# mypy: ignore-errors import collections import contextlib import functools import importlib import inspect import itertools import random import sys import threading import types from typing import Dict, List from ..bytecode_transformation import create_call_function try: import numpy as np except ModuleNotFoundError: np = None try: from torch.utils._cxx_pytree import PyTreeSpec except ImportError: PyTreeSpec = type(None) import torch._dynamo.config import torch.nn from torch._guards import TracingContext from .. import variables from ..exc import unimplemented from ..guards import GuardBuilder, install_guard from ..source import AttrSource, GetItemSource, ODictGetItemSource, RandomValueSource from ..utils import ( all_hook_names, build_checkpoint_variable, check_constant_args, get_custom_getattr, has_torch_function, is_namedtuple_cls, is_utils_checkpoint, istype, namedtuple_fields, object_has_getattribute, proxy_args_kwargs, tensortype_to_dtype, ) from .base import MutableLocal, VariableTracker from .ctx_manager import GenericContextWrappingVariable, NullContextVariable from .dicts import DefaultDictVariable class UserDefinedVariable(VariableTracker): pass class UserDefinedClassVariable(UserDefinedVariable): def __init__(self, value, **kwargs): super().__init__(**kwargs) self.value = value def as_python_constant(self): return self.value def python_type(self): return type(self.value) def as_proxy(self): return self.value def __str__(self): return f"UserDefinedClassVariable({self.value})" @staticmethod @functools.lru_cache(None) def _constant_fold_classes(): return { torch.device, torch.finfo, torch.iinfo, torch.Size, } @staticmethod @functools.lru_cache(None) def _in_graph_classes(): return set(tensortype_to_dtype.keys()) | { torch.Tensor, torch.cuda.Stream, torch.cuda.Event, } def can_constant_fold_through(self): return self.value in self._constant_fold_classes() def var_getattr(self, tx, name: str) -> "VariableTracker": from .. import trace_rules from . import ConstantVariable from .builder import VariableBuilder if name == "__name__": return ConstantVariable.create(self.value.__name__) source = AttrSource(self.source, name) if self.source is not None else None try: obj = inspect.getattr_static(self.value, name) except AttributeError: obj = None if isinstance(obj, staticmethod): func = obj.__get__(self.value) if source is not None: return trace_rules.lookup(func).create_with_source(func, source=source) else: return trace_rules.lookup(func)(func) elif isinstance(obj, classmethod): return variables.UserMethodVariable(obj.__func__, self, source=source) elif source and inspect.ismemberdescriptor(obj): return VariableBuilder(tx, source)(obj.__get__(self.value)) # Special handling of collections.OrderedDict.fromkeys() # Wrap it as GetAttrVariable(collections.OrderedDict, "fromkeys") to make it consistent with # collections.defaultdict, and both will be handled at UserDefinedClassVariable.call_method(). # Otherwise, it would be wrapped as UserDefinedObjectVariable(collections.OrderedDict.fromkeys), # and we need duplicate code to handle both cases. if self.value is collections.OrderedDict and name == "fromkeys": return super().var_getattr(tx, name) if name in getattr(self.value, "__dict__", {}) or ( self.value.__module__.startswith("torch.") or self.value.__module__ == "torch" ): if source: return VariableBuilder(tx, source)(obj) elif ConstantVariable.is_literal(obj): return ConstantVariable.create(obj) return super().var_getattr(tx, name) def _call_cross_entropy_loss(self, tx, args, kwargs): """ functional: input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean', label_smoothing=0.0 non functional ctor: weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean', label_smoothing=0.0 non functional loss call: input, target, optional_output """ from . import ConstantVariable def normalize_args( weight=ConstantVariable.create(None), size_average=ConstantVariable.create(None), ignore_index=ConstantVariable.create(-100), reduce=ConstantVariable.create(None), reduction=ConstantVariable.create("mean"), label_smoothing=ConstantVariable.create(0.0), ): return ( weight, size_average, ignore_index, reduce, reduction, label_smoothing, ) ( weight, size_average, ignore_index, reduce_arg, reduction, label_smoothing, ) = normalize_args(*args, **kwargs) def fake_cross_entropy_loss(input, target): from .builder import wrap_fx_proxy return wrap_fx_proxy( tx=tx, proxy=tx.output.create_proxy( "call_function", torch.nn.functional.cross_entropy, *proxy_args_kwargs( [ input, target, weight, size_average, ignore_index, reduce_arg, reduction, label_smoothing, ], {}, ), ), ) return variables.LambdaVariable(fake_cross_entropy_loss) def call_method( self, tx, name, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]", ) -> "VariableTracker": if ( name == "__subclasses__" and len(args) == 0 and not kwargs and "__subclasses__" not in self.value.