venv/lib/python3.12/site-packages/torch/_dynamo/variables/dicts.py

# mypy: ignore-errors

import collections
import dataclasses
import functools
import inspect
import sys
from typing import Dict, List, Optional

from torch._subclasses.fake_tensor import is_fake

from .. import polyfill, variables
from ..bytecode_transformation import (
    create_call_function,
    create_call_method,
    create_instruction,
    create_load_method,
)
from ..eval_frame import skip_code
from ..exc import unimplemented
from ..guards import GuardBuilder, install_guard
from ..source import AttrSource, GetItemSource
from ..utils import dict_keys, dict_values, istype, specialize_symnode
from .base import MutableLocal, VariableTracker
from .constant import ConstantVariable

# [Adding a new supported class within the keys of ConstDictVarialble]
# - Add its tracker type to is_hashable
# - (perhaps) Define how it is compared in _HashableTracker._eq_impl


def is_hashable(x):
    if isinstance(x, variables.TensorVariable):
        # Tensors are hashable if they have an example_value (a fake tensor)
        # Most VT's should have one.
        # It'd be nice if at some point we could assert that they all have one
        return x.as_proxy().node.meta.get("example_value") is not None
    elif isinstance(x, variables.TupleVariable):
        return all(is_hashable(e) for e in x.items)
    else:
        return isinstance(
            x,
            (
                variables.BuiltinVariable,
                variables.SymNodeVariable,
                variables.ConstantVariable,
                variables.EnumVariable,
                variables.user_defined.UserDefinedClassVariable,
                variables.UserFunctionVariable,
                variables.SkipFunctionVariable,
                variables.misc.NumpyVariable,
                variables.NNModuleVariable,
                variables.UnspecializedNNModuleVariable,
                variables.MethodWrapperVariable,
                variables.TorchInGraphFunctionVariable,
                variables.TypingVariable,
                variables.FunctoolsPartialVariable,
            ),
        )


class ConstDictVariable(VariableTracker):
    _nonvar_fields = {
        "user_cls",
        *VariableTracker._nonvar_fields,
    }

    class _HashableTracker:
        """
        Auxiliary opaque internal class that wraps a VariableTracker and makes it hashable
        This should not be seen or touched by anything outside of ConstDictVariable and its children
        Note that it's also fine to put VTs into dictionaries and sets, but doing so does not take into account aliasing
        """

        def __init__(self, vt):
            # We specialize SymNodes
            vt = specialize_symnode(vt)
            # TODO Temorarily remove to figure out what keys are we breaking on
            # and add proper support for them
            if not is_hashable(vt):
                unimplemented(f"Dict key of type {type(vt)}. Key: {vt}")
            self.vt = vt

        @property
        def underlying_value(self):
            if isinstance(self.vt, variables.TensorVariable):
                x = self.vt.as_proxy().node.meta["example_value"]
            elif isinstance(self.vt, variables.TupleVariable):
                Hashable = ConstDictVariable._HashableTracker
                x = tuple(Hashable(e).underlying_value for e in self.vt.items)
            elif isinstance(self.vt, variables.NNModuleVariable):
                return self.vt.module
            elif isinstance(self.vt, variables.UnspecializedNNModuleVariable):
                return self.vt.value
            elif isinstance(self.vt, variables.UserFunctionVariable):
                return self.vt.get_function()
            else:
                x = self.vt.as_python_constant()
            return x

        def __hash__(self):
            return hash(self.underlying_value)

        @staticmethod
        def _eq_impl(a, b):
            # TODO: Put this in utils and share it between variables/builtin.py and here
            if type(a) != type(b):
                return False
            elif isinstance(a, tuple):
                Hashable = ConstDictVariable._HashableTracker
                return len(a) == len(b) and all(
                    Hashable._eq_impl(u, v) for u, v in zip(a, b)
                )
            elif is_fake(a):
                return a is b
            else:
                return a == b

        def __eq__(self, other: "ConstDictVariable._HashableTracker") -> bool:
            Hashable = ConstDictVariable._HashableTracker
            assert isinstance(other, Hashable) or ConstantVariable.is_literal(
                other
            ), type(other)
            if isinstance(other, Hashable):
                return Hashable._eq_impl(self.underlying_value, other.underlying_value)

