reinplacing pass fixes for torchbench + huggingface (#83626)
I'm testing out turning on re-inplacing + functionalization by default with the AOTAutograd + eager backend on torchbench + huggingface models. This PR contains a few bug fixes from turning re-inplacing on:
(1) Handle more gracefully when FakeTensorMode is already turned on when you call reinplace
(2) More robust detection for when an inplace variant of an op exists (the dumb bug was that `pow.Scalar` doesn't have an inplace variant, even though there are several overloads of `pow_`. None of them are eligible though
(3) Avoid re-inplacing when it would require resizing the input buffer. This isn't allowed, because inplace ops aren't allowed to resize their inputs.
For the last one, I gave the two main examples in more detail in the comments. Important cases are:
```
# This should not be re-inplaced at all; the op broadcasts, so this would require resizing the self tensor
torch.add(tensor[1, 4], tensor[4, 4])
# This should not be re-inplaced, because the inplace and out-of-place variants of the op return different dtypes
torch.ge(a, b)
# However, this means that today when functionalization functionalists a `torch.ge_(a, b)` call, reinplacing won't properly de-functionalize it. I mentioned that optimization is worth adding later in the comments
```
(4) There's some logic around keeping `storage_to_nodes` up to date when we see a view op: if we re-inplace `out = a.add(...)`, and later in the program we encounter a "later_node",`out.view(..)`, and need to replace it with `a.view(...)`, then we need to update some metadata structures. I had to fix that logic: specifically, if "later_node" isn't a dispatcher op, (e.g. if it's an FX output node), I wasn't properly handling the case where the node's fake_meta info was not a tensor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83626
Approved by: https://github.com/ezyang
