { |
| 24420 | const auto& out = outputs_[output_idx].get(); |
| 24421 | resize_out(out, sizes, strides, options); |
| 24422 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24423 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24424 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24425 | } |
| 24426 | if (!names.empty()) { |
| 24427 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24428 | } |
| 24429 | // super must happen after, so that downstream can use maybe_get_output |
| 24430 | // to retrieve the output |
| 24431 | } |
| 24432 | void set_output_raw_strided( |
| 24433 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24434 | TensorOptions options, DimnameList names |
| 24435 | ) override { |
| 24436 | const auto& out = outputs_[output_idx].get(); |
| 24437 | resize_out(out, sizes, strides, options); |
| 24438 | if (!names.empty()) { |
| 24439 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24440 | } |
| 24441 | // super must happen after, so that downstream can use maybe_get_output |
| 24442 | // to retrieve the output |
| 24443 | } |
| 24444 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24445 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24446 | } |
| 24447 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24448 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24449 | }; |
| 24450 | at::Tensor & wrapper_CPU_replication_pad1d_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 24451 | structured_replication_pad1d_backward_out_cpu_out op(grad_input); |
| 24452 | op.meta(grad_output, self, padding); |
| 24453 | op.impl(grad_output, self, padding, op.maybe_get_output(0)); |
| 24454 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24455 | return grad_input; |
| 24456 | } |
| 24457 | struct structured_replication_pad2d_out_cpu_functional final : public at::native::structured_replication_pad2d_out_cpu { |
| 24458 | void set_output_strided( |
| 24459 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24460 | TensorOptions options, DimnameList names |
| 24461 | ) override { |
| 24462 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24463 | if (!names.empty()) { |
| 24464 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24465 | } |
| 24466 | // super must happen after, so that downstream can use maybe_get_output |
| 24467 | // to retrieve the output |
| 24468 | } |
| 24469 | void set_output_raw_strided( |
| 24470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24471 | TensorOptions options, DimnameList names |
| 24472 | ) override { |
| 24473 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24474 | if (!names.empty()) { |
| 24475 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24476 | } |
| 24477 | // super must happen after, so that downstream can use maybe_get_output |
| 24478 | // to retrieve the output |
| 24479 | } |
| 24480 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24481 | return *outputs_[output_idx]; |
| 24482 | } |
| 24483 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24484 | }; |
| 24485 | at::Tensor wrapper_CPU_replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) { |
| 24486 | structured_replication_pad2d_out_cpu_functional op; |
| 24487 | op.meta(self, padding); |
| 24488 | op.impl(self, padding, *op.outputs_[0]); |
| 24489 | return std::move(op.outputs_[0]).take(); |
| 24490 | } |
| 24491 | struct structured_replication_pad2d_out_cpu_out final : public at::native::structured_replication_pad2d_out_cpu { |
| 24492 | structured_replication_pad2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24493 | void set_output_strided( |
| 24494 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24495 | TensorOptions options, DimnameList names |
| 24496 | ) override { |
| 24497 | const auto& out = outputs_[output_idx].get(); |
| 24498 | resize_out(out, sizes, strides, options); |
| 24499 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24500 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24501 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24502 | } |
| 24503 | if (!names.empty()) { |
| 24504 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24505 | } |
| 24506 | // super must happen after, so that downstream can use maybe_get_output |
| 24507 | // to retrieve the output |
| 24508 | } |
| 24509 | void set_output_raw_strided( |
| 24510 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24511 | TensorOptions options, DimnameList names |
| 24512 | ) override { |
| 24513 | const auto& out = outputs_[output_idx].get(); |
| 24514 | resize_out(out, sizes, strides, options); |
| 24515 | if (!names.empty()) { |
| 24516 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24517 | } |
| 24518 | // super must happen after, so that downstream can use maybe_get_output |
| 24519 | // to retrieve the output |
| 24520 | } |
| 24521 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24522 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24523 | } |
| 24524 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24525 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24526 | }; |
| 24527 | at::Tensor & wrapper_CPU_replication_pad2d_out_out(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 24528 | structured_replication_pad2d_out_cpu_out op(out); |
| 24529 | op.meta(self, padding); |
| 24530 | op.impl(self, padding, op.maybe_get_output(0)); |
| 24531 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24532 | return out; |
| 24533 | } |
| 24534 | namespace { |
| 24535 | at::Tensor wrapper_CPU__replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 24536 | // No device check |
| 24537 | // DeviceGuard omitted |
| 24538 | return at::native::replication_pad2d_backward_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 24539 | } |
| 24540 | } // anonymous namespace |
| 24541 | namespace { |
| 24542 | at::Tensor & wrapper_CPU_grad_input_replication_pad2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 24543 | // No device check |
| 24544 | // DeviceGuard omitted |
| 24545 | return at::native::replication_pad2d_backward_out_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 24546 | } |
| 24547 | } // anonymous namespace |
| 24548 | struct structured_replication_pad3d_out_cpu_functional final : public at::native::structured_replication_pad3d_out_cpu { |
| 24549 | void set_output_strided( |
| 24550 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24551 | TensorOptions options, DimnameList names |
| 24552 | ) override { |
| 24553 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24554 | if (!names.empty()) { |
| 24555 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24556 | } |
| 24557 | // super must happen after, so that downstream can use maybe_get_output |
| 24558 | // to retrieve the output |
| 24559 | } |
| 24560 | void set_output_raw_strided( |
| 24561 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24562 | TensorOptions options, DimnameList names |
| 24563 | ) override { |
| 24564 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24565 | if (!names.empty()) { |
| 24566 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24567 | } |
| 24568 | // super must happen after, so that downstream can use maybe_get_output |
| 24569 | // to retrieve the output |
| 24570 | } |
| 24571 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24572 | return *outputs_[output_idx]; |
| 24573 | } |
| 24574 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24575 | }; |
| 24576 | at::Tensor wrapper_CPU_replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 24577 | structured_replication_pad3d_out_cpu_functional op; |
| 24578 | op.meta(self, padding); |
| 24579 | op.impl(self, padding, *op.outputs_[0]); |
| 24580 | return std::move(op.outputs_[0]).take(); |
| 24581 | } |
| 24582 | struct structured_replication_pad3d_out_cpu_out final : public at::native::structured_replication_pad3d_out_cpu { |
| 24583 | structured_replication_pad3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24584 | void set_output_strided( |
| 24585 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24586 | TensorOptions options, DimnameList names |
| 24587 | ) override { |
| 24588 | const auto& out = outputs_[output_idx].get(); |
| 24589 | resize_out(out, sizes, strides, options); |
| 24590 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24591 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24592 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24593 | } |
| 24594 | if (!names.empty()) { |
| 24595 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24596 | } |
| 24597 | // super must happen after, so that downstream can use maybe_get_output |
| 24598 | // to retrieve the output |
| 24599 | } |
| 24600 | void set_output_raw_strided( |
| 24601 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24602 | TensorOptions options, DimnameList names |
| 24603 | ) override { |
| 24604 | const auto& out = outputs_[output_idx].get(); |
| 24605 | resize_out(out, sizes, strides, options); |
| 24606 | if (!names.empty()) { |
| 24607 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24608 | } |
| 24609 | // super must happen after, so that downstream can use maybe_get_output |
| 24610 | // to retrieve the output |
| 24611 | } |
| 24612 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24613 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24614 | } |
| 24615 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24616 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24617 | }; |
| 24618 | at::Tensor & wrapper_CPU_replication_pad3d_out_out(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 24619 | structured_replication_pad3d_out_cpu_out op(out); |
| 24620 | op.meta(self, padding); |
| 24621 | op.impl(self, padding, op.maybe_get_output(0)); |
| 24622 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24623 | return out; |
| 24624 | } |
| 24625 | namespace { |
| 24626 | at::Tensor wrapper_CPU__replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 24627 | // No device check |
| 24628 | // DeviceGuard omitted |
| 24629 | return at::native::replication_pad3d_backward_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 24630 | } |
| 24631 | } // anonymous namespace |
| 24632 | namespace { |
| 24633 | at::Tensor & wrapper_CPU_grad_input_replication_pad3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 24634 | // No device check |
| 24635 | // DeviceGuard omitted |
| 24636 | return at::native::replication_pad3d_backward_out_cpu(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 24637 | } |
| 24638 | } // anonymous namespace |
| 24639 | struct structured_upsample_linear1d_out_cpu_functional final : public at::native::structured_upsample_linear1d_out_cpu { |
| 24640 | void set_output_strided( |
| 24641 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24642 | TensorOptions options, DimnameList names |
| 24643 | ) override { |
| 24644 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24645 | if (!names.empty()) { |
| 24646 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24647 | } |
| 24648 | // super must happen after, so that downstream can use maybe_get_output |
| 24649 | // to retrieve the output |
| 24650 | } |
| 24651 | void set_output_raw_strided( |
| 24652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24653 | TensorOptions options, DimnameList names |
| 24654 | ) override { |
| 24655 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24656 | if (!names.empty()) { |
| 24657 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24658 | } |
| 24659 | // super must happen after, so that downstream can use maybe_get_output |
| 24660 | // to retrieve the output |
| 24661 | } |
| 24662 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24663 | return *outputs_[output_idx]; |
| 24664 | } |
| 24665 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24666 | }; |
| 24667 | at::Tensor wrapper_CPU_upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 24668 | structured_upsample_linear1d_out_cpu_functional op; |
| 24669 | op.meta(self, output_size, align_corners, scales); |
| 24670 | op.impl(self, output_size, align_corners, scales, *op.outputs_[0]); |
| 24671 | return std::move(op.outputs_[0]).take(); |
| 24672 | } |
| 24673 | struct structured_upsample_linear1d_out_cpu_out final : public at::native::structured_upsample_linear1d_out_cpu { |
| 24674 | structured_upsample_linear1d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24675 | void set_output_strided( |
| 24676 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24677 | TensorOptions options, DimnameList names |
| 24678 | ) override { |
| 24679 | const auto& out = outputs_[output_idx].get(); |
| 24680 | resize_out(out, sizes, strides, options); |
| 24681 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24682 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24683 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24684 | } |
| 24685 | if (!names.empty()) { |
| 24686 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24687 | } |
| 24688 | // super must happen after, so that downstream can use maybe_get_output |
| 24689 | // to retrieve the output |
| 24690 | } |
| 24691 | void set_output_raw_strided( |
| 24692 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24693 | TensorOptions options, DimnameList names |
| 24694 | ) override { |
| 24695 | const auto& out = outputs_[output_idx].get(); |
| 24696 | resize_out(out, sizes, strides, options); |
| 24697 | if (!names.empty()) { |
| 24698 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24699 | } |
| 24700 | // super must happen after, so that downstream can use maybe_get_output |
| 24701 | // to retrieve the output |
| 24702 | } |
| 24703 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24704 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24705 | } |
| 24706 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24707 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24708 | }; |
| 24709 | at::Tensor & wrapper_CPU_upsample_linear1d_out_out(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 24710 | structured_upsample_linear1d_out_cpu_out op(out); |
| 24711 | op.meta(self, output_size, align_corners, scales); |
| 24712 | op.impl(self, output_size, align_corners, scales, op.maybe_get_output(0)); |
| 24713 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24714 | return out; |
| 24715 | } |
| 24716 | struct structured_upsample_linear1d_backward_out_cpu_functional final : public at::native::structured_upsample_linear1d_backward_out_cpu { |
| 24717 | void set_output_strided( |
| 24718 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24719 | TensorOptions options, DimnameList names |
| 24720 | ) override { |
| 24721 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24722 | if (!names.empty()) { |
| 24723 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24724 | } |
| 24725 | // super must happen after, so that downstream can use maybe_get_output |
| 24726 | // to retrieve the output |
| 24727 | } |
| 24728 | void set_output_raw_strided( |
| 24729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24730 | TensorOptions options, DimnameList names |
| 24731 | ) override { |
| 24732 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24733 | if (!names.empty()) { |
| 24734 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24735 | } |
| 24736 | // super must happen after, so that downstream can use maybe_get_output |
| 24737 | // to retrieve the output |
| 24738 | } |
| 24739 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24740 | return *outputs_[output_idx]; |
| 24741 | } |
| 24742 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24743 | }; |
| 24744 | at::Tensor wrapper_CPU_upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 24745 | structured_upsample_linear1d_backward_out_cpu_functional op; |
| 24746 | op.meta(grad_output, output_size, input_size, align_corners, scales); |
| 24747 | op.impl(grad_output, output_size, input_size, align_corners, scales, *op.outputs_[0]); |
| 24748 | return std::move(op.outputs_[0]).take(); |
| 24749 | } |
| 24750 | struct structured_upsample_linear1d_backward_out_cpu_out final : public at::native::structured_upsample_linear1d_backward_out_cpu { |
| 24751 | structured_upsample_linear1d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24752 | void set_output_strided( |
| 24753 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24754 | TensorOptions options, DimnameList names |
| 24755 | ) override { |
| 24756 | const auto& out = outputs_[output_idx].get(); |
| 24757 | resize_out(out, sizes, strides, options); |
| 24758 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24759 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24760 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24761 | } |
| 24762 | if (!names.empty()) { |
| 24763 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24764 | } |
| 24765 | // super must happen after, so that downstream can use maybe_get_output |
| 24766 | // to retrieve the output |
| 24767 | } |
| 24768 | void set_output_raw_strided( |
| 24769 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24770 | TensorOptions options, DimnameList names |
| 24771 | ) override { |
| 24772 | const auto& out = outputs_[output_idx].get(); |
| 24773 | resize_out(out, sizes, strides, options); |
| 24774 | if (!names.empty()) { |
| 24775 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24776 | } |
| 24777 | // super must happen after, so that downstream can use maybe_get_output |
| 24778 | // to retrieve the output |
| 24779 | } |
| 24780 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24781 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24782 | } |
| 24783 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24784 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24785 | }; |
| 24786 | at::Tensor & wrapper_CPU_upsample_linear1d_backward_out_grad_input(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales, at::Tensor & grad_input) { |
| 24787 | structured_upsample_linear1d_backward_out_cpu_out op(grad_input); |
| 24788 | op.meta(grad_output, output_size, input_size, align_corners, scales); |
| 24789 | op.impl(grad_output, output_size, input_size, align_corners, scales, op.maybe_get_output(0)); |
| 24790 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24791 | return grad_input; |
| 24792 | } |
| 24793 | struct structured_upsample_bilinear2d_out_cpu_functional final : public at::native::structured_upsample_bilinear2d_out_cpu { |
| 24794 | void set_output_strided( |
| 24795 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24796 | TensorOptions options, DimnameList names |
| 24797 | ) override { |
| 24798 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24799 | if (!names.empty()) { |
| 24800 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24801 | } |
| 24802 | // super must happen after, so that downstream can use maybe_get_output |
| 24803 | // to retrieve the output |
| 24804 | } |
| 24805 | void set_output_raw_strided( |
| 24806 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24807 | TensorOptions options, DimnameList names |
| 24808 | ) override { |
| 24809 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24810 | if (!names.empty()) { |
| 24811 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24812 | } |
| 24813 | // super must happen after, so that downstream can use maybe_get_output |
| 24814 | // to retrieve the output |
| 24815 | } |
| 24816 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24817 | return *outputs_[output_idx]; |
| 24818 | } |
| 24819 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24820 | }; |
| 24821 | at::Tensor wrapper_CPU_upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 24822 | structured_upsample_bilinear2d_out_cpu_functional op; |
| 24823 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 24824 | op.impl(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 24825 | return std::move(op.outputs_[0]).take(); |
| 24826 | } |
| 24827 | struct structured_upsample_bilinear2d_out_cpu_out final : public at::native::structured_upsample_bilinear2d_out_cpu { |
| 24828 | structured_upsample_bilinear2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24829 | void set_output_strided( |
| 24830 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24831 | TensorOptions options, DimnameList names |
| 24832 | ) override { |
| 24833 | const auto& out = outputs_[output_idx].get(); |
| 24834 | resize_out(out, sizes, strides, options); |
| 24835 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24836 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24837 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24838 | } |
| 24839 | if (!names.empty()) { |
| 24840 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24841 | } |
| 24842 | // super must happen after, so that downstream can use maybe_get_output |
| 24843 | // to retrieve the output |
| 24844 | } |
| 24845 | void set_output_raw_strided( |
| 24846 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24847 | TensorOptions options, DimnameList names |
| 24848 | ) override { |
| 24849 | const auto& out = outputs_[output_idx].get(); |
| 24850 | resize_out(out, sizes, strides, options); |
| 24851 | if (!names.empty()) { |
| 24852 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24853 | } |
| 24854 | // super must happen after, so that downstream can use maybe_get_output |
| 24855 | // to retrieve the output |
| 24856 | } |
| 24857 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24858 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24859 | } |
| 24860 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24861 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24862 | }; |
| 24863 | at::Tensor & wrapper_CPU_upsample_bilinear2d_out_out(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 24864 | structured_upsample_bilinear2d_out_cpu_out op(out); |
| 24865 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 24866 | op.impl(self, output_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 24867 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24868 | return out; |
| 24869 | } |
| 24870 | struct structured_upsample_bilinear2d_backward_out_cpu_functional final : public at::native::structured_upsample_bilinear2d_backward_out_cpu { |
| 24871 | void set_output_strided( |
| 24872 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24873 | TensorOptions options, DimnameList names |
| 24874 | ) override { |
| 24875 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24876 | if (!names.empty()) { |
| 24877 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24878 | } |
| 24879 | // super must happen after, so that downstream can use maybe_get_output |
| 24880 | // to retrieve the output |
| 24881 | } |
| 24882 | void set_output_raw_strided( |
| 24883 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24884 | TensorOptions options, DimnameList names |
| 24885 | ) override { |
| 24886 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24887 | if (!names.empty()) { |
| 24888 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24889 | } |
| 24890 | // super must happen after, so that downstream can use maybe_get_output |
| 24891 | // to retrieve the output |
| 24892 | } |
| 24893 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24894 | return *outputs_[output_idx]; |
| 24895 | } |
| 24896 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24897 | }; |
| 24898 | at::Tensor wrapper_CPU_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) { |
| 24899 | structured_upsample_bilinear2d_backward_out_cpu_functional op; |
| 24900 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 24901 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 24902 | return std::move(op.outputs_[0]).take(); |
| 24903 | } |
| 24904 | struct structured_upsample_bilinear2d_backward_out_cpu_out final : public at::native::structured_upsample_bilinear2d_backward_out_cpu { |
| 24905 | structured_upsample_bilinear2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24906 | void set_output_strided( |
| 24907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24908 | TensorOptions options, DimnameList names |
| 24909 | ) override { |
| 24910 | const auto& out = outputs_[output_idx].get(); |
| 24911 | resize_out(out, sizes, strides, options); |
| 24912 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24913 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24914 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24915 | } |
| 24916 | if (!names.empty()) { |
| 24917 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24918 | } |
| 24919 | // super must happen after, so that downstream can use maybe_get_output |
| 24920 | // to retrieve the output |
| 24921 | } |
| 24922 | void set_output_raw_strided( |
| 24923 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24924 | TensorOptions options, DimnameList names |
| 24925 | ) override { |
| 24926 | const auto& out = outputs_[output_idx].get(); |
| 24927 | resize_out(out, sizes, strides, options); |
| 24928 | if (!names.empty()) { |
| 24929 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24930 | } |
| 24931 | // super must happen after, so that downstream can use maybe_get_output |
| 24932 | // to retrieve the output |
| 24933 | } |
| 24934 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24935 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 24936 | } |
| 24937 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 24938 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 24939 | }; |
| 24940 | at::Tensor & wrapper_CPU_upsample_bilinear2d_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 24941 | structured_upsample_bilinear2d_backward_out_cpu_out op(grad_input); |
| 24942 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 24943 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 24944 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 24945 | return grad_input; |
| 24946 | } |
| 24947 | struct structured__upsample_bilinear2d_aa_out_cpu_functional final : public at::native::structured__upsample_bilinear2d_aa_out_cpu { |
| 24948 | void set_output_strided( |
| 24949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24950 | TensorOptions options, DimnameList names |
| 24951 | ) override { |
| 24952 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24953 | if (!names.empty()) { |
| 24954 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24955 | } |
| 24956 | // super must happen after, so that downstream can use maybe_get_output |
| 24957 | // to retrieve the output |
| 24958 | } |
| 24959 | void set_output_raw_strided( |
| 24960 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24961 | TensorOptions options, DimnameList names |
| 24962 | ) override { |
| 24963 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 24964 | if (!names.empty()) { |
| 24965 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 24966 | } |
| 24967 | // super must happen after, so that downstream can use maybe_get_output |
| 24968 | // to retrieve the output |
| 24969 | } |
| 24970 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 24971 | return *outputs_[output_idx]; |
| 24972 | } |
| 24973 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 24974 | }; |
| 24975 | at::Tensor wrapper_CPU__upsample_bilinear2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 24976 | structured__upsample_bilinear2d_aa_out_cpu_functional op; |
| 24977 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 24978 | op.impl(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 24979 | return std::move(op.outputs_[0]).take(); |
| 24980 | } |
| 24981 | struct structured__upsample_bilinear2d_aa_out_cpu_out final : public at::native::structured__upsample_bilinear2d_aa_out_cpu { |
| 24982 | structured__upsample_bilinear2d_aa_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 24983 | void set_output_strided( |
| 24984 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 24985 | TensorOptions options, DimnameList names |
| 24986 | ) override { |
| 24987 | const auto& out = outputs_[output_idx].get(); |
| 24988 | resize_out(out, sizes, strides, options); |
| 24989 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 24990 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 24991 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 24992 | } |
| 24993 | if (!names.empty()) { |
| 24994 | namedinference::propagate_names(outputs_[output_idx], names); |
| 24995 | } |
| 24996 | // super must happen after, so that downstream can use maybe_get_output |
| 24997 | // to retrieve the output |
| 24998 | } |
| 24999 | void set_output_raw_strided( |
| 25000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25001 | TensorOptions options, DimnameList names |
| 25002 | ) override { |
| 25003 | const auto& out = outputs_[output_idx].get(); |
| 25004 | resize_out(out, sizes, strides, options); |
| 25005 | if (!names.empty()) { |
| 25006 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25007 | } |
| 25008 | // super must happen after, so that downstream can use maybe_get_output |
| 25009 | // to retrieve the output |
| 25010 | } |
| 25011 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25012 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25013 | } |
| 25014 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25015 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25016 | }; |
| 25017 | at::Tensor & wrapper_CPU__upsample_bilinear2d_aa_out_out(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 25018 | structured__upsample_bilinear2d_aa_out_cpu_out op(out); |
| 25019 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 25020 | op.impl(self, output_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25021 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25022 | return out; |
| 25023 | } |
| 25024 | struct structured__upsample_bilinear2d_aa_backward_out_cpu_functional final : public at::native::structured__upsample_bilinear2d_aa_backward_out_cpu { |
| 25025 | void set_output_strided( |
| 25026 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25027 | TensorOptions options, DimnameList names |
| 25028 | ) override { |
| 25029 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25030 | if (!names.empty()) { |
| 25031 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25032 | } |
| 25033 | // super must happen after, so that downstream can use maybe_get_output |
| 25034 | // to retrieve the output |
| 25035 | } |
| 25036 | void set_output_raw_strided( |
| 25037 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25038 | TensorOptions options, DimnameList names |
| 25039 | ) override { |
| 25040 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25041 | if (!names.empty()) { |
| 25042 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25043 | } |
| 25044 | // super must happen after, so that downstream can use maybe_get_output |
| 25045 | // to retrieve the output |
| 25046 | } |
| 25047 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25048 | return *outputs_[output_idx]; |
| 25049 | } |
| 25050 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25051 | }; |
| 25052 | at::Tensor wrapper_CPU__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) { |
| 25053 | structured__upsample_bilinear2d_aa_backward_out_cpu_functional op; |
| 25054 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25055 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 25056 | return std::move(op.outputs_[0]).take(); |
| 25057 | } |
| 25058 | struct structured__upsample_bilinear2d_aa_backward_out_cpu_out final : public at::native::structured__upsample_bilinear2d_aa_backward_out_cpu { |
| 25059 | structured__upsample_bilinear2d_aa_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25060 | void set_output_strided( |
| 25061 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25062 | TensorOptions options, DimnameList names |
| 25063 | ) override { |
| 25064 | const auto& out = outputs_[output_idx].get(); |
| 25065 | resize_out(out, sizes, strides, options); |
| 25066 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25067 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25068 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25069 | } |
| 25070 | if (!names.empty()) { |
| 25071 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25072 | } |
| 25073 | // super must happen after, so that downstream can use maybe_get_output |
| 25074 | // to retrieve the output |
| 25075 | } |
| 25076 | void set_output_raw_strided( |
| 25077 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25078 | TensorOptions options, DimnameList names |
| 25079 | ) override { |
| 25080 | const auto& out = outputs_[output_idx].get(); |
| 25081 | resize_out(out, sizes, strides, options); |
| 25082 | if (!names.empty()) { |
| 25083 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25084 | } |
| 25085 | // super must happen after, so that downstream can use maybe_get_output |
| 25086 | // to retrieve the output |
| 25087 | } |
| 25088 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25089 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25090 | } |
| 25091 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25092 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25093 | }; |
| 25094 | at::Tensor & wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 25095 | structured__upsample_bilinear2d_aa_backward_out_cpu_out op(grad_input); |
| 25096 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25097 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25098 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25099 | return grad_input; |
| 25100 | } |
| 25101 | struct structured_upsample_bicubic2d_out_cpu_functional final : public at::native::structured_upsample_bicubic2d_out_cpu { |
| 25102 | void set_output_strided( |
| 25103 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25104 | TensorOptions options, DimnameList names |
| 25105 | ) override { |
| 25106 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25107 | if (!names.empty()) { |
| 25108 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25109 | } |
| 25110 | // super must happen after, so that downstream can use maybe_get_output |
| 25111 | // to retrieve the output |
| 25112 | } |
| 25113 | void set_output_raw_strided( |
| 25114 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25115 | TensorOptions options, DimnameList names |
| 25116 | ) override { |
| 25117 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25118 | if (!names.empty()) { |
| 25119 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25120 | } |
| 25121 | // super must happen after, so that downstream can use maybe_get_output |
| 25122 | // to retrieve the output |
| 25123 | } |
| 25124 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25125 | return *outputs_[output_idx]; |
| 25126 | } |
| 25127 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25128 | }; |
| 25129 | at::Tensor wrapper_CPU_upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 25130 | structured_upsample_bicubic2d_out_cpu_functional op; |
| 25131 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 25132 | op.impl(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 25133 | return std::move(op.outputs_[0]).take(); |
| 25134 | } |
| 25135 | struct structured_upsample_bicubic2d_out_cpu_out final : public at::native::structured_upsample_bicubic2d_out_cpu { |
| 25136 | structured_upsample_bicubic2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25137 | void set_output_strided( |
| 25138 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25139 | TensorOptions options, DimnameList names |
| 25140 | ) override { |
| 25141 | const auto& out = outputs_[output_idx].get(); |
| 25142 | resize_out(out, sizes, strides, options); |
| 25143 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25144 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25145 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25146 | } |
| 25147 | if (!names.empty()) { |
| 25148 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25149 | } |
| 25150 | // super must happen after, so that downstream can use maybe_get_output |
| 25151 | // to retrieve the output |
| 25152 | } |
| 25153 | void set_output_raw_strided( |
| 25154 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25155 | TensorOptions options, DimnameList names |
| 25156 | ) override { |
| 25157 | const auto& out = outputs_[output_idx].get(); |
| 25158 | resize_out(out, sizes, strides, options); |
| 25159 | if (!names.empty()) { |
| 25160 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25161 | } |
| 25162 | // super must happen after, so that downstream can use maybe_get_output |
| 25163 | // to retrieve the output |
| 25164 | } |
| 25165 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25166 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25167 | } |
| 25168 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25169 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25170 | }; |
| 25171 | at::Tensor & wrapper_CPU_upsample_bicubic2d_out_out(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 25172 | structured_upsample_bicubic2d_out_cpu_out op(out); |
| 25173 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 25174 | op.impl(self, output_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25175 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25176 | return out; |
| 25177 | } |
| 25178 | struct structured_upsample_bicubic2d_backward_out_cpu_functional final : public at::native::structured_upsample_bicubic2d_backward_out_cpu { |
| 25179 | void set_output_strided( |
| 25180 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25181 | TensorOptions options, DimnameList names |
| 25182 | ) override { |
| 25183 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25184 | if (!names.empty()) { |
| 25185 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25186 | } |
| 25187 | // super must happen after, so that downstream can use maybe_get_output |
| 25188 | // to retrieve the output |
| 25189 | } |
| 25190 | void set_output_raw_strided( |
| 25191 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25192 | TensorOptions options, DimnameList names |
| 25193 | ) override { |
| 25194 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25195 | if (!names.empty()) { |
| 25196 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25197 | } |
| 25198 | // super must happen after, so that downstream can use maybe_get_output |
| 25199 | // to retrieve the output |
| 25200 | } |
| 25201 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25202 | return *outputs_[output_idx]; |
| 25203 | } |
| 25204 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25205 | }; |
| 25206 | at::Tensor wrapper_CPU_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) { |
| 25207 | structured_upsample_bicubic2d_backward_out_cpu_functional op; |
| 25208 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25209 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 25210 | return std::move(op.outputs_[0]).take(); |
| 25211 | } |
| 25212 | struct structured_upsample_bicubic2d_backward_out_cpu_out final : public at::native::structured_upsample_bicubic2d_backward_out_cpu { |
| 25213 | structured_upsample_bicubic2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25214 | void set_output_strided( |
| 25215 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25216 | TensorOptions options, DimnameList names |
| 25217 | ) override { |
| 25218 | const auto& out = outputs_[output_idx].get(); |
| 25219 | resize_out(out, sizes, strides, options); |
| 25220 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25221 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25222 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25223 | } |
| 25224 | if (!names.empty()) { |
| 25225 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25226 | } |
| 25227 | // super must happen after, so that downstream can use maybe_get_output |
| 25228 | // to retrieve the output |
| 25229 | } |
| 25230 | void set_output_raw_strided( |
| 25231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25232 | TensorOptions options, DimnameList names |
| 25233 | ) override { |
| 25234 | const auto& out = outputs_[output_idx].get(); |
| 25235 | resize_out(out, sizes, strides, options); |
| 25236 | if (!names.empty()) { |
| 25237 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25238 | } |
| 25239 | // super must happen after, so that downstream can use maybe_get_output |
| 25240 | // to retrieve the output |
| 25241 | } |
| 25242 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25243 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25244 | } |
| 25245 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25246 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25247 | }; |
| 25248 | at::Tensor & wrapper_CPU_upsample_bicubic2d_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 25249 | structured_upsample_bicubic2d_backward_out_cpu_out op(grad_input); |
| 25250 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25251 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25252 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25253 | return grad_input; |
| 25254 | } |
| 25255 | struct structured__upsample_bicubic2d_aa_out_cpu_functional final : public at::native::structured__upsample_bicubic2d_aa_out_cpu { |
| 25256 | void set_output_strided( |
| 25257 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25258 | TensorOptions options, DimnameList names |
| 25259 | ) override { |
| 25260 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25261 | if (!names.empty()) { |
| 25262 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25263 | } |
| 25264 | // super must happen after, so that downstream can use maybe_get_output |
| 25265 | // to retrieve the output |
| 25266 | } |
| 25267 | void set_output_raw_strided( |
| 25268 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25269 | TensorOptions options, DimnameList names |
| 25270 | ) override { |
| 25271 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25272 | if (!names.empty()) { |
| 25273 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25274 | } |
| 25275 | // super must happen after, so that downstream can use maybe_get_output |
| 25276 | // to retrieve the output |
| 25277 | } |
| 25278 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25279 | return *outputs_[output_idx]; |
| 25280 | } |
| 25281 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25282 | }; |
| 25283 | at::Tensor wrapper_CPU__upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 25284 | structured__upsample_bicubic2d_aa_out_cpu_functional op; |
| 25285 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 25286 | op.impl(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 25287 | return std::move(op.outputs_[0]).take(); |
| 25288 | } |
| 25289 | struct structured__upsample_bicubic2d_aa_out_cpu_out final : public at::native::structured__upsample_bicubic2d_aa_out_cpu { |
| 25290 | structured__upsample_bicubic2d_aa_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25291 | void set_output_strided( |
| 25292 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25293 | TensorOptions options, DimnameList names |
| 25294 | ) override { |
| 25295 | const auto& out = outputs_[output_idx].get(); |
| 25296 | resize_out(out, sizes, strides, options); |
| 25297 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25298 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25299 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25300 | } |
| 25301 | if (!names.empty()) { |
| 25302 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25303 | } |
| 25304 | // super must happen after, so that downstream can use maybe_get_output |
| 25305 | // to retrieve the output |
| 25306 | } |
| 25307 | void set_output_raw_strided( |
| 25308 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25309 | TensorOptions options, DimnameList names |
| 25310 | ) override { |
| 25311 | const auto& out = outputs_[output_idx].get(); |
| 25312 | resize_out(out, sizes, strides, options); |
| 25313 | if (!names.empty()) { |
| 25314 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25315 | } |
| 25316 | // super must happen after, so that downstream can use maybe_get_output |
| 25317 | // to retrieve the output |
| 25318 | } |
| 25319 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25320 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25321 | } |
| 25322 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25323 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25324 | }; |
| 25325 | at::Tensor & wrapper_CPU__upsample_bicubic2d_aa_out_out(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 25326 | structured__upsample_bicubic2d_aa_out_cpu_out op(out); |
| 25327 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 25328 | op.impl(self, output_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25329 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25330 | return out; |
| 25331 | } |
| 25332 | struct structured__upsample_bicubic2d_aa_backward_out_cpu_functional final : public at::native::structured__upsample_bicubic2d_aa_backward_out_cpu { |
| 25333 | void set_output_strided( |
| 25334 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25335 | TensorOptions options, DimnameList names |
| 25336 | ) override { |
| 25337 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25338 | if (!names.empty()) { |
| 25339 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25340 | } |
| 25341 | // super must happen after, so that downstream can use maybe_get_output |
| 25342 | // to retrieve the output |
| 25343 | } |
| 25344 | void set_output_raw_strided( |
| 25345 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25346 | TensorOptions options, DimnameList names |
| 25347 | ) override { |
| 25348 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25349 | if (!names.empty()) { |
| 25350 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25351 | } |
| 25352 | // super must happen after, so that downstream can use maybe_get_output |
| 25353 | // to retrieve the output |
| 25354 | } |
| 25355 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25356 | return *outputs_[output_idx]; |
| 25357 | } |
| 25358 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25359 | }; |
| 25360 | at::Tensor wrapper_CPU__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) { |
| 25361 | structured__upsample_bicubic2d_aa_backward_out_cpu_functional op; |
| 25362 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25363 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 25364 | return std::move(op.outputs_[0]).take(); |
| 25365 | } |
| 25366 | struct structured__upsample_bicubic2d_aa_backward_out_cpu_out final : public at::native::structured__upsample_bicubic2d_aa_backward_out_cpu { |
| 25367 | structured__upsample_bicubic2d_aa_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25368 | void set_output_strided( |
| 25369 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25370 | TensorOptions options, DimnameList names |
| 25371 | ) override { |
| 25372 | const auto& out = outputs_[output_idx].get(); |
| 25373 | resize_out(out, sizes, strides, options); |
| 25374 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25375 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25376 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25377 | } |
| 25378 | if (!names.empty()) { |
| 25379 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25380 | } |
| 25381 | // super must happen after, so that downstream can use maybe_get_output |
| 25382 | // to retrieve the output |
| 25383 | } |
| 25384 | void set_output_raw_strided( |
| 25385 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25386 | TensorOptions options, DimnameList names |
| 25387 | ) override { |
| 25388 | const auto& out = outputs_[output_idx].get(); |
| 25389 | resize_out(out, sizes, strides, options); |
| 25390 | if (!names.empty()) { |
| 25391 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25392 | } |
| 25393 | // super must happen after, so that downstream can use maybe_get_output |
| 25394 | // to retrieve the output |
| 25395 | } |
| 25396 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25397 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25398 | } |
| 25399 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25400 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25401 | }; |
| 25402 | at::Tensor & wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 25403 | structured__upsample_bicubic2d_aa_backward_out_cpu_out op(grad_input); |
| 25404 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 25405 | op.impl(grad_output, output_size, input_size, align_corners, scales_h, scales_w, op.maybe_get_output(0)); |
| 25406 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25407 | return grad_input; |
| 25408 | } |
| 25409 | struct structured_upsample_trilinear3d_out_cpu_functional final : public at::native::structured_upsample_trilinear3d_out_cpu { |
| 25410 | void set_output_strided( |
| 25411 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25412 | TensorOptions options, DimnameList names |
| 25413 | ) override { |
| 25414 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25415 | if (!names.empty()) { |
| 25416 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25417 | } |
| 25418 | // super must happen after, so that downstream can use maybe_get_output |
| 25419 | // to retrieve the output |
| 25420 | } |
| 25421 | void set_output_raw_strided( |
| 25422 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25423 | TensorOptions options, DimnameList names |
| 25424 | ) override { |
| 25425 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25426 | if (!names.empty()) { |
| 25427 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25428 | } |
| 25429 | // super must happen after, so that downstream can use maybe_get_output |
| 25430 | // to retrieve the output |
| 25431 | } |
| 25432 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25433 | return *outputs_[output_idx]; |
| 25434 | } |
| 25435 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25436 | }; |
| 25437 | at::Tensor wrapper_CPU_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) { |
| 25438 | structured_upsample_trilinear3d_out_cpu_functional op; |
| 25439 | op.meta(self, output_size, align_corners, scales_d, scales_h, scales_w); |
| 25440 | op.impl(self, output_size, align_corners, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 25441 | return std::move(op.outputs_[0]).take(); |
| 25442 | } |
| 25443 | struct structured_upsample_trilinear3d_out_cpu_out final : public at::native::structured_upsample_trilinear3d_out_cpu { |
| 25444 | structured_upsample_trilinear3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25445 | void set_output_strided( |
| 25446 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25447 | TensorOptions options, DimnameList names |
| 25448 | ) override { |
| 25449 | const auto& out = outputs_[output_idx].get(); |
| 25450 | resize_out(out, sizes, strides, options); |
| 25451 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25452 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25453 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25454 | } |
| 25455 | if (!names.empty()) { |
| 25456 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25457 | } |
| 25458 | // super must happen after, so that downstream can use maybe_get_output |
| 25459 | // to retrieve the output |
| 25460 | } |
| 25461 | void set_output_raw_strided( |
| 25462 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25463 | TensorOptions options, DimnameList names |
| 25464 | ) override { |
| 25465 | const auto& out = outputs_[output_idx].get(); |
| 25466 | resize_out(out, sizes, strides, options); |
| 25467 | if (!names.empty()) { |
| 25468 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25469 | } |
| 25470 | // super must happen after, so that downstream can use maybe_get_output |
| 25471 | // to retrieve the output |
| 25472 | } |
| 25473 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25474 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25475 | } |
| 25476 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25477 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25478 | }; |
| 25479 | at::Tensor & wrapper_CPU_upsample_trilinear3d_out_out(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, at::Tensor & out) { |
| 25480 | structured_upsample_trilinear3d_out_cpu_out op(out); |
| 25481 | op.meta(self, output_size, align_corners, scales_d, scales_h, scales_w); |
| 25482 | op.impl(self, output_size, align_corners, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 25483 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25484 | return out; |
| 25485 | } |
| 25486 | struct structured_upsample_trilinear3d_backward_out_cpu_functional final : public at::native::structured_upsample_trilinear3d_backward_out_cpu { |
| 25487 | void set_output_strided( |
| 25488 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25489 | TensorOptions options, DimnameList names |
| 25490 | ) override { |
| 25491 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25492 | if (!names.empty()) { |
| 25493 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25494 | } |
| 25495 | // super must happen after, so that downstream can use maybe_get_output |
| 25496 | // to retrieve the output |
| 25497 | } |
| 25498 | void set_output_raw_strided( |
| 25499 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25500 | TensorOptions options, DimnameList names |
| 25501 | ) override { |
| 25502 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25503 | if (!names.empty()) { |
| 25504 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25505 | } |
| 25506 | // super must happen after, so that downstream can use maybe_get_output |
| 25507 | // to retrieve the output |
| 25508 | } |
| 25509 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25510 | return *outputs_[output_idx]; |
| 25511 | } |
| 25512 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25513 | }; |
| 25514 | at::Tensor wrapper_CPU_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) { |
| 25515 | structured_upsample_trilinear3d_backward_out_cpu_functional op; |
| 25516 | op.meta(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 25517 | op.impl(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 25518 | return std::move(op.outputs_[0]).take(); |
| 25519 | } |
| 25520 | struct structured_upsample_trilinear3d_backward_out_cpu_out final : public at::native::structured_upsample_trilinear3d_backward_out_cpu { |
| 25521 | structured_upsample_trilinear3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25522 | void set_output_strided( |
| 25523 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25524 | TensorOptions options, DimnameList names |
| 25525 | ) override { |
| 25526 | const auto& out = outputs_[output_idx].get(); |
| 25527 | resize_out(out, sizes, strides, options); |
| 25528 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25529 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25530 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25531 | } |
| 25532 | if (!names.empty()) { |
| 25533 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25534 | } |
| 25535 | // super must happen after, so that downstream can use maybe_get_output |
| 25536 | // to retrieve the output |
| 25537 | } |
| 25538 | void set_output_raw_strided( |
| 25539 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25540 | TensorOptions options, DimnameList names |
| 25541 | ) override { |
| 25542 | const auto& out = outputs_[output_idx].get(); |
| 25543 | resize_out(out, sizes, strides, options); |
| 25544 | if (!names.empty()) { |
| 25545 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25546 | } |
| 25547 | // super must happen after, so that downstream can use maybe_get_output |
| 25548 | // to retrieve the output |
| 25549 | } |
| 25550 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25551 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25552 | } |
| 25553 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25554 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25555 | }; |
| 25556 | at::Tensor & wrapper_CPU_upsample_trilinear3d_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 25557 | structured_upsample_trilinear3d_backward_out_cpu_out op(grad_input); |
| 25558 | op.meta(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 25559 | op.impl(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 25560 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25561 | return grad_input; |
| 25562 | } |
| 25563 | struct structured_upsample_nearest1d_out_cpu_functional final : public at::native::structured_upsample_nearest1d_out_cpu { |
| 25564 | void set_output_strided( |
| 25565 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25566 | TensorOptions options, DimnameList names |
| 25567 | ) override { |
| 25568 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25569 | if (!names.empty()) { |
| 25570 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25571 | } |
| 25572 | // super must happen after, so that downstream can use maybe_get_output |
| 25573 | // to retrieve the output |
| 25574 | } |
| 25575 | void set_output_raw_strided( |
| 25576 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25577 | TensorOptions options, DimnameList names |
| 25578 | ) override { |
| 25579 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25580 | if (!names.empty()) { |
| 25581 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25582 | } |
| 25583 | // super must happen after, so that downstream can use maybe_get_output |
| 25584 | // to retrieve the output |
| 25585 | } |
| 25586 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25587 | return *outputs_[output_idx]; |
| 25588 | } |
| 25589 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25590 | }; |
| 25591 | at::Tensor wrapper_CPU_upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 25592 | structured_upsample_nearest1d_out_cpu_functional op; |
| 25593 | op.meta(self, output_size, scales); |
| 25594 | op.impl(self, output_size, scales, *op.outputs_[0]); |
| 25595 | return std::move(op.outputs_[0]).take(); |
| 25596 | } |
| 25597 | struct structured_upsample_nearest1d_out_cpu_out final : public at::native::structured_upsample_nearest1d_out_cpu { |
| 25598 | structured_upsample_nearest1d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25599 | void set_output_strided( |
| 25600 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25601 | TensorOptions options, DimnameList names |
| 25602 | ) override { |
| 25603 | const auto& out = outputs_[output_idx].get(); |
| 25604 | resize_out(out, sizes, strides, options); |
| 25605 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25606 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25607 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25608 | } |
| 25609 | if (!names.empty()) { |
| 25610 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25611 | } |
| 25612 | // super must happen after, so that downstream can use maybe_get_output |
| 25613 | // to retrieve the output |
| 25614 | } |
| 25615 | void set_output_raw_strided( |
| 25616 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25617 | TensorOptions options, DimnameList names |
| 25618 | ) override { |
| 25619 | const auto& out = outputs_[output_idx].get(); |
| 25620 | resize_out(out, sizes, strides, options); |
| 25621 | if (!names.empty()) { |
| 25622 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25623 | } |
| 25624 | // super must happen after, so that downstream can use maybe_get_output |
| 25625 | // to retrieve the output |
| 25626 | } |
| 25627 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25628 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25629 | } |
| 25630 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25631 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25632 | }; |
| 25633 | at::Tensor & wrapper_CPU_upsample_nearest1d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 25634 | structured_upsample_nearest1d_out_cpu_out op(out); |
| 25635 | op.meta(self, output_size, scales); |
| 25636 | op.impl(self, output_size, scales, op.maybe_get_output(0)); |
| 25637 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25638 | return out; |
| 25639 | } |
| 25640 | struct structured__upsample_nearest_exact1d_out_cpu_functional final : public at::native::structured__upsample_nearest_exact1d_out_cpu { |
| 25641 | void set_output_strided( |
| 25642 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25643 | TensorOptions options, DimnameList names |
| 25644 | ) override { |
| 25645 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25646 | if (!names.empty()) { |
| 25647 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25648 | } |
| 25649 | // super must happen after, so that downstream can use maybe_get_output |
| 25650 | // to retrieve the output |
| 25651 | } |
| 25652 | void set_output_raw_strided( |
| 25653 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25654 | TensorOptions options, DimnameList names |
| 25655 | ) override { |
| 25656 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25657 | if (!names.empty()) { |
| 25658 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25659 | } |
| 25660 | // super must happen after, so that downstream can use maybe_get_output |
| 25661 | // to retrieve the output |
| 25662 | } |
| 25663 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25664 | return *outputs_[output_idx]; |
| 25665 | } |
| 25666 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25667 | }; |
| 25668 | at::Tensor wrapper_CPU__upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 25669 | structured__upsample_nearest_exact1d_out_cpu_functional op; |
| 25670 | op.meta(self, output_size, scales); |
| 25671 | op.impl(self, output_size, scales, *op.outputs_[0]); |
| 25672 | return std::move(op.outputs_[0]).take(); |
| 25673 | } |
| 25674 | struct structured__upsample_nearest_exact1d_out_cpu_out final : public at::native::structured__upsample_nearest_exact1d_out_cpu { |
| 25675 | structured__upsample_nearest_exact1d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25676 | void set_output_strided( |
| 25677 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25678 | TensorOptions options, DimnameList names |
| 25679 | ) override { |
| 25680 | const auto& out = outputs_[output_idx].get(); |
| 25681 | resize_out(out, sizes, strides, options); |
| 25682 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25683 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25684 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25685 | } |
| 25686 | if (!names.empty()) { |
| 25687 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25688 | } |
| 25689 | // super must happen after, so that downstream can use maybe_get_output |
| 25690 | // to retrieve the output |
| 25691 | } |
| 25692 | void set_output_raw_strided( |
| 25693 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25694 | TensorOptions options, DimnameList names |
| 25695 | ) override { |
| 25696 | const auto& out = outputs_[output_idx].get(); |
| 25697 | resize_out(out, sizes, strides, options); |
| 25698 | if (!names.empty()) { |
| 25699 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25700 | } |
| 25701 | // super must happen after, so that downstream can use maybe_get_output |
| 25702 | // to retrieve the output |
| 25703 | } |
| 25704 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25705 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25706 | } |
| 25707 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25708 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25709 | }; |
| 25710 | at::Tensor & wrapper_CPU__upsample_nearest_exact1d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 25711 | structured__upsample_nearest_exact1d_out_cpu_out op(out); |
| 25712 | op.meta(self, output_size, scales); |
| 25713 | op.impl(self, output_size, scales, op.maybe_get_output(0)); |
| 25714 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25715 | return out; |
| 25716 | } |
| 25717 | struct structured_upsample_nearest1d_backward_out_cpu_functional final : public at::native::structured_upsample_nearest1d_backward_out_cpu { |
| 25718 | void set_output_strided( |
| 25719 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25720 | TensorOptions options, DimnameList names |
| 25721 | ) override { |
| 25722 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25723 | if (!names.empty()) { |
| 25724 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25725 | } |
| 25726 | // super must happen after, so that downstream can use maybe_get_output |
| 25727 | // to retrieve the output |
| 25728 | } |
| 25729 | void set_output_raw_strided( |
| 25730 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25731 | TensorOptions options, DimnameList names |
| 25732 | ) override { |
| 25733 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25734 | if (!names.empty()) { |
| 25735 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25736 | } |
| 25737 | // super must happen after, so that downstream can use maybe_get_output |
| 25738 | // to retrieve the output |
| 25739 | } |
| 25740 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25741 | return *outputs_[output_idx]; |
| 25742 | } |
| 25743 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25744 | }; |
| 25745 | at::Tensor wrapper_CPU_upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 25746 | structured_upsample_nearest1d_backward_out_cpu_functional op; |
| 25747 | op.meta(grad_output, output_size, input_size, scales); |
| 25748 | op.impl(grad_output, output_size, input_size, scales, *op.outputs_[0]); |
| 25749 | return std::move(op.outputs_[0]).take(); |
| 25750 | } |
| 25751 | struct structured_upsample_nearest1d_backward_out_cpu_out final : public at::native::structured_upsample_nearest1d_backward_out_cpu { |
| 25752 | structured_upsample_nearest1d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25753 | void set_output_strided( |
| 25754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25755 | TensorOptions options, DimnameList names |
| 25756 | ) override { |
| 25757 | const auto& out = outputs_[output_idx].get(); |
| 25758 | resize_out(out, sizes, strides, options); |
| 25759 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25760 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25761 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25762 | } |
| 25763 | if (!names.empty()) { |
| 25764 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25765 | } |
| 25766 | // super must happen after, so that downstream can use maybe_get_output |
| 25767 | // to retrieve the output |
| 25768 | } |
| 25769 | void set_output_raw_strided( |
| 25770 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25771 | TensorOptions options, DimnameList names |
| 25772 | ) override { |
| 25773 | const auto& out = outputs_[output_idx].get(); |
| 25774 | resize_out(out, sizes, strides, options); |
| 25775 | if (!names.empty()) { |
| 25776 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25777 | } |
| 25778 | // super must happen after, so that downstream can use maybe_get_output |
| 25779 | // to retrieve the output |
| 25780 | } |
| 25781 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25782 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25783 | } |
| 25784 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25785 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25786 | }; |
| 25787 | at::Tensor & wrapper_CPU_upsample_nearest1d_backward_out_grad_input(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 25788 | structured_upsample_nearest1d_backward_out_cpu_out op(grad_input); |
| 25789 | op.meta(grad_output, output_size, input_size, scales); |
| 25790 | op.impl(grad_output, output_size, input_size, scales, op.maybe_get_output(0)); |
| 25791 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25792 | return grad_input; |
| 25793 | } |
| 25794 | struct structured__upsample_nearest_exact1d_backward_out_cpu_functional final : public at::native::structured__upsample_nearest_exact1d_backward_out_cpu { |
| 25795 | void set_output_strided( |
| 25796 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25797 | TensorOptions options, DimnameList names |
| 25798 | ) override { |
| 25799 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25800 | if (!names.empty()) { |
| 25801 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25802 | } |
| 25803 | // super must happen after, so that downstream can use maybe_get_output |
| 25804 | // to retrieve the output |
| 25805 | } |
| 25806 | void set_output_raw_strided( |
| 25807 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25808 | TensorOptions options, DimnameList names |
| 25809 | ) override { |
| 25810 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25811 | if (!names.empty()) { |
| 25812 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25813 | } |
| 25814 | // super must happen after, so that downstream can use maybe_get_output |
| 25815 | // to retrieve the output |
| 25816 | } |
| 25817 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25818 | return *outputs_[output_idx]; |
| 25819 | } |
| 25820 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25821 | }; |
| 25822 | at::Tensor wrapper_CPU__upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 25823 | structured__upsample_nearest_exact1d_backward_out_cpu_functional op; |
| 25824 | op.meta(grad_output, output_size, input_size, scales); |
| 25825 | op.impl(grad_output, output_size, input_size, scales, *op.outputs_[0]); |
| 25826 | return std::move(op.outputs_[0]).take(); |
| 25827 | } |
| 25828 | struct structured__upsample_nearest_exact1d_backward_out_cpu_out final : public at::native::structured__upsample_nearest_exact1d_backward_out_cpu { |
| 25829 | structured__upsample_nearest_exact1d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25830 | void set_output_strided( |
| 25831 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25832 | TensorOptions options, DimnameList names |
| 25833 | ) override { |
| 25834 | const auto& out = outputs_[output_idx].get(); |
| 25835 | resize_out(out, sizes, strides, options); |
| 25836 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25837 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25838 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25839 | } |
| 25840 | if (!names.empty()) { |
| 25841 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25842 | } |
| 25843 | // super must happen after, so that downstream can use maybe_get_output |
| 25844 | // to retrieve the output |
| 25845 | } |
| 25846 | void set_output_raw_strided( |
| 25847 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25848 | TensorOptions options, DimnameList names |
| 25849 | ) override { |
| 25850 | const auto& out = outputs_[output_idx].get(); |
| 25851 | resize_out(out, sizes, strides, options); |
| 25852 | if (!names.empty()) { |
| 25853 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25854 | } |
| 25855 | // super must happen after, so that downstream can use maybe_get_output |
| 25856 | // to retrieve the output |
| 25857 | } |
| 25858 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25859 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25860 | } |
| 25861 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25862 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25863 | }; |
| 25864 | at::Tensor & wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 25865 | structured__upsample_nearest_exact1d_backward_out_cpu_out op(grad_input); |
| 25866 | op.meta(grad_output, output_size, input_size, scales); |
| 25867 | op.impl(grad_output, output_size, input_size, scales, op.maybe_get_output(0)); |
| 25868 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25869 | return grad_input; |
| 25870 | } |
| 25871 | struct structured_upsample_nearest2d_out_cpu_functional final : public at::native::structured_upsample_nearest2d_out_cpu { |
| 25872 | void set_output_strided( |
| 25873 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25874 | TensorOptions options, DimnameList names |
| 25875 | ) override { |
| 25876 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25877 | if (!names.empty()) { |
| 25878 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25879 | } |
| 25880 | // super must happen after, so that downstream can use maybe_get_output |
| 25881 | // to retrieve the output |
| 25882 | } |
| 25883 | void set_output_raw_strided( |
| 25884 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25885 | TensorOptions options, DimnameList names |
| 25886 | ) override { |
| 25887 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25888 | if (!names.empty()) { |
| 25889 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25890 | } |
| 25891 | // super must happen after, so that downstream can use maybe_get_output |
| 25892 | // to retrieve the output |
| 25893 | } |
| 25894 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25895 | return *outputs_[output_idx]; |
| 25896 | } |
| 25897 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25898 | }; |
| 25899 | at::Tensor wrapper_CPU_upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 25900 | structured_upsample_nearest2d_out_cpu_functional op; |
| 25901 | op.meta(self, output_size, scales_h, scales_w); |
| 25902 | op.impl(self, output_size, scales_h, scales_w, *op.outputs_[0]); |
| 25903 | return std::move(op.outputs_[0]).take(); |
| 25904 | } |
| 25905 | struct structured_upsample_nearest2d_out_cpu_out final : public at::native::structured_upsample_nearest2d_out_cpu { |
| 25906 | structured_upsample_nearest2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25907 | void set_output_strided( |
| 25908 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25909 | TensorOptions options, DimnameList names |
| 25910 | ) override { |
| 25911 | const auto& out = outputs_[output_idx].get(); |
| 25912 | resize_out(out, sizes, strides, options); |
| 25913 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25914 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25915 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25916 | } |
| 25917 | if (!names.empty()) { |
| 25918 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25919 | } |
| 25920 | // super must happen after, so that downstream can use maybe_get_output |
| 25921 | // to retrieve the output |
| 25922 | } |
| 25923 | void set_output_raw_strided( |
| 25924 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25925 | TensorOptions options, DimnameList names |
| 25926 | ) override { |
| 25927 | const auto& out = outputs_[output_idx].get(); |
| 25928 | resize_out(out, sizes, strides, options); |
| 25929 | if (!names.empty()) { |
| 25930 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25931 | } |
| 25932 | // super must happen after, so that downstream can use maybe_get_output |
| 25933 | // to retrieve the output |
| 25934 | } |
| 25935 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25936 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 25937 | } |
| 25938 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 25939 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 25940 | }; |
| 25941 | at::Tensor & wrapper_CPU_upsample_nearest2d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 25942 | structured_upsample_nearest2d_out_cpu_out op(out); |
| 25943 | op.meta(self, output_size, scales_h, scales_w); |
| 25944 | op.impl(self, output_size, scales_h, scales_w, op.maybe_get_output(0)); |
| 25945 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 25946 | return out; |
| 25947 | } |
| 25948 | struct structured__upsample_nearest_exact2d_out_cpu_functional final : public at::native::structured__upsample_nearest_exact2d_out_cpu { |
| 25949 | void set_output_strided( |
| 25950 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25951 | TensorOptions options, DimnameList names |
| 25952 | ) override { |
| 25953 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25954 | if (!names.empty()) { |
| 25955 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25956 | } |
| 25957 | // super must happen after, so that downstream can use maybe_get_output |
| 25958 | // to retrieve the output |
| 25959 | } |
| 25960 | void set_output_raw_strided( |
| 25961 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25962 | TensorOptions options, DimnameList names |
| 25963 | ) override { |
| 25964 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 25965 | if (!names.empty()) { |
| 25966 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 25967 | } |
| 25968 | // super must happen after, so that downstream can use maybe_get_output |
| 25969 | // to retrieve the output |
| 25970 | } |
| 25971 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 25972 | return *outputs_[output_idx]; |
| 25973 | } |
| 25974 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 25975 | }; |
| 25976 | at::Tensor wrapper_CPU__upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 25977 | structured__upsample_nearest_exact2d_out_cpu_functional op; |
| 25978 | op.meta(self, output_size, scales_h, scales_w); |
| 25979 | op.impl(self, output_size, scales_h, scales_w, *op.outputs_[0]); |
| 25980 | return std::move(op.outputs_[0]).take(); |
| 25981 | } |
| 25982 | struct structured__upsample_nearest_exact2d_out_cpu_out final : public at::native::structured__upsample_nearest_exact2d_out_cpu { |
| 25983 | structured__upsample_nearest_exact2d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 25984 | void set_output_strided( |
| 25985 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 25986 | TensorOptions options, DimnameList names |
| 25987 | ) override { |
| 25988 | const auto& out = outputs_[output_idx].get(); |
| 25989 | resize_out(out, sizes, strides, options); |
| 25990 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 25991 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 25992 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 25993 | } |
| 25994 | if (!names.empty()) { |
| 25995 | namedinference::propagate_names(outputs_[output_idx], names); |
| 25996 | } |
| 25997 | // super must happen after, so that downstream can use maybe_get_output |
| 25998 | // to retrieve the output |
| 25999 | } |
| 26000 | void set_output_raw_strided( |
| 26001 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26002 | TensorOptions options, DimnameList names |
| 26003 | ) override { |
| 26004 | const auto& out = outputs_[output_idx].get(); |
| 26005 | resize_out(out, sizes, strides, options); |
| 26006 | if (!names.empty()) { |
| 26007 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26008 | } |
| 26009 | // super must happen after, so that downstream can use maybe_get_output |
| 26010 | // to retrieve the output |
| 26011 | } |
| 26012 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26013 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26014 | } |
| 26015 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26016 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26017 | }; |
| 26018 | at::Tensor & wrapper_CPU__upsample_nearest_exact2d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 26019 | structured__upsample_nearest_exact2d_out_cpu_out op(out); |
| 26020 | op.meta(self, output_size, scales_h, scales_w); |
| 26021 | op.impl(self, output_size, scales_h, scales_w, op.maybe_get_output(0)); |
| 26022 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26023 | return out; |
| 26024 | } |
| 26025 | struct structured_upsample_nearest2d_backward_out_cpu_functional final : public at::native::structured_upsample_nearest2d_backward_out_cpu { |
| 26026 | void set_output_strided( |
| 26027 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26028 | TensorOptions options, DimnameList names |
| 26029 | ) override { |
| 26030 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26031 | if (!names.empty()) { |
| 26032 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26033 | } |
| 26034 | // super must happen after, so that downstream can use maybe_get_output |
| 26035 | // to retrieve the output |
| 26036 | } |
| 26037 | void set_output_raw_strided( |
| 26038 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26039 | TensorOptions options, DimnameList names |
| 26040 | ) override { |
| 26041 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26042 | if (!names.empty()) { |
| 26043 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26044 | } |
| 26045 | // super must happen after, so that downstream can use maybe_get_output |
| 26046 | // to retrieve the output |
| 26047 | } |
| 26048 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26049 | return *outputs_[output_idx]; |
| 26050 | } |
| 26051 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26052 | }; |
| 26053 | at::Tensor wrapper_CPU_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) { |
| 26054 | structured_upsample_nearest2d_backward_out_cpu_functional op; |
| 26055 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 26056 | op.impl(grad_output, output_size, input_size, scales_h, scales_w, *op.outputs_[0]); |
| 26057 | return std::move(op.outputs_[0]).take(); |
| 26058 | } |
| 26059 | struct structured_upsample_nearest2d_backward_out_cpu_out final : public at::native::structured_upsample_nearest2d_backward_out_cpu { |
| 26060 | structured_upsample_nearest2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26061 | void set_output_strided( |
| 26062 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26063 | TensorOptions options, DimnameList names |
| 26064 | ) override { |
| 26065 | const auto& out = outputs_[output_idx].get(); |
| 26066 | resize_out(out, sizes, strides, options); |
| 26067 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26068 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26069 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26070 | } |
| 26071 | if (!names.empty()) { |
| 26072 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26073 | } |
| 26074 | // super must happen after, so that downstream can use maybe_get_output |
| 26075 | // to retrieve the output |
| 26076 | } |
| 26077 | void set_output_raw_strided( |
| 26078 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26079 | TensorOptions options, DimnameList names |
| 26080 | ) override { |
| 26081 | const auto& out = outputs_[output_idx].get(); |
| 26082 | resize_out(out, sizes, strides, options); |
| 26083 | if (!names.empty()) { |
| 26084 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26085 | } |
| 26086 | // super must happen after, so that downstream can use maybe_get_output |
| 26087 | // to retrieve the output |
| 26088 | } |
| 26089 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26090 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26091 | } |
| 26092 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26093 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26094 | }; |
| 26095 | at::Tensor & wrapper_CPU_upsample_nearest2d_backward_out_grad_input(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 26096 | structured_upsample_nearest2d_backward_out_cpu_out op(grad_input); |
| 26097 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 26098 | op.impl(grad_output, output_size, input_size, scales_h, scales_w, op.maybe_get_output(0)); |
| 26099 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26100 | return grad_input; |
| 26101 | } |
| 26102 | struct structured__upsample_nearest_exact2d_backward_out_cpu_functional final : public at::native::structured__upsample_nearest_exact2d_backward_out_cpu { |
| 26103 | void set_output_strided( |
| 26104 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26105 | TensorOptions options, DimnameList names |
| 26106 | ) override { |
| 26107 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26108 | if (!names.empty()) { |
| 26109 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26110 | } |
| 26111 | // super must happen after, so that downstream can use maybe_get_output |
| 26112 | // to retrieve the output |
| 26113 | } |
| 26114 | void set_output_raw_strided( |
| 26115 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26116 | TensorOptions options, DimnameList names |
| 26117 | ) override { |
| 26118 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26119 | if (!names.empty()) { |
| 26120 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26121 | } |
| 26122 | // super must happen after, so that downstream can use maybe_get_output |
| 26123 | // to retrieve the output |
| 26124 | } |
| 26125 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26126 | return *outputs_[output_idx]; |
| 26127 | } |
| 26128 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26129 | }; |
| 26130 | at::Tensor wrapper_CPU__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) { |
| 26131 | structured__upsample_nearest_exact2d_backward_out_cpu_functional op; |
| 26132 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 26133 | op.impl(grad_output, output_size, input_size, scales_h, scales_w, *op.outputs_[0]); |
| 26134 | return std::move(op.outputs_[0]).take(); |
| 26135 | } |
| 26136 | struct structured__upsample_nearest_exact2d_backward_out_cpu_out final : public at::native::structured__upsample_nearest_exact2d_backward_out_cpu { |
| 26137 | structured__upsample_nearest_exact2d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26138 | void set_output_strided( |
| 26139 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26140 | TensorOptions options, DimnameList names |
| 26141 | ) override { |
| 26142 | const auto& out = outputs_[output_idx].get(); |
| 26143 | resize_out(out, sizes, strides, options); |
| 26144 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26145 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26146 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26147 | } |
| 26148 | if (!names.empty()) { |
| 26149 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26150 | } |
| 26151 | // super must happen after, so that downstream can use maybe_get_output |
| 26152 | // to retrieve the output |
| 26153 | } |
| 26154 | void set_output_raw_strided( |
| 26155 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26156 | TensorOptions options, DimnameList names |
| 26157 | ) override { |
| 26158 | const auto& out = outputs_[output_idx].get(); |
| 26159 | resize_out(out, sizes, strides, options); |
| 26160 | if (!names.empty()) { |
| 26161 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26162 | } |
| 26163 | // super must happen after, so that downstream can use maybe_get_output |
| 26164 | // to retrieve the output |
| 26165 | } |
| 26166 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26167 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26168 | } |
| 26169 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26170 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26171 | }; |
| 26172 | at::Tensor & wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 26173 | structured__upsample_nearest_exact2d_backward_out_cpu_out op(grad_input); |
| 26174 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 26175 | op.impl(grad_output, output_size, input_size, scales_h, scales_w, op.maybe_get_output(0)); |
| 26176 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26177 | return grad_input; |
| 26178 | } |
| 26179 | struct structured_upsample_nearest3d_out_cpu_functional final : public at::native::structured_upsample_nearest3d_out_cpu { |
| 26180 | void set_output_strided( |
| 26181 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26182 | TensorOptions options, DimnameList names |
| 26183 | ) override { |
| 26184 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26185 | if (!names.empty()) { |
| 26186 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26187 | } |
| 26188 | // super must happen after, so that downstream can use maybe_get_output |
| 26189 | // to retrieve the output |
| 26190 | } |
| 26191 | void set_output_raw_strided( |
| 26192 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26193 | TensorOptions options, DimnameList names |
| 26194 | ) override { |
| 26195 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26196 | if (!names.empty()) { |
| 26197 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26198 | } |
| 26199 | // super must happen after, so that downstream can use maybe_get_output |
| 26200 | // to retrieve the output |
| 26201 | } |
| 26202 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26203 | return *outputs_[output_idx]; |
| 26204 | } |
| 26205 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26206 | }; |
| 26207 | at::Tensor wrapper_CPU_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) { |
| 26208 | structured_upsample_nearest3d_out_cpu_functional op; |
| 26209 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 26210 | op.impl(self, output_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 26211 | return std::move(op.outputs_[0]).take(); |
| 26212 | } |
| 26213 | struct structured_upsample_nearest3d_out_cpu_out final : public at::native::structured_upsample_nearest3d_out_cpu { |
| 26214 | structured_upsample_nearest3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26215 | void set_output_strided( |
| 26216 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26217 | TensorOptions options, DimnameList names |
| 26218 | ) override { |
| 26219 | const auto& out = outputs_[output_idx].get(); |
| 26220 | resize_out(out, sizes, strides, options); |
| 26221 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26222 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26223 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26224 | } |
| 26225 | if (!names.empty()) { |
| 26226 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26227 | } |
| 26228 | // super must happen after, so that downstream can use maybe_get_output |
| 26229 | // to retrieve the output |
| 26230 | } |
| 26231 | void set_output_raw_strided( |
| 26232 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26233 | TensorOptions options, DimnameList names |
| 26234 | ) override { |
| 26235 | const auto& out = outputs_[output_idx].get(); |
| 26236 | resize_out(out, sizes, strides, options); |
| 26237 | if (!names.empty()) { |
| 26238 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26239 | } |
| 26240 | // super must happen after, so that downstream can use maybe_get_output |
| 26241 | // to retrieve the output |
| 26242 | } |
| 26243 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26244 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26245 | } |
| 26246 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26247 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26248 | }; |
| 26249 | at::Tensor & wrapper_CPU_upsample_nearest3d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 26250 | structured_upsample_nearest3d_out_cpu_out op(out); |
| 26251 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 26252 | op.impl(self, output_size, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 26253 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26254 | return out; |
| 26255 | } |
| 26256 | struct structured__upsample_nearest_exact3d_out_cpu_functional final : public at::native::structured__upsample_nearest_exact3d_out_cpu { |
| 26257 | void set_output_strided( |
