# mypy: ignore-errors import collections import contextlib import enum import functools import importlib import inspect import itertools import random import re import sys import threading import types import warnings from typing import Dict, Generic, 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 def is_standard_setattr(val): return val in ( object.__setattr__, torch.nn.Module.__setattr__, ) 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, EnumVariable from .builder import VariableBuilder if name == "__name__": return ConstantVariable.create(self.value.__name__) elif name == "__qualname__": return ConstantVariable.create(self.value.__qualname__) 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: # __mro__ is a member in < 3.12, an attribute in >= 3.12 if inspect.ismemberdescriptor(obj) or ( sys.version_info >= (3, 12) and name == "__mro__" ): 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 ConstantVariable.is_literal(obj): return ConstantVariable.create(obj) elif isinstance(obj, enum.Enum): return EnumVariable(obj) elif name in getattr(self.value, "__dict__", {}) or ( self.value.__module__.startswith("torch.") or self.value.__module__ == "torch" ): if source: return VariableBuilder(tx, source)(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 ) elif name == "__eq__" and len(args) == 1 and hasattr(args[0], "value"): return variables.ConstantVariable(self.value == args[0].value) elif name == "__ne__" and len(args) == 1 and hasattr(args[0], "value"): return variables.ConstantVariable(self.value != args[0].value) 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 self.value is warnings.catch_warnings and not args: return variables.CatchWarningsCtxManagerVariable.create(tx, 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.create( 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 ( self.is_standard_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 elif issubclass(self.value, enum.Enum) and len(args) == 1 and not kwargs: options = {"mutable_local": MutableLocal()} return variables.EnumVariable.create(self.value, args[0], options) return super().call_function(tx, args, kwargs) def is_standard_new(self): """Check for __new__ being overridden""" new_fn = inspect.getattr_static(self.value, "__new__", None) if isinstance(new_fn, staticmethod): new_fn = new_fn.__func__ return new_fn in (object.__new__, Generic.__new__) def call_hasattr(self, tx, name: str) -> "VariableTracker": if self.source: source = AttrSource(self.source, name) install_guard(source.make_guard(GuardBuilder.HASATTR)) return variables.ConstantVariable(hasattr(self.value, name)) return super().call_hasattr(tx, name) def const_getattr(self, tx, name): if name == "__name__": return self.value.__name__ return super().const_getattr(tx, name) class NO_SUCH_SUBOBJ: pass 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) if is_standard_setattr(method): return self.method_setattr_standard(tx, *args, **kwargs) # [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)) tx.output.guard_on_key_order.add(self.source.name()) 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 # TODO(anijain2305) - Why do we need to guard on all keys? 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)], ) ) tx.output.guard_on_key_order.add(self.source.name()) 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]) if ( method in (object.__ne__, object.__eq__) and len(args) == 1 and not kwargs and hasattr(args[0], "value") ): return ConstantVariable( (self.value is args[0].value) is (method is object.__eq__) ) # 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 method_setattr_standard(self, tx, name, value): try: name = name.as_python_constant() except NotImplementedError: unimplemented(f"non-const setattr name: {name}") if not tx.output.side_effects.is_attribute_mutation(self): unimplemented(f"setattr({self}, {name}, ...)") tx.output.side_effects.store_attr(self, name, value) return variables.ConstantVariable(None) def needs_slow_setattr(self): return not is_standard_setattr( inspect.getattr_static(self.value, "__setattr__", None) ) 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 next_variable(self, tx): return self.call_method(tx, "__next__", [], {}) 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)) # TODO: arguably, this should route to wrap_symint/wrap_symfloat # (currently hypothetical), but I'm not going to poke my hand in # this nest for now 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) if self.source is None: unimplemented( "Sourceless UserDefinedObjectVariable method not supported" ) func_src = AttrSource(self.source, "__func__") func_var = VariableBuilder(tx, func_src)(func) obj_src = AttrSource(self.source, "__self__") obj_var = VariableBuilder(tx, obj_src)(obj) return func_var.call_function(tx, [obj_var] + args, 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 ): try: cls_var = inspect.getattr_static( self.value.__class__, name, NO_SUCH_SUBOBJ ) if cls_var is not NO_SUCH_SUBOBJ and name not in self.value.__dict__: # maybe user-defined @property that we need to inline return cls_var except AttributeError: pass # __slots__ # this might call torch.nn.Module.__getattr__ subobj = getattr(self.value, name) else: subobj = inspect.getattr_static(self.value, name) return subobj def has_key_in_generic_dict(self, tx, key): self._check_for_getattribute() if tx.output.side_effects.has_pending_mutation_of_attr(self, key): mutated_attr = tx.output.side_effects.load_attr(self, key, deleted_ok=True) return not isinstance(mutated_attr, variables.DeletedVariable) return key in self.value.__dict__ 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() if tx.output.side_effects.has_pending_mutation_of_attr(self, name): return tx.output.side_effects.load_attr(self, name) if name == "__dict__": options = {"source": source} return variables.GetAttrVariable(self, name, **options) try: subobj = self._getattr_static(name) except AttributeError: subobj = NO_SUCH_SUBOBJ getattr_fn = self._check_for_getattr() if isinstance(getattr_fn, types.FunctionType): # Dynamo is going to trace the __getattr__ function with # args=name. Set the source accordingly. new_source = None if self.source: new_source = AttrSource(self.source, "__getattr__") return variables.UserMethodVariable( getattr_fn, self, source=new_source ).call_function(tx, [ConstantVariable.create(name)], {}) elif getattr_fn is not None: unimplemented("UserDefined with non-function __getattr__") if isinstance(subobj, property): if self.source: # Read the class attribute to reach the property source = AttrSource(AttrSource(self.source, "__class__"), name) # Get the getter function 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, re.Pattern, ), ) ): if source: install_guard(source.make_guard(GuardBuilder.HASATTR)) return VariableBuilder(tx, source)(subobj) elif ConstantVariable.is_literal(subobj): return ConstantVariable.create(subobj) elif ( type(subobj) == torch.utils._pytree.TreeSpec or type(subobj) == torch.utils._pytree.LeafSpec or type(value) == torch.utils._pytree.TreeSpec ): from .builder import SourcelessBuilder return SourcelessBuilder.create(tx, subobj) if ( name not in getattr(value, "__dict__", {}) and ( type(value).__module__.startswith("torch.") or isinstance(subobj, re.Pattern) ) 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 SourcelessGraphModuleVariable(UserDefinedObjectVariable): def __init__( self, value, **kwargs, ): super().__init__(value, **kwargs) def call_method( self, tx, name, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]", ) -> "VariableTracker": fn_variable = variables.UserFunctionVariable(self.value.forward.__func__) args = [self] + args return tx.inline_user_function_return( fn_variable, args, kwargs, ) 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)