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# 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
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