__dict__ ): options = {"mutable_local": MutableLocal()} subs_as_vars: List[VariableTracker] = list() for sub in self.value.__subclasses__(): source = AttrSource(tx.import_source(sub.__module__), sub.__name__) subs_as_vars.append( variables.UserDefinedClassVariable(sub, source=source) ) return variables.ListVariable(subs_as_vars, **options) elif ( self.value in {collections.OrderedDict, collections.defaultdict} and name == "fromkeys" ): from .builtin import BuiltinVariable return BuiltinVariable.call_custom_dict_fromkeys( tx, self.value, *args, **kwargs ) return super().call_method(tx, name, args, kwargs) def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": from ..side_effects import SideEffects from .builder import SourcelessBuilder, wrap_fx_proxy from .builtin import BuiltinVariable constant_args = check_constant_args(args, kwargs) if self.can_constant_fold_through() and constant_args: # constant fold return variables.ConstantVariable.create( self.as_python_constant()( *[x.as_python_constant() for x in args], **{k: v.as_python_constant() for k, v in kwargs.items()}, ), ) elif self.value is torch.nn.CrossEntropyLoss: return self._call_cross_entropy_loss(tx, args, kwargs) elif self.value is contextlib.nullcontext: return NullContextVariable() elif self.value is collections.OrderedDict: return BuiltinVariable.call_custom_dict( tx, collections.OrderedDict, *args, **kwargs ) elif ( self.value is collections.defaultdict and len(args) <= 1 and DefaultDictVariable.is_supported_arg(args[0]) ): return DefaultDictVariable( {}, collections.defaultdict, args[0], mutable_local=MutableLocal(), ) elif self.value is collections.deque and not kwargs: if len(args) == 0: items = [] elif len(args) == 1 and args[0].has_unpack_var_sequence(tx): items = args[0].unpack_var_sequence(tx) else: unimplemented("deque() with more than 1 arg not supported") return variables.lists.DequeVariable(items, mutable_local=MutableLocal()) elif self.value is functools.partial: if not args: unimplemented("functools.partial malformed") # The first arg, a callable (the ctor below will assert on types) fn = args[0] rest_args = args[1:] # guards for the produced FunctoolsPartialVariable are installed in FunctoolsPartialVariable ctor from the # args and keywords return variables.functions.FunctoolsPartialVariable( fn, args=rest_args, keywords=kwargs ) elif ( issubclass(type(self.value), type) and hasattr( self.value, "__enter__" ) # TODO(voz): These can invoke user code! and hasattr( self.value, "__exit__" ) # TODO(voz): These can invoke user code! and check_constant_args(args, kwargs) and self.value.__init__ == object.__init__ and len(kwargs) == 0 # TODO(ybliang): support kwargs ): unwrapped_args = [x.as_python_constant() for x in args] return GenericContextWrappingVariable( unwrapped_args, cm_obj=self.value(*unwrapped_args), ) elif is_namedtuple_cls(self.value): fields = namedtuple_fields(self.value) # check if this a quasi-namedtuple or a real one if self.value.__module__ == "torch.return_types": # create pseudo-defaults from values of the quasi-namedtuple field_defaults = dict(zip(fields, args[0].items)) else: field_defaults = self.value._field_defaults items = list(args) items.extend([None] * (len(fields) - len(items))) var_tracker_kwargs = {} for field_name, var_tracker in zip(fields, items): if var_tracker is None: if field_name in kwargs: field_var = kwargs[field_name] else: assert field_name in field_defaults field_var = SourcelessBuilder()(tx, field_defaults[field_name]) var_tracker_kwargs[field_name] = field_var for name, value in var_tracker_kwargs.items(): assert name in fields items[fields.index(name)] = value assert all(x is not None for x in items) return variables.NamedTupleVariable(items, self.value) elif ( inspect.getattr_static(self.value, "__new__", None) in (object.