Source code for espnet2.schedulers.noam_lr

"""Noam learning rate scheduler module."""

import warnings
from typing import Union

import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import typechecked

from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler

[docs]class NoamLR(_LRScheduler, AbsBatchStepScheduler): """The LR scheduler proposed by Noam Ref: "Attention Is All You Need", FIXME(kamo): PyTorch doesn't provide _LRScheduler as public class, thus the behaviour isn't guaranteed at forward PyTorch version. NOTE(kamo): The "model_size" in original implementation is derived from the model, but in this implementation, this parameter is a constant value. You need to change it if the model is changed. """ @typechecked def __init__( self, optimizer: torch.optim.Optimizer, model_size: Union[int, float] = 320, warmup_steps: Union[int, float] = 25000, last_epoch: int = -1, ): self.model_size = model_size self.warmup_steps = warmup_steps lr = list(optimizer.param_groups)[0]["lr"] new_lr = self.lr_for_WarmupLR(lr) warnings.warn( f"NoamLR is deprecated. " f"Use WarmupLR(warmup_steps={warmup_steps}) with Optimizer(lr={new_lr})", ) # __init__() must be invoked before setting field # because step() is also invoked in __init__() super().__init__(optimizer, last_epoch)
[docs] def lr_for_WarmupLR(self, lr: float) -> float: return lr / self.model_size**0.5 / self.warmup_steps**0.5
def __repr__(self): return ( f"{self.__class__.__name__}(model_size={self.model_size}, " f"warmup_steps={self.warmup_steps})" )
[docs] def get_lr(self): step_num = self.last_epoch + 1 return [ lr * self.model_size**-0.5 * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) for lr in self.base_lrs ]