            # constant
            return Hashable._eq_impl(self.underlying_value, other)

    def __init__(
        self, items: Dict[VariableTracker, VariableTracker], user_cls=dict, **kwargs
    ):
        super().__init__(**kwargs)

        Hashable = ConstDictVariable._HashableTracker

        # Keys will just be HashableTrackers when cloning, in any other case they'll be VariableTrackers
        assert all(
            isinstance(x, (VariableTracker, Hashable))
            and isinstance(v, VariableTracker)
            for x, v in items.items()
        )

        def make_hashable(key):
            return key if isinstance(key, Hashable) else Hashable(key)

        self.items = {make_hashable(x): v for x, v in items.items()}
        self.user_cls = user_cls

    def as_proxy(self):
        return {k.vt.as_proxy(): v.as_proxy() for k, v in self.items.items()}

    def debug_repr(self):
        return (
            "{"
            + ", ".join(
                f"{k.vt.debug_repr()}: {v.debug_repr()}" for k, v in self.items.items()
            )
            + "}"
        )

    def as_python_constant(self):
        return {
            k.vt.as_python_constant(): v.as_python_constant()
            for k, v in self.items.items()
        }

    def keys_as_python_constant(self):
        return {k.vt.as_python_constant(): v for k, v in self.items.items()}

    def python_type(self):
        return self.user_cls

    def __contains__(self, vt):
        assert isinstance(vt, VariableTracker)
        Hashable = ConstDictVariable._HashableTracker
        return is_hashable(vt) and Hashable(vt) in self.items

    def reconstruct(self, codegen):
        # instructions to load collections.OrderedDict if necessary
        if self.user_cls is collections.OrderedDict:
            codegen.extend_output(
                [
                    codegen.create_load_python_module(collections, True),
                    codegen.create_load_attr("OrderedDict"),
                ]
            )
        # instructions to build the dict keys and values
        for key, value in self.items.items():
            codegen(key.vt)
            codegen(value)
        # BUILD_MAP and calling collections.OrderedDict if necessary
        if self.user_cls is collections.OrderedDict:
            codegen.extend_output(
                [
                    create_instruction("BUILD_MAP", arg=len(self.items)),
                    *create_call_function(1, False),
                ]
            )
        # BUILD_MAP only if user_cls is dict
        else:
            codegen.append_output(create_instruction("BUILD_MAP", arg=len(self.items)))

    def getitem_const(self, arg: VariableTracker):
        key = ConstDictVariable._HashableTracker(arg)
        if key not in self.items:
            unimplemented(f"dict KeyError: {arg.value}")
        return self.items[key]

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        from . import (
            BuiltinVariable,
            ConstantVariable,
            ListIteratorVariable,
            ListVariable,
            TupleVariable,
        )

        Hashable = ConstDictVariable._HashableTracker

        arg_hashable = args and is_hashable(args[0])

        if name == "__getitem__":
            assert len(args) == 1
            return self.getitem_const(args[0])
        elif name == "items":
            assert not (args or kwargs)
            if self.source:
                tx.output.guard_on_key_order.add(self.source.name())
            return TupleVariable(
                [TupleVariable([k.vt, v]) for k, v in self.items.items()]
            )
        elif name == "keys":
            if self.source:
                tx.output.guard_on_key_order.add(self.source.name())
            assert not (args or kwargs)
            return DictKeys(self)
        elif name == "values":
            if self.source:
                tx.output.guard_on_key_order.add(self.source.name())
            assert not (args or kwargs)
            return DictValues(self)
        elif name == "copy":
            assert not (args or kwargs)
            return self.clone(items=self.items.copy(), mutable_local=MutableLocal())
        elif name == "__len__":
            assert not (args or kwargs)
            return ConstantVariable.create(len(self.items))
        elif name == "__setitem__" and arg_hashable and self.mutable_local:
            assert not kwargs and len(args) == 2
            tx.output.side_effects.mutation(self)
            self.items[Hashable(args[0])] = args[1]
            return ConstantVariable.create(None)
        elif name == "__delitem__" and arg_hashable and self.mutable_local:
            tx.output.side_effects.mutation(self)
            self.items.__delitem__(Hashable(args[0]))
            return ConstantVariable.create(None)
        elif name in ("pop", "get") and len(args) in (1, 2) and args[0] not in self:
            # missing item, return the default value
            if len(args) == 1:
                return ConstantVariable(None)
            else:
                return args[1]
        elif name == "pop" and arg_hashable and self.mutable_local:
            tx.output.side_effects.mutation(self)
            return self.items.pop(Hashable(args[0]))
        elif name == "clear":
            tx.output.side_effects.mutation(self)
            self.items.clear()
            return ConstantVariable.create(None)
        elif (
            name == "update"
            and len(args) == 1
            and isinstance(
                args[0],
                (
                    ConstDictVariable,
                    ListVariable,
                    TupleVariable,
                    ListIteratorVariable,
                ),
            )
            and self.mutable_local
        ):
            tx.output.side_effects.mutation(self)
            if isinstance(args[0], ConstDictVariable):
                dict_vt = args[0]
            else:
                dict_vt = BuiltinVariable.call_custom_dict(tx, dict, args[0])
            self.items.update(dict_vt.items)
            # Wrap strings
            kwargs = {
                Hashable(ConstantVariable.create(k)): v for k, v in kwargs.items()
            }
            self.items.update(kwargs)
            return ConstantVariable.create(None)
        elif name in ("get", "__getattr__") and args[0] in self:
            return self.getitem_const(args[0])
        elif name == "__contains__" and len(args) == 1:
            return ConstantVariable.create(args[0] in self)
        else:
            return super().call_method(tx, name, args, kwargs)

