Lightstar/venv/Lib/site-packages/torch/_library/utils.py

# mypy: allow-untyped-defs
import dataclasses
import inspect
import sys
from typing import Any, Callable, Dict, Iterable, Tuple

import torch
import torch._utils_internal as _utils_internal
from torch import _C


@dataclasses.dataclass
class Kernel:
    """Models a (function, source location)"""

    func: Callable
    source: str

    def __call__(self, *args, **kwargs):
        return self.func(*args, **kwargs)


class RegistrationHandle:
    """Does something when someone calls .destroy() on it"""

    def __init__(self, on_destroy: Callable):
        self._on_destroy = on_destroy

    def destroy(self) -> None:
        self._on_destroy()


def get_source(stacklevel: int) -> str:
    """Get a string that represents the caller.

    Example: "/path/to/foo.py:42"

    Use stacklevel=1 to get the caller's source
    Use stacklevel=2 to get the caller's caller's source
    etc.
    """
    frame = inspect.getframeinfo(sys._getframe(stacklevel))
    source = f"{frame.filename}:{frame.lineno}"
    return source


def parse_namespace(qualname: str) -> Tuple[str, str]:
    splits = qualname.split("::")
    if len(splits) != 2:
        raise ValueError(
            f"Expected `qualname` to be of the form "
            f'"namespace::name", but got {qualname}. '
            f"The qualname passed to the torch.library APIs must consist "
            f"of a namespace and a name, e.g. aten::sin"
        )
    return splits[0], splits[1]


def lookup_op(qualname: str) -> torch._ops.OpOverload:
    namespace, name = parse_namespace(qualname)
    if "." in name:
        name, overload = name.split(".")
    else:
        overload = "default"
    ns = getattr(torch.ops, namespace)
    packet = getattr(ns, name)
    return getattr(packet, overload)


def is_builtin(op: torch._ops.OpOverload) -> bool:
    assert isinstance(op, torch._ops.OpOverload)
    return op.namespace in {"aten", "prim", "prims"}


def is_functional_schema(schema: Any) -> bool:
    """Check if the schema is functional.

    An operator is functional if:
    - it does not mutate any of its inputs
    - it does not return a view on any of its inputs
    - it has at least one return
    """

    def is_functional(schema):
        if schema.is_mutable:
            return False
        rets = schema.returns
        is_non_mutating_view = len(rets) > 0 and any(
            r.alias_info is not None and not r.alias_info.is_write for r in rets
        )
        if is_non_mutating_view:
            return False
        if not schema.returns:
            return False
        return True

    if isinstance(schema, torch._C.FunctionSchema):
        return is_functional(schema)

    # Lazy import because not all PyTorch builds have torchgen
    from torchgen.model import FunctionSchema

    if isinstance(schema, str):
        schema = FunctionSchema.parse(schema)
    assert isinstance(schema, FunctionSchema)
    return is_functional(schema)


# should be torch._C.JitType but that annotation is busted
def is_tensorlist_like_type(typ: Any) -> bool:
    return (
        typ == _C.ListType(_C.TensorType.get())
        or typ == _C.ListType(_C.OptionalType(_C.TensorType.get()))
        or typ == _C.OptionalType(_C.ListType(_C.TensorType.get()))
        or typ == _C.OptionalType(_C.ListType(_C.OptionalType(_C.TensorType.get())))
    )


# should be torch._C.JitType but that annotation is busted
def is_tensor_like_type(typ: Any) -> bool:
    return typ == _C.TensorType.get() or typ == _C.OptionalType(_C.TensorType.get())


def mutates_and_returns_first_arg(op: torch._ops.OpOverload):
    """Check if an op is an inplace aten op, i.e. it mutates and returns the first arg.

    TODO: torchgen/model.py's FunctionSchema.parse is the source of truth for this,
    but not all PyTorch builds have torchgen (due to the yaml dependency being weird).
    Figure this out.

