pytorch/torch/_prims/context.py

from typing import Callable, Sequence, Any, Dict
import functools


import torch
import torch.overrides

from torch._prims.utils import torch_function_passthrough

import torch._refs
import torch._refs.nn
import torch._refs.nn.functional
import torch._refs.special

import torch._prims


@functools.lru_cache(None)
def torch_to_refs_map():
    """
    Mapping of torch API functions to torch._refs functions.
    E.g. torch_to_refs_map()[torch.add] == torch._refs.add
    """
    modules = [
        (torch, torch._refs),
        (torch.nn, torch._refs.nn),
        (torch.nn.functional, torch._refs.nn.functional),
        (torch.special, torch._refs.special),
    ]
    r: Dict[Any, Any] = {
        torch.Tensor.__invert__: torch._refs.bitwise_not,
        torch.Tensor.__xor__: torch._refs.bitwise_xor,
        torch.Tensor.__and__: torch._refs.bitwise_and,
        torch.Tensor.__or__: torch._refs.bitwise_or,
    }
    for mod_torch, mod_refs in modules:
        for s in mod_refs.__all__:  # type: ignore[attr-defined]
            r[mod_torch.__dict__.get(s)] = mod_refs.__dict__.get(s)

    # Support remapping torch.Tensor.foo to _refs.foo
    for s in dir(torch.Tensor):
        if s in torch._refs.__all__:
            r[getattr(torch.Tensor, s)] = torch._refs.__dict__.get(s)
    return r


@functools.lru_cache(None)
def all_prims():
    """
    Set of all prim functions, e.g., torch._prims.add in all_prims()
    """
    return {torch._prims.__dict__.get(s) for s in torch._prims.__all__}


class TorchRefsMode(torch.overrides.TorchFunctionMode):
    """
    Switches the interpretation of torch.* functions and Tensor methods to
    use PrimTorch refs in torch._refs.  (Direct calls to _refs are unaffected.)

    >>> with TorchRefsMode.push():
    ...     torch.add(x, y)  # calls torch._refs.add(x, y)

    By default, this context manager will fall back on the torch.* if the
    ref does not exist; set strict=True to error if this occurs.
    """

    def __init__(self, strict=False):
        self.strict = strict

    def __torch_function__(
        self,
        orig_func: Callable,
        types: Sequence,
        args: Sequence[Any] = (),
        kwargs: Dict = None,
    ):
        if kwargs is None:
            kwargs = {}
        # For primitive operations, run them as is without interception
        if orig_func in torch_function_passthrough or orig_func in all_prims():
            return orig_func(*args, **kwargs)
        mapping = torch_to_refs_map()
        func = mapping.get(orig_func, None)
        if func is not None:
            return func(*args, **kwargs)
        if self.strict:
            raise RuntimeError(
                f"no _refs support for {torch.overrides.resolve_name(orig_func)}"
            )
        return orig_func(*args, **kwargs)