    def unpack_var_sequence(self, tx):
        return [x.vt for x in self.items.keys()]


class DefaultDictVariable(ConstDictVariable):
    def __init__(self, items, user_cls, default_factory=None, **kwargs):
        super().__init__(items, user_cls, **kwargs)
        assert user_cls is collections.defaultdict
        self.default_factory = default_factory

    def is_python_constant(self):
        # Return false for unsupported defaults. This ensures that a bad handler
        # path is not taken in BuiltinVariable for getitem.
        if self.default_factory not in [list, tuple, dict] and not self.items:
            return False
        return super().is_python_constant()

    def debug_repr(self):
        return (
            f"defaultdict({self.default_factory.debug_repr()}, {super().debug_repr()})"
        )

    @staticmethod
    def is_supported_arg(arg):
        if isinstance(arg, variables.BuiltinVariable):
            return arg.fn in [list, tuple, dict]
        else:
            return isinstance(arg, variables.functions.BaseUserFunctionVariable)

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        if name == "__getitem__":
            assert len(args) == 1

            if args[0] in self:
                return self.getitem_const(args[0])
            else:
                if self.default_factory is None:
                    raise KeyError(f"{args[0]}")
                else:
                    default_var = self.default_factory.call_function(tx, [], {})
                    super().call_method(
                        tx, "__setitem__", (args[0], default_var), kwargs
                    )
                    return default_var
        else:
            return super().call_method(tx, name, args, kwargs)


class SetVariable(ConstDictVariable):
    """We model a sets as dictonary with None values"""

    def __init__(
        self,
        items: List[VariableTracker],
        **kwargs,
    ):
        items = dict.fromkeys(items, SetVariable._default_value())
        super().__init__(items, **kwargs)

    def debug_repr(self):
        if not self.items:
            return "set()"
        else:
            return "{" + ",".join(k.vt.debug_repr() for k in self.items.keys()) + "}"

    @property
    def set_items(self):
        return set(self.items.keys())

    @staticmethod
    def _default_value():
        # Variable to fill in he keys of the dictinary
        return ConstantVariable.create(None)

    def as_proxy(self):
        return {k.vt.as_proxy() for k in self.set_items}

    def python_type(self):
        return set

    def as_python_constant(self):
        return {k.vt.as_python_constant() for k in self.set_items}

    def reconstruct(self, codegen):
        codegen.foreach([x.vt for x in self.set_items])
        codegen.append_output(create_instruction("BUILD_SET", arg=len(self.set_items)))

    def call_method(
        self,
        tx,
        name,
        args: List[VariableTracker],
        kwargs: Dict[str, VariableTracker],
    ) -> "VariableTracker":
        from . import ListVariable, TupleVariable

