# mypy: allow-untyped-defs import inspect from collections import defaultdict from functools import wraps from itertools import chain from typing import Callable, Dict, List, Sequence, TypeVar, Union from typing_extensions import ParamSpec import torch import torch.library from torch._ops import HigherOrderOperator, OpOverload, OpOverloadPacket from torch._prims_common import CustomOutParamAnnotation from torch.utils import _pytree as pytree __all__ = [ "decomposition_table", "pre_autograd_decomposition_table", "meta_table", "register_decomposition", "get_decompositions", "core_aten_decompositions", ] _T = TypeVar("_T") _P = ParamSpec("_P") # TODO: relax key type here; torch registrations should be possible to; but # right now this type is accurate global_decomposition_table: Dict[ str, Dict[torch._ops.OperatorBase, Callable] ] = defaultdict(dict) decomposition_table = global_decomposition_table["post_autograd"] pre_autograd_decomposition_table = global_decomposition_table["pre_autograd"] meta_table = global_decomposition_table["meta"] def _add_op_to_registry(registry, op, fn): """ This is an internal API for adding an op to the decomposition table. If op is OpOverload, it will be added to the registry directly. If op is OpOverloadPacket, all the valid op_overloads in the packet will be added to the registry. """ overloads: List[Union[torch._ops.OperatorBase]] = [] if isinstance(op, HigherOrderOperator): # There's no concept of overloads for HigherOrderOperator registry[op] = fn return elif isinstance(op, OpOverload): overloads.append(op) else: assert isinstance(op, OpOverloadPacket) for ol in op.overloads(): overloads.append(getattr(op, ol)) for op_overload in overloads: if op_overload in registry: raise RuntimeError(f"duplicate registrations for {op_overload}") # TorchScript dumps a bunch of extra nonsense overloads # which don't have corresponding dispatcher entries, we need # to filter those out, e.g aten.add.float_int if torch._C._dispatch_has_kernel(op_overload.name()): registry[op_overload] = fn def _convert_out_params(f): out_annotation = f.__annotations__.get("out") # If there are no out params, do not wrap the function. if not out_annotation: return f # Hack to detect when out is a Tuple. There seems to be no pretty way of doing this if getattr(out_annotation, "__origin__", None) is tuple: sig = inspect.signature(f) out_names = sig.return_annotation._fields # If out is a tuple, we need to register a function that unpacks all the out # elements as this is what native_functions.yaml expects @wraps(f) def _fn(*args, **kwargs): out_kwargs = tuple(kwargs.pop(o, None) for o in out_names) # Either all of the out kwargs are set or none of them is_none = out_kwargs[0] is None assert all((o is None) == is_none for o in out_kwargs) return f(*args, **kwargs, out=None if is_none else out_kwargs) out_params = [ inspect.Parameter( o, kind=inspect.Parameter.KEYWORD_ONLY, default=None, annotation=t, ) for o, t in zip(out_names, out_annotation.__args__) ] # Drop the out parameter and concatenate the new kwargs in the signature params = chain((v for k, v in sig.parameters.items() if k != "out"), out_params) _fn.__signature__ = inspect.Signature( # type: ignore[attr-defined] parameters=params, return_annotation=sig.return_annotation # type: ignore[arg-type] ) # Drop the out parameter and concatenate the new kwargs in the annotations _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"} for o in out_params: _fn.__annotations__[o.name] = o.annotation # Propagate that this function is wrapped by `out_wrapper` _fn._torch_decompositions_out_wrapper = f._torch_decompositions_out_wrapper # type: ignore[attr-defined] return _fn # Alternatively, there may be a single tensor out parameter with a name # other than "out". This will need special treatment and is indicated by an # annotation, which we will remove here so it is not exposed after wrapping. custom_out_param_name = f.__annotations__.pop(CustomOutParamAnnotation, None) if custom_out_param_name: @wraps(f) def _fn(*args, **kwargs): out_kwarg = kwargs.pop(custom_out_param_name, None) return f(*args, **kwargs, out=out_kwarg) out_param = inspect.Parameter( custom_out_param_name, kind=inspect.Parameter.KEYWORD_ONLY, default=None, annotation=out_annotation, ) # Drop the out parameter and concatenate the new kwarg in the signature sig = inspect.signature(f) params = chain( (v for k, v in sig.parameters.items() if k != "out"), (out_param,) ) _fn.