towhee.trainer.optimization.optimization.get_polynomial_decay_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, lr_end=1e-07, power=1.0, last_epoch=-1)[source]

Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.

  • optimizer (Optimizer) – The optimizer for which to schedule the learning rate.

  • num_warmup_steps (int) – The number of steps for the warmup phase.

  • num_training_steps (int) – The total number of training steps.

  • lr_end (float, optional, defaults to 1e-7) – The end LR.

  • power (float, optional, defaults to 1.0) – Power factor.

  • last_epoch (int, optional, defaults to -1) – The index of the last epoch when resuming training.

Note: power defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at


torch.optim.lr_scheduler.LambdaLR with the appropriate schedule.