        # We foward the calls to the dictionary model
        if name == "add":
            assert not kwargs
            assert len(args) == 1
            name = "__setitem__"
            args = (args[0], SetVariable._default_value())
        elif name == "pop":
            assert not kwargs
            assert not args
            # Choose an item at random and pop it via the Dict.pop method
            result = self.set_items.pop().vt
            super().call_method(tx, name, (result,), kwargs)
            return result
        elif name == "isdisjoint":
            assert not kwargs
            assert len(args) == 1
            return variables.UserFunctionVariable(
                polyfill.set_isdisjoint
            ).call_function(tx, [self, args[0]], {})
        elif (
            name == "update"
            and len(args) == 1
            and isinstance(
                args[0],
                (
                    SetVariable,
                    ListVariable,
                    TupleVariable,
                ),
            )
            and self.mutable_local
        ):
            if isinstance(args[0], (ListVariable, TupleVariable)):
                arg = SetVariable(args[0].unpack_var_sequence(tx))
            else:
                arg = args[0]
            return super().call_method(tx, "update", (arg,), kwargs)
        return super().call_method(tx, name, args, kwargs)

    def getitem_const(self, arg: VariableTracker):
        raise RuntimeError("Illegal to getitem on a set")


class DictView(VariableTracker):
    """
    Models _PyDictViewObject

    This is an "abstract" class. Subclasses will override kv and the items method
    """

    kv: Optional[str] = None

    def __init__(self, dv_dict: ConstDictVariable, **kwargs):
        super().__init__(**kwargs)
        assert self.kv in ("keys", "values")
        assert isinstance(dv_dict, ConstDictVariable)
        self.dv_dict = dv_dict

    @property
    def view_items(self):
        return getattr(self.dv_dict.items, self.kv)()

    @property
    def view_items_vt(self):
        # Returns an iterable of the unpacked items
        # Implement in the subclasses
        raise NotImplementedError

    def unpack_var_sequence(self, tx):
        def unwrap(x):
            return x.vt if self.kv == "keys" else x

        return [unwrap(x) for x in self.view_items]

    def reconstruct(self, codegen):
        codegen(self.dv_dict)
        codegen.extend_output(
            [
                create_load_method(self.kv),
                *create_call_method(0),
            ]
        )

    def call_method(
        self,
        tx,
        name,
        args: List["VariableTracker"],
        kwargs: Dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        if name == "__len__":
            return self.dv_dict.call_method(tx, name, args, kwargs)
        return super().call_method(tx, name, args, kwargs)


class DictKeys(DictView):
    kv = "keys"

    @property
    def set_items(self):
        return set(self.view_items)

    @property
    def view_items_vt(self):
        # Returns an iterable of the unpacked items
        return [x.vt for x in self.view_items]

    def python_type(self):
        return dict_keys

    def call_method(
        self,
        tx,
        name,
        args: List["VariableTracker"],
        kwargs: Dict[str, "VariableTracker"],
    ) -> "VariableTracker":
        if name == "__contains__":
            return self.dv_dict.call_method(tx, name, args, kwargs)
        return super().call_method(tx, name, args, kwargs)


class DictValues(DictView):
    # DictValues is an iterable but cannot be compared.
    kv = "values"

    @property
    def view_items_vt(self):
        return list(self.view_items)

    def python_type(self):
        return dict_values


def _is_matching_transformers_cls(cls) -> bool:
    mod = sys.modules.get("transformers.file_utils")
    return mod is not None and issubclass(cls, mod.ModelOutput)


def _is_matching_diffusers_cls(cls) -> bool:
    mod = sys.modules.get("diffusers.utils")
    return mod is not None and issubclass(cls, mod.BaseOutput)


def _call_hasattr_customobj(self, tx, name: str) -> "VariableTracker":
    """Shared method between DataClassVariable and CustomizedDictVariable where items are attrs"""
    if name in self.items or hasattr(self.user_cls, name):
        return ConstantVariable(True)
    elif istype(self.mutable_local, MutableLocal) and self.source is None:
        # Something created locally can't have any extra fields on it
        return ConstantVariable(False)
    elif self.mutable_local is None and self.source:
        # Maybe add a guard
        try:
            example = tx.output.root_tx.get_example_value(self.source)
            install_guard(
                AttrSource(self.source, name).make_guard(GuardBuilder.HASATTR)
            )
            return ConstantVariable(hasattr(example, name))
        except KeyError:
            pass
    unimplemented(
        f"hasattr({self.__class__.__name__}, {name}) {self.mutable_local} {self.source}"
    )


class DataClassVariable(ConstDictVariable):
    """
    This is a bit of a hack to deal with
    transformers.file_utils.ModelOutput() from huggingface.