| 26258 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26259 | TensorOptions options, DimnameList names |
| 26260 | ) override { |
| 26261 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26262 | if (!names.empty()) { |
| 26263 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26264 | } |
| 26265 | // super must happen after, so that downstream can use maybe_get_output |
| 26266 | // to retrieve the output |
| 26267 | } |
| 26268 | void set_output_raw_strided( |
| 26269 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26270 | TensorOptions options, DimnameList names |
| 26271 | ) override { |
| 26272 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26273 | if (!names.empty()) { |
| 26274 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26275 | } |
| 26276 | // super must happen after, so that downstream can use maybe_get_output |
| 26277 | // to retrieve the output |
| 26278 | } |
| 26279 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26280 | return *outputs_[output_idx]; |
| 26281 | } |
| 26282 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26283 | }; |
| 26284 | at::Tensor wrapper_CPU__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) { |
| 26285 | structured__upsample_nearest_exact3d_out_cpu_functional op; |
| 26286 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 26287 | op.impl(self, output_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 26288 | return std::move(op.outputs_[0]).take(); |
| 26289 | } |
| 26290 | struct structured__upsample_nearest_exact3d_out_cpu_out final : public at::native::structured__upsample_nearest_exact3d_out_cpu { |
| 26291 | structured__upsample_nearest_exact3d_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26292 | void set_output_strided( |
| 26293 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26294 | TensorOptions options, DimnameList names |
| 26295 | ) override { |
| 26296 | const auto& out = outputs_[output_idx].get(); |
| 26297 | resize_out(out, sizes, strides, options); |
| 26298 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26299 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26300 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26301 | } |
| 26302 | if (!names.empty()) { |
| 26303 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26304 | } |
| 26305 | // super must happen after, so that downstream can use maybe_get_output |
| 26306 | // to retrieve the output |
| 26307 | } |
| 26308 | void set_output_raw_strided( |
| 26309 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26310 | TensorOptions options, DimnameList names |
| 26311 | ) override { |
| 26312 | const auto& out = outputs_[output_idx].get(); |
| 26313 | resize_out(out, sizes, strides, options); |
| 26314 | if (!names.empty()) { |
| 26315 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26316 | } |
| 26317 | // super must happen after, so that downstream can use maybe_get_output |
| 26318 | // to retrieve the output |
| 26319 | } |
| 26320 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26321 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26322 | } |
| 26323 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26324 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26325 | }; |
| 26326 | at::Tensor & wrapper_CPU__upsample_nearest_exact3d_out_out(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 26327 | structured__upsample_nearest_exact3d_out_cpu_out op(out); |
| 26328 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 26329 | op.impl(self, output_size, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 26330 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26331 | return out; |
| 26332 | } |
| 26333 | struct structured_upsample_nearest3d_backward_out_cpu_functional final : public at::native::structured_upsample_nearest3d_backward_out_cpu { |
| 26334 | void set_output_strided( |
| 26335 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26336 | TensorOptions options, DimnameList names |
| 26337 | ) override { |
| 26338 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26339 | if (!names.empty()) { |
| 26340 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26341 | } |
| 26342 | // super must happen after, so that downstream can use maybe_get_output |
| 26343 | // to retrieve the output |
| 26344 | } |
| 26345 | void set_output_raw_strided( |
| 26346 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26347 | TensorOptions options, DimnameList names |
| 26348 | ) override { |
| 26349 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26350 | if (!names.empty()) { |
| 26351 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26352 | } |
| 26353 | // super must happen after, so that downstream can use maybe_get_output |
| 26354 | // to retrieve the output |
| 26355 | } |
| 26356 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26357 | return *outputs_[output_idx]; |
| 26358 | } |
| 26359 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26360 | }; |
| 26361 | at::Tensor wrapper_CPU_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) { |
| 26362 | structured_upsample_nearest3d_backward_out_cpu_functional op; |
| 26363 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 26364 | op.impl(grad_output, output_size, input_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 26365 | return std::move(op.outputs_[0]).take(); |
| 26366 | } |
| 26367 | struct structured_upsample_nearest3d_backward_out_cpu_out final : public at::native::structured_upsample_nearest3d_backward_out_cpu { |
| 26368 | structured_upsample_nearest3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26369 | void set_output_strided( |
| 26370 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26371 | TensorOptions options, DimnameList names |
| 26372 | ) override { |
| 26373 | const auto& out = outputs_[output_idx].get(); |
| 26374 | resize_out(out, sizes, strides, options); |
| 26375 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26376 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26377 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26378 | } |
| 26379 | if (!names.empty()) { |
| 26380 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26381 | } |
| 26382 | // super must happen after, so that downstream can use maybe_get_output |
| 26383 | // to retrieve the output |
| 26384 | } |
| 26385 | void set_output_raw_strided( |
| 26386 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26387 | TensorOptions options, DimnameList names |
| 26388 | ) override { |
| 26389 | const auto& out = outputs_[output_idx].get(); |
| 26390 | resize_out(out, sizes, strides, options); |
| 26391 | if (!names.empty()) { |
| 26392 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26393 | } |
| 26394 | // super must happen after, so that downstream can use maybe_get_output |
| 26395 | // to retrieve the output |
| 26396 | } |
| 26397 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26398 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26399 | } |
| 26400 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26401 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26402 | }; |
| 26403 | at::Tensor & wrapper_CPU_upsample_nearest3d_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 26404 | structured_upsample_nearest3d_backward_out_cpu_out op(grad_input); |
| 26405 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 26406 | op.impl(grad_output, output_size, input_size, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 26407 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26408 | return grad_input; |
| 26409 | } |
| 26410 | struct structured__upsample_nearest_exact3d_backward_out_cpu_functional final : public at::native::structured__upsample_nearest_exact3d_backward_out_cpu { |
| 26411 | void set_output_strided( |
| 26412 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26413 | TensorOptions options, DimnameList names |
| 26414 | ) override { |
| 26415 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26416 | if (!names.empty()) { |
| 26417 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26418 | } |
| 26419 | // super must happen after, so that downstream can use maybe_get_output |
| 26420 | // to retrieve the output |
| 26421 | } |
| 26422 | void set_output_raw_strided( |
| 26423 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26424 | TensorOptions options, DimnameList names |
| 26425 | ) override { |
| 26426 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26427 | if (!names.empty()) { |
| 26428 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26429 | } |
| 26430 | // super must happen after, so that downstream can use maybe_get_output |
| 26431 | // to retrieve the output |
| 26432 | } |
| 26433 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26434 | return *outputs_[output_idx]; |
| 26435 | } |
| 26436 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26437 | }; |
| 26438 | at::Tensor wrapper_CPU__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) { |
| 26439 | structured__upsample_nearest_exact3d_backward_out_cpu_functional op; |
| 26440 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 26441 | op.impl(grad_output, output_size, input_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 26442 | return std::move(op.outputs_[0]).take(); |
| 26443 | } |
| 26444 | struct structured__upsample_nearest_exact3d_backward_out_cpu_out final : public at::native::structured__upsample_nearest_exact3d_backward_out_cpu { |
| 26445 | structured__upsample_nearest_exact3d_backward_out_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26446 | void set_output_strided( |
| 26447 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26448 | TensorOptions options, DimnameList names |
| 26449 | ) override { |
| 26450 | const auto& out = outputs_[output_idx].get(); |
| 26451 | resize_out(out, sizes, strides, options); |
| 26452 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26453 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26454 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26455 | } |
| 26456 | if (!names.empty()) { |
| 26457 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26458 | } |
| 26459 | // super must happen after, so that downstream can use maybe_get_output |
| 26460 | // to retrieve the output |
| 26461 | } |
| 26462 | void set_output_raw_strided( |
| 26463 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26464 | TensorOptions options, DimnameList names |
| 26465 | ) override { |
| 26466 | const auto& out = outputs_[output_idx].get(); |
| 26467 | resize_out(out, sizes, strides, options); |
| 26468 | if (!names.empty()) { |
| 26469 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26470 | } |
| 26471 | // super must happen after, so that downstream can use maybe_get_output |
| 26472 | // to retrieve the output |
| 26473 | } |
| 26474 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26475 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26476 | } |
| 26477 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26478 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26479 | }; |
| 26480 | at::Tensor & wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input(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, at::Tensor & grad_input) { |
| 26481 | structured__upsample_nearest_exact3d_backward_out_cpu_out op(grad_input); |
| 26482 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 26483 | op.impl(grad_output, output_size, input_size, scales_d, scales_h, scales_w, op.maybe_get_output(0)); |
| 26484 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26485 | return grad_input; |
| 26486 | } |
| 26487 | struct structured_sigmoid_backward_out_functional final : public at::native::structured_sigmoid_backward_out { |
| 26488 | void set_output_strided( |
| 26489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26490 | TensorOptions options, DimnameList names |
| 26491 | ) override { |
| 26492 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26493 | if (!names.empty()) { |
| 26494 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26495 | } |
| 26496 | // super must happen after, so that downstream can use maybe_get_output |
| 26497 | // to retrieve the output |
| 26498 | at::native::structured_sigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26499 | } |
| 26500 | void set_output_raw_strided( |
| 26501 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26502 | TensorOptions options, DimnameList names |
| 26503 | ) override { |
| 26504 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26505 | if (!names.empty()) { |
| 26506 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26507 | } |
| 26508 | // super must happen after, so that downstream can use maybe_get_output |
| 26509 | // to retrieve the output |
| 26510 | at::native::structured_sigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26511 | } |
| 26512 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26513 | return *outputs_[output_idx]; |
| 26514 | } |
| 26515 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26516 | }; |
| 26517 | at::Tensor wrapper_CPU_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 26518 | structured_sigmoid_backward_out_functional op; |
| 26519 | op.meta(grad_output, output); |
| 26520 | op.impl(grad_output, output, *op.outputs_[0]); |
| 26521 | return std::move(op.outputs_[0]).take(); |
| 26522 | } |
| 26523 | struct structured_sigmoid_backward_out_out final : public at::native::structured_sigmoid_backward_out { |
| 26524 | structured_sigmoid_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26525 | void set_output_strided( |
| 26526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26527 | TensorOptions options, DimnameList names |
| 26528 | ) override { |
| 26529 | const auto& out = outputs_[output_idx].get(); |
| 26530 | resize_out(out, sizes, strides, options); |
| 26531 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26532 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26533 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26534 | } |
| 26535 | if (!names.empty()) { |
| 26536 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26537 | } |
| 26538 | // super must happen after, so that downstream can use maybe_get_output |
| 26539 | // to retrieve the output |
| 26540 | at::native::structured_sigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26541 | } |
| 26542 | void set_output_raw_strided( |
| 26543 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26544 | TensorOptions options, DimnameList names |
| 26545 | ) override { |
| 26546 | const auto& out = outputs_[output_idx].get(); |
| 26547 | resize_out(out, sizes, strides, options); |
| 26548 | if (!names.empty()) { |
| 26549 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26550 | } |
| 26551 | // super must happen after, so that downstream can use maybe_get_output |
| 26552 | // to retrieve the output |
| 26553 | at::native::structured_sigmoid_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26554 | } |
| 26555 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26556 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26557 | } |
| 26558 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26559 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26560 | }; |
| 26561 | at::Tensor & wrapper_CPU_sigmoid_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 26562 | structured_sigmoid_backward_out_out op(grad_input); |
| 26563 | op.meta(grad_output, output); |
| 26564 | op.impl(grad_output, output, op.maybe_get_output(0)); |
| 26565 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26566 | return grad_input; |
| 26567 | } |
| 26568 | struct structured_logit_backward_out_functional final : public at::native::structured_logit_backward_out { |
| 26569 | void set_output_strided( |
| 26570 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26571 | TensorOptions options, DimnameList names |
| 26572 | ) override { |
| 26573 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26574 | if (!names.empty()) { |
| 26575 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26576 | } |
| 26577 | // super must happen after, so that downstream can use maybe_get_output |
| 26578 | // to retrieve the output |
| 26579 | at::native::structured_logit_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26580 | } |
| 26581 | void set_output_raw_strided( |
| 26582 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26583 | TensorOptions options, DimnameList names |
| 26584 | ) override { |
| 26585 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26586 | if (!names.empty()) { |
| 26587 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26588 | } |
| 26589 | // super must happen after, so that downstream can use maybe_get_output |
| 26590 | // to retrieve the output |
| 26591 | at::native::structured_logit_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26592 | } |
| 26593 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26594 | return *outputs_[output_idx]; |
| 26595 | } |
| 26596 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26597 | }; |
| 26598 | at::Tensor wrapper_CPU_logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 26599 | structured_logit_backward_out_functional op; |
| 26600 | op.meta(grad_output, self, eps); |
| 26601 | op.impl(grad_output, self, eps, *op.outputs_[0]); |
| 26602 | return std::move(op.outputs_[0]).take(); |
| 26603 | } |
| 26604 | struct structured_logit_backward_out_out final : public at::native::structured_logit_backward_out { |
| 26605 | structured_logit_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26606 | void set_output_strided( |
| 26607 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26608 | TensorOptions options, DimnameList names |
| 26609 | ) override { |
| 26610 | const auto& out = outputs_[output_idx].get(); |
| 26611 | resize_out(out, sizes, strides, options); |
| 26612 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26613 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26614 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26615 | } |
| 26616 | if (!names.empty()) { |
| 26617 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26618 | } |
| 26619 | // super must happen after, so that downstream can use maybe_get_output |
| 26620 | // to retrieve the output |
| 26621 | at::native::structured_logit_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26622 | } |
| 26623 | void set_output_raw_strided( |
| 26624 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26625 | TensorOptions options, DimnameList names |
| 26626 | ) override { |
| 26627 | const auto& out = outputs_[output_idx].get(); |
| 26628 | resize_out(out, sizes, strides, options); |
| 26629 | if (!names.empty()) { |
| 26630 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26631 | } |
| 26632 | // super must happen after, so that downstream can use maybe_get_output |
| 26633 | // to retrieve the output |
| 26634 | at::native::structured_logit_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26635 | } |
| 26636 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26637 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26638 | } |
| 26639 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26640 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26641 | }; |
| 26642 | at::Tensor & wrapper_CPU_logit_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input) { |
| 26643 | structured_logit_backward_out_out op(grad_input); |
| 26644 | op.meta(grad_output, self, eps); |
| 26645 | op.impl(grad_output, self, eps, op.maybe_get_output(0)); |
| 26646 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26647 | return grad_input; |
| 26648 | } |
| 26649 | struct structured_tanh_backward_out_functional final : public at::native::structured_tanh_backward_out { |
| 26650 | void set_output_strided( |
| 26651 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26652 | TensorOptions options, DimnameList names |
| 26653 | ) override { |
| 26654 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26655 | if (!names.empty()) { |
| 26656 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26657 | } |
| 26658 | // super must happen after, so that downstream can use maybe_get_output |
| 26659 | // to retrieve the output |
| 26660 | at::native::structured_tanh_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26661 | } |
| 26662 | void set_output_raw_strided( |
| 26663 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26664 | TensorOptions options, DimnameList names |
| 26665 | ) override { |
| 26666 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26667 | if (!names.empty()) { |
| 26668 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26669 | } |
| 26670 | // super must happen after, so that downstream can use maybe_get_output |
| 26671 | // to retrieve the output |
| 26672 | at::native::structured_tanh_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26673 | } |
| 26674 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26675 | return *outputs_[output_idx]; |
| 26676 | } |
| 26677 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26678 | }; |
| 26679 | at::Tensor wrapper_CPU_tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 26680 | structured_tanh_backward_out_functional op; |
| 26681 | op.meta(grad_output, output); |
| 26682 | op.impl(grad_output, output, *op.outputs_[0]); |
| 26683 | return std::move(op.outputs_[0]).take(); |
| 26684 | } |
| 26685 | struct structured_tanh_backward_out_out final : public at::native::structured_tanh_backward_out { |
| 26686 | structured_tanh_backward_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26687 | void set_output_strided( |
| 26688 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26689 | TensorOptions options, DimnameList names |
| 26690 | ) override { |
| 26691 | const auto& out = outputs_[output_idx].get(); |
| 26692 | resize_out(out, sizes, strides, options); |
| 26693 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26694 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26695 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26696 | } |
| 26697 | if (!names.empty()) { |
| 26698 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26699 | } |
| 26700 | // super must happen after, so that downstream can use maybe_get_output |
| 26701 | // to retrieve the output |
| 26702 | at::native::structured_tanh_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26703 | } |
| 26704 | void set_output_raw_strided( |
| 26705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26706 | TensorOptions options, DimnameList names |
| 26707 | ) override { |
| 26708 | const auto& out = outputs_[output_idx].get(); |
| 26709 | resize_out(out, sizes, strides, options); |
| 26710 | if (!names.empty()) { |
| 26711 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26712 | } |
| 26713 | // super must happen after, so that downstream can use maybe_get_output |
| 26714 | // to retrieve the output |
| 26715 | at::native::structured_tanh_backward_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26716 | } |
| 26717 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26718 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26719 | } |
| 26720 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26721 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26722 | }; |
| 26723 | at::Tensor & wrapper_CPU_tanh_backward_out_grad_input(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 26724 | structured_tanh_backward_out_out op(grad_input); |
| 26725 | op.meta(grad_output, output); |
| 26726 | op.impl(grad_output, output, op.maybe_get_output(0)); |
| 26727 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26728 | return grad_input; |
| 26729 | } |
| 26730 | struct structured_slow_conv_transpose2d_structured_cpu_functional final : public at::native::structured_slow_conv_transpose2d_structured_cpu { |
| 26731 | void set_output_strided( |
| 26732 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26733 | TensorOptions options, DimnameList names |
| 26734 | ) override { |
| 26735 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26736 | if (!names.empty()) { |
| 26737 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26738 | } |
| 26739 | // super must happen after, so that downstream can use maybe_get_output |
| 26740 | // to retrieve the output |
| 26741 | } |
| 26742 | void set_output_raw_strided( |
| 26743 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26744 | TensorOptions options, DimnameList names |
| 26745 | ) override { |
| 26746 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26747 | if (!names.empty()) { |
| 26748 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26749 | } |
| 26750 | // super must happen after, so that downstream can use maybe_get_output |
| 26751 | // to retrieve the output |
| 26752 | } |
| 26753 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26754 | return *outputs_[output_idx]; |
| 26755 | } |
| 26756 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26757 | }; |
| 26758 | at::Tensor wrapper_CPU_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) { |
| 26759 | structured_slow_conv_transpose2d_structured_cpu_functional op; |
| 26760 | op.meta(self, weight, kernel_size, ((bias.has_value() && (*bias).defined()) ? at::OptionalTensorRef(*bias) : at::OptionalTensorRef()), stride, padding, output_padding, dilation); |
| 26761 | op.impl(self, weight, kernel_size, ((bias.has_value() && (*bias).defined()) ? at::OptionalTensorRef(*bias) : at::OptionalTensorRef()), stride, padding, output_padding, dilation, *op.outputs_[0]); |
| 26762 | return std::move(op.outputs_[0]).take(); |
| 26763 | } |
| 26764 | struct structured_slow_conv_transpose2d_structured_cpu_out final : public at::native::structured_slow_conv_transpose2d_structured_cpu { |
| 26765 | structured_slow_conv_transpose2d_structured_cpu_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26766 | void set_output_strided( |
| 26767 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26768 | TensorOptions options, DimnameList names |
| 26769 | ) override { |
| 26770 | const auto& out = outputs_[output_idx].get(); |
| 26771 | resize_out(out, sizes, strides, options); |
| 26772 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26773 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26774 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26775 | } |
| 26776 | if (!names.empty()) { |
| 26777 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26778 | } |
| 26779 | // super must happen after, so that downstream can use maybe_get_output |
| 26780 | // to retrieve the output |
| 26781 | } |
| 26782 | void set_output_raw_strided( |
| 26783 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26784 | TensorOptions options, DimnameList names |
| 26785 | ) override { |
| 26786 | const auto& out = outputs_[output_idx].get(); |
| 26787 | resize_out(out, sizes, strides, options); |
| 26788 | if (!names.empty()) { |
| 26789 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26790 | } |
| 26791 | // super must happen after, so that downstream can use maybe_get_output |
| 26792 | // to retrieve the output |
| 26793 | } |
| 26794 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26795 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26796 | } |
| 26797 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26798 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26799 | }; |
| 26800 | at::Tensor & wrapper_CPU_slow_conv_transpose2d_out_out(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, at::Tensor & out) { |
| 26801 | structured_slow_conv_transpose2d_structured_cpu_out op(out); |
| 26802 | op.meta(self, weight, kernel_size, ((bias.has_value() && (*bias).defined()) ? at::OptionalTensorRef(*bias) : at::OptionalTensorRef()), stride, padding, output_padding, dilation); |
| 26803 | op.impl(self, weight, kernel_size, ((bias.has_value() && (*bias).defined()) ? at::OptionalTensorRef(*bias) : at::OptionalTensorRef()), stride, padding, output_padding, dilation, op.maybe_get_output(0)); |
| 26804 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26805 | return out; |
| 26806 | } |
| 26807 | namespace { |
| 26808 | at::Tensor wrapper_CPU__slow_conv_transpose3d(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) { |
| 26809 | // No device check |
| 26810 | // DeviceGuard omitted |
| 26811 | return at::native::slow_conv_transpose3d_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation); |
| 26812 | } |
| 26813 | } // anonymous namespace |
| 26814 | namespace { |
| 26815 | at::Tensor & wrapper_CPU_out_slow_conv_transpose3d_out(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 26816 | // No device check |
| 26817 | // DeviceGuard omitted |
| 26818 | return at::native::slow_conv_transpose3d_out_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation, out); |
| 26819 | } |
| 26820 | } // anonymous namespace |
| 26821 | namespace { |
| 26822 | at::Tensor wrapper_CPU___slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 26823 | // No device check |
| 26824 | // DeviceGuard omitted |
| 26825 | return at::native::slow_conv2d_forward_cpu(self, weight, kernel_size, bias, stride, padding); |
| 26826 | } |
| 26827 | } // anonymous namespace |
| 26828 | namespace { |
| 26829 | at::Tensor & wrapper_CPU_output__slow_conv2d_forward_out(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::Tensor & output) { |
| 26830 | // No device check |
| 26831 | // DeviceGuard omitted |
| 26832 | return at::native::slow_conv2d_forward_out_cpu(self, weight, kernel_size, bias, stride, padding, output); |
| 26833 | } |
| 26834 | } // anonymous namespace |
| 26835 | namespace { |
| 26836 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_grad_input__slow_conv2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { |
| 26837 | // No device check |
| 26838 | // DeviceGuard omitted |
| 26839 | return at::native::slow_conv2d_backward_out_cpu(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); |
| 26840 | } |
| 26841 | } // anonymous namespace |
| 26842 | namespace { |
| 26843 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_output_mask__slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) { |
| 26844 | // No device check |
| 26845 | // DeviceGuard omitted |
| 26846 | return at::native::slow_conv2d_backward_cpu(grad_output, self, weight, kernel_size, stride, padding, output_mask); |
| 26847 | } |
| 26848 | } // anonymous namespace |
| 26849 | namespace { |
| 26850 | at::Tensor wrapper_CPU__slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding) { |
| 26851 | // No device check |
| 26852 | // DeviceGuard omitted |
| 26853 | return at::native::slow_conv3d_forward_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding)); |
| 26854 | } |
| 26855 | } // anonymous namespace |
| 26856 | namespace { |
| 26857 | at::Tensor & wrapper_CPU_output_slow_conv3d_forward_out(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) { |
| 26858 | // No device check |
| 26859 | // DeviceGuard omitted |
| 26860 | return at::native::slow_conv3d_forward_out_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), output); |
| 26861 | } |
| 26862 | } // anonymous namespace |
| 26863 | namespace { |
| 26864 | at::Tensor wrapper_CPU__slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation) { |
| 26865 | // No device check |
| 26866 | // DeviceGuard omitted |
| 26867 | return at::native::slow_conv_dilated2d_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), dilation); |
| 26868 | } |
| 26869 | } // anonymous namespace |
| 26870 | namespace { |
| 26871 | at::Tensor wrapper_CPU__slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation) { |
| 26872 | // No device check |
| 26873 | // DeviceGuard omitted |
| 26874 | return at::native::slow_conv_dilated3d_cpu(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), dilation); |
| 26875 | } |
| 26876 | } // anonymous namespace |
| 26877 | namespace { |
| 26878 | at::Tensor wrapper_CPU__col2im(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 26879 | // No device check |
| 26880 | // DeviceGuard omitted |
| 26881 | return at::native::col2im_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size), kernel_size, dilation, padding, stride); |
| 26882 | } |
| 26883 | } // anonymous namespace |
| 26884 | namespace { |
| 26885 | at::Tensor & wrapper_CPU_out_col2im_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 26886 | // No device check |
| 26887 | // DeviceGuard omitted |
| 26888 | return at::native::col2im_out_cpu(self, C10_AS_INTARRAYREF_SLOW(output_size), kernel_size, dilation, padding, stride, out); |
| 26889 | } |
| 26890 | } // anonymous namespace |
| 26891 | namespace { |
| 26892 | at::Tensor wrapper_CPU__im2col(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 26893 | // No device check |
| 26894 | // DeviceGuard omitted |
| 26895 | return at::native::im2col_cpu(self, kernel_size, dilation, padding, stride); |
| 26896 | } |
| 26897 | } // anonymous namespace |
| 26898 | namespace { |
| 26899 | at::Tensor & wrapper_CPU_out_im2col_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 26900 | // No device check |
| 26901 | // DeviceGuard omitted |
| 26902 | return at::native::im2col_out_cpu(self, kernel_size, dilation, padding, stride, out); |
| 26903 | } |
| 26904 | } // anonymous namespace |
| 26905 | struct structured_isposinf_out_functional final : public at::native::structured_isposinf_out { |
| 26906 | void set_output_strided( |
| 26907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26908 | TensorOptions options, DimnameList names |
| 26909 | ) override { |
| 26910 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26911 | if (!names.empty()) { |
| 26912 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26913 | } |
| 26914 | // super must happen after, so that downstream can use maybe_get_output |
| 26915 | // to retrieve the output |
| 26916 | at::native::structured_isposinf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26917 | } |
| 26918 | void set_output_raw_strided( |
| 26919 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26920 | TensorOptions options, DimnameList names |
| 26921 | ) override { |
| 26922 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26923 | if (!names.empty()) { |
| 26924 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26925 | } |
| 26926 | // super must happen after, so that downstream can use maybe_get_output |
| 26927 | // to retrieve the output |
| 26928 | at::native::structured_isposinf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26929 | } |
| 26930 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26931 | return *outputs_[output_idx]; |
| 26932 | } |
| 26933 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 26934 | }; |
| 26935 | at::Tensor wrapper_CPU_isposinf(const at::Tensor & self) { |
| 26936 | structured_isposinf_out_functional op; |
| 26937 | op.meta(self); |
| 26938 | op.impl(self, *op.outputs_[0]); |
| 26939 | return std::move(op.outputs_[0]).take(); |
| 26940 | } |
| 26941 | struct structured_isposinf_out_out final : public at::native::structured_isposinf_out { |
| 26942 | structured_isposinf_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 26943 | void set_output_strided( |
| 26944 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26945 | TensorOptions options, DimnameList names |
| 26946 | ) override { |
| 26947 | const auto& out = outputs_[output_idx].get(); |
| 26948 | resize_out(out, sizes, strides, options); |
| 26949 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 26950 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 26951 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 26952 | } |
| 26953 | if (!names.empty()) { |
| 26954 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26955 | } |
| 26956 | // super must happen after, so that downstream can use maybe_get_output |
| 26957 | // to retrieve the output |
| 26958 | at::native::structured_isposinf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26959 | } |
| 26960 | void set_output_raw_strided( |
| 26961 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26962 | TensorOptions options, DimnameList names |
| 26963 | ) override { |
| 26964 | const auto& out = outputs_[output_idx].get(); |
| 26965 | resize_out(out, sizes, strides, options); |
| 26966 | if (!names.empty()) { |
| 26967 | namedinference::propagate_names(outputs_[output_idx], names); |
| 26968 | } |
| 26969 | // super must happen after, so that downstream can use maybe_get_output |
| 26970 | // to retrieve the output |
| 26971 | at::native::structured_isposinf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26972 | } |
| 26973 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 26974 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 26975 | } |
| 26976 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 26977 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 26978 | }; |
| 26979 | at::Tensor & wrapper_CPU_isposinf_out_out(const at::Tensor & self, at::Tensor & out) { |
| 26980 | structured_isposinf_out_out op(out); |
| 26981 | op.meta(self); |
| 26982 | op.impl(self, op.maybe_get_output(0)); |
| 26983 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 26984 | return out; |
| 26985 | } |
| 26986 | struct structured_isneginf_out_functional final : public at::native::structured_isneginf_out { |
| 26987 | void set_output_strided( |
| 26988 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 26989 | TensorOptions options, DimnameList names |
| 26990 | ) override { |
| 26991 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 26992 | if (!names.empty()) { |
| 26993 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 26994 | } |
| 26995 | // super must happen after, so that downstream can use maybe_get_output |
| 26996 | // to retrieve the output |
| 26997 | at::native::structured_isneginf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 26998 | } |
| 26999 | void set_output_raw_strided( |
| 27000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27001 | TensorOptions options, DimnameList names |
| 27002 | ) override { |
| 27003 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27004 | if (!names.empty()) { |
| 27005 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27006 | } |
| 27007 | // super must happen after, so that downstream can use maybe_get_output |
| 27008 | // to retrieve the output |
| 27009 | at::native::structured_isneginf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27010 | } |
| 27011 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27012 | return *outputs_[output_idx]; |
| 27013 | } |
| 27014 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27015 | }; |
| 27016 | at::Tensor wrapper_CPU_isneginf(const at::Tensor & self) { |
| 27017 | structured_isneginf_out_functional op; |
| 27018 | op.meta(self); |
| 27019 | op.impl(self, *op.outputs_[0]); |
| 27020 | return std::move(op.outputs_[0]).take(); |
| 27021 | } |
| 27022 | struct structured_isneginf_out_out final : public at::native::structured_isneginf_out { |
| 27023 | structured_isneginf_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27024 | void set_output_strided( |
| 27025 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27026 | TensorOptions options, DimnameList names |
| 27027 | ) override { |
| 27028 | const auto& out = outputs_[output_idx].get(); |
| 27029 | resize_out(out, sizes, strides, options); |
| 27030 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27031 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27032 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27033 | } |
| 27034 | if (!names.empty()) { |
| 27035 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27036 | } |
| 27037 | // super must happen after, so that downstream can use maybe_get_output |
| 27038 | // to retrieve the output |
| 27039 | at::native::structured_isneginf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27040 | } |
| 27041 | void set_output_raw_strided( |
| 27042 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27043 | TensorOptions options, DimnameList names |
| 27044 | ) override { |
| 27045 | const auto& out = outputs_[output_idx].get(); |
| 27046 | resize_out(out, sizes, strides, options); |
| 27047 | if (!names.empty()) { |
| 27048 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27049 | } |
| 27050 | // super must happen after, so that downstream can use maybe_get_output |
| 27051 | // to retrieve the output |
| 27052 | at::native::structured_isneginf_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27053 | } |
| 27054 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27055 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27056 | } |
| 27057 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27058 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27059 | }; |
| 27060 | at::Tensor & wrapper_CPU_isneginf_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27061 | structured_isneginf_out_out op(out); |
| 27062 | op.meta(self); |
| 27063 | op.impl(self, op.maybe_get_output(0)); |
| 27064 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27065 | return out; |
| 27066 | } |
| 27067 | struct structured_special_entr_out_functional final : public at::native::structured_special_entr_out { |
| 27068 | void set_output_strided( |
| 27069 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27070 | TensorOptions options, DimnameList names |
| 27071 | ) override { |
| 27072 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27073 | if (!names.empty()) { |
| 27074 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27075 | } |
| 27076 | // super must happen after, so that downstream can use maybe_get_output |
| 27077 | // to retrieve the output |
| 27078 | at::native::structured_special_entr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27079 | } |
| 27080 | void set_output_raw_strided( |
| 27081 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27082 | TensorOptions options, DimnameList names |
| 27083 | ) override { |
| 27084 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27085 | if (!names.empty()) { |
| 27086 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27087 | } |
| 27088 | // super must happen after, so that downstream can use maybe_get_output |
| 27089 | // to retrieve the output |
| 27090 | at::native::structured_special_entr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27091 | } |
| 27092 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27093 | return *outputs_[output_idx]; |
| 27094 | } |
| 27095 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27096 | }; |
| 27097 | at::Tensor wrapper_CPU_special_entr(const at::Tensor & self) { |
| 27098 | structured_special_entr_out_functional op; |
| 27099 | op.meta(self); |
| 27100 | op.impl(self, *op.outputs_[0]); |
| 27101 | return std::move(op.outputs_[0]).take(); |
| 27102 | } |
| 27103 | struct structured_special_entr_out_out final : public at::native::structured_special_entr_out { |
| 27104 | structured_special_entr_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27105 | void set_output_strided( |
| 27106 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27107 | TensorOptions options, DimnameList names |
| 27108 | ) override { |
| 27109 | const auto& out = outputs_[output_idx].get(); |
| 27110 | resize_out(out, sizes, strides, options); |
| 27111 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27112 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27113 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27114 | } |
| 27115 | if (!names.empty()) { |
| 27116 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27117 | } |
| 27118 | // super must happen after, so that downstream can use maybe_get_output |
| 27119 | // to retrieve the output |
| 27120 | at::native::structured_special_entr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27121 | } |
| 27122 | void set_output_raw_strided( |
| 27123 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27124 | TensorOptions options, DimnameList names |
| 27125 | ) override { |
| 27126 | const auto& out = outputs_[output_idx].get(); |
| 27127 | resize_out(out, sizes, strides, options); |
| 27128 | if (!names.empty()) { |
| 27129 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27130 | } |
| 27131 | // super must happen after, so that downstream can use maybe_get_output |
| 27132 | // to retrieve the output |
| 27133 | at::native::structured_special_entr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27134 | } |
| 27135 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27136 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27137 | } |
| 27138 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27139 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27140 | }; |
| 27141 | at::Tensor & wrapper_CPU_special_entr_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27142 | structured_special_entr_out_out op(out); |
| 27143 | op.meta(self); |
| 27144 | op.impl(self, op.maybe_get_output(0)); |
| 27145 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27146 | return out; |
| 27147 | } |
| 27148 | struct structured_special_ndtri_out_functional final : public at::native::structured_special_ndtri_out { |
| 27149 | void set_output_strided( |
| 27150 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27151 | TensorOptions options, DimnameList names |
| 27152 | ) override { |
| 27153 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27154 | if (!names.empty()) { |
| 27155 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27156 | } |
| 27157 | // super must happen after, so that downstream can use maybe_get_output |
| 27158 | // to retrieve the output |
| 27159 | at::native::structured_special_ndtri_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27160 | } |
| 27161 | void set_output_raw_strided( |
| 27162 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27163 | TensorOptions options, DimnameList names |
| 27164 | ) override { |
| 27165 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27166 | if (!names.empty()) { |
| 27167 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27168 | } |
| 27169 | // super must happen after, so that downstream can use maybe_get_output |
| 27170 | // to retrieve the output |
| 27171 | at::native::structured_special_ndtri_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27172 | } |
| 27173 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27174 | return *outputs_[output_idx]; |
| 27175 | } |
| 27176 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27177 | }; |
| 27178 | at::Tensor wrapper_CPU_special_ndtri(const at::Tensor & self) { |
| 27179 | structured_special_ndtri_out_functional op; |
| 27180 | op.meta(self); |
| 27181 | op.impl(self, *op.outputs_[0]); |
| 27182 | return std::move(op.outputs_[0]).take(); |
| 27183 | } |
| 27184 | struct structured_special_ndtri_out_out final : public at::native::structured_special_ndtri_out { |
| 27185 | structured_special_ndtri_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27186 | void set_output_strided( |
| 27187 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27188 | TensorOptions options, DimnameList names |
| 27189 | ) override { |
| 27190 | const auto& out = outputs_[output_idx].get(); |
| 27191 | resize_out(out, sizes, strides, options); |
| 27192 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27193 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27194 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27195 | } |
| 27196 | if (!names.empty()) { |
| 27197 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27198 | } |
| 27199 | // super must happen after, so that downstream can use maybe_get_output |
| 27200 | // to retrieve the output |
| 27201 | at::native::structured_special_ndtri_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27202 | } |
| 27203 | void set_output_raw_strided( |
| 27204 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27205 | TensorOptions options, DimnameList names |
| 27206 | ) override { |
| 27207 | const auto& out = outputs_[output_idx].get(); |
| 27208 | resize_out(out, sizes, strides, options); |
| 27209 | if (!names.empty()) { |
| 27210 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27211 | } |
| 27212 | // super must happen after, so that downstream can use maybe_get_output |
| 27213 | // to retrieve the output |
| 27214 | at::native::structured_special_ndtri_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27215 | } |
| 27216 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27217 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27218 | } |
| 27219 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27220 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27221 | }; |
| 27222 | at::Tensor & wrapper_CPU_special_ndtri_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27223 | structured_special_ndtri_out_out op(out); |
| 27224 | op.meta(self); |
| 27225 | op.impl(self, op.maybe_get_output(0)); |
| 27226 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27227 | return out; |
| 27228 | } |
| 27229 | struct structured_special_log_ndtr_out_functional final : public at::native::structured_special_log_ndtr_out { |
| 27230 | void set_output_strided( |
| 27231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27232 | TensorOptions options, DimnameList names |
| 27233 | ) override { |
| 27234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27235 | if (!names.empty()) { |
| 27236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27237 | } |
| 27238 | // super must happen after, so that downstream can use maybe_get_output |
| 27239 | // to retrieve the output |
| 27240 | at::native::structured_special_log_ndtr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27241 | } |
| 27242 | void set_output_raw_strided( |
| 27243 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27244 | TensorOptions options, DimnameList names |
| 27245 | ) override { |
| 27246 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27247 | if (!names.empty()) { |
| 27248 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27249 | } |
| 27250 | // super must happen after, so that downstream can use maybe_get_output |
| 27251 | // to retrieve the output |
| 27252 | at::native::structured_special_log_ndtr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27253 | } |
| 27254 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27255 | return *outputs_[output_idx]; |
| 27256 | } |
| 27257 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27258 | }; |
| 27259 | at::Tensor wrapper_CPU_special_log_ndtr(const at::Tensor & self) { |
| 27260 | structured_special_log_ndtr_out_functional op; |
| 27261 | op.meta(self); |
| 27262 | op.impl(self, *op.outputs_[0]); |
| 27263 | return std::move(op.outputs_[0]).take(); |
| 27264 | } |
| 27265 | struct structured_special_log_ndtr_out_out final : public at::native::structured_special_log_ndtr_out { |
| 27266 | structured_special_log_ndtr_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27267 | void set_output_strided( |
| 27268 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27269 | TensorOptions options, DimnameList names |
| 27270 | ) override { |
| 27271 | const auto& out = outputs_[output_idx].get(); |
| 27272 | resize_out(out, sizes, strides, options); |
| 27273 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27274 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27275 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27276 | } |
| 27277 | if (!names.empty()) { |
| 27278 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27279 | } |
| 27280 | // super must happen after, so that downstream can use maybe_get_output |
| 27281 | // to retrieve the output |
| 27282 | at::native::structured_special_log_ndtr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27283 | } |
| 27284 | void set_output_raw_strided( |
| 27285 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27286 | TensorOptions options, DimnameList names |
| 27287 | ) override { |
| 27288 | const auto& out = outputs_[output_idx].get(); |
| 27289 | resize_out(out, sizes, strides, options); |
| 27290 | if (!names.empty()) { |
| 27291 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27292 | } |
| 27293 | // super must happen after, so that downstream can use maybe_get_output |
| 27294 | // to retrieve the output |
| 27295 | at::native::structured_special_log_ndtr_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27296 | } |
| 27297 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27298 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27299 | } |
| 27300 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27301 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27302 | }; |
| 27303 | at::Tensor & wrapper_CPU_special_log_ndtr_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27304 | structured_special_log_ndtr_out_out op(out); |
| 27305 | op.meta(self); |
| 27306 | op.impl(self, op.maybe_get_output(0)); |
| 27307 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27308 | return out; |
| 27309 | } |
| 27310 | struct structured_special_erfcx_out_functional final : public at::native::structured_special_erfcx_out { |
| 27311 | void set_output_strided( |
| 27312 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27313 | TensorOptions options, DimnameList names |
| 27314 | ) override { |
| 27315 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27316 | if (!names.empty()) { |
| 27317 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27318 | } |
| 27319 | // super must happen after, so that downstream can use maybe_get_output |
| 27320 | // to retrieve the output |
| 27321 | at::native::structured_special_erfcx_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27322 | } |
| 27323 | void set_output_raw_strided( |
| 27324 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27325 | TensorOptions options, DimnameList names |
| 27326 | ) override { |
| 27327 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27328 | if (!names.empty()) { |
| 27329 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27330 | } |
| 27331 | // super must happen after, so that downstream can use maybe_get_output |
| 27332 | // to retrieve the output |
| 27333 | at::native::structured_special_erfcx_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27334 | } |
| 27335 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27336 | return *outputs_[output_idx]; |
| 27337 | } |
| 27338 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27339 | }; |
| 27340 | at::Tensor wrapper_CPU_special_erfcx(const at::Tensor & self) { |
| 27341 | structured_special_erfcx_out_functional op; |
| 27342 | op.meta(self); |
| 27343 | op.impl(self, *op.outputs_[0]); |
| 27344 | return std::move(op.outputs_[0]).take(); |
| 27345 | } |
| 27346 | struct structured_special_erfcx_out_out final : public at::native::structured_special_erfcx_out { |
| 27347 | structured_special_erfcx_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27348 | void set_output_strided( |
| 27349 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27350 | TensorOptions options, DimnameList names |
| 27351 | ) override { |
| 27352 | const auto& out = outputs_[output_idx].get(); |
| 27353 | resize_out(out, sizes, strides, options); |
| 27354 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27355 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27356 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27357 | } |
| 27358 | if (!names.empty()) { |
| 27359 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27360 | } |
| 27361 | // super must happen after, so that downstream can use maybe_get_output |
| 27362 | // to retrieve the output |
| 27363 | at::native::structured_special_erfcx_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27364 | } |
| 27365 | void set_output_raw_strided( |
| 27366 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27367 | TensorOptions options, DimnameList names |
| 27368 | ) override { |
| 27369 | const auto& out = outputs_[output_idx].get(); |
| 27370 | resize_out(out, sizes, strides, options); |
| 27371 | if (!names.empty()) { |
| 27372 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27373 | } |
| 27374 | // super must happen after, so that downstream can use maybe_get_output |
| 27375 | // to retrieve the output |
| 27376 | at::native::structured_special_erfcx_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27377 | } |
| 27378 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27379 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27380 | } |
| 27381 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27382 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27383 | }; |
| 27384 | at::Tensor & wrapper_CPU_special_erfcx_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27385 | structured_special_erfcx_out_out op(out); |
| 27386 | op.meta(self); |
| 27387 | op.impl(self, op.maybe_get_output(0)); |
| 27388 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27389 | return out; |
| 27390 | } |
| 27391 | struct structured_special_xlog1py_out_functional final : public at::native::structured_special_xlog1py_out { |
| 27392 | void set_output_strided( |
| 27393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27394 | TensorOptions options, DimnameList names |
| 27395 | ) override { |
| 27396 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27397 | if (!names.empty()) { |
| 27398 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27399 | } |
| 27400 | // super must happen after, so that downstream can use maybe_get_output |
| 27401 | // to retrieve the output |
| 27402 | at::native::structured_special_xlog1py_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27403 | } |
| 27404 | void set_output_raw_strided( |
| 27405 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27406 | TensorOptions options, DimnameList names |
| 27407 | ) override { |
| 27408 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27409 | if (!names.empty()) { |
| 27410 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27411 | } |
| 27412 | // super must happen after, so that downstream can use maybe_get_output |
| 27413 | // to retrieve the output |
| 27414 | at::native::structured_special_xlog1py_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27415 | } |
| 27416 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27417 | return *outputs_[output_idx]; |
| 27418 | } |
| 27419 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27420 | }; |
| 27421 | at::Tensor wrapper_CPU_special_xlog1py(const at::Tensor & self, const at::Tensor & other) { |
| 27422 | structured_special_xlog1py_out_functional op; |
| 27423 | op.meta(self, other); |
| 27424 | op.impl(self, other, *op.outputs_[0]); |
| 27425 | return std::move(op.outputs_[0]).take(); |
| 27426 | } |
| 27427 | struct structured_special_xlog1py_out_out final : public at::native::structured_special_xlog1py_out { |
| 27428 | structured_special_xlog1py_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27429 | void set_output_strided( |
| 27430 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27431 | TensorOptions options, DimnameList names |
| 27432 | ) override { |
| 27433 | const auto& out = outputs_[output_idx].get(); |
| 27434 | resize_out(out, sizes, strides, options); |
| 27435 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27436 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27437 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27438 | } |
| 27439 | if (!names.empty()) { |
| 27440 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27441 | } |
| 27442 | // super must happen after, so that downstream can use maybe_get_output |
| 27443 | // to retrieve the output |
| 27444 | at::native::structured_special_xlog1py_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27445 | } |
| 27446 | void set_output_raw_strided( |
| 27447 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27448 | TensorOptions options, DimnameList names |
| 27449 | ) override { |
| 27450 | const auto& out = outputs_[output_idx].get(); |
| 27451 | resize_out(out, sizes, strides, options); |
| 27452 | if (!names.empty()) { |
| 27453 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27454 | } |
| 27455 | // super must happen after, so that downstream can use maybe_get_output |
| 27456 | // to retrieve the output |
| 27457 | at::native::structured_special_xlog1py_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27458 | } |
| 27459 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27460 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27461 | } |
| 27462 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27463 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27464 | }; |
| 27465 | at::Tensor & wrapper_CPU_special_xlog1py_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 27466 | structured_special_xlog1py_out_out op(out); |
| 27467 | op.meta(self, other); |
| 27468 | op.impl(self, other, op.maybe_get_output(0)); |
| 27469 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27470 | return out; |
| 27471 | } |
| 27472 | struct structured_special_zeta_out_functional final : public at::native::structured_special_zeta_out { |
| 27473 | void set_output_strided( |
| 27474 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27475 | TensorOptions options, DimnameList names |
| 27476 | ) override { |
| 27477 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27478 | if (!names.empty()) { |
| 27479 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27480 | } |
| 27481 | // super must happen after, so that downstream can use maybe_get_output |
| 27482 | // to retrieve the output |
| 27483 | at::native::structured_special_zeta_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27484 | } |
| 27485 | void set_output_raw_strided( |
| 27486 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27487 | TensorOptions options, DimnameList names |
| 27488 | ) override { |
| 27489 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27490 | if (!names.empty()) { |
| 27491 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27492 | } |
| 27493 | // super must happen after, so that downstream can use maybe_get_output |
| 27494 | // to retrieve the output |
| 27495 | at::native::structured_special_zeta_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27496 | } |
| 27497 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27498 | return *outputs_[output_idx]; |
| 27499 | } |
| 27500 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27501 | }; |
| 27502 | at::Tensor wrapper_CPU_special_zeta(const at::Tensor & self, const at::Tensor & other) { |
| 27503 | structured_special_zeta_out_functional op; |
| 27504 | op.meta(self, other); |
| 27505 | op.impl(self, other, *op.outputs_[0]); |
| 27506 | return std::move(op.outputs_[0]).take(); |
| 27507 | } |
| 27508 | struct structured_special_zeta_out_out final : public at::native::structured_special_zeta_out { |
| 27509 | structured_special_zeta_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27510 | void set_output_strided( |
| 27511 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27512 | TensorOptions options, DimnameList names |
| 27513 | ) override { |
| 27514 | const auto& out = outputs_[output_idx].get(); |
| 27515 | resize_out(out, sizes, strides, options); |
| 27516 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27517 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27518 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27519 | } |
| 27520 | if (!names.empty()) { |
| 27521 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27522 | } |
| 27523 | // super must happen after, so that downstream can use maybe_get_output |
| 27524 | // to retrieve the output |
| 27525 | at::native::structured_special_zeta_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27526 | } |
| 27527 | void set_output_raw_strided( |
| 27528 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27529 | TensorOptions options, DimnameList names |
| 27530 | ) override { |
| 27531 | const auto& out = outputs_[output_idx].get(); |
| 27532 | resize_out(out, sizes, strides, options); |
| 27533 | if (!names.empty()) { |
| 27534 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27535 | } |
| 27536 | // super must happen after, so that downstream can use maybe_get_output |
| 27537 | // to retrieve the output |
| 27538 | at::native::structured_special_zeta_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27539 | } |
| 27540 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27541 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27542 | } |
| 27543 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27544 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27545 | }; |
| 27546 | at::Tensor & wrapper_CPU_special_zeta_out_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 27547 | structured_special_zeta_out_out op(out); |
| 27548 | op.meta(self, other); |
| 27549 | op.impl(self, other, op.maybe_get_output(0)); |
| 27550 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27551 | return out; |
| 27552 | } |
| 27553 | struct structured_special_i0e_out_functional final : public at::native::structured_special_i0e_out { |
| 27554 | void set_output_strided( |
| 27555 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27556 | TensorOptions options, DimnameList names |
| 27557 | ) override { |
| 27558 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27559 | if (!names.empty()) { |
| 27560 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27561 | } |
| 27562 | // super must happen after, so that downstream can use maybe_get_output |
| 27563 | // to retrieve the output |
| 27564 | at::native::structured_special_i0e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27565 | } |
| 27566 | void set_output_raw_strided( |
| 27567 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27568 | TensorOptions options, DimnameList names |
| 27569 | ) override { |
| 27570 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27571 | if (!names.empty()) { |
| 27572 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27573 | } |
| 27574 | // super must happen after, so that downstream can use maybe_get_output |
| 27575 | // to retrieve the output |
| 27576 | at::native::structured_special_i0e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27577 | } |
| 27578 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27579 | return *outputs_[output_idx]; |
| 27580 | } |
| 27581 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27582 | }; |
| 27583 | at::Tensor wrapper_CPU_special_i0e(const at::Tensor & self) { |
| 27584 | structured_special_i0e_out_functional op; |
| 27585 | op.meta(self); |
| 27586 | op.impl(self, *op.outputs_[0]); |
| 27587 | return std::move(op.outputs_[0]).take(); |
| 27588 | } |
| 27589 | struct structured_special_i0e_out_out final : public at::native::structured_special_i0e_out { |
| 27590 | structured_special_i0e_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27591 | void set_output_strided( |
| 27592 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27593 | TensorOptions options, DimnameList names |
| 27594 | ) override { |
| 27595 | const auto& out = outputs_[output_idx].get(); |
| 27596 | resize_out(out, sizes, strides, options); |
| 27597 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27598 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27599 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27600 | } |
| 27601 | if (!names.empty()) { |
| 27602 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27603 | } |
| 27604 | // super must happen after, so that downstream can use maybe_get_output |
| 27605 | // to retrieve the output |
| 27606 | at::native::structured_special_i0e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27607 | } |
| 27608 | void set_output_raw_strided( |
| 27609 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27610 | TensorOptions options, DimnameList names |
| 27611 | ) override { |
| 27612 | const auto& out = outputs_[output_idx].get(); |
| 27613 | resize_out(out, sizes, strides, options); |
| 27614 | if (!names.empty()) { |
| 27615 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27616 | } |
| 27617 | // super must happen after, so that downstream can use maybe_get_output |
| 27618 | // to retrieve the output |
| 27619 | at::native::structured_special_i0e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27620 | } |
| 27621 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27622 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27623 | } |
| 27624 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27625 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27626 | }; |
| 27627 | at::Tensor & wrapper_CPU_special_i0e_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27628 | structured_special_i0e_out_out op(out); |
| 27629 | op.meta(self); |
| 27630 | op.impl(self, op.maybe_get_output(0)); |
| 27631 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27632 | return out; |
| 27633 | } |
| 27634 | struct structured_special_i1_out_functional final : public at::native::structured_special_i1_out { |
| 27635 | void set_output_strided( |
| 27636 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27637 | TensorOptions options, DimnameList names |
| 27638 | ) override { |
| 27639 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27640 | if (!names.empty()) { |
| 27641 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27642 | } |
| 27643 | // super must happen after, so that downstream can use maybe_get_output |
| 27644 | // to retrieve the output |
| 27645 | at::native::structured_special_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27646 | } |
| 27647 | void set_output_raw_strided( |
| 27648 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27649 | TensorOptions options, DimnameList names |
| 27650 | ) override { |
| 27651 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27652 | if (!names.empty()) { |
| 27653 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27654 | } |
| 27655 | // super must happen after, so that downstream can use maybe_get_output |
| 27656 | // to retrieve the output |
| 27657 | at::native::structured_special_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27658 | } |
| 27659 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27660 | return *outputs_[output_idx]; |
| 27661 | } |
| 27662 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27663 | }; |
| 27664 | at::Tensor wrapper_CPU_special_i1(const at::Tensor & self) { |
| 27665 | structured_special_i1_out_functional op; |
| 27666 | op.meta(self); |
| 27667 | op.impl(self, *op.outputs_[0]); |
| 27668 | return std::move(op.outputs_[0]).take(); |
| 27669 | } |
| 27670 | struct structured_special_i1_out_out final : public at::native::structured_special_i1_out { |
| 27671 | structured_special_i1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27672 | void set_output_strided( |
| 27673 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27674 | TensorOptions options, DimnameList names |
| 27675 | ) override { |
| 27676 | const auto& out = outputs_[output_idx].get(); |
| 27677 | resize_out(out, sizes, strides, options); |
| 27678 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27679 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27680 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27681 | } |
| 27682 | if (!names.empty()) { |
| 27683 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27684 | } |
| 27685 | // super must happen after, so that downstream can use maybe_get_output |
| 27686 | // to retrieve the output |
| 27687 | at::native::structured_special_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27688 | } |
| 27689 | void set_output_raw_strided( |
| 27690 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27691 | TensorOptions options, DimnameList names |
| 27692 | ) override { |
| 27693 | const auto& out = outputs_[output_idx].get(); |
| 27694 | resize_out(out, sizes, strides, options); |
| 27695 | if (!names.empty()) { |
| 27696 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27697 | } |
| 27698 | // super must happen after, so that downstream can use maybe_get_output |
| 27699 | // to retrieve the output |
| 27700 | at::native::structured_special_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27701 | } |
| 27702 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27703 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27704 | } |
| 27705 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27706 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27707 | }; |
| 27708 | at::Tensor & wrapper_CPU_special_i1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27709 | structured_special_i1_out_out op(out); |
| 27710 | op.meta(self); |
| 27711 | op.impl(self, op.maybe_get_output(0)); |
| 27712 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27713 | return out; |
| 27714 | } |
| 27715 | struct structured_special_i1e_out_functional final : public at::native::structured_special_i1e_out { |
| 27716 | void set_output_strided( |
| 27717 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27718 | TensorOptions options, DimnameList names |
| 27719 | ) override { |
| 27720 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27721 | if (!names.empty()) { |
| 27722 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27723 | } |
| 27724 | // super must happen after, so that downstream can use maybe_get_output |
| 27725 | // to retrieve the output |
| 27726 | at::native::structured_special_i1e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27727 | } |
| 27728 | void set_output_raw_strided( |
| 27729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27730 | TensorOptions options, DimnameList names |
| 27731 | ) override { |
| 27732 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27733 | if (!names.empty()) { |
| 27734 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27735 | } |
| 27736 | // super must happen after, so that downstream can use maybe_get_output |
| 27737 | // to retrieve the output |
| 27738 | at::native::structured_special_i1e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27739 | } |
| 27740 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27741 | return *outputs_[output_idx]; |
| 27742 | } |
| 27743 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27744 | }; |
| 27745 | at::Tensor wrapper_CPU_special_i1e(const at::Tensor & self) { |
| 27746 | structured_special_i1e_out_functional op; |
| 27747 | op.meta(self); |
| 27748 | op.impl(self, *op.outputs_[0]); |
| 27749 | return std::move(op.outputs_[0]).take(); |
| 27750 | } |
| 27751 | struct structured_special_i1e_out_out final : public at::native::structured_special_i1e_out { |
| 27752 | structured_special_i1e_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27753 | void set_output_strided( |
| 27754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27755 | TensorOptions options, DimnameList names |
| 27756 | ) override { |
| 27757 | const auto& out = outputs_[output_idx].get(); |
| 27758 | resize_out(out, sizes, strides, options); |
| 27759 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27760 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27761 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27762 | } |
| 27763 | if (!names.empty()) { |
| 27764 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27765 | } |
| 27766 | // super must happen after, so that downstream can use maybe_get_output |
| 27767 | // to retrieve the output |
| 27768 | at::native::structured_special_i1e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27769 | } |
| 27770 | void set_output_raw_strided( |
| 27771 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27772 | TensorOptions options, DimnameList names |
| 27773 | ) override { |
| 27774 | const auto& out = outputs_[output_idx].get(); |
| 27775 | resize_out(out, sizes, strides, options); |
| 27776 | if (!names.empty()) { |
| 27777 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27778 | } |
| 27779 | // super must happen after, so that downstream can use maybe_get_output |
| 27780 | // to retrieve the output |
| 27781 | at::native::structured_special_i1e_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 27782 | } |
| 27783 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27784 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27785 | } |
| 27786 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27787 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27788 | }; |
| 27789 | at::Tensor & wrapper_CPU_special_i1e_out_out(const at::Tensor & self, at::Tensor & out) { |
| 27790 | structured_special_i1e_out_out op(out); |
| 27791 | op.meta(self); |
| 27792 | op.impl(self, op.maybe_get_output(0)); |
| 27793 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27794 | return out; |
| 27795 | } |
| 27796 | struct structured_linalg_cholesky_ex_out_functional final : public at::native::structured_linalg_cholesky_ex_out { |
| 27797 | void set_output_strided( |
| 27798 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27799 | TensorOptions options, DimnameList names |
| 27800 | ) override { |
| 27801 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27802 | if (!names.empty()) { |
| 27803 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27804 | } |
| 27805 | // super must happen after, so that downstream can use maybe_get_output |
| 27806 | // to retrieve the output |
| 27807 | } |
| 27808 | void set_output_raw_strided( |
| 27809 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27810 | TensorOptions options, DimnameList names |
| 27811 | ) override { |
| 27812 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27813 | if (!names.empty()) { |
| 27814 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27815 | } |
| 27816 | // super must happen after, so that downstream can use maybe_get_output |
| 27817 | // to retrieve the output |
| 27818 | } |
| 27819 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27820 | return *outputs_[output_idx]; |
| 27821 | } |
| 27822 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 27823 | }; |
| 27824 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_linalg_cholesky_ex(const at::Tensor & self, bool upper, bool check_errors) { |
| 27825 | structured_linalg_cholesky_ex_out_functional op; |
| 27826 | op.meta(self, upper, check_errors); |
| 27827 | op.impl(self, upper, check_errors, *op.outputs_[0], *op.outputs_[1]); |
| 27828 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 27829 | } |
| 27830 | struct structured_linalg_cholesky_ex_out_out final : public at::native::structured_linalg_cholesky_ex_out { |
| 27831 | structured_linalg_cholesky_ex_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 27832 | void set_output_strided( |
| 27833 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27834 | TensorOptions options, DimnameList names |
| 27835 | ) override { |
| 27836 | const auto& out = outputs_[output_idx].get(); |
| 27837 | resize_out(out, sizes, strides, options); |
| 27838 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27839 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27840 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27841 | } |
| 27842 | if (!names.empty()) { |
| 27843 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27844 | } |
| 27845 | // super must happen after, so that downstream can use maybe_get_output |
| 27846 | // to retrieve the output |
| 27847 | } |
| 27848 | void set_output_raw_strided( |
| 27849 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27850 | TensorOptions options, DimnameList names |
| 27851 | ) override { |
| 27852 | const auto& out = outputs_[output_idx].get(); |
| 27853 | resize_out(out, sizes, strides, options); |
| 27854 | if (!names.empty()) { |
| 27855 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27856 | } |
| 27857 | // super must happen after, so that downstream can use maybe_get_output |
| 27858 | // to retrieve the output |
| 27859 | } |
| 27860 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27861 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27862 | } |
| 27863 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 27864 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 27865 | }; |
| 27866 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_linalg_cholesky_ex_out_L(const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { |
| 27867 | structured_linalg_cholesky_ex_out_out op(L, info); |
| 27868 | op.meta(self, upper, check_errors); |
| 27869 | op.impl(self, upper, check_errors, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 27870 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27871 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 27872 | return std::forward_as_tuple(L, info); |
| 27873 | } |
| 27874 | struct structured_linalg_cross_out_functional final : public at::native::structured_linalg_cross_out { |
| 27875 | void set_output_strided( |
| 27876 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27877 | TensorOptions options, DimnameList names |
| 27878 | ) override { |
| 27879 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27880 | if (!names.empty()) { |
| 27881 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27882 | } |
| 27883 | // super must happen after, so that downstream can use maybe_get_output |
| 27884 | // to retrieve the output |
| 27885 | } |
| 27886 | void set_output_raw_strided( |
| 27887 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27888 | TensorOptions options, DimnameList names |
| 27889 | ) override { |
| 27890 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27891 | if (!names.empty()) { |
| 27892 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27893 | } |
| 27894 | // super must happen after, so that downstream can use maybe_get_output |
| 27895 | // to retrieve the output |
| 27896 | } |
| 27897 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27898 | return *outputs_[output_idx]; |
| 27899 | } |
| 27900 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 27901 | }; |
| 27902 | at::Tensor wrapper_CPU_linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 27903 | structured_linalg_cross_out_functional op; |
| 27904 | op.meta(self, other, dim); |
| 27905 | op.impl(self, other, dim, *op.outputs_[0]); |
| 27906 | return std::move(op.outputs_[0]).take(); |
| 27907 | } |
| 27908 | struct structured_linalg_cross_out_out final : public at::native::structured_linalg_cross_out { |
| 27909 | structured_linalg_cross_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 27910 | void set_output_strided( |
| 27911 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27912 | TensorOptions options, DimnameList names |
| 27913 | ) override { |
| 27914 | const auto& out = outputs_[output_idx].get(); |
| 27915 | resize_out(out, sizes, strides, options); |
| 27916 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27917 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27918 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27919 | } |
| 27920 | if (!names.empty()) { |
| 27921 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27922 | } |
| 27923 | // super must happen after, so that downstream can use maybe_get_output |
| 27924 | // to retrieve the output |
| 27925 | } |
| 27926 | void set_output_raw_strided( |
| 27927 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27928 | TensorOptions options, DimnameList names |
| 27929 | ) override { |
| 27930 | const auto& out = outputs_[output_idx].get(); |
| 27931 | resize_out(out, sizes, strides, options); |
| 27932 | if (!names.empty()) { |
| 27933 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27934 | } |
| 27935 | // super must happen after, so that downstream can use maybe_get_output |
| 27936 | // to retrieve the output |
| 27937 | } |
| 27938 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27939 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 27940 | } |
| 27941 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 27942 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 27943 | }; |
| 27944 | at::Tensor & wrapper_CPU_linalg_cross_out_out(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { |
| 27945 | structured_linalg_cross_out_out op(out); |
| 27946 | op.meta(self, other, dim); |
| 27947 | op.impl(self, other, dim, op.maybe_get_output(0)); |
| 27948 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 27949 | return out; |
| 27950 | } |
| 27951 | struct structured_linalg_lu_factor_ex_out_functional final : public at::native::structured_linalg_lu_factor_ex_out { |
| 27952 | void set_output_strided( |
| 27953 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27954 | TensorOptions options, DimnameList names |
| 27955 | ) override { |
| 27956 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27957 | if (!names.empty()) { |
| 27958 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27959 | } |
| 27960 | // super must happen after, so that downstream can use maybe_get_output |
| 27961 | // to retrieve the output |
| 27962 | } |
| 27963 | void set_output_raw_strided( |
| 27964 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27965 | TensorOptions options, DimnameList names |
| 27966 | ) override { |
| 27967 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 27968 | if (!names.empty()) { |
| 27969 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 27970 | } |
| 27971 | // super must happen after, so that downstream can use maybe_get_output |
| 27972 | // to retrieve the output |
| 27973 | } |
| 27974 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 27975 | return *outputs_[output_idx]; |
| 27976 | } |
| 27977 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 27978 | }; |
| 27979 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_linalg_lu_factor_ex(const at::Tensor & A, bool pivot, bool check_errors) { |
| 27980 | structured_linalg_lu_factor_ex_out_functional op; |
| 27981 | op.meta(A, pivot, check_errors); |
| 27982 | op.impl(A, pivot, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 27983 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 27984 | } |
| 27985 | struct structured_linalg_lu_factor_ex_out_out final : public at::native::structured_linalg_lu_factor_ex_out { |
| 27986 | structured_linalg_lu_factor_ex_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 27987 | void set_output_strided( |
| 27988 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 27989 | TensorOptions options, DimnameList names |
| 27990 | ) override { |
| 27991 | const auto& out = outputs_[output_idx].get(); |
| 27992 | resize_out(out, sizes, strides, options); |
| 27993 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 27994 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 27995 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 27996 | } |
| 27997 | if (!names.empty()) { |
| 27998 | namedinference::propagate_names(outputs_[output_idx], names); |
| 27999 | } |
| 28000 | // super must happen after, so that downstream can use maybe_get_output |
| 28001 | // to retrieve the output |
| 28002 | } |
| 28003 | void set_output_raw_strided( |
| 28004 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28005 | TensorOptions options, DimnameList names |
| 28006 | ) override { |
| 28007 | const auto& out = outputs_[output_idx].get(); |
| 28008 | resize_out(out, sizes, strides, options); |
| 28009 | if (!names.empty()) { |
| 28010 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28011 | } |
| 28012 | // super must happen after, so that downstream can use maybe_get_output |
| 28013 | // to retrieve the output |
| 28014 | } |
| 28015 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28016 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28017 | } |
| 28018 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 28019 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 28020 | }; |
| 28021 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_linalg_lu_factor_ex_out_out(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 28022 | structured_linalg_lu_factor_ex_out_out op(LU, pivots, info); |
| 28023 | op.meta(A, pivot, check_errors); |
| 28024 | op.impl(A, pivot, check_errors, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 28025 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28026 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28027 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28028 | return std::forward_as_tuple(LU, pivots, info); |
| 28029 | } |
| 28030 | struct structured_linalg_lu_out_functional final : public at::native::structured_linalg_lu_out { |
| 28031 | void set_output_strided( |
| 28032 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28033 | TensorOptions options, DimnameList names |
| 28034 | ) override { |
| 28035 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28036 | if (!names.empty()) { |
| 28037 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28038 | } |
| 28039 | // super must happen after, so that downstream can use maybe_get_output |
| 28040 | // to retrieve the output |
| 28041 | } |
| 28042 | void set_output_raw_strided( |
| 28043 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28044 | TensorOptions options, DimnameList names |
| 28045 | ) override { |
| 28046 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28047 | if (!names.empty()) { |
| 28048 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28049 | } |
| 28050 | // super must happen after, so that downstream can use maybe_get_output |
| 28051 | // to retrieve the output |
| 28052 | } |
| 28053 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28054 | return *outputs_[output_idx]; |
| 28055 | } |
| 28056 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 28057 | }; |
| 28058 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_linalg_lu(const at::Tensor & A, bool pivot) { |
| 28059 | structured_linalg_lu_out_functional op; |
| 28060 | op.meta(A, pivot); |
| 28061 | op.impl(A, pivot, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 28062 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 28063 | } |
| 28064 | struct structured_linalg_lu_out_out final : public at::native::structured_linalg_lu_out { |
| 28065 | structured_linalg_lu_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 28066 | void set_output_strided( |
| 28067 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28068 | TensorOptions options, DimnameList names |
| 28069 | ) override { |
| 28070 | const auto& out = outputs_[output_idx].get(); |
| 28071 | resize_out(out, sizes, strides, options); |
| 28072 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28073 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28074 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28075 | } |
| 28076 | if (!names.empty()) { |
| 28077 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28078 | } |
| 28079 | // super must happen after, so that downstream can use maybe_get_output |
| 28080 | // to retrieve the output |
| 28081 | } |
| 28082 | void set_output_raw_strided( |
| 28083 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28084 | TensorOptions options, DimnameList names |
| 28085 | ) override { |
| 28086 | const auto& out = outputs_[output_idx].get(); |
| 28087 | resize_out(out, sizes, strides, options); |
| 28088 | if (!names.empty()) { |
| 28089 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28090 | } |
| 28091 | // super must happen after, so that downstream can use maybe_get_output |
| 28092 | // to retrieve the output |
| 28093 | } |
