[JIT] clear GraphFunction.optimized_graphs_ after freezing a module (#68316)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68316
Consider the following:
```
class Mod(nn.Module):
def __init__(self, val):
super().__init__()
self.param = nn.Parameter(val)
def forward(self, x):
# this method will change during freezing
return x + self.param
torch.jit.export
def make_prediction(self, x):
y = x + x
return self.forward(y)
param = torch.rand([2, 2])
unscripted_mod = Mod(param)
mod = torch.jit.script(unscripted_mod)
mod.eval()
mod = torch.jit.freeze(mod, preserved_attrs=["make_prediction"])`
```
During freezing the following will occur:
1. do some pre-freezing, including inlining; in particular, forward will be inlined into make_prediction. During inlining, forward.optimized_graph() is called, and the result is cached
2. freeze some methods. While freezing forward, the graph associated with the function will get updated. The cached optimized_graphs_ are not updated.
Previously, a call to `mod.forward(x)` would return an exectutor that would run on the old cached optimized_graph(). This would mean that the freezing optimizations would not apply, and potentially that the execution would fail because of parameters removed from the module.
This change clears the optimized_graphs_ cache after running freezing to prevent executing an old version of the graph.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D32410862
Pulled By: davidberard98
fbshipit-source-id: dd8bfe86ec2898b7c72813ab32c08f25c38e4cea
