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@ -1009,9 +1009,10 @@ 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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if scaler._scale.item() < 1.0e-05:
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continue
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scaler.scale(loss).backward()
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if scaler._scale.item() >= 1.0e-05:
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scaler.scale(loss).backward()
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else:
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logging.warning(f"Grad scale is small: {cur_grad_scale}")
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if params.multi_optim and batch_idx % params.accum_grads == 0:
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set_batch_count(model, params.batch_idx_train)
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scheduler_enc.step_batch(params.batch_idx_train)
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