Wrong result for Inplace tensor update on transpose for some devices with torch 2.3.0 about pytorch

[jerrychenhf]

This problem is related a few changes.

#104689 has a fix in 2.1.0 for "Inplace tensor update on transpose" problem for HPU or other devices that doens't have support on non contiguous output.But this PR cause some issues in part of the code calling wrap_output_with_input_device_.

#119868 reverted part of the code of #104689 to fix the new issues in 2.3.0. So the original issue in #103650 reopens in 2.3.0

[jerrychenhf]

There are cases that a FakeTensor is not reserve its fakeness when calling into convert_element_type.
For example, the following code run on CPU, the input FakeTensor is not preserved in output of type conversion in _to_copy decomposition for some calls.

import torch

def fn(a):
 b = a.t()
 b.mul_(1.0)
 return b

x = torch.arange(6).reshape([2, 3]).to('cpu')

print("x ", x.cpu())

compiled_fn = torch.compile(fn)
y = compiled_fn(x)

print("y ", y.cpu())

_to_copy start: FakeTensor(..., device='meta', size=(3, 2), dtype=torch.int64)
after type converted: tensor(..., device='meta', size=(3, 2))
_to_copy start: FakeTensor(..., size=(3, 2))
after type converted: FakeTensor(..., size=(3, 2), dtype=torch.int64)
_to_copy start: FakeTensor(..., size=(3, 2))
after type converted: FakeTensor(..., size=(3, 2), dtype=torch.int64)

I found usually the convert_element_type_meta is called under the fake mode if necessary and torch.empty_strided in the TensorMeta function will return FakeTensor in such case. But I am not sure why the first call in the above case doesn't preserve the Fakeness (Is this itself a bug?)

I read through the discussions in #119868. There were a few proposals.

Is there any problem of the initial proposal of "checking the input and current type as the fakeness and call wrap_output_with_input_device only necessary"?
I think convert_element_type_meta is much more impactful if we want to give convert_element_type_meta the semantics of preserving fakeness. Please suggest.

@ezyang @laithsakka