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@ -838,7 +838,7 @@ def train_one_epoch(
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# NOTE: We use reduction==sum and loss is computed over utterances
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# in the batch and there is no normalization to it so far.
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scaler.scale(loss).backward()
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scheduler.step_batch(params.batch_idx_train)
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#scheduler.step_batch(params.batch_idx_train)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad()
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@ -1121,7 +1121,7 @@ def run(rank, world_size, args):
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scaler.load_state_dict(checkpoints["grad_scaler"])
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for epoch in range(params.start_epoch, params.num_epochs + 1):
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scheduler.step_epoch(epoch - 1)
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#scheduler.step_epoch(epoch - 1)
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fix_random_seed(params.seed + epoch - 1)
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train_dl.sampler.set_epoch(epoch - 1)
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