    ModelOutput causes trouble because it a a mix of a dataclass and a
    OrderedDict and it calls super() methods implemented in C.
    """

    # ModelOutput() excludes None, though generic datclasses don't
    include_none = False

    @staticmethod
    @functools.lru_cache(None)
    def _patch_once():
        try:
            from transformers.file_utils import ModelOutput

            for obj in ModelOutput.__dict__.values():
                if callable(obj):
                    skip_code(obj.__code__)
        except ImportError:
            pass

        try:
            from diffusers.utils import BaseOutput

            for obj in BaseOutput.__dict__.values():
                if callable(obj):
                    skip_code(obj.__code__)
        except ImportError:
            pass

    @staticmethod
    def is_matching_cls(cls):
        return _is_matching_transformers_cls(cls) or _is_matching_diffusers_cls(cls)

    @classmethod
    def is_matching_object(cls, obj):
        return cls.is_matching_cls(type(obj))

    @classmethod
    def create(cls, user_cls, args, kwargs, options):
        DataClassVariable._patch_once()

        skip_code(user_cls.__init__.__code__)
        keys = [f.name for f in dataclasses.fields(user_cls)]
        bound = inspect.signature(user_cls).bind(*args, **kwargs)
        bound.apply_defaults()
        assert set(bound.arguments.keys()) == set(keys)
        items = {}
        for key in keys:
            val = bound.arguments[key]
            key = ConstantVariable.create(key)
            if isinstance(val, VariableTracker):
                items[key] = val
            else:
                if cls.include_none:
                    assert variables.ConstantVariable.is_literal(val)
                    items[key] = variables.ConstantVariable.create(val)
                else:
                    assert val is None, f"unexpected {val}"

        if len(items) == 1 and not isinstance(items[keys[0]], variables.TensorVariable):
            unimplemented("DataClassVariable iterator constructor")
            # TODO(jansel): implement unpacking logic in ModelOutput.__post_init__

        return cls(items, user_cls, **options)

    @classmethod
    def wrap(cls, builder, obj):
        user_cls = type(obj)
        keys = [f.name for f in dataclasses.fields(user_cls)]

        excluded = []
        items = {}
        for key in keys:
            # __init__ function of a dataclass might not have yet defined the key
            if hasattr(obj, key):
                val = getattr(obj, key)
                var = builder.__class__(
                    tx=builder.tx, source=AttrSource(builder.source, key)
                )(val)
                if val is not None or cls.include_none:
                    key = ConstantVariable.create(key)
                    items[key] = var
                else:
                    excluded.append(var)
        return cls(items, user_cls)

    def __init__(self, items, user_cls, **options):
        super().__init__(items, user_cls, **options)
        assert self.is_matching_cls(user_cls)

    def as_proxy(self):
        raise NotImplementedError

    def reconstruct(self, codegen):
        codegen.extend_output([codegen._create_load_const(self.user_cls)])
        # All the keys are just wrapped strings
        d = self.keys_as_python_constant()
        codegen.foreach(d.values())
        keys = tuple(d.keys())
        codegen.extend_output(codegen.create_call_function_kw(len(keys), keys, True))

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        if name == "__getitem__":
            assert not kwargs and len(args) == 1
            val = args[0]
            if val.python_type() == str:
                return self.getitem_const(val)
            else:
                return self.call_method(tx, "to_tuple", [], {}).call_method(
                    tx, "__getitem__", args, kwargs
                )
        elif name == "to_tuple":
            assert not (args or kwargs)
            return variables.TupleVariable(list(self.items.values()))
        elif name == "__setattr__":
            name = "__setitem__"
        return super().call_method(tx, name, args, kwargs)

    def var_getattr(self, tx, name: str) -> "VariableTracker":
        name_vt = ConstantVariable.create(name)
        if name_vt in self:
            return self.call_method(tx, "__getitem__", [name_vt], {})
        elif not self.include_none:
            defaults = {f.name: f.default for f in dataclasses.fields(self.user_cls)}
            if name in defaults:
                assert variables.ConstantVariable.is_literal(defaults[name])
                return variables.ConstantVariable.create(defaults[name])
        super().var_getattr(tx, name)

    call_hasattr = _call_hasattr_customobj


class CustomizedDictVariable(ConstDictVariable):
    @staticmethod
    def is_matching_cls(cls):
        # True if using default OrderedDict.__init__ and did not implement __post_init__
        if (
            issubclass(cls, collections.OrderedDict)
            and cls.__init__ is collections.OrderedDict.__init__
            and not hasattr(cls, "__post_init__")
        ):
            return True
        # hack for HF usecase:
        #   assume dataclass annotation for ModelOutput subclass
        #   assume self.create is AA to ModelOutput.__post_init__
        return _is_matching_transformers_cls(cls) or _is_matching_diffusers_cls(cls)

