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Any idea when this will be merged? I try to implement the prime_optimizer function but looks like require some of the changes on the torch_xla/csrc/init_python_bindings.cpp side. |
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Hi @dasoto, I've found that this approach will not guarantee the same sharding in the optimizer compared to running a full training step (this is due to sharding propagation decisions in the compiler). I believe the adagrad unit test was broken after an openxla pin update, for example. Since this is an experimental feature, I would be OK to merge after a rebase. cc @JackCaoG @alanwaketan |
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See also: #6546
The optimizer state must be primed before it can be restored. Optimizer state isn't materialized until the first
optim.stepcall, so to restore optimizer state before resuming training, a dummy step is needed.This PR introduces the
prime_optimizerAPI, which will run a dummy optimizer step with zeroed gradients. The gradient sharding is copied from the parameters to ensure the resulting sharding is the same.