| 28094 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28095 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28096 | } |
| 28097 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 28098 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 28099 | }; |
| 28100 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_linalg_lu_out_out(const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U) { |
| 28101 | structured_linalg_lu_out_out op(P, L, U); |
| 28102 | op.meta(A, pivot); |
| 28103 | op.impl(A, pivot, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 28104 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28105 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28106 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28107 | return std::forward_as_tuple(P, L, U); |
| 28108 | } |
| 28109 | struct structured_linalg_lu_solve_out_functional final : public at::native::structured_linalg_lu_solve_out { |
| 28110 | void set_output_strided( |
| 28111 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28112 | TensorOptions options, DimnameList names |
| 28113 | ) override { |
| 28114 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28115 | if (!names.empty()) { |
| 28116 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28117 | } |
| 28118 | // super must happen after, so that downstream can use maybe_get_output |
| 28119 | // to retrieve the output |
| 28120 | } |
| 28121 | void set_output_raw_strided( |
| 28122 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28123 | TensorOptions options, DimnameList names |
| 28124 | ) override { |
| 28125 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28126 | if (!names.empty()) { |
| 28127 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28128 | } |
| 28129 | // super must happen after, so that downstream can use maybe_get_output |
| 28130 | // to retrieve the output |
| 28131 | } |
| 28132 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28133 | return *outputs_[output_idx]; |
| 28134 | } |
| 28135 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 28136 | }; |
| 28137 | at::Tensor wrapper_CPU_linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { |
| 28138 | structured_linalg_lu_solve_out_functional op; |
| 28139 | op.meta(LU, pivots, B, left, adjoint); |
| 28140 | op.impl(LU, pivots, B, left, adjoint, *op.outputs_[0]); |
| 28141 | return std::move(op.outputs_[0]).take(); |
| 28142 | } |
| 28143 | struct structured_linalg_lu_solve_out_out final : public at::native::structured_linalg_lu_solve_out { |
| 28144 | structured_linalg_lu_solve_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 28145 | void set_output_strided( |
| 28146 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28147 | TensorOptions options, DimnameList names |
| 28148 | ) override { |
| 28149 | const auto& out = outputs_[output_idx].get(); |
| 28150 | resize_out(out, sizes, strides, options); |
| 28151 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28152 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28153 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28154 | } |
| 28155 | if (!names.empty()) { |
| 28156 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28157 | } |
| 28158 | // super must happen after, so that downstream can use maybe_get_output |
| 28159 | // to retrieve the output |
| 28160 | } |
| 28161 | void set_output_raw_strided( |
| 28162 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28163 | TensorOptions options, DimnameList names |
| 28164 | ) override { |
| 28165 | const auto& out = outputs_[output_idx].get(); |
| 28166 | resize_out(out, sizes, strides, options); |
| 28167 | if (!names.empty()) { |
| 28168 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28169 | } |
| 28170 | // super must happen after, so that downstream can use maybe_get_output |
| 28171 | // to retrieve the output |
| 28172 | } |
| 28173 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28174 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28175 | } |
| 28176 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 28177 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 28178 | }; |
| 28179 | at::Tensor & wrapper_CPU_linalg_lu_solve_out_out(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) { |
| 28180 | structured_linalg_lu_solve_out_out op(out); |
| 28181 | op.meta(LU, pivots, B, left, adjoint); |
| 28182 | op.impl(LU, pivots, B, left, adjoint, op.maybe_get_output(0)); |
| 28183 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28184 | return out; |
| 28185 | } |
| 28186 | struct structured__linalg_det_out_functional final : public at::native::structured__linalg_det_out { |
| 28187 | void set_output_strided( |
| 28188 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28189 | TensorOptions options, DimnameList names |
| 28190 | ) override { |
| 28191 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28192 | if (!names.empty()) { |
| 28193 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28194 | } |
| 28195 | // super must happen after, so that downstream can use maybe_get_output |
| 28196 | // to retrieve the output |
| 28197 | } |
| 28198 | void set_output_raw_strided( |
| 28199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28200 | TensorOptions options, DimnameList names |
| 28201 | ) override { |
| 28202 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28203 | if (!names.empty()) { |
| 28204 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28205 | } |
| 28206 | // super must happen after, so that downstream can use maybe_get_output |
| 28207 | // to retrieve the output |
| 28208 | } |
| 28209 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28210 | return *outputs_[output_idx]; |
| 28211 | } |
| 28212 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 28213 | }; |
| 28214 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__linalg_det(const at::Tensor & A) { |
| 28215 | structured__linalg_det_out_functional op; |
| 28216 | op.meta(A); |
| 28217 | op.impl(A, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 28218 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 28219 | } |
| 28220 | struct structured__linalg_det_out_out final : public at::native::structured__linalg_det_out { |
| 28221 | structured__linalg_det_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 28222 | void set_output_strided( |
| 28223 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28224 | TensorOptions options, DimnameList names |
| 28225 | ) override { |
| 28226 | const auto& out = outputs_[output_idx].get(); |
| 28227 | resize_out(out, sizes, strides, options); |
| 28228 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28229 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28230 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28231 | } |
| 28232 | if (!names.empty()) { |
| 28233 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28234 | } |
| 28235 | // super must happen after, so that downstream can use maybe_get_output |
| 28236 | // to retrieve the output |
| 28237 | } |
| 28238 | void set_output_raw_strided( |
| 28239 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28240 | TensorOptions options, DimnameList names |
| 28241 | ) override { |
| 28242 | const auto& out = outputs_[output_idx].get(); |
| 28243 | resize_out(out, sizes, strides, options); |
| 28244 | if (!names.empty()) { |
| 28245 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28246 | } |
| 28247 | // super must happen after, so that downstream can use maybe_get_output |
| 28248 | // to retrieve the output |
| 28249 | } |
| 28250 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28251 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28252 | } |
| 28253 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 28254 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 28255 | }; |
| 28256 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU__linalg_det_out_result(const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { |
| 28257 | structured__linalg_det_out_out op(result, LU, pivots); |
| 28258 | op.meta(A); |
| 28259 | op.impl(A, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 28260 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28261 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28262 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28263 | return std::forward_as_tuple(result, LU, pivots); |
| 28264 | } |
| 28265 | struct structured_linalg_ldl_factor_ex_out_functional final : public at::native::structured_linalg_ldl_factor_ex_out { |
| 28266 | void set_output_strided( |
| 28267 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28268 | TensorOptions options, DimnameList names |
| 28269 | ) override { |
| 28270 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28271 | if (!names.empty()) { |
| 28272 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28273 | } |
| 28274 | // super must happen after, so that downstream can use maybe_get_output |
| 28275 | // to retrieve the output |
| 28276 | } |
| 28277 | void set_output_raw_strided( |
| 28278 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28279 | TensorOptions options, DimnameList names |
| 28280 | ) override { |
| 28281 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28282 | if (!names.empty()) { |
| 28283 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28284 | } |
| 28285 | // super must happen after, so that downstream can use maybe_get_output |
| 28286 | // to retrieve the output |
| 28287 | } |
| 28288 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28289 | return *outputs_[output_idx]; |
| 28290 | } |
| 28291 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 28292 | }; |
| 28293 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU_linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian, bool check_errors) { |
| 28294 | structured_linalg_ldl_factor_ex_out_functional op; |
| 28295 | op.meta(self, hermitian, check_errors); |
| 28296 | op.impl(self, hermitian, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 28297 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 28298 | } |
| 28299 | struct structured_linalg_ldl_factor_ex_out_out final : public at::native::structured_linalg_ldl_factor_ex_out { |
| 28300 | structured_linalg_ldl_factor_ex_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 28301 | void set_output_strided( |
| 28302 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28303 | TensorOptions options, DimnameList names |
| 28304 | ) override { |
| 28305 | const auto& out = outputs_[output_idx].get(); |
| 28306 | resize_out(out, sizes, strides, options); |
| 28307 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28308 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28309 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28310 | } |
| 28311 | if (!names.empty()) { |
| 28312 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28313 | } |
| 28314 | // super must happen after, so that downstream can use maybe_get_output |
| 28315 | // to retrieve the output |
| 28316 | } |
| 28317 | void set_output_raw_strided( |
| 28318 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28319 | TensorOptions options, DimnameList names |
| 28320 | ) override { |
| 28321 | const auto& out = outputs_[output_idx].get(); |
| 28322 | resize_out(out, sizes, strides, options); |
| 28323 | if (!names.empty()) { |
| 28324 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28325 | } |
| 28326 | // super must happen after, so that downstream can use maybe_get_output |
| 28327 | // to retrieve the output |
| 28328 | } |
| 28329 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28330 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28331 | } |
| 28332 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 28333 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 28334 | }; |
| 28335 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_linalg_ldl_factor_ex_out_out(const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info) { |
| 28336 | structured_linalg_ldl_factor_ex_out_out op(LD, pivots, info); |
| 28337 | op.meta(self, hermitian, check_errors); |
| 28338 | op.impl(self, hermitian, check_errors, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 28339 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28340 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28341 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28342 | return std::forward_as_tuple(LD, pivots, info); |
| 28343 | } |
| 28344 | struct structured_linalg_ldl_solve_out_functional final : public at::native::structured_linalg_ldl_solve_out { |
| 28345 | void set_output_strided( |
| 28346 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28347 | TensorOptions options, DimnameList names |
| 28348 | ) override { |
| 28349 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28350 | if (!names.empty()) { |
| 28351 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28352 | } |
| 28353 | // super must happen after, so that downstream can use maybe_get_output |
| 28354 | // to retrieve the output |
| 28355 | } |
| 28356 | void set_output_raw_strided( |
| 28357 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28358 | TensorOptions options, DimnameList names |
| 28359 | ) override { |
| 28360 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28361 | if (!names.empty()) { |
| 28362 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28363 | } |
| 28364 | // super must happen after, so that downstream can use maybe_get_output |
| 28365 | // to retrieve the output |
| 28366 | } |
| 28367 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28368 | return *outputs_[output_idx]; |
| 28369 | } |
| 28370 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 28371 | }; |
| 28372 | at::Tensor wrapper_CPU_linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { |
| 28373 | structured_linalg_ldl_solve_out_functional op; |
| 28374 | op.meta(LD, pivots, B, hermitian); |
| 28375 | op.impl(LD, pivots, B, hermitian, *op.outputs_[0]); |
| 28376 | return std::move(op.outputs_[0]).take(); |
| 28377 | } |
| 28378 | struct structured_linalg_ldl_solve_out_out final : public at::native::structured_linalg_ldl_solve_out { |
| 28379 | structured_linalg_ldl_solve_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 28380 | void set_output_strided( |
| 28381 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28382 | TensorOptions options, DimnameList names |
| 28383 | ) override { |
| 28384 | const auto& out = outputs_[output_idx].get(); |
| 28385 | resize_out(out, sizes, strides, options); |
| 28386 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28387 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28388 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28389 | } |
| 28390 | if (!names.empty()) { |
| 28391 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28392 | } |
| 28393 | // super must happen after, so that downstream can use maybe_get_output |
| 28394 | // to retrieve the output |
| 28395 | } |
| 28396 | void set_output_raw_strided( |
| 28397 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28398 | TensorOptions options, DimnameList names |
| 28399 | ) override { |
| 28400 | const auto& out = outputs_[output_idx].get(); |
| 28401 | resize_out(out, sizes, strides, options); |
| 28402 | if (!names.empty()) { |
| 28403 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28404 | } |
| 28405 | // super must happen after, so that downstream can use maybe_get_output |
| 28406 | // to retrieve the output |
| 28407 | } |
| 28408 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28409 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28410 | } |
| 28411 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 28412 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 28413 | }; |
| 28414 | at::Tensor & wrapper_CPU_linalg_ldl_solve_out_out(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out) { |
| 28415 | structured_linalg_ldl_solve_out_out op(out); |
| 28416 | op.meta(LD, pivots, B, hermitian); |
| 28417 | op.impl(LD, pivots, B, hermitian, op.maybe_get_output(0)); |
| 28418 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28419 | return out; |
| 28420 | } |
| 28421 | namespace { |
| 28422 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU_out_linalg_lstsq_out(const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond, c10::optional<c10::string_view> driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values) { |
| 28423 | // No device check |
| 28424 | // DeviceGuard omitted |
| 28425 | return at::native::linalg_lstsq_out(self, b, rcond, driver, solution, residuals, rank, singular_values); |
| 28426 | } |
| 28427 | } // anonymous namespace |
| 28428 | namespace { |
| 28429 | at::Tensor wrapper_CPU__linalg_matrix_exp(const at::Tensor & self) { |
| 28430 | // No device check |
| 28431 | // DeviceGuard omitted |
| 28432 | return at::native::linalg_matrix_exp(self); |
| 28433 | } |
| 28434 | } // anonymous namespace |
| 28435 | struct structured__linalg_slogdet_out_functional final : public at::native::structured__linalg_slogdet_out { |
| 28436 | void set_output_strided( |
| 28437 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28438 | TensorOptions options, DimnameList names |
| 28439 | ) override { |
| 28440 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28441 | if (!names.empty()) { |
| 28442 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28443 | } |
| 28444 | // super must happen after, so that downstream can use maybe_get_output |
| 28445 | // to retrieve the output |
| 28446 | } |
| 28447 | void set_output_raw_strided( |
| 28448 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28449 | TensorOptions options, DimnameList names |
| 28450 | ) override { |
| 28451 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28452 | if (!names.empty()) { |
| 28453 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28454 | } |
| 28455 | // super must happen after, so that downstream can use maybe_get_output |
| 28456 | // to retrieve the output |
| 28457 | } |
| 28458 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28459 | return *outputs_[output_idx]; |
| 28460 | } |
| 28461 | std::array<c10::ExclusivelyOwned<Tensor>, 4> outputs_; |
| 28462 | }; |
| 28463 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__linalg_slogdet(const at::Tensor & A) { |
| 28464 | structured__linalg_slogdet_out_functional op; |
| 28465 | op.meta(A); |
| 28466 | op.impl(A, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2], *op.outputs_[3]); |
| 28467 | 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()); |
| 28468 | } |
| 28469 | struct structured__linalg_slogdet_out_out final : public at::native::structured__linalg_slogdet_out { |
| 28470 | structured__linalg_slogdet_out_out(Tensor& out0, Tensor& out1, Tensor& out2, Tensor& out3) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2), std::ref(out3) } {} |
| 28471 | void set_output_strided( |
| 28472 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28473 | TensorOptions options, DimnameList names |
| 28474 | ) override { |
| 28475 | const auto& out = outputs_[output_idx].get(); |
| 28476 | resize_out(out, sizes, strides, options); |
| 28477 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28478 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28479 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28480 | } |
| 28481 | if (!names.empty()) { |
| 28482 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28483 | } |
| 28484 | // super must happen after, so that downstream can use maybe_get_output |
| 28485 | // to retrieve the output |
| 28486 | } |
| 28487 | void set_output_raw_strided( |
| 28488 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28489 | TensorOptions options, DimnameList names |
| 28490 | ) override { |
| 28491 | const auto& out = outputs_[output_idx].get(); |
| 28492 | resize_out(out, sizes, strides, options); |
| 28493 | if (!names.empty()) { |
| 28494 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28495 | } |
| 28496 | // super must happen after, so that downstream can use maybe_get_output |
| 28497 | // to retrieve the output |
| 28498 | } |
| 28499 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28500 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28501 | } |
| 28502 | std::array<std::reference_wrapper<Tensor>, 4> outputs_; |
| 28503 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 4> proxy_outputs_; |
| 28504 | }; |
| 28505 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU__linalg_slogdet_out_sign(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots) { |
| 28506 | structured__linalg_slogdet_out_out op(sign, logabsdet, LU, pivots); |
| 28507 | op.meta(A); |
| 28508 | op.impl(A, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2), op.maybe_get_output(3)); |
| 28509 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28510 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28511 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28512 | if (op.proxy_outputs_[3].has_value()) op.outputs_[3].get().copy_(**op.proxy_outputs_[3]); |
| 28513 | return std::forward_as_tuple(sign, logabsdet, LU, pivots); |
| 28514 | } |
| 28515 | namespace { |
| 28516 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__linalg_eig(const at::Tensor & self) { |
| 28517 | // No device check |
| 28518 | // DeviceGuard omitted |
| 28519 | return at::native::linalg_eig(self); |
| 28520 | } |
| 28521 | } // anonymous namespace |
| 28522 | namespace { |
| 28523 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_out_linalg_eig_out(const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { |
| 28524 | // No device check |
| 28525 | // DeviceGuard omitted |
| 28526 | return at::native::linalg_eig_out(self, eigenvalues, eigenvectors); |
| 28527 | } |
| 28528 | } // anonymous namespace |
| 28529 | struct structured__linalg_eigh_out_functional final : public at::native::structured__linalg_eigh_out { |
| 28530 | void set_output_strided( |
| 28531 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28532 | TensorOptions options, DimnameList names |
| 28533 | ) override { |
| 28534 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28535 | if (!names.empty()) { |
| 28536 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28537 | } |
| 28538 | // super must happen after, so that downstream can use maybe_get_output |
| 28539 | // to retrieve the output |
| 28540 | } |
| 28541 | void set_output_raw_strided( |
| 28542 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28543 | TensorOptions options, DimnameList names |
| 28544 | ) override { |
| 28545 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28546 | if (!names.empty()) { |
| 28547 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28548 | } |
| 28549 | // super must happen after, so that downstream can use maybe_get_output |
| 28550 | // to retrieve the output |
| 28551 | } |
| 28552 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28553 | return *outputs_[output_idx]; |
| 28554 | } |
| 28555 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 28556 | }; |
| 28557 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU__linalg_eigh(const at::Tensor & A, c10::string_view UPLO, bool compute_v) { |
| 28558 | structured__linalg_eigh_out_functional op; |
| 28559 | op.meta(A, UPLO, compute_v); |
| 28560 | op.impl(A, UPLO, compute_v, *op.outputs_[0], *op.outputs_[1]); |
| 28561 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 28562 | } |
| 28563 | struct structured__linalg_eigh_out_out final : public at::native::structured__linalg_eigh_out { |
| 28564 | structured__linalg_eigh_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 28565 | void set_output_strided( |
| 28566 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28567 | TensorOptions options, DimnameList names |
| 28568 | ) override { |
| 28569 | const auto& out = outputs_[output_idx].get(); |
| 28570 | resize_out(out, sizes, strides, options); |
| 28571 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28572 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28573 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28574 | } |
| 28575 | if (!names.empty()) { |
| 28576 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28577 | } |
| 28578 | // super must happen after, so that downstream can use maybe_get_output |
| 28579 | // to retrieve the output |
| 28580 | } |
| 28581 | void set_output_raw_strided( |
| 28582 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28583 | TensorOptions options, DimnameList names |
| 28584 | ) override { |
| 28585 | const auto& out = outputs_[output_idx].get(); |
| 28586 | resize_out(out, sizes, strides, options); |
| 28587 | if (!names.empty()) { |
| 28588 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28589 | } |
| 28590 | // super must happen after, so that downstream can use maybe_get_output |
| 28591 | // to retrieve the output |
| 28592 | } |
| 28593 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28594 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28595 | } |
| 28596 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 28597 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 28598 | }; |
| 28599 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU__linalg_eigh_out_eigenvalues(const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { |
| 28600 | structured__linalg_eigh_out_out op(eigenvalues, eigenvectors); |
| 28601 | op.meta(A, UPLO, compute_v); |
| 28602 | op.impl(A, UPLO, compute_v, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 28603 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28604 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28605 | return std::forward_as_tuple(eigenvalues, eigenvectors); |
| 28606 | } |
| 28607 | namespace { |
| 28608 | at::Tensor wrapper_CPU__linalg_householder_product(const at::Tensor & input, const at::Tensor & tau) { |
| 28609 | // No device check |
| 28610 | // DeviceGuard omitted |
| 28611 | return at::native::linalg_householder_product(input, tau); |
| 28612 | } |
| 28613 | } // anonymous namespace |
| 28614 | namespace { |
| 28615 | at::Tensor & wrapper_CPU_out_linalg_householder_product_out(const at::Tensor & input, const at::Tensor & tau, at::Tensor & out) { |
| 28616 | // No device check |
| 28617 | // DeviceGuard omitted |
| 28618 | return at::native::linalg_householder_product_out(input, tau, out); |
| 28619 | } |
| 28620 | } // anonymous namespace |
| 28621 | struct structured_linalg_inv_ex_out_functional final : public at::native::structured_linalg_inv_ex_out { |
| 28622 | void set_output_strided( |
| 28623 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28624 | TensorOptions options, DimnameList names |
| 28625 | ) override { |
| 28626 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28627 | if (!names.empty()) { |
| 28628 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28629 | } |
| 28630 | // super must happen after, so that downstream can use maybe_get_output |
| 28631 | // to retrieve the output |
| 28632 | } |
| 28633 | void set_output_raw_strided( |
| 28634 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28635 | TensorOptions options, DimnameList names |
| 28636 | ) override { |
| 28637 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28638 | if (!names.empty()) { |
| 28639 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28640 | } |
| 28641 | // super must happen after, so that downstream can use maybe_get_output |
| 28642 | // to retrieve the output |
| 28643 | } |
| 28644 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28645 | return *outputs_[output_idx]; |
| 28646 | } |
| 28647 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 28648 | }; |
| 28649 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_linalg_inv_ex(const at::Tensor & A, bool check_errors) { |
| 28650 | structured_linalg_inv_ex_out_functional op; |
| 28651 | op.meta(A, check_errors); |
| 28652 | op.impl(A, check_errors, *op.outputs_[0], *op.outputs_[1]); |
| 28653 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 28654 | } |
| 28655 | struct structured_linalg_inv_ex_out_out final : public at::native::structured_linalg_inv_ex_out { |
| 28656 | structured_linalg_inv_ex_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 28657 | void set_output_strided( |
| 28658 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28659 | TensorOptions options, DimnameList names |
| 28660 | ) override { |
| 28661 | const auto& out = outputs_[output_idx].get(); |
| 28662 | resize_out(out, sizes, strides, options); |
| 28663 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28664 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28665 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28666 | } |
| 28667 | if (!names.empty()) { |
| 28668 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28669 | } |
| 28670 | // super must happen after, so that downstream can use maybe_get_output |
| 28671 | // to retrieve the output |
| 28672 | } |
| 28673 | void set_output_raw_strided( |
| 28674 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28675 | TensorOptions options, DimnameList names |
| 28676 | ) override { |
| 28677 | const auto& out = outputs_[output_idx].get(); |
| 28678 | resize_out(out, sizes, strides, options); |
| 28679 | if (!names.empty()) { |
| 28680 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28681 | } |
| 28682 | // super must happen after, so that downstream can use maybe_get_output |
| 28683 | // to retrieve the output |
| 28684 | } |
| 28685 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28686 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28687 | } |
| 28688 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 28689 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 28690 | }; |
| 28691 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_linalg_inv_ex_out_inverse(const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { |
| 28692 | structured_linalg_inv_ex_out_out op(inverse, info); |
| 28693 | op.meta(A, check_errors); |
| 28694 | op.impl(A, check_errors, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 28695 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28696 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28697 | return std::forward_as_tuple(inverse, info); |
| 28698 | } |
| 28699 | struct structured_linalg_vector_norm_out_functional final : public at::native::structured_linalg_vector_norm_out { |
| 28700 | void set_output_strided( |
| 28701 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28702 | TensorOptions options, DimnameList names |
| 28703 | ) override { |
| 28704 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28705 | if (!names.empty()) { |
| 28706 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28707 | } |
| 28708 | // super must happen after, so that downstream can use maybe_get_output |
| 28709 | // to retrieve the output |
| 28710 | } |
| 28711 | void set_output_raw_strided( |
| 28712 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28713 | TensorOptions options, DimnameList names |
| 28714 | ) override { |
| 28715 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28716 | if (!names.empty()) { |
| 28717 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28718 | } |
| 28719 | // super must happen after, so that downstream can use maybe_get_output |
| 28720 | // to retrieve the output |
| 28721 | } |
| 28722 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28723 | return *outputs_[output_idx]; |
| 28724 | } |
| 28725 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 28726 | }; |
| 28727 | at::Tensor wrapper_CPU_linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 28728 | structured_linalg_vector_norm_out_functional op; |
| 28729 | op.meta(self, ord, dim, keepdim, dtype); |
| 28730 | op.impl(self, ord, dim, keepdim, dtype, *op.outputs_[0]); |
| 28731 | return std::move(op.outputs_[0]).take(); |
| 28732 | } |
| 28733 | struct structured_linalg_vector_norm_out_out final : public at::native::structured_linalg_vector_norm_out { |
| 28734 | structured_linalg_vector_norm_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 28735 | void set_output_strided( |
| 28736 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28737 | TensorOptions options, DimnameList names |
| 28738 | ) override { |
| 28739 | const auto& out = outputs_[output_idx].get(); |
| 28740 | resize_out(out, sizes, strides, options); |
| 28741 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28742 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28743 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28744 | } |
| 28745 | if (!names.empty()) { |
| 28746 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28747 | } |
| 28748 | // super must happen after, so that downstream can use maybe_get_output |
| 28749 | // to retrieve the output |
| 28750 | } |
| 28751 | void set_output_raw_strided( |
| 28752 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28753 | TensorOptions options, DimnameList names |
| 28754 | ) override { |
| 28755 | const auto& out = outputs_[output_idx].get(); |
| 28756 | resize_out(out, sizes, strides, options); |
| 28757 | if (!names.empty()) { |
| 28758 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28759 | } |
| 28760 | // super must happen after, so that downstream can use maybe_get_output |
| 28761 | // to retrieve the output |
| 28762 | } |
| 28763 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28764 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28765 | } |
| 28766 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 28767 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 28768 | }; |
| 28769 | at::Tensor & wrapper_CPU_linalg_vector_norm_out_out(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 28770 | structured_linalg_vector_norm_out_out op(out); |
| 28771 | op.meta(self, ord, dim, keepdim, dtype); |
| 28772 | op.impl(self, ord, dim, keepdim, dtype, op.maybe_get_output(0)); |
| 28773 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28774 | return out; |
| 28775 | } |
| 28776 | struct structured__linalg_svd_out_functional final : public at::native::structured__linalg_svd_out { |
| 28777 | void set_output_strided( |
| 28778 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28779 | TensorOptions options, DimnameList names |
| 28780 | ) override { |
| 28781 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28782 | if (!names.empty()) { |
| 28783 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28784 | } |
| 28785 | // super must happen after, so that downstream can use maybe_get_output |
| 28786 | // to retrieve the output |
| 28787 | } |
| 28788 | void set_output_raw_strided( |
| 28789 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28790 | TensorOptions options, DimnameList names |
| 28791 | ) override { |
| 28792 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28793 | if (!names.empty()) { |
| 28794 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28795 | } |
| 28796 | // super must happen after, so that downstream can use maybe_get_output |
| 28797 | // to retrieve the output |
| 28798 | } |
| 28799 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28800 | return *outputs_[output_idx]; |
| 28801 | } |
| 28802 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 28803 | }; |
| 28804 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__linalg_svd(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver) { |
| 28805 | structured__linalg_svd_out_functional op; |
| 28806 | op.meta(A, full_matrices, compute_uv, driver); |
| 28807 | op.impl(A, full_matrices, compute_uv, driver, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 28808 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 28809 | } |
| 28810 | struct structured__linalg_svd_out_out final : public at::native::structured__linalg_svd_out { |
| 28811 | structured__linalg_svd_out_out(Tensor& out0, Tensor& out1, Tensor& out2) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2) } {} |
| 28812 | void set_output_strided( |
| 28813 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28814 | TensorOptions options, DimnameList names |
| 28815 | ) override { |
| 28816 | const auto& out = outputs_[output_idx].get(); |
| 28817 | resize_out(out, sizes, strides, options); |
| 28818 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28819 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28820 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28821 | } |
| 28822 | if (!names.empty()) { |
| 28823 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28824 | } |
| 28825 | // super must happen after, so that downstream can use maybe_get_output |
| 28826 | // to retrieve the output |
| 28827 | } |
| 28828 | void set_output_raw_strided( |
| 28829 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28830 | TensorOptions options, DimnameList names |
| 28831 | ) override { |
| 28832 | const auto& out = outputs_[output_idx].get(); |
| 28833 | resize_out(out, sizes, strides, options); |
| 28834 | if (!names.empty()) { |
| 28835 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28836 | } |
| 28837 | // super must happen after, so that downstream can use maybe_get_output |
| 28838 | // to retrieve the output |
| 28839 | } |
| 28840 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28841 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28842 | } |
| 28843 | std::array<std::reference_wrapper<Tensor>, 3> outputs_; |
| 28844 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 3> proxy_outputs_; |
| 28845 | }; |
| 28846 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU__linalg_svd_out_U(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { |
| 28847 | structured__linalg_svd_out_out op(U, S, Vh); |
| 28848 | op.meta(A, full_matrices, compute_uv, driver); |
| 28849 | op.impl(A, full_matrices, compute_uv, driver, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2)); |
| 28850 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28851 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28852 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28853 | return std::forward_as_tuple(U, S, Vh); |
| 28854 | } |
| 28855 | struct structured__linalg_solve_ex_out_functional final : public at::native::structured__linalg_solve_ex_out { |
| 28856 | void set_output_strided( |
| 28857 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28858 | TensorOptions options, DimnameList names |
| 28859 | ) override { |
| 28860 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28861 | if (!names.empty()) { |
| 28862 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28863 | } |
| 28864 | // super must happen after, so that downstream can use maybe_get_output |
| 28865 | // to retrieve the output |
| 28866 | } |
| 28867 | void set_output_raw_strided( |
| 28868 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28869 | TensorOptions options, DimnameList names |
| 28870 | ) override { |
| 28871 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28872 | if (!names.empty()) { |
| 28873 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28874 | } |
| 28875 | // super must happen after, so that downstream can use maybe_get_output |
| 28876 | // to retrieve the output |
| 28877 | } |
| 28878 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28879 | return *outputs_[output_idx]; |
| 28880 | } |
| 28881 | std::array<c10::ExclusivelyOwned<Tensor>, 4> outputs_; |
| 28882 | }; |
| 28883 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU__linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 28884 | structured__linalg_solve_ex_out_functional op; |
| 28885 | op.meta(A, B, left, check_errors); |
| 28886 | op.impl(A, B, left, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2], *op.outputs_[3]); |
| 28887 | 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()); |
| 28888 | } |
| 28889 | struct structured__linalg_solve_ex_out_out final : public at::native::structured__linalg_solve_ex_out { |
| 28890 | structured__linalg_solve_ex_out_out(Tensor& out0, Tensor& out1, Tensor& out2, Tensor& out3) : outputs_{ std::ref(out0), std::ref(out1), std::ref(out2), std::ref(out3) } {} |
| 28891 | void set_output_strided( |
| 28892 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28893 | TensorOptions options, DimnameList names |
| 28894 | ) override { |
| 28895 | const auto& out = outputs_[output_idx].get(); |
| 28896 | resize_out(out, sizes, strides, options); |
| 28897 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28898 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28899 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28900 | } |
| 28901 | if (!names.empty()) { |
| 28902 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28903 | } |
| 28904 | // super must happen after, so that downstream can use maybe_get_output |
| 28905 | // to retrieve the output |
| 28906 | } |
| 28907 | void set_output_raw_strided( |
| 28908 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28909 | TensorOptions options, DimnameList names |
| 28910 | ) override { |
| 28911 | const auto& out = outputs_[output_idx].get(); |
| 28912 | resize_out(out, sizes, strides, options); |
| 28913 | if (!names.empty()) { |
| 28914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28915 | } |
| 28916 | // super must happen after, so that downstream can use maybe_get_output |
| 28917 | // to retrieve the output |
| 28918 | } |
| 28919 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28920 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 28921 | } |
| 28922 | std::array<std::reference_wrapper<Tensor>, 4> outputs_; |
| 28923 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 4> proxy_outputs_; |
| 28924 | }; |
| 28925 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> wrapper_CPU__linalg_solve_ex_out_result(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 28926 | structured__linalg_solve_ex_out_out op(result, LU, pivots, info); |
| 28927 | op.meta(A, B, left, check_errors); |
| 28928 | op.impl(A, B, left, check_errors, op.maybe_get_output(0), op.maybe_get_output(1), op.maybe_get_output(2), op.maybe_get_output(3)); |
| 28929 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 28930 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 28931 | if (op.proxy_outputs_[2].has_value()) op.outputs_[2].get().copy_(**op.proxy_outputs_[2]); |
| 28932 | if (op.proxy_outputs_[3].has_value()) op.outputs_[3].get().copy_(**op.proxy_outputs_[3]); |
| 28933 | return std::forward_as_tuple(result, LU, pivots, info); |
| 28934 | } |
| 28935 | struct structured_linalg_qr_out_functional final : public at::native::structured_linalg_qr_out { |
| 28936 | void set_output_strided( |
| 28937 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28938 | TensorOptions options, DimnameList names |
| 28939 | ) override { |
| 28940 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28941 | if (!names.empty()) { |
| 28942 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28943 | } |
| 28944 | // super must happen after, so that downstream can use maybe_get_output |
| 28945 | // to retrieve the output |
| 28946 | } |
| 28947 | void set_output_raw_strided( |
| 28948 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28949 | TensorOptions options, DimnameList names |
| 28950 | ) override { |
| 28951 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 28952 | if (!names.empty()) { |
| 28953 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 28954 | } |
| 28955 | // super must happen after, so that downstream can use maybe_get_output |
| 28956 | // to retrieve the output |
| 28957 | } |
| 28958 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 28959 | return *outputs_[output_idx]; |
| 28960 | } |
| 28961 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 28962 | }; |
| 28963 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU_linalg_qr(const at::Tensor & A, c10::string_view mode) { |
| 28964 | structured_linalg_qr_out_functional op; |
| 28965 | op.meta(A, mode); |
| 28966 | op.impl(A, mode, *op.outputs_[0], *op.outputs_[1]); |
| 28967 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 28968 | } |
| 28969 | struct structured_linalg_qr_out_out final : public at::native::structured_linalg_qr_out { |
| 28970 | structured_linalg_qr_out_out(Tensor& out0, Tensor& out1) : outputs_{ std::ref(out0), std::ref(out1) } {} |
| 28971 | void set_output_strided( |
| 28972 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28973 | TensorOptions options, DimnameList names |
| 28974 | ) override { |
| 28975 | const auto& out = outputs_[output_idx].get(); |
| 28976 | resize_out(out, sizes, strides, options); |
| 28977 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 28978 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 28979 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 28980 | } |
| 28981 | if (!names.empty()) { |
| 28982 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28983 | } |
| 28984 | // super must happen after, so that downstream can use maybe_get_output |
| 28985 | // to retrieve the output |
| 28986 | } |
| 28987 | void set_output_raw_strided( |
| 28988 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 28989 | TensorOptions options, DimnameList names |
| 28990 | ) override { |
| 28991 | const auto& out = outputs_[output_idx].get(); |
| 28992 | resize_out(out, sizes, strides, options); |
| 28993 | if (!names.empty()) { |
| 28994 | namedinference::propagate_names(outputs_[output_idx], names); |
| 28995 | } |
| 28996 | // super must happen after, so that downstream can use maybe_get_output |
| 28997 | // to retrieve the output |
| 28998 | } |
| 28999 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29000 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29001 | } |
| 29002 | std::array<std::reference_wrapper<Tensor>, 2> outputs_; |
| 29003 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 2> proxy_outputs_; |
| 29004 | }; |
| 29005 | ::std::tuple<at::Tensor &,at::Tensor &> wrapper_CPU_linalg_qr_out_out(const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R) { |
| 29006 | structured_linalg_qr_out_out op(Q, R); |
| 29007 | op.meta(A, mode); |
| 29008 | op.impl(A, mode, op.maybe_get_output(0), op.maybe_get_output(1)); |
| 29009 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29010 | if (op.proxy_outputs_[1].has_value()) op.outputs_[1].get().copy_(**op.proxy_outputs_[1]); |
| 29011 | return std::forward_as_tuple(Q, R); |
| 29012 | } |
| 29013 | namespace { |
| 29014 | at::Tensor wrapper_CPU___test_optional_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 29015 | // No device check |
| 29016 | // DeviceGuard omitted |
| 29017 | return at::native::_test_optional_intlist(values, addends); |
| 29018 | } |
| 29019 | } // anonymous namespace |
| 29020 | namespace { |
| 29021 | at::Tensor wrapper_CPU___test_optional_filled_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 29022 | // No device check |
| 29023 | // DeviceGuard omitted |
| 29024 | return at::native::_test_optional_intlist(values, addends); |
| 29025 | } |
| 29026 | } // anonymous namespace |
| 29027 | namespace { |
| 29028 | at::Tensor wrapper_CPU___test_optional_floatlist(const at::Tensor & values, c10::optional<at::ArrayRef<double>> addends) { |
| 29029 | // No device check |
| 29030 | // DeviceGuard omitted |
| 29031 | return at::native::_test_optional_floatlist(values, addends); |
| 29032 | } |
| 29033 | } // anonymous namespace |
| 29034 | namespace { |
| 29035 | at::Tensor wrapper_CPU__segment_reduce(const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & indices, const c10::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const c10::optional<at::Scalar> & initial) { |
| 29036 | // No device check |
| 29037 | // DeviceGuard omitted |
| 29038 | return at::native::segment_reduce_kernel(data, reduce, lengths, indices, offsets, axis, unsafe, initial); |
| 29039 | } |
| 29040 | } // anonymous namespace |
| 29041 | namespace { |
| 29042 | at::Tensor wrapper_CPU___segment_reduce_backward(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & offsets, int64_t axis, const c10::optional<at::Scalar> & initial) { |
| 29043 | // No device check |
| 29044 | // DeviceGuard omitted |
| 29045 | return at::native::_segment_reduce_backward_kernel(grad, output, data, reduce, lengths, offsets, axis, initial); |
| 29046 | } |
| 29047 | } // anonymous namespace |
| 29048 | namespace { |
| 29049 | at::Tensor wrapper_CPU___transformer_encoder_layer_fwd(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, c10::optional<int64_t> mask_type) { |
| 29050 | // No device check |
| 29051 | // DeviceGuard omitted |
| 29052 | return at::native::transformer_encoder_layer_forward(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type); |
| 29053 | } |
| 29054 | } // anonymous namespace |
| 29055 | namespace { |
| 29056 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CPU___native_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type) { |
| 29057 | // No device check |
| 29058 | // DeviceGuard omitted |
| 29059 | return at::native::native_multi_head_attention_cpu(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); |
| 29060 | } |
| 29061 | } // anonymous namespace |
| 29062 | namespace { |
| 29063 | int64_t wrapper_CPU___fused_sdp_choice(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal) { |
| 29064 | // No device check |
| 29065 | // DeviceGuard omitted |
| 29066 | return at::native::_fused_sdp_choice_cpp(query, key, value, attn_mask, dropout_p, is_causal); |
| 29067 | } |
| 29068 | } // anonymous namespace |
| 29069 | struct structured_special_airy_ai_out_functional final : public at::native::structured_special_airy_ai_out { |
| 29070 | void set_output_strided( |
| 29071 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29072 | TensorOptions options, DimnameList names |
| 29073 | ) override { |
| 29074 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29075 | if (!names.empty()) { |
| 29076 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29077 | } |
| 29078 | // super must happen after, so that downstream can use maybe_get_output |
| 29079 | // to retrieve the output |
| 29080 | at::native::structured_special_airy_ai_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29081 | } |
| 29082 | void set_output_raw_strided( |
| 29083 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29084 | TensorOptions options, DimnameList names |
| 29085 | ) override { |
| 29086 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29087 | if (!names.empty()) { |
| 29088 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29089 | } |
| 29090 | // super must happen after, so that downstream can use maybe_get_output |
| 29091 | // to retrieve the output |
| 29092 | at::native::structured_special_airy_ai_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29093 | } |
| 29094 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29095 | return *outputs_[output_idx]; |
| 29096 | } |
| 29097 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29098 | }; |
| 29099 | at::Tensor wrapper_CPU_special_airy_ai(const at::Tensor & x) { |
| 29100 | structured_special_airy_ai_out_functional op; |
| 29101 | op.meta(x); |
| 29102 | op.impl(x, *op.outputs_[0]); |
| 29103 | return std::move(op.outputs_[0]).take(); |
| 29104 | } |
| 29105 | struct structured_special_airy_ai_out_out final : public at::native::structured_special_airy_ai_out { |
| 29106 | structured_special_airy_ai_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29107 | void set_output_strided( |
| 29108 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29109 | TensorOptions options, DimnameList names |
| 29110 | ) override { |
| 29111 | const auto& out = outputs_[output_idx].get(); |
| 29112 | resize_out(out, sizes, strides, options); |
| 29113 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29114 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29115 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29116 | } |
| 29117 | if (!names.empty()) { |
| 29118 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29119 | } |
| 29120 | // super must happen after, so that downstream can use maybe_get_output |
| 29121 | // to retrieve the output |
| 29122 | at::native::structured_special_airy_ai_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29123 | } |
| 29124 | void set_output_raw_strided( |
| 29125 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29126 | TensorOptions options, DimnameList names |
| 29127 | ) override { |
| 29128 | const auto& out = outputs_[output_idx].get(); |
| 29129 | resize_out(out, sizes, strides, options); |
| 29130 | if (!names.empty()) { |
| 29131 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29132 | } |
| 29133 | // super must happen after, so that downstream can use maybe_get_output |
| 29134 | // to retrieve the output |
| 29135 | at::native::structured_special_airy_ai_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29136 | } |
| 29137 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29138 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29139 | } |
| 29140 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29141 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29142 | }; |
| 29143 | at::Tensor & wrapper_CPU_special_airy_ai_out_out(const at::Tensor & x, at::Tensor & out) { |
| 29144 | structured_special_airy_ai_out_out op(out); |
| 29145 | op.meta(x); |
| 29146 | op.impl(x, op.maybe_get_output(0)); |
| 29147 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29148 | return out; |
| 29149 | } |
| 29150 | namespace { |
| 29151 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___transformer_decoder_only_layer_fwd(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value) { |
| 29152 | // No device check |
| 29153 | // DeviceGuard omitted |
| 29154 | return at::native::transformer_decoder_only_layer_forward(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value); |
| 29155 | } |
| 29156 | } // anonymous namespace |
| 29157 | namespace { |
| 29158 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CPU___native_decoder_only_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value, bool need_weights, bool average_attn_weights) { |
| 29159 | // No device check |
| 29160 | // DeviceGuard omitted |
| 29161 | return at::native::native_decoder_only_multi_head_attention(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, incr_key, incr_value, need_weights, average_attn_weights); |
| 29162 | } |
| 29163 | } // anonymous namespace |
| 29164 | struct structured_special_bessel_j0_out_functional final : public at::native::structured_special_bessel_j0_out { |
| 29165 | void set_output_strided( |
| 29166 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29167 | TensorOptions options, DimnameList names |
| 29168 | ) override { |
| 29169 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29170 | if (!names.empty()) { |
| 29171 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29172 | } |
| 29173 | // super must happen after, so that downstream can use maybe_get_output |
| 29174 | // to retrieve the output |
| 29175 | at::native::structured_special_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29176 | } |
| 29177 | void set_output_raw_strided( |
| 29178 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29179 | TensorOptions options, DimnameList names |
| 29180 | ) override { |
| 29181 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29182 | if (!names.empty()) { |
| 29183 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29184 | } |
| 29185 | // super must happen after, so that downstream can use maybe_get_output |
| 29186 | // to retrieve the output |
| 29187 | at::native::structured_special_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29188 | } |
| 29189 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29190 | return *outputs_[output_idx]; |
| 29191 | } |
| 29192 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29193 | }; |
| 29194 | at::Tensor wrapper_CPU_special_bessel_j0(const at::Tensor & self) { |
| 29195 | structured_special_bessel_j0_out_functional op; |
| 29196 | op.meta(self); |
| 29197 | op.impl(self, *op.outputs_[0]); |
| 29198 | return std::move(op.outputs_[0]).take(); |
| 29199 | } |
| 29200 | struct structured_special_bessel_j0_out_out final : public at::native::structured_special_bessel_j0_out { |
| 29201 | structured_special_bessel_j0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29202 | void set_output_strided( |
| 29203 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29204 | TensorOptions options, DimnameList names |
| 29205 | ) override { |
| 29206 | const auto& out = outputs_[output_idx].get(); |
| 29207 | resize_out(out, sizes, strides, options); |
| 29208 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29209 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29210 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29211 | } |
| 29212 | if (!names.empty()) { |
| 29213 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29214 | } |
| 29215 | // super must happen after, so that downstream can use maybe_get_output |
| 29216 | // to retrieve the output |
| 29217 | at::native::structured_special_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29218 | } |
| 29219 | void set_output_raw_strided( |
| 29220 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29221 | TensorOptions options, DimnameList names |
| 29222 | ) override { |
| 29223 | const auto& out = outputs_[output_idx].get(); |
| 29224 | resize_out(out, sizes, strides, options); |
| 29225 | if (!names.empty()) { |
| 29226 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29227 | } |
| 29228 | // super must happen after, so that downstream can use maybe_get_output |
| 29229 | // to retrieve the output |
| 29230 | at::native::structured_special_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29231 | } |
| 29232 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29233 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29234 | } |
| 29235 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29236 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29237 | }; |
| 29238 | at::Tensor & wrapper_CPU_special_bessel_j0_out_out(const at::Tensor & self, at::Tensor & out) { |
| 29239 | structured_special_bessel_j0_out_out op(out); |
| 29240 | op.meta(self); |
| 29241 | op.impl(self, op.maybe_get_output(0)); |
| 29242 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29243 | return out; |
| 29244 | } |
| 29245 | struct structured_special_bessel_j1_out_functional final : public at::native::structured_special_bessel_j1_out { |
| 29246 | void set_output_strided( |
| 29247 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29248 | TensorOptions options, DimnameList names |
| 29249 | ) override { |
| 29250 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29251 | if (!names.empty()) { |
| 29252 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29253 | } |
| 29254 | // super must happen after, so that downstream can use maybe_get_output |
| 29255 | // to retrieve the output |
| 29256 | at::native::structured_special_bessel_j1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29257 | } |
| 29258 | void set_output_raw_strided( |
| 29259 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29260 | TensorOptions options, DimnameList names |
| 29261 | ) override { |
| 29262 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29263 | if (!names.empty()) { |
| 29264 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29265 | } |
| 29266 | // super must happen after, so that downstream can use maybe_get_output |
| 29267 | // to retrieve the output |
| 29268 | at::native::structured_special_bessel_j1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29269 | } |
| 29270 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29271 | return *outputs_[output_idx]; |
| 29272 | } |
| 29273 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29274 | }; |
| 29275 | at::Tensor wrapper_CPU_special_bessel_j1(const at::Tensor & self) { |
| 29276 | structured_special_bessel_j1_out_functional op; |
| 29277 | op.meta(self); |
| 29278 | op.impl(self, *op.outputs_[0]); |
| 29279 | return std::move(op.outputs_[0]).take(); |
| 29280 | } |
| 29281 | struct structured_special_bessel_j1_out_out final : public at::native::structured_special_bessel_j1_out { |
| 29282 | structured_special_bessel_j1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29283 | void set_output_strided( |
| 29284 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29285 | TensorOptions options, DimnameList names |
| 29286 | ) override { |
| 29287 | const auto& out = outputs_[output_idx].get(); |
| 29288 | resize_out(out, sizes, strides, options); |
| 29289 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29290 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29291 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29292 | } |
| 29293 | if (!names.empty()) { |
| 29294 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29295 | } |
| 29296 | // super must happen after, so that downstream can use maybe_get_output |
| 29297 | // to retrieve the output |
| 29298 | at::native::structured_special_bessel_j1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29299 | } |
| 29300 | void set_output_raw_strided( |
| 29301 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29302 | TensorOptions options, DimnameList names |
| 29303 | ) override { |
| 29304 | const auto& out = outputs_[output_idx].get(); |
| 29305 | resize_out(out, sizes, strides, options); |
| 29306 | if (!names.empty()) { |
| 29307 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29308 | } |
| 29309 | // super must happen after, so that downstream can use maybe_get_output |
| 29310 | // to retrieve the output |
| 29311 | at::native::structured_special_bessel_j1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29312 | } |
| 29313 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29314 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29315 | } |
| 29316 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29317 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29318 | }; |
| 29319 | at::Tensor & wrapper_CPU_special_bessel_j1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 29320 | structured_special_bessel_j1_out_out op(out); |
| 29321 | op.meta(self); |
| 29322 | op.impl(self, op.maybe_get_output(0)); |
| 29323 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29324 | return out; |
| 29325 | } |
| 29326 | struct structured_special_bessel_y0_out_functional final : public at::native::structured_special_bessel_y0_out { |
| 29327 | void set_output_strided( |
| 29328 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29329 | TensorOptions options, DimnameList names |
| 29330 | ) override { |
| 29331 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29332 | if (!names.empty()) { |
| 29333 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29334 | } |
| 29335 | // super must happen after, so that downstream can use maybe_get_output |
| 29336 | // to retrieve the output |
| 29337 | at::native::structured_special_bessel_y0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29338 | } |
| 29339 | void set_output_raw_strided( |
| 29340 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29341 | TensorOptions options, DimnameList names |
| 29342 | ) override { |
| 29343 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29344 | if (!names.empty()) { |
| 29345 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29346 | } |
| 29347 | // super must happen after, so that downstream can use maybe_get_output |
| 29348 | // to retrieve the output |
| 29349 | at::native::structured_special_bessel_y0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29350 | } |
| 29351 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29352 | return *outputs_[output_idx]; |
| 29353 | } |
| 29354 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29355 | }; |
| 29356 | at::Tensor wrapper_CPU_special_bessel_y0(const at::Tensor & self) { |
| 29357 | structured_special_bessel_y0_out_functional op; |
| 29358 | op.meta(self); |
| 29359 | op.impl(self, *op.outputs_[0]); |
| 29360 | return std::move(op.outputs_[0]).take(); |
| 29361 | } |
| 29362 | struct structured_special_bessel_y0_out_out final : public at::native::structured_special_bessel_y0_out { |
| 29363 | structured_special_bessel_y0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29364 | void set_output_strided( |
| 29365 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29366 | TensorOptions options, DimnameList names |
| 29367 | ) override { |
| 29368 | const auto& out = outputs_[output_idx].get(); |
| 29369 | resize_out(out, sizes, strides, options); |
| 29370 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29371 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29372 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29373 | } |
| 29374 | if (!names.empty()) { |
| 29375 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29376 | } |
| 29377 | // super must happen after, so that downstream can use maybe_get_output |
| 29378 | // to retrieve the output |
| 29379 | at::native::structured_special_bessel_y0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29380 | } |
| 29381 | void set_output_raw_strided( |
| 29382 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29383 | TensorOptions options, DimnameList names |
| 29384 | ) override { |
| 29385 | const auto& out = outputs_[output_idx].get(); |
| 29386 | resize_out(out, sizes, strides, options); |
| 29387 | if (!names.empty()) { |
| 29388 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29389 | } |
| 29390 | // super must happen after, so that downstream can use maybe_get_output |
| 29391 | // to retrieve the output |
| 29392 | at::native::structured_special_bessel_y0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29393 | } |
| 29394 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29395 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29396 | } |
| 29397 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29398 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29399 | }; |
| 29400 | at::Tensor & wrapper_CPU_special_bessel_y0_out_out(const at::Tensor & self, at::Tensor & out) { |
| 29401 | structured_special_bessel_y0_out_out op(out); |
| 29402 | op.meta(self); |
| 29403 | op.impl(self, op.maybe_get_output(0)); |
| 29404 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29405 | return out; |
| 29406 | } |
| 29407 | struct structured_special_bessel_y1_out_functional final : public at::native::structured_special_bessel_y1_out { |
| 29408 | void set_output_strided( |
| 29409 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29410 | TensorOptions options, DimnameList names |
| 29411 | ) override { |
| 29412 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29413 | if (!names.empty()) { |
| 29414 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29415 | } |
| 29416 | // super must happen after, so that downstream can use maybe_get_output |
| 29417 | // to retrieve the output |
| 29418 | at::native::structured_special_bessel_y1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29419 | } |
| 29420 | void set_output_raw_strided( |
| 29421 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29422 | TensorOptions options, DimnameList names |
| 29423 | ) override { |
| 29424 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29425 | if (!names.empty()) { |
| 29426 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29427 | } |
| 29428 | // super must happen after, so that downstream can use maybe_get_output |
| 29429 | // to retrieve the output |
| 29430 | at::native::structured_special_bessel_y1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29431 | } |
| 29432 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29433 | return *outputs_[output_idx]; |
| 29434 | } |
| 29435 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29436 | }; |
| 29437 | at::Tensor wrapper_CPU_special_bessel_y1(const at::Tensor & self) { |
| 29438 | structured_special_bessel_y1_out_functional op; |
| 29439 | op.meta(self); |
| 29440 | op.impl(self, *op.outputs_[0]); |
| 29441 | return std::move(op.outputs_[0]).take(); |
| 29442 | } |
| 29443 | struct structured_special_bessel_y1_out_out final : public at::native::structured_special_bessel_y1_out { |
| 29444 | structured_special_bessel_y1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29445 | void set_output_strided( |
| 29446 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29447 | TensorOptions options, DimnameList names |
| 29448 | ) override { |
| 29449 | const auto& out = outputs_[output_idx].get(); |
| 29450 | resize_out(out, sizes, strides, options); |
| 29451 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29452 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29453 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29454 | } |
| 29455 | if (!names.empty()) { |
| 29456 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29457 | } |
| 29458 | // super must happen after, so that downstream can use maybe_get_output |
| 29459 | // to retrieve the output |
| 29460 | at::native::structured_special_bessel_y1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29461 | } |
| 29462 | void set_output_raw_strided( |
| 29463 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29464 | TensorOptions options, DimnameList names |
| 29465 | ) override { |
| 29466 | const auto& out = outputs_[output_idx].get(); |
| 29467 | resize_out(out, sizes, strides, options); |
| 29468 | if (!names.empty()) { |
| 29469 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29470 | } |
| 29471 | // super must happen after, so that downstream can use maybe_get_output |
| 29472 | // to retrieve the output |
| 29473 | at::native::structured_special_bessel_y1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29474 | } |
| 29475 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29476 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29477 | } |
| 29478 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29479 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29480 | }; |
| 29481 | at::Tensor & wrapper_CPU_special_bessel_y1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 29482 | structured_special_bessel_y1_out_out op(out); |
| 29483 | op.meta(self); |
| 29484 | op.impl(self, op.maybe_get_output(0)); |
| 29485 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29486 | return out; |
| 29487 | } |
| 29488 | struct structured_special_chebyshev_polynomial_t_out_functional final : public at::native::structured_special_chebyshev_polynomial_t_out { |
| 29489 | void set_output_strided( |
| 29490 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29491 | TensorOptions options, DimnameList names |
| 29492 | ) override { |
| 29493 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29494 | if (!names.empty()) { |
| 29495 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29496 | } |
| 29497 | // super must happen after, so that downstream can use maybe_get_output |
| 29498 | // to retrieve the output |
| 29499 | at::native::structured_special_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29500 | } |
| 29501 | void set_output_raw_strided( |
| 29502 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29503 | TensorOptions options, DimnameList names |
| 29504 | ) override { |
| 29505 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29506 | if (!names.empty()) { |
| 29507 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29508 | } |
| 29509 | // super must happen after, so that downstream can use maybe_get_output |
| 29510 | // to retrieve the output |
| 29511 | at::native::structured_special_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29512 | } |
| 29513 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29514 | return *outputs_[output_idx]; |
| 29515 | } |
| 29516 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29517 | }; |
| 29518 | at::Tensor wrapper_CPU_special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 29519 | structured_special_chebyshev_polynomial_t_out_functional op; |
| 29520 | op.meta(x, n); |
| 29521 | op.impl(x, n, *op.outputs_[0]); |
| 29522 | return std::move(op.outputs_[0]).take(); |
| 29523 | } |
| 29524 | struct structured_special_chebyshev_polynomial_t_out_out final : public at::native::structured_special_chebyshev_polynomial_t_out { |
| 29525 | structured_special_chebyshev_polynomial_t_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29526 | void set_output_strided( |
| 29527 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29528 | TensorOptions options, DimnameList names |
| 29529 | ) override { |
| 29530 | const auto& out = outputs_[output_idx].get(); |
| 29531 | resize_out(out, sizes, strides, options); |
| 29532 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29533 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29534 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29535 | } |
| 29536 | if (!names.empty()) { |
| 29537 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29538 | } |
| 29539 | // super must happen after, so that downstream can use maybe_get_output |
| 29540 | // to retrieve the output |
| 29541 | at::native::structured_special_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29542 | } |
| 29543 | void set_output_raw_strided( |
| 29544 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29545 | TensorOptions options, DimnameList names |
| 29546 | ) override { |
| 29547 | const auto& out = outputs_[output_idx].get(); |
| 29548 | resize_out(out, sizes, strides, options); |
| 29549 | if (!names.empty()) { |
| 29550 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29551 | } |
| 29552 | // super must happen after, so that downstream can use maybe_get_output |
| 29553 | // to retrieve the output |
| 29554 | at::native::structured_special_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29555 | } |
| 29556 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29557 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29558 | } |
| 29559 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29560 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29561 | }; |
| 29562 | at::Tensor & wrapper_CPU_special_chebyshev_polynomial_t_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29563 | structured_special_chebyshev_polynomial_t_out_out op(out); |
| 29564 | op.meta(x, n); |
| 29565 | op.impl(x, n, op.maybe_get_output(0)); |
| 29566 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29567 | return out; |
| 29568 | } |
| 29569 | struct structured_special_chebyshev_polynomial_u_out_functional final : public at::native::structured_special_chebyshev_polynomial_u_out { |
| 29570 | void set_output_strided( |
| 29571 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29572 | TensorOptions options, DimnameList names |
| 29573 | ) override { |
| 29574 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29575 | if (!names.empty()) { |
| 29576 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29577 | } |
| 29578 | // super must happen after, so that downstream can use maybe_get_output |
| 29579 | // to retrieve the output |
| 29580 | at::native::structured_special_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29581 | } |
| 29582 | void set_output_raw_strided( |
| 29583 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29584 | TensorOptions options, DimnameList names |
| 29585 | ) override { |
| 29586 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29587 | if (!names.empty()) { |
| 29588 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29589 | } |
| 29590 | // super must happen after, so that downstream can use maybe_get_output |
| 29591 | // to retrieve the output |
| 29592 | at::native::structured_special_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29593 | } |
| 29594 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29595 | return *outputs_[output_idx]; |
| 29596 | } |
| 29597 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29598 | }; |
| 29599 | at::Tensor wrapper_CPU_special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 29600 | structured_special_chebyshev_polynomial_u_out_functional op; |
| 29601 | op.meta(x, n); |
| 29602 | op.impl(x, n, *op.outputs_[0]); |
| 29603 | return std::move(op.outputs_[0]).take(); |
| 29604 | } |
| 29605 | struct structured_special_chebyshev_polynomial_u_out_out final : public at::native::structured_special_chebyshev_polynomial_u_out { |
| 29606 | structured_special_chebyshev_polynomial_u_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29607 | void set_output_strided( |
| 29608 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29609 | TensorOptions options, DimnameList names |
| 29610 | ) override { |
| 29611 | const auto& out = outputs_[output_idx].get(); |
| 29612 | resize_out(out, sizes, strides, options); |
| 29613 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29614 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29615 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29616 | } |
| 29617 | if (!names.empty()) { |
| 29618 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29619 | } |
| 29620 | // super must happen after, so that downstream can use maybe_get_output |
| 29621 | // to retrieve the output |
| 29622 | at::native::structured_special_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29623 | } |
| 29624 | void set_output_raw_strided( |
| 29625 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29626 | TensorOptions options, DimnameList names |
| 29627 | ) override { |
| 29628 | const auto& out = outputs_[output_idx].get(); |
| 29629 | resize_out(out, sizes, strides, options); |
| 29630 | if (!names.empty()) { |
| 29631 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29632 | } |
| 29633 | // super must happen after, so that downstream can use maybe_get_output |
| 29634 | // to retrieve the output |
| 29635 | at::native::structured_special_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29636 | } |
| 29637 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29638 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29639 | } |
| 29640 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29641 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29642 | }; |
| 29643 | at::Tensor & wrapper_CPU_special_chebyshev_polynomial_u_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29644 | structured_special_chebyshev_polynomial_u_out_out op(out); |
| 29645 | op.meta(x, n); |
| 29646 | op.impl(x, n, op.maybe_get_output(0)); |
| 29647 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29648 | return out; |
| 29649 | } |
| 29650 | struct structured_special_chebyshev_polynomial_v_out_functional final : public at::native::structured_special_chebyshev_polynomial_v_out { |
| 29651 | void set_output_strided( |
| 29652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29653 | TensorOptions options, DimnameList names |
| 29654 | ) override { |
| 29655 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29656 | if (!names.empty()) { |
| 29657 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29658 | } |
| 29659 | // super must happen after, so that downstream can use maybe_get_output |
| 29660 | // to retrieve the output |
| 29661 | at::native::structured_special_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29662 | } |
| 29663 | void set_output_raw_strided( |
| 29664 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29665 | TensorOptions options, DimnameList names |
| 29666 | ) override { |
| 29667 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29668 | if (!names.empty()) { |
| 29669 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29670 | } |
| 29671 | // super must happen after, so that downstream can use maybe_get_output |
| 29672 | // to retrieve the output |
| 29673 | at::native::structured_special_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29674 | } |
| 29675 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29676 | return *outputs_[output_idx]; |
| 29677 | } |
| 29678 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29679 | }; |
| 29680 | at::Tensor wrapper_CPU_special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 29681 | structured_special_chebyshev_polynomial_v_out_functional op; |
| 29682 | op.meta(x, n); |
| 29683 | op.impl(x, n, *op.outputs_[0]); |
| 29684 | return std::move(op.outputs_[0]).take(); |
| 29685 | } |
| 29686 | struct structured_special_chebyshev_polynomial_v_out_out final : public at::native::structured_special_chebyshev_polynomial_v_out { |
| 29687 | structured_special_chebyshev_polynomial_v_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29688 | void set_output_strided( |
| 29689 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29690 | TensorOptions options, DimnameList names |
| 29691 | ) override { |
| 29692 | const auto& out = outputs_[output_idx].get(); |
| 29693 | resize_out(out, sizes, strides, options); |
| 29694 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29695 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29696 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29697 | } |
| 29698 | if (!names.empty()) { |
| 29699 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29700 | } |
| 29701 | // super must happen after, so that downstream can use maybe_get_output |
| 29702 | // to retrieve the output |
| 29703 | at::native::structured_special_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29704 | } |
| 29705 | void set_output_raw_strided( |
| 29706 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29707 | TensorOptions options, DimnameList names |
| 29708 | ) override { |
| 29709 | const auto& out = outputs_[output_idx].get(); |
| 29710 | resize_out(out, sizes, strides, options); |
| 29711 | if (!names.empty()) { |
| 29712 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29713 | } |
| 29714 | // super must happen after, so that downstream can use maybe_get_output |
| 29715 | // to retrieve the output |
| 29716 | at::native::structured_special_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29717 | } |
| 29718 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29719 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29720 | } |
| 29721 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29722 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29723 | }; |
| 29724 | at::Tensor & wrapper_CPU_special_chebyshev_polynomial_v_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29725 | structured_special_chebyshev_polynomial_v_out_out op(out); |
| 29726 | op.meta(x, n); |
| 29727 | op.impl(x, n, op.maybe_get_output(0)); |
| 29728 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29729 | return out; |
| 29730 | } |
| 29731 | struct structured_special_chebyshev_polynomial_w_out_functional final : public at::native::structured_special_chebyshev_polynomial_w_out { |
| 29732 | void set_output_strided( |
| 29733 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29734 | TensorOptions options, DimnameList names |
| 29735 | ) override { |
| 29736 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29737 | if (!names.empty()) { |
| 29738 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29739 | } |
| 29740 | // super must happen after, so that downstream can use maybe_get_output |
| 29741 | // to retrieve the output |
| 29742 | at::native::structured_special_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29743 | } |
| 29744 | void set_output_raw_strided( |
| 29745 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29746 | TensorOptions options, DimnameList names |
| 29747 | ) override { |
| 29748 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29749 | if (!names.empty()) { |
| 29750 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29751 | } |
| 29752 | // super must happen after, so that downstream can use maybe_get_output |
| 29753 | // to retrieve the output |
| 29754 | at::native::structured_special_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29755 | } |
| 29756 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29757 | return *outputs_[output_idx]; |
| 29758 | } |
| 29759 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29760 | }; |
| 29761 | at::Tensor wrapper_CPU_special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 29762 | structured_special_chebyshev_polynomial_w_out_functional op; |
| 29763 | op.meta(x, n); |
| 29764 | op.impl(x, n, *op.outputs_[0]); |
| 29765 | return std::move(op.outputs_[0]).take(); |
| 29766 | } |
| 29767 | struct structured_special_chebyshev_polynomial_w_out_out final : public at::native::structured_special_chebyshev_polynomial_w_out { |
| 29768 | structured_special_chebyshev_polynomial_w_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29769 | void set_output_strided( |
| 29770 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29771 | TensorOptions options, DimnameList names |
| 29772 | ) override { |
| 29773 | const auto& out = outputs_[output_idx].get(); |
| 29774 | resize_out(out, sizes, strides, options); |
| 29775 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29776 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29777 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29778 | } |
| 29779 | if (!names.empty()) { |
| 29780 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29781 | } |
| 29782 | // super must happen after, so that downstream can use maybe_get_output |
| 29783 | // to retrieve the output |
| 29784 | at::native::structured_special_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29785 | } |
| 29786 | void set_output_raw_strided( |
| 29787 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29788 | TensorOptions options, DimnameList names |
| 29789 | ) override { |
| 29790 | const auto& out = outputs_[output_idx].get(); |
| 29791 | resize_out(out, sizes, strides, options); |
| 29792 | if (!names.empty()) { |
| 29793 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29794 | } |
| 29795 | // super must happen after, so that downstream can use maybe_get_output |
| 29796 | // to retrieve the output |
| 29797 | at::native::structured_special_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29798 | } |
| 29799 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29800 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29801 | } |
| 29802 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29803 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29804 | }; |
| 29805 | at::Tensor & wrapper_CPU_special_chebyshev_polynomial_w_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29806 | structured_special_chebyshev_polynomial_w_out_out op(out); |
| 29807 | op.meta(x, n); |
| 29808 | op.impl(x, n, op.maybe_get_output(0)); |
| 29809 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29810 | return out; |
| 29811 | } |
| 29812 | struct structured_special_hermite_polynomial_h_out_functional final : public at::native::structured_special_hermite_polynomial_h_out { |
| 29813 | void set_output_strided( |
| 29814 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29815 | TensorOptions options, DimnameList names |
| 29816 | ) override { |
| 29817 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29818 | if (!names.empty()) { |
| 29819 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29820 | } |
| 29821 | // super must happen after, so that downstream can use maybe_get_output |
| 29822 | // to retrieve the output |
| 29823 | at::native::structured_special_hermite_polynomial_h_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29824 | } |
| 29825 | void set_output_raw_strided( |
| 29826 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29827 | TensorOptions options, DimnameList names |
| 29828 | ) override { |
| 29829 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29830 | if (!names.empty()) { |
| 29831 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29832 | } |
| 29833 | // super must happen after, so that downstream can use maybe_get_output |
| 29834 | // to retrieve the output |
| 29835 | at::native::structured_special_hermite_polynomial_h_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29836 | } |
| 29837 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29838 | return *outputs_[output_idx]; |
| 29839 | } |
| 29840 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29841 | }; |
| 29842 | at::Tensor wrapper_CPU_special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) { |
| 29843 | structured_special_hermite_polynomial_h_out_functional op; |
| 29844 | op.meta(x, n); |
| 29845 | op.impl(x, n, *op.outputs_[0]); |
| 29846 | return std::move(op.outputs_[0]).take(); |
| 29847 | } |
| 29848 | struct structured_special_hermite_polynomial_h_out_out final : public at::native::structured_special_hermite_polynomial_h_out { |
| 29849 | structured_special_hermite_polynomial_h_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29850 | void set_output_strided( |
| 29851 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29852 | TensorOptions options, DimnameList names |
| 29853 | ) override { |
| 29854 | const auto& out = outputs_[output_idx].get(); |
| 29855 | resize_out(out, sizes, strides, options); |
| 29856 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29857 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29858 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29859 | } |
| 29860 | if (!names.empty()) { |
| 29861 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29862 | } |
| 29863 | // super must happen after, so that downstream can use maybe_get_output |
| 29864 | // to retrieve the output |
| 29865 | at::native::structured_special_hermite_polynomial_h_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29866 | } |
| 29867 | void set_output_raw_strided( |
| 29868 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29869 | TensorOptions options, DimnameList names |
| 29870 | ) override { |
| 29871 | const auto& out = outputs_[output_idx].get(); |
| 29872 | resize_out(out, sizes, strides, options); |
| 29873 | if (!names.empty()) { |
| 29874 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29875 | } |
| 29876 | // super must happen after, so that downstream can use maybe_get_output |
| 29877 | // to retrieve the output |
| 29878 | at::native::structured_special_hermite_polynomial_h_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29879 | } |
| 29880 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29881 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29882 | } |
| 29883 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29884 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29885 | }; |
| 29886 | at::Tensor & wrapper_CPU_special_hermite_polynomial_h_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29887 | structured_special_hermite_polynomial_h_out_out op(out); |
| 29888 | op.meta(x, n); |
| 29889 | op.impl(x, n, op.maybe_get_output(0)); |
| 29890 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29891 | return out; |
| 29892 | } |
| 29893 | struct structured_special_hermite_polynomial_he_out_functional final : public at::native::structured_special_hermite_polynomial_he_out { |
| 29894 | void set_output_strided( |
| 29895 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29896 | TensorOptions options, DimnameList names |
| 29897 | ) override { |
| 29898 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29899 | if (!names.empty()) { |
| 29900 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29901 | } |
| 29902 | // super must happen after, so that downstream can use maybe_get_output |
