159 lines
4.6 KiB
Python
159 lines
4.6 KiB
Python
import dataclasses
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import inspect
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import sys
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from typing import Any, Callable, Tuple
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import torch
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@dataclasses.dataclass
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class Kernel:
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"""Models a (function, source location)"""
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func: Callable
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source: str
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def __call__(self, *args, **kwargs):
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return self.func(*args, **kwargs)
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class RegistrationHandle:
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"""Does something when someone calls .destroy() on it"""
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def __init__(self, on_destroy: Callable):
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self._on_destroy = on_destroy
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def destroy(self) -> None:
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self._on_destroy()
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def get_source(stacklevel: int) -> str:
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"""Get a string that represents the caller.
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Example: "/path/to/foo.py:42"
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Use stacklevel=1 to get the caller's source
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Use stacklevel=2 to get the caller's caller's source
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etc.
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"""
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frame = inspect.getframeinfo(sys._getframe(stacklevel))
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source = f"{frame.filename}:{frame.lineno}"
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return source
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def parse_namespace(qualname: str) -> Tuple[str, str]:
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splits = qualname.split("::")
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if len(splits) != 2:
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raise ValueError(
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f"Expected `qualname` to be of the form "
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f'"namespace::name", but got {qualname}. '
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f"The qualname passed to the torch.library APIs must consist "
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f"of a namespace and a name, e.g. aten::sin"
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)
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return splits[0], splits[1]
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def lookup_op(qualname: str) -> torch._ops.OpOverloadPacket:
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namespace, name = parse_namespace(qualname)
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if "." in name:
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name, overload = name.split(".")
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else:
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overload = "default"
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ns = getattr(torch.ops, namespace)
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packet = getattr(ns, name)
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return getattr(packet, overload)
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def is_builtin(op: torch._ops.OpOverload) -> bool:
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assert isinstance(op, torch._ops.OpOverload)
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return op.namespace in {"aten", "prim", "prims"}
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def is_functional_schema(schema: Any) -> bool:
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"""Check if the schema is functional.
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An operator is functional if:
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- it does not mutate any of its inputs
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- it does not return a view on any of its inputs
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- it has at least one return
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"""
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# Lazy import because not all PyTorch builds have torchgen
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from torchgen.model import FunctionSchema, SchemaKind
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assert isinstance(schema, (str, FunctionSchema))
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if isinstance(schema, str):
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schema = FunctionSchema.parse(schema)
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if schema.kind() != SchemaKind.functional:
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return False
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rets = schema.returns
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is_non_mutating_view = len(rets) > 0 and any(
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r.annotation is not None and not r.annotation.is_write for r in rets
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)
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if is_non_mutating_view:
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return False
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if not schema.returns:
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return False
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return True
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def mutates_and_returns_first_arg(op: torch._ops.OpOverload):
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"""Check if an op is an inplace aten op, i.e. it mutates and returns the first arg.
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TODO: torchgen/model.py's FunctionSchema.parse is the source of truth for this,
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but not all PyTorch builds have torchgen (due to the yaml dependency being weird).
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Figure this out.
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Example: add_(Tensor(a!) x, Tensor y) -> Tensor(a)
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"""
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if op.namespace != "aten":
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return False
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schema = op._schema
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if not len(schema.returns) == 1:
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return False
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if schema.returns[0].alias_info is None:
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return False
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alias_set = schema.returns[0].alias_info.after_set
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if len(alias_set) != 1:
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return False
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loc = next(iter(alias_set))
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if len(schema.arguments) < 1:
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return False
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first_arg = schema.arguments[0]
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if first_arg.alias_info is None:
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return False
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if not first_arg.alias_info.is_write:
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return False
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alias_set = first_arg.alias_info.after_set
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if len(alias_set) != 1:
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return False
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if loc != next(iter(alias_set)):
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return False
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for arg in schema.arguments[1:]:
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if arg.alias_info is not None:
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return False
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return True
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def zip_schema(schema, args, kwargs):
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"""zips schema.arguments and (args, kwargs) together.
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Assumes that (args, kwargs) were the inputs to some torch._ops.OpOverload:
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that is, kwargs must be keyword-only arguments and default values may be omitted.
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"""
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assert len(schema.arguments) >= len(args) + len(kwargs)
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for i in range(len(schema.arguments)):
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info = schema.arguments[i]
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if info.kwarg_only:
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if info.name in kwargs:
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yield info, kwargs[info.name]
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continue
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if i >= len(args):
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# args that are equal to their default values are not populated
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# if they are followed by args that are equal to their defaults.
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# Skip these.
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continue
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yield info, args[i]
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return
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