Unexpected `__module__` attribute for `NamedTupleType.__new__` method: `namedtuple_{typename}`

[XuehaiPan]

Bug report

Bug description:

The __module__ attribute for NamedTupleType.__new__ method is namedtuple_{typename}. It does not exist. The __module__ attribute for methods should respect the module argument passed to collections.namedtuple.

In [1]: import collections

In [2]: MyTuple = collections.namedtuple('MyTuple', ['x', 'y', 'z'])

In [3]: MyTuple.__module__
Out[3]: '__main__'

In [4]: MyTuple.__new__.__module__
Out[4]: 'namedtuple_MyTuple'

In [5]: MyTuple._make.__module__
Out[5]: 'collections'

In [6]: import typing

In [7]: class MyAnotherTuple(typing.NamedTuple):
   ...:     a: int
   ...:     b: float
   ...:     

In [8]: MyAnotherTuple.__module__
Out[8]: '__main__'

In [9]: MyAnotherTuple.__new__.__module__
Out[9]: 'namedtuple_MyAnotherTuple'

In [10]: MyAnotherTuple._make.__module__
Out[10]: 'collections'

CPython versions tested on:

3.12

Operating systems tested on:

Linux

Linked PRs

• gh-127187: Set correct __module__ attribute for methods of named tuple types #127188

[rhettinger]

Is there any reason users would care about this? Is there any real world impact?

The current result seems reasonable to me. The __new__ method was created virtually so it is fair to report it as `'namedtuple_MyTuple'. It has been this way for a very long time and has not been a source of problems.

[XuehaiPan]

Is there any reason users would care about this? Is there any real world impact?

I'm trying to improve namedtuple support in PyTorch Dynamo, an ML compiler backend with a lightweight Python interpreter. Dynamo traces the Python frames and generates performat inlined functions via traced opcodes. The __module__ attribute is used to reconstruct the Python function during codegen phase.

The codegen tries to import the __name__ attribute during creating the __new__ function for namedtuple

https://github.com/pytorch/pytorch/blob/bae951030752f2d7f06c15af0c59e54026d851e9/torch/_dynamo/symbolic_convert.py#L3290-L3299

    def get_globals_source_and_value(self, name):
        if "__name__" in self.f_globals:
            module_name = self.f_globals["__name__"]
            module_source = self.import_source(module_name)
            if "torch_package" in module_name:
                fglobals_value = torch.package.package_importer._package_imported_modules[module_name]  # type: ignore[assignment]
            else:
                fglobals_value = importlib.import_module(module_name)  # type: ignore[assignment]
            fglobals_vt = VariableTracker.build(self, fglobals_value, module_source)
            global_source = AttrSource(module_source, name)

[rhettinger]

For PyTorch is this a show stopper or is a workaround available?

How would MyTuple.__new__.__module__ returning __main__ actually be useful for reconstructing a __new__ method? That isn't clear to me from your post. That attribute doesn't affect functionality. Technically, a namedtuple doesn't have to live in a module at all. It can be execed dynamically, created and used within a function's locals, stored in a class definination, or stored in a data structure rather than a module's globals. In these circumstances, returning __main__ isn't really correct.

[XuehaiPan]

For PyTorch is this a show stopper or is a workaround available?

PyTorch Dynamo handles NamedTuple via explicit special handling rather than a normal user-defined class. There is not a stopper for namedtuple types for now. Also, Dynamo is not a full-featured interpreter. We incrementally improve the coverage of the code in the ML community. That is fine for it to raise unimplemented for unhandled cases.

---

How would MyTuple.__new__.__module__ returning __main__ actually be useful for reconstructing a __new__ method? That isn't clear to me from your post. That attribute doesn't affect functionality.

The problem only shows up when the user tries to subclass a namedtuple type. The subclass needs the __module__ attribute to reconstruct its MyTupleSubType.__new__, which is MyTuple.__new__. See the example at the end of this post, the MyTupleSubType.__new__ method is used to create the instance of MyTupleSubType in polyfill instantiate_user_defined_class_object.

---

Technically, a namedtuple doesn't have to live in a module at all. It can be execed dynamically, created and used within a function's locals, stored in a class definination, or stored in a data structure rather than a module's globals. In these circumstances, returning __main__ isn't really correct.

The problem only shows up when the user tries to subclass a namedtuple type. Dynamo only supports classes defined in the global scope for now. Dynamo works fine with namedtuple types (locally or globally) that directly created by collections.namedtuple or typing.NamedTuple.

---

Let me give more context. cc @jansel, correct me if I am wrong.

In Dynamo, each specialized type has its own variable tracker type.

list object               -> ListVariable
tuple object              -> TupleVariable
namedtuple object         -> NamedTupleVariable
...

generic types             -> UserDefinedClassVaraible
instance of generic types -> UserDefinedVaraible

While creating an instance of a given type, there is a handcrafted dispatch path for namedtuple type here: https://github.com/pytorch/pytorch/blob/51b6126f5432b4cc446a0bb3882aa9d98124caa8/torch/_dynamo/variables/user_defined.py#L454-L485

# Python code: MyTuple(*values)
# Dynamo call stack:
   UserDefinedClassVaraible(MyTuple).call_function(tx, values, {})
-> NamedTupleVariable(MyTuple, values)

This handcrafted path bypasses to call MyTuple.__new__. So no errors are shown in the past.

For other types that do not have specialization, the route goes here: https://github.com/pytorch/pytorch/blob/51b6126f5432b4cc446a0bb3882aa9d98124caa8/torch/_dynamo/variables/user_defined.py#L587-L600

# Python code: SomeType(*args, **kwargs)
# Dynamo call stack:
   UserDefinedClassVaraible(SomeType).call_function(tx, args, kwargs)
-> UserFunctionVariable(instantiate_user_defined_class_object).call_function(tx, [SomeType, *args], **kwargs)
-> UserDefinedVaraible(obj)

# where:
def instantiate_user_defined_class_object(cls, /, *args, **kwargs):
    obj = cls.__new__(cls, *args, **kwargs)

    # Only call __init__ if the object is an instance of the class
    # Reference: https://github.com/python/cpython/blob/3.12/Objects/typeobject.c#L1670-L1673
    if isinstance(obj, cls):
        obj.__init__(*args, **kwargs)
    return obj

For subclasses of namedtuple type, they can not go through the NamedTupleVariable path. That results in Dynamo internal error in instantiate_user_defined_class_object for "can not reconstruct MyTupleSubType.__new__".

[jansel]

The CPython behavior here looks reasonable to me. I think we should find a workaround in PyTorch.

[rhettinger]

The CPython behavior here looks reasonable to me. I think we should find a workaround in PyTorch.

Thanks for the insight. I'll mark this as closed.