| 29903 | // to retrieve the output |
| 29904 | at::native::structured_special_hermite_polynomial_he_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29905 | } |
| 29906 | void set_output_raw_strided( |
| 29907 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29908 | TensorOptions options, DimnameList names |
| 29909 | ) override { |
| 29910 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29911 | if (!names.empty()) { |
| 29912 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29913 | } |
| 29914 | // super must happen after, so that downstream can use maybe_get_output |
| 29915 | // to retrieve the output |
| 29916 | at::native::structured_special_hermite_polynomial_he_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29917 | } |
| 29918 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29919 | return *outputs_[output_idx]; |
| 29920 | } |
| 29921 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 29922 | }; |
| 29923 | at::Tensor wrapper_CPU_special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) { |
| 29924 | structured_special_hermite_polynomial_he_out_functional op; |
| 29925 | op.meta(x, n); |
| 29926 | op.impl(x, n, *op.outputs_[0]); |
| 29927 | return std::move(op.outputs_[0]).take(); |
| 29928 | } |
| 29929 | struct structured_special_hermite_polynomial_he_out_out final : public at::native::structured_special_hermite_polynomial_he_out { |
| 29930 | structured_special_hermite_polynomial_he_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 29931 | void set_output_strided( |
| 29932 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29933 | TensorOptions options, DimnameList names |
| 29934 | ) override { |
| 29935 | const auto& out = outputs_[output_idx].get(); |
| 29936 | resize_out(out, sizes, strides, options); |
| 29937 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 29938 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 29939 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 29940 | } |
| 29941 | if (!names.empty()) { |
| 29942 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29943 | } |
| 29944 | // super must happen after, so that downstream can use maybe_get_output |
| 29945 | // to retrieve the output |
| 29946 | at::native::structured_special_hermite_polynomial_he_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29947 | } |
| 29948 | void set_output_raw_strided( |
| 29949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29950 | TensorOptions options, DimnameList names |
| 29951 | ) override { |
| 29952 | const auto& out = outputs_[output_idx].get(); |
| 29953 | resize_out(out, sizes, strides, options); |
| 29954 | if (!names.empty()) { |
| 29955 | namedinference::propagate_names(outputs_[output_idx], names); |
| 29956 | } |
| 29957 | // super must happen after, so that downstream can use maybe_get_output |
| 29958 | // to retrieve the output |
| 29959 | at::native::structured_special_hermite_polynomial_he_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29960 | } |
| 29961 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 29962 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 29963 | } |
| 29964 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 29965 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 29966 | }; |
| 29967 | at::Tensor & wrapper_CPU_special_hermite_polynomial_he_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 29968 | structured_special_hermite_polynomial_he_out_out op(out); |
| 29969 | op.meta(x, n); |
| 29970 | op.impl(x, n, op.maybe_get_output(0)); |
| 29971 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 29972 | return out; |
| 29973 | } |
| 29974 | struct structured_special_laguerre_polynomial_l_out_functional final : public at::native::structured_special_laguerre_polynomial_l_out { |
| 29975 | void set_output_strided( |
| 29976 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29977 | TensorOptions options, DimnameList names |
| 29978 | ) override { |
| 29979 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29980 | if (!names.empty()) { |
| 29981 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29982 | } |
| 29983 | // super must happen after, so that downstream can use maybe_get_output |
| 29984 | // to retrieve the output |
| 29985 | at::native::structured_special_laguerre_polynomial_l_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29986 | } |
| 29987 | void set_output_raw_strided( |
| 29988 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 29989 | TensorOptions options, DimnameList names |
| 29990 | ) override { |
| 29991 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 29992 | if (!names.empty()) { |
| 29993 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 29994 | } |
| 29995 | // super must happen after, so that downstream can use maybe_get_output |
| 29996 | // to retrieve the output |
| 29997 | at::native::structured_special_laguerre_polynomial_l_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 29998 | } |
| 29999 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30000 | return *outputs_[output_idx]; |
| 30001 | } |
| 30002 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30003 | }; |
| 30004 | at::Tensor wrapper_CPU_special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) { |
| 30005 | structured_special_laguerre_polynomial_l_out_functional op; |
| 30006 | op.meta(x, n); |
| 30007 | op.impl(x, n, *op.outputs_[0]); |
| 30008 | return std::move(op.outputs_[0]).take(); |
| 30009 | } |
| 30010 | struct structured_special_laguerre_polynomial_l_out_out final : public at::native::structured_special_laguerre_polynomial_l_out { |
| 30011 | structured_special_laguerre_polynomial_l_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30012 | void set_output_strided( |
| 30013 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30014 | TensorOptions options, DimnameList names |
| 30015 | ) override { |
| 30016 | const auto& out = outputs_[output_idx].get(); |
| 30017 | resize_out(out, sizes, strides, options); |
| 30018 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30019 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30020 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30021 | } |
| 30022 | if (!names.empty()) { |
| 30023 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30024 | } |
| 30025 | // super must happen after, so that downstream can use maybe_get_output |
| 30026 | // to retrieve the output |
| 30027 | at::native::structured_special_laguerre_polynomial_l_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30028 | } |
| 30029 | void set_output_raw_strided( |
| 30030 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30031 | TensorOptions options, DimnameList names |
| 30032 | ) override { |
| 30033 | const auto& out = outputs_[output_idx].get(); |
| 30034 | resize_out(out, sizes, strides, options); |
| 30035 | if (!names.empty()) { |
| 30036 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30037 | } |
| 30038 | // super must happen after, so that downstream can use maybe_get_output |
| 30039 | // to retrieve the output |
| 30040 | at::native::structured_special_laguerre_polynomial_l_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30041 | } |
| 30042 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30043 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30044 | } |
| 30045 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30046 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30047 | }; |
| 30048 | at::Tensor & wrapper_CPU_special_laguerre_polynomial_l_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30049 | structured_special_laguerre_polynomial_l_out_out op(out); |
| 30050 | op.meta(x, n); |
| 30051 | op.impl(x, n, op.maybe_get_output(0)); |
| 30052 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30053 | return out; |
| 30054 | } |
| 30055 | struct structured_special_legendre_polynomial_p_out_functional final : public at::native::structured_special_legendre_polynomial_p_out { |
| 30056 | void set_output_strided( |
| 30057 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30058 | TensorOptions options, DimnameList names |
| 30059 | ) override { |
| 30060 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30061 | if (!names.empty()) { |
| 30062 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30063 | } |
| 30064 | // super must happen after, so that downstream can use maybe_get_output |
| 30065 | // to retrieve the output |
| 30066 | at::native::structured_special_legendre_polynomial_p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30067 | } |
| 30068 | void set_output_raw_strided( |
| 30069 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30070 | TensorOptions options, DimnameList names |
| 30071 | ) override { |
| 30072 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30073 | if (!names.empty()) { |
| 30074 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30075 | } |
| 30076 | // super must happen after, so that downstream can use maybe_get_output |
| 30077 | // to retrieve the output |
| 30078 | at::native::structured_special_legendre_polynomial_p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30079 | } |
| 30080 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30081 | return *outputs_[output_idx]; |
| 30082 | } |
| 30083 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30084 | }; |
| 30085 | at::Tensor wrapper_CPU_special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) { |
| 30086 | structured_special_legendre_polynomial_p_out_functional op; |
| 30087 | op.meta(x, n); |
| 30088 | op.impl(x, n, *op.outputs_[0]); |
| 30089 | return std::move(op.outputs_[0]).take(); |
| 30090 | } |
| 30091 | struct structured_special_legendre_polynomial_p_out_out final : public at::native::structured_special_legendre_polynomial_p_out { |
| 30092 | structured_special_legendre_polynomial_p_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30093 | void set_output_strided( |
| 30094 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30095 | TensorOptions options, DimnameList names |
| 30096 | ) override { |
| 30097 | const auto& out = outputs_[output_idx].get(); |
| 30098 | resize_out(out, sizes, strides, options); |
| 30099 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30100 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30101 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30102 | } |
| 30103 | if (!names.empty()) { |
| 30104 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30105 | } |
| 30106 | // super must happen after, so that downstream can use maybe_get_output |
| 30107 | // to retrieve the output |
| 30108 | at::native::structured_special_legendre_polynomial_p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30109 | } |
| 30110 | void set_output_raw_strided( |
| 30111 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30112 | TensorOptions options, DimnameList names |
| 30113 | ) override { |
| 30114 | const auto& out = outputs_[output_idx].get(); |
| 30115 | resize_out(out, sizes, strides, options); |
| 30116 | if (!names.empty()) { |
| 30117 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30118 | } |
| 30119 | // super must happen after, so that downstream can use maybe_get_output |
| 30120 | // to retrieve the output |
| 30121 | at::native::structured_special_legendre_polynomial_p_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30122 | } |
| 30123 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30124 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30125 | } |
| 30126 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30127 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30128 | }; |
| 30129 | at::Tensor & wrapper_CPU_special_legendre_polynomial_p_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30130 | structured_special_legendre_polynomial_p_out_out op(out); |
| 30131 | op.meta(x, n); |
| 30132 | op.impl(x, n, op.maybe_get_output(0)); |
| 30133 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30134 | return out; |
| 30135 | } |
| 30136 | struct structured_special_modified_bessel_i0_out_functional final : public at::native::structured_special_modified_bessel_i0_out { |
| 30137 | void set_output_strided( |
| 30138 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30139 | TensorOptions options, DimnameList names |
| 30140 | ) override { |
| 30141 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30142 | if (!names.empty()) { |
| 30143 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30144 | } |
| 30145 | // super must happen after, so that downstream can use maybe_get_output |
| 30146 | // to retrieve the output |
| 30147 | at::native::structured_special_modified_bessel_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30148 | } |
| 30149 | void set_output_raw_strided( |
| 30150 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30151 | TensorOptions options, DimnameList names |
| 30152 | ) override { |
| 30153 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30154 | if (!names.empty()) { |
| 30155 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30156 | } |
| 30157 | // super must happen after, so that downstream can use maybe_get_output |
| 30158 | // to retrieve the output |
| 30159 | at::native::structured_special_modified_bessel_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30160 | } |
| 30161 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30162 | return *outputs_[output_idx]; |
| 30163 | } |
| 30164 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30165 | }; |
| 30166 | at::Tensor wrapper_CPU_special_modified_bessel_i0(const at::Tensor & self) { |
| 30167 | structured_special_modified_bessel_i0_out_functional op; |
| 30168 | op.meta(self); |
| 30169 | op.impl(self, *op.outputs_[0]); |
| 30170 | return std::move(op.outputs_[0]).take(); |
| 30171 | } |
| 30172 | struct structured_special_modified_bessel_i0_out_out final : public at::native::structured_special_modified_bessel_i0_out { |
| 30173 | structured_special_modified_bessel_i0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30174 | void set_output_strided( |
| 30175 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30176 | TensorOptions options, DimnameList names |
| 30177 | ) override { |
| 30178 | const auto& out = outputs_[output_idx].get(); |
| 30179 | resize_out(out, sizes, strides, options); |
| 30180 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30181 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30182 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30183 | } |
| 30184 | if (!names.empty()) { |
| 30185 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30186 | } |
| 30187 | // super must happen after, so that downstream can use maybe_get_output |
| 30188 | // to retrieve the output |
| 30189 | at::native::structured_special_modified_bessel_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30190 | } |
| 30191 | void set_output_raw_strided( |
| 30192 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30193 | TensorOptions options, DimnameList names |
| 30194 | ) override { |
| 30195 | const auto& out = outputs_[output_idx].get(); |
| 30196 | resize_out(out, sizes, strides, options); |
| 30197 | if (!names.empty()) { |
| 30198 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30199 | } |
| 30200 | // super must happen after, so that downstream can use maybe_get_output |
| 30201 | // to retrieve the output |
| 30202 | at::native::structured_special_modified_bessel_i0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30203 | } |
| 30204 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30205 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30206 | } |
| 30207 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30208 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30209 | }; |
| 30210 | at::Tensor & wrapper_CPU_special_modified_bessel_i0_out_out(const at::Tensor & self, at::Tensor & out) { |
| 30211 | structured_special_modified_bessel_i0_out_out op(out); |
| 30212 | op.meta(self); |
| 30213 | op.impl(self, op.maybe_get_output(0)); |
| 30214 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30215 | return out; |
| 30216 | } |
| 30217 | struct structured_special_modified_bessel_i1_out_functional final : public at::native::structured_special_modified_bessel_i1_out { |
| 30218 | void set_output_strided( |
| 30219 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30220 | TensorOptions options, DimnameList names |
| 30221 | ) override { |
| 30222 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30223 | if (!names.empty()) { |
| 30224 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30225 | } |
| 30226 | // super must happen after, so that downstream can use maybe_get_output |
| 30227 | // to retrieve the output |
| 30228 | at::native::structured_special_modified_bessel_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30229 | } |
| 30230 | void set_output_raw_strided( |
| 30231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30232 | TensorOptions options, DimnameList names |
| 30233 | ) override { |
| 30234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30235 | if (!names.empty()) { |
| 30236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30237 | } |
| 30238 | // super must happen after, so that downstream can use maybe_get_output |
| 30239 | // to retrieve the output |
| 30240 | at::native::structured_special_modified_bessel_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30241 | } |
| 30242 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30243 | return *outputs_[output_idx]; |
| 30244 | } |
| 30245 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30246 | }; |
| 30247 | at::Tensor wrapper_CPU_special_modified_bessel_i1(const at::Tensor & self) { |
| 30248 | structured_special_modified_bessel_i1_out_functional op; |
| 30249 | op.meta(self); |
| 30250 | op.impl(self, *op.outputs_[0]); |
| 30251 | return std::move(op.outputs_[0]).take(); |
| 30252 | } |
| 30253 | struct structured_special_modified_bessel_i1_out_out final : public at::native::structured_special_modified_bessel_i1_out { |
| 30254 | structured_special_modified_bessel_i1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30255 | void set_output_strided( |
| 30256 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30257 | TensorOptions options, DimnameList names |
| 30258 | ) override { |
| 30259 | const auto& out = outputs_[output_idx].get(); |
| 30260 | resize_out(out, sizes, strides, options); |
| 30261 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30262 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30263 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30264 | } |
| 30265 | if (!names.empty()) { |
| 30266 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30267 | } |
| 30268 | // super must happen after, so that downstream can use maybe_get_output |
| 30269 | // to retrieve the output |
| 30270 | at::native::structured_special_modified_bessel_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30271 | } |
| 30272 | void set_output_raw_strided( |
| 30273 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30274 | TensorOptions options, DimnameList names |
| 30275 | ) override { |
| 30276 | const auto& out = outputs_[output_idx].get(); |
| 30277 | resize_out(out, sizes, strides, options); |
| 30278 | if (!names.empty()) { |
| 30279 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30280 | } |
| 30281 | // super must happen after, so that downstream can use maybe_get_output |
| 30282 | // to retrieve the output |
| 30283 | at::native::structured_special_modified_bessel_i1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30284 | } |
| 30285 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30286 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30287 | } |
| 30288 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30289 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30290 | }; |
| 30291 | at::Tensor & wrapper_CPU_special_modified_bessel_i1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 30292 | structured_special_modified_bessel_i1_out_out op(out); |
| 30293 | op.meta(self); |
| 30294 | op.impl(self, op.maybe_get_output(0)); |
| 30295 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30296 | return out; |
| 30297 | } |
| 30298 | struct structured_special_modified_bessel_k0_out_functional final : public at::native::structured_special_modified_bessel_k0_out { |
| 30299 | void set_output_strided( |
| 30300 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30301 | TensorOptions options, DimnameList names |
| 30302 | ) override { |
| 30303 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30304 | if (!names.empty()) { |
| 30305 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30306 | } |
| 30307 | // super must happen after, so that downstream can use maybe_get_output |
| 30308 | // to retrieve the output |
| 30309 | at::native::structured_special_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30310 | } |
| 30311 | void set_output_raw_strided( |
| 30312 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30313 | TensorOptions options, DimnameList names |
| 30314 | ) override { |
| 30315 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30316 | if (!names.empty()) { |
| 30317 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30318 | } |
| 30319 | // super must happen after, so that downstream can use maybe_get_output |
| 30320 | // to retrieve the output |
| 30321 | at::native::structured_special_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30322 | } |
| 30323 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30324 | return *outputs_[output_idx]; |
| 30325 | } |
| 30326 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30327 | }; |
| 30328 | at::Tensor wrapper_CPU_special_modified_bessel_k0(const at::Tensor & self) { |
| 30329 | structured_special_modified_bessel_k0_out_functional op; |
| 30330 | op.meta(self); |
| 30331 | op.impl(self, *op.outputs_[0]); |
| 30332 | return std::move(op.outputs_[0]).take(); |
| 30333 | } |
| 30334 | struct structured_special_modified_bessel_k0_out_out final : public at::native::structured_special_modified_bessel_k0_out { |
| 30335 | structured_special_modified_bessel_k0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30336 | void set_output_strided( |
| 30337 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30338 | TensorOptions options, DimnameList names |
| 30339 | ) override { |
| 30340 | const auto& out = outputs_[output_idx].get(); |
| 30341 | resize_out(out, sizes, strides, options); |
| 30342 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30343 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30344 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30345 | } |
| 30346 | if (!names.empty()) { |
| 30347 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30348 | } |
| 30349 | // super must happen after, so that downstream can use maybe_get_output |
| 30350 | // to retrieve the output |
| 30351 | at::native::structured_special_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30352 | } |
| 30353 | void set_output_raw_strided( |
| 30354 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30355 | TensorOptions options, DimnameList names |
| 30356 | ) override { |
| 30357 | const auto& out = outputs_[output_idx].get(); |
| 30358 | resize_out(out, sizes, strides, options); |
| 30359 | if (!names.empty()) { |
| 30360 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30361 | } |
| 30362 | // super must happen after, so that downstream can use maybe_get_output |
| 30363 | // to retrieve the output |
| 30364 | at::native::structured_special_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30365 | } |
| 30366 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30367 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30368 | } |
| 30369 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30370 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30371 | }; |
| 30372 | at::Tensor & wrapper_CPU_special_modified_bessel_k0_out_out(const at::Tensor & self, at::Tensor & out) { |
| 30373 | structured_special_modified_bessel_k0_out_out op(out); |
| 30374 | op.meta(self); |
| 30375 | op.impl(self, op.maybe_get_output(0)); |
| 30376 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30377 | return out; |
| 30378 | } |
| 30379 | struct structured_special_modified_bessel_k1_out_functional final : public at::native::structured_special_modified_bessel_k1_out { |
| 30380 | void set_output_strided( |
| 30381 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30382 | TensorOptions options, DimnameList names |
| 30383 | ) override { |
| 30384 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30385 | if (!names.empty()) { |
| 30386 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30387 | } |
| 30388 | // super must happen after, so that downstream can use maybe_get_output |
| 30389 | // to retrieve the output |
| 30390 | at::native::structured_special_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30391 | } |
| 30392 | void set_output_raw_strided( |
| 30393 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30394 | TensorOptions options, DimnameList names |
| 30395 | ) override { |
| 30396 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30397 | if (!names.empty()) { |
| 30398 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30399 | } |
| 30400 | // super must happen after, so that downstream can use maybe_get_output |
| 30401 | // to retrieve the output |
| 30402 | at::native::structured_special_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30403 | } |
| 30404 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30405 | return *outputs_[output_idx]; |
| 30406 | } |
| 30407 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30408 | }; |
| 30409 | at::Tensor wrapper_CPU_special_modified_bessel_k1(const at::Tensor & self) { |
| 30410 | structured_special_modified_bessel_k1_out_functional op; |
| 30411 | op.meta(self); |
| 30412 | op.impl(self, *op.outputs_[0]); |
| 30413 | return std::move(op.outputs_[0]).take(); |
| 30414 | } |
| 30415 | struct structured_special_modified_bessel_k1_out_out final : public at::native::structured_special_modified_bessel_k1_out { |
| 30416 | structured_special_modified_bessel_k1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30417 | void set_output_strided( |
| 30418 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30419 | TensorOptions options, DimnameList names |
| 30420 | ) override { |
| 30421 | const auto& out = outputs_[output_idx].get(); |
| 30422 | resize_out(out, sizes, strides, options); |
| 30423 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30424 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30425 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30426 | } |
| 30427 | if (!names.empty()) { |
| 30428 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30429 | } |
| 30430 | // super must happen after, so that downstream can use maybe_get_output |
| 30431 | // to retrieve the output |
| 30432 | at::native::structured_special_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30433 | } |
| 30434 | void set_output_raw_strided( |
| 30435 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30436 | TensorOptions options, DimnameList names |
| 30437 | ) override { |
| 30438 | const auto& out = outputs_[output_idx].get(); |
| 30439 | resize_out(out, sizes, strides, options); |
| 30440 | if (!names.empty()) { |
| 30441 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30442 | } |
| 30443 | // super must happen after, so that downstream can use maybe_get_output |
| 30444 | // to retrieve the output |
| 30445 | at::native::structured_special_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30446 | } |
| 30447 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30448 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30449 | } |
| 30450 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30451 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30452 | }; |
| 30453 | at::Tensor & wrapper_CPU_special_modified_bessel_k1_out_out(const at::Tensor & self, at::Tensor & out) { |
| 30454 | structured_special_modified_bessel_k1_out_out op(out); |
| 30455 | op.meta(self); |
| 30456 | op.impl(self, op.maybe_get_output(0)); |
| 30457 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30458 | return out; |
| 30459 | } |
| 30460 | struct structured_special_scaled_modified_bessel_k0_out_functional final : public at::native::structured_special_scaled_modified_bessel_k0_out { |
| 30461 | void set_output_strided( |
| 30462 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30463 | TensorOptions options, DimnameList names |
| 30464 | ) override { |
| 30465 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30466 | if (!names.empty()) { |
| 30467 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30468 | } |
| 30469 | // super must happen after, so that downstream can use maybe_get_output |
| 30470 | // to retrieve the output |
| 30471 | at::native::structured_special_scaled_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30472 | } |
| 30473 | void set_output_raw_strided( |
| 30474 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30475 | TensorOptions options, DimnameList names |
| 30476 | ) override { |
| 30477 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30478 | if (!names.empty()) { |
| 30479 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30480 | } |
| 30481 | // super must happen after, so that downstream can use maybe_get_output |
| 30482 | // to retrieve the output |
| 30483 | at::native::structured_special_scaled_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30484 | } |
| 30485 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30486 | return *outputs_[output_idx]; |
| 30487 | } |
| 30488 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30489 | }; |
| 30490 | at::Tensor wrapper_CPU_special_scaled_modified_bessel_k0(const at::Tensor & x) { |
| 30491 | structured_special_scaled_modified_bessel_k0_out_functional op; |
| 30492 | op.meta(x); |
| 30493 | op.impl(x, *op.outputs_[0]); |
| 30494 | return std::move(op.outputs_[0]).take(); |
| 30495 | } |
| 30496 | struct structured_special_scaled_modified_bessel_k0_out_out final : public at::native::structured_special_scaled_modified_bessel_k0_out { |
| 30497 | structured_special_scaled_modified_bessel_k0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30498 | void set_output_strided( |
| 30499 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30500 | TensorOptions options, DimnameList names |
| 30501 | ) override { |
| 30502 | const auto& out = outputs_[output_idx].get(); |
| 30503 | resize_out(out, sizes, strides, options); |
| 30504 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30505 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30506 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30507 | } |
| 30508 | if (!names.empty()) { |
| 30509 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30510 | } |
| 30511 | // super must happen after, so that downstream can use maybe_get_output |
| 30512 | // to retrieve the output |
| 30513 | at::native::structured_special_scaled_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30514 | } |
| 30515 | void set_output_raw_strided( |
| 30516 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30517 | TensorOptions options, DimnameList names |
| 30518 | ) override { |
| 30519 | const auto& out = outputs_[output_idx].get(); |
| 30520 | resize_out(out, sizes, strides, options); |
| 30521 | if (!names.empty()) { |
| 30522 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30523 | } |
| 30524 | // super must happen after, so that downstream can use maybe_get_output |
| 30525 | // to retrieve the output |
| 30526 | at::native::structured_special_scaled_modified_bessel_k0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30527 | } |
| 30528 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30529 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30530 | } |
| 30531 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30532 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30533 | }; |
| 30534 | at::Tensor & wrapper_CPU_special_scaled_modified_bessel_k0_out_out(const at::Tensor & x, at::Tensor & out) { |
| 30535 | structured_special_scaled_modified_bessel_k0_out_out op(out); |
| 30536 | op.meta(x); |
| 30537 | op.impl(x, op.maybe_get_output(0)); |
| 30538 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30539 | return out; |
| 30540 | } |
| 30541 | struct structured_special_scaled_modified_bessel_k1_out_functional final : public at::native::structured_special_scaled_modified_bessel_k1_out { |
| 30542 | void set_output_strided( |
| 30543 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30544 | TensorOptions options, DimnameList names |
| 30545 | ) override { |
| 30546 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30547 | if (!names.empty()) { |
| 30548 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30549 | } |
| 30550 | // super must happen after, so that downstream can use maybe_get_output |
| 30551 | // to retrieve the output |
| 30552 | at::native::structured_special_scaled_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30553 | } |
| 30554 | void set_output_raw_strided( |
| 30555 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30556 | TensorOptions options, DimnameList names |
| 30557 | ) override { |
| 30558 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30559 | if (!names.empty()) { |
| 30560 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30561 | } |
| 30562 | // super must happen after, so that downstream can use maybe_get_output |
| 30563 | // to retrieve the output |
| 30564 | at::native::structured_special_scaled_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30565 | } |
| 30566 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30567 | return *outputs_[output_idx]; |
| 30568 | } |
| 30569 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30570 | }; |
| 30571 | at::Tensor wrapper_CPU_special_scaled_modified_bessel_k1(const at::Tensor & x) { |
| 30572 | structured_special_scaled_modified_bessel_k1_out_functional op; |
| 30573 | op.meta(x); |
| 30574 | op.impl(x, *op.outputs_[0]); |
| 30575 | return std::move(op.outputs_[0]).take(); |
| 30576 | } |
| 30577 | struct structured_special_scaled_modified_bessel_k1_out_out final : public at::native::structured_special_scaled_modified_bessel_k1_out { |
| 30578 | structured_special_scaled_modified_bessel_k1_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30579 | void set_output_strided( |
| 30580 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30581 | TensorOptions options, DimnameList names |
| 30582 | ) override { |
| 30583 | const auto& out = outputs_[output_idx].get(); |
| 30584 | resize_out(out, sizes, strides, options); |
| 30585 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30586 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30587 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30588 | } |
| 30589 | if (!names.empty()) { |
| 30590 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30591 | } |
| 30592 | // super must happen after, so that downstream can use maybe_get_output |
| 30593 | // to retrieve the output |
| 30594 | at::native::structured_special_scaled_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30595 | } |
| 30596 | void set_output_raw_strided( |
| 30597 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30598 | TensorOptions options, DimnameList names |
| 30599 | ) override { |
| 30600 | const auto& out = outputs_[output_idx].get(); |
| 30601 | resize_out(out, sizes, strides, options); |
| 30602 | if (!names.empty()) { |
| 30603 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30604 | } |
| 30605 | // super must happen after, so that downstream can use maybe_get_output |
| 30606 | // to retrieve the output |
| 30607 | at::native::structured_special_scaled_modified_bessel_k1_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30608 | } |
| 30609 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30610 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30611 | } |
| 30612 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30613 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30614 | }; |
| 30615 | at::Tensor & wrapper_CPU_special_scaled_modified_bessel_k1_out_out(const at::Tensor & x, at::Tensor & out) { |
| 30616 | structured_special_scaled_modified_bessel_k1_out_out op(out); |
| 30617 | op.meta(x); |
| 30618 | op.impl(x, op.maybe_get_output(0)); |
| 30619 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30620 | return out; |
| 30621 | } |
| 30622 | struct structured_special_shifted_chebyshev_polynomial_t_out_functional final : public at::native::structured_special_shifted_chebyshev_polynomial_t_out { |
| 30623 | void set_output_strided( |
| 30624 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30625 | TensorOptions options, DimnameList names |
| 30626 | ) override { |
| 30627 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30628 | if (!names.empty()) { |
| 30629 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30630 | } |
| 30631 | // super must happen after, so that downstream can use maybe_get_output |
| 30632 | // to retrieve the output |
| 30633 | at::native::structured_special_shifted_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30634 | } |
| 30635 | void set_output_raw_strided( |
| 30636 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30637 | TensorOptions options, DimnameList names |
| 30638 | ) override { |
| 30639 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30640 | if (!names.empty()) { |
| 30641 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30642 | } |
| 30643 | // super must happen after, so that downstream can use maybe_get_output |
| 30644 | // to retrieve the output |
| 30645 | at::native::structured_special_shifted_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30646 | } |
| 30647 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30648 | return *outputs_[output_idx]; |
| 30649 | } |
| 30650 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30651 | }; |
| 30652 | at::Tensor wrapper_CPU_special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 30653 | structured_special_shifted_chebyshev_polynomial_t_out_functional op; |
| 30654 | op.meta(x, n); |
| 30655 | op.impl(x, n, *op.outputs_[0]); |
| 30656 | return std::move(op.outputs_[0]).take(); |
| 30657 | } |
| 30658 | struct structured_special_shifted_chebyshev_polynomial_t_out_out final : public at::native::structured_special_shifted_chebyshev_polynomial_t_out { |
| 30659 | structured_special_shifted_chebyshev_polynomial_t_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30660 | void set_output_strided( |
| 30661 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30662 | TensorOptions options, DimnameList names |
| 30663 | ) override { |
| 30664 | const auto& out = outputs_[output_idx].get(); |
| 30665 | resize_out(out, sizes, strides, options); |
| 30666 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30667 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30668 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30669 | } |
| 30670 | if (!names.empty()) { |
| 30671 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30672 | } |
| 30673 | // super must happen after, so that downstream can use maybe_get_output |
| 30674 | // to retrieve the output |
| 30675 | at::native::structured_special_shifted_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30676 | } |
| 30677 | void set_output_raw_strided( |
| 30678 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30679 | TensorOptions options, DimnameList names |
| 30680 | ) override { |
| 30681 | const auto& out = outputs_[output_idx].get(); |
| 30682 | resize_out(out, sizes, strides, options); |
| 30683 | if (!names.empty()) { |
| 30684 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30685 | } |
| 30686 | // super must happen after, so that downstream can use maybe_get_output |
| 30687 | // to retrieve the output |
| 30688 | at::native::structured_special_shifted_chebyshev_polynomial_t_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30689 | } |
| 30690 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30691 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30692 | } |
| 30693 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30694 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30695 | }; |
| 30696 | at::Tensor & wrapper_CPU_special_shifted_chebyshev_polynomial_t_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30697 | structured_special_shifted_chebyshev_polynomial_t_out_out op(out); |
| 30698 | op.meta(x, n); |
| 30699 | op.impl(x, n, op.maybe_get_output(0)); |
| 30700 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30701 | return out; |
| 30702 | } |
| 30703 | struct structured_special_shifted_chebyshev_polynomial_u_out_functional final : public at::native::structured_special_shifted_chebyshev_polynomial_u_out { |
| 30704 | void set_output_strided( |
| 30705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30706 | TensorOptions options, DimnameList names |
| 30707 | ) override { |
| 30708 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30709 | if (!names.empty()) { |
| 30710 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30711 | } |
| 30712 | // super must happen after, so that downstream can use maybe_get_output |
| 30713 | // to retrieve the output |
| 30714 | at::native::structured_special_shifted_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30715 | } |
| 30716 | void set_output_raw_strided( |
| 30717 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30718 | TensorOptions options, DimnameList names |
| 30719 | ) override { |
| 30720 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30721 | if (!names.empty()) { |
| 30722 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30723 | } |
| 30724 | // super must happen after, so that downstream can use maybe_get_output |
| 30725 | // to retrieve the output |
| 30726 | at::native::structured_special_shifted_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30727 | } |
| 30728 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30729 | return *outputs_[output_idx]; |
| 30730 | } |
| 30731 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30732 | }; |
| 30733 | at::Tensor wrapper_CPU_special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 30734 | structured_special_shifted_chebyshev_polynomial_u_out_functional op; |
| 30735 | op.meta(x, n); |
| 30736 | op.impl(x, n, *op.outputs_[0]); |
| 30737 | return std::move(op.outputs_[0]).take(); |
| 30738 | } |
| 30739 | struct structured_special_shifted_chebyshev_polynomial_u_out_out final : public at::native::structured_special_shifted_chebyshev_polynomial_u_out { |
| 30740 | structured_special_shifted_chebyshev_polynomial_u_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30741 | void set_output_strided( |
| 30742 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30743 | TensorOptions options, DimnameList names |
| 30744 | ) override { |
| 30745 | const auto& out = outputs_[output_idx].get(); |
| 30746 | resize_out(out, sizes, strides, options); |
| 30747 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30748 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30749 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30750 | } |
| 30751 | if (!names.empty()) { |
| 30752 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30753 | } |
| 30754 | // super must happen after, so that downstream can use maybe_get_output |
| 30755 | // to retrieve the output |
| 30756 | at::native::structured_special_shifted_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30757 | } |
| 30758 | void set_output_raw_strided( |
| 30759 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30760 | TensorOptions options, DimnameList names |
| 30761 | ) override { |
| 30762 | const auto& out = outputs_[output_idx].get(); |
| 30763 | resize_out(out, sizes, strides, options); |
| 30764 | if (!names.empty()) { |
| 30765 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30766 | } |
| 30767 | // super must happen after, so that downstream can use maybe_get_output |
| 30768 | // to retrieve the output |
| 30769 | at::native::structured_special_shifted_chebyshev_polynomial_u_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30770 | } |
| 30771 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30772 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30773 | } |
| 30774 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30775 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30776 | }; |
| 30777 | at::Tensor & wrapper_CPU_special_shifted_chebyshev_polynomial_u_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30778 | structured_special_shifted_chebyshev_polynomial_u_out_out op(out); |
| 30779 | op.meta(x, n); |
| 30780 | op.impl(x, n, op.maybe_get_output(0)); |
| 30781 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30782 | return out; |
| 30783 | } |
| 30784 | struct structured_special_shifted_chebyshev_polynomial_v_out_functional final : public at::native::structured_special_shifted_chebyshev_polynomial_v_out { |
| 30785 | void set_output_strided( |
| 30786 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30787 | TensorOptions options, DimnameList names |
| 30788 | ) override { |
| 30789 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30790 | if (!names.empty()) { |
| 30791 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30792 | } |
| 30793 | // super must happen after, so that downstream can use maybe_get_output |
| 30794 | // to retrieve the output |
| 30795 | at::native::structured_special_shifted_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30796 | } |
| 30797 | void set_output_raw_strided( |
| 30798 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30799 | TensorOptions options, DimnameList names |
| 30800 | ) override { |
| 30801 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30802 | if (!names.empty()) { |
| 30803 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30804 | } |
| 30805 | // super must happen after, so that downstream can use maybe_get_output |
| 30806 | // to retrieve the output |
| 30807 | at::native::structured_special_shifted_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30808 | } |
| 30809 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30810 | return *outputs_[output_idx]; |
| 30811 | } |
| 30812 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30813 | }; |
| 30814 | at::Tensor wrapper_CPU_special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 30815 | structured_special_shifted_chebyshev_polynomial_v_out_functional op; |
| 30816 | op.meta(x, n); |
| 30817 | op.impl(x, n, *op.outputs_[0]); |
| 30818 | return std::move(op.outputs_[0]).take(); |
| 30819 | } |
| 30820 | struct structured_special_shifted_chebyshev_polynomial_v_out_out final : public at::native::structured_special_shifted_chebyshev_polynomial_v_out { |
| 30821 | structured_special_shifted_chebyshev_polynomial_v_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30822 | void set_output_strided( |
| 30823 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30824 | TensorOptions options, DimnameList names |
| 30825 | ) override { |
| 30826 | const auto& out = outputs_[output_idx].get(); |
| 30827 | resize_out(out, sizes, strides, options); |
| 30828 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30829 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30830 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30831 | } |
| 30832 | if (!names.empty()) { |
| 30833 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30834 | } |
| 30835 | // super must happen after, so that downstream can use maybe_get_output |
| 30836 | // to retrieve the output |
| 30837 | at::native::structured_special_shifted_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30838 | } |
| 30839 | void set_output_raw_strided( |
| 30840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30841 | TensorOptions options, DimnameList names |
| 30842 | ) override { |
| 30843 | const auto& out = outputs_[output_idx].get(); |
| 30844 | resize_out(out, sizes, strides, options); |
| 30845 | if (!names.empty()) { |
| 30846 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30847 | } |
| 30848 | // super must happen after, so that downstream can use maybe_get_output |
| 30849 | // to retrieve the output |
| 30850 | at::native::structured_special_shifted_chebyshev_polynomial_v_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30851 | } |
| 30852 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30853 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30854 | } |
| 30855 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30856 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30857 | }; |
| 30858 | at::Tensor & wrapper_CPU_special_shifted_chebyshev_polynomial_v_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30859 | structured_special_shifted_chebyshev_polynomial_v_out_out op(out); |
| 30860 | op.meta(x, n); |
| 30861 | op.impl(x, n, op.maybe_get_output(0)); |
| 30862 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30863 | return out; |
| 30864 | } |
| 30865 | struct structured_special_shifted_chebyshev_polynomial_w_out_functional final : public at::native::structured_special_shifted_chebyshev_polynomial_w_out { |
| 30866 | void set_output_strided( |
| 30867 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30868 | TensorOptions options, DimnameList names |
| 30869 | ) override { |
| 30870 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30871 | if (!names.empty()) { |
| 30872 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30873 | } |
| 30874 | // super must happen after, so that downstream can use maybe_get_output |
| 30875 | // to retrieve the output |
| 30876 | at::native::structured_special_shifted_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30877 | } |
| 30878 | void set_output_raw_strided( |
| 30879 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30880 | TensorOptions options, DimnameList names |
| 30881 | ) override { |
| 30882 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30883 | if (!names.empty()) { |
| 30884 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30885 | } |
| 30886 | // super must happen after, so that downstream can use maybe_get_output |
| 30887 | // to retrieve the output |
| 30888 | at::native::structured_special_shifted_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30889 | } |
| 30890 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30891 | return *outputs_[output_idx]; |
| 30892 | } |
| 30893 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30894 | }; |
| 30895 | at::Tensor wrapper_CPU_special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 30896 | structured_special_shifted_chebyshev_polynomial_w_out_functional op; |
| 30897 | op.meta(x, n); |
| 30898 | op.impl(x, n, *op.outputs_[0]); |
| 30899 | return std::move(op.outputs_[0]).take(); |
| 30900 | } |
| 30901 | struct structured_special_shifted_chebyshev_polynomial_w_out_out final : public at::native::structured_special_shifted_chebyshev_polynomial_w_out { |
| 30902 | structured_special_shifted_chebyshev_polynomial_w_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30903 | void set_output_strided( |
| 30904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30905 | TensorOptions options, DimnameList names |
| 30906 | ) override { |
| 30907 | const auto& out = outputs_[output_idx].get(); |
| 30908 | resize_out(out, sizes, strides, options); |
| 30909 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30910 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30911 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30912 | } |
| 30913 | if (!names.empty()) { |
| 30914 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30915 | } |
| 30916 | // super must happen after, so that downstream can use maybe_get_output |
| 30917 | // to retrieve the output |
| 30918 | at::native::structured_special_shifted_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30919 | } |
| 30920 | void set_output_raw_strided( |
| 30921 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30922 | TensorOptions options, DimnameList names |
| 30923 | ) override { |
| 30924 | const auto& out = outputs_[output_idx].get(); |
| 30925 | resize_out(out, sizes, strides, options); |
| 30926 | if (!names.empty()) { |
| 30927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30928 | } |
| 30929 | // super must happen after, so that downstream can use maybe_get_output |
| 30930 | // to retrieve the output |
| 30931 | at::native::structured_special_shifted_chebyshev_polynomial_w_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30932 | } |
| 30933 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30934 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 30935 | } |
| 30936 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 30937 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 30938 | }; |
| 30939 | at::Tensor & wrapper_CPU_special_shifted_chebyshev_polynomial_w_out_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 30940 | structured_special_shifted_chebyshev_polynomial_w_out_out op(out); |
| 30941 | op.meta(x, n); |
| 30942 | op.impl(x, n, op.maybe_get_output(0)); |
| 30943 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 30944 | return out; |
| 30945 | } |
| 30946 | struct structured_special_spherical_bessel_j0_out_functional final : public at::native::structured_special_spherical_bessel_j0_out { |
| 30947 | void set_output_strided( |
| 30948 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30949 | TensorOptions options, DimnameList names |
| 30950 | ) override { |
| 30951 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30952 | if (!names.empty()) { |
| 30953 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30954 | } |
| 30955 | // super must happen after, so that downstream can use maybe_get_output |
| 30956 | // to retrieve the output |
| 30957 | at::native::structured_special_spherical_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30958 | } |
| 30959 | void set_output_raw_strided( |
| 30960 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30961 | TensorOptions options, DimnameList names |
| 30962 | ) override { |
| 30963 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 30964 | if (!names.empty()) { |
| 30965 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 30966 | } |
| 30967 | // super must happen after, so that downstream can use maybe_get_output |
| 30968 | // to retrieve the output |
| 30969 | at::native::structured_special_spherical_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 30970 | } |
| 30971 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 30972 | return *outputs_[output_idx]; |
| 30973 | } |
| 30974 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 30975 | }; |
| 30976 | at::Tensor wrapper_CPU_special_spherical_bessel_j0(const at::Tensor & x) { |
| 30977 | structured_special_spherical_bessel_j0_out_functional op; |
| 30978 | op.meta(x); |
| 30979 | op.impl(x, *op.outputs_[0]); |
| 30980 | return std::move(op.outputs_[0]).take(); |
| 30981 | } |
| 30982 | struct structured_special_spherical_bessel_j0_out_out final : public at::native::structured_special_spherical_bessel_j0_out { |
| 30983 | structured_special_spherical_bessel_j0_out_out(Tensor& out0) : outputs_{ std::ref(out0) } {} |
| 30984 | void set_output_strided( |
| 30985 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 30986 | TensorOptions options, DimnameList names |
| 30987 | ) override { |
| 30988 | const auto& out = outputs_[output_idx].get(); |
| 30989 | resize_out(out, sizes, strides, options); |
| 30990 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 30991 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 30992 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 30993 | } |
| 30994 | if (!names.empty()) { |
| 30995 | namedinference::propagate_names(outputs_[output_idx], names); |
| 30996 | } |
| 30997 | // super must happen after, so that downstream can use maybe_get_output |
| 30998 | // to retrieve the output |
| 30999 | at::native::structured_special_spherical_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 31000 | } |
| 31001 | void set_output_raw_strided( |
| 31002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 31003 | TensorOptions options, DimnameList names |
| 31004 | ) override { |
| 31005 | const auto& out = outputs_[output_idx].get(); |
| 31006 | resize_out(out, sizes, strides, options); |
| 31007 | if (!names.empty()) { |
| 31008 | namedinference::propagate_names(outputs_[output_idx], names); |
| 31009 | } |
| 31010 | // super must happen after, so that downstream can use maybe_get_output |
| 31011 | // to retrieve the output |
| 31012 | at::native::structured_special_spherical_bessel_j0_out::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 31013 | } |
| 31014 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 31015 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 31016 | } |
| 31017 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 31018 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 31019 | }; |
| 31020 | at::Tensor & wrapper_CPU_special_spherical_bessel_j0_out_out(const at::Tensor & x, at::Tensor & out) { |
| 31021 | structured_special_spherical_bessel_j0_out_out op(out); |
| 31022 | op.meta(x); |
| 31023 | op.impl(x, op.maybe_get_output(0)); |
| 31024 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 31025 | return out; |
| 31026 | } |
| 31027 | namespace { |
| 31028 | at::Tensor wrapper_CPU___foobar(const at::Tensor & self, bool arg1, bool arg2, bool arg3) { |
| 31029 | // No device check |
| 31030 | // DeviceGuard omitted |
| 31031 | return at::native::foobar(self, arg1, arg2, arg3); |
| 31032 | } |
| 31033 | } // anonymous namespace |
| 31034 | TORCH_LIBRARY_IMPL(aten, CPU, m) { |
| 31035 | m.impl("_assert_async", |
| 31036 | TORCH_FN(wrapper_CPU___assert_async)); |
| 31037 | m.impl("native_dropout", |
| 31038 | TORCH_FN(wrapper_CPU__native_dropout)); |
| 31039 | m.impl("native_dropout_backward", |
| 31040 | TORCH_FN(wrapper_CPU__native_dropout_backward)); |
| 31041 | m.impl("abs.out", |
| 31042 | TORCH_FN(wrapper_CPU_out_abs_out)); |
| 31043 | m.impl("angle", |
| 31044 | TORCH_FN(wrapper_CPU__angle)); |
| 31045 | m.impl("angle.out", |
| 31046 | TORCH_FN(wrapper_CPU_out_angle_out)); |
| 31047 | m.impl("view_as_real", |
| 31048 | TORCH_FN(wrapper_CPU__view_as_real)); |
| 31049 | m.impl("view_as_complex", |
| 31050 | TORCH_FN(wrapper_CPU__view_as_complex)); |
| 31051 | m.impl("sgn", TORCH_FN(wrapper_CPU_sgn)); |
| 31052 | m.impl("sgn.out", TORCH_FN(wrapper_CPU_sgn_out_out)); |
| 31053 | m.impl("sgn_", TORCH_FN(wrapper_CPU_sgn_)); |
| 31054 | m.impl("conj_physical.out", |
| 31055 | TORCH_FN(wrapper_CPU_out_conj_physical_out)); |
| 31056 | m.impl("acos", TORCH_FN(wrapper_CPU_acos)); |
| 31057 | m.impl("acos.out", TORCH_FN(wrapper_CPU_acos_out_out)); |
| 31058 | m.impl("acos_", TORCH_FN(wrapper_CPU_acos_)); |
| 31059 | m.impl("add.Tensor", TORCH_FN(wrapper_CPU_add_Tensor)); |
| 31060 | m.impl("add.out", TORCH_FN(wrapper_CPU_add_out_out)); |
| 31061 | m.impl("add_.Tensor", TORCH_FN(wrapper_CPU_add__Tensor)); |
| 31062 | m.impl("_add_relu.Tensor", |
| 31063 | TORCH_FN(wrapper_CPU_Tensor__add_relu)); |
| 31064 | m.impl("_add_relu.out", |
| 31065 | TORCH_FN(wrapper_CPU_out__add_relu_out)); |
| 31066 | m.impl("_add_relu_.Tensor", |
| 31067 | TORCH_FN(wrapper_CPU_Tensor__add_relu_)); |
| 31068 | m.impl("_add_relu.Scalar", |
| 31069 | TORCH_FN(wrapper_CPU_Scalar__add_relu)); |
| 31070 | m.impl("_add_relu_.Scalar", |
| 31071 | TORCH_FN(wrapper_CPU_Scalar__add_relu_)); |
| 31072 | m.impl("addmv", TORCH_FN(wrapper_CPU_addmv)); |
| 31073 | m.impl("addmv.out", TORCH_FN(wrapper_CPU_addmv_out_out)); |
| 31074 | m.impl("addmv_", TORCH_FN(wrapper_CPU_addmv_)); |
| 31075 | m.impl("addr", |
| 31076 | TORCH_FN(wrapper_CPU__addr)); |
| 31077 | m.impl("addr.out", |
| 31078 | TORCH_FN(wrapper_CPU_out_addr_out)); |
| 31079 | m.impl("all.dim", TORCH_FN(wrapper_CPU_all_dim)); |
| 31080 | m.impl("all.out", TORCH_FN(wrapper_CPU_all_out_out)); |
| 31081 | m.impl("any.dim", TORCH_FN(wrapper_CPU_any_dim)); |
| 31082 | m.impl("any.out", TORCH_FN(wrapper_CPU_any_out_out)); |
| 31083 | m.impl("arange.start_out", |
| 31084 | TORCH_FN(wrapper_CPU_start_out_arange_out)); |
| 31085 | m.impl("argmax", TORCH_FN(wrapper_CPU_argmax)); |
| 31086 | m.impl("argmax.out", TORCH_FN(wrapper_CPU_argmax_out_out)); |
| 31087 | m.impl("argmin", TORCH_FN(wrapper_CPU_argmin)); |
| 31088 | m.impl("argmin.out", TORCH_FN(wrapper_CPU_argmin_out_out)); |
| 31089 | m.impl("acosh", TORCH_FN(wrapper_CPU_acosh)); |
| 31090 | m.impl("acosh.out", TORCH_FN(wrapper_CPU_acosh_out_out)); |
| 31091 | m.impl("acosh_", TORCH_FN(wrapper_CPU_acosh_)); |
| 31092 | m.impl("asinh", TORCH_FN(wrapper_CPU_asinh)); |
| 31093 | m.impl("asinh.out", TORCH_FN(wrapper_CPU_asinh_out_out)); |
| 31094 | m.impl("asinh_", TORCH_FN(wrapper_CPU_asinh_)); |
| 31095 | m.impl("atanh", TORCH_FN(wrapper_CPU_atanh)); |
| 31096 | m.impl("atanh.out", TORCH_FN(wrapper_CPU_atanh_out_out)); |
| 31097 | m.impl("atanh_", TORCH_FN(wrapper_CPU_atanh_)); |
| 31098 | m.impl("as_strided", |
| 31099 | TORCH_FN(wrapper_CPU__as_strided)); |
| 31100 | m.impl("asin", TORCH_FN(wrapper_CPU_asin)); |
| 31101 | m.impl("asin.out", TORCH_FN(wrapper_CPU_asin_out_out)); |
| 31102 | m.impl("asin_", TORCH_FN(wrapper_CPU_asin_)); |
| 31103 | m.impl("atan", TORCH_FN(wrapper_CPU_atan)); |
| 31104 | m.impl("atan.out", TORCH_FN(wrapper_CPU_atan_out_out)); |
| 31105 | m.impl("atan_", TORCH_FN(wrapper_CPU_atan_)); |
| 31106 | m.impl("baddbmm", TORCH_FN(wrapper_CPU_baddbmm)); |
| 31107 | m.impl("baddbmm.out", TORCH_FN(wrapper_CPU_baddbmm_out_out)); |
| 31108 | m.impl("baddbmm_", TORCH_FN(wrapper_CPU_baddbmm_)); |
| 31109 | m.impl("bernoulli.out", |
| 31110 | TORCH_FN(wrapper_CPU_out_bernoulli_out)); |
| 31111 | m.impl("bernoulli_.Tensor", |
| 31112 | TORCH_FN(wrapper_CPU_Tensor_bernoulli_)); |
| 31113 | m.impl("bernoulli_.float", |
| 31114 | TORCH_FN(wrapper_CPU_float_bernoulli_)); |
| 31115 | m.impl("binary_cross_entropy", |
| 31116 | TORCH_FN(wrapper_CPU__binary_cross_entropy)); |
| 31117 | m.impl("binary_cross_entropy.out", |
| 31118 | TORCH_FN(wrapper_CPU_out_binary_cross_entropy_out)); |
| 31119 | m.impl("binary_cross_entropy_backward", |
| 31120 | TORCH_FN(wrapper_CPU__binary_cross_entropy_backward)); |
| 31121 | m.impl("binary_cross_entropy_backward.grad_input", |
| 31122 | TORCH_FN(wrapper_CPU_grad_input_binary_cross_entropy_backward_out)); |
| 31123 | m.impl("bincount", |
| 31124 | TORCH_FN(wrapper_CPU__bincount)); |
| 31125 | m.impl("bitwise_not", TORCH_FN(wrapper_CPU_bitwise_not)); |
| 31126 | m.impl("bitwise_not.out", TORCH_FN(wrapper_CPU_bitwise_not_out_out)); |
| 31127 | m.impl("bitwise_not_", TORCH_FN(wrapper_CPU_bitwise_not_)); |
| 31128 | m.impl("copysign.Tensor", TORCH_FN(wrapper_CPU_copysign_Tensor)); |
| 31129 | m.impl("copysign.out", TORCH_FN(wrapper_CPU_copysign_out_out)); |
| 31130 | m.impl("copysign_.Tensor", TORCH_FN(wrapper_CPU_copysign__Tensor)); |
| 31131 | m.impl("logical_not.out", |
| 31132 | TORCH_FN(wrapper_CPU_out_logical_not_out)); |
| 31133 | m.impl("logical_xor.out", |
| 31134 | TORCH_FN(wrapper_CPU_out_logical_xor_out)); |
| 31135 | m.impl("logical_and.out", |
| 31136 | TORCH_FN(wrapper_CPU_out_logical_and_out)); |
| 31137 | m.impl("logical_or.out", |
| 31138 | TORCH_FN(wrapper_CPU_out_logical_or_out)); |
| 31139 | m.impl("bmm", TORCH_FN(wrapper_CPU_bmm)); |
| 31140 | m.impl("bmm.out", TORCH_FN(wrapper_CPU_bmm_out_out)); |
| 31141 | m.impl("cat", TORCH_FN(wrapper_CPU_cat)); |
| 31142 | m.impl("cat.out", TORCH_FN(wrapper_CPU_cat_out_out)); |
| 31143 | m.impl("ceil", TORCH_FN(wrapper_CPU_ceil)); |
| 31144 | m.impl("ceil.out", TORCH_FN(wrapper_CPU_ceil_out_out)); |
| 31145 | m.impl("ceil_", TORCH_FN(wrapper_CPU_ceil_)); |
| 31146 | m.impl("clamp", TORCH_FN(wrapper_CPU_clamp)); |
| 31147 | m.impl("clamp.out", TORCH_FN(wrapper_CPU_clamp_out_out)); |
| 31148 | m.impl("clamp_", TORCH_FN(wrapper_CPU_clamp_)); |
| 31149 | m.impl("clamp.Tensor", TORCH_FN(wrapper_CPU_clamp_Tensor)); |
| 31150 | m.impl("clamp.Tensor_out", TORCH_FN(wrapper_CPU_clamp_out_Tensor_out)); |
| 31151 | m.impl("clamp_.Tensor", TORCH_FN(wrapper_CPU_clamp__Tensor)); |
| 31152 | m.impl("clamp_max", TORCH_FN(wrapper_CPU_clamp_max)); |
| 31153 | m.impl("clamp_max.out", TORCH_FN(wrapper_CPU_clamp_max_out_out)); |
| 31154 | m.impl("clamp_max_", TORCH_FN(wrapper_CPU_clamp_max_)); |
| 31155 | m.impl("clamp_max.Tensor", TORCH_FN(wrapper_CPU_clamp_max_Tensor)); |
| 31156 | m.impl("clamp_max.Tensor_out", TORCH_FN(wrapper_CPU_clamp_max_out_Tensor_out)); |
| 31157 | m.impl("clamp_max_.Tensor", TORCH_FN(wrapper_CPU_clamp_max__Tensor)); |
| 31158 | m.impl("clamp_min", TORCH_FN(wrapper_CPU_clamp_min)); |
| 31159 | m.impl("clamp_min.out", TORCH_FN(wrapper_CPU_clamp_min_out_out)); |
| 31160 | m.impl("clamp_min_", TORCH_FN(wrapper_CPU_clamp_min_)); |
| 31161 | m.impl("clamp_min.Tensor", TORCH_FN(wrapper_CPU_clamp_min_Tensor)); |
| 31162 | m.impl("clamp_min.Tensor_out", TORCH_FN(wrapper_CPU_clamp_min_out_Tensor_out)); |
| 31163 | m.impl("clamp_min_.Tensor", TORCH_FN(wrapper_CPU_clamp_min__Tensor)); |
| 31164 | m.impl("complex.out", |
| 31165 | TORCH_FN(wrapper_CPU_out_complex_out)); |
| 31166 | m.impl("polar.out", |
| 31167 | TORCH_FN(wrapper_CPU_out_polar_out)); |
| 31168 | m.impl("cos", TORCH_FN(wrapper_CPU_cos)); |
| 31169 | m.impl("cos.out", TORCH_FN(wrapper_CPU_cos_out_out)); |
| 31170 | m.impl("cos_", TORCH_FN(wrapper_CPU_cos_)); |
| 31171 | m.impl("cosh", TORCH_FN(wrapper_CPU_cosh)); |
| 31172 | m.impl("cosh.out", TORCH_FN(wrapper_CPU_cosh_out_out)); |
| 31173 | m.impl("cosh_", TORCH_FN(wrapper_CPU_cosh_)); |
| 31174 | m.impl("count_nonzero.dim_IntList", |
| 31175 | TORCH_FN(wrapper_CPU_dim_IntList_count_nonzero)); |
| 31176 | m.impl("_cummax_helper", |
| 31177 | TORCH_FN(wrapper_CPU___cummax_helper)); |
| 31178 | m.impl("_cummin_helper", |
| 31179 | TORCH_FN(wrapper_CPU___cummin_helper)); |
| 31180 | m.impl("cumprod", TORCH_FN(wrapper_CPU_cumprod)); |
| 31181 | m.impl("cumprod.out", TORCH_FN(wrapper_CPU_cumprod_out_out)); |
| 31182 | m.impl("cumprod_", TORCH_FN(wrapper_CPU_cumprod_)); |
| 31183 | m.impl("cumsum", TORCH_FN(wrapper_CPU_cumsum)); |
| 31184 | m.impl("cumsum.out", TORCH_FN(wrapper_CPU_cumsum_out_out)); |
| 31185 | m.impl("cumsum_", TORCH_FN(wrapper_CPU_cumsum_)); |
| 31186 | m.impl("_ctc_loss", |
| 31187 | TORCH_FN(wrapper_CPU___ctc_loss)); |
| 31188 | m.impl("_ctc_loss.Tensor", |
| 31189 | TORCH_FN(wrapper_CPU_Tensor__ctc_loss)); |
| 31190 | m.impl("_ctc_loss_backward", |
| 31191 | TORCH_FN(wrapper_CPU___ctc_loss_backward)); |
| 31192 | m.impl("_ctc_loss_backward.Tensor", |
| 31193 | TORCH_FN(wrapper_CPU_Tensor__ctc_loss_backward)); |
| 31194 | m.impl("div.Tensor", TORCH_FN(wrapper_CPU_div_Tensor)); |
| 31195 | m.impl("div.out", TORCH_FN(wrapper_CPU_div_out_out)); |
| 31196 | m.impl("div_.Tensor", TORCH_FN(wrapper_CPU_div__Tensor)); |
| 31197 | m.impl("div.Tensor_mode", TORCH_FN(wrapper_CPU_div_Tensor_mode)); |
| 31198 | m.impl("div.out_mode", TORCH_FN(wrapper_CPU_div_out_out_mode)); |
| 31199 | m.impl("div_.Tensor_mode", TORCH_FN(wrapper_CPU_div__Tensor_mode)); |
| 31200 | m.impl("dot", |
| 31201 | TORCH_FN(wrapper_CPU__dot)); |
| 31202 | m.impl("vdot", |
| 31203 | TORCH_FN(wrapper_CPU__vdot)); |
| 31204 | m.impl("embedding_dense_backward", |
| 31205 | TORCH_FN(wrapper_CPU__embedding_dense_backward)); |
| 31206 | m.impl("embedding_renorm_", |
| 31207 | TORCH_FN(wrapper_CPU__embedding_renorm_)); |
| 31208 | m.impl("_embedding_bag_forward_only", |
| 31209 | TORCH_FN(wrapper_CPU___embedding_bag_forward_only)); |
| 31210 | m.impl("_embedding_bag", |
| 31211 | TORCH_FN(wrapper_CPU___embedding_bag)); |
| 31212 | m.impl("_embedding_bag_dense_backward", |
| 31213 | TORCH_FN(wrapper_CPU___embedding_bag_dense_backward)); |
| 31214 | m.impl("_embedding_bag_per_sample_weights_backward", |
| 31215 | TORCH_FN(wrapper_CPU___embedding_bag_per_sample_weights_backward)); |
| 31216 | m.impl("empty.memory_format", |
| 31217 | TORCH_FN(wrapper_CPU_memory_format_empty)); |
| 31218 | m.impl("_empty_affine_quantized", |
| 31219 | TORCH_FN(wrapper_CPU___empty_affine_quantized)); |
| 31220 | m.impl("_empty_per_channel_affine_quantized", |
| 31221 | TORCH_FN(wrapper_CPU___empty_per_channel_affine_quantized)); |
| 31222 | m.impl("resize_", |
| 31223 | TORCH_FN(wrapper_CPU__resize_)); |
| 31224 | m.impl("empty_strided", |
| 31225 | TORCH_FN(wrapper_CPU__empty_strided)); |
| 31226 | m.impl("erf", TORCH_FN(wrapper_CPU_erf)); |
| 31227 | m.impl("erf.out", TORCH_FN(wrapper_CPU_erf_out_out)); |
| 31228 | m.impl("erf_", TORCH_FN(wrapper_CPU_erf_)); |
| 31229 | m.impl("erfc", TORCH_FN(wrapper_CPU_erfc)); |
| 31230 | m.impl("erfc.out", TORCH_FN(wrapper_CPU_erfc_out_out)); |
| 31231 | m.impl("erfc_", TORCH_FN(wrapper_CPU_erfc_)); |
| 31232 | m.impl("exp", TORCH_FN(wrapper_CPU_exp)); |
| 31233 | m.impl("exp.out", TORCH_FN(wrapper_CPU_exp_out_out)); |
| 31234 | m.impl("exp_", TORCH_FN(wrapper_CPU_exp_)); |
| 31235 | m.impl("exp2", TORCH_FN(wrapper_CPU_exp2)); |
| 31236 | m.impl("exp2.out", TORCH_FN(wrapper_CPU_exp2_out_out)); |
| 31237 | m.impl("exp2_", TORCH_FN(wrapper_CPU_exp2_)); |
| 31238 | m.impl("expm1", TORCH_FN(wrapper_CPU_expm1)); |
| 31239 | m.impl("expm1.out", TORCH_FN(wrapper_CPU_expm1_out_out)); |
| 31240 | m.impl("expm1_", TORCH_FN(wrapper_CPU_expm1_)); |
| 31241 | m.impl("eye.out", |
| 31242 | TORCH_FN(wrapper_CPU_out_eye_out)); |
| 31243 | m.impl("eye.m_out", |
| 31244 | TORCH_FN(wrapper_CPU_m_out_eye_out)); |
| 31245 | m.impl("fill_.Scalar", |
| 31246 | TORCH_FN(wrapper_CPU_Scalar_fill_)); |
| 31247 | m.impl("fill_.Tensor", |
| 31248 | TORCH_FN(wrapper_CPU_Tensor_fill_)); |
| 31249 | m.impl("floor", TORCH_FN(wrapper_CPU_floor)); |
| 31250 | m.impl("floor.out", TORCH_FN(wrapper_CPU_floor_out_out)); |
| 31251 | m.impl("floor_", TORCH_FN(wrapper_CPU_floor_)); |
| 31252 | m.impl("floor_divide", |
| 31253 | TORCH_FN(wrapper_CPU__floor_divide)); |
| 31254 | m.impl("floor_divide.out", |
| 31255 | TORCH_FN(wrapper_CPU_out_floor_divide_out)); |
| 31256 | m.impl("floor_divide_.Tensor", |
| 31257 | TORCH_FN(wrapper_CPU_Tensor_floor_divide_)); |
| 31258 | m.impl("frac", TORCH_FN(wrapper_CPU_frac)); |
| 31259 | m.impl("frac.out", TORCH_FN(wrapper_CPU_frac_out_out)); |
| 31260 | m.impl("frac_", TORCH_FN(wrapper_CPU_frac_)); |
| 31261 | m.impl("from_file", |
| 31262 | TORCH_FN(wrapper_CPU__from_file)); |
| 31263 | m.impl("gcd", TORCH_FN(wrapper_CPU_gcd)); |
| 31264 | m.impl("gcd.out", TORCH_FN(wrapper_CPU_gcd_out_out)); |
| 31265 | m.impl("gcd_", TORCH_FN(wrapper_CPU_gcd_)); |
| 31266 | m.impl("lcm", TORCH_FN(wrapper_CPU_lcm)); |
| 31267 | m.impl("lcm.out", TORCH_FN(wrapper_CPU_lcm_out_out)); |
| 31268 | m.impl("lcm_", TORCH_FN(wrapper_CPU_lcm_)); |
| 31269 | m.impl("grid_sampler_2d", |
| 31270 | TORCH_FN(wrapper_CPU__grid_sampler_2d)); |
| 31271 | m.impl("grid_sampler_2d_backward", |
| 31272 | TORCH_FN(wrapper_CPU__grid_sampler_2d_backward)); |
| 31273 | m.impl("grid_sampler_3d", |
| 31274 | TORCH_FN(wrapper_CPU__grid_sampler_3d)); |
| 31275 | m.impl("grid_sampler_3d_backward", |
| 31276 | TORCH_FN(wrapper_CPU__grid_sampler_3d_backward)); |
| 31277 | m.impl("native_group_norm", |
| 31278 | TORCH_FN(wrapper_CPU__native_group_norm)); |
| 31279 | m.impl("native_group_norm_backward", |
| 31280 | TORCH_FN(wrapper_CPU__native_group_norm_backward)); |
| 31281 | m.impl("_fft_r2c", |
| 31282 | TORCH_FN(wrapper_CPU___fft_r2c)); |
| 31283 | m.impl("_fft_r2c.out", |
| 31284 | TORCH_FN(wrapper_CPU_out__fft_r2c_out)); |
| 31285 | m.impl("_fft_c2r", |
| 31286 | TORCH_FN(wrapper_CPU___fft_c2r)); |
| 31287 | m.impl("_fft_c2r.out", |
| 31288 | TORCH_FN(wrapper_CPU_out__fft_c2r_out)); |
| 31289 | m.impl("_fft_c2c", |
| 31290 | TORCH_FN(wrapper_CPU___fft_c2c)); |
| 31291 | m.impl("_fft_c2c.out", |
| 31292 | TORCH_FN(wrapper_CPU_out__fft_c2c_out)); |
| 31293 | m.impl("_validate_compressed_sparse_indices", |
| 31294 | TORCH_FN(wrapper_CPU___validate_compressed_sparse_indices)); |
| 31295 | m.impl("index.Tensor", TORCH_FN(wrapper_CPU_index_Tensor)); |
| 31296 | m.impl("index.Tensor_out", TORCH_FN(wrapper_CPU_index_out_Tensor_out)); |
| 31297 | m.impl("index_copy", TORCH_FN(wrapper_CPU_index_copy)); |
| 31298 | m.impl("index_copy.out", TORCH_FN(wrapper_CPU_index_copy_out_out)); |
| 31299 | m.impl("index_copy_", TORCH_FN(wrapper_CPU_index_copy_)); |
| 31300 | m.impl("_index_put_impl_", |
| 31301 | TORCH_FN(wrapper_CPU___index_put_impl_)); |
| 31302 | m.impl("isin.Tensor_Tensor", TORCH_FN(wrapper_CPU_isin_Tensor_Tensor)); |
| 31303 | m.impl("isin.Tensor_Tensor_out", TORCH_FN(wrapper_CPU_isin_out_Tensor_Tensor_out)); |
| 31304 | m.impl("isin.Tensor_Scalar", TORCH_FN(wrapper_CPU_isin_Tensor_Scalar)); |
| 31305 | m.impl("isin.Tensor_Scalar_out", TORCH_FN(wrapper_CPU_isin_out_Tensor_Scalar_out)); |
| 31306 | m.impl("isin.Scalar_Tensor", TORCH_FN(wrapper_CPU_isin_Scalar_Tensor)); |
| 31307 | m.impl("isin.Scalar_Tensor_out", TORCH_FN(wrapper_CPU_isin_out_Scalar_Tensor_out)); |
| 31308 | m.impl("isnan", |
| 31309 | TORCH_FN(wrapper_CPU__isnan)); |
| 31310 | m.impl("kthvalue.values", |
| 31311 | TORCH_FN(wrapper_CPU_values_kthvalue_out)); |
| 31312 | m.impl("native_layer_norm", |
| 31313 | TORCH_FN(wrapper_CPU__native_layer_norm)); |
| 31314 | m.impl("native_layer_norm_backward", |
| 31315 | TORCH_FN(wrapper_CPU__native_layer_norm_backward)); |
| 31316 | m.impl("nan_to_num.out", |
| 31317 | TORCH_FN(wrapper_CPU_out_nan_to_num_out)); |
| 31318 | m.impl("linspace.out", |
| 31319 | TORCH_FN(wrapper_CPU_out_linspace_out)); |
| 31320 | m.impl("log", TORCH_FN(wrapper_CPU_log)); |
| 31321 | m.impl("log.out", TORCH_FN(wrapper_CPU_log_out_out)); |
| 31322 | m.impl("log_", TORCH_FN(wrapper_CPU_log_)); |
| 31323 | m.impl("log10", TORCH_FN(wrapper_CPU_log10)); |
| 31324 | m.impl("log10.out", TORCH_FN(wrapper_CPU_log10_out_out)); |
| 31325 | m.impl("log10_", TORCH_FN(wrapper_CPU_log10_)); |
| 31326 | m.impl("log1p", TORCH_FN(wrapper_CPU_log1p)); |
| 31327 | m.impl("log1p.out", TORCH_FN(wrapper_CPU_log1p_out_out)); |
| 31328 | m.impl("log1p_", TORCH_FN(wrapper_CPU_log1p_)); |
| 31329 | m.impl("log2", TORCH_FN(wrapper_CPU_log2)); |
| 31330 | m.impl("log2.out", TORCH_FN(wrapper_CPU_log2_out_out)); |
| 31331 | m.impl("log2_", TORCH_FN(wrapper_CPU_log2_)); |
| 31332 | m.impl("logaddexp", TORCH_FN(wrapper_CPU_logaddexp)); |
| 31333 | m.impl("logaddexp.out", TORCH_FN(wrapper_CPU_logaddexp_out_out)); |
| 31334 | m.impl("logaddexp2", TORCH_FN(wrapper_CPU_logaddexp2)); |
| 31335 | m.impl("logaddexp2.out", TORCH_FN(wrapper_CPU_logaddexp2_out_out)); |
| 31336 | m.impl("xlogy.Tensor", TORCH_FN(wrapper_CPU_xlogy_Tensor)); |
| 31337 | m.impl("xlogy.OutTensor", TORCH_FN(wrapper_CPU_xlogy_out_OutTensor)); |
| 31338 | m.impl("xlogy_.Tensor", TORCH_FN(wrapper_CPU_xlogy__Tensor)); |
| 31339 | m.impl("logspace.out", |
| 31340 | TORCH_FN(wrapper_CPU_out_logspace_out)); |
| 31341 | m.impl("_log_softmax", TORCH_FN(wrapper_CPU__log_softmax)); |
| 31342 | m.impl("_log_softmax.out", TORCH_FN(wrapper_CPU__log_softmax_out_out)); |
| 31343 | m.impl("_log_softmax_backward_data", TORCH_FN(wrapper_CPU__log_softmax_backward_data)); |
| 31344 | m.impl("_log_softmax_backward_data.out", TORCH_FN(wrapper_CPU__log_softmax_backward_data_out_out)); |
| 31345 | m.impl("_logcumsumexp", |
| 31346 | TORCH_FN(wrapper_CPU___logcumsumexp)); |
| 31347 | m.impl("_logcumsumexp.out", |
| 31348 | TORCH_FN(wrapper_CPU_out__logcumsumexp_out)); |
| 31349 | m.impl("_aminmax", |
| 31350 | TORCH_FN(wrapper_CPU___aminmax)); |
| 31351 | m.impl("_aminmax.dim", |
| 31352 | TORCH_FN(wrapper_CPU_dim__aminmax)); |
| 31353 | m.impl("aminmax", TORCH_FN(wrapper_CPU_aminmax)); |
| 31354 | m.impl("aminmax.out", TORCH_FN(wrapper_CPU_aminmax_out_out)); |
| 31355 | m.impl("_compute_linear_combination", |
| 31356 | TORCH_FN(wrapper_CPU___compute_linear_combination)); |
| 31357 | m.impl("_compute_linear_combination.out", |
| 31358 | TORCH_FN(wrapper_CPU_out__compute_linear_combination_out)); |
| 31359 | m.impl("max.dim", TORCH_FN(wrapper_CPU_max_dim)); |
| 31360 | m.impl("max.dim_max", TORCH_FN(wrapper_CPU_max_out_dim_max)); |
| 31361 | m.impl("amax", TORCH_FN(wrapper_CPU_amax)); |
| 31362 | m.impl("amax.out", TORCH_FN(wrapper_CPU_amax_out_out)); |
| 31363 | m.impl("mean.dim", TORCH_FN(wrapper_CPU_mean_dim)); |
| 31364 | m.impl("mean.out", TORCH_FN(wrapper_CPU_mean_out_out)); |
| 31365 | m.impl("median", |
| 31366 | TORCH_FN(wrapper_CPU__median)); |
| 31367 | m.impl("median.dim_values", |
| 31368 | TORCH_FN(wrapper_CPU_dim_values_median_out)); |
| 31369 | m.impl("nanmedian", |
| 31370 | TORCH_FN(wrapper_CPU__nanmedian)); |
| 31371 | m.impl("nanmedian.dim_values", |
| 31372 | TORCH_FN(wrapper_CPU_dim_values_nanmedian_out)); |
| 31373 | m.impl("min.dim", TORCH_FN(wrapper_CPU_min_dim)); |
| 31374 | m.impl("min.dim_min", TORCH_FN(wrapper_CPU_min_out_dim_min)); |
| 31375 | m.impl("amin", TORCH_FN(wrapper_CPU_amin)); |
| 31376 | m.impl("amin.out", TORCH_FN(wrapper_CPU_amin_out_out)); |
| 31377 | m.impl("mkldnn_rnn_layer", |
| 31378 | TORCH_FN(wrapper_CPU__mkldnn_rnn_layer)); |
| 31379 | m.impl("mkldnn_rnn_layer_backward", |
| 31380 | TORCH_FN(wrapper_CPU__mkldnn_rnn_layer_backward)); |
| 31381 | m.impl("mm", TORCH_FN(wrapper_CPU_mm)); |
| 31382 | m.impl("mm.out", TORCH_FN(wrapper_CPU_mm_out_out)); |
| 31383 | m.impl("mode", |
| 31384 | TORCH_FN(wrapper_CPU__mode)); |
| 31385 | m.impl("mul.Tensor", TORCH_FN(wrapper_CPU_mul_Tensor)); |
| 31386 | m.impl("mul.out", TORCH_FN(wrapper_CPU_mul_out_out)); |
| 31387 | m.impl("mul_.Tensor", TORCH_FN(wrapper_CPU_mul__Tensor)); |
| 31388 | m.impl("mvlgamma.out", |
| 31389 | TORCH_FN(wrapper_CPU_out_mvlgamma_out)); |
| 31390 | m.impl("narrow_copy", |
| 31391 | TORCH_FN(wrapper_CPU__narrow_copy)); |
| 31392 | m.impl("narrow_copy.out", |
| 31393 | TORCH_FN(wrapper_CPU_out_narrow_copy_out)); |
| 31394 | m.impl("native_batch_norm", |
| 31395 | TORCH_FN(wrapper_CPU__native_batch_norm)); |
| 31396 | m.impl("native_batch_norm.out", |
| 31397 | TORCH_FN(wrapper_CPU_out_native_batch_norm_out)); |
| 31398 | m.impl("_native_batch_norm_legit.out", |
| 31399 | TORCH_FN(wrapper_CPU_out__native_batch_norm_legit_out)); |
| 31400 | m.impl("_native_batch_norm_legit", |
| 31401 | TORCH_FN(wrapper_CPU___native_batch_norm_legit)); |
| 31402 | m.impl("_native_batch_norm_legit.no_stats", |
| 31403 | TORCH_FN(wrapper_CPU_no_stats__native_batch_norm_legit)); |
| 31404 | m.impl("_native_batch_norm_legit.no_stats_out", |
| 31405 | TORCH_FN(wrapper_CPU_no_stats_out__native_batch_norm_legit_out)); |
| 31406 | m.impl("native_batch_norm_backward", |
| 31407 | TORCH_FN(wrapper_CPU__native_batch_norm_backward)); |
| 31408 | m.impl("batch_norm_update_stats", |
| 31409 | TORCH_FN(wrapper_CPU__batch_norm_update_stats)); |
| 31410 | m.impl("_cdist_forward", |
| 31411 | TORCH_FN(wrapper_CPU___cdist_forward)); |
| 31412 | m.impl("_cdist_backward", |
| 31413 | TORCH_FN(wrapper_CPU___cdist_backward)); |
| 31414 | m.impl("_pdist_forward", |
| 31415 | TORCH_FN(wrapper_CPU___pdist_forward)); |
| 31416 | m.impl("_pdist_backward", |
| 31417 | TORCH_FN(wrapper_CPU___pdist_backward)); |
| 31418 | m.impl("pixel_shuffle", |
| 31419 | TORCH_FN(wrapper_CPU__pixel_shuffle)); |
| 31420 | m.impl("pixel_unshuffle", |
| 31421 | TORCH_FN(wrapper_CPU__pixel_unshuffle)); |
| 31422 | m.impl("channel_shuffle", |
| 31423 | TORCH_FN(wrapper_CPU__channel_shuffle)); |
| 31424 | m.impl("native_channel_shuffle", |
| 31425 | TORCH_FN(wrapper_CPU__native_channel_shuffle)); |
| 31426 | m.impl("randperm.generator_out", |
| 31427 | TORCH_FN(wrapper_CPU_generator_out_randperm_out)); |
| 31428 | m.impl("range.out", |
| 31429 | TORCH_FN(wrapper_CPU_out_range_out)); |
| 31430 | m.impl("reciprocal", TORCH_FN(wrapper_CPU_reciprocal)); |
| 31431 | m.impl("reciprocal.out", TORCH_FN(wrapper_CPU_reciprocal_out_out)); |
| 31432 | m.impl("reciprocal_", TORCH_FN(wrapper_CPU_reciprocal_)); |
| 31433 | m.impl("neg", TORCH_FN(wrapper_CPU_neg)); |
| 31434 | m.impl("neg.out", TORCH_FN(wrapper_CPU_neg_out_out)); |
| 31435 | m.impl("neg_", TORCH_FN(wrapper_CPU_neg_)); |
| 31436 | m.impl("repeat_interleave.Tensor", |
| 31437 | TORCH_FN(wrapper_CPU_Tensor_repeat_interleave)); |
| 31438 | m.impl("_reshape_alias", |
| 31439 | TORCH_FN(wrapper_CPU___reshape_alias)); |
| 31440 | m.impl("round", TORCH_FN(wrapper_CPU_round)); |
| 31441 | m.impl("round.out", TORCH_FN(wrapper_CPU_round_out_out)); |
| 31442 | m.impl("round_", TORCH_FN(wrapper_CPU_round_)); |
| 31443 | m.impl("round.decimals", TORCH_FN(wrapper_CPU_round_decimals)); |
| 31444 | m.impl("round.decimals_out", TORCH_FN(wrapper_CPU_round_out_decimals_out)); |
| 31445 | m.impl("round_.decimals", TORCH_FN(wrapper_CPU_round__decimals)); |
| 31446 | m.impl("relu", |
| 31447 | TORCH_FN(wrapper_CPU__relu)); |
| 31448 | m.impl("relu_", |
| 31449 | TORCH_FN(wrapper_CPU__relu_)); |
| 31450 | m.impl("_prelu_kernel", |
| 31451 | TORCH_FN(wrapper_CPU___prelu_kernel)); |
| 31452 | m.impl("_prelu_kernel_backward", |
| 31453 | TORCH_FN(wrapper_CPU___prelu_kernel_backward)); |
| 31454 | m.impl("gelu", TORCH_FN(wrapper_CPU_gelu)); |
| 31455 | m.impl("gelu.out", TORCH_FN(wrapper_CPU_gelu_out_out)); |
| 31456 | m.impl("gelu_", TORCH_FN(wrapper_CPU_gelu_)); |
| 31457 | m.impl("gelu_backward", TORCH_FN(wrapper_CPU_gelu_backward)); |
| 31458 | m.impl("gelu_backward.grad_input", TORCH_FN(wrapper_CPU_gelu_backward_out_grad_input)); |
| 31459 | m.impl("hardshrink", TORCH_FN(wrapper_CPU_hardshrink)); |
| 31460 | m.impl("hardshrink.out", TORCH_FN(wrapper_CPU_hardshrink_out_out)); |
| 31461 | m.impl("hardshrink_backward", TORCH_FN(wrapper_CPU_hardshrink_backward)); |
| 31462 | m.impl("hardshrink_backward.grad_input", TORCH_FN(wrapper_CPU_hardshrink_backward_out_grad_input)); |
| 31463 | m.impl("rsqrt", TORCH_FN(wrapper_CPU_rsqrt)); |
| 31464 | m.impl("rsqrt.out", TORCH_FN(wrapper_CPU_rsqrt_out_out)); |
| 31465 | m.impl("rsqrt_", TORCH_FN(wrapper_CPU_rsqrt_)); |
| 31466 | m.impl("silu", TORCH_FN(wrapper_CPU_silu)); |
| 31467 | m.impl("silu.out", TORCH_FN(wrapper_CPU_silu_out_out)); |
| 31468 | m.impl("silu_", TORCH_FN(wrapper_CPU_silu_)); |
| 31469 | m.impl("silu_backward", TORCH_FN(wrapper_CPU_silu_backward)); |
| 31470 | m.impl("silu_backward.grad_input", TORCH_FN(wrapper_CPU_silu_backward_out_grad_input)); |
| 31471 | m.impl("mish", TORCH_FN(wrapper_CPU_mish)); |
| 31472 | m.impl("mish.out", TORCH_FN(wrapper_CPU_mish_out_out)); |
| 31473 | m.impl("mish_", TORCH_FN(wrapper_CPU_mish_)); |
| 31474 | m.impl("mish_backward", |
| 31475 | TORCH_FN(wrapper_CPU__mish_backward)); |
| 31476 | m.impl("sigmoid", TORCH_FN(wrapper_CPU_sigmoid)); |
| 31477 | m.impl("sigmoid.out", TORCH_FN(wrapper_CPU_sigmoid_out_out)); |
| 31478 | m.impl("sigmoid_", TORCH_FN(wrapper_CPU_sigmoid_)); |
| 31479 | m.impl("logit", |
| 31480 | TORCH_FN(wrapper_CPU__logit)); |
| 31481 | m.impl("logit.out", |
| 31482 | TORCH_FN(wrapper_CPU_out_logit_out)); |
| 31483 | m.impl("logit_", |
| 31484 | TORCH_FN(wrapper_CPU__logit_)); |
| 31485 | m.impl("sin", TORCH_FN(wrapper_CPU_sin)); |
| 31486 | m.impl("sin.out", TORCH_FN(wrapper_CPU_sin_out_out)); |
| 31487 | m.impl("sin_", TORCH_FN(wrapper_CPU_sin_)); |
| 31488 | m.impl("sinc", TORCH_FN(wrapper_CPU_sinc)); |
| 31489 | m.impl("sinc.out", TORCH_FN(wrapper_CPU_sinc_out_out)); |
| 31490 | m.impl("sinc_", TORCH_FN(wrapper_CPU_sinc_)); |
| 31491 | m.impl("sinh", TORCH_FN(wrapper_CPU_sinh)); |
| 31492 | m.impl("sinh.out", TORCH_FN(wrapper_CPU_sinh_out_out)); |
| 31493 | m.impl("sinh_", TORCH_FN(wrapper_CPU_sinh_)); |
| 31494 | m.impl("_softmax", TORCH_FN(wrapper_CPU__softmax)); |
| 31495 | m.impl("_softmax.out", TORCH_FN(wrapper_CPU__softmax_out_out)); |
| 31496 | m.impl("_softmax_backward_data", TORCH_FN(wrapper_CPU__softmax_backward_data)); |
| 31497 | m.impl("_softmax_backward_data.out", TORCH_FN(wrapper_CPU__softmax_backward_data_out_out)); |
| 31498 | m.impl("sspaddmm.out", |
| 31499 | TORCH_FN(wrapper_CPU_out_sspaddmm_out)); |
| 31500 | m.impl("_stack", |
| 31501 | TORCH_FN(wrapper_CPU___stack)); |
| 31502 | m.impl("_stack.out", |
| 31503 | TORCH_FN(wrapper_CPU_out__stack_out)); |
| 31504 | m.impl("sum.dim_IntList", TORCH_FN(wrapper_CPU_sum_dim_IntList)); |
| 31505 | m.impl("sum.IntList_out", TORCH_FN(wrapper_CPU_sum_out_IntList_out)); |
| 31506 | m.impl("nansum", |
| 31507 | TORCH_FN(wrapper_CPU__nansum)); |
| 31508 | m.impl("nansum.out", |
| 31509 | TORCH_FN(wrapper_CPU_out_nansum_out)); |
| 31510 | m.impl("sqrt", TORCH_FN(wrapper_CPU_sqrt)); |
| 31511 | m.impl("sqrt.out", TORCH_FN(wrapper_CPU_sqrt_out_out)); |
| 31512 | m.impl("sqrt_", TORCH_FN(wrapper_CPU_sqrt_)); |
| 31513 | m.impl("std.correction", |
| 31514 | TORCH_FN(wrapper_CPU_correction_std)); |
| 31515 | m.impl("std.correction_out", |
| 31516 | TORCH_FN(wrapper_CPU_correction_out_std_out)); |
| 31517 | m.impl("std_mean.correction", |
| 31518 | TORCH_FN(wrapper_CPU_correction_std_mean)); |
| 31519 | m.impl("prod", |
| 31520 | TORCH_FN(wrapper_CPU__prod)); |
| 31521 | m.impl("prod.dim_int", TORCH_FN(wrapper_CPU_prod_dim_int)); |
| 31522 | m.impl("prod.int_out", TORCH_FN(wrapper_CPU_prod_out_int_out)); |
| 31523 | m.impl("tan", TORCH_FN(wrapper_CPU_tan)); |
| 31524 | m.impl("tan.out", TORCH_FN(wrapper_CPU_tan_out_out)); |
| 31525 | m.impl("tan_", TORCH_FN(wrapper_CPU_tan_)); |
| 31526 | m.impl("tanh", TORCH_FN(wrapper_CPU_tanh)); |
| 31527 | m.impl("tanh.out", TORCH_FN(wrapper_CPU_tanh_out_out)); |
| 31528 | m.impl("tanh_", TORCH_FN(wrapper_CPU_tanh_)); |
| 31529 | m.impl("tensordot.out", |
| 31530 | TORCH_FN(wrapper_CPU_out_tensordot_out)); |
| 31531 | m.impl("threshold", TORCH_FN(wrapper_CPU_threshold)); |
| 31532 | m.impl("threshold.out", TORCH_FN(wrapper_CPU_threshold_out_out)); |
| 31533 | m.impl("threshold_", TORCH_FN(wrapper_CPU_threshold_)); |
| 31534 | m.impl("threshold_backward", TORCH_FN(wrapper_CPU_threshold_backward)); |
| 31535 | m.impl("threshold_backward.grad_input", TORCH_FN(wrapper_CPU_threshold_backward_out_grad_input)); |
| 31536 | m.impl("flip", |
| 31537 | TORCH_FN(wrapper_CPU__flip)); |
| 31538 | m.impl("roll", |
| 31539 | TORCH_FN(wrapper_CPU__roll)); |
| 31540 | m.impl("_transform_bias_rescale_qkv", |
| 31541 | TORCH_FN(wrapper_CPU___transform_bias_rescale_qkv)); |
| 31542 | m.impl("_nested_tensor_from_mask", |
| 31543 | TORCH_FN(wrapper_CPU___nested_tensor_from_mask)); |
| 31544 | m.impl("_nested_tensor_from_mask_left_aligned", |
| 31545 | TORCH_FN(wrapper_CPU___nested_tensor_from_mask_left_aligned)); |
| 31546 | m.impl("_nested_from_padded", |
| 31547 | TORCH_FN(wrapper_CPU___nested_from_padded)); |
| 31548 | m.impl("_nested_view_from_buffer", |
| 31549 | TORCH_FN(wrapper_CPU___nested_view_from_buffer)); |
| 31550 | m.impl("trunc", TORCH_FN(wrapper_CPU_trunc)); |
| 31551 | m.impl("trunc.out", TORCH_FN(wrapper_CPU_trunc_out_out)); |
| 31552 | m.impl("trunc_", TORCH_FN(wrapper_CPU_trunc_)); |
| 31553 | m.impl("_unique", |
| 31554 | TORCH_FN(wrapper_CPU___unique)); |
| 31555 | m.impl("unique_dim", |
| 31556 | TORCH_FN(wrapper_CPU__unique_dim)); |
| 31557 | m.impl("unique_consecutive", |
| 31558 | TORCH_FN(wrapper_CPU__unique_consecutive)); |
| 31559 | m.impl("unique_dim_consecutive", |
| 31560 | TORCH_FN(wrapper_CPU__unique_dim_consecutive)); |
| 31561 | m.impl("_unique2", |
| 31562 | TORCH_FN(wrapper_CPU___unique2)); |
| 31563 | m.impl("var.correction", |
| 31564 | TORCH_FN(wrapper_CPU_correction_var)); |
| 31565 | m.impl("var.correction_out", |
| 31566 | TORCH_FN(wrapper_CPU_correction_out_var_out)); |
| 31567 | m.impl("var_mean.correction", |
| 31568 | TORCH_FN(wrapper_CPU_correction_var_mean)); |
| 31569 | m.impl("where.self", |
| 31570 | TORCH_FN(wrapper_CPU_self_where)); |
| 31571 | m.impl("where.self_out", |
| 31572 | TORCH_FN(wrapper_CPU_self_out_where_out)); |
| 31573 | m.impl("_weight_norm_interface", |
| 31574 | TORCH_FN(wrapper_CPU___weight_norm_interface)); |
| 31575 | m.impl("_weight_norm_interface_backward", |
| 31576 | TORCH_FN(wrapper_CPU___weight_norm_interface_backward)); |
| 31577 | m.impl("_efficientzerotensor", |
| 31578 | TORCH_FN(wrapper_CPU___efficientzerotensor)); |
| 31579 | m.impl("_standard_gamma_grad", |
| 31580 | TORCH_FN(wrapper_CPU___standard_gamma_grad)); |
| 31581 | m.impl("_standard_gamma", |
| 31582 | TORCH_FN(wrapper_CPU___standard_gamma)); |
| 31583 | m.impl("_dirichlet_grad", |
| 31584 | TORCH_FN(wrapper_CPU___dirichlet_grad)); |
| 31585 | m.impl("_sample_dirichlet", |
| 31586 | TORCH_FN(wrapper_CPU___sample_dirichlet)); |
| 31587 | m.impl("poisson", |
| 31588 | TORCH_FN(wrapper_CPU__poisson)); |
| 31589 | m.impl("binomial", |
| 31590 | TORCH_FN(wrapper_CPU__binomial)); |
| 31591 | m.impl("_spdiags", |
| 31592 | TORCH_FN(wrapper_CPU___spdiags)); |
| 31593 | m.impl("norm.ScalarOpt_dim_dtype", TORCH_FN(wrapper_CPU_norm_ScalarOpt_dim_dtype)); |
| 31594 | m.impl("norm.dtype_out", TORCH_FN(wrapper_CPU_norm_out_dtype_out)); |
| 31595 | m.impl("norm.ScalarOpt_dim", TORCH_FN(wrapper_CPU_norm_ScalarOpt_dim)); |
| 31596 | m.impl("norm.out", TORCH_FN(wrapper_CPU_norm_out_out)); |
| 31597 | m.impl("frexp.Tensor_out", |
| 31598 | TORCH_FN(wrapper_CPU_Tensor_out_frexp_out)); |
| 31599 | m.impl("zero_", |
| 31600 | TORCH_FN(wrapper_CPU__zero_)); |
| 31601 | m.impl("sub.Tensor", TORCH_FN(wrapper_CPU_sub_Tensor)); |
| 31602 | m.impl("sub.out", TORCH_FN(wrapper_CPU_sub_out_out)); |
| 31603 | m.impl("sub_.Tensor", TORCH_FN(wrapper_CPU_sub__Tensor)); |
| 31604 | m.impl("rsub.Tensor", |
| 31605 | TORCH_FN(wrapper_CPU_Tensor_rsub)); |
| 31606 | m.impl("heaviside", TORCH_FN(wrapper_CPU_heaviside)); |
| 31607 | m.impl("heaviside.out", TORCH_FN(wrapper_CPU_heaviside_out_out)); |
| 31608 | m.impl("heaviside_", TORCH_FN(wrapper_CPU_heaviside_)); |
| 31609 | m.impl("addmm", TORCH_FN(wrapper_CPU_addmm)); |
| 31610 | m.impl("addmm.out", TORCH_FN(wrapper_CPU_addmm_out_out)); |
| 31611 | m.impl("addmm_", TORCH_FN(wrapper_CPU_addmm_)); |
| 31612 | m.impl("_addmm_activation", TORCH_FN(wrapper_CPU__addmm_activation)); |
| 31613 | m.impl("_addmm_activation.out", TORCH_FN(wrapper_CPU__addmm_activation_out_out)); |
| 31614 | m.impl("sparse_dim", |
| 31615 | TORCH_FN(wrapper_CPU__sparse_dim)); |
| 31616 | m.impl("dense_dim", |
| 31617 | TORCH_FN(wrapper_CPU__dense_dim)); |
| 31618 | m.impl("to_sparse.sparse_dim", |
| 31619 | TORCH_FN(wrapper_CPU_sparse_dim_to_sparse)); |
| 31620 | m.impl("to_sparse", |
| 31621 | TORCH_FN(wrapper_CPU__to_sparse)); |
| 31622 | m.impl("to_sparse_csr", |
| 31623 | TORCH_FN(wrapper_CPU__to_sparse_csr)); |
| 31624 | m.impl("to_sparse_csc", |
| 31625 | TORCH_FN(wrapper_CPU__to_sparse_csc)); |
| 31626 | m.impl("to_sparse_bsr", |
| 31627 | TORCH_FN(wrapper_CPU__to_sparse_bsr)); |
| 31628 | m.impl("to_sparse_bsc", |
| 31629 | TORCH_FN(wrapper_CPU__to_sparse_bsc)); |
| 31630 | m.impl("to_mkldnn", |
| 31631 | TORCH_FN(wrapper_CPU__to_mkldnn)); |
| 31632 | m.impl("quantize_per_tensor_dynamic", |
| 31633 | TORCH_FN(wrapper_CPU__quantize_per_tensor_dynamic)); |
| 31634 | m.impl("quantize_per_tensor", |
| 31635 | TORCH_FN(wrapper_CPU__quantize_per_tensor)); |
| 31636 | m.impl("quantize_per_tensor.tensor_qparams", |
| 31637 | TORCH_FN(wrapper_CPU_tensor_qparams_quantize_per_tensor)); |
