Source code for torch.library
from ._ops import OpOverload
from typing import Set
import traceback
import torch
__all__ = ['Library', 'impl', 'define']
# Set containing the combination of (namespace, operator, DispatchKey) for which a new kernel has been registered
# The keys in the set are of the form `namespace + "/" + op_name + "/" + dispatch_key`.
# This set is maintained to ensure that two libraries don't try to override the exact same functionality to avoid
# libraries calling into kernels not intended to be called.
_impls: Set[str] = set()
# prim is reserved by TorchScript interpreter
_reserved_namespaces = ['prim']
[docs]class Library:
"""
A class to create libraries that can be used to register new operators or
override operators in existing libraries from Python.
A user can optionally pass in a dispatch keyname if they only want to register
kernels corresponding to only one specific dispatch key.
To create a library to override operators in an existing library (with name ns), set the kind to "IMPL".
To create a new library (with name ns) to register new operators, set the kind to "DEF".
Args:
ns: library name
kind: "DEF", "IMPL" (default: "IMPL")
dispatch_key: PyTorch dispatch key (default: "")
"""
def __init__(self, ns, kind, dispatch_key=""):
if kind != "IMPL" and kind != "DEF":
raise ValueError("Unsupported kind: ", kind)
if ns in _reserved_namespaces and kind == "DEF":
raise ValueError(ns, " is a reserved namespace. Please try creating a library with another name.")
frame = traceback.extract_stack(limit=3)[0]
filename, lineno = frame.filename, frame.lineno
self.m = torch._C._dispatch_library(kind, ns, dispatch_key, filename, lineno)
self.ns = ns
self._op_impls = set()
self.kind = kind
self.dispatch_key = dispatch_key
def __repr__(self):
return "Library(kind={}, ns={}, dispatch_key={})>".format(self.kind, self.ns, self.dispatch_key)
[docs] def define(self, schema, alias_analysis=""):
r'''Defines a new operator and its semantics in the ns namespace.
Args:
schema: function schema to define a new operator.
alias_analysis (optional): Indicates if the aliasing properties of the operator arguments can be
inferred from the schema (default behavior) or not ("CONSERVATIVE").
Returns:
name of the operator as inferred from the schema.
Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LIBRARY)
>>> my_lib = Library("foo", "DEF")
>>> my_lib.define("sum(Tensor self) -> Tensor")
'''
# This is added because we also want to disallow PURE_FUNCTION alias analysis which is a valid
# AliasAnalysis type in C++
if alias_analysis not in ["", "FROM_SCHEMA", "CONSERVATIVE"]:
raise RuntimeError("Invalid alias_analysis type {}".format(alias_analysis))
return self.m.define(schema, alias_analysis)
[docs] def impl(self, op_name, fn, dispatch_key=''):
r'''Registers the function implementation for an operator defined in the library.
Args:
op_name: operator name (along with the overload) or OpOverload object.
fn: function that's the operator implementation for the input dispatch key.
dispatch_key: dispatch key that the input function should be registered for. By default, it uses
the dispatch key that the library was created with.
Example::
>>> # xdoctest: +SKIP
>>> my_lib = Library("aten", "IMPL")
>>> def div_cpu(self, other):
>>> return self * (1 / other)
>>> my_lib.impl("div.Tensor", "CPU")
'''
if not callable(fn):
raise TypeError("Input function is required to be a callable but found type {}".format(type(fn)))
if dispatch_key == '':
dispatch_key = self.dispatch_key
if isinstance(op_name, str):
name = op_name
elif isinstance(op_name, OpOverload):
name = op_name._schema.name
overload_name = op_name._schema.overload_name
if overload_name != '':
name = name + '.' + overload_name
else:
raise RuntimeError("impl should be passed either a name or an OpOverload object as the first argument")
key = self.ns + "/" + name.split("::")[-1] + "/" + dispatch_key
if key in _impls:
# TODO: in future, add more info about where the existing function is registered (this info is
# today already returned by the C++ warning when impl is called but we error out before that)
raise RuntimeError("This is not allowed since there's already a kernel registered from python overriding {}"
"'s behavior for {} dispatch key and {} namespace.".
format(name.split("::")[-1], dispatch_key, self.ns))
if dispatch_key == "Meta":
dispatcher_op_name = name
if '::' not in dispatcher_op_name:
dispatcher_op_name = f'{self.ns}::{dispatcher_op_name}'
# Internally, we shouldn't be registering meta kernels for any operators that
# have CompositeImplicitAutograd kernels.
# Instead, we should be letting those decompositions run, and writing meta kernels
# only for the base operators.
if torch._C._dispatch_has_kernel_for_dispatch_key(dispatcher_op_name, "CompositeImplicitAutograd"):
raise RuntimeError(
f"We should not register a meta kernel directly to the operator '{name}',"
" because it has a CompositeImplicitAutograd kernel in core."
" Instead we should let the operator decompose, and ensure that we have meta kernels"
" for the base ops that it decomposes into.")
self.m.impl(name, dispatch_key if dispatch_key != "" else "CompositeImplicitAutograd", fn)
_impls.add(key)
self._op_impls.add(key)
def __del__(self):
# _op_impls might not have been initialized if an error was thrown in __init__
_op_impls_ = getattr(self, '_op_impls', None)
if _op_impls_:
for key in self._op_impls:
_impls.remove(key)
del self.m
# decorator to register python functions for library ops
# Note: this decorator API should remain consistent with `Library.impl` API
def impl(lib, name, dispatch_key=""):
def wrap(f):
lib.impl(name, f, dispatch_key)
return f
return wrap
def define(lib, schema, alias_analysis=""):
def wrap(f):
name = lib.define(schema, alias_analysis)
lib.impl(name, f)
return f
return wrap