    @classmethod
    def is_matching_object(cls, obj):
        return cls.is_matching_cls(type(obj))

    # called from user_defined.py
    # when is_matching_cls(cls) is true
    @classmethod
    def create(cls, user_cls, args, kwargs, options):
        # avoid tracing when returning ModelOutput from forward func
        for attr_name in ("__init__", "__post_init__", "__setattr__", "__setitem__"):
            if hasattr(user_cls, attr_name):
                fn = getattr(user_cls, attr_name)
                assert callable(fn), f"expect callable attr {attr_name}"
                if hasattr(fn, "__code__"):
                    skip_code(fn.__code__)

        if dataclasses.is_dataclass(user_cls):
            # @dataclass CustomDict(a=1, b=2)
            bound = inspect.signature(user_cls).bind(*args, **kwargs)
            bound.apply_defaults()

            def make_var(x):
                if isinstance(x, VariableTracker):
                    return x
                elif ConstantVariable.is_literal(x):
                    return ConstantVariable.create(x)
                else:
                    unimplemented(
                        "expect VariableTracker or ConstantVariable.is_literal"
                    )

            bound_args = {}
            if _is_matching_transformers_cls(user_cls) or _is_matching_diffusers_cls(
                user_cls
            ):
                # Skip none
                for k, v in bound.arguments.items():
                    if isinstance(v, ConstantVariable) and v.value is None or v is None:
                        continue
                    bound_args[k] = v
            else:
                bound_args = bound.arguments

            items = {
                ConstantVariable.create(k): make_var(v) for k, v in bound_args.items()
            }
        elif not args:
            # CustomDict(a=1, b=2) in the general (non-dataclass) case.
            items = {ConstantVariable.create(k): v for k, v in kwargs.items()}
        elif len(args) == 1 and isinstance(args[0], ConstDictVariable) and not kwargs:
            # CustomDict({'a': 1, 'b': 2})
            items = args[0].items
        else:
            unimplemented("custom dict init with args/kwargs unimplemented")

        return cls(items, user_cls, **options)

    # called from builder.py
    @classmethod
    def wrap(cls, builder, obj):
        raise NotImplementedError

    def __init__(self, items, user_cls, **options):
        super().__init__(items, user_cls, **options)
        assert self.is_matching_cls(user_cls)

    def as_proxy(self):
        raise NotImplementedError

    # 'RETURN_VALUE triggered compile'
    # called from torch/_dynamo/codegen.py
    def reconstruct(self, codegen):
        is_hf_model_output = _is_matching_transformers_cls(
            self.user_cls
        ) or _is_matching_diffusers_cls(self.user_cls)

        # If the user class is a ModelOutput, then wrap the instance creation in
        # torch._dynamo.disable(). Even though we mark the __post_init__ as skip
        # in `create` function, this is not enough. TorchDynamo can still get
        # triggered on the child functions of __post_init__. This upsets export.
        # Since, we know that ModelOutput __post_init__ is not worth optimizing,
        # we just wrap the instance creation in torch._dynamo.disable(),
        # regardless whether its export or not.
        if is_hf_model_output:
            # load torch._dynamo.disable
            codegen.append_output(codegen.create_load_global("torch", True, add=True))
            codegen.append_output(codegen.create_load_attr("_dynamo"))
            codegen.append_output(codegen.create_load_attr("disable"))
        codegen.extend_output([codegen._create_load_const(self.user_cls)])

        if is_hf_model_output:
            # Wrap user_cls with disable
            codegen.extend_output(create_call_function(1, False))

        # All the keys are just wrapped strings
        d = self.keys_as_python_constant()
        codegen.foreach(d.values())
        keys = tuple(d.keys())
        codegen.extend_output(codegen.create_call_function_kw(len(keys), keys, True))