__new__,) and SideEffects.cls_supports_mutation_side_effects(self.value) and self.source ): var = tx.output.side_effects.track_object_new( self.source, self.value, variables.UnspecializedNNModuleVariable if issubclass(self.value, torch.nn.Module) else UserDefinedObjectVariable, {}, ) if ( inspect.getattr_static(self.value, "__init__", None) is torch.nn.Module.__init__ ): tx.output.side_effects.store_attr( var, "__call_nn_module_init", variables.ConstantVariable.create(True), ) return var else: var.call_method(tx, "__init__", args, kwargs) return var elif variables.CustomizedDictVariable.is_matching_cls(self.value): options = {"mutable_local": MutableLocal()} return variables.CustomizedDictVariable.create( self.value, args, kwargs, options ) elif variables.DataClassVariable.is_matching_cls(self.value): options = {"mutable_local": MutableLocal()} return variables.DataClassVariable.create(self.value, args, kwargs, options) elif ( variables.RestrictedListSubclassVariable.is_matching_cls(self.value) and self.source ): return variables.RestrictedListSubclassVariable( variables.BuiltinVariable(list).call_function(tx, args, kwargs).items, user_cls=self.value, user_cls_source=self.source, mutable_local=MutableLocal(), ) elif self.value in self._in_graph_classes(): # torch.LongTensor cannot accept a list of FakeTensors. # So we stack the list of FakeTensors instead. if ( np and self.value in tensortype_to_dtype and len(args) == 1 and isinstance(args[0], variables.ListVariable) and len(args[0].items) > 1 and all(isinstance(x, variables.TensorVariable) for x in args[0].items) ): # Stack FakeTensor stacked = wrap_fx_proxy( tx=tx, proxy=tx.output.create_proxy( "call_function", torch.stack, *proxy_args_kwargs(args, kwargs), ), ) args = [stacked] tensor_variable = wrap_fx_proxy( tx=tx, proxy=tx.output.create_proxy( "call_function", self.value, *proxy_args_kwargs(args, kwargs), ), ) return tensor_variable return super().call_function(tx, args, kwargs) def const_getattr(self, tx, name): if name == "__name__": return self.value.__name__ return super().const_getattr(tx, name) class UserDefinedObjectVariable(UserDefinedVariable): """ Mostly objects of defined type. Catch-all for something where we only know the type. """ _nonvar_fields = {"value", "value_type", *UserDefinedVariable._nonvar_fields} def __init__(self, value, value_type=None, **kwargs): super().__init__(**kwargs) self.value = value self.value_type = value_type or type(value) assert type(value) is self.value_type def __str__(self): inner = self.value_type.__name__ if inner in [ "builtin_function_or_method", "getset_descriptor", "method_descriptor", "method", ]: inner = str(getattr(self.value, "__name__", None)) return f"{self.__class__.__name__}({inner})" def python_type(self): return self.value_type def guard_as_python_constant(self): if self.source: install_guard(self.source.make_guard(GuardBuilder.ID_MATCH)) return self.value return super().guard_as_python_constant() def torch_function_check(self): assert has_torch_function( self ), f"calling torch function on object without __torch_function__ {self}" def get_torch_fn(self, tx): self.torch_function_check() from .torch_function import build_torch_function_fn return build_torch_function_fn(tx, self.value, self.source) def call_torch_function(self, tx, fn, types, args, kwargs): self.torch_function_check() from .torch_function import _get_subclass_type_var, call_torch_function return call_torch_function( tx, _get_subclass_type_var(tx, self), self.get_torch_fn(tx), fn, types, args, kwargs, ) @staticmethod @functools.lru_cache(None) def _supported_random_functions(): fns = { random.random, random.randint, random.randrange, random.uniform, } return fns def _maybe_get_baseclass_method(self, name): if name not in getattr(self.value, "__dict__", {}): try: return inspect.getattr_static(type(self.value), name) except AttributeError: pass return None def call_method( self, tx, name, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]", ) -> "VariableTracker": from . import ( BuiltinVariable, ConstantVariable, TupleVariable, UserMethodVariable, ) method = self._maybe_get_baseclass_method(name) if method is not None: if method is object.