Update requires grad property (#43634)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43634
Because differentiable graphs detach the gradients of input Tensors, creating and inlining differentiable graphs changes the requires_grad property of tensors in the graph. In the legacy executor, this was not a problem as the Fuser would simply ignore the gradient property because it would be invariant that the LegacyExecutor only passed tensors with grad = False. This is not the case with the profiler, as the Fuser does it's own guarding.
Updating the type also helps with other typechecks, e.g. the ones specializing the backward, and with debugging the graph.
Other possibilities considered were:
- Fuser/Specialize AutogradZero always guards against requires_grad=False regardless of the profiled type
- Re-profile forward execution of differentiable graph
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D23358803
Pulled By: eellison
fbshipit-source-id: b106998accd5d0f718527bc00177de9af5bad5fc
