Support calling __torch_function__ attribute access (#111737)
Summary:
Triggers `__torch_function__` tracing on attribute/method/property access matching the eager behavior for non-overridden attributes/methods/properties that are present on `torch.Tensor`.
Some caveats:
1. for methods there doesn't seem to be a way to check if the original implementation of a method is overridden via monkey patching or not. For example:
```
class LocalSubclass(torch.Tensor):
classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
return super().__torch_function__(func, types, args, kwargs)
x = torch.ones(2, 2).as_subclass(LocalSubclass)
> x.sigmoid
<built-in method sigmoid of LocalSubclass object at 0x7f8d305bb5e0>
```
There isn't a way to verify that this built-in method is equivalent to the base `torch.Tensor` implementation as each instance will have a different built-in method object that can't be traced back to the original `torch.Tensor` impl. You can check that the class itself has the original implementation via
```
> inspect.getattr_static(LocalSubclass, "sigmoid")
<method 'sigmoid' of 'torch._C.TensorBase' objects>
```
But we can't detect if the user dynamically patches an object with a built-in method called sigmoid which does something completely different.
2. If a user overrides a method but calls the original implementation we will still graph break. This will require modifying `SuperVariable` (and any other way to get the original impl) to handle tensor subclasses.
X-link: https://github.com/pytorch/pytorch/pull/111737
Approved by: https://github.com/jansel, https://github.com/ezyang
Reviewed By: izaitsevfb
Differential Revision: D50778866
Pulled By: mlazos
fbshipit-source-id: e87006fd04b4818b171c18cf623fd1f0fd358196