| 31638 | m.impl("quantize_per_tensor.tensors", |
| 31639 | TORCH_FN(wrapper_CPU_tensors_quantize_per_tensor)); |
| 31640 | m.impl("quantize_per_channel", |
| 31641 | TORCH_FN(wrapper_CPU__quantize_per_channel)); |
| 31642 | m.impl("dequantize.self", |
| 31643 | TORCH_FN(wrapper_CPU_self_dequantize)); |
| 31644 | m.impl("_make_per_tensor_quantized_tensor", |
| 31645 | TORCH_FN(wrapper_CPU___make_per_tensor_quantized_tensor)); |
| 31646 | m.impl("_make_per_channel_quantized_tensor", |
| 31647 | TORCH_FN(wrapper_CPU___make_per_channel_quantized_tensor)); |
| 31648 | m.impl("fake_quantize_per_tensor_affine_cachemask", |
| 31649 | TORCH_FN(wrapper_CPU__fake_quantize_per_tensor_affine_cachemask)); |
| 31650 | m.impl("_fake_quantize_per_tensor_affine_cachemask_tensor_qparams", |
| 31651 | TORCH_FN(wrapper_CPU___fake_quantize_per_tensor_affine_cachemask_tensor_qparams)); |
| 31652 | m.impl("_fake_quantize_learnable_per_tensor_affine", |
| 31653 | TORCH_FN(wrapper_CPU___fake_quantize_learnable_per_tensor_affine)); |
| 31654 | m.impl("_fake_quantize_learnable_per_tensor_affine_backward", |
| 31655 | TORCH_FN(wrapper_CPU___fake_quantize_learnable_per_tensor_affine_backward)); |
| 31656 | m.impl("fake_quantize_per_channel_affine_cachemask", |
| 31657 | TORCH_FN(wrapper_CPU__fake_quantize_per_channel_affine_cachemask)); |
| 31658 | m.impl("_fake_quantize_learnable_per_channel_affine", |
| 31659 | TORCH_FN(wrapper_CPU___fake_quantize_learnable_per_channel_affine)); |
| 31660 | m.impl("_fake_quantize_learnable_per_channel_affine_backward", |
| 31661 | TORCH_FN(wrapper_CPU___fake_quantize_learnable_per_channel_affine_backward)); |
| 31662 | m.impl("_fused_moving_avg_obs_fq_helper", |
| 31663 | TORCH_FN(wrapper_CPU___fused_moving_avg_obs_fq_helper)); |
| 31664 | m.impl("_local_scalar_dense", |
| 31665 | TORCH_FN(wrapper_CPU___local_scalar_dense)); |
| 31666 | m.impl("set_.source_Storage", |
| 31667 | TORCH_FN(wrapper_CPU_source_Storage_set_)); |
| 31668 | m.impl("set_.source_Storage_storage_offset", |
| 31669 | TORCH_FN(wrapper_CPU_source_Storage_storage_offset_set_)); |
| 31670 | m.impl("set_.source_Tensor", |
| 31671 | TORCH_FN(wrapper_CPU_source_Tensor_set_)); |
| 31672 | m.impl("set_", |
| 31673 | TORCH_FN(wrapper_CPU__set_)); |
| 31674 | m.impl("is_set_to", |
| 31675 | TORCH_FN(wrapper_CPU__is_set_to)); |
| 31676 | m.impl("masked_fill_.Scalar", |
| 31677 | TORCH_FN(wrapper_CPU_Scalar_masked_fill_)); |
| 31678 | m.impl("masked_fill_.Tensor", |
| 31679 | TORCH_FN(wrapper_CPU_Tensor_masked_fill_)); |
| 31680 | m.impl("masked_scatter_", |
| 31681 | TORCH_FN(wrapper_CPU__masked_scatter_)); |
| 31682 | m.impl("_masked_softmax", |
| 31683 | TORCH_FN(wrapper_CPU___masked_softmax)); |
| 31684 | m.impl("_masked_softmax_backward", |
| 31685 | TORCH_FN(wrapper_CPU___masked_softmax_backward)); |
| 31686 | m.impl("view", |
| 31687 | TORCH_FN(wrapper_CPU__view)); |
| 31688 | m.impl("put_", |
| 31689 | TORCH_FN(wrapper_CPU__put_)); |
| 31690 | m.impl("index_add", TORCH_FN(wrapper_CPU_index_add)); |
| 31691 | m.impl("index_add.out", TORCH_FN(wrapper_CPU_index_add_out_out)); |
| 31692 | m.impl("index_add_", TORCH_FN(wrapper_CPU_index_add_)); |
| 31693 | m.impl("index_reduce", TORCH_FN(wrapper_CPU_index_reduce)); |
| 31694 | m.impl("index_reduce.out", TORCH_FN(wrapper_CPU_index_reduce_out_out)); |
| 31695 | m.impl("index_reduce_", TORCH_FN(wrapper_CPU_index_reduce_)); |
| 31696 | m.impl("index_fill_.int_Scalar", |
| 31697 | TORCH_FN(wrapper_CPU_int_Scalar_index_fill_)); |
| 31698 | m.impl("index_fill_.int_Tensor", |
| 31699 | TORCH_FN(wrapper_CPU_int_Tensor_index_fill_)); |
| 31700 | m.impl("scatter.src", TORCH_FN(wrapper_CPU_scatter_src)); |
| 31701 | m.impl("scatter.src_out", TORCH_FN(wrapper_CPU_scatter_out_src_out)); |
| 31702 | m.impl("scatter_.src", TORCH_FN(wrapper_CPU_scatter__src)); |
| 31703 | m.impl("scatter.value", TORCH_FN(wrapper_CPU_scatter_value)); |
| 31704 | m.impl("scatter.value_out", TORCH_FN(wrapper_CPU_scatter_out_value_out)); |
| 31705 | m.impl("scatter_.value", TORCH_FN(wrapper_CPU_scatter__value)); |
| 31706 | m.impl("scatter.reduce", TORCH_FN(wrapper_CPU_scatter_reduce)); |
| 31707 | m.impl("scatter.reduce_out", TORCH_FN(wrapper_CPU_scatter_out_reduce_out)); |
| 31708 | m.impl("scatter_.reduce", TORCH_FN(wrapper_CPU_scatter__reduce)); |
| 31709 | m.impl("scatter.value_reduce", TORCH_FN(wrapper_CPU_scatter_value_reduce)); |
| 31710 | m.impl("scatter.value_reduce_out", TORCH_FN(wrapper_CPU_scatter_out_value_reduce_out)); |
| 31711 | m.impl("scatter_.value_reduce", TORCH_FN(wrapper_CPU_scatter__value_reduce)); |
| 31712 | m.impl("scatter_add", TORCH_FN(wrapper_CPU_scatter_add)); |
| 31713 | m.impl("scatter_add.out", TORCH_FN(wrapper_CPU_scatter_add_out_out)); |
| 31714 | m.impl("scatter_add_", TORCH_FN(wrapper_CPU_scatter_add_)); |
| 31715 | m.impl("scatter_reduce.two", TORCH_FN(wrapper_CPU_scatter_reduce_two)); |
| 31716 | m.impl("scatter_reduce.two_out", TORCH_FN(wrapper_CPU_scatter_reduce_out_two_out)); |
| 31717 | m.impl("scatter_reduce_.two", TORCH_FN(wrapper_CPU_scatter_reduce__two)); |
| 31718 | m.impl("eq.Scalar", TORCH_FN(wrapper_CPU_eq_Scalar)); |
| 31719 | m.impl("eq.Scalar_out", TORCH_FN(wrapper_CPU_eq_out_Scalar_out)); |
| 31720 | m.impl("eq_.Scalar", TORCH_FN(wrapper_CPU_eq__Scalar)); |
| 31721 | m.impl("eq.Tensor", TORCH_FN(wrapper_CPU_eq_Tensor)); |
| 31722 | m.impl("eq.Tensor_out", TORCH_FN(wrapper_CPU_eq_out_Tensor_out)); |
| 31723 | m.impl("eq_.Tensor", TORCH_FN(wrapper_CPU_eq__Tensor)); |
| 31724 | m.impl("bitwise_and.Tensor", TORCH_FN(wrapper_CPU_bitwise_and_Tensor)); |
| 31725 | m.impl("bitwise_and.Tensor_out", TORCH_FN(wrapper_CPU_bitwise_and_out_Tensor_out)); |
| 31726 | m.impl("bitwise_and_.Tensor", TORCH_FN(wrapper_CPU_bitwise_and__Tensor)); |
| 31727 | m.impl("bitwise_or.Tensor", TORCH_FN(wrapper_CPU_bitwise_or_Tensor)); |
| 31728 | m.impl("bitwise_or.Tensor_out", TORCH_FN(wrapper_CPU_bitwise_or_out_Tensor_out)); |
| 31729 | m.impl("bitwise_or_.Tensor", TORCH_FN(wrapper_CPU_bitwise_or__Tensor)); |
| 31730 | m.impl("bitwise_xor.Tensor", TORCH_FN(wrapper_CPU_bitwise_xor_Tensor)); |
| 31731 | m.impl("bitwise_xor.Tensor_out", TORCH_FN(wrapper_CPU_bitwise_xor_out_Tensor_out)); |
| 31732 | m.impl("bitwise_xor_.Tensor", TORCH_FN(wrapper_CPU_bitwise_xor__Tensor)); |
| 31733 | m.impl("__lshift__.Scalar", |
| 31734 | TORCH_FN(wrapper_CPU_Scalar___lshift__)); |
| 31735 | m.impl("__ilshift__.Scalar", |
| 31736 | TORCH_FN(wrapper_CPU_Scalar___ilshift__)); |
| 31737 | m.impl("__lshift__.Tensor", |
| 31738 | TORCH_FN(wrapper_CPU_Tensor___lshift__)); |
| 31739 | m.impl("__ilshift__.Tensor", |
| 31740 | TORCH_FN(wrapper_CPU_Tensor___ilshift__)); |
| 31741 | m.impl("bitwise_left_shift.Tensor", TORCH_FN(wrapper_CPU_bitwise_left_shift_Tensor)); |
| 31742 | m.impl("bitwise_left_shift.Tensor_out", TORCH_FN(wrapper_CPU_bitwise_left_shift_out_Tensor_out)); |
| 31743 | m.impl("bitwise_left_shift_.Tensor", TORCH_FN(wrapper_CPU_bitwise_left_shift__Tensor)); |
| 31744 | m.impl("__rshift__.Scalar", |
| 31745 | TORCH_FN(wrapper_CPU_Scalar___rshift__)); |
| 31746 | m.impl("__irshift__.Scalar", |
| 31747 | TORCH_FN(wrapper_CPU_Scalar___irshift__)); |
| 31748 | m.impl("__rshift__.Tensor", |
| 31749 | TORCH_FN(wrapper_CPU_Tensor___rshift__)); |
| 31750 | m.impl("__irshift__.Tensor", |
| 31751 | TORCH_FN(wrapper_CPU_Tensor___irshift__)); |
| 31752 | m.impl("bitwise_right_shift.Tensor", TORCH_FN(wrapper_CPU_bitwise_right_shift_Tensor)); |
| 31753 | m.impl("bitwise_right_shift.Tensor_out", TORCH_FN(wrapper_CPU_bitwise_right_shift_out_Tensor_out)); |
| 31754 | m.impl("bitwise_right_shift_.Tensor", TORCH_FN(wrapper_CPU_bitwise_right_shift__Tensor)); |
| 31755 | m.impl("tril", TORCH_FN(wrapper_CPU_tril)); |
| 31756 | m.impl("tril.out", TORCH_FN(wrapper_CPU_tril_out_out)); |
| 31757 | m.impl("tril_", TORCH_FN(wrapper_CPU_tril_)); |
| 31758 | m.impl("triu", TORCH_FN(wrapper_CPU_triu)); |
| 31759 | m.impl("triu.out", TORCH_FN(wrapper_CPU_triu_out_out)); |
| 31760 | m.impl("triu_", TORCH_FN(wrapper_CPU_triu_)); |
| 31761 | m.impl("digamma", TORCH_FN(wrapper_CPU_digamma)); |
| 31762 | m.impl("digamma.out", TORCH_FN(wrapper_CPU_digamma_out_out)); |
| 31763 | m.impl("digamma_", TORCH_FN(wrapper_CPU_digamma_)); |
| 31764 | m.impl("lerp.Scalar", TORCH_FN(wrapper_CPU_lerp_Scalar)); |
| 31765 | m.impl("lerp.Scalar_out", TORCH_FN(wrapper_CPU_lerp_out_Scalar_out)); |
| 31766 | m.impl("lerp_.Scalar", TORCH_FN(wrapper_CPU_lerp__Scalar)); |
| 31767 | m.impl("lerp.Tensor", TORCH_FN(wrapper_CPU_lerp_Tensor)); |
| 31768 | m.impl("lerp.Tensor_out", TORCH_FN(wrapper_CPU_lerp_out_Tensor_out)); |
| 31769 | m.impl("lerp_.Tensor", TORCH_FN(wrapper_CPU_lerp__Tensor)); |
| 31770 | m.impl("addbmm", |
| 31771 | TORCH_FN(wrapper_CPU__addbmm)); |
| 31772 | m.impl("addbmm.out", |
| 31773 | TORCH_FN(wrapper_CPU_out_addbmm_out)); |
| 31774 | m.impl("addbmm_", |
| 31775 | TORCH_FN(wrapper_CPU__addbmm_)); |
| 31776 | m.impl("random_.from", |
| 31777 | TORCH_FN(wrapper_CPU_from_random_)); |
| 31778 | m.impl("random_.to", |
| 31779 | TORCH_FN(wrapper_CPU_to_random_)); |
| 31780 | m.impl("random_", |
| 31781 | TORCH_FN(wrapper_CPU__random_)); |
| 31782 | m.impl("uniform_", |
| 31783 | TORCH_FN(wrapper_CPU__uniform_)); |
| 31784 | m.impl("cauchy_", |
| 31785 | TORCH_FN(wrapper_CPU__cauchy_)); |
| 31786 | m.impl("log_normal_", |
| 31787 | TORCH_FN(wrapper_CPU__log_normal_)); |
| 31788 | m.impl("exponential_", |
| 31789 | TORCH_FN(wrapper_CPU__exponential_)); |
| 31790 | m.impl("geometric_", |
| 31791 | TORCH_FN(wrapper_CPU__geometric_)); |
| 31792 | m.impl("tril_indices", |
| 31793 | TORCH_FN(wrapper_CPU__tril_indices)); |
| 31794 | m.impl("triu_indices", |
| 31795 | TORCH_FN(wrapper_CPU__triu_indices)); |
| 31796 | m.impl("trace", |
| 31797 | TORCH_FN(wrapper_CPU__trace)); |
| 31798 | m.impl("ne.Scalar", TORCH_FN(wrapper_CPU_ne_Scalar)); |
| 31799 | m.impl("ne.Scalar_out", TORCH_FN(wrapper_CPU_ne_out_Scalar_out)); |
| 31800 | m.impl("ne_.Scalar", TORCH_FN(wrapper_CPU_ne__Scalar)); |
| 31801 | m.impl("ne.Tensor", TORCH_FN(wrapper_CPU_ne_Tensor)); |
| 31802 | m.impl("ne.Tensor_out", TORCH_FN(wrapper_CPU_ne_out_Tensor_out)); |
| 31803 | m.impl("ne_.Tensor", TORCH_FN(wrapper_CPU_ne__Tensor)); |
| 31804 | m.impl("ge.Scalar", TORCH_FN(wrapper_CPU_ge_Scalar)); |
| 31805 | m.impl("ge.Scalar_out", TORCH_FN(wrapper_CPU_ge_out_Scalar_out)); |
| 31806 | m.impl("ge_.Scalar", TORCH_FN(wrapper_CPU_ge__Scalar)); |
| 31807 | m.impl("ge.Tensor", TORCH_FN(wrapper_CPU_ge_Tensor)); |
| 31808 | m.impl("ge.Tensor_out", TORCH_FN(wrapper_CPU_ge_out_Tensor_out)); |
| 31809 | m.impl("ge_.Tensor", TORCH_FN(wrapper_CPU_ge__Tensor)); |
| 31810 | m.impl("le.Scalar", TORCH_FN(wrapper_CPU_le_Scalar)); |
| 31811 | m.impl("le.Scalar_out", TORCH_FN(wrapper_CPU_le_out_Scalar_out)); |
| 31812 | m.impl("le_.Scalar", TORCH_FN(wrapper_CPU_le__Scalar)); |
| 31813 | m.impl("le.Tensor", TORCH_FN(wrapper_CPU_le_Tensor)); |
| 31814 | m.impl("le.Tensor_out", TORCH_FN(wrapper_CPU_le_out_Tensor_out)); |
| 31815 | m.impl("le_.Tensor", TORCH_FN(wrapper_CPU_le__Tensor)); |
| 31816 | m.impl("gt.Scalar", TORCH_FN(wrapper_CPU_gt_Scalar)); |
| 31817 | m.impl("gt.Scalar_out", TORCH_FN(wrapper_CPU_gt_out_Scalar_out)); |
| 31818 | m.impl("gt_.Scalar", TORCH_FN(wrapper_CPU_gt__Scalar)); |
| 31819 | m.impl("gt.Tensor", TORCH_FN(wrapper_CPU_gt_Tensor)); |
| 31820 | m.impl("gt.Tensor_out", TORCH_FN(wrapper_CPU_gt_out_Tensor_out)); |
| 31821 | m.impl("gt_.Tensor", TORCH_FN(wrapper_CPU_gt__Tensor)); |
| 31822 | m.impl("lt.Scalar", TORCH_FN(wrapper_CPU_lt_Scalar)); |
| 31823 | m.impl("lt.Scalar_out", TORCH_FN(wrapper_CPU_lt_out_Scalar_out)); |
| 31824 | m.impl("lt_.Scalar", TORCH_FN(wrapper_CPU_lt__Scalar)); |
| 31825 | m.impl("lt.Tensor", TORCH_FN(wrapper_CPU_lt_Tensor)); |
| 31826 | m.impl("lt.Tensor_out", TORCH_FN(wrapper_CPU_lt_out_Tensor_out)); |
| 31827 | m.impl("lt_.Tensor", TORCH_FN(wrapper_CPU_lt__Tensor)); |
| 31828 | m.impl("take", |
| 31829 | TORCH_FN(wrapper_CPU__take)); |
| 31830 | m.impl("take.out", |
| 31831 | TORCH_FN(wrapper_CPU_out_take_out)); |
| 31832 | m.impl("index_select", |
| 31833 | TORCH_FN(wrapper_CPU__index_select)); |
| 31834 | m.impl("index_select.out", |
| 31835 | TORCH_FN(wrapper_CPU_out_index_select_out)); |
| 31836 | m.impl("masked_select", |
| 31837 | TORCH_FN(wrapper_CPU__masked_select)); |
| 31838 | m.impl("masked_select.out", |
| 31839 | TORCH_FN(wrapper_CPU_out_masked_select_out)); |
| 31840 | m.impl("nonzero", |
| 31841 | TORCH_FN(wrapper_CPU__nonzero)); |
| 31842 | m.impl("nonzero.out", |
| 31843 | TORCH_FN(wrapper_CPU_out_nonzero_out)); |
| 31844 | m.impl("gather", TORCH_FN(wrapper_CPU_gather)); |
| 31845 | m.impl("gather.out", TORCH_FN(wrapper_CPU_gather_out_out)); |
| 31846 | m.impl("addcmul", TORCH_FN(wrapper_CPU_addcmul)); |
| 31847 | m.impl("addcmul.out", TORCH_FN(wrapper_CPU_addcmul_out_out)); |
| 31848 | m.impl("addcmul_", TORCH_FN(wrapper_CPU_addcmul_)); |
| 31849 | m.impl("addcdiv", TORCH_FN(wrapper_CPU_addcdiv)); |
| 31850 | m.impl("addcdiv.out", TORCH_FN(wrapper_CPU_addcdiv_out_out)); |
| 31851 | m.impl("addcdiv_", TORCH_FN(wrapper_CPU_addcdiv_)); |
| 31852 | m.impl("triangular_solve", TORCH_FN(wrapper_CPU_triangular_solve)); |
| 31853 | m.impl("triangular_solve.X", TORCH_FN(wrapper_CPU_triangular_solve_out_X)); |
| 31854 | m.impl("linalg_solve_triangular", |
| 31855 | TORCH_FN(wrapper_CPU__linalg_solve_triangular)); |
| 31856 | m.impl("linalg_solve_triangular.out", |
| 31857 | TORCH_FN(wrapper_CPU_out_linalg_solve_triangular_out)); |
| 31858 | m.impl("cholesky", |
| 31859 | TORCH_FN(wrapper_CPU__cholesky)); |
| 31860 | m.impl("cholesky.out", |
| 31861 | TORCH_FN(wrapper_CPU_out_cholesky_out)); |
| 31862 | m.impl("_cholesky_solve_helper", |
| 31863 | TORCH_FN(wrapper_CPU___cholesky_solve_helper)); |
| 31864 | m.impl("cholesky_inverse", |
| 31865 | TORCH_FN(wrapper_CPU__cholesky_inverse)); |
| 31866 | m.impl("cholesky_inverse.out", |
| 31867 | TORCH_FN(wrapper_CPU_out_cholesky_inverse_out)); |
| 31868 | m.impl("geqrf", |
| 31869 | TORCH_FN(wrapper_CPU__geqrf)); |
| 31870 | m.impl("geqrf.a", |
| 31871 | TORCH_FN(wrapper_CPU_a_geqrf_out)); |
| 31872 | m.impl("ormqr", |
| 31873 | TORCH_FN(wrapper_CPU__ormqr)); |
| 31874 | m.impl("ormqr.out", |
| 31875 | TORCH_FN(wrapper_CPU_out_ormqr_out)); |
| 31876 | m.impl("lu_unpack", TORCH_FN(wrapper_CPU_lu_unpack)); |
| 31877 | m.impl("lu_unpack.out", TORCH_FN(wrapper_CPU_lu_unpack_out_out)); |
| 31878 | m.impl("multinomial", |
| 31879 | TORCH_FN(wrapper_CPU__multinomial)); |
| 31880 | m.impl("multinomial.out", |
| 31881 | TORCH_FN(wrapper_CPU_out_multinomial_out)); |
| 31882 | m.impl("lgamma", TORCH_FN(wrapper_CPU_lgamma)); |
| 31883 | m.impl("lgamma.out", TORCH_FN(wrapper_CPU_lgamma_out_out)); |
| 31884 | m.impl("lgamma_", TORCH_FN(wrapper_CPU_lgamma_)); |
| 31885 | m.impl("polygamma", TORCH_FN(wrapper_CPU_polygamma)); |
| 31886 | m.impl("polygamma.out", TORCH_FN(wrapper_CPU_polygamma_out_out)); |
| 31887 | m.impl("erfinv", TORCH_FN(wrapper_CPU_erfinv)); |
| 31888 | m.impl("erfinv.out", TORCH_FN(wrapper_CPU_erfinv_out_out)); |
| 31889 | m.impl("erfinv_", TORCH_FN(wrapper_CPU_erfinv_)); |
| 31890 | m.impl("i0", TORCH_FN(wrapper_CPU_i0)); |
| 31891 | m.impl("i0.out", TORCH_FN(wrapper_CPU_i0_out_out)); |
| 31892 | m.impl("i0_", TORCH_FN(wrapper_CPU_i0_)); |
| 31893 | m.impl("sign", TORCH_FN(wrapper_CPU_sign)); |
| 31894 | m.impl("sign.out", TORCH_FN(wrapper_CPU_sign_out_out)); |
| 31895 | m.impl("sign_", TORCH_FN(wrapper_CPU_sign_)); |
| 31896 | m.impl("signbit", TORCH_FN(wrapper_CPU_signbit)); |
| 31897 | m.impl("signbit.out", TORCH_FN(wrapper_CPU_signbit_out_out)); |
| 31898 | m.impl("atan2", TORCH_FN(wrapper_CPU_atan2)); |
| 31899 | m.impl("atan2.out", TORCH_FN(wrapper_CPU_atan2_out_out)); |
| 31900 | m.impl("atan2_", TORCH_FN(wrapper_CPU_atan2_)); |
| 31901 | m.impl("histc", |
| 31902 | TORCH_FN(wrapper_CPU__histc)); |
| 31903 | m.impl("histc.out", |
| 31904 | TORCH_FN(wrapper_CPU_out_histc_out)); |
| 31905 | m.impl("histogram.bins_tensor", |
| 31906 | TORCH_FN(wrapper_CPU_bins_tensor_histogram)); |
| 31907 | m.impl("histogram.bins_tensor_out", |
| 31908 | TORCH_FN(wrapper_CPU_bins_tensor_out_histogram_out)); |
| 31909 | m.impl("histogram.bin_ct", |
| 31910 | TORCH_FN(wrapper_CPU_bin_ct_histogram)); |
| 31911 | m.impl("histogram.bin_ct_out", |
| 31912 | TORCH_FN(wrapper_CPU_bin_ct_out_histogram_out)); |
| 31913 | m.impl("_histogramdd_bin_edges", |
| 31914 | TORCH_FN(wrapper_CPU___histogramdd_bin_edges)); |
| 31915 | m.impl("_histogramdd_from_bin_cts", |
| 31916 | TORCH_FN(wrapper_CPU___histogramdd_from_bin_cts)); |
| 31917 | m.impl("_histogramdd_from_bin_tensors", |
| 31918 | TORCH_FN(wrapper_CPU___histogramdd_from_bin_tensors)); |
| 31919 | m.impl("fmod.Tensor", TORCH_FN(wrapper_CPU_fmod_Tensor)); |
| 31920 | m.impl("fmod.Tensor_out", TORCH_FN(wrapper_CPU_fmod_out_Tensor_out)); |
| 31921 | m.impl("fmod_.Tensor", TORCH_FN(wrapper_CPU_fmod__Tensor)); |
| 31922 | m.impl("hypot", TORCH_FN(wrapper_CPU_hypot)); |
| 31923 | m.impl("hypot.out", TORCH_FN(wrapper_CPU_hypot_out_out)); |
| 31924 | m.impl("hypot_", TORCH_FN(wrapper_CPU_hypot_)); |
| 31925 | m.impl("igamma", TORCH_FN(wrapper_CPU_igamma)); |
| 31926 | m.impl("igamma.out", TORCH_FN(wrapper_CPU_igamma_out_out)); |
| 31927 | m.impl("igamma_", TORCH_FN(wrapper_CPU_igamma_)); |
| 31928 | m.impl("igammac", TORCH_FN(wrapper_CPU_igammac)); |
| 31929 | m.impl("igammac.out", TORCH_FN(wrapper_CPU_igammac_out_out)); |
| 31930 | m.impl("igammac_", TORCH_FN(wrapper_CPU_igammac_)); |
| 31931 | m.impl("nextafter", TORCH_FN(wrapper_CPU_nextafter)); |
| 31932 | m.impl("nextafter.out", TORCH_FN(wrapper_CPU_nextafter_out_out)); |
| 31933 | m.impl("nextafter_", TORCH_FN(wrapper_CPU_nextafter_)); |
| 31934 | m.impl("remainder.Tensor", TORCH_FN(wrapper_CPU_remainder_Tensor)); |
| 31935 | m.impl("remainder.Tensor_out", TORCH_FN(wrapper_CPU_remainder_out_Tensor_out)); |
| 31936 | m.impl("remainder_.Tensor", TORCH_FN(wrapper_CPU_remainder__Tensor)); |
| 31937 | m.impl("remainder.Scalar_Tensor", |
| 31938 | TORCH_FN(wrapper_CPU_Scalar_Tensor_remainder)); |
| 31939 | m.impl("min", |
| 31940 | TORCH_FN(wrapper_CPU__min)); |
| 31941 | m.impl("fmin", TORCH_FN(wrapper_CPU_fmin)); |
| 31942 | m.impl("fmin.out", TORCH_FN(wrapper_CPU_fmin_out_out)); |
| 31943 | m.impl("max", |
| 31944 | TORCH_FN(wrapper_CPU__max)); |
| 31945 | m.impl("max.unary_out", |
| 31946 | TORCH_FN(wrapper_CPU_unary_out_max_out)); |
| 31947 | m.impl("fmax", TORCH_FN(wrapper_CPU_fmax)); |
| 31948 | m.impl("fmax.out", TORCH_FN(wrapper_CPU_fmax_out_out)); |
| 31949 | m.impl("maximum", TORCH_FN(wrapper_CPU_maximum)); |
| 31950 | m.impl("maximum.out", TORCH_FN(wrapper_CPU_maximum_out_out)); |
| 31951 | m.impl("minimum", TORCH_FN(wrapper_CPU_minimum)); |
| 31952 | m.impl("minimum.out", TORCH_FN(wrapper_CPU_minimum_out_out)); |
| 31953 | m.impl("sort.stable", TORCH_FN(wrapper_CPU_sort_stable)); |
| 31954 | m.impl("sort.values_stable", TORCH_FN(wrapper_CPU_sort_out_values_stable)); |
| 31955 | m.impl("argsort.stable", |
| 31956 | TORCH_FN(wrapper_CPU_stable_argsort)); |
| 31957 | m.impl("topk", TORCH_FN(wrapper_CPU_topk)); |
| 31958 | m.impl("topk.values", TORCH_FN(wrapper_CPU_topk_out_values)); |
| 31959 | m.impl("all", TORCH_FN(wrapper_CPU_all)); |
| 31960 | m.impl("all.all_out", TORCH_FN(wrapper_CPU_all_out_all_out)); |
| 31961 | m.impl("any", TORCH_FN(wrapper_CPU_any)); |
| 31962 | m.impl("any.all_out", TORCH_FN(wrapper_CPU_any_out_all_out)); |
| 31963 | m.impl("renorm", TORCH_FN(wrapper_CPU_renorm)); |
| 31964 | m.impl("renorm.out", TORCH_FN(wrapper_CPU_renorm_out_out)); |
| 31965 | m.impl("renorm_", TORCH_FN(wrapper_CPU_renorm_)); |
| 31966 | m.impl("unfold", |
| 31967 | TORCH_FN(wrapper_CPU__unfold)); |
| 31968 | m.impl("unfold_backward", |
| 31969 | TORCH_FN(wrapper_CPU__unfold_backward)); |
| 31970 | m.impl("equal", |
| 31971 | TORCH_FN(wrapper_CPU__equal)); |
| 31972 | m.impl("pow.Tensor_Tensor", TORCH_FN(wrapper_CPU_pow_Tensor_Tensor)); |
| 31973 | m.impl("pow.Tensor_Tensor_out", TORCH_FN(wrapper_CPU_pow_out_Tensor_Tensor_out)); |
| 31974 | m.impl("pow_.Tensor", TORCH_FN(wrapper_CPU_pow__Tensor)); |
| 31975 | m.impl("pow.Scalar", TORCH_FN(wrapper_CPU_pow_Scalar)); |
| 31976 | m.impl("pow.Scalar_out", TORCH_FN(wrapper_CPU_pow_out_Scalar_out)); |
| 31977 | m.impl("pow.Tensor_Scalar", TORCH_FN(wrapper_CPU_pow_Tensor_Scalar)); |
| 31978 | m.impl("pow.Tensor_Scalar_out", TORCH_FN(wrapper_CPU_pow_out_Tensor_Scalar_out)); |
| 31979 | m.impl("pow_.Scalar", TORCH_FN(wrapper_CPU_pow__Scalar)); |
| 31980 | m.impl("normal_", |
| 31981 | TORCH_FN(wrapper_CPU__normal_)); |
| 31982 | m.impl("normal.Tensor_float", |
| 31983 | TORCH_FN(wrapper_CPU_Tensor_float_normal)); |
| 31984 | m.impl("normal.Tensor_float_out", |
| 31985 | TORCH_FN(wrapper_CPU_Tensor_float_out_normal_out)); |
| 31986 | m.impl("normal.float_Tensor", |
| 31987 | TORCH_FN(wrapper_CPU_float_Tensor_normal)); |
| 31988 | m.impl("normal.float_Tensor_out", |
| 31989 | TORCH_FN(wrapper_CPU_float_Tensor_out_normal_out)); |
| 31990 | m.impl("normal.Tensor_Tensor", |
| 31991 | TORCH_FN(wrapper_CPU_Tensor_Tensor_normal)); |
| 31992 | m.impl("normal.Tensor_Tensor_out", |
| 31993 | TORCH_FN(wrapper_CPU_Tensor_Tensor_out_normal_out)); |
| 31994 | m.impl("_foreach_add.Scalar", |
| 31995 | TORCH_FN(wrapper_CPU_Scalar__foreach_add)); |
| 31996 | m.impl("_foreach_add_.Scalar", |
| 31997 | TORCH_FN(wrapper_CPU_Scalar__foreach_add_)); |
| 31998 | m.impl("_foreach_sub.Scalar", |
| 31999 | TORCH_FN(wrapper_CPU_Scalar__foreach_sub)); |
| 32000 | m.impl("_foreach_sub_.Scalar", |
| 32001 | TORCH_FN(wrapper_CPU_Scalar__foreach_sub_)); |
| 32002 | m.impl("_foreach_mul.Scalar", |
| 32003 | TORCH_FN(wrapper_CPU_Scalar__foreach_mul)); |
| 32004 | m.impl("_foreach_mul_.Scalar", |
| 32005 | TORCH_FN(wrapper_CPU_Scalar__foreach_mul_)); |
| 32006 | m.impl("_foreach_div.Scalar", |
| 32007 | TORCH_FN(wrapper_CPU_Scalar__foreach_div)); |
| 32008 | m.impl("_foreach_div_.Scalar", |
| 32009 | TORCH_FN(wrapper_CPU_Scalar__foreach_div_)); |
| 32010 | m.impl("_foreach_clamp_min.Scalar", |
| 32011 | TORCH_FN(wrapper_CPU_Scalar__foreach_clamp_min)); |
| 32012 | m.impl("_foreach_clamp_min_.Scalar", |
| 32013 | TORCH_FN(wrapper_CPU_Scalar__foreach_clamp_min_)); |
| 32014 | m.impl("_foreach_clamp_max.Scalar", |
| 32015 | TORCH_FN(wrapper_CPU_Scalar__foreach_clamp_max)); |
| 32016 | m.impl("_foreach_clamp_max_.Scalar", |
| 32017 | TORCH_FN(wrapper_CPU_Scalar__foreach_clamp_max_)); |
| 32018 | m.impl("_foreach_maximum.Scalar", |
| 32019 | TORCH_FN(wrapper_CPU_Scalar__foreach_maximum)); |
| 32020 | m.impl("_foreach_maximum_.Scalar", |
| 32021 | TORCH_FN(wrapper_CPU_Scalar__foreach_maximum_)); |
| 32022 | m.impl("_foreach_minimum.Scalar", |
| 32023 | TORCH_FN(wrapper_CPU_Scalar__foreach_minimum)); |
| 32024 | m.impl("_foreach_minimum_.Scalar", |
| 32025 | TORCH_FN(wrapper_CPU_Scalar__foreach_minimum_)); |
| 32026 | m.impl("_foreach_add.List", |
| 32027 | TORCH_FN(wrapper_CPU_List__foreach_add)); |
| 32028 | m.impl("_foreach_add_.List", |
| 32029 | TORCH_FN(wrapper_CPU_List__foreach_add_)); |
| 32030 | m.impl("_foreach_sub.List", |
| 32031 | TORCH_FN(wrapper_CPU_List__foreach_sub)); |
| 32032 | m.impl("_foreach_sub_.List", |
| 32033 | TORCH_FN(wrapper_CPU_List__foreach_sub_)); |
| 32034 | m.impl("_foreach_mul.List", |
| 32035 | TORCH_FN(wrapper_CPU_List__foreach_mul)); |
| 32036 | m.impl("_foreach_mul_.List", |
| 32037 | TORCH_FN(wrapper_CPU_List__foreach_mul_)); |
| 32038 | m.impl("_foreach_div.List", |
| 32039 | TORCH_FN(wrapper_CPU_List__foreach_div)); |
| 32040 | m.impl("_foreach_div_.List", |
| 32041 | TORCH_FN(wrapper_CPU_List__foreach_div_)); |
| 32042 | m.impl("_foreach_clamp_min.List", |
| 32043 | TORCH_FN(wrapper_CPU_List__foreach_clamp_min)); |
| 32044 | m.impl("_foreach_clamp_min_.List", |
| 32045 | TORCH_FN(wrapper_CPU_List__foreach_clamp_min_)); |
| 32046 | m.impl("_foreach_clamp_max.List", |
| 32047 | TORCH_FN(wrapper_CPU_List__foreach_clamp_max)); |
| 32048 | m.impl("_foreach_clamp_max_.List", |
| 32049 | TORCH_FN(wrapper_CPU_List__foreach_clamp_max_)); |
| 32050 | m.impl("_foreach_maximum.List", |
| 32051 | TORCH_FN(wrapper_CPU_List__foreach_maximum)); |
| 32052 | m.impl("_foreach_maximum_.List", |
| 32053 | TORCH_FN(wrapper_CPU_List__foreach_maximum_)); |
| 32054 | m.impl("_foreach_minimum.List", |
| 32055 | TORCH_FN(wrapper_CPU_List__foreach_minimum)); |
| 32056 | m.impl("_foreach_minimum_.List", |
| 32057 | TORCH_FN(wrapper_CPU_List__foreach_minimum_)); |
| 32058 | m.impl("_foreach_add.ScalarList", |
| 32059 | TORCH_FN(wrapper_CPU_ScalarList__foreach_add)); |
| 32060 | m.impl("_foreach_add_.ScalarList", |
| 32061 | TORCH_FN(wrapper_CPU_ScalarList__foreach_add_)); |
| 32062 | m.impl("_foreach_sub.ScalarList", |
| 32063 | TORCH_FN(wrapper_CPU_ScalarList__foreach_sub)); |
| 32064 | m.impl("_foreach_sub_.ScalarList", |
| 32065 | TORCH_FN(wrapper_CPU_ScalarList__foreach_sub_)); |
| 32066 | m.impl("_foreach_div.ScalarList", |
| 32067 | TORCH_FN(wrapper_CPU_ScalarList__foreach_div)); |
| 32068 | m.impl("_foreach_div_.ScalarList", |
| 32069 | TORCH_FN(wrapper_CPU_ScalarList__foreach_div_)); |
| 32070 | m.impl("_foreach_mul.ScalarList", |
| 32071 | TORCH_FN(wrapper_CPU_ScalarList__foreach_mul)); |
| 32072 | m.impl("_foreach_mul_.ScalarList", |
| 32073 | TORCH_FN(wrapper_CPU_ScalarList__foreach_mul_)); |
| 32074 | m.impl("_foreach_clamp_min.ScalarList", |
| 32075 | TORCH_FN(wrapper_CPU_ScalarList__foreach_clamp_min)); |
| 32076 | m.impl("_foreach_clamp_min_.ScalarList", |
| 32077 | TORCH_FN(wrapper_CPU_ScalarList__foreach_clamp_min_)); |
| 32078 | m.impl("_foreach_clamp_max.ScalarList", |
| 32079 | TORCH_FN(wrapper_CPU_ScalarList__foreach_clamp_max)); |
| 32080 | m.impl("_foreach_clamp_max_.ScalarList", |
| 32081 | TORCH_FN(wrapper_CPU_ScalarList__foreach_clamp_max_)); |
| 32082 | m.impl("_foreach_maximum.ScalarList", |
| 32083 | TORCH_FN(wrapper_CPU_ScalarList__foreach_maximum)); |
| 32084 | m.impl("_foreach_maximum_.ScalarList", |
| 32085 | TORCH_FN(wrapper_CPU_ScalarList__foreach_maximum_)); |
| 32086 | m.impl("_foreach_minimum.ScalarList", |
| 32087 | TORCH_FN(wrapper_CPU_ScalarList__foreach_minimum)); |
| 32088 | m.impl("_foreach_minimum_.ScalarList", |
| 32089 | TORCH_FN(wrapper_CPU_ScalarList__foreach_minimum_)); |
| 32090 | m.impl("_foreach_exp", |
| 32091 | TORCH_FN(wrapper_CPU___foreach_exp)); |
| 32092 | m.impl("_foreach_exp_", |
| 32093 | TORCH_FN(wrapper_CPU___foreach_exp_)); |
| 32094 | m.impl("_foreach_zero_", |
| 32095 | TORCH_FN(wrapper_CPU___foreach_zero_)); |
| 32096 | m.impl("_foreach_sqrt", |
| 32097 | TORCH_FN(wrapper_CPU___foreach_sqrt)); |
| 32098 | m.impl("_foreach_sqrt_", |
| 32099 | TORCH_FN(wrapper_CPU___foreach_sqrt_)); |
| 32100 | m.impl("_foreach_abs", |
| 32101 | TORCH_FN(wrapper_CPU___foreach_abs)); |
| 32102 | m.impl("_foreach_abs_", |
| 32103 | TORCH_FN(wrapper_CPU___foreach_abs_)); |
| 32104 | m.impl("_foreach_acos", |
| 32105 | TORCH_FN(wrapper_CPU___foreach_acos)); |
| 32106 | m.impl("_foreach_acos_", |
| 32107 | TORCH_FN(wrapper_CPU___foreach_acos_)); |
| 32108 | m.impl("_foreach_asin", |
| 32109 | TORCH_FN(wrapper_CPU___foreach_asin)); |
| 32110 | m.impl("_foreach_asin_", |
| 32111 | TORCH_FN(wrapper_CPU___foreach_asin_)); |
| 32112 | m.impl("_foreach_atan", |
| 32113 | TORCH_FN(wrapper_CPU___foreach_atan)); |
| 32114 | m.impl("_foreach_atan_", |
| 32115 | TORCH_FN(wrapper_CPU___foreach_atan_)); |
| 32116 | m.impl("_foreach_ceil", |
| 32117 | TORCH_FN(wrapper_CPU___foreach_ceil)); |
| 32118 | m.impl("_foreach_ceil_", |
| 32119 | TORCH_FN(wrapper_CPU___foreach_ceil_)); |
| 32120 | m.impl("_foreach_cos", |
| 32121 | TORCH_FN(wrapper_CPU___foreach_cos)); |
| 32122 | m.impl("_foreach_cos_", |
| 32123 | TORCH_FN(wrapper_CPU___foreach_cos_)); |
| 32124 | m.impl("_foreach_cosh", |
| 32125 | TORCH_FN(wrapper_CPU___foreach_cosh)); |
| 32126 | m.impl("_foreach_cosh_", |
| 32127 | TORCH_FN(wrapper_CPU___foreach_cosh_)); |
| 32128 | m.impl("_foreach_erf", |
| 32129 | TORCH_FN(wrapper_CPU___foreach_erf)); |
| 32130 | m.impl("_foreach_erf_", |
| 32131 | TORCH_FN(wrapper_CPU___foreach_erf_)); |
| 32132 | m.impl("_foreach_erfc", |
| 32133 | TORCH_FN(wrapper_CPU___foreach_erfc)); |
| 32134 | m.impl("_foreach_erfc_", |
| 32135 | TORCH_FN(wrapper_CPU___foreach_erfc_)); |
| 32136 | m.impl("_foreach_expm1", |
| 32137 | TORCH_FN(wrapper_CPU___foreach_expm1)); |
| 32138 | m.impl("_foreach_expm1_", |
| 32139 | TORCH_FN(wrapper_CPU___foreach_expm1_)); |
| 32140 | m.impl("_foreach_floor", |
| 32141 | TORCH_FN(wrapper_CPU___foreach_floor)); |
| 32142 | m.impl("_foreach_floor_", |
| 32143 | TORCH_FN(wrapper_CPU___foreach_floor_)); |
| 32144 | m.impl("_foreach_log", |
| 32145 | TORCH_FN(wrapper_CPU___foreach_log)); |
| 32146 | m.impl("_foreach_log_", |
| 32147 | TORCH_FN(wrapper_CPU___foreach_log_)); |
| 32148 | m.impl("_foreach_log10", |
| 32149 | TORCH_FN(wrapper_CPU___foreach_log10)); |
| 32150 | m.impl("_foreach_log10_", |
| 32151 | TORCH_FN(wrapper_CPU___foreach_log10_)); |
| 32152 | m.impl("_foreach_log1p", |
| 32153 | TORCH_FN(wrapper_CPU___foreach_log1p)); |
| 32154 | m.impl("_foreach_log1p_", |
| 32155 | TORCH_FN(wrapper_CPU___foreach_log1p_)); |
| 32156 | m.impl("_foreach_log2", |
| 32157 | TORCH_FN(wrapper_CPU___foreach_log2)); |
| 32158 | m.impl("_foreach_log2_", |
| 32159 | TORCH_FN(wrapper_CPU___foreach_log2_)); |
| 32160 | m.impl("_foreach_neg", |
| 32161 | TORCH_FN(wrapper_CPU___foreach_neg)); |
| 32162 | m.impl("_foreach_neg_", |
| 32163 | TORCH_FN(wrapper_CPU___foreach_neg_)); |
| 32164 | m.impl("_foreach_tan", |
| 32165 | TORCH_FN(wrapper_CPU___foreach_tan)); |
| 32166 | m.impl("_foreach_tan_", |
| 32167 | TORCH_FN(wrapper_CPU___foreach_tan_)); |
| 32168 | m.impl("_foreach_tanh", |
| 32169 | TORCH_FN(wrapper_CPU___foreach_tanh)); |
| 32170 | m.impl("_foreach_tanh_", |
| 32171 | TORCH_FN(wrapper_CPU___foreach_tanh_)); |
| 32172 | m.impl("_foreach_sin", |
| 32173 | TORCH_FN(wrapper_CPU___foreach_sin)); |
| 32174 | m.impl("_foreach_sin_", |
| 32175 | TORCH_FN(wrapper_CPU___foreach_sin_)); |
| 32176 | m.impl("_foreach_sinh", |
| 32177 | TORCH_FN(wrapper_CPU___foreach_sinh)); |
| 32178 | m.impl("_foreach_sinh_", |
| 32179 | TORCH_FN(wrapper_CPU___foreach_sinh_)); |
| 32180 | m.impl("_foreach_round", |
| 32181 | TORCH_FN(wrapper_CPU___foreach_round)); |
| 32182 | m.impl("_foreach_round_", |
| 32183 | TORCH_FN(wrapper_CPU___foreach_round_)); |
| 32184 | m.impl("_foreach_lgamma", |
| 32185 | TORCH_FN(wrapper_CPU___foreach_lgamma)); |
| 32186 | m.impl("_foreach_lgamma_", |
| 32187 | TORCH_FN(wrapper_CPU___foreach_lgamma_)); |
| 32188 | m.impl("_foreach_frac", |
| 32189 | TORCH_FN(wrapper_CPU___foreach_frac)); |
| 32190 | m.impl("_foreach_frac_", |
| 32191 | TORCH_FN(wrapper_CPU___foreach_frac_)); |
| 32192 | m.impl("_foreach_reciprocal", |
| 32193 | TORCH_FN(wrapper_CPU___foreach_reciprocal)); |
| 32194 | m.impl("_foreach_reciprocal_", |
| 32195 | TORCH_FN(wrapper_CPU___foreach_reciprocal_)); |
| 32196 | m.impl("_foreach_sigmoid", |
| 32197 | TORCH_FN(wrapper_CPU___foreach_sigmoid)); |
| 32198 | m.impl("_foreach_sigmoid_", |
| 32199 | TORCH_FN(wrapper_CPU___foreach_sigmoid_)); |
| 32200 | m.impl("_foreach_trunc", |
| 32201 | TORCH_FN(wrapper_CPU___foreach_trunc)); |
| 32202 | m.impl("_foreach_trunc_", |
| 32203 | TORCH_FN(wrapper_CPU___foreach_trunc_)); |
| 32204 | m.impl("_foreach_addcdiv.Scalar", |
| 32205 | TORCH_FN(wrapper_CPU_Scalar__foreach_addcdiv)); |
| 32206 | m.impl("_foreach_addcdiv_.Scalar", |
| 32207 | TORCH_FN(wrapper_CPU_Scalar__foreach_addcdiv_)); |
| 32208 | m.impl("_foreach_addcmul.Scalar", |
| 32209 | TORCH_FN(wrapper_CPU_Scalar__foreach_addcmul)); |
| 32210 | m.impl("_foreach_addcmul_.Scalar", |
| 32211 | TORCH_FN(wrapper_CPU_Scalar__foreach_addcmul_)); |
| 32212 | m.impl("_foreach_addcdiv.ScalarList", |
| 32213 | TORCH_FN(wrapper_CPU_ScalarList__foreach_addcdiv)); |
| 32214 | m.impl("_foreach_addcdiv_.ScalarList", |
| 32215 | TORCH_FN(wrapper_CPU_ScalarList__foreach_addcdiv_)); |
| 32216 | m.impl("_foreach_addcdiv.Tensor", |
| 32217 | TORCH_FN(wrapper_CPU_Tensor__foreach_addcdiv)); |
| 32218 | m.impl("_foreach_addcdiv_.Tensor", |
| 32219 | TORCH_FN(wrapper_CPU_Tensor__foreach_addcdiv_)); |
| 32220 | m.impl("_foreach_addcmul.ScalarList", |
| 32221 | TORCH_FN(wrapper_CPU_ScalarList__foreach_addcmul)); |
| 32222 | m.impl("_foreach_addcmul_.ScalarList", |
| 32223 | TORCH_FN(wrapper_CPU_ScalarList__foreach_addcmul_)); |
| 32224 | m.impl("_foreach_addcmul.Tensor", |
| 32225 | TORCH_FN(wrapper_CPU_Tensor__foreach_addcmul)); |
| 32226 | m.impl("_foreach_addcmul_.Tensor", |
| 32227 | TORCH_FN(wrapper_CPU_Tensor__foreach_addcmul_)); |
| 32228 | m.impl("_foreach_norm.Scalar", |
| 32229 | TORCH_FN(wrapper_CPU_Scalar__foreach_norm)); |
| 32230 | m.impl("_foreach_lerp.List", |
| 32231 | TORCH_FN(wrapper_CPU_List__foreach_lerp)); |
| 32232 | m.impl("_foreach_lerp_.List", |
| 32233 | TORCH_FN(wrapper_CPU_List__foreach_lerp_)); |
| 32234 | m.impl("_foreach_lerp.Scalar", |
| 32235 | TORCH_FN(wrapper_CPU_Scalar__foreach_lerp)); |
| 32236 | m.impl("_foreach_lerp_.Scalar", |
| 32237 | TORCH_FN(wrapper_CPU_Scalar__foreach_lerp_)); |
| 32238 | m.impl("bucketize.Tensor", |
| 32239 | TORCH_FN(wrapper_CPU_Tensor_bucketize)); |
| 32240 | m.impl("bucketize.Tensor_out", |
| 32241 | TORCH_FN(wrapper_CPU_Tensor_out_bucketize_out)); |
| 32242 | m.impl("bucketize.Scalar", |
| 32243 | TORCH_FN(wrapper_CPU_Scalar_bucketize)); |
| 32244 | m.impl("searchsorted.Tensor", |
| 32245 | TORCH_FN(wrapper_CPU_Tensor_searchsorted)); |
| 32246 | m.impl("searchsorted.Tensor_out", |
| 32247 | TORCH_FN(wrapper_CPU_Tensor_out_searchsorted_out)); |
| 32248 | m.impl("searchsorted.Scalar", |
| 32249 | TORCH_FN(wrapper_CPU_Scalar_searchsorted)); |
| 32250 | m.impl("_convert_indices_from_coo_to_csr", TORCH_FN(wrapper_CPU__convert_indices_from_coo_to_csr)); |
| 32251 | m.impl("_convert_indices_from_coo_to_csr.out", TORCH_FN(wrapper_CPU__convert_indices_from_coo_to_csr_out_out)); |
| 32252 | m.impl("_convert_indices_from_csr_to_coo", TORCH_FN(wrapper_CPU__convert_indices_from_csr_to_coo)); |
| 32253 | m.impl("_convert_indices_from_csr_to_coo.out", TORCH_FN(wrapper_CPU__convert_indices_from_csr_to_coo_out_out)); |
| 32254 | m.impl("mse_loss", TORCH_FN(wrapper_CPU_mse_loss)); |
| 32255 | m.impl("mse_loss.out", TORCH_FN(wrapper_CPU_mse_loss_out_out)); |
| 32256 | m.impl("mse_loss_backward", |
| 32257 | TORCH_FN(wrapper_CPU__mse_loss_backward)); |
| 32258 | m.impl("mse_loss_backward.grad_input", |
| 32259 | TORCH_FN(wrapper_CPU_grad_input_mse_loss_backward_out)); |
| 32260 | m.impl("multi_margin_loss", |
| 32261 | TORCH_FN(wrapper_CPU__multi_margin_loss)); |
| 32262 | m.impl("multi_margin_loss.out", |
| 32263 | TORCH_FN(wrapper_CPU_out_multi_margin_loss_out)); |
| 32264 | m.impl("multi_margin_loss_backward", |
| 32265 | TORCH_FN(wrapper_CPU__multi_margin_loss_backward)); |
| 32266 | m.impl("multi_margin_loss_backward.grad_input", |
| 32267 | TORCH_FN(wrapper_CPU_grad_input_multi_margin_loss_backward_out)); |
| 32268 | m.impl("multilabel_margin_loss_forward", |
| 32269 | TORCH_FN(wrapper_CPU__multilabel_margin_loss_forward)); |
| 32270 | m.impl("multilabel_margin_loss_forward.output", |
| 32271 | TORCH_FN(wrapper_CPU_output_multilabel_margin_loss_forward_out)); |
| 32272 | m.impl("multilabel_margin_loss_backward", |
| 32273 | TORCH_FN(wrapper_CPU__multilabel_margin_loss_backward)); |
| 32274 | m.impl("multilabel_margin_loss_backward.grad_input", |
| 32275 | TORCH_FN(wrapper_CPU_grad_input_multilabel_margin_loss_backward_out)); |
| 32276 | m.impl("nll_loss_forward", TORCH_FN(wrapper_CPU_nll_loss_forward)); |
| 32277 | m.impl("nll_loss_forward.output", TORCH_FN(wrapper_CPU_nll_loss_forward_out_output)); |
| 32278 | m.impl("nll_loss_backward", TORCH_FN(wrapper_CPU_nll_loss_backward)); |
| 32279 | m.impl("nll_loss_backward.grad_input", TORCH_FN(wrapper_CPU_nll_loss_backward_out_grad_input)); |
| 32280 | m.impl("nll_loss2d_forward", |
| 32281 | TORCH_FN(wrapper_CPU__nll_loss2d_forward)); |
| 32282 | m.impl("nll_loss2d_forward.output", |
| 32283 | TORCH_FN(wrapper_CPU_output_nll_loss2d_forward_out)); |
| 32284 | m.impl("nll_loss2d_backward", |
| 32285 | TORCH_FN(wrapper_CPU__nll_loss2d_backward)); |
| 32286 | m.impl("nll_loss2d_backward.grad_input", |
| 32287 | TORCH_FN(wrapper_CPU_grad_input_nll_loss2d_backward_out)); |
| 32288 | m.impl("smooth_l1_loss", TORCH_FN(wrapper_CPU_smooth_l1_loss)); |
| 32289 | m.impl("smooth_l1_loss.out", TORCH_FN(wrapper_CPU_smooth_l1_loss_out_out)); |
| 32290 | m.impl("smooth_l1_loss_backward.grad_input", |
| 32291 | TORCH_FN(wrapper_CPU_grad_input_smooth_l1_loss_backward_out)); |
| 32292 | m.impl("huber_loss", |
| 32293 | TORCH_FN(wrapper_CPU__huber_loss)); |
| 32294 | m.impl("huber_loss.out", |
| 32295 | TORCH_FN(wrapper_CPU_out_huber_loss_out)); |
| 32296 | m.impl("huber_loss_backward.out", |
| 32297 | TORCH_FN(wrapper_CPU_out_huber_loss_backward_out)); |
| 32298 | m.impl("elu", TORCH_FN(wrapper_CPU_elu)); |
| 32299 | m.impl("elu.out", TORCH_FN(wrapper_CPU_elu_out_out)); |
| 32300 | m.impl("elu_", TORCH_FN(wrapper_CPU_elu_)); |
| 32301 | m.impl("elu_backward", TORCH_FN(wrapper_CPU_elu_backward)); |
| 32302 | m.impl("elu_backward.grad_input", TORCH_FN(wrapper_CPU_elu_backward_out_grad_input)); |
| 32303 | m.impl("glu", TORCH_FN(wrapper_CPU_glu)); |
| 32304 | m.impl("glu.out", TORCH_FN(wrapper_CPU_glu_out_out)); |
| 32305 | m.impl("glu_backward", |
| 32306 | TORCH_FN(wrapper_CPU__glu_backward)); |
| 32307 | m.impl("glu_backward.grad_input", |
| 32308 | TORCH_FN(wrapper_CPU_grad_input_glu_backward_out)); |
| 32309 | m.impl("glu_jvp", |
| 32310 | TORCH_FN(wrapper_CPU__glu_jvp)); |
| 32311 | m.impl("glu_backward_jvp", |
| 32312 | TORCH_FN(wrapper_CPU__glu_backward_jvp)); |
| 32313 | m.impl("hardsigmoid", TORCH_FN(wrapper_CPU_hardsigmoid)); |
| 32314 | m.impl("hardsigmoid.out", TORCH_FN(wrapper_CPU_hardsigmoid_out_out)); |
| 32315 | m.impl("hardsigmoid_", TORCH_FN(wrapper_CPU_hardsigmoid_)); |
| 32316 | m.impl("hardsigmoid_backward", TORCH_FN(wrapper_CPU_hardsigmoid_backward)); |
| 32317 | m.impl("hardsigmoid_backward.grad_input", TORCH_FN(wrapper_CPU_hardsigmoid_backward_out_grad_input)); |
| 32318 | m.impl("hardtanh", |
| 32319 | TORCH_FN(wrapper_CPU__hardtanh)); |
| 32320 | m.impl("hardtanh.out", |
| 32321 | TORCH_FN(wrapper_CPU_out_hardtanh_out)); |
| 32322 | m.impl("hardtanh_", |
| 32323 | TORCH_FN(wrapper_CPU__hardtanh_)); |
| 32324 | m.impl("hardtanh_backward", |
| 32325 | TORCH_FN(wrapper_CPU__hardtanh_backward)); |
| 32326 | m.impl("hardtanh_backward.grad_input", |
| 32327 | TORCH_FN(wrapper_CPU_grad_input_hardtanh_backward_out)); |
| 32328 | m.impl("hardswish", |
| 32329 | TORCH_FN(wrapper_CPU__hardswish)); |
| 32330 | m.impl("hardswish.out", |
| 32331 | TORCH_FN(wrapper_CPU_out_hardswish_out)); |
| 32332 | m.impl("hardswish_", |
| 32333 | TORCH_FN(wrapper_CPU__hardswish_)); |
| 32334 | m.impl("hardswish_backward", |
| 32335 | TORCH_FN(wrapper_CPU__hardswish_backward)); |
| 32336 | m.impl("leaky_relu", TORCH_FN(wrapper_CPU_leaky_relu)); |
| 32337 | m.impl("leaky_relu.out", TORCH_FN(wrapper_CPU_leaky_relu_out_out)); |
| 32338 | m.impl("leaky_relu_", TORCH_FN(wrapper_CPU_leaky_relu_)); |
| 32339 | m.impl("leaky_relu_backward", TORCH_FN(wrapper_CPU_leaky_relu_backward)); |
| 32340 | m.impl("leaky_relu_backward.grad_input", TORCH_FN(wrapper_CPU_leaky_relu_backward_out_grad_input)); |
| 32341 | m.impl("log_sigmoid_forward", |
| 32342 | TORCH_FN(wrapper_CPU__log_sigmoid_forward)); |
| 32343 | m.impl("log_sigmoid_forward.output", |
| 32344 | TORCH_FN(wrapper_CPU_output_log_sigmoid_forward_out)); |
| 32345 | m.impl("log_sigmoid_backward", |
| 32346 | TORCH_FN(wrapper_CPU__log_sigmoid_backward)); |
| 32347 | m.impl("log_sigmoid_backward.grad_input", |
| 32348 | TORCH_FN(wrapper_CPU_grad_input_log_sigmoid_backward_out)); |
| 32349 | m.impl("rrelu_with_noise", |
| 32350 | TORCH_FN(wrapper_CPU__rrelu_with_noise)); |
| 32351 | m.impl("rrelu_with_noise.out", |
| 32352 | TORCH_FN(wrapper_CPU_out_rrelu_with_noise_out)); |
| 32353 | m.impl("rrelu_with_noise_", |
| 32354 | TORCH_FN(wrapper_CPU__rrelu_with_noise_)); |
| 32355 | m.impl("softplus", TORCH_FN(wrapper_CPU_softplus)); |
| 32356 | m.impl("softplus.out", TORCH_FN(wrapper_CPU_softplus_out_out)); |
| 32357 | m.impl("softplus_backward", TORCH_FN(wrapper_CPU_softplus_backward)); |
| 32358 | m.impl("softplus_backward.grad_input", TORCH_FN(wrapper_CPU_softplus_backward_out_grad_input)); |
| 32359 | m.impl("softshrink", TORCH_FN(wrapper_CPU_softshrink)); |
| 32360 | m.impl("softshrink.out", TORCH_FN(wrapper_CPU_softshrink_out_out)); |
| 32361 | m.impl("softshrink_backward", TORCH_FN(wrapper_CPU_softshrink_backward)); |
| 32362 | m.impl("softshrink_backward.grad_input", TORCH_FN(wrapper_CPU_softshrink_backward_out_grad_input)); |
| 32363 | m.impl("adaptive_avg_pool2d.out", |
| 32364 | TORCH_FN(wrapper_CPU_out_adaptive_avg_pool2d_out)); |
| 32365 | m.impl("_adaptive_avg_pool2d", |
| 32366 | TORCH_FN(wrapper_CPU___adaptive_avg_pool2d)); |
| 32367 | m.impl("_adaptive_avg_pool2d_backward", |
| 32368 | TORCH_FN(wrapper_CPU___adaptive_avg_pool2d_backward)); |
| 32369 | m.impl("adaptive_avg_pool3d.out", |
| 32370 | TORCH_FN(wrapper_CPU_out_adaptive_avg_pool3d_out)); |
| 32371 | m.impl("_adaptive_avg_pool3d", |
| 32372 | TORCH_FN(wrapper_CPU___adaptive_avg_pool3d)); |
| 32373 | m.impl("adaptive_avg_pool3d_backward.grad_input", |
| 32374 | TORCH_FN(wrapper_CPU_grad_input_adaptive_avg_pool3d_backward_out)); |
| 32375 | m.impl("_adaptive_avg_pool3d_backward", |
| 32376 | TORCH_FN(wrapper_CPU___adaptive_avg_pool3d_backward)); |
| 32377 | m.impl("adaptive_max_pool2d", TORCH_FN(wrapper_CPU_adaptive_max_pool2d)); |
| 32378 | m.impl("adaptive_max_pool2d.out", TORCH_FN(wrapper_CPU_adaptive_max_pool2d_out_out)); |
| 32379 | m.impl("adaptive_max_pool2d_backward", TORCH_FN(wrapper_CPU_adaptive_max_pool2d_backward)); |
| 32380 | m.impl("adaptive_max_pool2d_backward.grad_input", TORCH_FN(wrapper_CPU_adaptive_max_pool2d_backward_out_grad_input)); |
| 32381 | m.impl("adaptive_max_pool3d", TORCH_FN(wrapper_CPU_adaptive_max_pool3d)); |
| 32382 | m.impl("adaptive_max_pool3d.out", TORCH_FN(wrapper_CPU_adaptive_max_pool3d_out_out)); |
| 32383 | m.impl("adaptive_max_pool3d_backward", TORCH_FN(wrapper_CPU_adaptive_max_pool3d_backward)); |
| 32384 | m.impl("adaptive_max_pool3d_backward.grad_input", TORCH_FN(wrapper_CPU_adaptive_max_pool3d_backward_out_grad_input)); |
| 32385 | m.impl("avg_pool2d", TORCH_FN(wrapper_CPU_avg_pool2d)); |
| 32386 | m.impl("avg_pool2d.out", TORCH_FN(wrapper_CPU_avg_pool2d_out_out)); |
| 32387 | m.impl("avg_pool2d_backward", TORCH_FN(wrapper_CPU_avg_pool2d_backward)); |
| 32388 | m.impl("avg_pool2d_backward.grad_input", TORCH_FN(wrapper_CPU_avg_pool2d_backward_out_grad_input)); |
| 32389 | m.impl("avg_pool3d", TORCH_FN(wrapper_CPU_avg_pool3d)); |
| 32390 | m.impl("avg_pool3d.out", TORCH_FN(wrapper_CPU_avg_pool3d_out_out)); |
| 32391 | m.impl("avg_pool3d_backward", TORCH_FN(wrapper_CPU_avg_pool3d_backward)); |
| 32392 | m.impl("avg_pool3d_backward.grad_input", TORCH_FN(wrapper_CPU_avg_pool3d_backward_out_grad_input)); |
| 32393 | m.impl("fractional_max_pool2d", TORCH_FN(wrapper_CPU_fractional_max_pool2d)); |
| 32394 | m.impl("fractional_max_pool2d.output", TORCH_FN(wrapper_CPU_fractional_max_pool2d_out_output)); |
| 32395 | m.impl("fractional_max_pool2d_backward", TORCH_FN(wrapper_CPU_fractional_max_pool2d_backward)); |
| 32396 | m.impl("fractional_max_pool2d_backward.grad_input", TORCH_FN(wrapper_CPU_fractional_max_pool2d_backward_out_grad_input)); |
| 32397 | m.impl("fractional_max_pool3d", TORCH_FN(wrapper_CPU_fractional_max_pool3d)); |
| 32398 | m.impl("fractional_max_pool3d.output", TORCH_FN(wrapper_CPU_fractional_max_pool3d_out_output)); |
| 32399 | m.impl("fractional_max_pool3d_backward", |
| 32400 | TORCH_FN(wrapper_CPU__fractional_max_pool3d_backward)); |
| 32401 | m.impl("fractional_max_pool3d_backward.grad_input", |
| 32402 | TORCH_FN(wrapper_CPU_grad_input_fractional_max_pool3d_backward_out)); |
| 32403 | m.impl("max_pool2d_with_indices", TORCH_FN(wrapper_CPU_max_pool2d_with_indices)); |
| 32404 | m.impl("max_pool2d_with_indices.out", TORCH_FN(wrapper_CPU_max_pool2d_with_indices_out_out)); |
| 32405 | m.impl("max_pool2d_with_indices_backward", TORCH_FN(wrapper_CPU_max_pool2d_with_indices_backward)); |
| 32406 | m.impl("max_pool2d_with_indices_backward.grad_input", TORCH_FN(wrapper_CPU_max_pool2d_with_indices_backward_out_grad_input)); |
| 32407 | m.impl("max_pool3d_with_indices", |
| 32408 | TORCH_FN(wrapper_CPU__max_pool3d_with_indices)); |
| 32409 | m.impl("max_pool3d_with_indices.out", |
| 32410 | TORCH_FN(wrapper_CPU_out_max_pool3d_with_indices_out)); |
| 32411 | m.impl("max_pool3d_with_indices_backward", |
| 32412 | TORCH_FN(wrapper_CPU__max_pool3d_with_indices_backward)); |
| 32413 | m.impl("max_pool3d_with_indices_backward.grad_input", |
| 32414 | TORCH_FN(wrapper_CPU_grad_input_max_pool3d_with_indices_backward_out)); |
| 32415 | m.impl("max_unpool2d", |
| 32416 | TORCH_FN(wrapper_CPU__max_unpool2d)); |
| 32417 | m.impl("max_unpool2d.out", |
| 32418 | TORCH_FN(wrapper_CPU_out_max_unpool2d_out)); |
| 32419 | m.impl("max_unpool3d", |
| 32420 | TORCH_FN(wrapper_CPU__max_unpool3d)); |
| 32421 | m.impl("max_unpool3d.out", |
| 32422 | TORCH_FN(wrapper_CPU_out_max_unpool3d_out)); |
| 32423 | m.impl("reflection_pad1d", TORCH_FN(wrapper_CPU_reflection_pad1d)); |
| 32424 | m.impl("reflection_pad1d.out", TORCH_FN(wrapper_CPU_reflection_pad1d_out_out)); |
| 32425 | m.impl("reflection_pad1d_backward", TORCH_FN(wrapper_CPU_reflection_pad1d_backward)); |
| 32426 | m.impl("reflection_pad1d_backward.grad_input", TORCH_FN(wrapper_CPU_reflection_pad1d_backward_out_grad_input)); |
| 32427 | m.impl("reflection_pad2d", |
| 32428 | TORCH_FN(wrapper_CPU__reflection_pad2d)); |
| 32429 | m.impl("reflection_pad2d.out", |
| 32430 | TORCH_FN(wrapper_CPU_out_reflection_pad2d_out)); |
| 32431 | m.impl("reflection_pad2d_backward", |
| 32432 | TORCH_FN(wrapper_CPU__reflection_pad2d_backward)); |
| 32433 | m.impl("reflection_pad2d_backward.grad_input", |
| 32434 | TORCH_FN(wrapper_CPU_grad_input_reflection_pad2d_backward_out)); |
| 32435 | m.impl("reflection_pad3d", TORCH_FN(wrapper_CPU_reflection_pad3d)); |
| 32436 | m.impl("reflection_pad3d.out", TORCH_FN(wrapper_CPU_reflection_pad3d_out_out)); |
| 32437 | m.impl("reflection_pad3d_backward", TORCH_FN(wrapper_CPU_reflection_pad3d_backward)); |
| 32438 | m.impl("reflection_pad3d_backward.grad_input", TORCH_FN(wrapper_CPU_reflection_pad3d_backward_out_grad_input)); |
| 32439 | m.impl("replication_pad1d", TORCH_FN(wrapper_CPU_replication_pad1d)); |
| 32440 | m.impl("replication_pad1d.out", TORCH_FN(wrapper_CPU_replication_pad1d_out_out)); |
| 32441 | m.impl("replication_pad1d_backward", TORCH_FN(wrapper_CPU_replication_pad1d_backward)); |
| 32442 | m.impl("replication_pad1d_backward.grad_input", TORCH_FN(wrapper_CPU_replication_pad1d_backward_out_grad_input)); |
| 32443 | m.impl("replication_pad2d", TORCH_FN(wrapper_CPU_replication_pad2d)); |
| 32444 | m.impl("replication_pad2d.out", TORCH_FN(wrapper_CPU_replication_pad2d_out_out)); |
| 32445 | m.impl("replication_pad2d_backward", |
| 32446 | TORCH_FN(wrapper_CPU__replication_pad2d_backward)); |
| 32447 | m.impl("replication_pad2d_backward.grad_input", |
| 32448 | TORCH_FN(wrapper_CPU_grad_input_replication_pad2d_backward_out)); |
| 32449 | m.impl("replication_pad3d", TORCH_FN(wrapper_CPU_replication_pad3d)); |
| 32450 | m.impl("replication_pad3d.out", TORCH_FN(wrapper_CPU_replication_pad3d_out_out)); |
| 32451 | m.impl("replication_pad3d_backward", |
| 32452 | TORCH_FN(wrapper_CPU__replication_pad3d_backward)); |
| 32453 | m.impl("replication_pad3d_backward.grad_input", |
| 32454 | TORCH_FN(wrapper_CPU_grad_input_replication_pad3d_backward_out)); |
| 32455 | m.impl("upsample_linear1d", TORCH_FN(wrapper_CPU_upsample_linear1d)); |
| 32456 | m.impl("upsample_linear1d.out", TORCH_FN(wrapper_CPU_upsample_linear1d_out_out)); |
| 32457 | m.impl("upsample_linear1d_backward", TORCH_FN(wrapper_CPU_upsample_linear1d_backward)); |
| 32458 | m.impl("upsample_linear1d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_linear1d_backward_out_grad_input)); |
| 32459 | m.impl("upsample_bilinear2d", TORCH_FN(wrapper_CPU_upsample_bilinear2d)); |
| 32460 | m.impl("upsample_bilinear2d.out", TORCH_FN(wrapper_CPU_upsample_bilinear2d_out_out)); |
| 32461 | m.impl("upsample_bilinear2d_backward", TORCH_FN(wrapper_CPU_upsample_bilinear2d_backward)); |
| 32462 | m.impl("upsample_bilinear2d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_bilinear2d_backward_out_grad_input)); |
| 32463 | m.impl("_upsample_bilinear2d_aa", TORCH_FN(wrapper_CPU__upsample_bilinear2d_aa)); |
| 32464 | m.impl("_upsample_bilinear2d_aa.out", TORCH_FN(wrapper_CPU__upsample_bilinear2d_aa_out_out)); |
| 32465 | m.impl("_upsample_bilinear2d_aa_backward", TORCH_FN(wrapper_CPU__upsample_bilinear2d_aa_backward)); |
| 32466 | m.impl("_upsample_bilinear2d_aa_backward.grad_input", TORCH_FN(wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input)); |
| 32467 | m.impl("upsample_bicubic2d", TORCH_FN(wrapper_CPU_upsample_bicubic2d)); |
| 32468 | m.impl("upsample_bicubic2d.out", TORCH_FN(wrapper_CPU_upsample_bicubic2d_out_out)); |
| 32469 | m.impl("upsample_bicubic2d_backward", TORCH_FN(wrapper_CPU_upsample_bicubic2d_backward)); |
| 32470 | m.impl("upsample_bicubic2d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_bicubic2d_backward_out_grad_input)); |
| 32471 | m.impl("_upsample_bicubic2d_aa", TORCH_FN(wrapper_CPU__upsample_bicubic2d_aa)); |
| 32472 | m.impl("_upsample_bicubic2d_aa.out", TORCH_FN(wrapper_CPU__upsample_bicubic2d_aa_out_out)); |
| 32473 | m.impl("_upsample_bicubic2d_aa_backward", TORCH_FN(wrapper_CPU__upsample_bicubic2d_aa_backward)); |
| 32474 | m.impl("_upsample_bicubic2d_aa_backward.grad_input", TORCH_FN(wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input)); |
| 32475 | m.impl("upsample_trilinear3d", TORCH_FN(wrapper_CPU_upsample_trilinear3d)); |
| 32476 | m.impl("upsample_trilinear3d.out", TORCH_FN(wrapper_CPU_upsample_trilinear3d_out_out)); |
| 32477 | m.impl("upsample_trilinear3d_backward", TORCH_FN(wrapper_CPU_upsample_trilinear3d_backward)); |
| 32478 | m.impl("upsample_trilinear3d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_trilinear3d_backward_out_grad_input)); |
| 32479 | m.impl("upsample_nearest1d", TORCH_FN(wrapper_CPU_upsample_nearest1d)); |
| 32480 | m.impl("upsample_nearest1d.out", TORCH_FN(wrapper_CPU_upsample_nearest1d_out_out)); |
| 32481 | m.impl("_upsample_nearest_exact1d", TORCH_FN(wrapper_CPU__upsample_nearest_exact1d)); |
| 32482 | m.impl("_upsample_nearest_exact1d.out", TORCH_FN(wrapper_CPU__upsample_nearest_exact1d_out_out)); |
| 32483 | m.impl("upsample_nearest1d_backward", TORCH_FN(wrapper_CPU_upsample_nearest1d_backward)); |
| 32484 | m.impl("upsample_nearest1d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_nearest1d_backward_out_grad_input)); |
| 32485 | m.impl("_upsample_nearest_exact1d_backward", TORCH_FN(wrapper_CPU__upsample_nearest_exact1d_backward)); |
| 32486 | m.impl("_upsample_nearest_exact1d_backward.grad_input", TORCH_FN(wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input)); |
| 32487 | m.impl("upsample_nearest2d", TORCH_FN(wrapper_CPU_upsample_nearest2d)); |
| 32488 | m.impl("upsample_nearest2d.out", TORCH_FN(wrapper_CPU_upsample_nearest2d_out_out)); |
| 32489 | m.impl("_upsample_nearest_exact2d", TORCH_FN(wrapper_CPU__upsample_nearest_exact2d)); |
| 32490 | m.impl("_upsample_nearest_exact2d.out", TORCH_FN(wrapper_CPU__upsample_nearest_exact2d_out_out)); |
| 32491 | m.impl("upsample_nearest2d_backward", TORCH_FN(wrapper_CPU_upsample_nearest2d_backward)); |
| 32492 | m.impl("upsample_nearest2d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_nearest2d_backward_out_grad_input)); |
| 32493 | m.impl("_upsample_nearest_exact2d_backward", TORCH_FN(wrapper_CPU__upsample_nearest_exact2d_backward)); |
| 32494 | m.impl("_upsample_nearest_exact2d_backward.grad_input", TORCH_FN(wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input)); |
| 32495 | m.impl("upsample_nearest3d", TORCH_FN(wrapper_CPU_upsample_nearest3d)); |
| 32496 | m.impl("upsample_nearest3d.out", TORCH_FN(wrapper_CPU_upsample_nearest3d_out_out)); |
| 32497 | m.impl("_upsample_nearest_exact3d", TORCH_FN(wrapper_CPU__upsample_nearest_exact3d)); |
| 32498 | m.impl("_upsample_nearest_exact3d.out", TORCH_FN(wrapper_CPU__upsample_nearest_exact3d_out_out)); |
| 32499 | m.impl("upsample_nearest3d_backward", TORCH_FN(wrapper_CPU_upsample_nearest3d_backward)); |
| 32500 | m.impl("upsample_nearest3d_backward.grad_input", TORCH_FN(wrapper_CPU_upsample_nearest3d_backward_out_grad_input)); |
| 32501 | m.impl("_upsample_nearest_exact3d_backward", TORCH_FN(wrapper_CPU__upsample_nearest_exact3d_backward)); |
| 32502 | m.impl("_upsample_nearest_exact3d_backward.grad_input", TORCH_FN(wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input)); |
| 32503 | m.impl("sigmoid_backward", TORCH_FN(wrapper_CPU_sigmoid_backward)); |
| 32504 | m.impl("sigmoid_backward.grad_input", TORCH_FN(wrapper_CPU_sigmoid_backward_out_grad_input)); |
| 32505 | m.impl("logit_backward", TORCH_FN(wrapper_CPU_logit_backward)); |
| 32506 | m.impl("logit_backward.grad_input", TORCH_FN(wrapper_CPU_logit_backward_out_grad_input)); |
| 32507 | m.impl("tanh_backward", TORCH_FN(wrapper_CPU_tanh_backward)); |
| 32508 | m.impl("tanh_backward.grad_input", TORCH_FN(wrapper_CPU_tanh_backward_out_grad_input)); |
| 32509 | m.impl("slow_conv_transpose2d", TORCH_FN(wrapper_CPU_slow_conv_transpose2d)); |
| 32510 | m.impl("slow_conv_transpose2d.out", TORCH_FN(wrapper_CPU_slow_conv_transpose2d_out_out)); |
| 32511 | m.impl("slow_conv_transpose3d", |
| 32512 | TORCH_FN(wrapper_CPU__slow_conv_transpose3d)); |
| 32513 | m.impl("slow_conv_transpose3d.out", |
| 32514 | TORCH_FN(wrapper_CPU_out_slow_conv_transpose3d_out)); |
| 32515 | m.impl("_slow_conv2d_forward", |
| 32516 | TORCH_FN(wrapper_CPU___slow_conv2d_forward)); |
| 32517 | m.impl("_slow_conv2d_forward.output", |
| 32518 | TORCH_FN(wrapper_CPU_output__slow_conv2d_forward_out)); |
| 32519 | m.impl("_slow_conv2d_backward.grad_input", |
| 32520 | TORCH_FN(wrapper_CPU_grad_input__slow_conv2d_backward_out)); |
| 32521 | m.impl("_slow_conv2d_backward.output_mask", |
| 32522 | TORCH_FN(wrapper_CPU_output_mask__slow_conv2d_backward)); |
| 32523 | m.impl("slow_conv3d_forward", |
| 32524 | TORCH_FN(wrapper_CPU__slow_conv3d_forward)); |
| 32525 | m.impl("slow_conv3d_forward.output", |
| 32526 | TORCH_FN(wrapper_CPU_output_slow_conv3d_forward_out)); |
| 32527 | m.impl("slow_conv_dilated2d", |
| 32528 | TORCH_FN(wrapper_CPU__slow_conv_dilated2d)); |
| 32529 | m.impl("slow_conv_dilated3d", |
| 32530 | TORCH_FN(wrapper_CPU__slow_conv_dilated3d)); |
| 32531 | m.impl("col2im", |
| 32532 | TORCH_FN(wrapper_CPU__col2im)); |
| 32533 | m.impl("col2im.out", |
| 32534 | TORCH_FN(wrapper_CPU_out_col2im_out)); |
| 32535 | m.impl("im2col", |
| 32536 | TORCH_FN(wrapper_CPU__im2col)); |
| 32537 | m.impl("im2col.out", |
| 32538 | TORCH_FN(wrapper_CPU_out_im2col_out)); |
| 32539 | m.impl("isposinf", TORCH_FN(wrapper_CPU_isposinf)); |
| 32540 | m.impl("isposinf.out", TORCH_FN(wrapper_CPU_isposinf_out_out)); |
| 32541 | m.impl("isneginf", TORCH_FN(wrapper_CPU_isneginf)); |
| 32542 | m.impl("isneginf.out", TORCH_FN(wrapper_CPU_isneginf_out_out)); |
| 32543 | m.impl("special_entr", TORCH_FN(wrapper_CPU_special_entr)); |
| 32544 | m.impl("special_entr.out", TORCH_FN(wrapper_CPU_special_entr_out_out)); |
| 32545 | m.impl("special_ndtri", TORCH_FN(wrapper_CPU_special_ndtri)); |
| 32546 | m.impl("special_ndtri.out", TORCH_FN(wrapper_CPU_special_ndtri_out_out)); |
| 32547 | m.impl("special_log_ndtr", TORCH_FN(wrapper_CPU_special_log_ndtr)); |
| 32548 | m.impl("special_log_ndtr.out", TORCH_FN(wrapper_CPU_special_log_ndtr_out_out)); |
| 32549 | m.impl("special_erfcx", TORCH_FN(wrapper_CPU_special_erfcx)); |
| 32550 | m.impl("special_erfcx.out", TORCH_FN(wrapper_CPU_special_erfcx_out_out)); |
| 32551 | m.impl("special_xlog1py", TORCH_FN(wrapper_CPU_special_xlog1py)); |
| 32552 | m.impl("special_xlog1py.out", TORCH_FN(wrapper_CPU_special_xlog1py_out_out)); |
| 32553 | m.impl("special_zeta", TORCH_FN(wrapper_CPU_special_zeta)); |
| 32554 | m.impl("special_zeta.out", TORCH_FN(wrapper_CPU_special_zeta_out_out)); |
| 32555 | m.impl("special_i0e", TORCH_FN(wrapper_CPU_special_i0e)); |
| 32556 | m.impl("special_i0e.out", TORCH_FN(wrapper_CPU_special_i0e_out_out)); |
| 32557 | m.impl("special_i1", TORCH_FN(wrapper_CPU_special_i1)); |
| 32558 | m.impl("special_i1.out", TORCH_FN(wrapper_CPU_special_i1_out_out)); |
| 32559 | m.impl("special_i1e", TORCH_FN(wrapper_CPU_special_i1e)); |
| 32560 | m.impl("special_i1e.out", TORCH_FN(wrapper_CPU_special_i1e_out_out)); |
| 32561 | m.impl("linalg_cholesky_ex", TORCH_FN(wrapper_CPU_linalg_cholesky_ex)); |
| 32562 | m.impl("linalg_cholesky_ex.L", TORCH_FN(wrapper_CPU_linalg_cholesky_ex_out_L)); |
| 32563 | m.impl("linalg_cross", TORCH_FN(wrapper_CPU_linalg_cross)); |
| 32564 | m.impl("linalg_cross.out", TORCH_FN(wrapper_CPU_linalg_cross_out_out)); |
| 32565 | m.impl("linalg_lu_factor_ex", TORCH_FN(wrapper_CPU_linalg_lu_factor_ex)); |
| 32566 | m.impl("linalg_lu_factor_ex.out", TORCH_FN(wrapper_CPU_linalg_lu_factor_ex_out_out)); |
| 32567 | m.impl("linalg_lu", TORCH_FN(wrapper_CPU_linalg_lu)); |
| 32568 | m.impl("linalg_lu.out", TORCH_FN(wrapper_CPU_linalg_lu_out_out)); |
| 32569 | m.impl("linalg_lu_solve", TORCH_FN(wrapper_CPU_linalg_lu_solve)); |
| 32570 | m.impl("linalg_lu_solve.out", TORCH_FN(wrapper_CPU_linalg_lu_solve_out_out)); |
| 32571 | m.impl("_linalg_det", TORCH_FN(wrapper_CPU__linalg_det)); |
| 32572 | m.impl("_linalg_det.result", TORCH_FN(wrapper_CPU__linalg_det_out_result)); |
| 32573 | m.impl("linalg_ldl_factor_ex", TORCH_FN(wrapper_CPU_linalg_ldl_factor_ex)); |
| 32574 | m.impl("linalg_ldl_factor_ex.out", TORCH_FN(wrapper_CPU_linalg_ldl_factor_ex_out_out)); |
| 32575 | m.impl("linalg_ldl_solve", TORCH_FN(wrapper_CPU_linalg_ldl_solve)); |
| 32576 | m.impl("linalg_ldl_solve.out", TORCH_FN(wrapper_CPU_linalg_ldl_solve_out_out)); |
| 32577 | m.impl("linalg_lstsq.out", |
| 32578 | TORCH_FN(wrapper_CPU_out_linalg_lstsq_out)); |
| 32579 | m.impl("linalg_matrix_exp", |
| 32580 | TORCH_FN(wrapper_CPU__linalg_matrix_exp)); |
| 32581 | m.impl("_linalg_slogdet", TORCH_FN(wrapper_CPU__linalg_slogdet)); |
| 32582 | m.impl("_linalg_slogdet.sign", TORCH_FN(wrapper_CPU__linalg_slogdet_out_sign)); |
| 32583 | m.impl("linalg_eig", |
| 32584 | TORCH_FN(wrapper_CPU__linalg_eig)); |
| 32585 | m.impl("linalg_eig.out", |
| 32586 | TORCH_FN(wrapper_CPU_out_linalg_eig_out)); |
| 32587 | m.impl("_linalg_eigh", TORCH_FN(wrapper_CPU__linalg_eigh)); |
| 32588 | m.impl("_linalg_eigh.eigenvalues", TORCH_FN(wrapper_CPU__linalg_eigh_out_eigenvalues)); |
| 32589 | m.impl("linalg_householder_product", |
| 32590 | TORCH_FN(wrapper_CPU__linalg_householder_product)); |
| 32591 | m.impl("linalg_householder_product.out", |
| 32592 | TORCH_FN(wrapper_CPU_out_linalg_householder_product_out)); |
| 32593 | m.impl("linalg_inv_ex", TORCH_FN(wrapper_CPU_linalg_inv_ex)); |
| 32594 | m.impl("linalg_inv_ex.inverse", TORCH_FN(wrapper_CPU_linalg_inv_ex_out_inverse)); |
| 32595 | m.impl("linalg_vector_norm", TORCH_FN(wrapper_CPU_linalg_vector_norm)); |
| 32596 | m.impl("linalg_vector_norm.out", TORCH_FN(wrapper_CPU_linalg_vector_norm_out_out)); |
| 32597 | m.impl("_linalg_svd", TORCH_FN(wrapper_CPU__linalg_svd)); |
| 32598 | m.impl("_linalg_svd.U", TORCH_FN(wrapper_CPU__linalg_svd_out_U)); |
| 32599 | m.impl("_linalg_solve_ex", TORCH_FN(wrapper_CPU__linalg_solve_ex)); |
| 32600 | m.impl("_linalg_solve_ex.result", TORCH_FN(wrapper_CPU__linalg_solve_ex_out_result)); |
| 32601 | m.impl("linalg_qr", TORCH_FN(wrapper_CPU_linalg_qr)); |
| 32602 | m.impl("linalg_qr.out", TORCH_FN(wrapper_CPU_linalg_qr_out_out)); |
| 32603 | m.impl("_test_optional_intlist", |
| 32604 | TORCH_FN(wrapper_CPU___test_optional_intlist)); |
| 32605 | m.impl("_test_optional_filled_intlist", |
| 32606 | TORCH_FN(wrapper_CPU___test_optional_filled_intlist)); |
| 32607 | m.impl("_test_optional_floatlist", |
| 32608 | TORCH_FN(wrapper_CPU___test_optional_floatlist)); |
| 32609 | m.impl("segment_reduce", |
| 32610 | TORCH_FN(wrapper_CPU__segment_reduce)); |
| 32611 | m.impl("_segment_reduce_backward", |
| 32612 | TORCH_FN(wrapper_CPU___segment_reduce_backward)); |
| 32613 | m.impl("_transformer_encoder_layer_fwd", |
| 32614 | TORCH_FN(wrapper_CPU___transformer_encoder_layer_fwd)); |
| 32615 | m.impl("_native_multi_head_attention", |
| 32616 | TORCH_FN(wrapper_CPU___native_multi_head_attention)); |
| 32617 | m.impl("_fused_sdp_choice", |
| 32618 | TORCH_FN(wrapper_CPU___fused_sdp_choice)); |
| 32619 | m.impl("special_airy_ai", TORCH_FN(wrapper_CPU_special_airy_ai)); |
| 32620 | m.impl("special_airy_ai.out", TORCH_FN(wrapper_CPU_special_airy_ai_out_out)); |
| 32621 | m.impl("_transformer_decoder_only_layer_fwd", |
| 32622 | TORCH_FN(wrapper_CPU___transformer_decoder_only_layer_fwd)); |
| 32623 | m.impl("_native_decoder_only_multi_head_attention", |
| 32624 | TORCH_FN(wrapper_CPU___native_decoder_only_multi_head_attention)); |
| 32625 | m.impl("special_bessel_j0", TORCH_FN(wrapper_CPU_special_bessel_j0)); |
| 32626 | m.impl("special_bessel_j0.out", TORCH_FN(wrapper_CPU_special_bessel_j0_out_out)); |
| 32627 | m.impl("special_bessel_j1", TORCH_FN(wrapper_CPU_special_bessel_j1)); |
| 32628 | m.impl("special_bessel_j1.out", TORCH_FN(wrapper_CPU_special_bessel_j1_out_out)); |
| 32629 | m.impl("special_bessel_y0", TORCH_FN(wrapper_CPU_special_bessel_y0)); |
| 32630 | m.impl("special_bessel_y0.out", TORCH_FN(wrapper_CPU_special_bessel_y0_out_out)); |
| 32631 | m.impl("special_bessel_y1", TORCH_FN(wrapper_CPU_special_bessel_y1)); |
| 32632 | m.impl("special_bessel_y1.out", TORCH_FN(wrapper_CPU_special_bessel_y1_out_out)); |
| 32633 | m.impl("special_chebyshev_polynomial_t", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_t)); |
| 32634 | m.impl("special_chebyshev_polynomial_t.out", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_t_out_out)); |
| 32635 | m.impl("special_chebyshev_polynomial_u", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_u)); |
| 32636 | m.impl("special_chebyshev_polynomial_u.out", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_u_out_out)); |
| 32637 | m.impl("special_chebyshev_polynomial_v", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_v)); |
| 32638 | m.impl("special_chebyshev_polynomial_v.out", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_v_out_out)); |
| 32639 | m.impl("special_chebyshev_polynomial_w", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_w)); |
| 32640 | m.impl("special_chebyshev_polynomial_w.out", TORCH_FN(wrapper_CPU_special_chebyshev_polynomial_w_out_out)); |
| 32641 | m.impl("special_hermite_polynomial_h", TORCH_FN(wrapper_CPU_special_hermite_polynomial_h)); |
| 32642 | m.impl("special_hermite_polynomial_h.out", TORCH_FN(wrapper_CPU_special_hermite_polynomial_h_out_out)); |
| 32643 | m.impl("special_hermite_polynomial_he", TORCH_FN(wrapper_CPU_special_hermite_polynomial_he)); |
| 32644 | m.impl("special_hermite_polynomial_he.out", TORCH_FN(wrapper_CPU_special_hermite_polynomial_he_out_out)); |
| 32645 | m.impl("special_laguerre_polynomial_l", TORCH_FN(wrapper_CPU_special_laguerre_polynomial_l)); |
| 32646 | m.impl("special_laguerre_polynomial_l.out", TORCH_FN(wrapper_CPU_special_laguerre_polynomial_l_out_out)); |
| 32647 | m.impl("special_legendre_polynomial_p", TORCH_FN(wrapper_CPU_special_legendre_polynomial_p)); |
| 32648 | m.impl("special_legendre_polynomial_p.out", TORCH_FN(wrapper_CPU_special_legendre_polynomial_p_out_out)); |
| 32649 | m.impl("special_modified_bessel_i0", TORCH_FN(wrapper_CPU_special_modified_bessel_i0)); |
| 32650 | m.impl("special_modified_bessel_i0.out", TORCH_FN(wrapper_CPU_special_modified_bessel_i0_out_out)); |
| 32651 | m.impl("special_modified_bessel_i1", TORCH_FN(wrapper_CPU_special_modified_bessel_i1)); |
| 32652 | m.impl("special_modified_bessel_i1.out", TORCH_FN(wrapper_CPU_special_modified_bessel_i1_out_out)); |
| 32653 | m.impl("special_modified_bessel_k0", TORCH_FN(wrapper_CPU_special_modified_bessel_k0)); |
| 32654 | m.impl("special_modified_bessel_k0.out", TORCH_FN(wrapper_CPU_special_modified_bessel_k0_out_out)); |
| 32655 | m.impl("special_modified_bessel_k1", TORCH_FN(wrapper_CPU_special_modified_bessel_k1)); |
| 32656 | m.impl("special_modified_bessel_k1.out", TORCH_FN(wrapper_CPU_special_modified_bessel_k1_out_out)); |
| 32657 | m.impl("special_scaled_modified_bessel_k0", TORCH_FN(wrapper_CPU_special_scaled_modified_bessel_k0)); |
| 32658 | m.impl("special_scaled_modified_bessel_k0.out", TORCH_FN(wrapper_CPU_special_scaled_modified_bessel_k0_out_out)); |
| 32659 | m.impl("special_scaled_modified_bessel_k1", TORCH_FN(wrapper_CPU_special_scaled_modified_bessel_k1)); |
| 32660 | m.impl("special_scaled_modified_bessel_k1.out", TORCH_FN(wrapper_CPU_special_scaled_modified_bessel_k1_out_out)); |
| 32661 | m.impl("special_shifted_chebyshev_polynomial_t", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_t)); |
| 32662 | m.impl("special_shifted_chebyshev_polynomial_t.out", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_t_out_out)); |
| 32663 | m.impl("special_shifted_chebyshev_polynomial_u", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_u)); |
| 32664 | m.impl("special_shifted_chebyshev_polynomial_u.out", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_u_out_out)); |
| 32665 | m.impl("special_shifted_chebyshev_polynomial_v", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_v)); |
| 32666 | m.impl("special_shifted_chebyshev_polynomial_v.out", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_v_out_out)); |
| 32667 | m.impl("special_shifted_chebyshev_polynomial_w", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_w)); |
| 32668 | m.impl("special_shifted_chebyshev_polynomial_w.out", TORCH_FN(wrapper_CPU_special_shifted_chebyshev_polynomial_w_out_out)); |
| 32669 | m.impl("special_spherical_bessel_j0", TORCH_FN(wrapper_CPU_special_spherical_bessel_j0)); |
| 32670 | m.impl("special_spherical_bessel_j0.out", TORCH_FN(wrapper_CPU_special_spherical_bessel_j0_out_out)); |
| 32671 | m.impl("_foobar", |
| 32672 | TORCH_FN(wrapper_CPU___foobar)); |
| 32673 | }; |
| 32674 | } // anonymous namespace |
| 32675 | namespace cpu { |
| 32676 | void _assert_async(const at::Tensor & self) { |
| 32677 | return wrapper_CPU___assert_async(self); |
| 32678 | } |
| 32679 | ::std::tuple<at::Tensor,at::Tensor> native_dropout(const at::Tensor & input, double p, c10::optional<bool> train) { |
| 32680 | return wrapper_CPU__native_dropout(input, p, train); |
| 32681 | } |
| 32682 | at::Tensor native_dropout_backward(const at::Tensor & grad_output, const at::Tensor & mask, double scale) { |
| 32683 | return wrapper_CPU__native_dropout_backward(grad_output, mask, scale); |
| 32684 | } |
| 32685 | at::Tensor & abs_out(at::Tensor & out, const at::Tensor & self) { |
| 32686 | return wrapper_CPU_out_abs_out(self, out); |
| 32687 | } |
| 32688 | at::Tensor & abs_outf(const at::Tensor & self, at::Tensor & out) { |
| 32689 | return wrapper_CPU_out_abs_out(self, out); |
| 32690 | } |
| 32691 | at::Tensor angle(const at::Tensor & self) { |
| 32692 | return wrapper_CPU__angle(self); |
| 32693 | } |
| 32694 | at::Tensor & angle_out(at::Tensor & out, const at::Tensor & self) { |
| 32695 | return wrapper_CPU_out_angle_out(self, out); |
| 32696 | } |
| 32697 | at::Tensor & angle_outf(const at::Tensor & self, at::Tensor & out) { |
| 32698 | return wrapper_CPU_out_angle_out(self, out); |
| 32699 | } |
| 32700 | at::Tensor view_as_real(const at::Tensor & self) { |
| 32701 | return wrapper_CPU__view_as_real(self); |
| 32702 | } |
| 32703 | at::Tensor view_as_complex(const at::Tensor & self) { |
| 32704 | return wrapper_CPU__view_as_complex(self); |
| 32705 | } |
| 32706 | at::Tensor sgn(const at::Tensor & self) { |
| 32707 | return wrapper_CPU_sgn(self); |
| 32708 | } |
| 32709 | at::Tensor & sgn_out(at::Tensor & out, const at::Tensor & self) { |
| 32710 | return wrapper_CPU_sgn_out_out(self, out); |
| 32711 | } |
| 32712 | at::Tensor & sgn_outf(const at::Tensor & self, at::Tensor & out) { |
| 32713 | return wrapper_CPU_sgn_out_out(self, out); |
| 32714 | } |
| 32715 | at::Tensor & sgn_(at::Tensor & self) { |
| 32716 | return wrapper_CPU_sgn_(self); |
| 32717 | } |
| 32718 | at::Tensor & conj_physical_out(at::Tensor & out, const at::Tensor & self) { |
| 32719 | return wrapper_CPU_out_conj_physical_out(self, out); |
| 32720 | } |
| 32721 | at::Tensor & conj_physical_outf(const at::Tensor & self, at::Tensor & out) { |
| 32722 | return wrapper_CPU_out_conj_physical_out(self, out); |
| 32723 | } |
| 32724 | at::Tensor acos(const at::Tensor & self) { |
| 32725 | return wrapper_CPU_acos(self); |
| 32726 | } |
| 32727 | at::Tensor & acos_out(at::Tensor & out, const at::Tensor & self) { |
| 32728 | return wrapper_CPU_acos_out_out(self, out); |
| 32729 | } |
| 32730 | at::Tensor & acos_outf(const at::Tensor & self, at::Tensor & out) { |
| 32731 | return wrapper_CPU_acos_out_out(self, out); |
| 32732 | } |
| 32733 | at::Tensor & acos_(at::Tensor & self) { |
| 32734 | return wrapper_CPU_acos_(self); |
| 32735 | } |
| 32736 | at::Tensor add(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32737 | return wrapper_CPU_add_Tensor(self, other, alpha); |
| 32738 | } |
| 32739 | at::Tensor & add_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32740 | return wrapper_CPU_add_out_out(self, other, alpha, out); |
| 32741 | } |
| 32742 | at::Tensor & add_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 32743 | return wrapper_CPU_add_out_out(self, other, alpha, out); |
| 32744 | } |
| 32745 | at::Tensor & add_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32746 | return wrapper_CPU_add__Tensor(self, other, alpha); |
| 32747 | } |
| 32748 | at::Tensor _add_relu(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32749 | return wrapper_CPU_Tensor__add_relu(self, other, alpha); |
| 32750 | } |
| 32751 | at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32752 | return wrapper_CPU_out__add_relu_out(self, other, alpha, out); |
| 32753 | } |
| 32754 | at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 32755 | return wrapper_CPU_out__add_relu_out(self, other, alpha, out); |
| 32756 | } |
| 32757 | at::Tensor & _add_relu_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 32758 | return wrapper_CPU_Tensor__add_relu_(self, other, alpha); |
| 32759 | } |
| 32760 | at::Tensor _add_relu(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 32761 | return wrapper_CPU_Scalar__add_relu(self, other, alpha); |
| 32762 | } |
| 32763 | at::Tensor & _add_relu_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 32764 | return wrapper_CPU_Scalar__add_relu_(self, other, alpha); |
| 32765 | } |
| 32766 | at::Tensor addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32767 | return wrapper_CPU_addmv(self, mat, vec, beta, alpha); |
| 32768 | } |
| 32769 | at::Tensor & addmv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32770 | return wrapper_CPU_addmv_out_out(self, mat, vec, beta, alpha, out); |
| 32771 | } |
| 32772 | at::Tensor & addmv_outf(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 32773 | return wrapper_CPU_addmv_out_out(self, mat, vec, beta, alpha, out); |
| 32774 | } |
| 32775 | at::Tensor & addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32776 | return wrapper_CPU_addmv_(self, mat, vec, beta, alpha); |
| 32777 | } |
| 32778 | at::Tensor addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32779 | return wrapper_CPU__addr(self, vec1, vec2, beta, alpha); |
| 32780 | } |
| 32781 | at::Tensor & addr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32782 | return wrapper_CPU_out_addr_out(self, vec1, vec2, beta, alpha, out); |
| 32783 | } |
| 32784 | at::Tensor & addr_outf(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 32785 | return wrapper_CPU_out_addr_out(self, vec1, vec2, beta, alpha, out); |
| 32786 | } |
| 32787 | at::Tensor all(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 32788 | return wrapper_CPU_all_dim(self, dim, keepdim); |
| 32789 | } |
| 32790 | at::Tensor & all_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 32791 | return wrapper_CPU_all_out_out(self, dim, keepdim, out); |
| 32792 | } |
| 32793 | at::Tensor & all_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 32794 | return wrapper_CPU_all_out_out(self, dim, keepdim, out); |
| 32795 | } |
| 32796 | at::Tensor any(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 32797 | return wrapper_CPU_any_dim(self, dim, keepdim); |
| 32798 | } |
| 32799 | at::Tensor & any_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 32800 | return wrapper_CPU_any_out_out(self, dim, keepdim, out); |
| 32801 | } |
| 32802 | at::Tensor & any_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 32803 | return wrapper_CPU_any_out_out(self, dim, keepdim, out); |
| 32804 | } |
| 32805 | at::Tensor & arange_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) { |
| 32806 | return wrapper_CPU_start_out_arange_out(start, end, step, out); |
| 32807 | } |
| 32808 | at::Tensor & arange_outf(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 32809 | return wrapper_CPU_start_out_arange_out(start, end, step, out); |
| 32810 | } |
| 32811 | at::Tensor argmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 32812 | return wrapper_CPU_argmax(self, dim, keepdim); |
| 32813 | } |
| 32814 | at::Tensor & argmax_out(at::Tensor & out, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 32815 | return wrapper_CPU_argmax_out_out(self, dim, keepdim, out); |
| 32816 | } |
| 32817 | at::Tensor & argmax_outf(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 32818 | return wrapper_CPU_argmax_out_out(self, dim, keepdim, out); |
| 32819 | } |
| 32820 | at::Tensor argmin(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 32821 | return wrapper_CPU_argmin(self, dim, keepdim); |
| 32822 | } |
| 32823 | at::Tensor & argmin_out(at::Tensor & out, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 32824 | return wrapper_CPU_argmin_out_out(self, dim, keepdim, out); |
| 32825 | } |
| 32826 | at::Tensor & argmin_outf(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 32827 | return wrapper_CPU_argmin_out_out(self, dim, keepdim, out); |
| 32828 | } |
| 32829 | at::Tensor acosh(const at::Tensor & self) { |
| 32830 | return wrapper_CPU_acosh(self); |
| 32831 | } |
| 32832 | at::Tensor & acosh_out(at::Tensor & out, const at::Tensor & self) { |
| 32833 | return wrapper_CPU_acosh_out_out(self, out); |
| 32834 | } |
| 32835 | at::Tensor & acosh_outf(const at::Tensor & self, at::Tensor & out) { |
| 32836 | return wrapper_CPU_acosh_out_out(self, out); |
| 32837 | } |
| 32838 | at::Tensor & acosh_(at::Tensor & self) { |
| 32839 | return wrapper_CPU_acosh_(self); |
| 32840 | } |
| 32841 | at::Tensor asinh(const at::Tensor & self) { |
| 32842 | return wrapper_CPU_asinh(self); |
| 32843 | } |
| 32844 | at::Tensor & asinh_out(at::Tensor & out, const at::Tensor & self) { |
| 32845 | return wrapper_CPU_asinh_out_out(self, out); |
| 32846 | } |
| 32847 | at::Tensor & asinh_outf(const at::Tensor & self, at::Tensor & out) { |
| 32848 | return wrapper_CPU_asinh_out_out(self, out); |
| 32849 | } |
| 32850 | at::Tensor & asinh_(at::Tensor & self) { |
| 32851 | return wrapper_CPU_asinh_(self); |
| 32852 | } |
| 32853 | at::Tensor atanh(const at::Tensor & self) { |
| 32854 | return wrapper_CPU_atanh(self); |
| 32855 | } |
| 32856 | at::Tensor & atanh_out(at::Tensor & out, const at::Tensor & self) { |
| 32857 | return wrapper_CPU_atanh_out_out(self, out); |
| 32858 | } |
| 32859 | at::Tensor & atanh_outf(const at::Tensor & self, at::Tensor & out) { |
| 32860 | return wrapper_CPU_atanh_out_out(self, out); |
| 32861 | } |
| 32862 | at::Tensor & atanh_(at::Tensor & self) { |
| 32863 | return wrapper_CPU_atanh_(self); |
| 32864 | } |
| 32865 | at::Tensor as_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<int64_t> storage_offset) { |
| 32866 | return wrapper_CPU__as_strided(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); |
| 32867 | } |
| 32868 | at::Tensor as_strided_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 32869 | return wrapper_CPU__as_strided(self, size, stride, storage_offset); |
| 32870 | } |
| 32871 | at::Tensor asin(const at::Tensor & self) { |
| 32872 | return wrapper_CPU_asin(self); |
| 32873 | } |
| 32874 | at::Tensor & asin_out(at::Tensor & out, const at::Tensor & self) { |
| 32875 | return wrapper_CPU_asin_out_out(self, out); |
| 32876 | } |
| 32877 | at::Tensor & asin_outf(const at::Tensor & self, at::Tensor & out) { |
| 32878 | return wrapper_CPU_asin_out_out(self, out); |
| 32879 | } |
| 32880 | at::Tensor & asin_(at::Tensor & self) { |
| 32881 | return wrapper_CPU_asin_(self); |
| 32882 | } |
| 32883 | at::Tensor atan(const at::Tensor & self) { |
| 32884 | return wrapper_CPU_atan(self); |
| 32885 | } |
| 32886 | at::Tensor & atan_out(at::Tensor & out, const at::Tensor & self) { |
| 32887 | return wrapper_CPU_atan_out_out(self, out); |
| 32888 | } |
| 32889 | at::Tensor & atan_outf(const at::Tensor & self, at::Tensor & out) { |
| 32890 | return wrapper_CPU_atan_out_out(self, out); |
| 32891 | } |
| 32892 | at::Tensor & atan_(at::Tensor & self) { |
| 32893 | return wrapper_CPU_atan_(self); |
| 32894 | } |
| 32895 | at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32896 | return wrapper_CPU_baddbmm(self, batch1, batch2, beta, alpha); |
| 32897 | } |
| 32898 | at::Tensor & baddbmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32899 | return wrapper_CPU_baddbmm_out_out(self, batch1, batch2, beta, alpha, out); |
| 32900 | } |
| 32901 | at::Tensor & baddbmm_outf(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 32902 | return wrapper_CPU_baddbmm_out_out(self, batch1, batch2, beta, alpha, out); |
| 32903 | } |
| 32904 | at::Tensor & baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 32905 | return wrapper_CPU_baddbmm_(self, batch1, batch2, beta, alpha); |
| 32906 | } |
| 32907 | at::Tensor & bernoulli_out(at::Tensor & out, const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 32908 | return wrapper_CPU_out_bernoulli_out(self, generator, out); |
| 32909 | } |
| 32910 | at::Tensor & bernoulli_outf(const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 32911 | return wrapper_CPU_out_bernoulli_out(self, generator, out); |
| 32912 | } |
| 32913 | at::Tensor & bernoulli_(at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator) { |
| 32914 | return wrapper_CPU_Tensor_bernoulli_(self, p, generator); |
| 32915 | } |
| 32916 | at::Tensor & bernoulli_(at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 32917 | return wrapper_CPU_float_bernoulli_(self, p, generator); |
| 32918 | } |
| 32919 | at::Tensor binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 32920 | return wrapper_CPU__binary_cross_entropy(self, target, weight, reduction); |
| 32921 | } |
| 32922 | at::Tensor & binary_cross_entropy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 32923 | return wrapper_CPU_out_binary_cross_entropy_out(self, target, weight, reduction, out); |
| 32924 | } |
| 32925 | at::Tensor & binary_cross_entropy_outf(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 32926 | return wrapper_CPU_out_binary_cross_entropy_out(self, target, weight, reduction, out); |
| 32927 | } |
| 32928 | at::Tensor binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 32929 | return wrapper_CPU__binary_cross_entropy_backward(grad_output, self, target, weight, reduction); |
| 32930 | } |
| 32931 | at::Tensor & binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 32932 | return wrapper_CPU_grad_input_binary_cross_entropy_backward_out(grad_output, self, target, weight, reduction, grad_input); |
| 32933 | } |
| 32934 | at::Tensor & binary_cross_entropy_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) { |
| 32935 | return wrapper_CPU_grad_input_binary_cross_entropy_backward_out(grad_output, self, target, weight, reduction, grad_input); |
| 32936 | } |
| 32937 | at::Tensor bincount(const at::Tensor & self, const c10::optional<at::Tensor> & weights, int64_t minlength) { |
| 32938 | return wrapper_CPU__bincount(self, weights, minlength); |
| 32939 | } |
| 32940 | at::Tensor bitwise_not(const at::Tensor & self) { |
| 32941 | return wrapper_CPU_bitwise_not(self); |
| 32942 | } |
| 32943 | at::Tensor & bitwise_not_out(at::Tensor & out, const at::Tensor & self) { |
| 32944 | return wrapper_CPU_bitwise_not_out_out(self, out); |
| 32945 | } |
| 32946 | at::Tensor & bitwise_not_outf(const at::Tensor & self, at::Tensor & out) { |
| 32947 | return wrapper_CPU_bitwise_not_out_out(self, out); |
| 32948 | } |
| 32949 | at::Tensor & bitwise_not_(at::Tensor & self) { |
| 32950 | return wrapper_CPU_bitwise_not_(self); |
| 32951 | } |
| 32952 | at::Tensor copysign(const at::Tensor & self, const at::Tensor & other) { |
| 32953 | return wrapper_CPU_copysign_Tensor(self, other); |
| 32954 | } |
| 32955 | at::Tensor & copysign_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 32956 | return wrapper_CPU_copysign_out_out(self, other, out); |
| 32957 | } |
| 32958 | at::Tensor & copysign_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 32959 | return wrapper_CPU_copysign_out_out(self, other, out); |
| 32960 | } |
| 32961 | at::Tensor & copysign_(at::Tensor & self, const at::Tensor & other) { |
| 32962 | return wrapper_CPU_copysign__Tensor(self, other); |
| 32963 | } |
| 32964 | at::Tensor & logical_not_out(at::Tensor & out, const at::Tensor & self) { |
| 32965 | return wrapper_CPU_out_logical_not_out(self, out); |
| 32966 | } |
| 32967 | at::Tensor & logical_not_outf(const at::Tensor & self, at::Tensor & out) { |
| 32968 | return wrapper_CPU_out_logical_not_out(self, out); |
| 32969 | } |
| 32970 | at::Tensor & logical_xor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 32971 | return wrapper_CPU_out_logical_xor_out(self, other, out); |
| 32972 | } |
| 32973 | at::Tensor & logical_xor_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 32974 | return wrapper_CPU_out_logical_xor_out(self, other, out); |
| 32975 | } |
| 32976 | at::Tensor & logical_and_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 32977 | return wrapper_CPU_out_logical_and_out(self, other, out); |
| 32978 | } |
| 32979 | at::Tensor & logical_and_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 32980 | return wrapper_CPU_out_logical_and_out(self, other, out); |
| 32981 | } |
| 32982 | at::Tensor & logical_or_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 32983 | return wrapper_CPU_out_logical_or_out(self, other, out); |
| 32984 | } |
| 32985 | at::Tensor & logical_or_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 32986 | return wrapper_CPU_out_logical_or_out(self, other, out); |
| 32987 | } |
| 32988 | at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2) { |
| 32989 | return wrapper_CPU_bmm(self, mat2); |
| 32990 | } |
| 32991 | at::Tensor & bmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { |
| 32992 | return wrapper_CPU_bmm_out_out(self, mat2, out); |
| 32993 | } |
| 32994 | at::Tensor & bmm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 32995 | return wrapper_CPU_bmm_out_out(self, mat2, out); |
| 32996 | } |
| 32997 | at::Tensor cat(const at::ITensorListRef & tensors, int64_t dim) { |
| 32998 | return wrapper_CPU_cat(tensors, dim); |
| 32999 | } |
| 33000 | at::Tensor & cat_out(at::Tensor & out, const at::ITensorListRef & tensors, int64_t dim) { |
| 33001 | return wrapper_CPU_cat_out_out(tensors, dim, out); |
| 33002 | } |
| 33003 | at::Tensor & cat_outf(const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out) { |
| 33004 | return wrapper_CPU_cat_out_out(tensors, dim, out); |
| 33005 | } |
| 33006 | at::Tensor ceil(const at::Tensor & self) { |
| 33007 | return wrapper_CPU_ceil(self); |
| 33008 | } |
| 33009 | at::Tensor & ceil_out(at::Tensor & out, const at::Tensor & self) { |
| 33010 | return wrapper_CPU_ceil_out_out(self, out); |
| 33011 | } |
| 33012 | at::Tensor & ceil_outf(const at::Tensor & self, at::Tensor & out) { |
| 33013 | return wrapper_CPU_ceil_out_out(self, out); |
| 33014 | } |
| 33015 | at::Tensor & ceil_(at::Tensor & self) { |
| 33016 | return wrapper_CPU_ceil_(self); |
| 33017 | } |
| 33018 | at::Tensor clamp(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 33019 | return wrapper_CPU_clamp(self, min, max); |
| 33020 | } |
| 33021 | at::Tensor & clamp_out(at::Tensor & out, const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 33022 | return wrapper_CPU_clamp_out_out(self, min, max, out); |
| 33023 | } |
| 33024 | at::Tensor & clamp_outf(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max, at::Tensor & out) { |
| 33025 | return wrapper_CPU_clamp_out_out(self, min, max, out); |
| 33026 | } |
| 33027 | at::Tensor & clamp_(at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 33028 | return wrapper_CPU_clamp_(self, min, max); |
| 33029 | } |
| 33030 | at::Tensor clamp(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 33031 | return wrapper_CPU_clamp_Tensor(self, min, max); |
| 33032 | } |
| 33033 | at::Tensor & clamp_out(at::Tensor & out, const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 33034 | return wrapper_CPU_clamp_out_Tensor_out(self, min, max, out); |
| 33035 | } |
| 33036 | at::Tensor & clamp_outf(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max, at::Tensor & out) { |
| 33037 | return wrapper_CPU_clamp_out_Tensor_out(self, min, max, out); |
| 33038 | } |
| 33039 | at::Tensor & clamp_(at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 33040 | return wrapper_CPU_clamp__Tensor(self, min, max); |
| 33041 | } |
| 33042 | at::Tensor clamp_max(const at::Tensor & self, const at::Scalar & max) { |
| 33043 | return wrapper_CPU_clamp_max(self, max); |
| 33044 | } |
| 33045 | at::Tensor & clamp_max_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & max) { |
| 33046 | return wrapper_CPU_clamp_max_out_out(self, max, out); |
| 33047 | } |
| 33048 | at::Tensor & clamp_max_outf(const at::Tensor & self, const at::Scalar & max, at::Tensor & out) { |
| 33049 | return wrapper_CPU_clamp_max_out_out(self, max, out); |
| 33050 | } |
| 33051 | at::Tensor & clamp_max_(at::Tensor & self, const at::Scalar & max) { |
| 33052 | return wrapper_CPU_clamp_max_(self, max); |
| 33053 | } |
| 33054 | at::Tensor clamp_max(const at::Tensor & self, const at::Tensor & max) { |
| 33055 | return wrapper_CPU_clamp_max_Tensor(self, max); |
| 33056 | } |
| 33057 | at::Tensor & clamp_max_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & max) { |
| 33058 | return wrapper_CPU_clamp_max_out_Tensor_out(self, max, out); |
| 33059 | } |
| 33060 | at::Tensor & clamp_max_outf(const at::Tensor & self, const at::Tensor & max, at::Tensor & out) { |
| 33061 | return wrapper_CPU_clamp_max_out_Tensor_out(self, max, out); |
| 33062 | } |
| 33063 | at::Tensor & clamp_max_(at::Tensor & self, const at::Tensor & max) { |
| 33064 | return wrapper_CPU_clamp_max__Tensor(self, max); |
| 33065 | } |
| 33066 | at::Tensor clamp_min(const at::Tensor & self, const at::Scalar & min) { |
| 33067 | return wrapper_CPU_clamp_min(self, min); |
| 33068 | } |
| 33069 | at::Tensor & clamp_min_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & min) { |
| 33070 | return wrapper_CPU_clamp_min_out_out(self, min, out); |
| 33071 | } |
| 33072 | at::Tensor & clamp_min_outf(const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { |
| 33073 | return wrapper_CPU_clamp_min_out_out(self, min, out); |
| 33074 | } |
| 33075 | at::Tensor & clamp_min_(at::Tensor & self, const at::Scalar & min) { |
| 33076 | return wrapper_CPU_clamp_min_(self, min); |
| 33077 | } |
| 33078 | at::Tensor clamp_min(const at::Tensor & self, const at::Tensor & min) { |
| 33079 | return wrapper_CPU_clamp_min_Tensor(self, min); |
| 33080 | } |
| 33081 | at::Tensor & clamp_min_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & min) { |
| 33082 | return wrapper_CPU_clamp_min_out_Tensor_out(self, min, out); |
| 33083 | } |
| 33084 | at::Tensor & clamp_min_outf(const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { |
| 33085 | return wrapper_CPU_clamp_min_out_Tensor_out(self, min, out); |
| 33086 | } |
| 33087 | at::Tensor & clamp_min_(at::Tensor & self, const at::Tensor & min) { |
| 33088 | return wrapper_CPU_clamp_min__Tensor(self, min); |
| 33089 | } |
| 33090 | at::Tensor & complex_out(at::Tensor & out, const at::Tensor & real, const at::Tensor & imag) { |
| 33091 | return wrapper_CPU_out_complex_out(real, imag, out); |
| 33092 | } |
| 33093 | at::Tensor & complex_outf(const at::Tensor & real, const at::Tensor & imag, at::Tensor & out) { |
| 33094 | return wrapper_CPU_out_complex_out(real, imag, out); |
| 33095 | } |
| 33096 | at::Tensor & polar_out(at::Tensor & out, const at::Tensor & abs, const at::Tensor & angle) { |
| 33097 | return wrapper_CPU_out_polar_out(abs, angle, out); |
| 33098 | } |
| 33099 | at::Tensor & polar_outf(const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out) { |
| 33100 | return wrapper_CPU_out_polar_out(abs, angle, out); |
| 33101 | } |
| 33102 | at::Tensor cos(const at::Tensor & self) { |
| 33103 | return wrapper_CPU_cos(self); |
| 33104 | } |
| 33105 | at::Tensor & cos_out(at::Tensor & out, const at::Tensor & self) { |
| 33106 | return wrapper_CPU_cos_out_out(self, out); |
| 33107 | } |
| 33108 | at::Tensor & cos_outf(const at::Tensor & self, at::Tensor & out) { |
| 33109 | return wrapper_CPU_cos_out_out(self, out); |
| 33110 | } |
| 33111 | at::Tensor & cos_(at::Tensor & self) { |
| 33112 | return wrapper_CPU_cos_(self); |
| 33113 | } |
| 33114 | at::Tensor cosh(const at::Tensor & self) { |
| 33115 | return wrapper_CPU_cosh(self); |
| 33116 | } |
| 33117 | at::Tensor & cosh_out(at::Tensor & out, const at::Tensor & self) { |
| 33118 | return wrapper_CPU_cosh_out_out(self, out); |
| 33119 | } |
| 33120 | at::Tensor & cosh_outf(const at::Tensor & self, at::Tensor & out) { |
| 33121 | return wrapper_CPU_cosh_out_out(self, out); |
| 33122 | } |
| 33123 | at::Tensor & cosh_(at::Tensor & self) { |
| 33124 | return wrapper_CPU_cosh_(self); |
| 33125 | } |
| 33126 | at::Tensor count_nonzero(const at::Tensor & self, at::IntArrayRef dim) { |
| 33127 | return wrapper_CPU_dim_IntList_count_nonzero(self, dim); |
| 33128 | } |
| 33129 | void _cummax_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 33130 | return wrapper_CPU___cummax_helper(self, values, indices, dim); |
| 33131 | } |
| 33132 | void _cummin_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 33133 | return wrapper_CPU___cummin_helper(self, values, indices, dim); |
| 33134 | } |
| 33135 | at::Tensor cumprod(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33136 | return wrapper_CPU_cumprod(self, dim, dtype); |
| 33137 | } |
| 33138 | at::Tensor & cumprod_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33139 | return wrapper_CPU_cumprod_out_out(self, dim, dtype, out); |
| 33140 | } |
| 33141 | at::Tensor & cumprod_outf(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 33142 | return wrapper_CPU_cumprod_out_out(self, dim, dtype, out); |
| 33143 | } |
| 33144 | at::Tensor & cumprod_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33145 | return wrapper_CPU_cumprod_(self, dim, dtype); |