    Example: add_(Tensor(a!) x, Tensor y) -> Tensor(a)
    """
    if op.namespace != "aten":
        return False
    schema = op._schema
    if not len(schema.returns) == 1:
        return False
    if schema.returns[0].alias_info is None:
        return False
    alias_set = schema.returns[0].alias_info.after_set
    if len(alias_set) != 1:
        return False
    loc = next(iter(alias_set))
    if len(schema.arguments) < 1:
        return False
    first_arg = schema.arguments[0]
    if first_arg.alias_info is None:
        return False
    if not first_arg.alias_info.is_write:
        return False
    alias_set = first_arg.alias_info.after_set
    if len(alias_set) != 1:
        return False
    if loc != next(iter(alias_set)):
        return False
    for arg in schema.arguments[1:]:
        if arg.alias_info is not None:
            return False
    return True


def fill_defaults(schema, args, kwargs):
    new_args = []
    new_kwargs = {}
    for i in range(len(schema.arguments)):
        info = schema.arguments[i]
        if info.kwarg_only:
            if info.name in kwargs:
                new_kwargs[info.name] = kwargs[info.name]
            else:
                new_kwargs[info.name] = info.default_value
        else:
            if i < len(args):
                new_args.append(args[i])
            else:
                new_args.append(info.default_value)
    return tuple(new_args), new_kwargs


def zip_schema(
    schema: _C.FunctionSchema, args: Tuple[Any, ...], kwargs: Dict[str, Any]
) -> Iterable[Tuple[_C.Argument, Any]]:
    """zips schema.arguments and (args, kwargs) together.

    Assumes that (args, kwargs) were the inputs to some torch._ops.OpOverload:
    that is, kwargs must be keyword-only arguments and default values may be omitted.
    """
    assert len(schema.arguments) >= len(args) + len(kwargs)
    for i in range(len(schema.arguments)):
        info = schema.arguments[i]
        if info.kwarg_only:
            if info.name in kwargs:
                yield info, kwargs[info.name]
            continue
        if i >= len(args):
            # args that are equal to their default values are not populated
            # if they are followed by args that are equal to their defaults.
            # Skip these.
            continue
        yield info, args[i]
    return


def can_generate_trivial_fake_impl(op: torch._ops.OpOverload) -> bool:
    assert isinstance(op, torch._ops.OpOverload)
    if is_builtin(op):
        # We control the built-ins. These may (in rare cases)
        # do input metadata mutation (which we have banned on custom ops)
        return False
    schema = op._schema
    # It's suspicious if the op is not mutable but returns nothing, so we return False out of an abundance of caution
    if not schema.is_mutable:
        return False
    if len(schema.returns) > 0:
        return False
    # If the op returns nothing, then it has a trivial fake impl.
    return True


def requires_set_python_module() -> bool:
    """If an op was defined in C++ and extended from Python using the
    torch.library APIs, returns if we require that there have been a
    m.set_python_module("mylib.ops") call from C++ that associates
    the C++ op with a python module.
    """
    return getattr(_utils_internal, "REQUIRES_SET_PYTHON_MODULE", True)


def handle_dispatch_mode(curr_mode, op_overload, *args, **kwargs):
    assert isinstance(curr_mode, torch.utils._python_dispatch.TorchDispatchMode)
    overload_types = []
    args_flattened, _ = torch.utils._pytree.tree_flatten((args, kwargs.values()))
    for a in args_flattened:
        # TODO: need to double check the semantics of the "types" argument to torch_dispatch.
        # It's generated in PyInterpreter.cpp, but seems to be generated in two places,
        # where in one case we only include tensors with the python key, and in another
        # we include **all** tensors.
        if isinstance(a, torch.Tensor) and torch._C._dispatch_keys(a).has(
            torch._C.DispatchKey.Python
        ):
            overload_types.append(type(a))
    # TODO: check that I got these args correct (in C++, we pass in "0000"??)

    return curr_mode.__torch_dispatch__(op_overload, overload_types, args, kwargs)


def has_kwarg_only_args(schema: _C.FunctionSchema):
    return any(a.kwarg_only for a in schema.arguments)


def has_kwarg_only_tensors(schema: _C.FunctionSchema):
    for a in schema.arguments:
        if not (is_tensor_like_type(a.type) or is_tensorlist_like_type(a.type)):
            continue
        if not a.kwarg_only:
            continue
        return True
    return False