ai-content-maker/.venv/Lib/site-packages/torch/fx/experimental/normalize.py

import operator
from typing import Any, Callable, Dict, Tuple, Optional

import torch
import torch.fx
import torch.fx as fx
from torch.fx import Transformer, Proxy
from torch.fx.node import Argument, Target, Node, map_aggregate
from torch.fx.operator_schemas import (
    normalize_module,
    normalize_function,
    create_type_hint,
)

from .schema_type_annotation import AnnotateTypesWithSchema


class NormalizeArgs(Transformer):
    """
    Normalize arguments to Python targets. This means that
    `args/kwargs` will be matched up to the module/functional's
    signature and rewritten to exclusively kwargs in positional order
    if `normalize_to_only_use_kwargs` is true. Also populates default
    values. Does not support positional-only parameters or varargs
    parameters (*args, **kwargs).

    If the nodes have 'type' metadata, it will use it to disambiguate
    overloads. Otherwise, it will throw an error.

    Example usage:
        m = torchvision.models.resnet18()
        traced = torch.fx.symbolic_trace(m)
        traced = NormalizeArgs(traced).transform()
    """

    def __init__(
        self, module: torch.fx.GraphModule, normalize_to_only_use_kwargs: bool = True
    ):
        super().__init__(module)
        self.node_map: Dict[Proxy, Node] = {}
        self.normalize_to_only_use_kwargs = normalize_to_only_use_kwargs

    def run_node(self, n: Node) -> Any:
        args, kwargs = self.fetch_args_kwargs_from_env(n)

        def get_type(arg):
            if isinstance(arg, fx.Node):
                return n.meta["type"] if "type" in n.meta else None
            return type(arg)

        arg_types = map_aggregate(n.args, get_type)
        assert isinstance(arg_types, tuple)
        arg_types = tuple([create_type_hint(i) for i in arg_types])
        kwarg_types = {k: get_type(v) for k, v in kwargs.items()}
        if n.op == "call_function":
            out = self.call_function(n.target, args, kwargs, arg_types, kwarg_types)
        else:
            out = super().run_node(n)
        if n.op != "output":
            self.node_map[out] = n
            out.node.meta = n.meta
            out.node.type = n.type
        return out

    def call_function(
        self,
        target: Target,
        args: Tuple[Argument, ...],
        kwargs: Dict[str, Any],
        arg_types: Optional[Tuple[Any, ...]] = None,
        kwarg_types: Optional[Dict[str, Any]] = None,
    ):
        assert callable(target)
        new_args_and_kwargs = normalize_function(
            target,
            args,  # type: ignore[arg-type]
            kwargs,
            arg_types,  # type: ignore[arg-type]
            kwarg_types,
            self.normalize_to_only_use_kwargs,
        )
        if new_args_and_kwargs:
            new_args, new_kwargs = new_args_and_kwargs
            return self.tracer.create_proxy(
                "call_function", target, new_args, new_kwargs
            )
        else:
            return super().call_function(target, args, kwargs)

    def call_module(
        self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]
    ):
        assert isinstance(target, str)
        new_args_and_kwargs = normalize_module(
            self.module,
            target,
            args,  # type: ignore[arg-type]
            kwargs,
            self.normalize_to_only_use_kwargs,
        )
        if new_args_and_kwargs:
            new_args, new_kwargs = new_args_and_kwargs
            return super().call_module(target, new_args, new_kwargs)
        else:
            return super().call_module(target, args, kwargs)


class NormalizeOperators(AnnotateTypesWithSchema):
    """
    Normalize callsites that are different ways of "spelling" the same
    invocation into a single, canonical call. Currently supports:

    1. Normalize operators (e.g. operator.add) to the `torch` ops they
       ultimately invoke (e.g. torch.add) when it is possible to statically
       reason that

    Example usage:

        m = torchvision.models.resnet18()

        traced = torch.fx.symbolic_trace(m)

        traced = NormalizeOperators(traced).transform()
    """

    binary_magic_method_remap: Dict[
        Callable[[Any, Any], Any], Callable[[Any, Any], Any]
    ] = {
        torch.add: operator.add,
        torch.mul: operator.mul,
        torch.sub: operator.sub,
        torch.div: operator.truediv,
        torch.floor_divide: operator.floordiv,
        torch.remainder: operator.mod,
        torch.eq: operator.eq,
        torch.ne: operator.ne,
        torch.lt: operator.lt,
        torch.le: operator.le,
        torch.gt: operator.gt,
        torch.ge: operator.ge,
    }

    def call_function(
        self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]
    ):
        # Normalize operators according to the magic methods implemented on tensors here:
        # https://github.com/pytorch/pytorch/blob/28c5d90b679c6b38bf4183ec99f16d933c2f1bcd/tools/autograd/templates/python_variable_methods.cpp#L1137 # noqa: B950

        assert callable(target)

        if target in self.binary_magic_method_remap:
            if len(args) != 2:
                return super().call_function(target, args, kwargs)
            lhs, rhs = args

            return super().call_function(
                target=self.binary_magic_method_remap[target],
                args=(lhs, rhs),
                kwargs={},
            )

        return super().call_function(target, args, kwargs)