| 33146 | } |
| 33147 | at::Tensor cumsum(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33148 | return wrapper_CPU_cumsum(self, dim, dtype); |
| 33149 | } |
| 33150 | at::Tensor & cumsum_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33151 | return wrapper_CPU_cumsum_out_out(self, dim, dtype, out); |
| 33152 | } |
| 33153 | at::Tensor & cumsum_outf(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 33154 | return wrapper_CPU_cumsum_out_out(self, dim, dtype, out); |
| 33155 | } |
| 33156 | at::Tensor & cumsum_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 33157 | return wrapper_CPU_cumsum_(self, dim, dtype); |
| 33158 | } |
| 33159 | ::std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity) { |
| 33160 | return wrapper_CPU___ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); |
| 33161 | } |
| 33162 | ::std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity) { |
| 33163 | return wrapper_CPU_Tensor__ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); |
| 33164 | } |
| 33165 | at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { |
| 33166 | return wrapper_CPU___ctc_loss_backward(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); |
| 33167 | } |
| 33168 | at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { |
| 33169 | return wrapper_CPU_Tensor__ctc_loss_backward(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); |
| 33170 | } |
| 33171 | at::Tensor div(const at::Tensor & self, const at::Tensor & other) { |
| 33172 | return wrapper_CPU_div_Tensor(self, other); |
| 33173 | } |
| 33174 | at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33175 | return wrapper_CPU_div_out_out(self, other, out); |
| 33176 | } |
| 33177 | at::Tensor & div_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33178 | return wrapper_CPU_div_out_out(self, other, out); |
| 33179 | } |
| 33180 | at::Tensor & div_(at::Tensor & self, const at::Tensor & other) { |
| 33181 | return wrapper_CPU_div__Tensor(self, other); |
| 33182 | } |
| 33183 | at::Tensor div(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 33184 | return wrapper_CPU_div_Tensor_mode(self, other, rounding_mode); |
| 33185 | } |
| 33186 | at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 33187 | return wrapper_CPU_div_out_out_mode(self, other, rounding_mode, out); |
| 33188 | } |
| 33189 | at::Tensor & div_outf(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 33190 | return wrapper_CPU_div_out_out_mode(self, other, rounding_mode, out); |
| 33191 | } |
| 33192 | at::Tensor & div_(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 33193 | return wrapper_CPU_div__Tensor_mode(self, other, rounding_mode); |
| 33194 | } |
| 33195 | at::Tensor dot(const at::Tensor & self, const at::Tensor & tensor) { |
| 33196 | return wrapper_CPU__dot(self, tensor); |
| 33197 | } |
| 33198 | at::Tensor vdot(const at::Tensor & self, const at::Tensor & other) { |
| 33199 | return wrapper_CPU__vdot(self, other); |
| 33200 | } |
| 33201 | at::Tensor embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { |
| 33202 | return wrapper_CPU__embedding_dense_backward(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); |
| 33203 | } |
| 33204 | at::Tensor embedding_dense_backward_symint(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) { |
| 33205 | return wrapper_CPU__embedding_dense_backward(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); |
| 33206 | } |
| 33207 | at::Tensor & embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { |
| 33208 | return wrapper_CPU__embedding_renorm_(self, indices, max_norm, norm_type); |
| 33209 | } |
| 33210 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_forward_only(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 33211 | return wrapper_CPU___embedding_bag_forward_only(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 33212 | } |
| 33213 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 33214 | return wrapper_CPU___embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 33215 | } |
| 33216 | at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx) { |
| 33217 | return wrapper_CPU___embedding_bag_dense_backward(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); |
| 33218 | } |
| 33219 | at::Tensor _embedding_bag_dense_backward_symint(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx) { |
| 33220 | return wrapper_CPU___embedding_bag_dense_backward(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); |
| 33221 | } |
| 33222 | at::Tensor _embedding_bag_per_sample_weights_backward(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx) { |
| 33223 | return wrapper_CPU___embedding_bag_per_sample_weights_backward(grad, weight, indices, offsets, offset2bag, mode, padding_idx); |
| 33224 | } |
| 33225 | at::Tensor empty(at::IntArrayRef size, at::TensorOptions options, c10::optional<at::MemoryFormat> memory_format) { |
| 33226 | return wrapper_CPU_memory_format_empty(c10::fromIntArrayRefSlow(size), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); |
| 33227 | } |
| 33228 | at::Tensor empty(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 33229 | return wrapper_CPU_memory_format_empty(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, memory_format); |
| 33230 | } |
| 33231 | at::Tensor empty_symint(c10::SymIntArrayRef size, at::TensorOptions options, c10::optional<at::MemoryFormat> memory_format) { |
| 33232 | return wrapper_CPU_memory_format_empty(size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); |
| 33233 | } |
| 33234 | at::Tensor empty_symint(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 33235 | return wrapper_CPU_memory_format_empty(size, dtype, layout, device, pin_memory, memory_format); |
| 33236 | } |
| 33237 | at::Tensor _empty_affine_quantized(at::IntArrayRef size, at::TensorOptions options, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format) { |
| 33238 | return wrapper_CPU___empty_affine_quantized(size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); |
| 33239 | } |
| 33240 | at::Tensor _empty_affine_quantized(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format) { |
| 33241 | return wrapper_CPU___empty_affine_quantized(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); |
| 33242 | } |
| 33243 | at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options, c10::optional<at::MemoryFormat> memory_format) { |
| 33244 | return wrapper_CPU___empty_per_channel_affine_quantized(size, scales, zero_points, axis, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); |
| 33245 | } |
| 33246 | at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 33247 | return wrapper_CPU___empty_per_channel_affine_quantized(size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format); |
| 33248 | } |
| 33249 | const at::Tensor & resize_(const at::Tensor & self, at::IntArrayRef size, c10::optional<at::MemoryFormat> memory_format) { |
| 33250 | return wrapper_CPU__resize_(self, c10::fromIntArrayRefSlow(size), memory_format); |
| 33251 | } |
| 33252 | const at::Tensor & resize__symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::MemoryFormat> memory_format) { |
| 33253 | return wrapper_CPU__resize_(self, size, memory_format); |
| 33254 | } |
| 33255 | at::Tensor empty_strided(at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options) { |
| 33256 | return wrapper_CPU__empty_strided(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 33257 | } |
| 33258 | at::Tensor empty_strided(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) { |
| 33259 | return wrapper_CPU__empty_strided(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory); |
| 33260 | } |
| 33261 | at::Tensor empty_strided_symint(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options) { |
| 33262 | return wrapper_CPU__empty_strided(size, stride, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 33263 | } |
| 33264 | at::Tensor empty_strided_symint(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) { |
| 33265 | return wrapper_CPU__empty_strided(size, stride, dtype, layout, device, pin_memory); |
| 33266 | } |
| 33267 | at::Tensor erf(const at::Tensor & self) { |
| 33268 | return wrapper_CPU_erf(self); |
| 33269 | } |
| 33270 | at::Tensor & erf_out(at::Tensor & out, const at::Tensor & self) { |
| 33271 | return wrapper_CPU_erf_out_out(self, out); |
| 33272 | } |
| 33273 | at::Tensor & erf_outf(const at::Tensor & self, at::Tensor & out) { |
| 33274 | return wrapper_CPU_erf_out_out(self, out); |
| 33275 | } |
| 33276 | at::Tensor & erf_(at::Tensor & self) { |
| 33277 | return wrapper_CPU_erf_(self); |
| 33278 | } |
| 33279 | at::Tensor erfc(const at::Tensor & self) { |
| 33280 | return wrapper_CPU_erfc(self); |
| 33281 | } |
| 33282 | at::Tensor & erfc_out(at::Tensor & out, const at::Tensor & self) { |
| 33283 | return wrapper_CPU_erfc_out_out(self, out); |
| 33284 | } |
| 33285 | at::Tensor & erfc_outf(const at::Tensor & self, at::Tensor & out) { |
| 33286 | return wrapper_CPU_erfc_out_out(self, out); |
| 33287 | } |
| 33288 | at::Tensor & erfc_(at::Tensor & self) { |
| 33289 | return wrapper_CPU_erfc_(self); |
| 33290 | } |
| 33291 | at::Tensor exp(const at::Tensor & self) { |
| 33292 | return wrapper_CPU_exp(self); |
| 33293 | } |
| 33294 | at::Tensor & exp_out(at::Tensor & out, const at::Tensor & self) { |
| 33295 | return wrapper_CPU_exp_out_out(self, out); |
| 33296 | } |
| 33297 | at::Tensor & exp_outf(const at::Tensor & self, at::Tensor & out) { |
| 33298 | return wrapper_CPU_exp_out_out(self, out); |
| 33299 | } |
| 33300 | at::Tensor & exp_(at::Tensor & self) { |
| 33301 | return wrapper_CPU_exp_(self); |
| 33302 | } |
| 33303 | at::Tensor exp2(const at::Tensor & self) { |
| 33304 | return wrapper_CPU_exp2(self); |
| 33305 | } |
| 33306 | at::Tensor & exp2_out(at::Tensor & out, const at::Tensor & self) { |
| 33307 | return wrapper_CPU_exp2_out_out(self, out); |
| 33308 | } |
| 33309 | at::Tensor & exp2_outf(const at::Tensor & self, at::Tensor & out) { |
| 33310 | return wrapper_CPU_exp2_out_out(self, out); |
| 33311 | } |
| 33312 | at::Tensor & exp2_(at::Tensor & self) { |
| 33313 | return wrapper_CPU_exp2_(self); |
| 33314 | } |
| 33315 | at::Tensor expm1(const at::Tensor & self) { |
| 33316 | return wrapper_CPU_expm1(self); |
| 33317 | } |
| 33318 | at::Tensor & expm1_out(at::Tensor & out, const at::Tensor & self) { |
| 33319 | return wrapper_CPU_expm1_out_out(self, out); |
| 33320 | } |
| 33321 | at::Tensor & expm1_outf(const at::Tensor & self, at::Tensor & out) { |
| 33322 | return wrapper_CPU_expm1_out_out(self, out); |
| 33323 | } |
| 33324 | at::Tensor & expm1_(at::Tensor & self) { |
| 33325 | return wrapper_CPU_expm1_(self); |
| 33326 | } |
| 33327 | at::Tensor & eye_out(at::Tensor & out, int64_t n) { |
| 33328 | return wrapper_CPU_out_eye_out(n, out); |
| 33329 | } |
| 33330 | at::Tensor & eye_outf(int64_t n, at::Tensor & out) { |
| 33331 | return wrapper_CPU_out_eye_out(n, out); |
| 33332 | } |
| 33333 | at::Tensor & eye_out(at::Tensor & out, int64_t n, int64_t m) { |
| 33334 | return wrapper_CPU_m_out_eye_out(n, m, out); |
| 33335 | } |
| 33336 | at::Tensor & eye_outf(int64_t n, int64_t m, at::Tensor & out) { |
| 33337 | return wrapper_CPU_m_out_eye_out(n, m, out); |
| 33338 | } |
| 33339 | at::Tensor & fill_(at::Tensor & self, const at::Scalar & value) { |
| 33340 | return wrapper_CPU_Scalar_fill_(self, value); |
| 33341 | } |
| 33342 | at::Tensor & fill_(at::Tensor & self, const at::Tensor & value) { |
| 33343 | return wrapper_CPU_Tensor_fill_(self, value); |
| 33344 | } |
| 33345 | at::Tensor floor(const at::Tensor & self) { |
| 33346 | return wrapper_CPU_floor(self); |
| 33347 | } |
| 33348 | at::Tensor & floor_out(at::Tensor & out, const at::Tensor & self) { |
| 33349 | return wrapper_CPU_floor_out_out(self, out); |
| 33350 | } |
| 33351 | at::Tensor & floor_outf(const at::Tensor & self, at::Tensor & out) { |
| 33352 | return wrapper_CPU_floor_out_out(self, out); |
| 33353 | } |
| 33354 | at::Tensor & floor_(at::Tensor & self) { |
| 33355 | return wrapper_CPU_floor_(self); |
| 33356 | } |
| 33357 | at::Tensor floor_divide(const at::Tensor & self, const at::Tensor & other) { |
| 33358 | return wrapper_CPU__floor_divide(self, other); |
| 33359 | } |
| 33360 | at::Tensor & floor_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33361 | return wrapper_CPU_out_floor_divide_out(self, other, out); |
| 33362 | } |
| 33363 | at::Tensor & floor_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33364 | return wrapper_CPU_out_floor_divide_out(self, other, out); |
| 33365 | } |
| 33366 | at::Tensor & floor_divide_(at::Tensor & self, const at::Tensor & other) { |
| 33367 | return wrapper_CPU_Tensor_floor_divide_(self, other); |
| 33368 | } |
| 33369 | at::Tensor frac(const at::Tensor & self) { |
| 33370 | return wrapper_CPU_frac(self); |
| 33371 | } |
| 33372 | at::Tensor & frac_out(at::Tensor & out, const at::Tensor & self) { |
| 33373 | return wrapper_CPU_frac_out_out(self, out); |
| 33374 | } |
| 33375 | at::Tensor & frac_outf(const at::Tensor & self, at::Tensor & out) { |
| 33376 | return wrapper_CPU_frac_out_out(self, out); |
| 33377 | } |
| 33378 | at::Tensor & frac_(at::Tensor & self) { |
| 33379 | return wrapper_CPU_frac_(self); |
| 33380 | } |
| 33381 | at::Tensor from_file(c10::string_view filename, c10::optional<bool> shared, c10::optional<int64_t> size, at::TensorOptions options) { |
| 33382 | return wrapper_CPU__from_file(filename, shared, size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 33383 | } |
| 33384 | at::Tensor from_file(c10::string_view filename, c10::optional<bool> shared, c10::optional<int64_t> size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 33385 | return wrapper_CPU__from_file(filename, shared, size, dtype, layout, device, pin_memory); |
| 33386 | } |
| 33387 | at::Tensor gcd(const at::Tensor & self, const at::Tensor & other) { |
| 33388 | return wrapper_CPU_gcd(self, other); |
| 33389 | } |
| 33390 | at::Tensor & gcd_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33391 | return wrapper_CPU_gcd_out_out(self, other, out); |
| 33392 | } |
| 33393 | at::Tensor & gcd_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33394 | return wrapper_CPU_gcd_out_out(self, other, out); |
| 33395 | } |
| 33396 | at::Tensor & gcd_(at::Tensor & self, const at::Tensor & other) { |
| 33397 | return wrapper_CPU_gcd_(self, other); |
| 33398 | } |
| 33399 | at::Tensor lcm(const at::Tensor & self, const at::Tensor & other) { |
| 33400 | return wrapper_CPU_lcm(self, other); |
| 33401 | } |
| 33402 | at::Tensor & lcm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33403 | return wrapper_CPU_lcm_out_out(self, other, out); |
| 33404 | } |
| 33405 | at::Tensor & lcm_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33406 | return wrapper_CPU_lcm_out_out(self, other, out); |
| 33407 | } |
| 33408 | at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other) { |
| 33409 | return wrapper_CPU_lcm_(self, other); |
| 33410 | } |
| 33411 | at::Tensor grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 33412 | return wrapper_CPU__grid_sampler_2d(input, grid, interpolation_mode, padding_mode, align_corners); |
| 33413 | } |
| 33414 | ::std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 33415 | return wrapper_CPU__grid_sampler_2d_backward(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 33416 | } |
| 33417 | at::Tensor grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 33418 | return wrapper_CPU__grid_sampler_3d(input, grid, interpolation_mode, padding_mode, align_corners); |
| 33419 | } |
| 33420 | ::std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 33421 | return wrapper_CPU__grid_sampler_3d_backward(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 33422 | } |
| 33423 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) { |
| 33424 | return wrapper_CPU__native_group_norm(input, weight, bias, N, C, HxW, group, eps); |
| 33425 | } |
| 33426 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_symint(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { |
| 33427 | return wrapper_CPU__native_group_norm(input, weight, bias, N, C, HxW, group, eps); |
| 33428 | } |
| 33429 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask) { |
| 33430 | return wrapper_CPU__native_group_norm_backward(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); |
| 33431 | } |
| 33432 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward_symint(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) { |
| 33433 | return wrapper_CPU__native_group_norm_backward(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); |
| 33434 | } |
| 33435 | at::Tensor _fft_r2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { |
| 33436 | return wrapper_CPU___fft_r2c(self, dim, normalization, onesided); |
| 33437 | } |
| 33438 | at::Tensor & _fft_r2c_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { |
| 33439 | return wrapper_CPU_out__fft_r2c_out(self, dim, normalization, onesided, out); |
| 33440 | } |
| 33441 | at::Tensor & _fft_r2c_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { |
| 33442 | return wrapper_CPU_out__fft_r2c_out(self, dim, normalization, onesided, out); |
| 33443 | } |
| 33444 | at::Tensor _fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) { |
| 33445 | return wrapper_CPU___fft_c2r(self, dim, normalization, last_dim_size); |
| 33446 | } |
| 33447 | at::Tensor & _fft_c2r_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) { |
| 33448 | return wrapper_CPU_out__fft_c2r_out(self, dim, normalization, last_dim_size, out); |
| 33449 | } |
| 33450 | at::Tensor & _fft_c2r_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size, at::Tensor & out) { |
| 33451 | return wrapper_CPU_out__fft_c2r_out(self, dim, normalization, last_dim_size, out); |
| 33452 | } |
| 33453 | at::Tensor _fft_c2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) { |
| 33454 | return wrapper_CPU___fft_c2c(self, c10::fromIntArrayRefSlow(dim), normalization, forward); |
| 33455 | } |
| 33456 | at::Tensor _fft_c2c_symint(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { |
| 33457 | return wrapper_CPU___fft_c2c(self, dim, normalization, forward); |
| 33458 | } |
| 33459 | at::Tensor & _fft_c2c_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) { |
| 33460 | return wrapper_CPU_out__fft_c2c_out(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out); |
| 33461 | } |
| 33462 | at::Tensor & _fft_c2c_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 33463 | return wrapper_CPU_out__fft_c2c_out(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out); |
| 33464 | } |
| 33465 | at::Tensor & _fft_c2c_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { |
| 33466 | return wrapper_CPU_out__fft_c2c_out(self, dim, normalization, forward, out); |
| 33467 | } |
| 33468 | at::Tensor & _fft_c2c_symint_outf(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 33469 | return wrapper_CPU_out__fft_c2c_out(self, dim, normalization, forward, out); |
| 33470 | } |
| 33471 | void _validate_compressed_sparse_indices(bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { |
| 33472 | return wrapper_CPU___validate_compressed_sparse_indices(is_crow, compressed_idx, plain_idx, cdim, dim, nnz); |
| 33473 | } |
| 33474 | at::Tensor index(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 33475 | return wrapper_CPU_index_Tensor(self, indices); |
| 33476 | } |
| 33477 | at::Tensor & index_out(at::Tensor & out, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 33478 | return wrapper_CPU_index_out_Tensor_out(self, indices, out); |
| 33479 | } |
| 33480 | at::Tensor & index_outf(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, at::Tensor & out) { |
| 33481 | return wrapper_CPU_index_out_Tensor_out(self, indices, out); |
| 33482 | } |
| 33483 | at::Tensor index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 33484 | return wrapper_CPU_index_copy(self, dim, index, source); |
| 33485 | } |
| 33486 | at::Tensor & index_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 33487 | return wrapper_CPU_index_copy_out_out(self, dim, index, source, out); |
| 33488 | } |
| 33489 | at::Tensor & index_copy_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out) { |
| 33490 | return wrapper_CPU_index_copy_out_out(self, dim, index, source, out); |
| 33491 | } |
| 33492 | at::Tensor & index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 33493 | return wrapper_CPU_index_copy_(self, dim, index, source); |
| 33494 | } |
| 33495 | at::Tensor & _index_put_impl_(at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe) { |
| 33496 | return wrapper_CPU___index_put_impl_(self, indices, values, accumulate, unsafe); |
| 33497 | } |
| 33498 | at::Tensor isin(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 33499 | return wrapper_CPU_isin_Tensor_Tensor(elements, test_elements, assume_unique, invert); |
| 33500 | } |
| 33501 | at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 33502 | return wrapper_CPU_isin_out_Tensor_Tensor_out(elements, test_elements, assume_unique, invert, out); |
| 33503 | } |
| 33504 | at::Tensor & isin_outf(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 33505 | return wrapper_CPU_isin_out_Tensor_Tensor_out(elements, test_elements, assume_unique, invert, out); |
| 33506 | } |
| 33507 | at::Tensor isin(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { |
| 33508 | return wrapper_CPU_isin_Tensor_Scalar(elements, test_element, assume_unique, invert); |
| 33509 | } |
| 33510 | at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { |
| 33511 | return wrapper_CPU_isin_out_Tensor_Scalar_out(elements, test_element, assume_unique, invert, out); |
| 33512 | } |
| 33513 | at::Tensor & isin_outf(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) { |
| 33514 | return wrapper_CPU_isin_out_Tensor_Scalar_out(elements, test_element, assume_unique, invert, out); |
| 33515 | } |
| 33516 | at::Tensor isin(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 33517 | return wrapper_CPU_isin_Scalar_Tensor(element, test_elements, assume_unique, invert); |
| 33518 | } |
| 33519 | at::Tensor & isin_out(at::Tensor & out, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 33520 | return wrapper_CPU_isin_out_Scalar_Tensor_out(element, test_elements, assume_unique, invert, out); |
| 33521 | } |
| 33522 | at::Tensor & isin_outf(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 33523 | return wrapper_CPU_isin_out_Scalar_Tensor_out(element, test_elements, assume_unique, invert, out); |
| 33524 | } |
| 33525 | at::Tensor isnan(const at::Tensor & self) { |
| 33526 | return wrapper_CPU__isnan(self); |
| 33527 | } |
| 33528 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) { |
| 33529 | return wrapper_CPU_values_kthvalue_out(self, k, dim, keepdim, values, indices); |
| 33530 | } |
| 33531 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_outf(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 33532 | return wrapper_CPU_values_kthvalue_out(self, k, dim, keepdim, values, indices); |
| 33533 | } |
| 33534 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps) { |
| 33535 | return wrapper_CPU__native_layer_norm(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps); |
| 33536 | } |
| 33537 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_symint(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps) { |
| 33538 | return wrapper_CPU__native_layer_norm(input, normalized_shape, weight, bias, eps); |
| 33539 | } |
| 33540 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) { |
| 33541 | return wrapper_CPU__native_layer_norm_backward(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask); |
| 33542 | } |
| 33543 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward_symint(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) { |
| 33544 | return wrapper_CPU__native_layer_norm_backward(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask); |
| 33545 | } |
| 33546 | at::Tensor & nan_to_num_out(at::Tensor & out, const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 33547 | return wrapper_CPU_out_nan_to_num_out(self, nan, posinf, neginf, out); |
| 33548 | } |
| 33549 | at::Tensor & nan_to_num_outf(const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out) { |
| 33550 | return wrapper_CPU_out_nan_to_num_out(self, nan, posinf, neginf, out); |
| 33551 | } |
| 33552 | at::Tensor & linspace_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps) { |
| 33553 | return wrapper_CPU_out_linspace_out(start, end, steps, out); |
| 33554 | } |
| 33555 | at::Tensor & linspace_outf(const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out) { |
| 33556 | return wrapper_CPU_out_linspace_out(start, end, steps, out); |
| 33557 | } |
| 33558 | at::Tensor log(const at::Tensor & self) { |
| 33559 | return wrapper_CPU_log(self); |
| 33560 | } |
| 33561 | at::Tensor & log_out(at::Tensor & out, const at::Tensor & self) { |
| 33562 | return wrapper_CPU_log_out_out(self, out); |
| 33563 | } |
| 33564 | at::Tensor & log_outf(const at::Tensor & self, at::Tensor & out) { |
| 33565 | return wrapper_CPU_log_out_out(self, out); |
| 33566 | } |
| 33567 | at::Tensor & log_(at::Tensor & self) { |
| 33568 | return wrapper_CPU_log_(self); |
| 33569 | } |
| 33570 | at::Tensor log10(const at::Tensor & self) { |
| 33571 | return wrapper_CPU_log10(self); |
| 33572 | } |
| 33573 | at::Tensor & log10_out(at::Tensor & out, const at::Tensor & self) { |
| 33574 | return wrapper_CPU_log10_out_out(self, out); |
| 33575 | } |
| 33576 | at::Tensor & log10_outf(const at::Tensor & self, at::Tensor & out) { |
| 33577 | return wrapper_CPU_log10_out_out(self, out); |
| 33578 | } |
| 33579 | at::Tensor & log10_(at::Tensor & self) { |
| 33580 | return wrapper_CPU_log10_(self); |
| 33581 | } |
| 33582 | at::Tensor log1p(const at::Tensor & self) { |
| 33583 | return wrapper_CPU_log1p(self); |
| 33584 | } |
| 33585 | at::Tensor & log1p_out(at::Tensor & out, const at::Tensor & self) { |
| 33586 | return wrapper_CPU_log1p_out_out(self, out); |
| 33587 | } |
| 33588 | at::Tensor & log1p_outf(const at::Tensor & self, at::Tensor & out) { |
| 33589 | return wrapper_CPU_log1p_out_out(self, out); |
| 33590 | } |
| 33591 | at::Tensor & log1p_(at::Tensor & self) { |
| 33592 | return wrapper_CPU_log1p_(self); |
| 33593 | } |
| 33594 | at::Tensor log2(const at::Tensor & self) { |
| 33595 | return wrapper_CPU_log2(self); |
| 33596 | } |
| 33597 | at::Tensor & log2_out(at::Tensor & out, const at::Tensor & self) { |
| 33598 | return wrapper_CPU_log2_out_out(self, out); |
| 33599 | } |
| 33600 | at::Tensor & log2_outf(const at::Tensor & self, at::Tensor & out) { |
| 33601 | return wrapper_CPU_log2_out_out(self, out); |
| 33602 | } |
| 33603 | at::Tensor & log2_(at::Tensor & self) { |
| 33604 | return wrapper_CPU_log2_(self); |
| 33605 | } |
| 33606 | at::Tensor logaddexp(const at::Tensor & self, const at::Tensor & other) { |
| 33607 | return wrapper_CPU_logaddexp(self, other); |
| 33608 | } |
| 33609 | at::Tensor & logaddexp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33610 | return wrapper_CPU_logaddexp_out_out(self, other, out); |
| 33611 | } |
| 33612 | at::Tensor & logaddexp_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33613 | return wrapper_CPU_logaddexp_out_out(self, other, out); |
| 33614 | } |
| 33615 | at::Tensor logaddexp2(const at::Tensor & self, const at::Tensor & other) { |
| 33616 | return wrapper_CPU_logaddexp2(self, other); |
| 33617 | } |
| 33618 | at::Tensor & logaddexp2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33619 | return wrapper_CPU_logaddexp2_out_out(self, other, out); |
| 33620 | } |
| 33621 | at::Tensor & logaddexp2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33622 | return wrapper_CPU_logaddexp2_out_out(self, other, out); |
| 33623 | } |
| 33624 | at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other) { |
| 33625 | return wrapper_CPU_xlogy_Tensor(self, other); |
| 33626 | } |
| 33627 | at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33628 | return wrapper_CPU_xlogy_out_OutTensor(self, other, out); |
| 33629 | } |
| 33630 | at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33631 | return wrapper_CPU_xlogy_out_OutTensor(self, other, out); |
| 33632 | } |
| 33633 | at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other) { |
| 33634 | return wrapper_CPU_xlogy__Tensor(self, other); |
| 33635 | } |
| 33636 | at::Tensor & logspace_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base) { |
| 33637 | return wrapper_CPU_out_logspace_out(start, end, steps, base, out); |
| 33638 | } |
| 33639 | at::Tensor & logspace_outf(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) { |
| 33640 | return wrapper_CPU_out_logspace_out(start, end, steps, base, out); |
| 33641 | } |
| 33642 | at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 33643 | return wrapper_CPU__log_softmax(self, dim, half_to_float); |
| 33644 | } |
| 33645 | at::Tensor & _log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 33646 | return wrapper_CPU__log_softmax_out_out(self, dim, half_to_float, out); |
| 33647 | } |
| 33648 | at::Tensor & _log_softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 33649 | return wrapper_CPU__log_softmax_out_out(self, dim, half_to_float, out); |
| 33650 | } |
| 33651 | at::Tensor _log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 33652 | return wrapper_CPU__log_softmax_backward_data(grad_output, output, dim, input_dtype); |
| 33653 | } |
| 33654 | at::Tensor & _log_softmax_backward_data_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 33655 | return wrapper_CPU__log_softmax_backward_data_out_out(grad_output, output, dim, input_dtype, out); |
| 33656 | } |
| 33657 | at::Tensor & _log_softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { |
| 33658 | return wrapper_CPU__log_softmax_backward_data_out_out(grad_output, output, dim, input_dtype, out); |
| 33659 | } |
| 33660 | at::Tensor _logcumsumexp(const at::Tensor & self, int64_t dim) { |
| 33661 | return wrapper_CPU___logcumsumexp(self, dim); |
| 33662 | } |
| 33663 | at::Tensor & _logcumsumexp_out(at::Tensor & out, const at::Tensor & self, int64_t dim) { |
| 33664 | return wrapper_CPU_out__logcumsumexp_out(self, dim, out); |
| 33665 | } |
| 33666 | at::Tensor & _logcumsumexp_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 33667 | return wrapper_CPU_out__logcumsumexp_out(self, dim, out); |
| 33668 | } |
| 33669 | ::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self) { |
| 33670 | return wrapper_CPU___aminmax(self); |
| 33671 | } |
| 33672 | ::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33673 | return wrapper_CPU_dim__aminmax(self, dim, keepdim); |
| 33674 | } |
| 33675 | ::std::tuple<at::Tensor,at::Tensor> aminmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 33676 | return wrapper_CPU_aminmax(self, dim, keepdim); |
| 33677 | } |
| 33678 | ::std::tuple<at::Tensor &,at::Tensor &> aminmax_out(at::Tensor & min, at::Tensor & max, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 33679 | return wrapper_CPU_aminmax_out_out(self, dim, keepdim, min, max); |
| 33680 | } |
| 33681 | ::std::tuple<at::Tensor &,at::Tensor &> aminmax_outf(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & min, at::Tensor & max) { |
| 33682 | return wrapper_CPU_aminmax_out_out(self, dim, keepdim, min, max); |
| 33683 | } |
| 33684 | at::Tensor _compute_linear_combination(const at::Tensor & input, const at::Tensor & coefficients) { |
| 33685 | return wrapper_CPU___compute_linear_combination(input, coefficients); |
| 33686 | } |
| 33687 | at::Tensor & _compute_linear_combination_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & coefficients) { |
| 33688 | return wrapper_CPU_out__compute_linear_combination_out(input, coefficients, out); |
| 33689 | } |
| 33690 | at::Tensor & _compute_linear_combination_outf(const at::Tensor & input, const at::Tensor & coefficients, at::Tensor & out) { |
| 33691 | return wrapper_CPU_out__compute_linear_combination_out(input, coefficients, out); |
| 33692 | } |
| 33693 | ::std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33694 | return wrapper_CPU_max_dim(self, dim, keepdim); |
| 33695 | } |
| 33696 | ::std::tuple<at::Tensor &,at::Tensor &> max_out(at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33697 | return wrapper_CPU_max_out_dim_max(self, dim, keepdim, max, max_values); |
| 33698 | } |
| 33699 | ::std::tuple<at::Tensor &,at::Tensor &> max_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { |
| 33700 | return wrapper_CPU_max_out_dim_max(self, dim, keepdim, max, max_values); |
| 33701 | } |
| 33702 | at::Tensor amax(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 33703 | return wrapper_CPU_amax(self, dim, keepdim); |
| 33704 | } |
| 33705 | at::Tensor & amax_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 33706 | return wrapper_CPU_amax_out_out(self, dim, keepdim, out); |
| 33707 | } |
| 33708 | at::Tensor & amax_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 33709 | return wrapper_CPU_amax_out_out(self, dim, keepdim, out); |
| 33710 | } |
| 33711 | at::Tensor mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 33712 | return wrapper_CPU_mean_dim(self, dim, keepdim, dtype); |
| 33713 | } |
| 33714 | at::Tensor & mean_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 33715 | return wrapper_CPU_mean_out_out(self, dim, keepdim, dtype, out); |
| 33716 | } |
| 33717 | at::Tensor & mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 33718 | return wrapper_CPU_mean_out_out(self, dim, keepdim, dtype, out); |
| 33719 | } |
| 33720 | at::Tensor median(const at::Tensor & self) { |
| 33721 | return wrapper_CPU__median(self); |
| 33722 | } |
| 33723 | ::std::tuple<at::Tensor &,at::Tensor &> median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33724 | return wrapper_CPU_dim_values_median_out(self, dim, keepdim, values, indices); |
| 33725 | } |
| 33726 | ::std::tuple<at::Tensor &,at::Tensor &> median_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 33727 | return wrapper_CPU_dim_values_median_out(self, dim, keepdim, values, indices); |
| 33728 | } |
| 33729 | at::Tensor nanmedian(const at::Tensor & self) { |
| 33730 | return wrapper_CPU__nanmedian(self); |
| 33731 | } |
| 33732 | ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33733 | return wrapper_CPU_dim_values_nanmedian_out(self, dim, keepdim, values, indices); |
| 33734 | } |
| 33735 | ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 33736 | return wrapper_CPU_dim_values_nanmedian_out(self, dim, keepdim, values, indices); |
| 33737 | } |
| 33738 | ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33739 | return wrapper_CPU_min_dim(self, dim, keepdim); |
| 33740 | } |
| 33741 | ::std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33742 | return wrapper_CPU_min_out_dim_min(self, dim, keepdim, min, min_indices); |
| 33743 | } |
| 33744 | ::std::tuple<at::Tensor &,at::Tensor &> min_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 33745 | return wrapper_CPU_min_out_dim_min(self, dim, keepdim, min, min_indices); |
| 33746 | } |
| 33747 | at::Tensor amin(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 33748 | return wrapper_CPU_amin(self, dim, keepdim); |
| 33749 | } |
| 33750 | at::Tensor & amin_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 33751 | return wrapper_CPU_amin_out_out(self, dim, keepdim, out); |
| 33752 | } |
| 33753 | at::Tensor & amin_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 33754 | return wrapper_CPU_amin_out_out(self, dim, keepdim, out); |
| 33755 | } |
| 33756 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer(const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) { |
| 33757 | return wrapper_CPU__mkldnn_rnn_layer(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); |
| 33758 | } |
| 33759 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer_backward(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { |
| 33760 | return wrapper_CPU__mkldnn_rnn_layer_backward(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); |
| 33761 | } |
| 33762 | at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2) { |
| 33763 | return wrapper_CPU_mm(self, mat2); |
| 33764 | } |
| 33765 | at::Tensor & mm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { |
| 33766 | return wrapper_CPU_mm_out_out(self, mat2, out); |
| 33767 | } |
| 33768 | at::Tensor & mm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 33769 | return wrapper_CPU_mm_out_out(self, mat2, out); |
| 33770 | } |
| 33771 | ::std::tuple<at::Tensor,at::Tensor> mode(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 33772 | return wrapper_CPU__mode(self, dim, keepdim); |
| 33773 | } |
| 33774 | at::Tensor mul(const at::Tensor & self, const at::Tensor & other) { |
| 33775 | return wrapper_CPU_mul_Tensor(self, other); |
| 33776 | } |
| 33777 | at::Tensor & mul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 33778 | return wrapper_CPU_mul_out_out(self, other, out); |
| 33779 | } |
| 33780 | at::Tensor & mul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 33781 | return wrapper_CPU_mul_out_out(self, other, out); |
| 33782 | } |
| 33783 | at::Tensor & mul_(at::Tensor & self, const at::Tensor & other) { |
| 33784 | return wrapper_CPU_mul__Tensor(self, other); |
| 33785 | } |
| 33786 | at::Tensor & mvlgamma_out(at::Tensor & out, const at::Tensor & self, int64_t p) { |
| 33787 | return wrapper_CPU_out_mvlgamma_out(self, p, out); |
| 33788 | } |
| 33789 | at::Tensor & mvlgamma_outf(const at::Tensor & self, int64_t p, at::Tensor & out) { |
| 33790 | return wrapper_CPU_out_mvlgamma_out(self, p, out); |
| 33791 | } |
| 33792 | at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { |
| 33793 | return wrapper_CPU__narrow_copy(self, dim, start, length); |
| 33794 | } |
| 33795 | at::Tensor narrow_copy_symint(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 33796 | return wrapper_CPU__narrow_copy(self, dim, start, length); |
| 33797 | } |
| 33798 | at::Tensor & narrow_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { |
| 33799 | return wrapper_CPU_out_narrow_copy_out(self, dim, start, length, out); |
| 33800 | } |
| 33801 | at::Tensor & narrow_copy_outf(const at::Tensor & self, int64_t dim, int64_t start, int64_t length, at::Tensor & out) { |
| 33802 | return wrapper_CPU_out_narrow_copy_out(self, dim, start, length, out); |
| 33803 | } |
| 33804 | at::Tensor & narrow_copy_symint_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 33805 | return wrapper_CPU_out_narrow_copy_out(self, dim, start, length, out); |
| 33806 | } |
| 33807 | at::Tensor & narrow_copy_symint_outf(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { |
| 33808 | return wrapper_CPU_out_narrow_copy_out(self, dim, start, length, out); |
| 33809 | } |
| 33810 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps) { |
| 33811 | return wrapper_CPU__native_batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 33812 | } |
| 33813 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps) { |
| 33814 | return wrapper_CPU_out_native_batch_norm_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 33815 | } |
| 33816 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_outf(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 33817 | return wrapper_CPU_out_native_batch_norm_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 33818 | } |
| 33819 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) { |
| 33820 | return wrapper_CPU_out__native_batch_norm_legit_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 33821 | } |
| 33822 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_outf(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 33823 | return wrapper_CPU_out__native_batch_norm_legit_out(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 33824 | } |
| 33825 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) { |
| 33826 | return wrapper_CPU___native_batch_norm_legit(input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 33827 | } |
| 33828 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, bool training, double momentum, double eps) { |
| 33829 | return wrapper_CPU_no_stats__native_batch_norm_legit(input, weight, bias, training, momentum, eps); |
| 33830 | } |
| 33831 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, bool training, double momentum, double eps) { |
| 33832 | return wrapper_CPU_no_stats_out__native_batch_norm_legit_out(input, weight, bias, training, momentum, eps, out, save_mean, save_invstd); |
| 33833 | } |
| 33834 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_outf(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 33835 | return wrapper_CPU_no_stats_out__native_batch_norm_legit_out(input, weight, bias, training, momentum, eps, out, save_mean, save_invstd); |
| 33836 | } |
| 33837 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) { |
| 33838 | return wrapper_CPU__native_batch_norm_backward(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); |
| 33839 | } |
| 33840 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_update_stats(const at::Tensor & input, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum) { |
| 33841 | return wrapper_CPU__batch_norm_update_stats(input, running_mean, running_var, momentum); |
| 33842 | } |
| 33843 | at::Tensor _cdist_forward(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode) { |
| 33844 | return wrapper_CPU___cdist_forward(x1, x2, p, compute_mode); |
| 33845 | } |
| 33846 | at::Tensor _cdist_backward(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) { |
| 33847 | return wrapper_CPU___cdist_backward(grad, x1, x2, p, cdist); |
| 33848 | } |
| 33849 | at::Tensor _pdist_forward(const at::Tensor & self, double p) { |
| 33850 | return wrapper_CPU___pdist_forward(self, p); |
| 33851 | } |
| 33852 | at::Tensor _pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { |
| 33853 | return wrapper_CPU___pdist_backward(grad, self, p, pdist); |
| 33854 | } |
| 33855 | at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) { |
| 33856 | return wrapper_CPU__pixel_shuffle(self, upscale_factor); |
| 33857 | } |
| 33858 | at::Tensor pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor) { |
| 33859 | return wrapper_CPU__pixel_unshuffle(self, downscale_factor); |
| 33860 | } |
| 33861 | at::Tensor channel_shuffle(const at::Tensor & self, int64_t groups) { |
| 33862 | return wrapper_CPU__channel_shuffle(self, groups); |
| 33863 | } |
| 33864 | at::Tensor native_channel_shuffle(const at::Tensor & self, int64_t groups) { |
| 33865 | return wrapper_CPU__native_channel_shuffle(self, groups); |
| 33866 | } |
| 33867 | at::Tensor & randperm_out(at::Tensor & out, int64_t n, c10::optional<at::Generator> generator) { |
| 33868 | return wrapper_CPU_generator_out_randperm_out(n, generator, out); |
| 33869 | } |
| 33870 | at::Tensor & randperm_outf(int64_t n, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 33871 | return wrapper_CPU_generator_out_randperm_out(n, generator, out); |
| 33872 | } |
| 33873 | at::Tensor & range_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) { |
| 33874 | return wrapper_CPU_out_range_out(start, end, step, out); |
| 33875 | } |
| 33876 | at::Tensor & range_outf(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 33877 | return wrapper_CPU_out_range_out(start, end, step, out); |
| 33878 | } |
| 33879 | at::Tensor reciprocal(const at::Tensor & self) { |
| 33880 | return wrapper_CPU_reciprocal(self); |
| 33881 | } |
| 33882 | at::Tensor & reciprocal_out(at::Tensor & out, const at::Tensor & self) { |
| 33883 | return wrapper_CPU_reciprocal_out_out(self, out); |
| 33884 | } |
| 33885 | at::Tensor & reciprocal_outf(const at::Tensor & self, at::Tensor & out) { |
| 33886 | return wrapper_CPU_reciprocal_out_out(self, out); |
| 33887 | } |
| 33888 | at::Tensor & reciprocal_(at::Tensor & self) { |
| 33889 | return wrapper_CPU_reciprocal_(self); |
| 33890 | } |
| 33891 | at::Tensor neg(const at::Tensor & self) { |
| 33892 | return wrapper_CPU_neg(self); |
| 33893 | } |
| 33894 | at::Tensor & neg_out(at::Tensor & out, const at::Tensor & self) { |
| 33895 | return wrapper_CPU_neg_out_out(self, out); |
| 33896 | } |
| 33897 | at::Tensor & neg_outf(const at::Tensor & self, at::Tensor & out) { |
| 33898 | return wrapper_CPU_neg_out_out(self, out); |
| 33899 | } |
| 33900 | at::Tensor & neg_(at::Tensor & self) { |
| 33901 | return wrapper_CPU_neg_(self); |
| 33902 | } |
| 33903 | at::Tensor repeat_interleave(const at::Tensor & repeats, c10::optional<int64_t> output_size) { |
| 33904 | return wrapper_CPU_Tensor_repeat_interleave(repeats, output_size); |
| 33905 | } |
| 33906 | at::Tensor _reshape_alias(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { |
| 33907 | return wrapper_CPU___reshape_alias(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); |
| 33908 | } |
| 33909 | at::Tensor _reshape_alias_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 33910 | return wrapper_CPU___reshape_alias(self, size, stride); |
| 33911 | } |
| 33912 | at::Tensor round(const at::Tensor & self) { |
| 33913 | return wrapper_CPU_round(self); |
| 33914 | } |
| 33915 | at::Tensor & round_out(at::Tensor & out, const at::Tensor & self) { |
| 33916 | return wrapper_CPU_round_out_out(self, out); |
| 33917 | } |
| 33918 | at::Tensor & round_outf(const at::Tensor & self, at::Tensor & out) { |
| 33919 | return wrapper_CPU_round_out_out(self, out); |
| 33920 | } |
| 33921 | at::Tensor & round_(at::Tensor & self) { |
| 33922 | return wrapper_CPU_round_(self); |
| 33923 | } |
| 33924 | at::Tensor round(const at::Tensor & self, int64_t decimals) { |
| 33925 | return wrapper_CPU_round_decimals(self, decimals); |
| 33926 | } |
| 33927 | at::Tensor & round_out(at::Tensor & out, const at::Tensor & self, int64_t decimals) { |
| 33928 | return wrapper_CPU_round_out_decimals_out(self, decimals, out); |
| 33929 | } |
| 33930 | at::Tensor & round_outf(const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 33931 | return wrapper_CPU_round_out_decimals_out(self, decimals, out); |
| 33932 | } |
| 33933 | at::Tensor & round_(at::Tensor & self, int64_t decimals) { |
| 33934 | return wrapper_CPU_round__decimals(self, decimals); |
| 33935 | } |
| 33936 | at::Tensor relu(const at::Tensor & self) { |
| 33937 | return wrapper_CPU__relu(self); |
| 33938 | } |
| 33939 | at::Tensor & relu_(at::Tensor & self) { |
| 33940 | return wrapper_CPU__relu_(self); |
| 33941 | } |
| 33942 | at::Tensor _prelu_kernel(const at::Tensor & self, const at::Tensor & weight) { |
| 33943 | return wrapper_CPU___prelu_kernel(self, weight); |
| 33944 | } |
| 33945 | ::std::tuple<at::Tensor,at::Tensor> _prelu_kernel_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) { |
| 33946 | return wrapper_CPU___prelu_kernel_backward(grad_output, self, weight); |
| 33947 | } |
| 33948 | at::Tensor gelu(const at::Tensor & self, c10::string_view approximate) { |
| 33949 | return wrapper_CPU_gelu(self, approximate); |
| 33950 | } |
| 33951 | at::Tensor & gelu_out(at::Tensor & out, const at::Tensor & self, c10::string_view approximate) { |
| 33952 | return wrapper_CPU_gelu_out_out(self, approximate, out); |
| 33953 | } |
| 33954 | at::Tensor & gelu_outf(const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { |
| 33955 | return wrapper_CPU_gelu_out_out(self, approximate, out); |
| 33956 | } |
| 33957 | at::Tensor & gelu_(at::Tensor & self, c10::string_view approximate) { |
| 33958 | return wrapper_CPU_gelu_(self, approximate); |
| 33959 | } |
| 33960 | at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { |
| 33961 | return wrapper_CPU_gelu_backward(grad_output, self, approximate); |
| 33962 | } |
| 33963 | at::Tensor & gelu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { |
| 33964 | return wrapper_CPU_gelu_backward_out_grad_input(grad_output, self, approximate, grad_input); |
| 33965 | } |
| 33966 | at::Tensor & gelu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input) { |
| 33967 | return wrapper_CPU_gelu_backward_out_grad_input(grad_output, self, approximate, grad_input); |
| 33968 | } |
| 33969 | at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 33970 | return wrapper_CPU_hardshrink(self, lambd); |
| 33971 | } |
| 33972 | at::Tensor & hardshrink_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd) { |
| 33973 | return wrapper_CPU_hardshrink_out_out(self, lambd, out); |
| 33974 | } |
| 33975 | at::Tensor & hardshrink_outf(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 33976 | return wrapper_CPU_hardshrink_out_out(self, lambd, out); |
| 33977 | } |
| 33978 | at::Tensor hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { |
| 33979 | return wrapper_CPU_hardshrink_backward(grad_out, self, lambd); |
| 33980 | } |
| 33981 | at::Tensor & hardshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { |
| 33982 | return wrapper_CPU_hardshrink_backward_out_grad_input(grad_out, self, lambd, grad_input); |
| 33983 | } |
| 33984 | at::Tensor & hardshrink_backward_outf(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 33985 | return wrapper_CPU_hardshrink_backward_out_grad_input(grad_out, self, lambd, grad_input); |
| 33986 | } |
| 33987 | at::Tensor rsqrt(const at::Tensor & self) { |
| 33988 | return wrapper_CPU_rsqrt(self); |
| 33989 | } |
| 33990 | at::Tensor & rsqrt_out(at::Tensor & out, const at::Tensor & self) { |
| 33991 | return wrapper_CPU_rsqrt_out_out(self, out); |
| 33992 | } |
| 33993 | at::Tensor & rsqrt_outf(const at::Tensor & self, at::Tensor & out) { |
| 33994 | return wrapper_CPU_rsqrt_out_out(self, out); |
| 33995 | } |
| 33996 | at::Tensor & rsqrt_(at::Tensor & self) { |
| 33997 | return wrapper_CPU_rsqrt_(self); |
| 33998 | } |
| 33999 | at::Tensor silu(const at::Tensor & self) { |
| 34000 | return wrapper_CPU_silu(self); |
| 34001 | } |
| 34002 | at::Tensor & silu_out(at::Tensor & out, const at::Tensor & self) { |
| 34003 | return wrapper_CPU_silu_out_out(self, out); |
| 34004 | } |
| 34005 | at::Tensor & silu_outf(const at::Tensor & self, at::Tensor & out) { |
| 34006 | return wrapper_CPU_silu_out_out(self, out); |
| 34007 | } |
| 34008 | at::Tensor & silu_(at::Tensor & self) { |
| 34009 | return wrapper_CPU_silu_(self); |
| 34010 | } |
| 34011 | at::Tensor silu_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 34012 | return wrapper_CPU_silu_backward(grad_output, self); |
| 34013 | } |
| 34014 | at::Tensor & silu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { |
| 34015 | return wrapper_CPU_silu_backward_out_grad_input(grad_output, self, grad_input); |
| 34016 | } |
| 34017 | at::Tensor & silu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 34018 | return wrapper_CPU_silu_backward_out_grad_input(grad_output, self, grad_input); |
| 34019 | } |
| 34020 | at::Tensor mish(const at::Tensor & self) { |
| 34021 | return wrapper_CPU_mish(self); |
| 34022 | } |
| 34023 | at::Tensor & mish_out(at::Tensor & out, const at::Tensor & self) { |
| 34024 | return wrapper_CPU_mish_out_out(self, out); |
| 34025 | } |
| 34026 | at::Tensor & mish_outf(const at::Tensor & self, at::Tensor & out) { |
| 34027 | return wrapper_CPU_mish_out_out(self, out); |
| 34028 | } |
| 34029 | at::Tensor & mish_(at::Tensor & self) { |
| 34030 | return wrapper_CPU_mish_(self); |
| 34031 | } |
| 34032 | at::Tensor mish_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 34033 | return wrapper_CPU__mish_backward(grad_output, self); |
| 34034 | } |
| 34035 | at::Tensor sigmoid(const at::Tensor & self) { |
| 34036 | return wrapper_CPU_sigmoid(self); |
| 34037 | } |
| 34038 | at::Tensor & sigmoid_out(at::Tensor & out, const at::Tensor & self) { |
| 34039 | return wrapper_CPU_sigmoid_out_out(self, out); |
| 34040 | } |
| 34041 | at::Tensor & sigmoid_outf(const at::Tensor & self, at::Tensor & out) { |
| 34042 | return wrapper_CPU_sigmoid_out_out(self, out); |
| 34043 | } |
| 34044 | at::Tensor & sigmoid_(at::Tensor & self) { |
| 34045 | return wrapper_CPU_sigmoid_(self); |
| 34046 | } |
| 34047 | at::Tensor logit(const at::Tensor & self, c10::optional<double> eps) { |
| 34048 | return wrapper_CPU__logit(self, eps); |
| 34049 | } |
| 34050 | at::Tensor & logit_out(at::Tensor & out, const at::Tensor & self, c10::optional<double> eps) { |
| 34051 | return wrapper_CPU_out_logit_out(self, eps, out); |
| 34052 | } |
| 34053 | at::Tensor & logit_outf(const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) { |
| 34054 | return wrapper_CPU_out_logit_out(self, eps, out); |
| 34055 | } |
| 34056 | at::Tensor & logit_(at::Tensor & self, c10::optional<double> eps) { |
| 34057 | return wrapper_CPU__logit_(self, eps); |
| 34058 | } |
| 34059 | at::Tensor sin(const at::Tensor & self) { |
| 34060 | return wrapper_CPU_sin(self); |
| 34061 | } |
| 34062 | at::Tensor & sin_out(at::Tensor & out, const at::Tensor & self) { |
| 34063 | return wrapper_CPU_sin_out_out(self, out); |
| 34064 | } |
| 34065 | at::Tensor & sin_outf(const at::Tensor & self, at::Tensor & out) { |
| 34066 | return wrapper_CPU_sin_out_out(self, out); |
| 34067 | } |
| 34068 | at::Tensor & sin_(at::Tensor & self) { |
| 34069 | return wrapper_CPU_sin_(self); |
| 34070 | } |
| 34071 | at::Tensor sinc(const at::Tensor & self) { |
| 34072 | return wrapper_CPU_sinc(self); |
| 34073 | } |
| 34074 | at::Tensor & sinc_out(at::Tensor & out, const at::Tensor & self) { |
| 34075 | return wrapper_CPU_sinc_out_out(self, out); |
| 34076 | } |
| 34077 | at::Tensor & sinc_outf(const at::Tensor & self, at::Tensor & out) { |
| 34078 | return wrapper_CPU_sinc_out_out(self, out); |
| 34079 | } |
| 34080 | at::Tensor & sinc_(at::Tensor & self) { |
| 34081 | return wrapper_CPU_sinc_(self); |
| 34082 | } |
| 34083 | at::Tensor sinh(const at::Tensor & self) { |
| 34084 | return wrapper_CPU_sinh(self); |
| 34085 | } |
| 34086 | at::Tensor & sinh_out(at::Tensor & out, const at::Tensor & self) { |
| 34087 | return wrapper_CPU_sinh_out_out(self, out); |
| 34088 | } |
| 34089 | at::Tensor & sinh_outf(const at::Tensor & self, at::Tensor & out) { |
| 34090 | return wrapper_CPU_sinh_out_out(self, out); |
| 34091 | } |
| 34092 | at::Tensor & sinh_(at::Tensor & self) { |
| 34093 | return wrapper_CPU_sinh_(self); |
| 34094 | } |
| 34095 | at::Tensor _softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 34096 | return wrapper_CPU__softmax(self, dim, half_to_float); |
| 34097 | } |
| 34098 | at::Tensor & _softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 34099 | return wrapper_CPU__softmax_out_out(self, dim, half_to_float, out); |
| 34100 | } |
| 34101 | at::Tensor & _softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 34102 | return wrapper_CPU__softmax_out_out(self, dim, half_to_float, out); |
| 34103 | } |
| 34104 | at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 34105 | return wrapper_CPU__softmax_backward_data(grad_output, output, dim, input_dtype); |
| 34106 | } |
| 34107 | at::Tensor & _softmax_backward_data_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 34108 | return wrapper_CPU__softmax_backward_data_out_out(grad_output, output, dim, input_dtype, grad_input); |
| 34109 | } |
| 34110 | at::Tensor & _softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { |
| 34111 | return wrapper_CPU__softmax_backward_data_out_out(grad_output, output, dim, input_dtype, grad_input); |
| 34112 | } |
| 34113 | at::Tensor & sspaddmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34114 | return wrapper_CPU_out_sspaddmm_out(self, mat1, mat2, beta, alpha, out); |
| 34115 | } |
| 34116 | at::Tensor & sspaddmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 34117 | return wrapper_CPU_out_sspaddmm_out(self, mat1, mat2, beta, alpha, out); |
| 34118 | } |
| 34119 | at::Tensor _stack(at::TensorList tensors, int64_t dim) { |
| 34120 | return wrapper_CPU___stack(tensors, dim); |
| 34121 | } |
| 34122 | at::Tensor & _stack_out(at::Tensor & out, at::TensorList tensors, int64_t dim) { |
| 34123 | return wrapper_CPU_out__stack_out(tensors, dim, out); |
| 34124 | } |
| 34125 | at::Tensor & _stack_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 34126 | return wrapper_CPU_out__stack_out(tensors, dim, out); |
| 34127 | } |
| 34128 | at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34129 | return wrapper_CPU_sum_dim_IntList(self, dim, keepdim, dtype); |
| 34130 | } |
| 34131 | at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34132 | return wrapper_CPU_sum_out_IntList_out(self, dim, keepdim, dtype, out); |
| 34133 | } |
| 34134 | at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 34135 | return wrapper_CPU_sum_out_IntList_out(self, dim, keepdim, dtype, out); |
| 34136 | } |
| 34137 | at::Tensor nansum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34138 | return wrapper_CPU__nansum(self, dim, keepdim, dtype); |
| 34139 | } |
| 34140 | at::Tensor & nansum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34141 | return wrapper_CPU_out_nansum_out(self, dim, keepdim, dtype, out); |
| 34142 | } |
| 34143 | at::Tensor & nansum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 34144 | return wrapper_CPU_out_nansum_out(self, dim, keepdim, dtype, out); |
| 34145 | } |
| 34146 | at::Tensor sqrt(const at::Tensor & self) { |
| 34147 | return wrapper_CPU_sqrt(self); |
| 34148 | } |
| 34149 | at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self) { |
| 34150 | return wrapper_CPU_sqrt_out_out(self, out); |
| 34151 | } |
| 34152 | at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out) { |
| 34153 | return wrapper_CPU_sqrt_out_out(self, out); |
| 34154 | } |
| 34155 | at::Tensor & sqrt_(at::Tensor & self) { |
| 34156 | return wrapper_CPU_sqrt_(self); |
| 34157 | } |
| 34158 | at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34159 | return wrapper_CPU_correction_std(self, dim, correction, keepdim); |
| 34160 | } |
| 34161 | at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34162 | return wrapper_CPU_correction_out_std_out(self, dim, correction, keepdim, out); |
| 34163 | } |
| 34164 | at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 34165 | return wrapper_CPU_correction_out_std_out(self, dim, correction, keepdim, out); |
| 34166 | } |
| 34167 | ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34168 | return wrapper_CPU_correction_std_mean(self, dim, correction, keepdim); |
| 34169 | } |
| 34170 | at::Tensor prod(const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 34171 | return wrapper_CPU__prod(self, dtype); |
| 34172 | } |
| 34173 | at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34174 | return wrapper_CPU_prod_dim_int(self, dim, keepdim, dtype); |
| 34175 | } |
| 34176 | at::Tensor & prod_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 34177 | return wrapper_CPU_prod_out_int_out(self, dim, keepdim, dtype, out); |
| 34178 | } |
| 34179 | at::Tensor & prod_outf(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 34180 | return wrapper_CPU_prod_out_int_out(self, dim, keepdim, dtype, out); |
| 34181 | } |
| 34182 | at::Tensor tan(const at::Tensor & self) { |
| 34183 | return wrapper_CPU_tan(self); |
| 34184 | } |
| 34185 | at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self) { |
| 34186 | return wrapper_CPU_tan_out_out(self, out); |
| 34187 | } |
| 34188 | at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out) { |
| 34189 | return wrapper_CPU_tan_out_out(self, out); |
| 34190 | } |
| 34191 | at::Tensor & tan_(at::Tensor & self) { |
| 34192 | return wrapper_CPU_tan_(self); |
| 34193 | } |
| 34194 | at::Tensor tanh(const at::Tensor & self) { |
| 34195 | return wrapper_CPU_tanh(self); |
| 34196 | } |
| 34197 | at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self) { |
| 34198 | return wrapper_CPU_tanh_out_out(self, out); |
| 34199 | } |
| 34200 | at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out) { |
| 34201 | return wrapper_CPU_tanh_out_out(self, out); |
| 34202 | } |
| 34203 | at::Tensor & tanh_(at::Tensor & self) { |
| 34204 | return wrapper_CPU_tanh_(self); |
| 34205 | } |
| 34206 | at::Tensor & tensordot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { |
| 34207 | return wrapper_CPU_out_tensordot_out(self, other, dims_self, dims_other, out); |
| 34208 | } |
| 34209 | at::Tensor & tensordot_outf(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { |
| 34210 | return wrapper_CPU_out_tensordot_out(self, other, dims_self, dims_other, out); |
| 34211 | } |
| 34212 | at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 34213 | return wrapper_CPU_threshold(self, threshold, value); |
| 34214 | } |
| 34215 | at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 34216 | return wrapper_CPU_threshold_out_out(self, threshold, value, out); |
| 34217 | } |
| 34218 | at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { |
| 34219 | return wrapper_CPU_threshold_out_out(self, threshold, value, out); |
| 34220 | } |
| 34221 | at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 34222 | return wrapper_CPU_threshold_(self, threshold, value); |
| 34223 | } |
| 34224 | at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { |
| 34225 | return wrapper_CPU_threshold_backward(grad_output, self, threshold); |
| 34226 | } |
| 34227 | at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { |
| 34228 | return wrapper_CPU_threshold_backward_out_grad_input(grad_output, self, threshold, grad_input); |
| 34229 | } |
| 34230 | at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input) { |
| 34231 | return wrapper_CPU_threshold_backward_out_grad_input(grad_output, self, threshold, grad_input); |
| 34232 | } |
| 34233 | at::Tensor flip(const at::Tensor & self, at::IntArrayRef dims) { |
| 34234 | return wrapper_CPU__flip(self, dims); |
| 34235 | } |
| 34236 | at::Tensor roll(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims) { |
| 34237 | return wrapper_CPU__roll(self, shifts, dims); |
| 34238 | } |
| 34239 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transform_bias_rescale_qkv(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) { |
| 34240 | return wrapper_CPU___transform_bias_rescale_qkv(qkv, qkv_bias, num_heads); |
| 34241 | } |
| 34242 | at::Tensor _nested_tensor_from_mask(const at::Tensor & t, const at::Tensor & mask, bool mask_check) { |
| 34243 | return wrapper_CPU___nested_tensor_from_mask(t, mask, mask_check); |
| 34244 | } |
| 34245 | bool _nested_tensor_from_mask_left_aligned(const at::Tensor & t, const at::Tensor & mask) { |
| 34246 | return wrapper_CPU___nested_tensor_from_mask_left_aligned(t, mask); |
| 34247 | } |
| 34248 | at::Tensor _nested_from_padded(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) { |
| 34249 | return wrapper_CPU___nested_from_padded(padded, cpu_nested_shape_example, fuse_transform_0213); |
| 34250 | } |
| 34251 | at::Tensor _nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 34252 | return wrapper_CPU___nested_view_from_buffer(self, nested_size, nested_strides, offsets); |
| 34253 | } |
| 34254 | at::Tensor trunc(const at::Tensor & self) { |
| 34255 | return wrapper_CPU_trunc(self); |
| 34256 | } |
| 34257 | at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self) { |
| 34258 | return wrapper_CPU_trunc_out_out(self, out); |
| 34259 | } |
| 34260 | at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out) { |
| 34261 | return wrapper_CPU_trunc_out_out(self, out); |
| 34262 | } |
| 34263 | at::Tensor & trunc_(at::Tensor & self) { |
| 34264 | return wrapper_CPU_trunc_(self); |
| 34265 | } |
| 34266 | ::std::tuple<at::Tensor,at::Tensor> _unique(const at::Tensor & self, bool sorted, bool return_inverse) { |
| 34267 | return wrapper_CPU___unique(self, sorted, return_inverse); |
| 34268 | } |
| 34269 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { |
| 34270 | return wrapper_CPU__unique_dim(self, dim, sorted, return_inverse, return_counts); |
| 34271 | } |
| 34272 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim) { |
| 34273 | return wrapper_CPU__unique_consecutive(self, return_inverse, return_counts, dim); |
| 34274 | } |
| 34275 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts) { |
| 34276 | return wrapper_CPU__unique_dim_consecutive(self, dim, return_inverse, return_counts); |
| 34277 | } |
| 34278 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _unique2(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) { |
| 34279 | return wrapper_CPU___unique2(self, sorted, return_inverse, return_counts); |
| 34280 | } |
| 34281 | at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34282 | return wrapper_CPU_correction_var(self, dim, correction, keepdim); |
| 34283 | } |
| 34284 | at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34285 | return wrapper_CPU_correction_out_var_out(self, dim, correction, keepdim, out); |
| 34286 | } |
| 34287 | at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 34288 | return wrapper_CPU_correction_out_var_out(self, dim, correction, keepdim, out); |
| 34289 | } |
| 34290 | ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 34291 | return wrapper_CPU_correction_var_mean(self, dim, correction, keepdim); |
| 34292 | } |
| 34293 | at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { |
| 34294 | return wrapper_CPU_self_where(condition, self, other); |
| 34295 | } |
| 34296 | at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { |
| 34297 | return wrapper_CPU_self_out_where_out(condition, self, other, out); |
| 34298 | } |
| 34299 | at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34300 | return wrapper_CPU_self_out_where_out(condition, self, other, out); |
| 34301 | } |
| 34302 | ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim) { |
| 34303 | return wrapper_CPU___weight_norm_interface(v, g, dim); |
| 34304 | } |
| 34305 | ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { |
| 34306 | return wrapper_CPU___weight_norm_interface_backward(grad_w, saved_v, saved_g, saved_norms, dim); |
| 34307 | } |
| 34308 | at::Tensor _efficientzerotensor(at::IntArrayRef size, at::TensorOptions options) { |
| 34309 | return wrapper_CPU___efficientzerotensor(size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 34310 | } |
| 34311 | at::Tensor _efficientzerotensor(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 34312 | return wrapper_CPU___efficientzerotensor(size, dtype, layout, device, pin_memory); |
| 34313 | } |
| 34314 | at::Tensor _standard_gamma_grad(const at::Tensor & self, const at::Tensor & output) { |
| 34315 | return wrapper_CPU___standard_gamma_grad(self, output); |
| 34316 | } |
| 34317 | at::Tensor _standard_gamma(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 34318 | return wrapper_CPU___standard_gamma(self, generator); |
| 34319 | } |
| 34320 | at::Tensor _dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { |
| 34321 | return wrapper_CPU___dirichlet_grad(x, alpha, total); |
| 34322 | } |
| 34323 | at::Tensor _sample_dirichlet(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 34324 | return wrapper_CPU___sample_dirichlet(self, generator); |
| 34325 | } |
| 34326 | at::Tensor poisson(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 34327 | return wrapper_CPU__poisson(self, generator); |
| 34328 | } |
| 34329 | at::Tensor binomial(const at::Tensor & count, const at::Tensor & prob, c10::optional<at::Generator> generator) { |
| 34330 | return wrapper_CPU__binomial(count, prob, generator); |
| 34331 | } |
| 34332 | at::Tensor _spdiags(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout) { |
| 34333 | return wrapper_CPU___spdiags(diagonals, offsets, shape, layout); |
| 34334 | } |
| 34335 | at::Tensor norm(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { |
| 34336 | return wrapper_CPU_norm_ScalarOpt_dim_dtype(self, p, dim, keepdim, dtype); |
| 34337 | } |
| 34338 | at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { |
| 34339 | return wrapper_CPU_norm_out_dtype_out(self, p, dim, keepdim, dtype, out); |
| 34340 | } |
| 34341 | at::Tensor & norm_outf(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { |
| 34342 | return wrapper_CPU_norm_out_dtype_out(self, p, dim, keepdim, dtype, out); |
| 34343 | } |
| 34344 | at::Tensor norm(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim) { |
| 34345 | return wrapper_CPU_norm_ScalarOpt_dim(self, p, dim, keepdim); |
| 34346 | } |
| 34347 | at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim) { |
| 34348 | return wrapper_CPU_norm_out_out(self, p, dim, keepdim, out); |
| 34349 | } |
| 34350 | at::Tensor & norm_outf(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 34351 | return wrapper_CPU_norm_out_out(self, p, dim, keepdim, out); |
| 34352 | } |
| 34353 | ::std::tuple<at::Tensor &,at::Tensor &> frexp_out(at::Tensor & mantissa, at::Tensor & exponent, const at::Tensor & self) { |
| 34354 | return wrapper_CPU_Tensor_out_frexp_out(self, mantissa, exponent); |
| 34355 | } |
| 34356 | ::std::tuple<at::Tensor &,at::Tensor &> frexp_outf(const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { |
| 34357 | return wrapper_CPU_Tensor_out_frexp_out(self, mantissa, exponent); |
| 34358 | } |
| 34359 | at::Tensor & zero_(at::Tensor & self) { |
| 34360 | return wrapper_CPU__zero_(self); |
| 34361 | } |
| 34362 | at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 34363 | return wrapper_CPU_sub_Tensor(self, other, alpha); |
| 34364 | } |
| 34365 | at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 34366 | return wrapper_CPU_sub_out_out(self, other, alpha, out); |
| 34367 | } |
| 34368 | at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 34369 | return wrapper_CPU_sub_out_out(self, other, alpha, out); |
| 34370 | } |
| 34371 | at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 34372 | return wrapper_CPU_sub__Tensor(self, other, alpha); |
| 34373 | } |
| 34374 | at::Tensor rsub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 34375 | return wrapper_CPU_Tensor_rsub(self, other, alpha); |
| 34376 | } |
| 34377 | at::Tensor heaviside(const at::Tensor & self, const at::Tensor & values) { |
| 34378 | return wrapper_CPU_heaviside(self, values); |
| 34379 | } |
| 34380 | at::Tensor & heaviside_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & values) { |
| 34381 | return wrapper_CPU_heaviside_out_out(self, values, out); |
| 34382 | } |
| 34383 | at::Tensor & heaviside_outf(const at::Tensor & self, const at::Tensor & values, at::Tensor & out) { |
| 34384 | return wrapper_CPU_heaviside_out_out(self, values, out); |
| 34385 | } |
| 34386 | at::Tensor & heaviside_(at::Tensor & self, const at::Tensor & values) { |
| 34387 | return wrapper_CPU_heaviside_(self, values); |
| 34388 | } |
| 34389 | at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34390 | return wrapper_CPU_addmm(self, mat1, mat2, beta, alpha); |
| 34391 | } |
| 34392 | at::Tensor & addmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34393 | return wrapper_CPU_addmm_out_out(self, mat1, mat2, beta, alpha, out); |
| 34394 | } |
| 34395 | at::Tensor & addmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 34396 | return wrapper_CPU_addmm_out_out(self, mat1, mat2, beta, alpha, out); |
| 34397 | } |
| 34398 | at::Tensor & addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34399 | return wrapper_CPU_addmm_(self, mat1, mat2, beta, alpha); |
| 34400 | } |
| 34401 | 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) { |
| 34402 | return wrapper_CPU__addmm_activation(self, mat1, mat2, beta, alpha, use_gelu); |
| 34403 | } |
| 34404 | at::Tensor & _addmm_activation_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) { |
| 34405 | return wrapper_CPU__addmm_activation_out_out(self, mat1, mat2, beta, alpha, use_gelu, out); |
| 34406 | } |
| 34407 | at::Tensor & _addmm_activation_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu, at::Tensor & out) { |
| 34408 | return wrapper_CPU__addmm_activation_out_out(self, mat1, mat2, beta, alpha, use_gelu, out); |
| 34409 | } |
| 34410 | int64_t sparse_dim(const at::Tensor & self) { |
| 34411 | return wrapper_CPU__sparse_dim(self); |
| 34412 | } |
| 34413 | int64_t dense_dim(const at::Tensor & self) { |
| 34414 | return wrapper_CPU__dense_dim(self); |
| 34415 | } |
| 34416 | at::Tensor to_sparse(const at::Tensor & self, int64_t sparse_dim) { |
| 34417 | return wrapper_CPU_sparse_dim_to_sparse(self, sparse_dim); |
| 34418 | } |
| 34419 | at::Tensor to_sparse(const at::Tensor & self, c10::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 34420 | return wrapper_CPU__to_sparse(self, layout, blocksize, dense_dim); |
| 34421 | } |
| 34422 | at::Tensor to_sparse_csr(const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 34423 | return wrapper_CPU__to_sparse_csr(self, dense_dim); |
| 34424 | } |
| 34425 | at::Tensor to_sparse_csc(const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 34426 | return wrapper_CPU__to_sparse_csc(self, dense_dim); |
| 34427 | } |
| 34428 | at::Tensor to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 34429 | return wrapper_CPU__to_sparse_bsr(self, blocksize, dense_dim); |
| 34430 | } |
| 34431 | at::Tensor to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional<int64_t> dense_dim) { |
| 34432 | return wrapper_CPU__to_sparse_bsc(self, blocksize, dense_dim); |
| 34433 | } |
| 34434 | at::Tensor to_mkldnn(const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 34435 | return wrapper_CPU__to_mkldnn(self, dtype); |
| 34436 | } |
| 34437 | at::Tensor quantize_per_tensor_dynamic(const at::Tensor & self, at::ScalarType dtype, bool reduce_range) { |
| 34438 | return wrapper_CPU__quantize_per_tensor_dynamic(self, dtype, reduce_range); |
| 34439 | } |
| 34440 | at::Tensor quantize_per_tensor(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) { |
| 34441 | return wrapper_CPU__quantize_per_tensor(self, scale, zero_point, dtype); |
| 34442 | } |
| 34443 | at::Tensor quantize_per_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) { |
| 34444 | return wrapper_CPU_tensor_qparams_quantize_per_tensor(self, scale, zero_point, dtype); |
| 34445 | } |
| 34446 | ::std::vector<at::Tensor> quantize_per_tensor(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) { |
| 34447 | return wrapper_CPU_tensors_quantize_per_tensor(tensors, scales, zero_points, dtype); |
| 34448 | } |
| 34449 | at::Tensor quantize_per_channel(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { |
| 34450 | return wrapper_CPU__quantize_per_channel(self, scales, zero_points, axis, dtype); |
| 34451 | } |
| 34452 | at::Tensor dequantize(const at::Tensor & self) { |
| 34453 | return wrapper_CPU_self_dequantize(self); |
| 34454 | } |
| 34455 | at::Tensor _make_per_tensor_quantized_tensor(const at::Tensor & self, double scale, int64_t zero_point) { |
| 34456 | return wrapper_CPU___make_per_tensor_quantized_tensor(self, scale, zero_point); |
| 34457 | } |
| 34458 | at::Tensor _make_per_channel_quantized_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) { |
| 34459 | return wrapper_CPU___make_per_channel_quantized_tensor(self, scale, zero_point, axis); |
| 34460 | } |
| 34461 | ::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_tensor_affine_cachemask(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { |
| 34462 | return wrapper_CPU__fake_quantize_per_tensor_affine_cachemask(self, scale, zero_point, quant_min, quant_max); |
| 34463 | } |
| 34464 | ::std::tuple<at::Tensor,at::Tensor> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) { |
| 34465 | return wrapper_CPU___fake_quantize_per_tensor_affine_cachemask_tensor_qparams(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max); |
| 34466 | } |
| 34467 | at::Tensor _fake_quantize_learnable_per_tensor_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 34468 | return wrapper_CPU___fake_quantize_learnable_per_tensor_affine(self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 34469 | } |
| 34470 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_tensor_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 34471 | return wrapper_CPU___fake_quantize_learnable_per_tensor_affine_backward(grad, self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 34472 | } |
| 34473 | ::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_channel_affine_cachemask(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { |
| 34474 | return wrapper_CPU__fake_quantize_per_channel_affine_cachemask(self, scale, zero_point, axis, quant_min, quant_max); |
| 34475 | } |
| 34476 | at::Tensor _fake_quantize_learnable_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 34477 | return wrapper_CPU___fake_quantize_learnable_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 34478 | } |
| 34479 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_channel_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 34480 | return wrapper_CPU___fake_quantize_learnable_per_channel_affine_backward(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 34481 | } |
| 34482 | ::std::tuple<at::Tensor,at::Tensor> _fused_moving_avg_obs_fq_helper(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) { |
| 34483 | return wrapper_CPU___fused_moving_avg_obs_fq_helper(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 34484 | } |
| 34485 | at::Scalar _local_scalar_dense(const at::Tensor & self) { |
| 34486 | return wrapper_CPU___local_scalar_dense(self); |
| 34487 | } |
| 34488 | at::Tensor & set_(at::Tensor & self, at::Storage source) { |
| 34489 | return wrapper_CPU_source_Storage_set_(self, source); |
| 34490 | } |
| 34491 | at::Tensor & set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride) { |
| 34492 | return wrapper_CPU_source_Storage_storage_offset_set_(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); |
| 34493 | } |
| 34494 | at::Tensor & set__symint(at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 34495 | return wrapper_CPU_source_Storage_storage_offset_set_(self, source, storage_offset, size, stride); |
| 34496 | } |
| 34497 | at::Tensor & set_(at::Tensor & self, const at::Tensor & source) { |
| 34498 | return wrapper_CPU_source_Tensor_set_(self, source); |
| 34499 | } |
| 34500 | at::Tensor & set_(at::Tensor & self) { |
| 34501 | return wrapper_CPU__set_(self); |
| 34502 | } |
| 34503 | bool is_set_to(const at::Tensor & self, const at::Tensor & tensor) { |
| 34504 | return wrapper_CPU__is_set_to(self, tensor); |
| 34505 | } |
| 34506 | at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { |
| 34507 | return wrapper_CPU_Scalar_masked_fill_(self, mask, value); |
| 34508 | } |
| 34509 | at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { |
| 34510 | return wrapper_CPU_Tensor_masked_fill_(self, mask, value); |
| 34511 | } |
| 34512 | at::Tensor & masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { |
| 34513 | return wrapper_CPU__masked_scatter_(self, mask, source); |
| 34514 | } |
| 34515 | at::Tensor _masked_softmax(const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type) { |
| 34516 | return wrapper_CPU___masked_softmax(self, mask, dim, mask_type); |
| 34517 | } |
| 34518 | at::Tensor _masked_softmax_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional<int64_t> dim) { |
| 34519 | return wrapper_CPU___masked_softmax_backward(grad_output, output, mask, dim); |
| 34520 | } |
| 34521 | at::Tensor view(const at::Tensor & self, at::IntArrayRef size) { |
| 34522 | return wrapper_CPU__view(self, c10::fromIntArrayRefSlow(size)); |
| 34523 | } |
| 34524 | at::Tensor view_symint(const at::Tensor & self, c10::SymIntArrayRef size) { |
| 34525 | return wrapper_CPU__view(self, size); |
| 34526 | } |
| 34527 | at::Tensor & put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) { |
| 34528 | return wrapper_CPU__put_(self, index, source, accumulate); |
| 34529 | } |
| 34530 | at::Tensor index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 34531 | return wrapper_CPU_index_add(self, dim, index, source, alpha); |
| 34532 | } |
| 34533 | at::Tensor & index_add_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 34534 | return wrapper_CPU_index_add_out_out(self, dim, index, source, alpha, out); |
| 34535 | } |
| 34536 | at::Tensor & index_add_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out) { |
| 34537 | return wrapper_CPU_index_add_out_out(self, dim, index, source, alpha, out); |
| 34538 | } |
| 34539 | at::Tensor & index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 34540 | return wrapper_CPU_index_add_(self, dim, index, source, alpha); |
| 34541 | } |
| 34542 | 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) { |
| 34543 | return wrapper_CPU_index_reduce(self, dim, index, source, reduce, include_self); |
| 34544 | } |
| 34545 | at::Tensor & index_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 34546 | return wrapper_CPU_index_reduce_out_out(self, dim, index, source, reduce, include_self, out); |
| 34547 | } |
| 34548 | at::Tensor & index_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 34549 | return wrapper_CPU_index_reduce_out_out(self, dim, index, source, reduce, include_self, out); |
| 34550 | } |
| 34551 | 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) { |
| 34552 | return wrapper_CPU_index_reduce_(self, dim, index, source, reduce, include_self); |
| 34553 | } |
| 34554 | at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 34555 | return wrapper_CPU_int_Scalar_index_fill_(self, dim, index, value); |
| 34556 | } |
| 34557 | at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 34558 | return wrapper_CPU_int_Tensor_index_fill_(self, dim, index, value); |
| 34559 | } |
| 34560 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34561 | return wrapper_CPU_scatter_src(self, dim, index, src); |
| 34562 | } |
| 34563 | at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34564 | return wrapper_CPU_scatter_out_src_out(self, dim, index, src, out); |
| 34565 | } |
| 34566 | at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 34567 | return wrapper_CPU_scatter_out_src_out(self, dim, index, src, out); |
| 34568 | } |
| 34569 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34570 | return wrapper_CPU_scatter__src(self, dim, index, src); |
| 34571 | } |
| 34572 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 34573 | return wrapper_CPU_scatter_value(self, dim, index, value); |
| 34574 | } |
| 34575 | at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 34576 | return wrapper_CPU_scatter_out_value_out(self, dim, index, value, out); |
| 34577 | } |
| 34578 | at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 34579 | return wrapper_CPU_scatter_out_value_out(self, dim, index, value, out); |
| 34580 | } |
| 34581 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 34582 | return wrapper_CPU_scatter__value(self, dim, index, value); |
| 34583 | } |
| 34584 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 34585 | return wrapper_CPU_scatter_reduce(self, dim, index, src, reduce); |
| 34586 | } |
| 34587 | at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 34588 | return wrapper_CPU_scatter_out_reduce_out(self, dim, index, src, reduce, out); |
| 34589 | } |
| 34590 | at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) { |
| 34591 | return wrapper_CPU_scatter_out_reduce_out(self, dim, index, src, reduce, out); |
| 34592 | } |
| 34593 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 34594 | return wrapper_CPU_scatter__reduce(self, dim, index, src, reduce); |
| 34595 | } |
| 34596 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 34597 | return wrapper_CPU_scatter_value_reduce(self, dim, index, value, reduce); |
| 34598 | } |
| 34599 | at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 34600 | return wrapper_CPU_scatter_out_value_reduce_out(self, dim, index, value, reduce, out); |
| 34601 | } |
| 34602 | at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out) { |
| 34603 | return wrapper_CPU_scatter_out_value_reduce_out(self, dim, index, value, reduce, out); |
| 34604 | } |
| 34605 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 34606 | return wrapper_CPU_scatter__value_reduce(self, dim, index, value, reduce); |
| 34607 | } |
| 34608 | at::Tensor scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34609 | return wrapper_CPU_scatter_add(self, dim, index, src); |
| 34610 | } |
| 34611 | at::Tensor & scatter_add_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34612 | return wrapper_CPU_scatter_add_out_out(self, dim, index, src, out); |
| 34613 | } |
| 34614 | at::Tensor & scatter_add_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 34615 | return wrapper_CPU_scatter_add_out_out(self, dim, index, src, out); |
| 34616 | } |
| 34617 | at::Tensor & scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 34618 | return wrapper_CPU_scatter_add_(self, dim, index, src); |
| 34619 | } |
| 34620 | 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) { |
| 34621 | return wrapper_CPU_scatter_reduce_two(self, dim, index, src, reduce, include_self); |
| 34622 | } |
| 34623 | at::Tensor & scatter_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 34624 | return wrapper_CPU_scatter_reduce_out_two_out(self, dim, index, src, reduce, include_self, out); |
| 34625 | } |
| 34626 | at::Tensor & scatter_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 34627 | return wrapper_CPU_scatter_reduce_out_two_out(self, dim, index, src, reduce, include_self, out); |
| 34628 | } |
| 34629 | 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) { |
| 34630 | return wrapper_CPU_scatter_reduce__two(self, dim, index, src, reduce, include_self); |
| 34631 | } |
| 34632 | at::Tensor eq(const at::Tensor & self, const at::Scalar & other) { |
| 34633 | return wrapper_CPU_eq_Scalar(self, other); |
| 34634 | } |
| 34635 | at::Tensor & eq_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34636 | return wrapper_CPU_eq_out_Scalar_out(self, other, out); |
| 34637 | } |
| 34638 | at::Tensor & eq_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34639 | return wrapper_CPU_eq_out_Scalar_out(self, other, out); |
| 34640 | } |
| 34641 | at::Tensor & eq_(at::Tensor & self, const at::Scalar & other) { |
| 34642 | return wrapper_CPU_eq__Scalar(self, other); |
| 34643 | } |
| 34644 | at::Tensor eq(const at::Tensor & self, const at::Tensor & other) { |
| 34645 | return wrapper_CPU_eq_Tensor(self, other); |
| 34646 | } |
| 34647 | at::Tensor & eq_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34648 | return wrapper_CPU_eq_out_Tensor_out(self, other, out); |
| 34649 | } |
| 34650 | at::Tensor & eq_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34651 | return wrapper_CPU_eq_out_Tensor_out(self, other, out); |
| 34652 | } |
| 34653 | at::Tensor & eq_(at::Tensor & self, const at::Tensor & other) { |
| 34654 | return wrapper_CPU_eq__Tensor(self, other); |
| 34655 | } |
| 34656 | at::Tensor bitwise_and(const at::Tensor & self, const at::Tensor & other) { |
| 34657 | return wrapper_CPU_bitwise_and_Tensor(self, other); |
| 34658 | } |
| 34659 | at::Tensor & bitwise_and_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34660 | return wrapper_CPU_bitwise_and_out_Tensor_out(self, other, out); |
| 34661 | } |
| 34662 | at::Tensor & bitwise_and_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34663 | return wrapper_CPU_bitwise_and_out_Tensor_out(self, other, out); |
| 34664 | } |
| 34665 | at::Tensor & bitwise_and_(at::Tensor & self, const at::Tensor & other) { |
| 34666 | return wrapper_CPU_bitwise_and__Tensor(self, other); |
| 34667 | } |
| 34668 | at::Tensor bitwise_or(const at::Tensor & self, const at::Tensor & other) { |
| 34669 | return wrapper_CPU_bitwise_or_Tensor(self, other); |
| 34670 | } |
| 34671 | at::Tensor & bitwise_or_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34672 | return wrapper_CPU_bitwise_or_out_Tensor_out(self, other, out); |
| 34673 | } |
| 34674 | at::Tensor & bitwise_or_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34675 | return wrapper_CPU_bitwise_or_out_Tensor_out(self, other, out); |
| 34676 | } |
| 34677 | at::Tensor & bitwise_or_(at::Tensor & self, const at::Tensor & other) { |
| 34678 | return wrapper_CPU_bitwise_or__Tensor(self, other); |
| 34679 | } |
| 34680 | at::Tensor bitwise_xor(const at::Tensor & self, const at::Tensor & other) { |
| 34681 | return wrapper_CPU_bitwise_xor_Tensor(self, other); |
| 34682 | } |
| 34683 | at::Tensor & bitwise_xor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34684 | return wrapper_CPU_bitwise_xor_out_Tensor_out(self, other, out); |
| 34685 | } |
| 34686 | at::Tensor & bitwise_xor_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34687 | return wrapper_CPU_bitwise_xor_out_Tensor_out(self, other, out); |
| 34688 | } |
| 34689 | at::Tensor & bitwise_xor_(at::Tensor & self, const at::Tensor & other) { |
| 34690 | return wrapper_CPU_bitwise_xor__Tensor(self, other); |
| 34691 | } |
| 34692 | at::Tensor __lshift__(const at::Tensor & self, const at::Scalar & other) { |
| 34693 | return wrapper_CPU_Scalar___lshift__(self, other); |
| 34694 | } |
| 34695 | at::Tensor & __ilshift__(at::Tensor & self, const at::Scalar & other) { |
| 34696 | return wrapper_CPU_Scalar___ilshift__(self, other); |
| 34697 | } |
| 34698 | at::Tensor __lshift__(const at::Tensor & self, const at::Tensor & other) { |
| 34699 | return wrapper_CPU_Tensor___lshift__(self, other); |
| 34700 | } |
| 34701 | at::Tensor & __ilshift__(at::Tensor & self, const at::Tensor & other) { |
| 34702 | return wrapper_CPU_Tensor___ilshift__(self, other); |
| 34703 | } |
| 34704 | at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Tensor & other) { |
| 34705 | return wrapper_CPU_bitwise_left_shift_Tensor(self, other); |
| 34706 | } |
| 34707 | at::Tensor & bitwise_left_shift_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34708 | return wrapper_CPU_bitwise_left_shift_out_Tensor_out(self, other, out); |
| 34709 | } |
| 34710 | at::Tensor & bitwise_left_shift_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34711 | return wrapper_CPU_bitwise_left_shift_out_Tensor_out(self, other, out); |
| 34712 | } |
| 34713 | at::Tensor & bitwise_left_shift_(at::Tensor & self, const at::Tensor & other) { |
| 34714 | return wrapper_CPU_bitwise_left_shift__Tensor(self, other); |
| 34715 | } |
| 34716 | at::Tensor __rshift__(const at::Tensor & self, const at::Scalar & other) { |
| 34717 | return wrapper_CPU_Scalar___rshift__(self, other); |
| 34718 | } |
| 34719 | at::Tensor & __irshift__(at::Tensor & self, const at::Scalar & other) { |
| 34720 | return wrapper_CPU_Scalar___irshift__(self, other); |
| 34721 | } |
| 34722 | at::Tensor __rshift__(const at::Tensor & self, const at::Tensor & other) { |
| 34723 | return wrapper_CPU_Tensor___rshift__(self, other); |
| 34724 | } |
| 34725 | at::Tensor & __irshift__(at::Tensor & self, const at::Tensor & other) { |
| 34726 | return wrapper_CPU_Tensor___irshift__(self, other); |
| 34727 | } |
| 34728 | at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Tensor & other) { |
| 34729 | return wrapper_CPU_bitwise_right_shift_Tensor(self, other); |
| 34730 | } |
| 34731 | at::Tensor & bitwise_right_shift_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34732 | return wrapper_CPU_bitwise_right_shift_out_Tensor_out(self, other, out); |
| 34733 | } |
| 34734 | at::Tensor & bitwise_right_shift_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34735 | return wrapper_CPU_bitwise_right_shift_out_Tensor_out(self, other, out); |
| 34736 | } |
| 34737 | at::Tensor & bitwise_right_shift_(at::Tensor & self, const at::Tensor & other) { |
| 34738 | return wrapper_CPU_bitwise_right_shift__Tensor(self, other); |
| 34739 | } |
| 34740 | at::Tensor tril(const at::Tensor & self, int64_t diagonal) { |
| 34741 | return wrapper_CPU_tril(self, diagonal); |
| 34742 | } |
| 34743 | at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal) { |
| 34744 | return wrapper_CPU_tril_out_out(self, diagonal, out); |
| 34745 | } |