__init__: return ConstantVariable.create(None) # [NOTE] OrderedDict, dict subtypes must always have source # We cannot instantiate such subtypes in-graph due to builtin __new__ if method is collections.OrderedDict.keys: # subclass of OrderedDict assert not (args or kwargs) assert self.source # OrderedDict, dict subtypes must always have source keys = list(self.value.keys()) assert all(map(ConstantVariable.is_literal, keys)) install_guard(self.source.make_guard(GuardBuilder.DICT_CONST_KEYS)) return TupleVariable([ConstantVariable.create(k) for k in keys]) if ( method in (collections.OrderedDict.__contains__, dict.__contains__) and len(args) == 1 and isinstance(args[0], (ConstantVariable, BuiltinVariable)) and inspect.getattr_static(type(self.value), "keys") in (collections.OrderedDict.keys, dict.keys) ): assert not kwargs assert self.source # OrderedDict, dict subtypes must always have source install_guard(self.source.make_guard(GuardBuilder.DICT_CONST_KEYS)) return ConstantVariable.create( args[0].as_python_constant() in self.value ) if method is collections.OrderedDict.items and isinstance( self.value, collections.OrderedDict ): assert self.source # OrderedDict, dict subtypes must always have source assert not (args or kwargs) items = [] keys = self.call_method(tx, "keys", [], {}) for key in keys.unpack_var_sequence(tx): items.append( TupleVariable( [key, self.odict_getitem(tx, key)], ) ) return TupleVariable(items) if method is collections.OrderedDict.__getitem__ and len(args) == 1: assert not kwargs assert self.source # OrderedDict, dict subtypes must always have source return self.odict_getitem(tx, args[0]) # check for methods implemented in C++ if isinstance(method, types.FunctionType): source = ( None if self.source is None else AttrSource(AttrSource(self.source, "__class__"), name) ) # TODO(jansel): add a guard to check for monkey patching? return UserMethodVariable(method, self, source=source).call_function( tx, args, kwargs ) if method is list.__len__ and self.source and not (args or kwargs): install_guard(self.source.make_guard(GuardBuilder.SEQUENCE_LENGTH)) return ConstantVariable(len(self.value)) return super().call_method(tx, name, args, kwargs) def unpack_var_sequence(self, tx): if ( self.source and self._maybe_get_baseclass_method("__iter__") is list.__iter__ and self._maybe_get_baseclass_method("__len__") is list.__len__ and self._maybe_get_baseclass_method("__getitem__") is list.__getitem__ ): install_guard(self.source.make_guard(GuardBuilder.SEQUENCE_LENGTH)) return [ variables.LazyVariableTracker.create( self.value[k], source=GetItemSource(self.source, k), ) for k in range(len(self.value)) ] return super().unpack_var_sequence(tx) def is_supported_random(self): try: return self.value in self._supported_random_functions() except TypeError: # TypeError: unhashable type return False def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": from .. import trace_rules from .builder import VariableBuilder if ( self.is_supported_random() and all(k.is_python_constant() for k in args) and all(v.is_python_constant() for v in kwargs.values()) ): args = [x.as_python_constant() for x in args] kwargs = {k: v.as_python_constant() for k, v in kwargs.items()} random_call_index = len(tx.output.random_calls) example_value = self.value(*args, **kwargs) source = RandomValueSource(random_call_index) tx.output.random_calls.append((self.value, args, kwargs)) return VariableBuilder(tx, source).wrap_unspecialized_primitive( example_value ) elif istype(self.value, types.MethodType): func = self.value.__func__ obj = self.value.__self__ if ( func is torch.utils._contextlib._DecoratorContextManager.clone and variables.TorchCtxManagerClassVariable.is_matching_cls( obj.__class__ ) and not (args or kwargs) ): return variables.TorchCtxManagerClassVariable( obj.__class__ ).call_function(tx, args, kwargs) if ( func is torch.autograd.grad_mode.inference_mode.clone and obj.__class__ is torch.autograd.grad_mode.inference_mode ): # simulate the inference_mode.clone implementation var = variables.ConstantVariable(obj.mode) return variables.TorchCtxManagerClassVariable( obj.