[dynamo] Issue with construction nn.Parameter about pytorch HOT 5 OPEN

cc @jansel @bdhirsh

from pytorch.

This is sort of like need proper subclass support in Dynamo :P

cc @mlazos

from pytorch.

I have a very simple repo
The first print(parameters) in the code changes the output from Parameter containing, to tensor with grad_fn

    def test_ParameterList(self):

        @torch.compile(backend="eager")
        def func():
            def make_param():
                return Parameter(torch.randn(2, 2))

            parameters = []
            # without the following print line we generate:
#             [Parameter containing:
#              tensor([[ 0.0461,  0.4024],
#              [-1.0115,  0.2167]], requires_grad=True)]
                    #
            # with it we geenrate 
# [tensor([[ 0.0461,  0.4024],
#         [-1.0115,  0.2167]], grad_fn=<TracableCreateParameterBackward>)]
            print(parameters)


            parameters.append(make_param())
            print(parameters)

        ```

from pytorch.

@laithsakka I wasn't able to repro using using the test_ParameterList example on nightly (it still prints Parameter containing: ...). Is it expected?

@anijain2305 I have seen this error before when working on Traceable FSDP+TP. Some thoughts:

• The suggestion from error message (cloning the output) is not correct because we do want to preserve the nn.Parameter placeholder tensor identity (i.e. we must return the original nn.Parameter placeholder).
• I wonder if you have high-level insight on why the nn.Parameter (or its base tensor) was mutated. I suspect it's from nn.Linear's reset_parameters method, but I haven't verified it myself yet.

from pytorch.

@yf225 sorry I updated the test I missed one print before parameters.append(make_param())

for the second print with the print we get

[tensor([[ 0.0461, 0.4024], [-1.0115, 0.2167]], grad_fn=)]

without it we get :

[Parameter containing:
tensor([[ 0.0461, 0.4024],
[-1.0115, 0.2167]], requires_grad=True)]

from pytorch.