| 34746 | at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 34747 | return wrapper_CPU_tril_out_out(self, diagonal, out); |
| 34748 | } |
| 34749 | at::Tensor & tril_(at::Tensor & self, int64_t diagonal) { |
| 34750 | return wrapper_CPU_tril_(self, diagonal); |
| 34751 | } |
| 34752 | at::Tensor triu(const at::Tensor & self, int64_t diagonal) { |
| 34753 | return wrapper_CPU_triu(self, diagonal); |
| 34754 | } |
| 34755 | at::Tensor & triu_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal) { |
| 34756 | return wrapper_CPU_triu_out_out(self, diagonal, out); |
| 34757 | } |
| 34758 | at::Tensor & triu_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 34759 | return wrapper_CPU_triu_out_out(self, diagonal, out); |
| 34760 | } |
| 34761 | at::Tensor & triu_(at::Tensor & self, int64_t diagonal) { |
| 34762 | return wrapper_CPU_triu_(self, diagonal); |
| 34763 | } |
| 34764 | at::Tensor digamma(const at::Tensor & self) { |
| 34765 | return wrapper_CPU_digamma(self); |
| 34766 | } |
| 34767 | at::Tensor & digamma_out(at::Tensor & out, const at::Tensor & self) { |
| 34768 | return wrapper_CPU_digamma_out_out(self, out); |
| 34769 | } |
| 34770 | at::Tensor & digamma_outf(const at::Tensor & self, at::Tensor & out) { |
| 34771 | return wrapper_CPU_digamma_out_out(self, out); |
| 34772 | } |
| 34773 | at::Tensor & digamma_(at::Tensor & self) { |
| 34774 | return wrapper_CPU_digamma_(self); |
| 34775 | } |
| 34776 | at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 34777 | return wrapper_CPU_lerp_Scalar(self, end, weight); |
| 34778 | } |
| 34779 | at::Tensor & lerp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 34780 | return wrapper_CPU_lerp_out_Scalar_out(self, end, weight, out); |
| 34781 | } |
| 34782 | at::Tensor & lerp_outf(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { |
| 34783 | return wrapper_CPU_lerp_out_Scalar_out(self, end, weight, out); |
| 34784 | } |
| 34785 | at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 34786 | return wrapper_CPU_lerp__Scalar(self, end, weight); |
| 34787 | } |
| 34788 | at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 34789 | return wrapper_CPU_lerp_Tensor(self, end, weight); |
| 34790 | } |
| 34791 | at::Tensor & lerp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 34792 | return wrapper_CPU_lerp_out_Tensor_out(self, end, weight, out); |
| 34793 | } |
| 34794 | at::Tensor & lerp_outf(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { |
| 34795 | return wrapper_CPU_lerp_out_Tensor_out(self, end, weight, out); |
| 34796 | } |
| 34797 | at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 34798 | return wrapper_CPU_lerp__Tensor(self, end, weight); |
| 34799 | } |
| 34800 | at::Tensor addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34801 | return wrapper_CPU__addbmm(self, batch1, batch2, beta, alpha); |
| 34802 | } |
| 34803 | at::Tensor & addbmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34804 | return wrapper_CPU_out_addbmm_out(self, batch1, batch2, beta, alpha, out); |
| 34805 | } |
| 34806 | at::Tensor & addbmm_outf(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 34807 | return wrapper_CPU_out_addbmm_out(self, batch1, batch2, beta, alpha, out); |
| 34808 | } |
| 34809 | at::Tensor & addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 34810 | return wrapper_CPU__addbmm_(self, batch1, batch2, beta, alpha); |
| 34811 | } |
| 34812 | at::Tensor & random_(at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 34813 | return wrapper_CPU_from_random_(self, from, to, generator); |
| 34814 | } |
| 34815 | at::Tensor & random_(at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 34816 | return wrapper_CPU_to_random_(self, to, generator); |
| 34817 | } |
| 34818 | at::Tensor & random_(at::Tensor & self, c10::optional<at::Generator> generator) { |
| 34819 | return wrapper_CPU__random_(self, generator); |
| 34820 | } |
| 34821 | at::Tensor & uniform_(at::Tensor & self, double from, double to, c10::optional<at::Generator> generator) { |
| 34822 | return wrapper_CPU__uniform_(self, from, to, generator); |
| 34823 | } |
| 34824 | at::Tensor & cauchy_(at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 34825 | return wrapper_CPU__cauchy_(self, median, sigma, generator); |
| 34826 | } |
| 34827 | at::Tensor & log_normal_(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 34828 | return wrapper_CPU__log_normal_(self, mean, std, generator); |
| 34829 | } |
| 34830 | at::Tensor & exponential_(at::Tensor & self, double lambd, c10::optional<at::Generator> generator) { |
| 34831 | return wrapper_CPU__exponential_(self, lambd, generator); |
| 34832 | } |
| 34833 | at::Tensor & geometric_(at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 34834 | return wrapper_CPU__geometric_(self, p, generator); |
| 34835 | } |
| 34836 | at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, at::TensorOptions options) { |
| 34837 | return wrapper_CPU__tril_indices(row, col, offset, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 34838 | } |
| 34839 | at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 34840 | return wrapper_CPU__tril_indices(row, col, offset, dtype, layout, device, pin_memory); |
| 34841 | } |
| 34842 | at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, at::TensorOptions options) { |
| 34843 | return wrapper_CPU__triu_indices(row, col, offset, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 34844 | } |
| 34845 | at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 34846 | return wrapper_CPU__triu_indices(row, col, offset, dtype, layout, device, pin_memory); |
| 34847 | } |
| 34848 | at::Tensor trace(const at::Tensor & self) { |
| 34849 | return wrapper_CPU__trace(self); |
| 34850 | } |
| 34851 | at::Tensor ne(const at::Tensor & self, const at::Scalar & other) { |
| 34852 | return wrapper_CPU_ne_Scalar(self, other); |
| 34853 | } |
| 34854 | at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34855 | return wrapper_CPU_ne_out_Scalar_out(self, other, out); |
| 34856 | } |
| 34857 | at::Tensor & ne_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34858 | return wrapper_CPU_ne_out_Scalar_out(self, other, out); |
| 34859 | } |
| 34860 | at::Tensor & ne_(at::Tensor & self, const at::Scalar & other) { |
| 34861 | return wrapper_CPU_ne__Scalar(self, other); |
| 34862 | } |
| 34863 | at::Tensor ne(const at::Tensor & self, const at::Tensor & other) { |
| 34864 | return wrapper_CPU_ne_Tensor(self, other); |
| 34865 | } |
| 34866 | at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34867 | return wrapper_CPU_ne_out_Tensor_out(self, other, out); |
| 34868 | } |
| 34869 | at::Tensor & ne_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34870 | return wrapper_CPU_ne_out_Tensor_out(self, other, out); |
| 34871 | } |
| 34872 | at::Tensor & ne_(at::Tensor & self, const at::Tensor & other) { |
| 34873 | return wrapper_CPU_ne__Tensor(self, other); |
| 34874 | } |
| 34875 | at::Tensor ge(const at::Tensor & self, const at::Scalar & other) { |
| 34876 | return wrapper_CPU_ge_Scalar(self, other); |
| 34877 | } |
| 34878 | at::Tensor & ge_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34879 | return wrapper_CPU_ge_out_Scalar_out(self, other, out); |
| 34880 | } |
| 34881 | at::Tensor & ge_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34882 | return wrapper_CPU_ge_out_Scalar_out(self, other, out); |
| 34883 | } |
| 34884 | at::Tensor & ge_(at::Tensor & self, const at::Scalar & other) { |
| 34885 | return wrapper_CPU_ge__Scalar(self, other); |
| 34886 | } |
| 34887 | at::Tensor ge(const at::Tensor & self, const at::Tensor & other) { |
| 34888 | return wrapper_CPU_ge_Tensor(self, other); |
| 34889 | } |
| 34890 | at::Tensor & ge_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34891 | return wrapper_CPU_ge_out_Tensor_out(self, other, out); |
| 34892 | } |
| 34893 | at::Tensor & ge_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34894 | return wrapper_CPU_ge_out_Tensor_out(self, other, out); |
| 34895 | } |
| 34896 | at::Tensor & ge_(at::Tensor & self, const at::Tensor & other) { |
| 34897 | return wrapper_CPU_ge__Tensor(self, other); |
| 34898 | } |
| 34899 | at::Tensor le(const at::Tensor & self, const at::Scalar & other) { |
| 34900 | return wrapper_CPU_le_Scalar(self, other); |
| 34901 | } |
| 34902 | at::Tensor & le_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34903 | return wrapper_CPU_le_out_Scalar_out(self, other, out); |
| 34904 | } |
| 34905 | at::Tensor & le_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34906 | return wrapper_CPU_le_out_Scalar_out(self, other, out); |
| 34907 | } |
| 34908 | at::Tensor & le_(at::Tensor & self, const at::Scalar & other) { |
| 34909 | return wrapper_CPU_le__Scalar(self, other); |
| 34910 | } |
| 34911 | at::Tensor le(const at::Tensor & self, const at::Tensor & other) { |
| 34912 | return wrapper_CPU_le_Tensor(self, other); |
| 34913 | } |
| 34914 | at::Tensor & le_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34915 | return wrapper_CPU_le_out_Tensor_out(self, other, out); |
| 34916 | } |
| 34917 | at::Tensor & le_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34918 | return wrapper_CPU_le_out_Tensor_out(self, other, out); |
| 34919 | } |
| 34920 | at::Tensor & le_(at::Tensor & self, const at::Tensor & other) { |
| 34921 | return wrapper_CPU_le__Tensor(self, other); |
| 34922 | } |
| 34923 | at::Tensor gt(const at::Tensor & self, const at::Scalar & other) { |
| 34924 | return wrapper_CPU_gt_Scalar(self, other); |
| 34925 | } |
| 34926 | at::Tensor & gt_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34927 | return wrapper_CPU_gt_out_Scalar_out(self, other, out); |
| 34928 | } |
| 34929 | at::Tensor & gt_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34930 | return wrapper_CPU_gt_out_Scalar_out(self, other, out); |
| 34931 | } |
| 34932 | at::Tensor & gt_(at::Tensor & self, const at::Scalar & other) { |
| 34933 | return wrapper_CPU_gt__Scalar(self, other); |
| 34934 | } |
| 34935 | at::Tensor gt(const at::Tensor & self, const at::Tensor & other) { |
| 34936 | return wrapper_CPU_gt_Tensor(self, other); |
| 34937 | } |
| 34938 | at::Tensor & gt_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34939 | return wrapper_CPU_gt_out_Tensor_out(self, other, out); |
| 34940 | } |
| 34941 | at::Tensor & gt_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34942 | return wrapper_CPU_gt_out_Tensor_out(self, other, out); |
| 34943 | } |
| 34944 | at::Tensor & gt_(at::Tensor & self, const at::Tensor & other) { |
| 34945 | return wrapper_CPU_gt__Tensor(self, other); |
| 34946 | } |
| 34947 | at::Tensor lt(const at::Tensor & self, const at::Scalar & other) { |
| 34948 | return wrapper_CPU_lt_Scalar(self, other); |
| 34949 | } |
| 34950 | at::Tensor & lt_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { |
| 34951 | return wrapper_CPU_lt_out_Scalar_out(self, other, out); |
| 34952 | } |
| 34953 | at::Tensor & lt_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 34954 | return wrapper_CPU_lt_out_Scalar_out(self, other, out); |
| 34955 | } |
| 34956 | at::Tensor & lt_(at::Tensor & self, const at::Scalar & other) { |
| 34957 | return wrapper_CPU_lt__Scalar(self, other); |
| 34958 | } |
| 34959 | at::Tensor lt(const at::Tensor & self, const at::Tensor & other) { |
| 34960 | return wrapper_CPU_lt_Tensor(self, other); |
| 34961 | } |
| 34962 | at::Tensor & lt_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 34963 | return wrapper_CPU_lt_out_Tensor_out(self, other, out); |
| 34964 | } |
| 34965 | at::Tensor & lt_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 34966 | return wrapper_CPU_lt_out_Tensor_out(self, other, out); |
| 34967 | } |
| 34968 | at::Tensor & lt_(at::Tensor & self, const at::Tensor & other) { |
| 34969 | return wrapper_CPU_lt__Tensor(self, other); |
| 34970 | } |
| 34971 | at::Tensor take(const at::Tensor & self, const at::Tensor & index) { |
| 34972 | return wrapper_CPU__take(self, index); |
| 34973 | } |
| 34974 | at::Tensor & take_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index) { |
| 34975 | return wrapper_CPU_out_take_out(self, index, out); |
| 34976 | } |
| 34977 | at::Tensor & take_outf(const at::Tensor & self, const at::Tensor & index, at::Tensor & out) { |
| 34978 | return wrapper_CPU_out_take_out(self, index, out); |
| 34979 | } |
| 34980 | at::Tensor index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) { |
| 34981 | return wrapper_CPU__index_select(self, dim, index); |
| 34982 | } |
| 34983 | at::Tensor & index_select_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index) { |
| 34984 | return wrapper_CPU_out_index_select_out(self, dim, index, out); |
| 34985 | } |
| 34986 | at::Tensor & index_select_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out) { |
| 34987 | return wrapper_CPU_out_index_select_out(self, dim, index, out); |
| 34988 | } |
| 34989 | at::Tensor masked_select(const at::Tensor & self, const at::Tensor & mask) { |
| 34990 | return wrapper_CPU__masked_select(self, mask); |
| 34991 | } |
| 34992 | at::Tensor & masked_select_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask) { |
| 34993 | return wrapper_CPU_out_masked_select_out(self, mask, out); |
| 34994 | } |
| 34995 | at::Tensor & masked_select_outf(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { |
| 34996 | return wrapper_CPU_out_masked_select_out(self, mask, out); |
| 34997 | } |
| 34998 | at::Tensor nonzero(const at::Tensor & self) { |
| 34999 | return wrapper_CPU__nonzero(self); |
| 35000 | } |
| 35001 | at::Tensor & nonzero_out(at::Tensor & out, const at::Tensor & self) { |
| 35002 | return wrapper_CPU_out_nonzero_out(self, out); |
| 35003 | } |
| 35004 | at::Tensor & nonzero_outf(const at::Tensor & self, at::Tensor & out) { |
| 35005 | return wrapper_CPU_out_nonzero_out(self, out); |
| 35006 | } |
| 35007 | at::Tensor gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 35008 | return wrapper_CPU_gather(self, dim, index, sparse_grad); |
| 35009 | } |
| 35010 | at::Tensor & gather_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 35011 | return wrapper_CPU_gather_out_out(self, dim, index, sparse_grad, out); |
| 35012 | } |
| 35013 | at::Tensor & gather_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 35014 | return wrapper_CPU_gather_out_out(self, dim, index, sparse_grad, out); |
| 35015 | } |
| 35016 | at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35017 | return wrapper_CPU_addcmul(self, tensor1, tensor2, value); |
| 35018 | } |
| 35019 | at::Tensor & addcmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35020 | return wrapper_CPU_addcmul_out_out(self, tensor1, tensor2, value, out); |
| 35021 | } |
| 35022 | at::Tensor & addcmul_outf(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { |
| 35023 | return wrapper_CPU_addcmul_out_out(self, tensor1, tensor2, value, out); |
| 35024 | } |
| 35025 | at::Tensor & addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35026 | return wrapper_CPU_addcmul_(self, tensor1, tensor2, value); |
| 35027 | } |
| 35028 | at::Tensor addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35029 | return wrapper_CPU_addcdiv(self, tensor1, tensor2, value); |
| 35030 | } |
| 35031 | at::Tensor & addcdiv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35032 | return wrapper_CPU_addcdiv_out_out(self, tensor1, tensor2, value, out); |
| 35033 | } |
| 35034 | at::Tensor & addcdiv_outf(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { |
| 35035 | return wrapper_CPU_addcdiv_out_out(self, tensor1, tensor2, value, out); |
| 35036 | } |
| 35037 | at::Tensor & addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 35038 | return wrapper_CPU_addcdiv_(self, tensor1, tensor2, value); |
| 35039 | } |
| 35040 | ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 35041 | return wrapper_CPU_triangular_solve(self, A, upper, transpose, unitriangular); |
| 35042 | } |
| 35043 | ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 35044 | return wrapper_CPU_triangular_solve_out_X(self, A, upper, transpose, unitriangular, X, M); |
| 35045 | } |
| 35046 | ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { |
| 35047 | return wrapper_CPU_triangular_solve_out_X(self, A, upper, transpose, unitriangular, X, M); |
| 35048 | } |
| 35049 | at::Tensor linalg_solve_triangular(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { |
| 35050 | return wrapper_CPU__linalg_solve_triangular(self, B, upper, left, unitriangular); |
| 35051 | } |
| 35052 | at::Tensor & linalg_solve_triangular_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { |
| 35053 | return wrapper_CPU_out_linalg_solve_triangular_out(self, B, upper, left, unitriangular, out); |
| 35054 | } |
| 35055 | at::Tensor & linalg_solve_triangular_outf(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { |
| 35056 | return wrapper_CPU_out_linalg_solve_triangular_out(self, B, upper, left, unitriangular, out); |
| 35057 | } |
| 35058 | at::Tensor cholesky(const at::Tensor & self, bool upper) { |
| 35059 | return wrapper_CPU__cholesky(self, upper); |
| 35060 | } |
| 35061 | at::Tensor & cholesky_out(at::Tensor & out, const at::Tensor & self, bool upper) { |
| 35062 | return wrapper_CPU_out_cholesky_out(self, upper, out); |
| 35063 | } |
| 35064 | at::Tensor & cholesky_outf(const at::Tensor & self, bool upper, at::Tensor & out) { |
| 35065 | return wrapper_CPU_out_cholesky_out(self, upper, out); |
| 35066 | } |
| 35067 | at::Tensor _cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper) { |
| 35068 | return wrapper_CPU___cholesky_solve_helper(self, A, upper); |
| 35069 | } |
| 35070 | at::Tensor cholesky_inverse(const at::Tensor & self, bool upper) { |
| 35071 | return wrapper_CPU__cholesky_inverse(self, upper); |
| 35072 | } |
| 35073 | at::Tensor & cholesky_inverse_out(at::Tensor & out, const at::Tensor & self, bool upper) { |
| 35074 | return wrapper_CPU_out_cholesky_inverse_out(self, upper, out); |
| 35075 | } |
| 35076 | at::Tensor & cholesky_inverse_outf(const at::Tensor & self, bool upper, at::Tensor & out) { |
| 35077 | return wrapper_CPU_out_cholesky_inverse_out(self, upper, out); |
| 35078 | } |
| 35079 | ::std::tuple<at::Tensor,at::Tensor> geqrf(const at::Tensor & self) { |
| 35080 | return wrapper_CPU__geqrf(self); |
| 35081 | } |
| 35082 | ::std::tuple<at::Tensor &,at::Tensor &> geqrf_out(at::Tensor & a, at::Tensor & tau, const at::Tensor & self) { |
| 35083 | return wrapper_CPU_a_geqrf_out(self, a, tau); |
| 35084 | } |
| 35085 | ::std::tuple<at::Tensor &,at::Tensor &> geqrf_outf(const at::Tensor & self, at::Tensor & a, at::Tensor & tau) { |
| 35086 | return wrapper_CPU_a_geqrf_out(self, a, tau); |
| 35087 | } |
| 35088 | at::Tensor ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { |
| 35089 | return wrapper_CPU__ormqr(self, input2, input3, left, transpose); |
| 35090 | } |
| 35091 | at::Tensor & ormqr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { |
| 35092 | return wrapper_CPU_out_ormqr_out(self, input2, input3, left, transpose, out); |
| 35093 | } |
| 35094 | at::Tensor & ormqr_outf(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) { |
| 35095 | return wrapper_CPU_out_ormqr_out(self, input2, input3, left, transpose, out); |
| 35096 | } |
| 35097 | ::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) { |
| 35098 | return wrapper_CPU_lu_unpack(LU_data, LU_pivots, unpack_data, unpack_pivots); |
| 35099 | } |
| 35100 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> lu_unpack_out(at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) { |
| 35101 | return wrapper_CPU_lu_unpack_out_out(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U); |
| 35102 | } |
| 35103 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> lu_unpack_outf(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U) { |
| 35104 | return wrapper_CPU_lu_unpack_out_out(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U); |
| 35105 | } |
| 35106 | at::Tensor multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator) { |
| 35107 | return wrapper_CPU__multinomial(self, num_samples, replacement, generator); |
| 35108 | } |
| 35109 | at::Tensor & multinomial_out(at::Tensor & out, const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator) { |
| 35110 | return wrapper_CPU_out_multinomial_out(self, num_samples, replacement, generator, out); |
| 35111 | } |
| 35112 | at::Tensor & multinomial_outf(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 35113 | return wrapper_CPU_out_multinomial_out(self, num_samples, replacement, generator, out); |
| 35114 | } |
| 35115 | at::Tensor lgamma(const at::Tensor & self) { |
| 35116 | return wrapper_CPU_lgamma(self); |
| 35117 | } |
| 35118 | at::Tensor & lgamma_out(at::Tensor & out, const at::Tensor & self) { |
| 35119 | return wrapper_CPU_lgamma_out_out(self, out); |
| 35120 | } |
| 35121 | at::Tensor & lgamma_outf(const at::Tensor & self, at::Tensor & out) { |
| 35122 | return wrapper_CPU_lgamma_out_out(self, out); |
| 35123 | } |
| 35124 | at::Tensor & lgamma_(at::Tensor & self) { |
| 35125 | return wrapper_CPU_lgamma_(self); |
| 35126 | } |
| 35127 | at::Tensor polygamma(int64_t n, const at::Tensor & self) { |
| 35128 | return wrapper_CPU_polygamma(n, self); |
| 35129 | } |
| 35130 | at::Tensor & polygamma_out(at::Tensor & out, int64_t n, const at::Tensor & self) { |
| 35131 | return wrapper_CPU_polygamma_out_out(n, self, out); |
| 35132 | } |
| 35133 | at::Tensor & polygamma_outf(int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 35134 | return wrapper_CPU_polygamma_out_out(n, self, out); |
| 35135 | } |
| 35136 | at::Tensor erfinv(const at::Tensor & self) { |
| 35137 | return wrapper_CPU_erfinv(self); |
| 35138 | } |
| 35139 | at::Tensor & erfinv_out(at::Tensor & out, const at::Tensor & self) { |
| 35140 | return wrapper_CPU_erfinv_out_out(self, out); |
| 35141 | } |
| 35142 | at::Tensor & erfinv_outf(const at::Tensor & self, at::Tensor & out) { |
| 35143 | return wrapper_CPU_erfinv_out_out(self, out); |
| 35144 | } |
| 35145 | at::Tensor & erfinv_(at::Tensor & self) { |
| 35146 | return wrapper_CPU_erfinv_(self); |
| 35147 | } |
| 35148 | at::Tensor i0(const at::Tensor & self) { |
| 35149 | return wrapper_CPU_i0(self); |
| 35150 | } |
| 35151 | at::Tensor & i0_out(at::Tensor & out, const at::Tensor & self) { |
| 35152 | return wrapper_CPU_i0_out_out(self, out); |
| 35153 | } |
| 35154 | at::Tensor & i0_outf(const at::Tensor & self, at::Tensor & out) { |
| 35155 | return wrapper_CPU_i0_out_out(self, out); |
| 35156 | } |
| 35157 | at::Tensor & i0_(at::Tensor & self) { |
| 35158 | return wrapper_CPU_i0_(self); |
| 35159 | } |
| 35160 | at::Tensor sign(const at::Tensor & self) { |
| 35161 | return wrapper_CPU_sign(self); |
| 35162 | } |
| 35163 | at::Tensor & sign_out(at::Tensor & out, const at::Tensor & self) { |
| 35164 | return wrapper_CPU_sign_out_out(self, out); |
| 35165 | } |
| 35166 | at::Tensor & sign_outf(const at::Tensor & self, at::Tensor & out) { |
| 35167 | return wrapper_CPU_sign_out_out(self, out); |
| 35168 | } |
| 35169 | at::Tensor & sign_(at::Tensor & self) { |
| 35170 | return wrapper_CPU_sign_(self); |
| 35171 | } |
| 35172 | at::Tensor signbit(const at::Tensor & self) { |
| 35173 | return wrapper_CPU_signbit(self); |
| 35174 | } |
| 35175 | at::Tensor & signbit_out(at::Tensor & out, const at::Tensor & self) { |
| 35176 | return wrapper_CPU_signbit_out_out(self, out); |
| 35177 | } |
| 35178 | at::Tensor & signbit_outf(const at::Tensor & self, at::Tensor & out) { |
| 35179 | return wrapper_CPU_signbit_out_out(self, out); |
| 35180 | } |
| 35181 | at::Tensor atan2(const at::Tensor & self, const at::Tensor & other) { |
| 35182 | return wrapper_CPU_atan2(self, other); |
| 35183 | } |
| 35184 | at::Tensor & atan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35185 | return wrapper_CPU_atan2_out_out(self, other, out); |
| 35186 | } |
| 35187 | at::Tensor & atan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35188 | return wrapper_CPU_atan2_out_out(self, other, out); |
| 35189 | } |
| 35190 | at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other) { |
| 35191 | return wrapper_CPU_atan2_(self, other); |
| 35192 | } |
| 35193 | at::Tensor histc(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { |
| 35194 | return wrapper_CPU__histc(self, bins, min, max); |
| 35195 | } |
| 35196 | at::Tensor & histc_out(at::Tensor & out, const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { |
| 35197 | return wrapper_CPU_out_histc_out(self, bins, min, max, out); |
| 35198 | } |
| 35199 | at::Tensor & histc_outf(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) { |
| 35200 | return wrapper_CPU_out_histc_out(self, bins, min, max, out); |
| 35201 | } |
| 35202 | ::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 35203 | return wrapper_CPU_bins_tensor_histogram(self, bins, weight, density); |
| 35204 | } |
| 35205 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_out(at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 35206 | return wrapper_CPU_bins_tensor_out_histogram_out(self, bins, weight, density, hist, bin_edges); |
| 35207 | } |
| 35208 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_outf(const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 35209 | return wrapper_CPU_bins_tensor_out_histogram_out(self, bins, weight, density, hist, bin_edges); |
| 35210 | } |
| 35211 | ::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 35212 | return wrapper_CPU_bin_ct_histogram(self, bins, range, weight, density); |
| 35213 | } |
| 35214 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_out(at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 35215 | return wrapper_CPU_bin_ct_out_histogram_out(self, bins, range, weight, density, hist, bin_edges); |
| 35216 | } |
| 35217 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_outf(const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 35218 | return wrapper_CPU_bin_ct_out_histogram_out(self, bins, range, weight, density, hist, bin_edges); |
| 35219 | } |
| 35220 | ::std::vector<at::Tensor> _histogramdd_bin_edges(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 35221 | return wrapper_CPU___histogramdd_bin_edges(self, bins, range, weight, density); |
| 35222 | } |
| 35223 | at::Tensor _histogramdd_from_bin_cts(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 35224 | return wrapper_CPU___histogramdd_from_bin_cts(self, bins, range, weight, density); |
| 35225 | } |
| 35226 | at::Tensor _histogramdd_from_bin_tensors(const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 35227 | return wrapper_CPU___histogramdd_from_bin_tensors(self, bins, weight, density); |
| 35228 | } |
| 35229 | at::Tensor fmod(const at::Tensor & self, const at::Tensor & other) { |
| 35230 | return wrapper_CPU_fmod_Tensor(self, other); |
| 35231 | } |
| 35232 | at::Tensor & fmod_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35233 | return wrapper_CPU_fmod_out_Tensor_out(self, other, out); |
| 35234 | } |
| 35235 | at::Tensor & fmod_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35236 | return wrapper_CPU_fmod_out_Tensor_out(self, other, out); |
| 35237 | } |
| 35238 | at::Tensor & fmod_(at::Tensor & self, const at::Tensor & other) { |
| 35239 | return wrapper_CPU_fmod__Tensor(self, other); |
| 35240 | } |
| 35241 | at::Tensor hypot(const at::Tensor & self, const at::Tensor & other) { |
| 35242 | return wrapper_CPU_hypot(self, other); |
| 35243 | } |
| 35244 | at::Tensor & hypot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35245 | return wrapper_CPU_hypot_out_out(self, other, out); |
| 35246 | } |
| 35247 | at::Tensor & hypot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35248 | return wrapper_CPU_hypot_out_out(self, other, out); |
| 35249 | } |
| 35250 | at::Tensor & hypot_(at::Tensor & self, const at::Tensor & other) { |
| 35251 | return wrapper_CPU_hypot_(self, other); |
| 35252 | } |
| 35253 | at::Tensor igamma(const at::Tensor & self, const at::Tensor & other) { |
| 35254 | return wrapper_CPU_igamma(self, other); |
| 35255 | } |
| 35256 | at::Tensor & igamma_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35257 | return wrapper_CPU_igamma_out_out(self, other, out); |
| 35258 | } |
| 35259 | at::Tensor & igamma_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35260 | return wrapper_CPU_igamma_out_out(self, other, out); |
| 35261 | } |
| 35262 | at::Tensor & igamma_(at::Tensor & self, const at::Tensor & other) { |
| 35263 | return wrapper_CPU_igamma_(self, other); |
| 35264 | } |
| 35265 | at::Tensor igammac(const at::Tensor & self, const at::Tensor & other) { |
| 35266 | return wrapper_CPU_igammac(self, other); |
| 35267 | } |
| 35268 | at::Tensor & igammac_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35269 | return wrapper_CPU_igammac_out_out(self, other, out); |
| 35270 | } |
| 35271 | at::Tensor & igammac_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35272 | return wrapper_CPU_igammac_out_out(self, other, out); |
| 35273 | } |
| 35274 | at::Tensor & igammac_(at::Tensor & self, const at::Tensor & other) { |
| 35275 | return wrapper_CPU_igammac_(self, other); |
| 35276 | } |
| 35277 | at::Tensor nextafter(const at::Tensor & self, const at::Tensor & other) { |
| 35278 | return wrapper_CPU_nextafter(self, other); |
| 35279 | } |
| 35280 | at::Tensor & nextafter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35281 | return wrapper_CPU_nextafter_out_out(self, other, out); |
| 35282 | } |
| 35283 | at::Tensor & nextafter_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35284 | return wrapper_CPU_nextafter_out_out(self, other, out); |
| 35285 | } |
| 35286 | at::Tensor & nextafter_(at::Tensor & self, const at::Tensor & other) { |
| 35287 | return wrapper_CPU_nextafter_(self, other); |
| 35288 | } |
| 35289 | at::Tensor remainder(const at::Tensor & self, const at::Tensor & other) { |
| 35290 | return wrapper_CPU_remainder_Tensor(self, other); |
| 35291 | } |
| 35292 | at::Tensor & remainder_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35293 | return wrapper_CPU_remainder_out_Tensor_out(self, other, out); |
| 35294 | } |
| 35295 | at::Tensor & remainder_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35296 | return wrapper_CPU_remainder_out_Tensor_out(self, other, out); |
| 35297 | } |
| 35298 | at::Tensor & remainder_(at::Tensor & self, const at::Tensor & other) { |
| 35299 | return wrapper_CPU_remainder__Tensor(self, other); |
| 35300 | } |
| 35301 | at::Tensor remainder(const at::Scalar & self, const at::Tensor & other) { |
| 35302 | return wrapper_CPU_Scalar_Tensor_remainder(self, other); |
| 35303 | } |
| 35304 | at::Tensor min(const at::Tensor & self) { |
| 35305 | return wrapper_CPU__min(self); |
| 35306 | } |
| 35307 | at::Tensor fmin(const at::Tensor & self, const at::Tensor & other) { |
| 35308 | return wrapper_CPU_fmin(self, other); |
| 35309 | } |
| 35310 | at::Tensor & fmin_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35311 | return wrapper_CPU_fmin_out_out(self, other, out); |
| 35312 | } |
| 35313 | at::Tensor & fmin_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35314 | return wrapper_CPU_fmin_out_out(self, other, out); |
| 35315 | } |
| 35316 | at::Tensor max(const at::Tensor & self) { |
| 35317 | return wrapper_CPU__max(self); |
| 35318 | } |
| 35319 | at::Tensor & max_out(at::Tensor & out, const at::Tensor & self) { |
| 35320 | return wrapper_CPU_unary_out_max_out(self, out); |
| 35321 | } |
| 35322 | at::Tensor & max_outf(const at::Tensor & self, at::Tensor & out) { |
| 35323 | return wrapper_CPU_unary_out_max_out(self, out); |
| 35324 | } |
| 35325 | at::Tensor fmax(const at::Tensor & self, const at::Tensor & other) { |
| 35326 | return wrapper_CPU_fmax(self, other); |
| 35327 | } |
| 35328 | at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35329 | return wrapper_CPU_fmax_out_out(self, other, out); |
| 35330 | } |
| 35331 | at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35332 | return wrapper_CPU_fmax_out_out(self, other, out); |
| 35333 | } |
| 35334 | at::Tensor maximum(const at::Tensor & self, const at::Tensor & other) { |
| 35335 | return wrapper_CPU_maximum(self, other); |
| 35336 | } |
| 35337 | at::Tensor & maximum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35338 | return wrapper_CPU_maximum_out_out(self, other, out); |
| 35339 | } |
| 35340 | at::Tensor & maximum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35341 | return wrapper_CPU_maximum_out_out(self, other, out); |
| 35342 | } |
| 35343 | at::Tensor minimum(const at::Tensor & self, const at::Tensor & other) { |
| 35344 | return wrapper_CPU_minimum(self, other); |
| 35345 | } |
| 35346 | at::Tensor & minimum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 35347 | return wrapper_CPU_minimum_out_out(self, other, out); |
| 35348 | } |
| 35349 | at::Tensor & minimum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 35350 | return wrapper_CPU_minimum_out_out(self, other, out); |
| 35351 | } |
| 35352 | ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending) { |
| 35353 | return wrapper_CPU_sort_stable(self, stable, dim, descending); |
| 35354 | } |
| 35355 | ::std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending) { |
| 35356 | return wrapper_CPU_sort_out_values_stable(self, stable, dim, descending, values, indices); |
| 35357 | } |
| 35358 | ::std::tuple<at::Tensor &,at::Tensor &> sort_outf(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 35359 | return wrapper_CPU_sort_out_values_stable(self, stable, dim, descending, values, indices); |
| 35360 | } |
| 35361 | at::Tensor argsort(const at::Tensor & self, bool stable, int64_t dim, bool descending) { |
| 35362 | return wrapper_CPU_stable_argsort(self, stable, dim, descending); |
| 35363 | } |
| 35364 | ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 35365 | return wrapper_CPU_topk(self, k, dim, largest, sorted); |
| 35366 | } |
| 35367 | ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 35368 | return wrapper_CPU_topk_out_values(self, k, dim, largest, sorted, values, indices); |
| 35369 | } |
| 35370 | ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { |
| 35371 | return wrapper_CPU_topk_out_values(self, k, dim, largest, sorted, values, indices); |
| 35372 | } |
| 35373 | at::Tensor all(const at::Tensor & self) { |
| 35374 | return wrapper_CPU_all(self); |
| 35375 | } |
| 35376 | at::Tensor & all_out(at::Tensor & out, const at::Tensor & self) { |
| 35377 | return wrapper_CPU_all_out_all_out(self, out); |
| 35378 | } |
| 35379 | at::Tensor & all_outf(const at::Tensor & self, at::Tensor & out) { |
| 35380 | return wrapper_CPU_all_out_all_out(self, out); |
| 35381 | } |
| 35382 | at::Tensor any(const at::Tensor & self) { |
| 35383 | return wrapper_CPU_any(self); |
| 35384 | } |
| 35385 | at::Tensor & any_out(at::Tensor & out, const at::Tensor & self) { |
| 35386 | return wrapper_CPU_any_out_all_out(self, out); |
| 35387 | } |
| 35388 | at::Tensor & any_outf(const at::Tensor & self, at::Tensor & out) { |
| 35389 | return wrapper_CPU_any_out_all_out(self, out); |
| 35390 | } |
| 35391 | at::Tensor renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 35392 | return wrapper_CPU_renorm(self, p, dim, maxnorm); |
| 35393 | } |
| 35394 | at::Tensor & renorm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 35395 | return wrapper_CPU_renorm_out_out(self, p, dim, maxnorm, out); |
| 35396 | } |
| 35397 | at::Tensor & renorm_outf(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out) { |
| 35398 | return wrapper_CPU_renorm_out_out(self, p, dim, maxnorm, out); |
| 35399 | } |
| 35400 | at::Tensor & renorm_(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 35401 | return wrapper_CPU_renorm_(self, p, dim, maxnorm); |
| 35402 | } |
| 35403 | at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { |
| 35404 | return wrapper_CPU__unfold(self, dimension, size, step); |
| 35405 | } |
| 35406 | at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { |
| 35407 | return wrapper_CPU__unfold_backward(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step); |
| 35408 | } |
| 35409 | at::Tensor unfold_backward_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { |
| 35410 | return wrapper_CPU__unfold_backward(grad_in, input_sizes, dim, size, step); |
| 35411 | } |
| 35412 | bool equal(const at::Tensor & self, const at::Tensor & other) { |
| 35413 | return wrapper_CPU__equal(self, other); |
| 35414 | } |
| 35415 | at::Tensor pow(const at::Tensor & self, const at::Tensor & exponent) { |
| 35416 | return wrapper_CPU_pow_Tensor_Tensor(self, exponent); |
| 35417 | } |
| 35418 | at::Tensor & pow_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & exponent) { |
| 35419 | return wrapper_CPU_pow_out_Tensor_Tensor_out(self, exponent, out); |
| 35420 | } |
| 35421 | at::Tensor & pow_outf(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { |
| 35422 | return wrapper_CPU_pow_out_Tensor_Tensor_out(self, exponent, out); |
| 35423 | } |
| 35424 | at::Tensor & pow_(at::Tensor & self, const at::Tensor & exponent) { |
| 35425 | return wrapper_CPU_pow__Tensor(self, exponent); |
| 35426 | } |
| 35427 | at::Tensor pow(const at::Scalar & self, const at::Tensor & exponent) { |
| 35428 | return wrapper_CPU_pow_Scalar(self, exponent); |
| 35429 | } |
| 35430 | at::Tensor & pow_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & exponent) { |
| 35431 | return wrapper_CPU_pow_out_Scalar_out(self, exponent, out); |
| 35432 | } |
| 35433 | at::Tensor & pow_outf(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { |
| 35434 | return wrapper_CPU_pow_out_Scalar_out(self, exponent, out); |
| 35435 | } |
| 35436 | at::Tensor pow(const at::Tensor & self, const at::Scalar & exponent) { |
| 35437 | return wrapper_CPU_pow_Tensor_Scalar(self, exponent); |
| 35438 | } |
| 35439 | at::Tensor & pow_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & exponent) { |
| 35440 | return wrapper_CPU_pow_out_Tensor_Scalar_out(self, exponent, out); |
| 35441 | } |
| 35442 | at::Tensor & pow_outf(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { |
| 35443 | return wrapper_CPU_pow_out_Tensor_Scalar_out(self, exponent, out); |
| 35444 | } |
| 35445 | at::Tensor & pow_(at::Tensor & self, const at::Scalar & exponent) { |
| 35446 | return wrapper_CPU_pow__Scalar(self, exponent); |
| 35447 | } |
| 35448 | at::Tensor & normal_(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 35449 | return wrapper_CPU__normal_(self, mean, std, generator); |
| 35450 | } |
| 35451 | at::Tensor normal(const at::Tensor & mean, double std, c10::optional<at::Generator> generator) { |
| 35452 | return wrapper_CPU_Tensor_float_normal(mean, std, generator); |
| 35453 | } |
| 35454 | at::Tensor & normal_out(at::Tensor & out, const at::Tensor & mean, double std, c10::optional<at::Generator> generator) { |
| 35455 | return wrapper_CPU_Tensor_float_out_normal_out(mean, std, generator, out); |
| 35456 | } |
| 35457 | at::Tensor & normal_outf(const at::Tensor & mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 35458 | return wrapper_CPU_Tensor_float_out_normal_out(mean, std, generator, out); |
| 35459 | } |
| 35460 | at::Tensor normal(double mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 35461 | return wrapper_CPU_float_Tensor_normal(mean, std, generator); |
| 35462 | } |
| 35463 | at::Tensor & normal_out(at::Tensor & out, double mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 35464 | return wrapper_CPU_float_Tensor_out_normal_out(mean, std, generator, out); |
| 35465 | } |
| 35466 | at::Tensor & normal_outf(double mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 35467 | return wrapper_CPU_float_Tensor_out_normal_out(mean, std, generator, out); |
| 35468 | } |
| 35469 | at::Tensor normal(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 35470 | return wrapper_CPU_Tensor_Tensor_normal(mean, std, generator); |
| 35471 | } |
| 35472 | at::Tensor & normal_out(at::Tensor & out, const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 35473 | return wrapper_CPU_Tensor_Tensor_out_normal_out(mean, std, generator, out); |
| 35474 | } |
| 35475 | at::Tensor & normal_outf(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 35476 | return wrapper_CPU_Tensor_Tensor_out_normal_out(mean, std, generator, out); |
| 35477 | } |
| 35478 | ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Scalar & scalar) { |
| 35479 | return wrapper_CPU_Scalar__foreach_add(self, scalar); |
| 35480 | } |
| 35481 | void _foreach_add_(at::TensorList self, const at::Scalar & scalar) { |
| 35482 | return wrapper_CPU_Scalar__foreach_add_(self, scalar); |
| 35483 | } |
| 35484 | ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, const at::Scalar & scalar) { |
| 35485 | return wrapper_CPU_Scalar__foreach_sub(self, scalar); |
| 35486 | } |
| 35487 | void _foreach_sub_(at::TensorList self, const at::Scalar & scalar) { |
| 35488 | return wrapper_CPU_Scalar__foreach_sub_(self, scalar); |
| 35489 | } |
| 35490 | ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, const at::Scalar & scalar) { |
| 35491 | return wrapper_CPU_Scalar__foreach_mul(self, scalar); |
| 35492 | } |
| 35493 | void _foreach_mul_(at::TensorList self, const at::Scalar & scalar) { |
| 35494 | return wrapper_CPU_Scalar__foreach_mul_(self, scalar); |
| 35495 | } |
| 35496 | ::std::vector<at::Tensor> _foreach_div(at::TensorList self, const at::Scalar & scalar) { |
| 35497 | return wrapper_CPU_Scalar__foreach_div(self, scalar); |
| 35498 | } |
| 35499 | void _foreach_div_(at::TensorList self, const at::Scalar & scalar) { |
| 35500 | return wrapper_CPU_Scalar__foreach_div_(self, scalar); |
| 35501 | } |
| 35502 | ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, const at::Scalar & scalar) { |
| 35503 | return wrapper_CPU_Scalar__foreach_clamp_min(self, scalar); |
| 35504 | } |
| 35505 | void _foreach_clamp_min_(at::TensorList self, const at::Scalar & scalar) { |
| 35506 | return wrapper_CPU_Scalar__foreach_clamp_min_(self, scalar); |
| 35507 | } |
| 35508 | ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, const at::Scalar & scalar) { |
| 35509 | return wrapper_CPU_Scalar__foreach_clamp_max(self, scalar); |
| 35510 | } |
| 35511 | void _foreach_clamp_max_(at::TensorList self, const at::Scalar & scalar) { |
| 35512 | return wrapper_CPU_Scalar__foreach_clamp_max_(self, scalar); |
| 35513 | } |
| 35514 | ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, const at::Scalar & scalar) { |
| 35515 | return wrapper_CPU_Scalar__foreach_maximum(self, scalar); |
| 35516 | } |
| 35517 | void _foreach_maximum_(at::TensorList self, const at::Scalar & scalar) { |
| 35518 | return wrapper_CPU_Scalar__foreach_maximum_(self, scalar); |
| 35519 | } |
| 35520 | ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, const at::Scalar & scalar) { |
| 35521 | return wrapper_CPU_Scalar__foreach_minimum(self, scalar); |
| 35522 | } |
| 35523 | void _foreach_minimum_(at::TensorList self, const at::Scalar & scalar) { |
| 35524 | return wrapper_CPU_Scalar__foreach_minimum_(self, scalar); |
| 35525 | } |
| 35526 | ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 35527 | return wrapper_CPU_List__foreach_add(self, other, alpha); |
| 35528 | } |
| 35529 | void _foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 35530 | return wrapper_CPU_List__foreach_add_(self, other, alpha); |
| 35531 | } |
| 35532 | ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 35533 | return wrapper_CPU_List__foreach_sub(self, other, alpha); |
| 35534 | } |
| 35535 | void _foreach_sub_(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 35536 | return wrapper_CPU_List__foreach_sub_(self, other, alpha); |
| 35537 | } |
| 35538 | ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::TensorList other) { |
| 35539 | return wrapper_CPU_List__foreach_mul(self, other); |
| 35540 | } |
| 35541 | void _foreach_mul_(at::TensorList self, at::TensorList other) { |
| 35542 | return wrapper_CPU_List__foreach_mul_(self, other); |
| 35543 | } |
| 35544 | ::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::TensorList other) { |
| 35545 | return wrapper_CPU_List__foreach_div(self, other); |
| 35546 | } |
| 35547 | void _foreach_div_(at::TensorList self, at::TensorList other) { |
| 35548 | return wrapper_CPU_List__foreach_div_(self, other); |
| 35549 | } |
| 35550 | ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::TensorList other) { |
| 35551 | return wrapper_CPU_List__foreach_clamp_min(self, other); |
| 35552 | } |
| 35553 | void _foreach_clamp_min_(at::TensorList self, at::TensorList other) { |
| 35554 | return wrapper_CPU_List__foreach_clamp_min_(self, other); |
| 35555 | } |
| 35556 | ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::TensorList other) { |
| 35557 | return wrapper_CPU_List__foreach_clamp_max(self, other); |
| 35558 | } |
| 35559 | void _foreach_clamp_max_(at::TensorList self, at::TensorList other) { |
| 35560 | return wrapper_CPU_List__foreach_clamp_max_(self, other); |
| 35561 | } |
| 35562 | ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::TensorList other) { |
| 35563 | return wrapper_CPU_List__foreach_maximum(self, other); |
| 35564 | } |
| 35565 | void _foreach_maximum_(at::TensorList self, at::TensorList other) { |
| 35566 | return wrapper_CPU_List__foreach_maximum_(self, other); |
| 35567 | } |
| 35568 | ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::TensorList other) { |
| 35569 | return wrapper_CPU_List__foreach_minimum(self, other); |
| 35570 | } |
| 35571 | void _foreach_minimum_(at::TensorList self, at::TensorList other) { |
| 35572 | return wrapper_CPU_List__foreach_minimum_(self, other); |
| 35573 | } |
| 35574 | ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35575 | return wrapper_CPU_ScalarList__foreach_add(self, scalars); |
| 35576 | } |
| 35577 | void _foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35578 | return wrapper_CPU_ScalarList__foreach_add_(self, scalars); |
| 35579 | } |
| 35580 | ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35581 | return wrapper_CPU_ScalarList__foreach_sub(self, scalars); |
| 35582 | } |
| 35583 | void _foreach_sub_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35584 | return wrapper_CPU_ScalarList__foreach_sub_(self, scalars); |
| 35585 | } |
| 35586 | ::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35587 | return wrapper_CPU_ScalarList__foreach_div(self, scalars); |
| 35588 | } |
| 35589 | void _foreach_div_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35590 | return wrapper_CPU_ScalarList__foreach_div_(self, scalars); |
| 35591 | } |
| 35592 | ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35593 | return wrapper_CPU_ScalarList__foreach_mul(self, scalars); |
| 35594 | } |
| 35595 | void _foreach_mul_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35596 | return wrapper_CPU_ScalarList__foreach_mul_(self, scalars); |
| 35597 | } |
| 35598 | ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35599 | return wrapper_CPU_ScalarList__foreach_clamp_min(self, scalars); |
| 35600 | } |
| 35601 | void _foreach_clamp_min_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35602 | return wrapper_CPU_ScalarList__foreach_clamp_min_(self, scalars); |
| 35603 | } |
| 35604 | ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35605 | return wrapper_CPU_ScalarList__foreach_clamp_max(self, scalars); |
| 35606 | } |
| 35607 | void _foreach_clamp_max_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35608 | return wrapper_CPU_ScalarList__foreach_clamp_max_(self, scalars); |
| 35609 | } |
| 35610 | ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35611 | return wrapper_CPU_ScalarList__foreach_maximum(self, scalars); |
| 35612 | } |
| 35613 | void _foreach_maximum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35614 | return wrapper_CPU_ScalarList__foreach_maximum_(self, scalars); |
| 35615 | } |
| 35616 | ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35617 | return wrapper_CPU_ScalarList__foreach_minimum(self, scalars); |
| 35618 | } |
| 35619 | void _foreach_minimum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 35620 | return wrapper_CPU_ScalarList__foreach_minimum_(self, scalars); |
| 35621 | } |
| 35622 | ::std::vector<at::Tensor> _foreach_exp(at::TensorList self) { |
| 35623 | return wrapper_CPU___foreach_exp(self); |
| 35624 | } |
| 35625 | void _foreach_exp_(at::TensorList self) { |
| 35626 | return wrapper_CPU___foreach_exp_(self); |
| 35627 | } |
| 35628 | void _foreach_zero_(at::TensorList self) { |
| 35629 | return wrapper_CPU___foreach_zero_(self); |
| 35630 | } |
| 35631 | ::std::vector<at::Tensor> _foreach_sqrt(at::TensorList self) { |
| 35632 | return wrapper_CPU___foreach_sqrt(self); |
| 35633 | } |
| 35634 | void _foreach_sqrt_(at::TensorList self) { |
| 35635 | return wrapper_CPU___foreach_sqrt_(self); |
| 35636 | } |
| 35637 | ::std::vector<at::Tensor> _foreach_abs(at::TensorList self) { |
| 35638 | return wrapper_CPU___foreach_abs(self); |
| 35639 | } |
| 35640 | void _foreach_abs_(at::TensorList self) { |
| 35641 | return wrapper_CPU___foreach_abs_(self); |
| 35642 | } |
| 35643 | ::std::vector<at::Tensor> _foreach_acos(at::TensorList self) { |
| 35644 | return wrapper_CPU___foreach_acos(self); |
| 35645 | } |
| 35646 | void _foreach_acos_(at::TensorList self) { |
| 35647 | return wrapper_CPU___foreach_acos_(self); |
| 35648 | } |
| 35649 | ::std::vector<at::Tensor> _foreach_asin(at::TensorList self) { |
| 35650 | return wrapper_CPU___foreach_asin(self); |
| 35651 | } |
| 35652 | void _foreach_asin_(at::TensorList self) { |
| 35653 | return wrapper_CPU___foreach_asin_(self); |
| 35654 | } |
| 35655 | ::std::vector<at::Tensor> _foreach_atan(at::TensorList self) { |
| 35656 | return wrapper_CPU___foreach_atan(self); |
| 35657 | } |
| 35658 | void _foreach_atan_(at::TensorList self) { |
| 35659 | return wrapper_CPU___foreach_atan_(self); |
| 35660 | } |
| 35661 | ::std::vector<at::Tensor> _foreach_ceil(at::TensorList self) { |
| 35662 | return wrapper_CPU___foreach_ceil(self); |
| 35663 | } |
| 35664 | void _foreach_ceil_(at::TensorList self) { |
| 35665 | return wrapper_CPU___foreach_ceil_(self); |
| 35666 | } |
| 35667 | ::std::vector<at::Tensor> _foreach_cos(at::TensorList self) { |
| 35668 | return wrapper_CPU___foreach_cos(self); |
| 35669 | } |
| 35670 | void _foreach_cos_(at::TensorList self) { |
| 35671 | return wrapper_CPU___foreach_cos_(self); |
| 35672 | } |
| 35673 | ::std::vector<at::Tensor> _foreach_cosh(at::TensorList self) { |
| 35674 | return wrapper_CPU___foreach_cosh(self); |
| 35675 | } |
| 35676 | void _foreach_cosh_(at::TensorList self) { |
| 35677 | return wrapper_CPU___foreach_cosh_(self); |
| 35678 | } |
| 35679 | ::std::vector<at::Tensor> _foreach_erf(at::TensorList self) { |
| 35680 | return wrapper_CPU___foreach_erf(self); |
| 35681 | } |
| 35682 | void _foreach_erf_(at::TensorList self) { |
| 35683 | return wrapper_CPU___foreach_erf_(self); |
| 35684 | } |
| 35685 | ::std::vector<at::Tensor> _foreach_erfc(at::TensorList self) { |
| 35686 | return wrapper_CPU___foreach_erfc(self); |
| 35687 | } |
| 35688 | void _foreach_erfc_(at::TensorList self) { |
| 35689 | return wrapper_CPU___foreach_erfc_(self); |
| 35690 | } |
| 35691 | ::std::vector<at::Tensor> _foreach_expm1(at::TensorList self) { |
| 35692 | return wrapper_CPU___foreach_expm1(self); |
| 35693 | } |
| 35694 | void _foreach_expm1_(at::TensorList self) { |
| 35695 | return wrapper_CPU___foreach_expm1_(self); |
| 35696 | } |
| 35697 | ::std::vector<at::Tensor> _foreach_floor(at::TensorList self) { |
| 35698 | return wrapper_CPU___foreach_floor(self); |
| 35699 | } |
| 35700 | void _foreach_floor_(at::TensorList self) { |
| 35701 | return wrapper_CPU___foreach_floor_(self); |
| 35702 | } |
| 35703 | ::std::vector<at::Tensor> _foreach_log(at::TensorList self) { |
| 35704 | return wrapper_CPU___foreach_log(self); |
| 35705 | } |
| 35706 | void _foreach_log_(at::TensorList self) { |
| 35707 | return wrapper_CPU___foreach_log_(self); |
| 35708 | } |
| 35709 | ::std::vector<at::Tensor> _foreach_log10(at::TensorList self) { |
| 35710 | return wrapper_CPU___foreach_log10(self); |
| 35711 | } |
| 35712 | void _foreach_log10_(at::TensorList self) { |
| 35713 | return wrapper_CPU___foreach_log10_(self); |
| 35714 | } |
| 35715 | ::std::vector<at::Tensor> _foreach_log1p(at::TensorList self) { |
| 35716 | return wrapper_CPU___foreach_log1p(self); |
| 35717 | } |
| 35718 | void _foreach_log1p_(at::TensorList self) { |
| 35719 | return wrapper_CPU___foreach_log1p_(self); |
| 35720 | } |
| 35721 | ::std::vector<at::Tensor> _foreach_log2(at::TensorList self) { |
| 35722 | return wrapper_CPU___foreach_log2(self); |
| 35723 | } |
| 35724 | void _foreach_log2_(at::TensorList self) { |
| 35725 | return wrapper_CPU___foreach_log2_(self); |
| 35726 | } |
| 35727 | ::std::vector<at::Tensor> _foreach_neg(at::TensorList self) { |
| 35728 | return wrapper_CPU___foreach_neg(self); |
| 35729 | } |
| 35730 | void _foreach_neg_(at::TensorList self) { |
| 35731 | return wrapper_CPU___foreach_neg_(self); |
| 35732 | } |
| 35733 | ::std::vector<at::Tensor> _foreach_tan(at::TensorList self) { |
| 35734 | return wrapper_CPU___foreach_tan(self); |
| 35735 | } |
| 35736 | void _foreach_tan_(at::TensorList self) { |
| 35737 | return wrapper_CPU___foreach_tan_(self); |
| 35738 | } |
| 35739 | ::std::vector<at::Tensor> _foreach_tanh(at::TensorList self) { |
| 35740 | return wrapper_CPU___foreach_tanh(self); |
| 35741 | } |
| 35742 | void _foreach_tanh_(at::TensorList self) { |
| 35743 | return wrapper_CPU___foreach_tanh_(self); |
| 35744 | } |
| 35745 | ::std::vector<at::Tensor> _foreach_sin(at::TensorList self) { |
| 35746 | return wrapper_CPU___foreach_sin(self); |
| 35747 | } |
| 35748 | void _foreach_sin_(at::TensorList self) { |
| 35749 | return wrapper_CPU___foreach_sin_(self); |
| 35750 | } |
| 35751 | ::std::vector<at::Tensor> _foreach_sinh(at::TensorList self) { |
| 35752 | return wrapper_CPU___foreach_sinh(self); |
| 35753 | } |
| 35754 | void _foreach_sinh_(at::TensorList self) { |
| 35755 | return wrapper_CPU___foreach_sinh_(self); |
| 35756 | } |
| 35757 | ::std::vector<at::Tensor> _foreach_round(at::TensorList self) { |
| 35758 | return wrapper_CPU___foreach_round(self); |
| 35759 | } |
| 35760 | void _foreach_round_(at::TensorList self) { |
| 35761 | return wrapper_CPU___foreach_round_(self); |
| 35762 | } |
| 35763 | ::std::vector<at::Tensor> _foreach_lgamma(at::TensorList self) { |
| 35764 | return wrapper_CPU___foreach_lgamma(self); |
| 35765 | } |
| 35766 | void _foreach_lgamma_(at::TensorList self) { |
| 35767 | return wrapper_CPU___foreach_lgamma_(self); |
| 35768 | } |
| 35769 | ::std::vector<at::Tensor> _foreach_frac(at::TensorList self) { |
| 35770 | return wrapper_CPU___foreach_frac(self); |
| 35771 | } |
| 35772 | void _foreach_frac_(at::TensorList self) { |
| 35773 | return wrapper_CPU___foreach_frac_(self); |
| 35774 | } |
| 35775 | ::std::vector<at::Tensor> _foreach_reciprocal(at::TensorList self) { |
| 35776 | return wrapper_CPU___foreach_reciprocal(self); |
| 35777 | } |
| 35778 | void _foreach_reciprocal_(at::TensorList self) { |
| 35779 | return wrapper_CPU___foreach_reciprocal_(self); |
| 35780 | } |
| 35781 | ::std::vector<at::Tensor> _foreach_sigmoid(at::TensorList self) { |
| 35782 | return wrapper_CPU___foreach_sigmoid(self); |
| 35783 | } |
| 35784 | void _foreach_sigmoid_(at::TensorList self) { |
| 35785 | return wrapper_CPU___foreach_sigmoid_(self); |
| 35786 | } |
| 35787 | ::std::vector<at::Tensor> _foreach_trunc(at::TensorList self) { |
| 35788 | return wrapper_CPU___foreach_trunc(self); |
| 35789 | } |
| 35790 | void _foreach_trunc_(at::TensorList self) { |
| 35791 | return wrapper_CPU___foreach_trunc_(self); |
| 35792 | } |
| 35793 | ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 35794 | return wrapper_CPU_Scalar__foreach_addcdiv(self, tensor1, tensor2, value); |
| 35795 | } |
| 35796 | void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 35797 | return wrapper_CPU_Scalar__foreach_addcdiv_(self, tensor1, tensor2, value); |
| 35798 | } |
| 35799 | ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 35800 | return wrapper_CPU_Scalar__foreach_addcmul(self, tensor1, tensor2, value); |
| 35801 | } |
| 35802 | void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 35803 | return wrapper_CPU_Scalar__foreach_addcmul_(self, tensor1, tensor2, value); |
| 35804 | } |
| 35805 | ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 35806 | return wrapper_CPU_ScalarList__foreach_addcdiv(self, tensor1, tensor2, scalars); |
| 35807 | } |
| 35808 | void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 35809 | return wrapper_CPU_ScalarList__foreach_addcdiv_(self, tensor1, tensor2, scalars); |
| 35810 | } |
| 35811 | ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 35812 | return wrapper_CPU_Tensor__foreach_addcdiv(self, tensor1, tensor2, scalars); |
| 35813 | } |
| 35814 | void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 35815 | return wrapper_CPU_Tensor__foreach_addcdiv_(self, tensor1, tensor2, scalars); |
| 35816 | } |
| 35817 | ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 35818 | return wrapper_CPU_ScalarList__foreach_addcmul(self, tensor1, tensor2, scalars); |
| 35819 | } |
| 35820 | void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 35821 | return wrapper_CPU_ScalarList__foreach_addcmul_(self, tensor1, tensor2, scalars); |
| 35822 | } |
| 35823 | ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 35824 | return wrapper_CPU_Tensor__foreach_addcmul(self, tensor1, tensor2, scalars); |
| 35825 | } |
| 35826 | void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 35827 | return wrapper_CPU_Tensor__foreach_addcmul_(self, tensor1, tensor2, scalars); |
| 35828 | } |
| 35829 | ::std::vector<at::Tensor> _foreach_norm(at::TensorList self, const at::Scalar & ord) { |
| 35830 | return wrapper_CPU_Scalar__foreach_norm(self, ord); |
| 35831 | } |
| 35832 | ::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { |
| 35833 | return wrapper_CPU_List__foreach_lerp(self, tensors1, weights); |
| 35834 | } |
| 35835 | void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { |
| 35836 | return wrapper_CPU_List__foreach_lerp_(self, tensors1, weights); |
| 35837 | } |
| 35838 | ::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { |
| 35839 | return wrapper_CPU_Scalar__foreach_lerp(self, tensors1, weight); |
| 35840 | } |
| 35841 | void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { |
| 35842 | return wrapper_CPU_Scalar__foreach_lerp_(self, tensors1, weight); |
| 35843 | } |
| 35844 | at::Tensor bucketize(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 35845 | return wrapper_CPU_Tensor_bucketize(self, boundaries, out_int32, right); |
| 35846 | } |
| 35847 | at::Tensor & bucketize_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 35848 | return wrapper_CPU_Tensor_out_bucketize_out(self, boundaries, out_int32, right, out); |
| 35849 | } |
| 35850 | at::Tensor & bucketize_outf(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 35851 | return wrapper_CPU_Tensor_out_bucketize_out(self, boundaries, out_int32, right, out); |
| 35852 | } |
| 35853 | at::Tensor bucketize(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 35854 | return wrapper_CPU_Scalar_bucketize(self, boundaries, out_int32, right); |
| 35855 | } |
| 35856 | at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter) { |
| 35857 | return wrapper_CPU_Tensor_searchsorted(sorted_sequence, self, out_int32, right, side, sorter); |
| 35858 | } |
| 35859 | at::Tensor & searchsorted_out(at::Tensor & out, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter) { |
| 35860 | return wrapper_CPU_Tensor_out_searchsorted_out(sorted_sequence, self, out_int32, right, side, sorter, out); |
| 35861 | } |
| 35862 | at::Tensor & searchsorted_outf(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter, at::Tensor & out) { |
| 35863 | return wrapper_CPU_Tensor_out_searchsorted_out(sorted_sequence, self, out_int32, right, side, sorter, out); |
| 35864 | } |
| 35865 | at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter) { |
| 35866 | return wrapper_CPU_Scalar_searchsorted(sorted_sequence, self, out_int32, right, side, sorter); |
| 35867 | } |
| 35868 | at::Tensor _convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32) { |
| 35869 | return wrapper_CPU__convert_indices_from_coo_to_csr(self, size, out_int32); |
| 35870 | } |
| 35871 | at::Tensor & _convert_indices_from_coo_to_csr_out(at::Tensor & out, const at::Tensor & self, int64_t size, bool out_int32) { |
| 35872 | return wrapper_CPU__convert_indices_from_coo_to_csr_out_out(self, size, out_int32, out); |
| 35873 | } |
| 35874 | at::Tensor & _convert_indices_from_coo_to_csr_outf(const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out) { |
| 35875 | return wrapper_CPU__convert_indices_from_coo_to_csr_out_out(self, size, out_int32, out); |
| 35876 | } |
| 35877 | at::Tensor _convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 35878 | return wrapper_CPU__convert_indices_from_csr_to_coo(crow_indices, col_indices, out_int32, transpose); |
| 35879 | } |
| 35880 | at::Tensor & _convert_indices_from_csr_to_coo_out(at::Tensor & out, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 35881 | return wrapper_CPU__convert_indices_from_csr_to_coo_out_out(crow_indices, col_indices, out_int32, transpose, out); |
| 35882 | } |
| 35883 | at::Tensor & _convert_indices_from_csr_to_coo_outf(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { |
| 35884 | return wrapper_CPU__convert_indices_from_csr_to_coo_out_out(crow_indices, col_indices, out_int32, transpose, out); |
| 35885 | } |
| 35886 | at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35887 | return wrapper_CPU_mse_loss(self, target, reduction); |
| 35888 | } |
| 35889 | at::Tensor & mse_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35890 | return wrapper_CPU_mse_loss_out_out(self, target, reduction, out); |
| 35891 | } |
| 35892 | at::Tensor & mse_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 35893 | return wrapper_CPU_mse_loss_out_out(self, target, reduction, out); |
| 35894 | } |
| 35895 | at::Tensor mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35896 | return wrapper_CPU__mse_loss_backward(grad_output, self, target, reduction); |
| 35897 | } |
| 35898 | at::Tensor & mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35899 | return wrapper_CPU_grad_input_mse_loss_backward_out(grad_output, self, target, reduction, grad_input); |
| 35900 | } |
| 35901 | at::Tensor & mse_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) { |
| 35902 | return wrapper_CPU_grad_input_mse_loss_backward_out(grad_output, self, target, reduction, grad_input); |
| 35903 | } |
| 35904 | at::Tensor multi_margin_loss(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 35905 | return wrapper_CPU__multi_margin_loss(self, target, p, margin, weight, reduction); |
| 35906 | } |
| 35907 | at::Tensor & multi_margin_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 35908 | return wrapper_CPU_out_multi_margin_loss_out(self, target, p, margin, weight, reduction, out); |
| 35909 | } |
| 35910 | at::Tensor & multi_margin_loss_outf(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 35911 | return wrapper_CPU_out_multi_margin_loss_out(self, target, p, margin, weight, reduction, out); |
| 35912 | } |
| 35913 | at::Tensor multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 35914 | return wrapper_CPU__multi_margin_loss_backward(grad_output, self, target, p, margin, weight, reduction); |
| 35915 | } |
| 35916 | at::Tensor & multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 35917 | return wrapper_CPU_grad_input_multi_margin_loss_backward_out(grad_output, self, target, p, margin, weight, reduction, grad_input); |
| 35918 | } |
| 35919 | at::Tensor & multi_margin_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) { |
| 35920 | return wrapper_CPU_grad_input_multi_margin_loss_backward_out(grad_output, self, target, p, margin, weight, reduction, grad_input); |
| 35921 | } |
| 35922 | ::std::tuple<at::Tensor,at::Tensor> multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35923 | return wrapper_CPU__multilabel_margin_loss_forward(self, target, reduction); |
| 35924 | } |
| 35925 | ::std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 35926 | return wrapper_CPU_output_multilabel_margin_loss_forward_out(self, target, reduction, output, is_target); |
| 35927 | } |
| 35928 | ::std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & output, at::Tensor & is_target) { |
| 35929 | return wrapper_CPU_output_multilabel_margin_loss_forward_out(self, target, reduction, output, is_target); |
| 35930 | } |
| 35931 | at::Tensor multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { |
| 35932 | return wrapper_CPU__multilabel_margin_loss_backward(grad_output, self, target, reduction, is_target); |
| 35933 | } |
| 35934 | at::Tensor & multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { |
| 35935 | return wrapper_CPU_grad_input_multilabel_margin_loss_backward_out(grad_output, self, target, reduction, is_target, grad_input); |
| 35936 | } |
| 35937 | at::Tensor & multilabel_margin_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input) { |
| 35938 | return wrapper_CPU_grad_input_multilabel_margin_loss_backward_out(grad_output, self, target, reduction, is_target, grad_input); |
| 35939 | } |
| 35940 | ::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) { |
| 35941 | return wrapper_CPU_nll_loss_forward(self, target, weight, reduction, ignore_index); |
| 35942 | } |
| 35943 | ::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) { |
| 35944 | return wrapper_CPU_nll_loss_forward(self, target, weight, reduction, ignore_index.expect_int()); |
| 35945 | } |
| 35946 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 35947 | return wrapper_CPU_nll_loss_forward_out_output(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35948 | } |
| 35949 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 35950 | return wrapper_CPU_nll_loss_forward_out_output(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35951 | } |
| 35952 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_symint_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 35953 | return wrapper_CPU_nll_loss_forward_out_output(self, target, weight, reduction, ignore_index.expect_int(), output, total_weight); |
| 35954 | } |
| 35955 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_symint_outf(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 35956 | return wrapper_CPU_nll_loss_forward_out_output(self, target, weight, reduction, ignore_index.expect_int(), output, total_weight); |
| 35957 | } |
| 35958 | 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) { |
| 35959 | return wrapper_CPU_nll_loss_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 35960 | } |
| 35961 | 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) { |
| 35962 | return wrapper_CPU_nll_loss_backward(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight); |
| 35963 | } |
| 35964 | at::Tensor & nll_loss_backward_out(at::Tensor & grad_input, 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) { |
| 35965 | return wrapper_CPU_nll_loss_backward_out_grad_input(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 35966 | } |
| 35967 | at::Tensor & nll_loss_backward_outf(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, at::Tensor & grad_input) { |
| 35968 | return wrapper_CPU_nll_loss_backward_out_grad_input(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 35969 | } |
| 35970 | at::Tensor & nll_loss_backward_symint_out(at::Tensor & grad_input, 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) { |
| 35971 | return wrapper_CPU_nll_loss_backward_out_grad_input(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight, grad_input); |
| 35972 | } |
| 35973 | at::Tensor & nll_loss_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 35974 | return wrapper_CPU_nll_loss_backward_out_grad_input(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight, grad_input); |
| 35975 | } |
| 35976 | ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 35977 | return wrapper_CPU__nll_loss2d_forward(self, target, weight, reduction, ignore_index); |
| 35978 | } |
| 35979 | ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward_symint(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 35980 | return wrapper_CPU__nll_loss2d_forward(self, target, weight, reduction, ignore_index); |
| 35981 | } |
| 35982 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 35983 | return wrapper_CPU_output_nll_loss2d_forward_out(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35984 | } |
| 35985 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_outf(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 35986 | return wrapper_CPU_output_nll_loss2d_forward_out(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35987 | } |
| 35988 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_symint_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 35989 | return wrapper_CPU_output_nll_loss2d_forward_out(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35990 | } |
| 35991 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_symint_outf(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 35992 | return wrapper_CPU_output_nll_loss2d_forward_out(self, target, weight, reduction, ignore_index, output, total_weight); |
| 35993 | } |
| 35994 | at::Tensor nll_loss2d_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) { |
| 35995 | return wrapper_CPU__nll_loss2d_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 35996 | } |
| 35997 | at::Tensor nll_loss2d_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) { |
| 35998 | return wrapper_CPU__nll_loss2d_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 35999 | } |
| 36000 | at::Tensor & nll_loss2d_backward_out(at::Tensor & grad_input, 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) { |
| 36001 | return wrapper_CPU_grad_input_nll_loss2d_backward_out(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 36002 | } |
| 36003 | at::Tensor & nll_loss2d_backward_outf(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, at::Tensor & grad_input) { |
| 36004 | return wrapper_CPU_grad_input_nll_loss2d_backward_out(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 36005 | } |
| 36006 | at::Tensor & nll_loss2d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36007 | return wrapper_CPU_grad_input_nll_loss2d_backward_out(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 36008 | } |
| 36009 | at::Tensor & nll_loss2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 36010 | return wrapper_CPU_grad_input_nll_loss2d_backward_out(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 36011 | } |
| 36012 | at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 36013 | return wrapper_CPU_smooth_l1_loss(self, target, reduction, beta); |
| 36014 | } |
| 36015 | at::Tensor & smooth_l1_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 36016 | return wrapper_CPU_smooth_l1_loss_out_out(self, target, reduction, beta, out); |
| 36017 | } |
| 36018 | at::Tensor & smooth_l1_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out) { |
| 36019 | return wrapper_CPU_smooth_l1_loss_out_out(self, target, reduction, beta, out); |
| 36020 | } |
| 36021 | at::Tensor & smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 36022 | return wrapper_CPU_grad_input_smooth_l1_loss_backward_out(grad_output, self, target, reduction, beta, grad_input); |
| 36023 | } |
| 36024 | at::Tensor & smooth_l1_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { |
| 36025 | return wrapper_CPU_grad_input_smooth_l1_loss_backward_out(grad_output, self, target, reduction, beta, grad_input); |
| 36026 | } |
| 36027 | at::Tensor huber_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 36028 | return wrapper_CPU__huber_loss(self, target, reduction, delta); |
| 36029 | } |
| 36030 | at::Tensor & huber_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 36031 | return wrapper_CPU_out_huber_loss_out(self, target, reduction, delta, out); |
| 36032 | } |
| 36033 | at::Tensor & huber_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { |
| 36034 | return wrapper_CPU_out_huber_loss_out(self, target, reduction, delta, out); |
| 36035 | } |
| 36036 | at::Tensor & huber_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 36037 | return wrapper_CPU_out_huber_loss_backward_out(grad_output, self, target, reduction, delta, grad_input); |
| 36038 | } |
| 36039 | at::Tensor & huber_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { |
| 36040 | return wrapper_CPU_out_huber_loss_backward_out(grad_output, self, target, reduction, delta, grad_input); |
| 36041 | } |
| 36042 | at::Tensor elu(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 36043 | return wrapper_CPU_elu(self, alpha, scale, input_scale); |
| 36044 | } |
| 36045 | at::Tensor & elu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 36046 | return wrapper_CPU_elu_out_out(self, alpha, scale, input_scale, out); |
| 36047 | } |
| 36048 | at::Tensor & elu_outf(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out) { |
| 36049 | return wrapper_CPU_elu_out_out(self, alpha, scale, input_scale, out); |
| 36050 | } |
| 36051 | at::Tensor & elu_(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 36052 | return wrapper_CPU_elu_(self, alpha, scale, input_scale); |
| 36053 | } |
| 36054 | 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) { |
| 36055 | return wrapper_CPU_elu_backward(grad_output, alpha, scale, input_scale, is_result, self_or_result); |
| 36056 | } |
| 36057 | at::Tensor & elu_backward_out(at::Tensor & grad_input, 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) { |
| 36058 | return wrapper_CPU_elu_backward_out_grad_input(grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input); |
| 36059 | } |
| 36060 | at::Tensor & elu_backward_outf(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input) { |
| 36061 | return wrapper_CPU_elu_backward_out_grad_input(grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input); |
| 36062 | } |
| 36063 | at::Tensor glu(const at::Tensor & self, int64_t dim) { |
| 36064 | return wrapper_CPU_glu(self, dim); |
| 36065 | } |
| 36066 | at::Tensor & glu_out(at::Tensor & out, const at::Tensor & self, int64_t dim) { |
| 36067 | return wrapper_CPU_glu_out_out(self, dim, out); |
| 36068 | } |
| 36069 | at::Tensor & glu_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 36070 | return wrapper_CPU_glu_out_out(self, dim, out); |
| 36071 | } |
| 36072 | at::Tensor glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { |
| 36073 | return wrapper_CPU__glu_backward(grad_output, self, dim); |
| 36074 | } |
| 36075 | at::Tensor & glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { |
| 36076 | return wrapper_CPU_grad_input_glu_backward_out(grad_output, self, dim, grad_input); |
| 36077 | } |
| 36078 | at::Tensor & glu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, at::Tensor & grad_input) { |
| 36079 | return wrapper_CPU_grad_input_glu_backward_out(grad_output, self, dim, grad_input); |
| 36080 | } |
| 36081 | at::Tensor glu_jvp(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) { |
| 36082 | return wrapper_CPU__glu_jvp(glu, x, dx, dim); |
| 36083 | } |
| 36084 | at::Tensor glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { |
| 36085 | return wrapper_CPU__glu_backward_jvp(grad_x, grad_glu, x, dgrad_glu, dx, dim); |
| 36086 | } |
| 36087 | at::Tensor hardsigmoid(const at::Tensor & self) { |
| 36088 | return wrapper_CPU_hardsigmoid(self); |
| 36089 | } |
| 36090 | at::Tensor & hardsigmoid_out(at::Tensor & out, const at::Tensor & self) { |
| 36091 | return wrapper_CPU_hardsigmoid_out_out(self, out); |
| 36092 | } |
| 36093 | at::Tensor & hardsigmoid_outf(const at::Tensor & self, at::Tensor & out) { |
| 36094 | return wrapper_CPU_hardsigmoid_out_out(self, out); |
| 36095 | } |
| 36096 | at::Tensor & hardsigmoid_(at::Tensor & self) { |
| 36097 | return wrapper_CPU_hardsigmoid_(self); |
| 36098 | } |
| 36099 | at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 36100 | return wrapper_CPU_hardsigmoid_backward(grad_output, self); |
| 36101 | } |
| 36102 | at::Tensor & hardsigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { |
| 36103 | return wrapper_CPU_hardsigmoid_backward_out_grad_input(grad_output, self, grad_input); |
| 36104 | } |
| 36105 | at::Tensor & hardsigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 36106 | return wrapper_CPU_hardsigmoid_backward_out_grad_input(grad_output, self, grad_input); |
| 36107 | } |
| 36108 | at::Tensor hardtanh(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 36109 | return wrapper_CPU__hardtanh(self, min_val, max_val); |
| 36110 | } |
| 36111 | at::Tensor & hardtanh_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 36112 | return wrapper_CPU_out_hardtanh_out(self, min_val, max_val, out); |
| 36113 | } |
| 36114 | at::Tensor & hardtanh_outf(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) { |
| 36115 | return wrapper_CPU_out_hardtanh_out(self, min_val, max_val, out); |
| 36116 | } |
| 36117 | at::Tensor & hardtanh_(at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 36118 | return wrapper_CPU__hardtanh_(self, min_val, max_val); |
| 36119 | } |
| 36120 | at::Tensor hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 36121 | return wrapper_CPU__hardtanh_backward(grad_output, self, min_val, max_val); |
| 36122 | } |
| 36123 | at::Tensor & hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 36124 | return wrapper_CPU_grad_input_hardtanh_backward_out(grad_output, self, min_val, max_val, grad_input); |
| 36125 | } |
| 36126 | at::Tensor & hardtanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & grad_input) { |
| 36127 | return wrapper_CPU_grad_input_hardtanh_backward_out(grad_output, self, min_val, max_val, grad_input); |
| 36128 | } |
| 36129 | at::Tensor hardswish(const at::Tensor & self) { |
| 36130 | return wrapper_CPU__hardswish(self); |
| 36131 | } |
| 36132 | at::Tensor & hardswish_out(at::Tensor & out, const at::Tensor & self) { |
| 36133 | return wrapper_CPU_out_hardswish_out(self, out); |
| 36134 | } |
| 36135 | at::Tensor & hardswish_outf(const at::Tensor & self, at::Tensor & out) { |
| 36136 | return wrapper_CPU_out_hardswish_out(self, out); |
| 36137 | } |
| 36138 | at::Tensor & hardswish_(at::Tensor & self) { |
| 36139 | return wrapper_CPU__hardswish_(self); |
| 36140 | } |
| 36141 | at::Tensor hardswish_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 36142 | return wrapper_CPU__hardswish_backward(grad_output, self); |
| 36143 | } |
| 36144 | at::Tensor leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope) { |
| 36145 | return wrapper_CPU_leaky_relu(self, negative_slope); |
| 36146 | } |
| 36147 | at::Tensor & leaky_relu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & negative_slope) { |
| 36148 | return wrapper_CPU_leaky_relu_out_out(self, negative_slope, out); |
| 36149 | } |
| 36150 | at::Tensor & leaky_relu_outf(const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) { |
| 36151 | return wrapper_CPU_leaky_relu_out_out(self, negative_slope, out); |
| 36152 | } |
| 36153 | at::Tensor & leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope) { |
| 36154 | return wrapper_CPU_leaky_relu_(self, negative_slope); |
| 36155 | } |
| 36156 | at::Tensor leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { |
| 36157 | return wrapper_CPU_leaky_relu_backward(grad_output, self, negative_slope, self_is_result); |
| 36158 | } |
| 36159 | at::Tensor & leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { |
| 36160 | return wrapper_CPU_leaky_relu_backward_out_grad_input(grad_output, self, negative_slope, self_is_result, grad_input); |
| 36161 | } |
| 36162 | at::Tensor & leaky_relu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input) { |
| 36163 | return wrapper_CPU_leaky_relu_backward_out_grad_input(grad_output, self, negative_slope, self_is_result, grad_input); |
| 36164 | } |
| 36165 | ::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward(const at::Tensor & self) { |
| 36166 | return wrapper_CPU__log_sigmoid_forward(self); |
| 36167 | } |
| 36168 | ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) { |
| 36169 | return wrapper_CPU_output_log_sigmoid_forward_out(self, output, buffer); |
| 36170 | } |
| 36171 | ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_outf(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { |
| 36172 | return wrapper_CPU_output_log_sigmoid_forward_out(self, output, buffer); |
| 36173 | } |
| 36174 | at::Tensor log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { |
| 36175 | return wrapper_CPU__log_sigmoid_backward(grad_output, self, buffer); |
| 36176 | } |
| 36177 | at::Tensor & log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { |
| 36178 | return wrapper_CPU_grad_input_log_sigmoid_backward_out(grad_output, self, buffer, grad_input); |
| 36179 | } |
| 36180 | at::Tensor & log_sigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) { |
| 36181 | return wrapper_CPU_grad_input_log_sigmoid_backward_out(grad_output, self, buffer, grad_input); |
| 36182 | } |
| 36183 | at::Tensor rrelu_with_noise(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 36184 | return wrapper_CPU__rrelu_with_noise(self, noise, lower, upper, training, generator); |
| 36185 | } |
| 36186 | at::Tensor & rrelu_with_noise_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 36187 | return wrapper_CPU_out_rrelu_with_noise_out(self, noise, lower, upper, training, generator, out); |
| 36188 | } |
| 36189 | at::Tensor & rrelu_with_noise_outf(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 36190 | return wrapper_CPU_out_rrelu_with_noise_out(self, noise, lower, upper, training, generator, out); |
| 36191 | } |
| 36192 | at::Tensor & rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 36193 | return wrapper_CPU__rrelu_with_noise_(self, noise, lower, upper, training, generator); |
| 36194 | } |
| 36195 | at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 36196 | return wrapper_CPU_softplus(self, beta, threshold); |
| 36197 | } |
| 36198 | at::Tensor & softplus_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 36199 | return wrapper_CPU_softplus_out_out(self, beta, threshold, out); |
| 36200 | } |
| 36201 | at::Tensor & softplus_outf(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out) { |
| 36202 | return wrapper_CPU_softplus_out_out(self, beta, threshold, out); |
| 36203 | } |
| 36204 | at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 36205 | return wrapper_CPU_softplus_backward(grad_output, self, beta, threshold); |
| 36206 | } |
| 36207 | at::Tensor & softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 36208 | return wrapper_CPU_softplus_backward_out_grad_input(grad_output, self, beta, threshold, grad_input); |
| 36209 | } |
| 36210 | at::Tensor & softplus_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) { |
| 36211 | return wrapper_CPU_softplus_backward_out_grad_input(grad_output, self, beta, threshold, grad_input); |
| 36212 | } |
| 36213 | at::Tensor softshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 36214 | return wrapper_CPU_softshrink(self, lambd); |
| 36215 | } |
| 36216 | at::Tensor & softshrink_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd) { |
| 36217 | return wrapper_CPU_softshrink_out_out(self, lambd, out); |
| 36218 | } |
| 36219 | at::Tensor & softshrink_outf(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 36220 | return wrapper_CPU_softshrink_out_out(self, lambd, out); |
| 36221 | } |
| 36222 | at::Tensor softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { |
| 36223 | return wrapper_CPU_softshrink_backward(grad_output, self, lambd); |
| 36224 | } |
| 36225 | at::Tensor & softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { |
| 36226 | return wrapper_CPU_softshrink_backward_out_grad_input(grad_output, self, lambd, grad_input); |
| 36227 | } |
| 36228 | at::Tensor & softshrink_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 36229 | return wrapper_CPU_softshrink_backward_out_grad_input(grad_output, self, lambd, grad_input); |
| 36230 | } |
| 36231 | at::Tensor & adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { |
| 36232 | return wrapper_CPU_out_adaptive_avg_pool2d_out(self, c10::fromIntArrayRefSlow(output_size), out); |
| 36233 | } |
| 36234 | at::Tensor & adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { |
| 36235 | return wrapper_CPU_out_adaptive_avg_pool2d_out(self, c10::fromIntArrayRefSlow(output_size), out); |
| 36236 | } |
| 36237 | at::Tensor & adaptive_avg_pool2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 36238 | return wrapper_CPU_out_adaptive_avg_pool2d_out(self, output_size, out); |
| 36239 | } |
| 36240 | at::Tensor & adaptive_avg_pool2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 36241 | return wrapper_CPU_out_adaptive_avg_pool2d_out(self, output_size, out); |
| 36242 | } |
| 36243 | at::Tensor _adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 36244 | return wrapper_CPU___adaptive_avg_pool2d(self, c10::fromIntArrayRefSlow(output_size)); |
| 36245 | } |
| 36246 | at::Tensor _adaptive_avg_pool2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 36247 | return wrapper_CPU___adaptive_avg_pool2d(self, output_size); |
| 36248 | } |
| 36249 | at::Tensor _adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 36250 | return wrapper_CPU___adaptive_avg_pool2d_backward(grad_output, self); |
| 36251 | } |
| 36252 | at::Tensor & adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { |
| 36253 | return wrapper_CPU_out_adaptive_avg_pool3d_out(self, c10::fromIntArrayRefSlow(output_size), out); |
| 36254 | } |
| 36255 | at::Tensor & adaptive_avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { |
| 36256 | return wrapper_CPU_out_adaptive_avg_pool3d_out(self, c10::fromIntArrayRefSlow(output_size), out); |
| 36257 | } |
| 36258 | at::Tensor & adaptive_avg_pool3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 36259 | return wrapper_CPU_out_adaptive_avg_pool3d_out(self, output_size, out); |
| 36260 | } |
| 36261 | at::Tensor & adaptive_avg_pool3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 36262 | return wrapper_CPU_out_adaptive_avg_pool3d_out(self, output_size, out); |
| 36263 | } |
| 36264 | at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 36265 | return wrapper_CPU___adaptive_avg_pool3d(self, c10::fromIntArrayRefSlow(output_size)); |
| 36266 | } |
| 36267 | at::Tensor _adaptive_avg_pool3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 36268 | return wrapper_CPU___adaptive_avg_pool3d(self, output_size); |
| 36269 | } |
| 36270 | at::Tensor & adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { |
| 36271 | return wrapper_CPU_grad_input_adaptive_avg_pool3d_backward_out(grad_output, self, grad_input); |
| 36272 | } |
| 36273 | at::Tensor & adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 36274 | return wrapper_CPU_grad_input_adaptive_avg_pool3d_backward_out(grad_output, self, grad_input); |
| 36275 | } |
| 36276 | at::Tensor _adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 36277 | return wrapper_CPU___adaptive_avg_pool3d_backward(grad_output, self); |
| 36278 | } |
| 36279 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 36280 | return wrapper_CPU_adaptive_max_pool2d(self, output_size); |
| 36281 | } |
| 36282 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) { |
| 36283 | return wrapper_CPU_adaptive_max_pool2d_out_out(self, output_size, out, indices); |
| 36284 | } |
| 36285 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 36286 | return wrapper_CPU_adaptive_max_pool2d_out_out(self, output_size, out, indices); |
| 36287 | } |
| 36288 | at::Tensor adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 36289 | return wrapper_CPU_adaptive_max_pool2d_backward(grad_output, self, indices); |
| 36290 | } |
| 36291 | at::Tensor & adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 36292 | return wrapper_CPU_adaptive_max_pool2d_backward_out_grad_input(grad_output, self, indices, grad_input); |
| 36293 | } |
| 36294 | at::Tensor & adaptive_max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 36295 | return wrapper_CPU_adaptive_max_pool2d_backward_out_grad_input(grad_output, self, indices, grad_input); |
| 36296 | } |
| 36297 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 36298 | return wrapper_CPU_adaptive_max_pool3d(self, output_size); |
| 36299 | } |
| 36300 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) { |
| 36301 | return wrapper_CPU_adaptive_max_pool3d_out_out(self, output_size, out, indices); |
| 36302 | } |
| 36303 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 36304 | return wrapper_CPU_adaptive_max_pool3d_out_out(self, output_size, out, indices); |
| 36305 | } |
| 36306 | at::Tensor adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 36307 | return wrapper_CPU_adaptive_max_pool3d_backward(grad_output, self, indices); |
| 36308 | } |
| 36309 | at::Tensor & adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 36310 | return wrapper_CPU_adaptive_max_pool3d_backward_out_grad_input(grad_output, self, indices, grad_input); |
| 36311 | } |
| 36312 | at::Tensor & adaptive_max_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 36313 | return wrapper_CPU_adaptive_max_pool3d_backward_out_grad_input(grad_output, self, indices, grad_input); |
| 36314 | } |
| 36315 | 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) { |
| 36316 | return wrapper_CPU_avg_pool2d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 36317 | } |
| 36318 | at::Tensor & avg_pool2d_out(at::Tensor & out, 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) { |
| 36319 | return wrapper_CPU_avg_pool2d_out_out(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); |
| 36320 | } |
| 36321 | at::Tensor & avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 36322 | return wrapper_CPU_avg_pool2d_out_out(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); |
| 36323 | } |
| 36324 | 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) { |
| 36325 | return wrapper_CPU_avg_pool2d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 36326 | } |
| 36327 | at::Tensor & avg_pool2d_backward_out(at::Tensor & grad_input, 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) { |
| 36328 | return wrapper_CPU_avg_pool2d_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 36329 | } |
| 36330 | at::Tensor & avg_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & grad_input) { |
| 36331 | return wrapper_CPU_avg_pool2d_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 36332 | } |
| 36333 | 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) { |
| 36334 | return wrapper_CPU_avg_pool3d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 36335 | } |
| 36336 | at::Tensor & avg_pool3d_out(at::Tensor & out, 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) { |
| 36337 | return wrapper_CPU_avg_pool3d_out_out(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); |
| 36338 | } |
| 36339 | at::Tensor & avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 36340 | return wrapper_CPU_avg_pool3d_out_out(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); |
| 36341 | } |
| 36342 | 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) { |
| 36343 | return wrapper_CPU_avg_pool3d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 36344 | } |
| 36345 | at::Tensor & avg_pool3d_backward_out(at::Tensor & grad_input, 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) { |
