Comments (3)
Oh, thanks for pointing this out. You're absolutely correct. The AcceleratedOptimizer
object has the right wraps that disable zero_grad
on non-sync steps, and should be used for clearing gradients, as opposed to the wrapped model.
Looking at the history of our internal codebase, we were actually calling self.optimizer.zero_grad(set_to_none=True)
until a commit on May 7 2023 changed this to the model. Note we have trained similarly performing PPO models both before and after that commit, so I think it's safe to say that this bug hasn't affected the results in noticeable ways.
Note the quark trainer doesn't have this problem, since I'm not using the accelerator ctx manager there.
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Resolved in #58
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Thanks for fixing this! However, I find that removing self.policy.zero_grad(set_to_none=True)
immediately causes a cuda out-of-memory error. I'm running PPO training on 8 A100 GPUs. Have you encountered this issue?
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Related Issues (20)
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