__signature__ = inspect.Signature( # type: ignore[attr-defined] parameters=params, return_annotation=sig.return_annotation # type: ignore[arg-type] ) # Drop the out parameter and concatenate the new kwargs in the annotations _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"} _fn.__annotations__[out_param.name] = out_param.annotation return _fn return f def register_decomposition( aten_op, registry=None, *, type="post_autograd", unsafe=False ) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: """ A decorator to register a function as a decomposition to the Python decomposition table. Use it like this:: @register_decomposition(torch.ops.aten.clamp_min) def clamp_min(x): return torch.clamp(self, min=min) If you are writing a new decomposition, consider contributing it directly to PyTorch in torch._decomp.decompositions. This API is experimental; we are almost certainly going to extend the API when we make decompositions eligible for use in transforms (e.g., autograd) and not just backend tracing, where we then need to know if a decomposition can be used to simulate a transform. By default, we also will register it to the Meta key of dispatcher, and replace the c++ Meta implementation if there is already one. unsafe kwarg is for reuse of this function for registering non-function things """ assert type in {"post_autograd", "pre_autograd", "meta"} def decomposition_decorator(fn: Callable[_P, _T]) -> Callable[_P, _T]: orig_fn = fn if not unsafe: fn = _convert_out_params(fn) nonlocal registry if registry is None: registry = global_decomposition_table[type] def register(op): _add_op_to_registry(registry, op, fn) # To handle allowing multiple aten_ops at once pytree.tree_map_(register, aten_op) return orig_fn return decomposition_decorator def get_decompositions( aten_ops: Sequence[Union[torch._ops.OperatorBase, OpOverloadPacket]], type: str = "post_autograd", ) -> Dict[torch._ops.OperatorBase, Callable]: """ Retrieve a dictionary of decompositions corresponding to the list of operator overloads and overload packets passed as input. Overload packets will include all decomposed overloads in the packet. If there is no decomposition for a requested operator, it is silently ignored. This API is experimental; we are almost certainly going to give an alternate, more recommended formulation, where a user provides the set of operators they know how to implement, and we provide decompositions for everything not in this set. """ assert type in {"post_autograd", "pre_autograd", "meta"} registry = global_decomposition_table[type] packets_to_overloads = defaultdict(list) for opo in registry: if isinstance(opo, (OpOverload, OpOverloadPacket)): packets_to_overloads[opo.overloadpacket].append(opo) decompositions: Dict[torch._ops.OperatorBase, Callable] = {} for op in aten_ops: if isinstance(op, OpOverloadPacket) and op in packets_to_overloads: for op_overload in packets_to_overloads[op]: decompositions[op_overload] = registry[op_overload] elif isinstance(op, (torch._ops.OperatorBase)) and op in registry: decompositions[op] = registry[op] return decompositions def remove_decompositions( decompositions: Dict[torch._ops.OperatorBase, Callable], aten_ops: Sequence[Union[OpOverload, OpOverloadPacket]], ) -> None: """ Given a dictionary of decompositions obtained from get_decompositions(), removes operators associated with a list of operator overloads and overload packets passed as input. If the decomposition dictionary does not contain a decomposition that is specified to be removed, it is silently ignored. """ for op in aten_ops: if isinstance(op, OpOverloadPacket): for overload_name in op.overloads(): opo = getattr(op, overload_name) decompositions.pop(opo, None) elif isinstance(op, OpOverload): decompositions.pop(op, None) # populate the table import torch._decomp.decompositions import torch._refs # See NOTE [Core ATen Ops] # # list was copied from torch/_inductor/decomposition.py # excluding decompositions that results in prim ops # Resulting opset of decomposition is core aten ops def core_aten_decompositions() -> Dict[torch._ops.OperatorBase, Callable]: aten = torch.ops.aten return get_decompositions( [ aten.addcdiv, aten.addcdiv_, aten.addcmul, aten.addcmul_, aten.addr, aten.affine_grid_generator, aten.alias_copy, aten.all, aten.aminmax, aten.arange.default, aten.arange.start, aten.avg_pool2d_backward, aten.baddbmm, aten.binary_cross_entropy, aten.binary_cross_entropy_backward, aten.binary_cross_entropy_with_logits, aten.block_diag, aten.celu, aten.celu_, aten.channel_shuffle, aten.clamp_max, aten.clamp_min, aten.col2im, aten.count_nonzero, aten.linalg_cross, aten.cudnn_batch_norm, aten.cudnn_batch_norm_backward, aten.miopen_batch_norm_backward, aten.deg2rad, aten.deg2rad_, aten.detach, aten.diag_embed, aten.diagonal_backward, aten.dot, aten.vdot, aten.elu, aten.elu_, aten.elu_backward, aten._embedding_bag, aten.embedding_dense_backward, aten.empty_like, aten._euclidean_dist.default, aten.expand_as, aten.expand_copy, aten.eye, aten.fill, aten.fill_, aten.floor_divide, aten.frac, aten.frac_, aten._fused_moving_avg_obs_fq_helper, aten.gelu_, aten.gelu_backward, aten.glu, aten.glu_backward, aten.hardshrink, aten.hardsigmoid, aten.hardsigmoid_, aten.hardsigmoid_backward, aten.hardswish, aten.hardswish_, aten.hardswish_backward, aten.hardtanh_, aten.hardtanh_backward, aten.heaviside, aten.heaviside_, aten.huber_loss, aten.huber_loss_backward, aten.im2col, aten.index_add, aten.index_add_, aten.index_copy, aten.index_copy_, aten.index_fill, aten.index_fill_, aten.isin, aten.isneginf, aten.isposinf, aten.l1_loss, aten._lazy_clone, aten._test_parallel_materialize, aten.leaky_relu_, aten.leaky_relu_backward, aten.lerp, aten.lerp_, aten.linspace, aten.logaddexp, aten.logaddexp2, aten.logit, aten.logit_, aten.logit_backward, aten.log_sigmoid_backward, aten.log_sigmoid_forward, aten._log_softmax_backward_data, aten.logspace, aten.logsumexp.default, aten.masked_fill, aten.masked_fill_, aten.mish, aten.mish_, aten.mse_loss, aten.mse_loss_backward, aten.multi_margin_loss, aten.multilabel_margin_loss_forward, aten.mv, aten.mvlgamma, aten.mvlgamma_, aten.nansum, aten.nan_to_num, aten.nan_to_num_, aten.narrow, aten.native_batch_norm_backward, aten.native_dropout_backward, aten.native_group_norm_backward, aten.native_layer_norm_backward, aten.new_empty, aten.new_full, aten.new_ones, aten.new_zeros, aten.nll_loss2d_forward, aten.nll_loss2d_backward, aten.nll_loss_backward, aten.nll_loss_forward, aten.norm, aten.ones, aten.ones_like, aten.pixel_shuffle, aten.pixel_unshuffle, aten._prelu_kernel, aten._prelu_kernel_backward, aten._reshape_alias, aten.rad2deg, aten.rad2deg_, aten.reflection_pad1d, aten.reflection_pad1d_backward, aten.reflection_pad2d, aten.reflection_pad2d_backward, aten.reflection_pad3d, aten.reflection_pad3d_backward, aten.replication_pad1d, aten.replication_pad2d, aten.replication_pad3d, aten.renorm, aten.renorm_, aten.replication_pad2d, aten.resize_as, aten.roll, aten.rot90, aten.rrelu_with_noise, aten.rrelu_with_noise_, aten.rsub, aten._safe_softmax, aten._scaled_dot_product_flash_attention_for_cpu.default, aten.select_backward, aten.select_scatter, aten.sgn, aten.sgn_, aten.sigmoid_backward, aten.silu, aten.silu_, aten.silu_backward, aten.sinc, aten.sinc_, aten.slice_backward, aten.smooth_l1_loss, aten.smooth_l1_loss_backward, aten.soft_margin_loss, aten.soft_margin_loss_backward, aten._softmax_backward_data, aten.softplus, aten.softplus_backward, aten.softshrink, aten.special_entr, aten.special_log_ndtr, aten.special_xlog1py, aten.split.Tensor, aten.split_with_sizes_copy, aten.squeeze.default, aten.squeeze.dim, aten.std, aten.std_mean, aten.stack, aten.sum.default, aten.sum.out, aten.t, aten.t_copy, aten.take, aten.tanh_backward, aten.threshold, aten.threshold_, aten.threshold_backward, aten.trace, aten.transpose.int, aten.tril, aten.tril_, aten.triu, aten.triu_, aten.unbind, aten.unfold_backward, aten.unfold_copy, aten._unsafe_index, aten._unsafe_index_put, aten._unsafe_masked_index, aten._unsafe_masked_index_put_accumulate, aten.unsafe_split.Tensor, aten.unsafe_split_with_sizes, aten.unsqueeze_copy, aten._unsafe_view, aten.upsample_linear1d, aten.upsample_bilinear2d, aten.upsample_trilinear3d, aten.upsample_nearest2d_backward, aten.view_as_complex, aten.xlogy, aten.xlogy_, aten.zero, aten.zero_, aten.zeros, aten.zeros_like, aten._chunk_cat, aten._weight_norm_interface, ] )