| 36346 | return wrapper_CPU_avg_pool3d_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 36347 | } |
| 36348 | at::Tensor & avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & grad_input) { |
| 36349 | return wrapper_CPU_avg_pool3d_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 36350 | } |
| 36351 | ::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) { |
| 36352 | return wrapper_CPU_fractional_max_pool2d(self, kernel_size, output_size, random_samples); |
| 36353 | } |
| 36354 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 36355 | return wrapper_CPU_fractional_max_pool2d_out_output(self, kernel_size, output_size, random_samples, output, indices); |
| 36356 | } |
| 36357 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 36358 | return wrapper_CPU_fractional_max_pool2d_out_output(self, kernel_size, output_size, random_samples, output, indices); |
| 36359 | } |
| 36360 | 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) { |
| 36361 | return wrapper_CPU_fractional_max_pool2d_backward(grad_output, self, kernel_size, output_size, indices); |
| 36362 | } |
| 36363 | at::Tensor & fractional_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 36364 | return wrapper_CPU_fractional_max_pool2d_backward_out_grad_input(grad_output, self, kernel_size, output_size, indices, grad_input); |
| 36365 | } |
| 36366 | at::Tensor & fractional_max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { |
| 36367 | return wrapper_CPU_fractional_max_pool2d_backward_out_grad_input(grad_output, self, kernel_size, output_size, indices, grad_input); |
| 36368 | } |
| 36369 | ::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) { |
| 36370 | return wrapper_CPU_fractional_max_pool3d(self, kernel_size, output_size, random_samples); |
| 36371 | } |
| 36372 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 36373 | return wrapper_CPU_fractional_max_pool3d_out_output(self, kernel_size, output_size, random_samples, output, indices); |
| 36374 | } |
| 36375 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 36376 | return wrapper_CPU_fractional_max_pool3d_out_output(self, kernel_size, output_size, random_samples, output, indices); |
| 36377 | } |
| 36378 | at::Tensor fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 36379 | return wrapper_CPU__fractional_max_pool3d_backward(grad_output, self, kernel_size, output_size, indices); |
| 36380 | } |
| 36381 | at::Tensor & fractional_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 36382 | return wrapper_CPU_grad_input_fractional_max_pool3d_backward_out(grad_output, self, kernel_size, output_size, indices, grad_input); |
| 36383 | } |
| 36384 | at::Tensor & fractional_max_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { |
| 36385 | return wrapper_CPU_grad_input_fractional_max_pool3d_backward_out(grad_output, self, kernel_size, output_size, indices, grad_input); |
| 36386 | } |
| 36387 | ::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) { |
| 36388 | return wrapper_CPU_max_pool2d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 36389 | } |
| 36390 | ::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 36391 | return wrapper_CPU_max_pool2d_with_indices_out_out(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); |
| 36392 | } |
| 36393 | ::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { |
| 36394 | return wrapper_CPU_max_pool2d_with_indices_out_out(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); |
| 36395 | } |
| 36396 | 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) { |
| 36397 | return wrapper_CPU_max_pool2d_with_indices_backward(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 36398 | } |
| 36399 | at::Tensor & max_pool2d_with_indices_backward_out(at::Tensor & grad_input, 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) { |
| 36400 | return wrapper_CPU_max_pool2d_with_indices_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); |
| 36401 | } |
| 36402 | at::Tensor & max_pool2d_with_indices_backward_outf(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, at::Tensor & grad_input) { |
| 36403 | return wrapper_CPU_max_pool2d_with_indices_backward_out_grad_input(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); |
| 36404 | } |
| 36405 | ::std::tuple<at::Tensor,at::Tensor> max_pool3d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 36406 | return wrapper_CPU__max_pool3d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 36407 | } |
| 36408 | ::std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 36409 | return wrapper_CPU_out_max_pool3d_with_indices_out(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); |
| 36410 | } |
| 36411 | ::std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { |
| 36412 | return wrapper_CPU_out_max_pool3d_with_indices_out(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); |
| 36413 | } |
| 36414 | at::Tensor max_pool3d_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) { |
| 36415 | return wrapper_CPU__max_pool3d_with_indices_backward(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 36416 | } |
| 36417 | at::Tensor & max_pool3d_with_indices_backward_out(at::Tensor & grad_input, 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) { |
| 36418 | return wrapper_CPU_grad_input_max_pool3d_with_indices_backward_out(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); |
| 36419 | } |
| 36420 | at::Tensor & max_pool3d_with_indices_backward_outf(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, at::Tensor & grad_input) { |
| 36421 | return wrapper_CPU_grad_input_max_pool3d_with_indices_backward_out(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); |
| 36422 | } |
| 36423 | at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { |
| 36424 | return wrapper_CPU__max_unpool2d(self, indices, output_size); |
| 36425 | } |
| 36426 | at::Tensor & max_unpool2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { |
| 36427 | return wrapper_CPU_out_max_unpool2d_out(self, indices, output_size, out); |
| 36428 | } |
| 36429 | at::Tensor & max_unpool2d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) { |
| 36430 | return wrapper_CPU_out_max_unpool2d_out(self, indices, output_size, out); |
| 36431 | } |
| 36432 | at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 36433 | return wrapper_CPU__max_unpool3d(self, indices, output_size, stride, padding); |
| 36434 | } |
| 36435 | at::Tensor & max_unpool3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 36436 | return wrapper_CPU_out_max_unpool3d_out(self, indices, output_size, stride, padding, out); |
| 36437 | } |
| 36438 | at::Tensor & max_unpool3d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { |
| 36439 | return wrapper_CPU_out_max_unpool3d_out(self, indices, output_size, stride, padding, out); |
| 36440 | } |
| 36441 | at::Tensor reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36442 | return wrapper_CPU_reflection_pad1d(self, padding); |
| 36443 | } |
| 36444 | at::Tensor reflection_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36445 | return wrapper_CPU_reflection_pad1d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36446 | } |
| 36447 | at::Tensor & reflection_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36448 | return wrapper_CPU_reflection_pad1d_out_out(self, padding, out); |
| 36449 | } |
| 36450 | at::Tensor & reflection_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36451 | return wrapper_CPU_reflection_pad1d_out_out(self, padding, out); |
| 36452 | } |
| 36453 | at::Tensor & reflection_pad1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36454 | return wrapper_CPU_reflection_pad1d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36455 | } |
| 36456 | at::Tensor & reflection_pad1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36457 | return wrapper_CPU_reflection_pad1d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36458 | } |
| 36459 | at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36460 | return wrapper_CPU_reflection_pad1d_backward(grad_output, self, padding); |
| 36461 | } |
| 36462 | at::Tensor reflection_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36463 | return wrapper_CPU_reflection_pad1d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36464 | } |
| 36465 | at::Tensor & reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36466 | return wrapper_CPU_reflection_pad1d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36467 | } |
| 36468 | at::Tensor & reflection_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36469 | return wrapper_CPU_reflection_pad1d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36470 | } |
| 36471 | at::Tensor & reflection_pad1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36472 | return wrapper_CPU_reflection_pad1d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36473 | } |
| 36474 | at::Tensor & reflection_pad1d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36475 | return wrapper_CPU_reflection_pad1d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36476 | } |
| 36477 | at::Tensor reflection_pad2d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36478 | return wrapper_CPU__reflection_pad2d(self, c10::fromIntArrayRefSlow(padding)); |
| 36479 | } |
| 36480 | at::Tensor reflection_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36481 | return wrapper_CPU__reflection_pad2d(self, padding); |
| 36482 | } |
| 36483 | at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36484 | return wrapper_CPU_out_reflection_pad2d_out(self, c10::fromIntArrayRefSlow(padding), out); |
| 36485 | } |
| 36486 | at::Tensor & reflection_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36487 | return wrapper_CPU_out_reflection_pad2d_out(self, c10::fromIntArrayRefSlow(padding), out); |
| 36488 | } |
| 36489 | at::Tensor & reflection_pad2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36490 | return wrapper_CPU_out_reflection_pad2d_out(self, padding, out); |
| 36491 | } |
| 36492 | at::Tensor & reflection_pad2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36493 | return wrapper_CPU_out_reflection_pad2d_out(self, padding, out); |
| 36494 | } |
| 36495 | at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36496 | return wrapper_CPU__reflection_pad2d_backward(grad_output, self, c10::fromIntArrayRefSlow(padding)); |
| 36497 | } |
| 36498 | at::Tensor reflection_pad2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36499 | return wrapper_CPU__reflection_pad2d_backward(grad_output, self, padding); |
| 36500 | } |
| 36501 | at::Tensor & reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36502 | return wrapper_CPU_grad_input_reflection_pad2d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36503 | } |
| 36504 | at::Tensor & reflection_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36505 | return wrapper_CPU_grad_input_reflection_pad2d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36506 | } |
| 36507 | at::Tensor & reflection_pad2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36508 | return wrapper_CPU_grad_input_reflection_pad2d_backward_out(grad_output, self, padding, grad_input); |
| 36509 | } |
| 36510 | at::Tensor & reflection_pad2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36511 | return wrapper_CPU_grad_input_reflection_pad2d_backward_out(grad_output, self, padding, grad_input); |
| 36512 | } |
| 36513 | at::Tensor reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36514 | return wrapper_CPU_reflection_pad3d(self, padding); |
| 36515 | } |
| 36516 | at::Tensor reflection_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36517 | return wrapper_CPU_reflection_pad3d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36518 | } |
| 36519 | at::Tensor & reflection_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36520 | return wrapper_CPU_reflection_pad3d_out_out(self, padding, out); |
| 36521 | } |
| 36522 | at::Tensor & reflection_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36523 | return wrapper_CPU_reflection_pad3d_out_out(self, padding, out); |
| 36524 | } |
| 36525 | at::Tensor & reflection_pad3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36526 | return wrapper_CPU_reflection_pad3d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36527 | } |
| 36528 | at::Tensor & reflection_pad3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36529 | return wrapper_CPU_reflection_pad3d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36530 | } |
| 36531 | at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36532 | return wrapper_CPU_reflection_pad3d_backward(grad_output, self, padding); |
| 36533 | } |
| 36534 | at::Tensor reflection_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36535 | return wrapper_CPU_reflection_pad3d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36536 | } |
| 36537 | at::Tensor & reflection_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36538 | return wrapper_CPU_reflection_pad3d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36539 | } |
| 36540 | at::Tensor & reflection_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36541 | return wrapper_CPU_reflection_pad3d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36542 | } |
| 36543 | at::Tensor & reflection_pad3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36544 | return wrapper_CPU_reflection_pad3d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36545 | } |
| 36546 | at::Tensor & reflection_pad3d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36547 | return wrapper_CPU_reflection_pad3d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36548 | } |
| 36549 | at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36550 | return wrapper_CPU_replication_pad1d(self, padding); |
| 36551 | } |
| 36552 | at::Tensor replication_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36553 | return wrapper_CPU_replication_pad1d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36554 | } |
| 36555 | at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36556 | return wrapper_CPU_replication_pad1d_out_out(self, padding, out); |
| 36557 | } |
| 36558 | at::Tensor & replication_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36559 | return wrapper_CPU_replication_pad1d_out_out(self, padding, out); |
| 36560 | } |
| 36561 | at::Tensor & replication_pad1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36562 | return wrapper_CPU_replication_pad1d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36563 | } |
| 36564 | at::Tensor & replication_pad1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36565 | return wrapper_CPU_replication_pad1d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36566 | } |
| 36567 | at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36568 | return wrapper_CPU_replication_pad1d_backward(grad_output, self, padding); |
| 36569 | } |
| 36570 | at::Tensor replication_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36571 | return wrapper_CPU_replication_pad1d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36572 | } |
| 36573 | at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36574 | return wrapper_CPU_replication_pad1d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36575 | } |
| 36576 | at::Tensor & replication_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36577 | return wrapper_CPU_replication_pad1d_backward_out_grad_input(grad_output, self, padding, grad_input); |
| 36578 | } |
| 36579 | at::Tensor & replication_pad1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36580 | return wrapper_CPU_replication_pad1d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36581 | } |
| 36582 | at::Tensor & replication_pad1d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36583 | return wrapper_CPU_replication_pad1d_backward_out_grad_input(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding), grad_input); |
| 36584 | } |
| 36585 | at::Tensor replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36586 | return wrapper_CPU_replication_pad2d(self, padding); |
| 36587 | } |
| 36588 | at::Tensor replication_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36589 | return wrapper_CPU_replication_pad2d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36590 | } |
| 36591 | at::Tensor & replication_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36592 | return wrapper_CPU_replication_pad2d_out_out(self, padding, out); |
| 36593 | } |
| 36594 | at::Tensor & replication_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36595 | return wrapper_CPU_replication_pad2d_out_out(self, padding, out); |
| 36596 | } |
| 36597 | at::Tensor & replication_pad2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36598 | return wrapper_CPU_replication_pad2d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36599 | } |
| 36600 | at::Tensor & replication_pad2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36601 | return wrapper_CPU_replication_pad2d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36602 | } |
| 36603 | at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36604 | return wrapper_CPU__replication_pad2d_backward(grad_output, self, c10::fromIntArrayRefSlow(padding)); |
| 36605 | } |
| 36606 | at::Tensor replication_pad2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36607 | return wrapper_CPU__replication_pad2d_backward(grad_output, self, padding); |
| 36608 | } |
| 36609 | at::Tensor & replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36610 | return wrapper_CPU_grad_input_replication_pad2d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36611 | } |
| 36612 | at::Tensor & replication_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36613 | return wrapper_CPU_grad_input_replication_pad2d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36614 | } |
| 36615 | at::Tensor & replication_pad2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36616 | return wrapper_CPU_grad_input_replication_pad2d_backward_out(grad_output, self, padding, grad_input); |
| 36617 | } |
| 36618 | at::Tensor & replication_pad2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36619 | return wrapper_CPU_grad_input_replication_pad2d_backward_out(grad_output, self, padding, grad_input); |
| 36620 | } |
| 36621 | at::Tensor replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 36622 | return wrapper_CPU_replication_pad3d(self, padding); |
| 36623 | } |
| 36624 | at::Tensor replication_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36625 | return wrapper_CPU_replication_pad3d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 36626 | } |
| 36627 | at::Tensor & replication_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { |
| 36628 | return wrapper_CPU_replication_pad3d_out_out(self, padding, out); |
| 36629 | } |
| 36630 | at::Tensor & replication_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { |
| 36631 | return wrapper_CPU_replication_pad3d_out_out(self, padding, out); |
| 36632 | } |
| 36633 | at::Tensor & replication_pad3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36634 | return wrapper_CPU_replication_pad3d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36635 | } |
| 36636 | at::Tensor & replication_pad3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 36637 | return wrapper_CPU_replication_pad3d_out_out(self, C10_AS_INTARRAYREF_SLOW(padding), out); |
| 36638 | } |
| 36639 | at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36640 | return wrapper_CPU__replication_pad3d_backward(grad_output, self, c10::fromIntArrayRefSlow(padding)); |
| 36641 | } |
| 36642 | at::Tensor replication_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36643 | return wrapper_CPU__replication_pad3d_backward(grad_output, self, padding); |
| 36644 | } |
| 36645 | at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 36646 | return wrapper_CPU_grad_input_replication_pad3d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36647 | } |
| 36648 | at::Tensor & replication_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { |
| 36649 | return wrapper_CPU_grad_input_replication_pad3d_backward_out(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); |
| 36650 | } |
| 36651 | at::Tensor & replication_pad3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 36652 | return wrapper_CPU_grad_input_replication_pad3d_backward_out(grad_output, self, padding, grad_input); |
| 36653 | } |
| 36654 | at::Tensor & replication_pad3d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 36655 | return wrapper_CPU_grad_input_replication_pad3d_backward_out(grad_output, self, padding, grad_input); |
| 36656 | } |
| 36657 | at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 36658 | return wrapper_CPU_upsample_linear1d(self, output_size, align_corners, scales); |
| 36659 | } |
| 36660 | at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 36661 | return wrapper_CPU_upsample_linear1d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales); |
| 36662 | } |
| 36663 | at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 36664 | return wrapper_CPU_upsample_linear1d_out_out(self, output_size, align_corners, scales, out); |
| 36665 | } |
| 36666 | at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 36667 | return wrapper_CPU_upsample_linear1d_out_out(self, output_size, align_corners, scales, out); |
| 36668 | } |
| 36669 | at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 36670 | return wrapper_CPU_upsample_linear1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales, out); |
| 36671 | } |
| 36672 | at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 36673 | return wrapper_CPU_upsample_linear1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales, out); |
| 36674 | } |
| 36675 | 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) { |
| 36676 | return wrapper_CPU_upsample_linear1d_backward(grad_output, output_size, input_size, align_corners, scales); |
| 36677 | } |
| 36678 | 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) { |
| 36679 | return wrapper_CPU_upsample_linear1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales); |
| 36680 | } |
| 36681 | at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 36682 | return wrapper_CPU_upsample_linear1d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales, grad_input); |
| 36683 | } |
| 36684 | at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36685 | return wrapper_CPU_upsample_linear1d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales, grad_input); |
| 36686 | } |
| 36687 | at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 36688 | return wrapper_CPU_upsample_linear1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales, grad_input); |
| 36689 | } |
| 36690 | at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36691 | return wrapper_CPU_upsample_linear1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales, grad_input); |
| 36692 | } |
| 36693 | 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) { |
| 36694 | return wrapper_CPU_upsample_bilinear2d(self, output_size, align_corners, scales_h, scales_w); |
| 36695 | } |
| 36696 | 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) { |
| 36697 | return wrapper_CPU_upsample_bilinear2d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 36698 | } |
| 36699 | at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36700 | return wrapper_CPU_upsample_bilinear2d_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36701 | } |
| 36702 | at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36703 | return wrapper_CPU_upsample_bilinear2d_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36704 | } |
| 36705 | at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36706 | return wrapper_CPU_upsample_bilinear2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36707 | } |
| 36708 | at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36709 | return wrapper_CPU_upsample_bilinear2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36710 | } |
| 36711 | 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) { |
| 36712 | return wrapper_CPU_upsample_bilinear2d_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 36713 | } |
| 36714 | 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) { |
| 36715 | return wrapper_CPU_upsample_bilinear2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 36716 | } |
| 36717 | at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, 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) { |
| 36718 | return wrapper_CPU_upsample_bilinear2d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36719 | } |
| 36720 | at::Tensor & upsample_bilinear2d_backward_outf(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, at::Tensor & grad_input) { |
| 36721 | return wrapper_CPU_upsample_bilinear2d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36722 | } |
| 36723 | at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36724 | return wrapper_CPU_upsample_bilinear2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36725 | } |
| 36726 | at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36727 | return wrapper_CPU_upsample_bilinear2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36728 | } |
| 36729 | 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) { |
| 36730 | return wrapper_CPU__upsample_bilinear2d_aa(self, output_size, align_corners, scales_h, scales_w); |
| 36731 | } |
| 36732 | 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) { |
| 36733 | return wrapper_CPU__upsample_bilinear2d_aa(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 36734 | } |
| 36735 | at::Tensor & _upsample_bilinear2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36736 | return wrapper_CPU__upsample_bilinear2d_aa_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36737 | } |
| 36738 | at::Tensor & _upsample_bilinear2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36739 | return wrapper_CPU__upsample_bilinear2d_aa_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36740 | } |
| 36741 | at::Tensor & _upsample_bilinear2d_aa_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36742 | return wrapper_CPU__upsample_bilinear2d_aa_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36743 | } |
| 36744 | at::Tensor & _upsample_bilinear2d_aa_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36745 | return wrapper_CPU__upsample_bilinear2d_aa_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36746 | } |
| 36747 | 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) { |
| 36748 | return wrapper_CPU__upsample_bilinear2d_aa_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 36749 | } |
| 36750 | 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) { |
| 36751 | return wrapper_CPU__upsample_bilinear2d_aa_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 36752 | } |
| 36753 | at::Tensor & _upsample_bilinear2d_aa_backward_out(at::Tensor & grad_input, 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) { |
| 36754 | return wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36755 | } |
| 36756 | at::Tensor & _upsample_bilinear2d_aa_backward_outf(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, at::Tensor & grad_input) { |
| 36757 | return wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36758 | } |
| 36759 | at::Tensor & _upsample_bilinear2d_aa_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36760 | return wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36761 | } |
| 36762 | at::Tensor & _upsample_bilinear2d_aa_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36763 | return wrapper_CPU__upsample_bilinear2d_aa_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36764 | } |
| 36765 | 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) { |
| 36766 | return wrapper_CPU_upsample_bicubic2d(self, output_size, align_corners, scales_h, scales_w); |
| 36767 | } |
| 36768 | 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) { |
| 36769 | return wrapper_CPU_upsample_bicubic2d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 36770 | } |
| 36771 | at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36772 | return wrapper_CPU_upsample_bicubic2d_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36773 | } |
| 36774 | at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36775 | return wrapper_CPU_upsample_bicubic2d_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36776 | } |
| 36777 | at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36778 | return wrapper_CPU_upsample_bicubic2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36779 | } |
| 36780 | at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36781 | return wrapper_CPU_upsample_bicubic2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36782 | } |
| 36783 | 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) { |
| 36784 | return wrapper_CPU_upsample_bicubic2d_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 36785 | } |
| 36786 | 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) { |
| 36787 | return wrapper_CPU_upsample_bicubic2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 36788 | } |
| 36789 | at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, 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) { |
| 36790 | return wrapper_CPU_upsample_bicubic2d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36791 | } |
| 36792 | at::Tensor & upsample_bicubic2d_backward_outf(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, at::Tensor & grad_input) { |
| 36793 | return wrapper_CPU_upsample_bicubic2d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36794 | } |
| 36795 | at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36796 | return wrapper_CPU_upsample_bicubic2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36797 | } |
| 36798 | at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36799 | return wrapper_CPU_upsample_bicubic2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36800 | } |
| 36801 | 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) { |
| 36802 | return wrapper_CPU__upsample_bicubic2d_aa(self, output_size, align_corners, scales_h, scales_w); |
| 36803 | } |
| 36804 | 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) { |
| 36805 | return wrapper_CPU__upsample_bicubic2d_aa(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 36806 | } |
| 36807 | at::Tensor & _upsample_bicubic2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36808 | return wrapper_CPU__upsample_bicubic2d_aa_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36809 | } |
| 36810 | at::Tensor & _upsample_bicubic2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36811 | return wrapper_CPU__upsample_bicubic2d_aa_out_out(self, output_size, align_corners, scales_h, scales_w, out); |
| 36812 | } |
| 36813 | at::Tensor & _upsample_bicubic2d_aa_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36814 | return wrapper_CPU__upsample_bicubic2d_aa_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36815 | } |
| 36816 | at::Tensor & _upsample_bicubic2d_aa_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36817 | return wrapper_CPU__upsample_bicubic2d_aa_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w, out); |
| 36818 | } |
| 36819 | 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) { |
| 36820 | return wrapper_CPU__upsample_bicubic2d_aa_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 36821 | } |
| 36822 | 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) { |
| 36823 | return wrapper_CPU__upsample_bicubic2d_aa_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 36824 | } |
| 36825 | at::Tensor & _upsample_bicubic2d_aa_backward_out(at::Tensor & grad_input, 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) { |
| 36826 | return wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36827 | } |
| 36828 | at::Tensor & _upsample_bicubic2d_aa_backward_outf(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, at::Tensor & grad_input) { |
| 36829 | return wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 36830 | } |
| 36831 | at::Tensor & _upsample_bicubic2d_aa_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36832 | return wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36833 | } |
| 36834 | at::Tensor & _upsample_bicubic2d_aa_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36835 | return wrapper_CPU__upsample_bicubic2d_aa_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w, grad_input); |
| 36836 | } |
| 36837 | 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) { |
| 36838 | return wrapper_CPU_upsample_trilinear3d(self, output_size, align_corners, scales_d, scales_h, scales_w); |
| 36839 | } |
| 36840 | 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) { |
| 36841 | return wrapper_CPU_upsample_trilinear3d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_d, scales_h, scales_w); |
| 36842 | } |
| 36843 | at::Tensor & upsample_trilinear3d_out(at::Tensor & out, 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) { |
| 36844 | return wrapper_CPU_upsample_trilinear3d_out_out(self, output_size, align_corners, scales_d, scales_h, scales_w, out); |
| 36845 | } |
| 36846 | at::Tensor & upsample_trilinear3d_outf(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, at::Tensor & out) { |
| 36847 | return wrapper_CPU_upsample_trilinear3d_out_out(self, output_size, align_corners, scales_d, scales_h, scales_w, out); |
| 36848 | } |
| 36849 | at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, 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) { |
| 36850 | return wrapper_CPU_upsample_trilinear3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_d, scales_h, scales_w, out); |
| 36851 | } |
| 36852 | at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36853 | return wrapper_CPU_upsample_trilinear3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_d, scales_h, scales_w, out); |
| 36854 | } |
| 36855 | 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) { |
| 36856 | return wrapper_CPU_upsample_trilinear3d_backward(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 36857 | } |
| 36858 | 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) { |
| 36859 | return wrapper_CPU_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); |
| 36860 | } |
| 36861 | at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, 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) { |
| 36862 | return wrapper_CPU_upsample_trilinear3d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); |
| 36863 | } |
| 36864 | at::Tensor & upsample_trilinear3d_backward_outf(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, at::Tensor & grad_input) { |
| 36865 | return wrapper_CPU_upsample_trilinear3d_backward_out_grad_input(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); |
| 36866 | } |
| 36867 | at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 36868 | return wrapper_CPU_upsample_trilinear3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_d, scales_h, scales_w, grad_input); |
| 36869 | } |
| 36870 | at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36871 | return wrapper_CPU_upsample_trilinear3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_d, scales_h, scales_w, grad_input); |
| 36872 | } |
| 36873 | at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 36874 | return wrapper_CPU_upsample_nearest1d(self, output_size, scales); |
| 36875 | } |
| 36876 | at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 36877 | return wrapper_CPU_upsample_nearest1d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales); |
| 36878 | } |
| 36879 | at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 36880 | return wrapper_CPU_upsample_nearest1d_out_out(self, output_size, scales, out); |
| 36881 | } |
| 36882 | at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 36883 | return wrapper_CPU_upsample_nearest1d_out_out(self, output_size, scales, out); |
| 36884 | } |
| 36885 | at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 36886 | return wrapper_CPU_upsample_nearest1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales, out); |
| 36887 | } |
| 36888 | at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 36889 | return wrapper_CPU_upsample_nearest1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales, out); |
| 36890 | } |
| 36891 | at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 36892 | return wrapper_CPU__upsample_nearest_exact1d(self, output_size, scales); |
| 36893 | } |
| 36894 | at::Tensor _upsample_nearest_exact1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 36895 | return wrapper_CPU__upsample_nearest_exact1d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales); |
| 36896 | } |
| 36897 | at::Tensor & _upsample_nearest_exact1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 36898 | return wrapper_CPU__upsample_nearest_exact1d_out_out(self, output_size, scales, out); |
| 36899 | } |
| 36900 | at::Tensor & _upsample_nearest_exact1d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 36901 | return wrapper_CPU__upsample_nearest_exact1d_out_out(self, output_size, scales, out); |
| 36902 | } |
| 36903 | at::Tensor & _upsample_nearest_exact1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 36904 | return wrapper_CPU__upsample_nearest_exact1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales, out); |
| 36905 | } |
| 36906 | at::Tensor & _upsample_nearest_exact1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 36907 | return wrapper_CPU__upsample_nearest_exact1d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales, out); |
| 36908 | } |
| 36909 | at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 36910 | return wrapper_CPU_upsample_nearest1d_backward(grad_output, output_size, input_size, scales); |
| 36911 | } |
| 36912 | at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 36913 | return wrapper_CPU_upsample_nearest1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales); |
| 36914 | } |
| 36915 | at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 36916 | return wrapper_CPU_upsample_nearest1d_backward_out_grad_input(grad_output, output_size, input_size, scales, grad_input); |
| 36917 | } |
| 36918 | at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36919 | return wrapper_CPU_upsample_nearest1d_backward_out_grad_input(grad_output, output_size, input_size, scales, grad_input); |
| 36920 | } |
| 36921 | at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 36922 | return wrapper_CPU_upsample_nearest1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales, grad_input); |
| 36923 | } |
| 36924 | at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36925 | return wrapper_CPU_upsample_nearest1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales, grad_input); |
| 36926 | } |
| 36927 | at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 36928 | return wrapper_CPU__upsample_nearest_exact1d_backward(grad_output, output_size, input_size, scales); |
| 36929 | } |
| 36930 | at::Tensor _upsample_nearest_exact1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 36931 | return wrapper_CPU__upsample_nearest_exact1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales); |
| 36932 | } |
| 36933 | at::Tensor & _upsample_nearest_exact1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 36934 | return wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input(grad_output, output_size, input_size, scales, grad_input); |
| 36935 | } |
| 36936 | at::Tensor & _upsample_nearest_exact1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36937 | return wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input(grad_output, output_size, input_size, scales, grad_input); |
| 36938 | } |
| 36939 | at::Tensor & _upsample_nearest_exact1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 36940 | return wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales, grad_input); |
| 36941 | } |
| 36942 | at::Tensor & _upsample_nearest_exact1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 36943 | return wrapper_CPU__upsample_nearest_exact1d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales, grad_input); |
| 36944 | } |
| 36945 | at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36946 | return wrapper_CPU_upsample_nearest2d(self, output_size, scales_h, scales_w); |
| 36947 | } |
| 36948 | at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36949 | return wrapper_CPU_upsample_nearest2d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w); |
| 36950 | } |
| 36951 | at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36952 | return wrapper_CPU_upsample_nearest2d_out_out(self, output_size, scales_h, scales_w, out); |
| 36953 | } |
| 36954 | at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36955 | return wrapper_CPU_upsample_nearest2d_out_out(self, output_size, scales_h, scales_w, out); |
| 36956 | } |
| 36957 | at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36958 | return wrapper_CPU_upsample_nearest2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w, out); |
| 36959 | } |
| 36960 | at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36961 | return wrapper_CPU_upsample_nearest2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w, out); |
| 36962 | } |
| 36963 | at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36964 | return wrapper_CPU__upsample_nearest_exact2d(self, output_size, scales_h, scales_w); |
| 36965 | } |
| 36966 | at::Tensor _upsample_nearest_exact2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36967 | return wrapper_CPU__upsample_nearest_exact2d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w); |
| 36968 | } |
| 36969 | at::Tensor & _upsample_nearest_exact2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36970 | return wrapper_CPU__upsample_nearest_exact2d_out_out(self, output_size, scales_h, scales_w, out); |
| 36971 | } |
| 36972 | at::Tensor & _upsample_nearest_exact2d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36973 | return wrapper_CPU__upsample_nearest_exact2d_out_out(self, output_size, scales_h, scales_w, out); |
| 36974 | } |
| 36975 | at::Tensor & _upsample_nearest_exact2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36976 | return wrapper_CPU__upsample_nearest_exact2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w, out); |
| 36977 | } |
| 36978 | at::Tensor & _upsample_nearest_exact2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 36979 | return wrapper_CPU__upsample_nearest_exact2d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w, out); |
| 36980 | } |
| 36981 | 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) { |
| 36982 | return wrapper_CPU_upsample_nearest2d_backward(grad_output, output_size, input_size, scales_h, scales_w); |
| 36983 | } |
| 36984 | 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) { |
| 36985 | return wrapper_CPU_upsample_nearest2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w); |
| 36986 | } |
| 36987 | at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36988 | return wrapper_CPU_upsample_nearest2d_backward_out_grad_input(grad_output, output_size, input_size, scales_h, scales_w, grad_input); |
| 36989 | } |
| 36990 | at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36991 | return wrapper_CPU_upsample_nearest2d_backward_out_grad_input(grad_output, output_size, input_size, scales_h, scales_w, grad_input); |
| 36992 | } |
| 36993 | at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 36994 | return wrapper_CPU_upsample_nearest2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w, grad_input); |
| 36995 | } |
| 36996 | at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 36997 | return wrapper_CPU_upsample_nearest2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w, grad_input); |
| 36998 | } |
| 36999 | 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) { |
| 37000 | return wrapper_CPU__upsample_nearest_exact2d_backward(grad_output, output_size, input_size, scales_h, scales_w); |
| 37001 | } |
| 37002 | 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) { |
| 37003 | return wrapper_CPU__upsample_nearest_exact2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w); |
| 37004 | } |
| 37005 | at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37006 | return wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input(grad_output, output_size, input_size, scales_h, scales_w, grad_input); |
| 37007 | } |
| 37008 | at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 37009 | return wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input(grad_output, output_size, input_size, scales_h, scales_w, grad_input); |
| 37010 | } |
| 37011 | at::Tensor & _upsample_nearest_exact2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37012 | return wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w, grad_input); |
| 37013 | } |
| 37014 | at::Tensor & _upsample_nearest_exact2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 37015 | return wrapper_CPU__upsample_nearest_exact2d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w, grad_input); |
| 37016 | } |
| 37017 | 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) { |
| 37018 | return wrapper_CPU_upsample_nearest3d(self, output_size, scales_d, scales_h, scales_w); |
| 37019 | } |
| 37020 | 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) { |
| 37021 | return wrapper_CPU_upsample_nearest3d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w); |
| 37022 | } |
| 37023 | at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37024 | return wrapper_CPU_upsample_nearest3d_out_out(self, output_size, scales_d, scales_h, scales_w, out); |
| 37025 | } |
| 37026 | at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 37027 | return wrapper_CPU_upsample_nearest3d_out_out(self, output_size, scales_d, scales_h, scales_w, out); |
| 37028 | } |
| 37029 | at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37030 | return wrapper_CPU_upsample_nearest3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w, out); |
| 37031 | } |
| 37032 | at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 37033 | return wrapper_CPU_upsample_nearest3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w, out); |
| 37034 | } |
| 37035 | 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) { |
| 37036 | return wrapper_CPU__upsample_nearest_exact3d(self, output_size, scales_d, scales_h, scales_w); |
| 37037 | } |
| 37038 | 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) { |
| 37039 | return wrapper_CPU__upsample_nearest_exact3d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w); |
| 37040 | } |
| 37041 | at::Tensor & _upsample_nearest_exact3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37042 | return wrapper_CPU__upsample_nearest_exact3d_out_out(self, output_size, scales_d, scales_h, scales_w, out); |
| 37043 | } |
| 37044 | at::Tensor & _upsample_nearest_exact3d_outf(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 37045 | return wrapper_CPU__upsample_nearest_exact3d_out_out(self, output_size, scales_d, scales_h, scales_w, out); |
| 37046 | } |
| 37047 | at::Tensor & _upsample_nearest_exact3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 37048 | return wrapper_CPU__upsample_nearest_exact3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w, out); |
| 37049 | } |
| 37050 | at::Tensor & _upsample_nearest_exact3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 37051 | return wrapper_CPU__upsample_nearest_exact3d_out_out(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w, out); |
| 37052 | } |
| 37053 | 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) { |
| 37054 | return wrapper_CPU_upsample_nearest3d_backward(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 37055 | } |
| 37056 | 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) { |
| 37057 | return wrapper_CPU_upsample_nearest3d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w); |
| 37058 | } |
| 37059 | at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, 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) { |
| 37060 | return wrapper_CPU_upsample_nearest3d_backward_out_grad_input(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 37061 | } |
| 37062 | at::Tensor & upsample_nearest3d_backward_outf(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, at::Tensor & grad_input) { |
| 37063 | return wrapper_CPU_upsample_nearest3d_backward_out_grad_input(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 37064 | } |
| 37065 | at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 37066 | return wrapper_CPU_upsample_nearest3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w, grad_input); |
| 37067 | } |
| 37068 | at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 37069 | return wrapper_CPU_upsample_nearest3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w, grad_input); |
| 37070 | } |
| 37071 | 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) { |
| 37072 | return wrapper_CPU__upsample_nearest_exact3d_backward(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 37073 | } |
| 37074 | 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) { |
| 37075 | return wrapper_CPU__upsample_nearest_exact3d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w); |
| 37076 | } |
| 37077 | at::Tensor & _upsample_nearest_exact3d_backward_out(at::Tensor & grad_input, 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) { |
| 37078 | return wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 37079 | } |
| 37080 | at::Tensor & _upsample_nearest_exact3d_backward_outf(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, at::Tensor & grad_input) { |
| 37081 | return wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 37082 | } |
| 37083 | at::Tensor & _upsample_nearest_exact3d_backward_symint_out(at::Tensor & grad_input, 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) { |
| 37084 | return wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w, grad_input); |
| 37085 | } |
| 37086 | at::Tensor & _upsample_nearest_exact3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 37087 | return wrapper_CPU__upsample_nearest_exact3d_backward_out_grad_input(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w, grad_input); |
| 37088 | } |
| 37089 | at::Tensor sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 37090 | return wrapper_CPU_sigmoid_backward(grad_output, output); |
| 37091 | } |
| 37092 | at::Tensor & sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) { |
| 37093 | return wrapper_CPU_sigmoid_backward_out_grad_input(grad_output, output, grad_input); |
| 37094 | } |
| 37095 | at::Tensor & sigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 37096 | return wrapper_CPU_sigmoid_backward_out_grad_input(grad_output, output, grad_input); |
| 37097 | } |
| 37098 | at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 37099 | return wrapper_CPU_logit_backward(grad_output, self, eps); |
| 37100 | } |
| 37101 | at::Tensor & logit_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 37102 | return wrapper_CPU_logit_backward_out_grad_input(grad_output, self, eps, grad_input); |
| 37103 | } |
| 37104 | at::Tensor & logit_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input) { |
| 37105 | return wrapper_CPU_logit_backward_out_grad_input(grad_output, self, eps, grad_input); |
| 37106 | } |
| 37107 | at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 37108 | return wrapper_CPU_tanh_backward(grad_output, output); |
| 37109 | } |
| 37110 | at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) { |
| 37111 | return wrapper_CPU_tanh_backward_out_grad_input(grad_output, output, grad_input); |
| 37112 | } |
| 37113 | at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 37114 | return wrapper_CPU_tanh_backward_out_grad_input(grad_output, output, grad_input); |
| 37115 | } |
| 37116 | 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) { |
| 37117 | return wrapper_CPU_slow_conv_transpose2d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); |
| 37118 | } |
| 37119 | 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) { |
| 37120 | return wrapper_CPU_slow_conv_transpose2d(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation); |
| 37121 | } |
| 37122 | at::Tensor & slow_conv_transpose2d_out(at::Tensor & out, 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) { |
| 37123 | return wrapper_CPU_slow_conv_transpose2d_out_out(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 37124 | } |
| 37125 | at::Tensor & slow_conv_transpose2d_outf(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, at::Tensor & out) { |
| 37126 | return wrapper_CPU_slow_conv_transpose2d_out_out(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 37127 | } |
| 37128 | at::Tensor & slow_conv_transpose2d_symint_out(at::Tensor & out, 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) { |
| 37129 | return wrapper_CPU_slow_conv_transpose2d_out_out(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation, out); |
| 37130 | } |
| 37131 | at::Tensor & slow_conv_transpose2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 37132 | return wrapper_CPU_slow_conv_transpose2d_out_out(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation, out); |
| 37133 | } |
| 37134 | at::Tensor slow_conv_transpose3d(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) { |
| 37135 | return wrapper_CPU__slow_conv_transpose3d(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), dilation); |
| 37136 | } |
| 37137 | at::Tensor slow_conv_transpose3d_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) { |
| 37138 | return wrapper_CPU__slow_conv_transpose3d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); |
| 37139 | } |
| 37140 | at::Tensor & slow_conv_transpose3d_out(at::Tensor & out, 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) { |
| 37141 | return wrapper_CPU_out_slow_conv_transpose3d_out(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), dilation, out); |
| 37142 | } |
| 37143 | at::Tensor & slow_conv_transpose3d_outf(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, at::Tensor & out) { |
| 37144 | return wrapper_CPU_out_slow_conv_transpose3d_out(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), dilation, out); |
| 37145 | } |
| 37146 | at::Tensor & slow_conv_transpose3d_symint_out(at::Tensor & out, 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) { |
| 37147 | return wrapper_CPU_out_slow_conv_transpose3d_out(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 37148 | } |
| 37149 | at::Tensor & slow_conv_transpose3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 37150 | return wrapper_CPU_out_slow_conv_transpose3d_out(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 37151 | } |
| 37152 | at::Tensor _slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 37153 | return wrapper_CPU___slow_conv2d_forward(self, weight, kernel_size, bias, stride, padding); |
| 37154 | } |
| 37155 | at::Tensor & _slow_conv2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 37156 | return wrapper_CPU_output__slow_conv2d_forward_out(self, weight, kernel_size, bias, stride, padding, output); |
| 37157 | } |
| 37158 | at::Tensor & _slow_conv2d_forward_outf(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::Tensor & output) { |
| 37159 | return wrapper_CPU_output__slow_conv2d_forward_out(self, weight, kernel_size, bias, stride, padding, output); |
| 37160 | } |
| 37161 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 37162 | return wrapper_CPU_grad_input__slow_conv2d_backward_out(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); |
| 37163 | } |
| 37164 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { |
| 37165 | return wrapper_CPU_grad_input__slow_conv2d_backward_out(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); |
| 37166 | } |
| 37167 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) { |
| 37168 | return wrapper_CPU_output_mask__slow_conv2d_backward(grad_output, self, weight, kernel_size, stride, padding, output_mask); |
| 37169 | } |
| 37170 | at::Tensor slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 37171 | return wrapper_CPU__slow_conv3d_forward(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding)); |
| 37172 | } |
| 37173 | at::Tensor slow_conv3d_forward_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) { |
| 37174 | return wrapper_CPU__slow_conv3d_forward(self, weight, kernel_size, bias, stride, padding); |
| 37175 | } |
| 37176 | at::Tensor & slow_conv3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 37177 | return wrapper_CPU_output_slow_conv3d_forward_out(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), output); |
| 37178 | } |
| 37179 | at::Tensor & slow_conv3d_forward_outf(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::Tensor & output) { |
| 37180 | return wrapper_CPU_output_slow_conv3d_forward_out(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), output); |
| 37181 | } |
| 37182 | at::Tensor & slow_conv3d_forward_symint_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding) { |
| 37183 | return wrapper_CPU_output_slow_conv3d_forward_out(self, weight, kernel_size, bias, stride, padding, output); |
| 37184 | } |
| 37185 | at::Tensor & slow_conv3d_forward_symint_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) { |
| 37186 | return wrapper_CPU_output_slow_conv3d_forward_out(self, weight, kernel_size, bias, stride, padding, output); |
| 37187 | } |
| 37188 | at::Tensor slow_conv_dilated2d(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 dilation) { |
| 37189 | return wrapper_CPU__slow_conv_dilated2d(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), dilation); |
| 37190 | } |
| 37191 | at::Tensor slow_conv_dilated2d_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, at::IntArrayRef dilation) { |
| 37192 | return wrapper_CPU__slow_conv_dilated2d(self, weight, kernel_size, bias, stride, padding, dilation); |
| 37193 | } |
| 37194 | at::Tensor slow_conv_dilated3d(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 dilation) { |
| 37195 | return wrapper_CPU__slow_conv_dilated3d(self, weight, kernel_size, bias, stride, c10::fromIntArrayRefSlow(padding), dilation); |
| 37196 | } |
| 37197 | at::Tensor slow_conv_dilated3d_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, at::IntArrayRef dilation) { |
| 37198 | return wrapper_CPU__slow_conv_dilated3d(self, weight, kernel_size, bias, stride, padding, dilation); |
| 37199 | } |
| 37200 | at::Tensor col2im(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37201 | return wrapper_CPU__col2im(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride); |
| 37202 | } |
| 37203 | at::Tensor col2im_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37204 | return wrapper_CPU__col2im(self, output_size, kernel_size, dilation, padding, stride); |
| 37205 | } |
| 37206 | at::Tensor & col2im_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37207 | return wrapper_CPU_out_col2im_out(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out); |
| 37208 | } |
| 37209 | at::Tensor & col2im_outf(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 37210 | return wrapper_CPU_out_col2im_out(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out); |
| 37211 | } |
| 37212 | at::Tensor & col2im_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37213 | return wrapper_CPU_out_col2im_out(self, output_size, kernel_size, dilation, padding, stride, out); |
| 37214 | } |
| 37215 | at::Tensor & col2im_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 37216 | return wrapper_CPU_out_col2im_out(self, output_size, kernel_size, dilation, padding, stride, out); |
| 37217 | } |
| 37218 | at::Tensor im2col(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37219 | return wrapper_CPU__im2col(self, kernel_size, dilation, padding, stride); |
| 37220 | } |
| 37221 | at::Tensor & im2col_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 37222 | return wrapper_CPU_out_im2col_out(self, kernel_size, dilation, padding, stride, out); |
| 37223 | } |
| 37224 | at::Tensor & im2col_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 37225 | return wrapper_CPU_out_im2col_out(self, kernel_size, dilation, padding, stride, out); |
| 37226 | } |
| 37227 | at::Tensor isposinf(const at::Tensor & self) { |
| 37228 | return wrapper_CPU_isposinf(self); |
| 37229 | } |
| 37230 | at::Tensor & isposinf_out(at::Tensor & out, const at::Tensor & self) { |
| 37231 | return wrapper_CPU_isposinf_out_out(self, out); |
| 37232 | } |
| 37233 | at::Tensor & isposinf_outf(const at::Tensor & self, at::Tensor & out) { |
| 37234 | return wrapper_CPU_isposinf_out_out(self, out); |
| 37235 | } |
| 37236 | at::Tensor isneginf(const at::Tensor & self) { |
| 37237 | return wrapper_CPU_isneginf(self); |
| 37238 | } |
| 37239 | at::Tensor & isneginf_out(at::Tensor & out, const at::Tensor & self) { |
| 37240 | return wrapper_CPU_isneginf_out_out(self, out); |
| 37241 | } |
| 37242 | at::Tensor & isneginf_outf(const at::Tensor & self, at::Tensor & out) { |
| 37243 | return wrapper_CPU_isneginf_out_out(self, out); |
| 37244 | } |
| 37245 | at::Tensor special_entr(const at::Tensor & self) { |
| 37246 | return wrapper_CPU_special_entr(self); |
| 37247 | } |
| 37248 | at::Tensor & special_entr_out(at::Tensor & out, const at::Tensor & self) { |
| 37249 | return wrapper_CPU_special_entr_out_out(self, out); |
| 37250 | } |
| 37251 | at::Tensor & special_entr_outf(const at::Tensor & self, at::Tensor & out) { |
| 37252 | return wrapper_CPU_special_entr_out_out(self, out); |
| 37253 | } |
| 37254 | at::Tensor special_ndtri(const at::Tensor & self) { |
| 37255 | return wrapper_CPU_special_ndtri(self); |
| 37256 | } |
| 37257 | at::Tensor & special_ndtri_out(at::Tensor & out, const at::Tensor & self) { |
| 37258 | return wrapper_CPU_special_ndtri_out_out(self, out); |
| 37259 | } |
| 37260 | at::Tensor & special_ndtri_outf(const at::Tensor & self, at::Tensor & out) { |
| 37261 | return wrapper_CPU_special_ndtri_out_out(self, out); |
| 37262 | } |
| 37263 | at::Tensor special_log_ndtr(const at::Tensor & self) { |
| 37264 | return wrapper_CPU_special_log_ndtr(self); |
| 37265 | } |
| 37266 | at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self) { |
| 37267 | return wrapper_CPU_special_log_ndtr_out_out(self, out); |
| 37268 | } |
| 37269 | at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out) { |
| 37270 | return wrapper_CPU_special_log_ndtr_out_out(self, out); |
| 37271 | } |
| 37272 | at::Tensor special_erfcx(const at::Tensor & self) { |
| 37273 | return wrapper_CPU_special_erfcx(self); |
| 37274 | } |
| 37275 | at::Tensor & special_erfcx_out(at::Tensor & out, const at::Tensor & self) { |
| 37276 | return wrapper_CPU_special_erfcx_out_out(self, out); |
| 37277 | } |
| 37278 | at::Tensor & special_erfcx_outf(const at::Tensor & self, at::Tensor & out) { |
| 37279 | return wrapper_CPU_special_erfcx_out_out(self, out); |
| 37280 | } |
| 37281 | at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other) { |
| 37282 | return wrapper_CPU_special_xlog1py(self, other); |
| 37283 | } |
| 37284 | at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 37285 | return wrapper_CPU_special_xlog1py_out_out(self, other, out); |
| 37286 | } |
| 37287 | at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 37288 | return wrapper_CPU_special_xlog1py_out_out(self, other, out); |
| 37289 | } |
| 37290 | at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other) { |
| 37291 | return wrapper_CPU_special_zeta(self, other); |
| 37292 | } |
| 37293 | at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { |
| 37294 | return wrapper_CPU_special_zeta_out_out(self, other, out); |
| 37295 | } |
| 37296 | at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 37297 | return wrapper_CPU_special_zeta_out_out(self, other, out); |
| 37298 | } |
| 37299 | at::Tensor special_i0e(const at::Tensor & self) { |
| 37300 | return wrapper_CPU_special_i0e(self); |
| 37301 | } |
| 37302 | at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self) { |
| 37303 | return wrapper_CPU_special_i0e_out_out(self, out); |
| 37304 | } |
| 37305 | at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out) { |
| 37306 | return wrapper_CPU_special_i0e_out_out(self, out); |
| 37307 | } |
| 37308 | at::Tensor special_i1(const at::Tensor & self) { |
| 37309 | return wrapper_CPU_special_i1(self); |
| 37310 | } |
| 37311 | at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self) { |
| 37312 | return wrapper_CPU_special_i1_out_out(self, out); |
| 37313 | } |
| 37314 | at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out) { |
| 37315 | return wrapper_CPU_special_i1_out_out(self, out); |
| 37316 | } |
| 37317 | at::Tensor special_i1e(const at::Tensor & self) { |
| 37318 | return wrapper_CPU_special_i1e(self); |
| 37319 | } |
| 37320 | at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self) { |
| 37321 | return wrapper_CPU_special_i1e_out_out(self, out); |
| 37322 | } |
| 37323 | at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out) { |
| 37324 | return wrapper_CPU_special_i1e_out_out(self, out); |
| 37325 | } |
| 37326 | ::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex(const at::Tensor & self, bool upper, bool check_errors) { |
| 37327 | return wrapper_CPU_linalg_cholesky_ex(self, upper, check_errors); |
| 37328 | } |
| 37329 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_out(at::Tensor & L, at::Tensor & info, const at::Tensor & self, bool upper, bool check_errors) { |
| 37330 | return wrapper_CPU_linalg_cholesky_ex_out_L(self, upper, check_errors, L, info); |
| 37331 | } |
| 37332 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_outf(const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { |
| 37333 | return wrapper_CPU_linalg_cholesky_ex_out_L(self, upper, check_errors, L, info); |
| 37334 | } |
| 37335 | at::Tensor linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 37336 | return wrapper_CPU_linalg_cross(self, other, dim); |
| 37337 | } |
| 37338 | at::Tensor & linalg_cross_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 37339 | return wrapper_CPU_linalg_cross_out_out(self, other, dim, out); |
| 37340 | } |
| 37341 | at::Tensor & linalg_cross_outf(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { |
| 37342 | return wrapper_CPU_linalg_cross_out_out(self, other, dim, out); |
| 37343 | } |
| 37344 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex(const at::Tensor & A, bool pivot, bool check_errors) { |
| 37345 | return wrapper_CPU_linalg_lu_factor_ex(A, pivot, check_errors); |
| 37346 | } |
| 37347 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out(at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, bool pivot, bool check_errors) { |
| 37348 | return wrapper_CPU_linalg_lu_factor_ex_out_out(A, pivot, check_errors, LU, pivots, info); |
| 37349 | } |
| 37350 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_outf(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 37351 | return wrapper_CPU_linalg_lu_factor_ex_out_out(A, pivot, check_errors, LU, pivots, info); |
| 37352 | } |
| 37353 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu(const at::Tensor & A, bool pivot) { |
| 37354 | return wrapper_CPU_linalg_lu(A, pivot); |
| 37355 | } |
| 37356 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_out(at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & A, bool pivot) { |
| 37357 | return wrapper_CPU_linalg_lu_out_out(A, pivot, P, L, U); |
| 37358 | } |
| 37359 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_outf(const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U) { |
| 37360 | return wrapper_CPU_linalg_lu_out_out(A, pivot, P, L, U); |
| 37361 | } |
| 37362 | at::Tensor linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { |
| 37363 | return wrapper_CPU_linalg_lu_solve(LU, pivots, B, left, adjoint); |
| 37364 | } |
| 37365 | at::Tensor & linalg_lu_solve_out(at::Tensor & out, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { |
| 37366 | return wrapper_CPU_linalg_lu_solve_out_out(LU, pivots, B, left, adjoint, out); |
| 37367 | } |
| 37368 | at::Tensor & linalg_lu_solve_outf(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) { |
| 37369 | return wrapper_CPU_linalg_lu_solve_out_out(LU, pivots, B, left, adjoint, out); |
| 37370 | } |
| 37371 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det(const at::Tensor & A) { |
| 37372 | return wrapper_CPU__linalg_det(A); |
| 37373 | } |
| 37374 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_out(at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) { |
| 37375 | return wrapper_CPU__linalg_det_out_result(A, result, LU, pivots); |
| 37376 | } |
| 37377 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_outf(const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { |
| 37378 | return wrapper_CPU__linalg_det_out_result(A, result, LU, pivots); |
| 37379 | } |
| 37380 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian, bool check_errors) { |
| 37381 | return wrapper_CPU_linalg_ldl_factor_ex(self, hermitian, check_errors); |
| 37382 | } |
| 37383 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_out(at::Tensor & LD, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool hermitian, bool check_errors) { |
| 37384 | return wrapper_CPU_linalg_ldl_factor_ex_out_out(self, hermitian, check_errors, LD, pivots, info); |
| 37385 | } |
| 37386 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_outf(const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info) { |
| 37387 | return wrapper_CPU_linalg_ldl_factor_ex_out_out(self, hermitian, check_errors, LD, pivots, info); |
| 37388 | } |
| 37389 | at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { |
| 37390 | return wrapper_CPU_linalg_ldl_solve(LD, pivots, B, hermitian); |
| 37391 | } |
| 37392 | at::Tensor & linalg_ldl_solve_out(at::Tensor & out, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { |
| 37393 | return wrapper_CPU_linalg_ldl_solve_out_out(LD, pivots, B, hermitian, out); |
| 37394 | } |
| 37395 | at::Tensor & linalg_ldl_solve_outf(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out) { |
| 37396 | return wrapper_CPU_linalg_ldl_solve_out_out(LD, pivots, B, hermitian, out); |
| 37397 | } |
| 37398 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_out(at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values, const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond, c10::optional<c10::string_view> driver) { |
| 37399 | return wrapper_CPU_out_linalg_lstsq_out(self, b, rcond, driver, solution, residuals, rank, singular_values); |
| 37400 | } |
| 37401 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_outf(const at::Tensor & self, const at::Tensor & b, c10::optional<double> rcond, c10::optional<c10::string_view> driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values) { |
| 37402 | return wrapper_CPU_out_linalg_lstsq_out(self, b, rcond, driver, solution, residuals, rank, singular_values); |
| 37403 | } |
| 37404 | at::Tensor linalg_matrix_exp(const at::Tensor & self) { |
| 37405 | return wrapper_CPU__linalg_matrix_exp(self); |
| 37406 | } |
| 37407 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_slogdet(const at::Tensor & A) { |
| 37408 | return wrapper_CPU__linalg_slogdet(A); |
| 37409 | } |
| 37410 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_out(at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) { |
| 37411 | return wrapper_CPU__linalg_slogdet_out_sign(A, sign, logabsdet, LU, pivots); |
| 37412 | } |
| 37413 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_outf(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots) { |
| 37414 | return wrapper_CPU__linalg_slogdet_out_sign(A, sign, logabsdet, LU, pivots); |
| 37415 | } |
| 37416 | ::std::tuple<at::Tensor,at::Tensor> linalg_eig(const at::Tensor & self) { |
| 37417 | return wrapper_CPU__linalg_eig(self); |
| 37418 | } |
| 37419 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & self) { |
| 37420 | return wrapper_CPU_out_linalg_eig_out(self, eigenvalues, eigenvectors); |
| 37421 | } |
| 37422 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_outf(const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { |
| 37423 | return wrapper_CPU_out_linalg_eig_out(self, eigenvalues, eigenvectors); |
| 37424 | } |
| 37425 | ::std::tuple<at::Tensor,at::Tensor> _linalg_eigh(const at::Tensor & A, c10::string_view UPLO, bool compute_v) { |
| 37426 | return wrapper_CPU__linalg_eigh(A, UPLO, compute_v); |
| 37427 | } |
| 37428 | ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & A, c10::string_view UPLO, bool compute_v) { |
| 37429 | return wrapper_CPU__linalg_eigh_out_eigenvalues(A, UPLO, compute_v, eigenvalues, eigenvectors); |
| 37430 | } |
| 37431 | ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_outf(const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { |
| 37432 | return wrapper_CPU__linalg_eigh_out_eigenvalues(A, UPLO, compute_v, eigenvalues, eigenvectors); |
| 37433 | } |
| 37434 | at::Tensor linalg_householder_product(const at::Tensor & input, const at::Tensor & tau) { |
| 37435 | return wrapper_CPU__linalg_householder_product(input, tau); |
| 37436 | } |
| 37437 | at::Tensor & linalg_householder_product_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & tau) { |
| 37438 | return wrapper_CPU_out_linalg_householder_product_out(input, tau, out); |
| 37439 | } |
| 37440 | at::Tensor & linalg_householder_product_outf(const at::Tensor & input, const at::Tensor & tau, at::Tensor & out) { |
| 37441 | return wrapper_CPU_out_linalg_householder_product_out(input, tau, out); |
| 37442 | } |
| 37443 | ::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex(const at::Tensor & A, bool check_errors) { |
| 37444 | return wrapper_CPU_linalg_inv_ex(A, check_errors); |
| 37445 | } |
| 37446 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_out(at::Tensor & inverse, at::Tensor & info, const at::Tensor & A, bool check_errors) { |
| 37447 | return wrapper_CPU_linalg_inv_ex_out_inverse(A, check_errors, inverse, info); |
| 37448 | } |
| 37449 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_outf(const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { |
| 37450 | return wrapper_CPU_linalg_inv_ex_out_inverse(A, check_errors, inverse, info); |
| 37451 | } |
| 37452 | at::Tensor linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 37453 | return wrapper_CPU_linalg_vector_norm(self, ord, dim, keepdim, dtype); |
| 37454 | } |
| 37455 | at::Tensor & linalg_vector_norm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 37456 | return wrapper_CPU_linalg_vector_norm_out_out(self, ord, dim, keepdim, dtype, out); |
| 37457 | } |
| 37458 | at::Tensor & linalg_vector_norm_outf(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 37459 | return wrapper_CPU_linalg_vector_norm_out_out(self, ord, dim, keepdim, dtype, out); |
| 37460 | } |
| 37461 | ::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) { |
| 37462 | return wrapper_CPU__linalg_svd(A, full_matrices, compute_uv, driver); |
| 37463 | } |
| 37464 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver) { |
| 37465 | return wrapper_CPU__linalg_svd_out_U(A, full_matrices, compute_uv, driver, U, S, Vh); |
| 37466 | } |
| 37467 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_outf(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { |
| 37468 | return wrapper_CPU__linalg_svd_out_U(A, full_matrices, compute_uv, driver, U, S, Vh); |
| 37469 | } |
| 37470 | ::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) { |
| 37471 | return wrapper_CPU__linalg_solve_ex(A, B, left, check_errors); |
| 37472 | } |
| 37473 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_out(at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 37474 | return wrapper_CPU__linalg_solve_ex_out_result(A, B, left, check_errors, result, LU, pivots, info); |
| 37475 | } |
| 37476 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_outf(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 37477 | return wrapper_CPU__linalg_solve_ex_out_result(A, B, left, check_errors, result, LU, pivots, info); |
| 37478 | } |
| 37479 | ::std::tuple<at::Tensor,at::Tensor> linalg_qr(const at::Tensor & A, c10::string_view mode) { |
| 37480 | return wrapper_CPU_linalg_qr(A, mode); |
| 37481 | } |
| 37482 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_out(at::Tensor & Q, at::Tensor & R, const at::Tensor & A, c10::string_view mode) { |
| 37483 | return wrapper_CPU_linalg_qr_out_out(A, mode, Q, R); |
| 37484 | } |
| 37485 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_outf(const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R) { |
| 37486 | return wrapper_CPU_linalg_qr_out_out(A, mode, Q, R); |
| 37487 | } |
| 37488 | at::Tensor _test_optional_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 37489 | return wrapper_CPU___test_optional_intlist(values, addends); |
| 37490 | } |
| 37491 | at::Tensor _test_optional_filled_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 37492 | return wrapper_CPU___test_optional_filled_intlist(values, addends); |
| 37493 | } |
| 37494 | at::Tensor _test_optional_floatlist(const at::Tensor & values, c10::optional<at::ArrayRef<double>> addends) { |
| 37495 | return wrapper_CPU___test_optional_floatlist(values, addends); |
| 37496 | } |
| 37497 | at::Tensor segment_reduce(const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & indices, const c10::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const c10::optional<at::Scalar> & initial) { |
| 37498 | return wrapper_CPU__segment_reduce(data, reduce, lengths, indices, offsets, axis, unsafe, initial); |
| 37499 | } |
| 37500 | at::Tensor _segment_reduce_backward(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & offsets, int64_t axis, const c10::optional<at::Scalar> & initial) { |
| 37501 | return wrapper_CPU___segment_reduce_backward(grad, output, data, reduce, lengths, offsets, axis, initial); |
| 37502 | } |
| 37503 | at::Tensor _transformer_encoder_layer_fwd(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, c10::optional<int64_t> mask_type) { |
| 37504 | return wrapper_CPU___transformer_encoder_layer_fwd(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type); |
| 37505 | } |
| 37506 | ::std::tuple<at::Tensor,at::Tensor> _native_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type) { |
| 37507 | return wrapper_CPU___native_multi_head_attention(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); |
| 37508 | } |
| 37509 | int64_t _fused_sdp_choice(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal) { |
| 37510 | return wrapper_CPU___fused_sdp_choice(query, key, value, attn_mask, dropout_p, is_causal); |
| 37511 | } |
| 37512 | at::Tensor special_airy_ai(const at::Tensor & x) { |
| 37513 | return wrapper_CPU_special_airy_ai(x); |
| 37514 | } |
| 37515 | at::Tensor & special_airy_ai_out(at::Tensor & out, const at::Tensor & x) { |
| 37516 | return wrapper_CPU_special_airy_ai_out_out(x, out); |
| 37517 | } |
| 37518 | at::Tensor & special_airy_ai_outf(const at::Tensor & x, at::Tensor & out) { |
| 37519 | return wrapper_CPU_special_airy_ai_out_out(x, out); |
| 37520 | } |
| 37521 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transformer_decoder_only_layer_fwd(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value) { |
| 37522 | return wrapper_CPU___transformer_decoder_only_layer_fwd(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value); |
| 37523 | } |
| 37524 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _native_decoder_only_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value, bool need_weights, bool average_attn_weights) { |
| 37525 | return wrapper_CPU___native_decoder_only_multi_head_attention(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, incr_key, incr_value, need_weights, average_attn_weights); |
| 37526 | } |
| 37527 | at::Tensor special_bessel_j0(const at::Tensor & self) { |
| 37528 | return wrapper_CPU_special_bessel_j0(self); |
| 37529 | } |
| 37530 | at::Tensor & special_bessel_j0_out(at::Tensor & out, const at::Tensor & self) { |
| 37531 | return wrapper_CPU_special_bessel_j0_out_out(self, out); |
| 37532 | } |
| 37533 | at::Tensor & special_bessel_j0_outf(const at::Tensor & self, at::Tensor & out) { |
| 37534 | return wrapper_CPU_special_bessel_j0_out_out(self, out); |
| 37535 | } |
| 37536 | at::Tensor special_bessel_j1(const at::Tensor & self) { |
| 37537 | return wrapper_CPU_special_bessel_j1(self); |
| 37538 | } |
| 37539 | at::Tensor & special_bessel_j1_out(at::Tensor & out, const at::Tensor & self) { |
| 37540 | return wrapper_CPU_special_bessel_j1_out_out(self, out); |
| 37541 | } |
| 37542 | at::Tensor & special_bessel_j1_outf(const at::Tensor & self, at::Tensor & out) { |
| 37543 | return wrapper_CPU_special_bessel_j1_out_out(self, out); |
| 37544 | } |
| 37545 | at::Tensor special_bessel_y0(const at::Tensor & self) { |
| 37546 | return wrapper_CPU_special_bessel_y0(self); |
| 37547 | } |
| 37548 | at::Tensor & special_bessel_y0_out(at::Tensor & out, const at::Tensor & self) { |
| 37549 | return wrapper_CPU_special_bessel_y0_out_out(self, out); |
| 37550 | } |
| 37551 | at::Tensor & special_bessel_y0_outf(const at::Tensor & self, at::Tensor & out) { |
| 37552 | return wrapper_CPU_special_bessel_y0_out_out(self, out); |
| 37553 | } |
| 37554 | at::Tensor special_bessel_y1(const at::Tensor & self) { |
| 37555 | return wrapper_CPU_special_bessel_y1(self); |
| 37556 | } |
| 37557 | at::Tensor & special_bessel_y1_out(at::Tensor & out, const at::Tensor & self) { |
| 37558 | return wrapper_CPU_special_bessel_y1_out_out(self, out); |
| 37559 | } |
| 37560 | at::Tensor & special_bessel_y1_outf(const at::Tensor & self, at::Tensor & out) { |
| 37561 | return wrapper_CPU_special_bessel_y1_out_out(self, out); |
| 37562 | } |
| 37563 | at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 37564 | return wrapper_CPU_special_chebyshev_polynomial_t(x, n); |
| 37565 | } |
| 37566 | at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37567 | return wrapper_CPU_special_chebyshev_polynomial_t_out_out(x, n, out); |
| 37568 | } |
| 37569 | at::Tensor & special_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37570 | return wrapper_CPU_special_chebyshev_polynomial_t_out_out(x, n, out); |
| 37571 | } |
| 37572 | at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 37573 | return wrapper_CPU_special_chebyshev_polynomial_u(x, n); |
| 37574 | } |
| 37575 | at::Tensor & special_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37576 | return wrapper_CPU_special_chebyshev_polynomial_u_out_out(x, n, out); |
| 37577 | } |
| 37578 | at::Tensor & special_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37579 | return wrapper_CPU_special_chebyshev_polynomial_u_out_out(x, n, out); |
| 37580 | } |
| 37581 | at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 37582 | return wrapper_CPU_special_chebyshev_polynomial_v(x, n); |
| 37583 | } |
| 37584 | at::Tensor & special_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37585 | return wrapper_CPU_special_chebyshev_polynomial_v_out_out(x, n, out); |
| 37586 | } |
| 37587 | at::Tensor & special_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37588 | return wrapper_CPU_special_chebyshev_polynomial_v_out_out(x, n, out); |
| 37589 | } |
| 37590 | at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 37591 | return wrapper_CPU_special_chebyshev_polynomial_w(x, n); |
| 37592 | } |
| 37593 | at::Tensor & special_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37594 | return wrapper_CPU_special_chebyshev_polynomial_w_out_out(x, n, out); |
| 37595 | } |
| 37596 | at::Tensor & special_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37597 | return wrapper_CPU_special_chebyshev_polynomial_w_out_out(x, n, out); |
| 37598 | } |
| 37599 | at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) { |
| 37600 | return wrapper_CPU_special_hermite_polynomial_h(x, n); |
| 37601 | } |
| 37602 | at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37603 | return wrapper_CPU_special_hermite_polynomial_h_out_out(x, n, out); |
| 37604 | } |
| 37605 | at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37606 | return wrapper_CPU_special_hermite_polynomial_h_out_out(x, n, out); |
| 37607 | } |
| 37608 | at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) { |
| 37609 | return wrapper_CPU_special_hermite_polynomial_he(x, n); |
| 37610 | } |
| 37611 | at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37612 | return wrapper_CPU_special_hermite_polynomial_he_out_out(x, n, out); |
| 37613 | } |
| 37614 | at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37615 | return wrapper_CPU_special_hermite_polynomial_he_out_out(x, n, out); |
| 37616 | } |
| 37617 | at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) { |
| 37618 | return wrapper_CPU_special_laguerre_polynomial_l(x, n); |
| 37619 | } |
| 37620 | at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37621 | return wrapper_CPU_special_laguerre_polynomial_l_out_out(x, n, out); |
| 37622 | } |
| 37623 | at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37624 | return wrapper_CPU_special_laguerre_polynomial_l_out_out(x, n, out); |
| 37625 | } |
| 37626 | at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) { |
| 37627 | return wrapper_CPU_special_legendre_polynomial_p(x, n); |
| 37628 | } |
| 37629 | at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37630 | return wrapper_CPU_special_legendre_polynomial_p_out_out(x, n, out); |
| 37631 | } |
| 37632 | at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37633 | return wrapper_CPU_special_legendre_polynomial_p_out_out(x, n, out); |
| 37634 | } |
| 37635 | at::Tensor special_modified_bessel_i0(const at::Tensor & self) { |
| 37636 | return wrapper_CPU_special_modified_bessel_i0(self); |
| 37637 | } |
| 37638 | at::Tensor & special_modified_bessel_i0_out(at::Tensor & out, const at::Tensor & self) { |
| 37639 | return wrapper_CPU_special_modified_bessel_i0_out_out(self, out); |
| 37640 | } |
| 37641 | at::Tensor & special_modified_bessel_i0_outf(const at::Tensor & self, at::Tensor & out) { |
| 37642 | return wrapper_CPU_special_modified_bessel_i0_out_out(self, out); |
| 37643 | } |
| 37644 | at::Tensor special_modified_bessel_i1(const at::Tensor & self) { |
| 37645 | return wrapper_CPU_special_modified_bessel_i1(self); |
| 37646 | } |
| 37647 | at::Tensor & special_modified_bessel_i1_out(at::Tensor & out, const at::Tensor & self) { |
| 37648 | return wrapper_CPU_special_modified_bessel_i1_out_out(self, out); |
| 37649 | } |
| 37650 | at::Tensor & special_modified_bessel_i1_outf(const at::Tensor & self, at::Tensor & out) { |
| 37651 | return wrapper_CPU_special_modified_bessel_i1_out_out(self, out); |
| 37652 | } |
| 37653 | at::Tensor special_modified_bessel_k0(const at::Tensor & self) { |
| 37654 | return wrapper_CPU_special_modified_bessel_k0(self); |
| 37655 | } |
| 37656 | at::Tensor & special_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & self) { |
| 37657 | return wrapper_CPU_special_modified_bessel_k0_out_out(self, out); |
| 37658 | } |
| 37659 | at::Tensor & special_modified_bessel_k0_outf(const at::Tensor & self, at::Tensor & out) { |
| 37660 | return wrapper_CPU_special_modified_bessel_k0_out_out(self, out); |
| 37661 | } |
| 37662 | at::Tensor special_modified_bessel_k1(const at::Tensor & self) { |
| 37663 | return wrapper_CPU_special_modified_bessel_k1(self); |
| 37664 | } |
| 37665 | at::Tensor & special_modified_bessel_k1_out(at::Tensor & out, const at::Tensor & self) { |
| 37666 | return wrapper_CPU_special_modified_bessel_k1_out_out(self, out); |
| 37667 | } |
| 37668 | at::Tensor & special_modified_bessel_k1_outf(const at::Tensor & self, at::Tensor & out) { |
| 37669 | return wrapper_CPU_special_modified_bessel_k1_out_out(self, out); |
| 37670 | } |
| 37671 | at::Tensor special_scaled_modified_bessel_k0(const at::Tensor & x) { |
| 37672 | return wrapper_CPU_special_scaled_modified_bessel_k0(x); |
| 37673 | } |
| 37674 | at::Tensor & special_scaled_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & x) { |
| 37675 | return wrapper_CPU_special_scaled_modified_bessel_k0_out_out(x, out); |
| 37676 | } |
| 37677 | at::Tensor & special_scaled_modified_bessel_k0_outf(const at::Tensor & x, at::Tensor & out) { |
| 37678 | return wrapper_CPU_special_scaled_modified_bessel_k0_out_out(x, out); |
| 37679 | } |
| 37680 | at::Tensor special_scaled_modified_bessel_k1(const at::Tensor & x) { |
| 37681 | return wrapper_CPU_special_scaled_modified_bessel_k1(x); |
| 37682 | } |
| 37683 | at::Tensor & special_scaled_modified_bessel_k1_out(at::Tensor & out, const at::Tensor & x) { |
| 37684 | return wrapper_CPU_special_scaled_modified_bessel_k1_out_out(x, out); |
| 37685 | } |
| 37686 | at::Tensor & special_scaled_modified_bessel_k1_outf(const at::Tensor & x, at::Tensor & out) { |
| 37687 | return wrapper_CPU_special_scaled_modified_bessel_k1_out_out(x, out); |
| 37688 | } |
| 37689 | at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 37690 | return wrapper_CPU_special_shifted_chebyshev_polynomial_t(x, n); |
| 37691 | } |
| 37692 | at::Tensor & special_shifted_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37693 | return wrapper_CPU_special_shifted_chebyshev_polynomial_t_out_out(x, n, out); |
| 37694 | } |
| 37695 | at::Tensor & special_shifted_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37696 | return wrapper_CPU_special_shifted_chebyshev_polynomial_t_out_out(x, n, out); |
| 37697 | } |
| 37698 | at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 37699 | return wrapper_CPU_special_shifted_chebyshev_polynomial_u(x, n); |
| 37700 | } |
| 37701 | at::Tensor & special_shifted_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37702 | return wrapper_CPU_special_shifted_chebyshev_polynomial_u_out_out(x, n, out); |
| 37703 | } |
| 37704 | at::Tensor & special_shifted_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37705 | return wrapper_CPU_special_shifted_chebyshev_polynomial_u_out_out(x, n, out); |
| 37706 | } |
| 37707 | at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 37708 | return wrapper_CPU_special_shifted_chebyshev_polynomial_v(x, n); |
| 37709 | } |
| 37710 | at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37711 | return wrapper_CPU_special_shifted_chebyshev_polynomial_v_out_out(x, n, out); |
| 37712 | } |
| 37713 | at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37714 | return wrapper_CPU_special_shifted_chebyshev_polynomial_v_out_out(x, n, out); |
| 37715 | } |
| 37716 | at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 37717 | return wrapper_CPU_special_shifted_chebyshev_polynomial_w(x, n); |
| 37718 | } |
| 37719 | at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { |
| 37720 | return wrapper_CPU_special_shifted_chebyshev_polynomial_w_out_out(x, n, out); |
| 37721 | } |
| 37722 | at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 37723 | return wrapper_CPU_special_shifted_chebyshev_polynomial_w_out_out(x, n, out); |
| 37724 | } |
| 37725 | at::Tensor special_spherical_bessel_j0(const at::Tensor & x) { |
| 37726 | return wrapper_CPU_special_spherical_bessel_j0(x); |
| 37727 | } |
| 37728 | at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x) { |
| 37729 | return wrapper_CPU_special_spherical_bessel_j0_out_out(x, out); |
| 37730 | } |
| 37731 | at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out) { |
| 37732 | return wrapper_CPU_special_spherical_bessel_j0_out_out(x, out); |
| 37733 | } |
| 37734 | at::Tensor _foobar(const at::Tensor & self, bool arg1, bool arg2, bool arg3) { |
| 37735 | return wrapper_CPU___foobar(self, arg1, arg2, arg3); |
| 37736 | } |
| 37737 | } // namespace cpu |
| 37738 | } // namespace at |
| 37739 |
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