__class__ ).call_function(tx, [var], kwargs) elif ( istype(self.value, functools.partial) and trace_rules.lookup(self.value.func) == variables.TorchInGraphFunctionVariable and all( variables.ConstantVariable.is_literal(v) for v in itertools.chain(self.value.args, self.value.keywords.values()) ) ): if self.source: install_guard( AttrSource(self.source, "func").make_guard(GuardBuilder.ID_MATCH), AttrSource(self.source, "args").make_guard( GuardBuilder.CONSTANT_MATCH ), AttrSource(self.source, "keywords").make_guard( GuardBuilder.CONSTANT_MATCH ), ) partial_args = [ variables.ConstantVariable.create(v) for v in self.value.args ] partial_args.extend(args) partial_kwargs = { k: variables.ConstantVariable.create(v) for k, v in self.value.keywords.items() } partial_kwargs.update(kwargs) if is_utils_checkpoint(self.value.func): return build_checkpoint_variable().call_function( tx, partial_args, partial_kwargs ) return variables.TorchInGraphFunctionVariable( self.value.func ).call_function(tx, partial_args, partial_kwargs) elif callable(self.value): if self.source: install_guard(self.source.make_guard(GuardBuilder.FUNCTION_MATCH)) return self.call_method(tx, "__call__", args, kwargs) return super().call_function(tx, args, kwargs) def _check_for_getattribute(self): if object_has_getattribute(self.value): unimplemented("UserDefinedObjectVariable with custom __getattribute__") def _check_for_getattr(self): return get_custom_getattr(self.value) def _getattr_static(self, name): if ( isinstance(self.value, (torch.nn.Module, PyTreeSpec)) or "__slots__" in self.value.__class__.__dict__ or type(self.value) == threading.local ): # getattr_static doesn't work on these subobj = getattr(self.value, name) else: subobj = inspect.getattr_static(self.value, name) return subobj def var_getattr(self, tx, name): from .. import trace_rules from . import ConstantVariable from .builder import VariableBuilder value = self.value source = AttrSource(self.source, name) if self.source else None self._check_for_getattribute() getattr_fn = self._check_for_getattr() class NO_SUCH_SUBOBJ: pass try: subobj = self._getattr_static(name) except AttributeError: subobj = NO_SUCH_SUBOBJ if isinstance(getattr_fn, types.FunctionType): return variables.UserMethodVariable( getattr_fn, self, source=source ).call_function(tx, [ConstantVariable.create(name)], {}) elif getattr_fn is not None: unimplemented("UserDefined with non-function __getattr__") if isinstance(subobj, property): # Rewrite the source being explicit about reading it statically. if self.source: source = AttrSource(self.source, name, get_static=True) source = AttrSource(source, "fget") return variables.UserMethodVariable( subobj.fget, self, source=source ).call_function(tx, [], {}) elif isinstance(subobj, torch.distributions.utils.lazy_property): subobj_var = UserDefinedObjectVariable(subobj, source=source) return variables.UserMethodVariable( subobj.__get__.__func__, subobj_var, source=source ).call_function(tx, [self], {}) elif isinstance(subobj, staticmethod): func = subobj.__get__(self.value) if source is not None: return trace_rules.lookup(func).create_with_source(func, source=source) else: return trace_rules.lookup(func)(func) elif isinstance(subobj, classmethod): return variables.UserMethodVariable( subobj.__func__, self.var_getattr(tx, "__class__"), source=source ) elif isinstance(subobj, types.FunctionType) or ( isinstance(subobj, types.MethodType) and isinstance(self.value, torch.nn.Module) ): # Since we get subobj via self._getattr_static, which may not trigger dynamic lookup. # Static lookup can't tell us it's a method or function correctly, # so we trigger dynamic lookup here to get the correct type. dynamic_subobj = getattr(self.value, name) while dynamic_subobj is subobj and hasattr(subobj, "_torchdynamo_inline"): subobj = subobj._torchdynamo_inline dynamic_subobj = subobj source = AttrSource(source, "_torchdynamo_inline") if source else None if isinstance(subobj, types.MethodType): if dynamic_subobj.__self__ is not self.value: unimplemented("__self__ mismatch for bound method") func = subobj.