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dc8fe8f971
From https://github.com/pytorch/audio/blob/main/src/libtorchaudio/stable/ops.h Technically it should have been ok not to port these but looking at these carefully I realized the subtract ported to audio ~would have undefined behavior :/~ is broken ``` inline Tensor subtract(const Tensor& self, const Tensor& other) { const auto num_args = 2; std::array<StableIValue, num_args> stack{ torch::stable::detail::from(self), torch::stable::detail::from(other)}; TORCH_ERROR_CODE_CHECK(torch_call_dispatcher( "aten::subtract", "Tensor", stack.data(), TORCH_ABI_VERSION)); return torch::stable::detail::to<torch::stable::Tensor>(stack[0]); } ``` as it missed `alpha` the signature for `subtract.Tensor` is `func: subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor`. ~This is also our bad as although out of bounds reads on the stableivalue stack would be caught by asan, without asan they are silent correctness issues (PR coming to fix).~ Use the old path to support this as we don't support stableivalue conversion for Scalar yet. Pull Request resolved: https://github.com/pytorch/pytorch/pull/169880 Approved by: https://github.com/albanD ghstack dependencies: #169703, #169709, #169711, #168062, #169872
195 lines
8.1 KiB
Python
195 lines
8.1 KiB
Python
# Be extra careful when you edit this file, because it affects AOTInductor ABI compatibility. See
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# https://github.com/pytorch/pytorch/blob/7e86a7c0155295539996e0cf422883571126073e/torchgen/gen.py#L2424-L2436
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# for details.
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#
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# The inductor_fallback_ops list is based on the fallback ops from torch/_inductor/lowering.py.
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#
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# Generally speaking, it is ok to add a new op to the list, but you need to run
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# `python torchgen/gen.py --update-aoti-c-shim` in order to regenerate C shim header files.
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# But it is NOT ok to remove an existing fallback op from the list, since that will break
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# some existing AOTInductor-compiled models.
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#
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# A fallback op version defaults to 1. If you want to extend an existing fallback op by adding
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# a new argument with a default value, while it is fine in the Python world, it will be BC-breaking
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# when generating C shim. Thus you need to bump up the version number of that fallback op by
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# updating the entry in the inductor_fallback_ops list, adding a new version number with a list
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# of new arguments, and then run `python torchgen/gen.py --update-aoti-c-shim` to regenerate.
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inductor_fallback_ops: dict[str, dict[str, list[str]]] = {
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"aten._adaptive_avg_pool2d_backward.default": {},
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"aten._adaptive_avg_pool2d.default": {},
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"aten._adaptive_avg_pool3d_backward.default": {},
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"aten._adaptive_avg_pool3d.default": {},
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"aten._addmm_activation.default": {},
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"aten._cdist_backward.default": {},
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"aten._cdist_forward.default": {},
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"aten._cudnn_rnn.default": {},
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"aten._dyn_quant_matmul_4bit.default": {},
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"aten._dyn_quant_pack_4bit_weight.default": {},
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"aten._efficient_attention_backward.default": {},
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"aten._efficient_attention_forward.default": {},
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"aten._efficientzerotensor.default": {},
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"aten._embedding_bag_dense_backward.default": {},
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"aten._embedding_bag_forward_only.default": {},
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"aten._embedding_bag_per_sample_weights_backward.default": {},
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"aten._embedding_bag.default": {},
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"aten._fft_c2c.default": {},
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"aten._fft_r2c.default": {},
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"aten._flash_attention_backward.default": {},
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"aten._flash_attention_forward.default": {},
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"aten._fused_moving_avg_obs_fq_helper_functional.default": {},
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"aten._fused_moving_avg_obs_fq_helper.default": {},
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"aten._fused_rms_norm.default": {},
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"aten._histogramdd_from_bin_cts.default": {},
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"aten._int_mm.out": {},
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"aten._pdist_backward.default": {},
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"aten._pdist_forward.default": {},
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"aten._scaled_dot_product_attention_math_for_mps.default": {},
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"aten._scaled_dot_product_cudnn_attention_backward.default": {},
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"aten._scaled_dot_product_cudnn_attention.default": {},
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"aten._scaled_dot_product_efficient_attention_backward.default": {},
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"aten._scaled_dot_product_efficient_attention.default": {},
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"aten._scaled_dot_product_flash_attention_backward.default": {},
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"aten._scaled_dot_product_flash_attention_for_cpu_backward.default": {},
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"aten._scaled_dot_product_flash_attention_for_cpu.default": {},
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"aten._scaled_dot_product_flash_attention.default": {},
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"aten._scaled_dot_product_fused_attention_overrideable_backward.default": {},
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"aten._scaled_dot_product_fused_attention_overrideable.default": {},
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"aten._scaled_mm.default": {},
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"aten._scaled_grouped_mm.default": {},
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"aten._scaled_mm.out": {},
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"aten._segment_reduce_backward.default": {},
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"aten._thnn_fused_lstm_cell.default": {},
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"aten._to_sparse.default": {},
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"aten._trilinear.default": {},
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"aten._weight_int4pack_mm.default": {},
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"aten._weight_int8pack_mm.default": {},
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"aten.abs.default": {},
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"aten.adaptive_max_pool2d_backward.default": {},
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"aten.adaptive_max_pool2d.default": {},
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"aten.adaptive_max_pool3d_backward.default": {},
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"aten.adaptive_max_pool3d.default": {},
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"aten.add.Scalar": {},
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"aten.add.Tensor": {},
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"aten.addbmm.default": {},
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"aten.addmm.out": {},
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"aten.addmv.default": {},
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"aten.angle.default": {},
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"aten.avg_pool2d_backward.default": {},
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"aten.avg_pool2d.default": {},
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"aten.avg_pool3d_backward.default": {},
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"aten.avg_pool3d.default": {},
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"aten.baddbmm.out": {},
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"aten.bernoulli_.float": {},
