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@ -1086,6 +1086,12 @@ def train_one_epoch(
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f"grad_scale is too small, exiting: {cur_grad_scale}"
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)
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if params.batch_idx_train > 4000 and loss > 300:
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wb.log({"valid/loss": 10000})
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raise RunteimError(
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f"divergence... exiting: loss={loss}"
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)
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if batch_idx % (params.log_interval*params.accum_grads) == 0:
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if params.multi_optim:
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cur_enc_lr = scheduler_enc.get_last_lr()[0]
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