Fix deadlock in some edge case in autograd (#73961)
Summary:
Minimal example that deadlocks before but not after:
```python
import torch
from torch.autograd import Function
class Foo(Function):
staticmethod
def forward(ctx, x):
return x.clone()
staticmethod
def forward(ctx, gO):
return gO.clone()
def get_out():
inp = torch.rand(2, requires_grad=True)
# The python function is first so that it runs
# last in the backward pass
right = Foo.apply(inp)
# An op that creates new memory
left1 = inp.clone()
# An op that saves its input
left2 = left1 ** 2
# Inplace modify so that the backward for
# left2 always raises an error
left1 += 1
# An op that takes both side as input.
# After running, both side's last op will be in
# the ready queue
# And the op for left will run first as it was
# executed last during the forward
out = left2 + right
return out
# Nothing should be global variables here as, from what
# I can see, python leaks all the global objects
get_out().sum().backward()
```
Since this requires the python interpreter to die, it is hard to test in CI.
Let me know if you have an idea how to do it though.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73961
Reviewed By: malfet
Differential Revision: D34752747
Pulled By: albanD
fbshipit-source-id: 1a537b1f733e161e8d3ff053cd432b37b34d432a
(cherry picked from commit 17943e4c04c782d81deab439e010195f04e75bbd)
