Partially addresses #111081 This fixes the majority of the slowness from https://fb.workplace.com/groups/1405155842844877/permalink/7491314274228973/. In particular, the type of example that suffers the most perf-wise in AOTAutograd looks like this: ``` @torch.compile def f(x): intermediate = x.mul(2) outs = intermediate.unbind(0) return *outs x = torch.randn(50, 50, requires_grad=True) outs = f(x) sum(outs).sum().backward() ``` There are 50 output tensors in the above function, that all alias each other. AOTAutograd will dutifully exercise its intermediate base [logic](https://github.com/pytorch/pytorch/blob/main/torch/_functorch/aot_autograd.py#L294), and try to regenerate the aliases outside of the compiled `autograd.Function` at runtime, to ensure that the autograd engine is aware of the aliasing. In this case, this will result in **50 AsStridedBackward nodes in the backward**, because we will fall back to using as_strided to generate each of those 50 outputs. The current PR as is (somewhat unsafely) ensures that the backward graph consists of a single `UnbindBackward`, or a call to `aten.cat()`. I left a long comment in the code describing the situation, but the core idea is that **autograd does not let you mutate grad_fn of tensor aliases that come from multi-output views**. So if we have `k` outputs that alias each other, but `k-1` of them are aliases that came from multi-output views, then in eager mode, it would not be possible to mutate one of the aliases in a way that would change the grad_fn of any of the other aliases, without causing an error in the backward. So the claim I'm making is that if we hide this aliasing from the autograd engine, then it is impossible for the user to perform any mutations that would cause autograd metadata to diverge between torch.compile and eager in a way that isn't an error in eager mode. To be fair, I think that taking the approach outlined in https://docs.google.com/document/d/1DlfFq8TKbuAn2zyJxLfoW-X1qkkm5PLdHFtySo03QAk/edit would also help us avoid the as_strided calls in this particularly egregious case, **and** keep the autograd error messages. This relies on both pre-dispatch functionalization being fully hardened **and** adding some pretty invasive changes to AOTAutograd though, and is probably at least several months out. Pull Request resolved: #111411 Approved by: https://github.com/ezyang