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        fn = getattr(self.user_cls, name)
        source = None if self.source is None else AttrSource(self.source, name)

        if hasattr(fn, "__objclass__") and fn.__objclass__ in (
            dict,
            collections.OrderedDict,
        ):
            # for python dict method without overridden
            return super().call_method(tx, name, args, kwargs)
        elif name in ("__getitem__", "to_tuple", "__setitem__", "__setattr__"):
            # for user overridden method
            return tx.inline_user_function_return(
                variables.UserFunctionVariable(fn, source=source),
                [self] + list(args),
                kwargs,
            )

        unimplemented("custom dict: call_method unimplemented name=%s", name)

    def var_getattr(self, tx, name: str) -> "VariableTracker":
        name_vt = ConstantVariable.create(name)
        if name_vt in self:
            return self.call_method(tx, "__getitem__", [name_vt], {})
        super().var_getattr(tx, name)

    call_hasattr = _call_hasattr_customobj


@functools.lru_cache(None)
def _install_PretrainedConfig_patch():
    import transformers

    # We need to monkeypatch transformers here, sadly.
    # TODO(voz): Upstream to transformers lib

    def _dynamo_overriden_transformers_eq(self, other):
        if not hasattr(other, "__dict__"):
            return False
        return self.__dict__ == other.__dict__

    transformers.configuration_utils.PretrainedConfig.__eq__ = (
        _dynamo_overriden_transformers_eq
    )


class HFPretrainedConfigVariable(VariableTracker):
    """
    Hack for HuggingFace PretrainedConfig
    """

    @staticmethod
    def is_matching_cls(cls):
        mod = sys.modules.get("transformers.configuration_utils")
        is_match = mod is not None and issubclass(cls, mod.PretrainedConfig)

        # Lazily install monkeypatch the first time we see it in dynamo
        if is_match:
            _install_PretrainedConfig_patch()
        return is_match

    @classmethod
    def is_matching_object(cls, obj):
        return cls.is_matching_cls(type(obj))

    def __init__(self, obj, **kwargs):
        super().__init__(**kwargs)
        self.obj = obj
        assert self.is_matching_cls(type(obj))

    def var_getattr(self, tx, name: str) -> "VariableTracker":
        from . import ConstantVariable

        return ConstantVariable.create(getattr(self.obj, name))

    def call_hasattr(self, tx, name: str) -> "VariableTracker":
        return variables.ConstantVariable.create(hasattr(self.obj, name))


class PythonSysModulesVariable(VariableTracker):
    """Special case for sys.modules.

    Without this we will guard on the exact set of modules imported in the
    lifetime of the python program.
    """

    def python_type(self):
        return dict

    def reconstruct(self, codegen):
        codegen.extend_output(
            [
                codegen.create_load_python_module(sys, True),
                codegen.create_load_attr("modules"),
            ]
        )

    def call_method(
        self, tx, name, args: List[VariableTracker], kwargs: Dict[str, VariableTracker]
    ):
        if name == "__getitem__":
            return self.call_getitem(tx, *args, **kwargs)
        elif name == "get":
            return self.call_get(tx, *args, **kwargs)
        elif name == "__contains__":
            return self.call_contains(tx, *args, **kwargs)
        unimplemented(f"sys.modules.{name}(*{args}, **{kwargs})")

    def _contains_helper(self, tx, key: VariableTracker):
        k = key.as_python_constant()
        has_key = k in sys.modules
        install_guard(
            self.make_guard(
                functools.partial(GuardBuilder.DICT_CONTAINS, key=k, invert=not has_key)
            )
        )
        return k, has_key

    def call_contains(self, tx, key: VariableTracker):
        k, has_key = self._contains_helper(tx, key)
        return ConstantVariable.create(value=has_key)

    def call_get(
        self, tx, key: VariableTracker, default: Optional[VariableTracker] = None
    ):
        from .builder import VariableBuilder

        k, has_key = self._contains_helper(tx, key)

        if has_key:
            return VariableBuilder(
                tx,
                GetItemSource(self.source, k),
            )(sys.modules[k])

        if default is not None:
            return default

        return ConstantVariable.create(value=None)

    def call_getitem(self, tx, key: VariableTracker):
        from .builder import VariableBuilder

        k, has_key = self._contains_helper(tx, key)
        return VariableBuilder(
            tx,
            GetItemSource(self.source, k),
        )(sys.modules[k])