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Implement 2p version of learning rate schedule.
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@ -791,7 +791,7 @@ def run(rank, world_size, args):
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scheduler = torch.optim.lr_scheduler.LambdaLR(
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optimizer,
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lambda step: (((step**2 + params.lr_steps**2) / params.lr_steps**2) ** -0.25 *
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(((epoch**2 + params.lr_epochs**2) / params.lr_epochs**2) ** -0.25))
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(((epoch**2 + params.lr_epochs**2) / params.lr_epochs**2) ** -0.25)))
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if checkpoints and "optimizer" in checkpoints:
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