library.py

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()

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)
        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)

    def impl(self, op_name, fn, dispatch_key=''):
        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}'
            # get a string containing the names of every dispatch key that the operator has a registration for.
            dispatch_key_registration = torch._C._dispatch_dump(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 'CompositeImplicitAutograd' in dispatch_key_registration:
                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, fn)
        _impls.add(key)
        self._op_impls.add(key)

    def define(self, schema, alias_analysis=""):
        '''
        Takes a schema to define a new operator.
        Also, optionally takes `alias_analysis` argument to indicate if the aliasing properties of the arguments
        can be inferred from the schema (default behavior) or not ("CONSERVATIVE").

        Returns the name of the operator as inferred from the schema.
        '''
        # 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)

    def __del__(self):
        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