__func__ else: assert isinstance(subobj, types.FunctionType) func = subobj if inspect.ismethod(dynamic_subobj): return variables.UserMethodVariable(func, self, source=source) elif inspect.isfunction(dynamic_subobj): if is_utils_checkpoint(func): return build_checkpoint_variable(source=source) elif source is not None: return trace_rules.lookup(func).create_with_source( func, source=source ) else: return trace_rules.lookup(func)(func) if ( name in getattr(value, "__dict__", {}) or ConstantVariable.is_literal(subobj) or isinstance( subobj, ( torch.Tensor, torch.nn.Module, ), ) ): if source: return VariableBuilder(tx, source)(subobj) elif ConstantVariable.is_literal(subobj): return ConstantVariable.create(subobj) if ( name not in getattr(value, "__dict__", {}) and type(value).__module__.startswith("torch.") and "torch.optim" not in type(value).__module__ and not callable(value) and not isinstance(subobj, types.MethodDescriptorType) ): if not source: assert getattr( importlib.import_module(type(value).__module__), type(value).__name__, ) is type(value) source = AttrSource( AttrSource( tx.import_source(type(value).__module__), type(value).__name__ ), name, ) return VariableBuilder(tx, source)(subobj) options = {"source": source} if isinstance( subobj, ( torch.distributions.constraints._Interval, torch.distributions.constraints._Real, torch.distributions.constraints.Constraint, ), ): return UserDefinedObjectVariable(subobj, **options) elif isinstance(self.value, torch.nn.Module) and name in all_hook_names: assert isinstance(subobj, collections.OrderedDict) if not subobj: return variables.ConstDictVariable( subobj, collections.OrderedDict, **options ) if name == "__class__": return UserDefinedClassVariable(type(self.value), **options) return variables.GetAttrVariable(self, name, **options) def call_hasattr(self, tx, name: str) -> "VariableTracker": if tx.output.side_effects.is_attribute_mutation(self): try: result = tx.output.side_effects.load_attr(self, name, deleted_ok=True) return variables.ConstantVariable.create( not isinstance(result, variables.DeletedVariable) ) except KeyError: pass if self.source: install_guard( AttrSource(self.source, name).make_guard(GuardBuilder.HASATTR) ) if self._check_for_getattribute() or self._check_for_getattr(): unimplemented("hasattr with custom __getattr__") try: self._getattr_static(name) return variables.ConstantVariable.create(True) except AttributeError: return variables.ConstantVariable.create(False) def odict_getitem(self, tx, key): from .builder import VariableBuilder from .dicts import is_hashable # TODO this should probably be merged with the dict handling index = ( key.source if is_hashable(key) and key.source is not None else key.as_python_constant() ) return VariableBuilder( tx, ODictGetItemSource(self.source, index), )(collections.OrderedDict.__getitem__(self.value, key.as_python_constant())) class KeyedJaggedTensorVariable(UserDefinedObjectVariable): @staticmethod def is_matching_object(obj): mod = sys.modules.get("torchrec.sparse.jagged_tensor") return mod is not None and type(obj) is mod.KeyedJaggedTensor def __init__(self, value, **kwargs): from torchrec.sparse.jagged_tensor import KeyedJaggedTensor assert type(value) is KeyedJaggedTensor super().__init__(value, **kwargs) def var_getattr(self, tx, name): if ( torch._dynamo.config.force_unspec_int_unbacked_size_like_on_torchrec_kjt and self.source is not None and name in ("_length_per_key", "_offset_per_key") ): with TracingContext.patch(force_unspec_int_unbacked_size_like=True): return super().var_getattr(tx, name) return super().var_getattr(tx, name) class RemovableHandleVariable(VariableTracker): REMOVED = -1 def __init__( self, mutable_local=None, # index of the registration in the side_effects owned register_hook/handle list, used during removal. idx=None, **kwargs, ): super().__init__(**kwargs) self.mutable_local = mutable_local self.idx = idx def call_method(self, tx, method_name, args, kwargs): if method_name == "remove": if self.idx != self.REMOVED: tx.output.side_effects.remove_hook(self.idx) self.idx = self.REMOVED return variables.ConstantVariable.create(None) super().call_method(tx, method_name, args, kwargs) def reconstruct(self, codegen): if self.idx == self.REMOVED: # Hook has already been removed, return a dummy handle codegen.load_import_from("torch._dynamo.utils", "invalid_removeable_handle") codegen.extend_output(create_call_function(0, True)) return # unreachable due to codegen.add_cache() when the hook is installed super().reconstruct(codegen)