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"aten.bernoulli_.Tensor": {},
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"aten.bmm.out": {},
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"aten.bucketize.Tensor": {},
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"aten.cat.default": {},
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"aten.cholesky_inverse.default": {},
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"aten.cholesky_solve.default": {},
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"aten.convolution_backward.default": {},
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"aten.convolution.default": {},
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"aten.cummax.default": {},
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"aten.cummin.default": {},
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"aten.cumprod.default": {},
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"aten.cumsum.default": {},
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"aten.exponential.default": {},
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"aten.fill_.Scalar": {},
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"aten.fractional_max_pool2d_backward.default": {},
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"aten.fractional_max_pool2d.default": {},
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"aten.fractional_max_pool3d_backward.default": {},
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"aten.fractional_max_pool3d.default": {},
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"aten.gcd.default": {},
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"aten.geqrf.default": {},
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"aten.grid_sampler_2d_backward.default": {},
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"aten.hann_window.default": {},
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"aten.histc.default": {},
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"aten.histogram.bin_ct": {},
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"aten.index_put.default": {},
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"aten.index_reduce.default": {},
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"aten.index.Tensor": {},
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"aten.kthvalue.default": {},
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"aten.logcumsumexp.default": {},
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"aten.lu_unpack.default": {},
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"aten.masked_scatter_backward.default": {},
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"aten.masked_scatter.default": {},
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"aten.masked_select.default": {},
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"aten.max_pool2d_with_indices_backward.default": {},
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"aten.max_pool2d_with_indices.default": {},
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"aten.max_pool3d_with_indices_backward.default": {},
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"aten.max_pool3d_with_indices.default": {},
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"aten.max_unpool2d.default": {},
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"aten.max_unpool3d.default": {},
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"aten.median.default": {},
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"aten.mm.out": {},
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"aten.mode.default": {},
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"aten.mul.Scalar": {},
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"aten.mul.Tensor": {},
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"aten.nanmedian.default": {},
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"aten.narrow.default": {},
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"aten.native_dropout.default": {},
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"aten.nonzero.default": {},
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"aten.normal_functional.default": {},
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"aten.ormqr.default": {},
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"aten.pad.default": {},
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"aten.permute.default": {},
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"aten.polar.default": {},
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"aten.pow.Scalar": {},
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"aten.pow.Tensor_Scalar": {},
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"aten.pow.Tensor_Tensor": {},
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"aten.rand.default": {},
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"aten.rand.generator": {},
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"aten.randint.default": {},
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"aten.randint.generator": {},
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"aten.randint.low_out": {},
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"aten.randint.low": {},
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"aten.randn.default": {},
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"aten.randn.generator": {},
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"aten.randperm.default": {},
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"aten.repeat_interleave.Tensor": {},
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"aten.replication_pad1d_backward.default": {},
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"aten.replication_pad2d_backward.default": {},
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"aten.reshape.default": {},
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"aten.resize_.default": {},
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"aten.resize_as_.default": {},
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"aten.scatter_reduce.two_out": {},
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"aten.scatter.src_out": {},
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"aten.scatter.value_out": {},
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"aten.searchsorted.Scalar": {},
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"aten.searchsorted.Tensor": {},
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"aten.segment_reduce.default": {},
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"aten.set_.source_Tensor": {},
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"aten.slice.Tensor": {},
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"aten.soft_margin_loss_backward.default": {},
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"aten.sort.default": {},
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"aten.sort.stable": {},
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"aten.squeeze.dim": {},
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"aten.to_sparse.default": {},
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"aten.topk.default": {},
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"aten.triangular_solve.default": {},
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"aten.uniform.default": {},
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"aten.upsample_bicubic2d_backward.default": {},
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"aten.upsample_linear1d_backward.default": {},
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"aten.upsample_trilinear3d_backward.default": {},
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"aten.view_as_complex.default": {},
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"aten.view_as_real.default": {},
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"aten.view.dtype": {},
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"aten._weight_int4pack_mm_with_scales_and_zeros.default": {},
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}
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# `python torchgen/gen.py --update-aoti-c-shim` will automatically generate
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# c_shim_aten.{h/cpp} based on the list below.
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# Operators in this list are intended to be used in torch/csrc/stable/ops.h
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# Unlike other c_shims, operators in this file do not bypass the dispatcher.
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# The same BC rules apply as inductor_fallback_ops.
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aten_shimified_ops: dict[str, dict[str, list[str]]] = {
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"aten.fill_.Scalar": {},
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"aten.pad.default": {},
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"aten.narrow.default": {},
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"aten.amax.default": {},
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"aten.new_empty.default": {},
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"aten.new_zeros.default": {},
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"aten.full.default": {},
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"aten.subtract.Tensor": {},
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}
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