pytorch

2f98fa91 - [SR] Do not manage tensors that escape scope via container (#74966)

[SR] Do not manage tensors that escape scope via container (#74966) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74966 It's clear that we don't want to manage tensors that escape their scope. Previously, we handled this by checking whether the tensor aliased the graph outputs. But there's actually another way to escape scope: by aliasing the wildcard set. The following graph demonstrates this: ``` def forward(self, cond: bool, a, b): lst = [] if cond: res = a + b # res should not be managed!!! lst.append(res) return lst ``` The `if cond:` sub-block returns nothing, but `res` escapes the scope through `lst`. The fix is simple: we simply have to mark values that alias the wildcard set as an `external_alias_` in `ValueGroup`. This diff also exposed another issue (via unit tests) in `checkOutputTensorMemoryLeaks`: it assumes that, if a node's `Value*` is managed, the underlying `IValue` must be a tensor. But this is not true after the addition of `to_maybe_copy_out`; TMCO does not produce a tensor in its first output slot if it does not copy. ghstack-source-id: 153288188 Test Plan: New unit tests cover the problematic case Reviewed By: navahgar Differential Revision: D35257087 fbshipit-source-id: 853a761dffe51f2c70720759664dd8dfcd56d1d7 (cherry picked from commit 2c7f519354041975f33626eab6b7f16c2494bbf8)