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import torch
from torch.optim.optimizer import Optimizer
import pytorch_lightning as pl
class BaseScheduler(object):
"""Base class for the step-wise scheduler logic.
Args:
optimizer (Optimize): Optimizer instance to apply lr schedule on.
Subclass this and overwrite ``_get_lr`` to write your own step-wise scheduler.
"""
def __init__(self, optimizer):
self.optimizer = optimizer
self.step_num = 0
def zero_grad(self):
self.optimizer.zero_grad()
def _get_lr(self):
raise NotImplementedError
def _set_lr(self, lr):
for param_group in self.optimizer.param_groups:
param_group["lr"] = lr
def step(self, metrics=None, epoch=None):
"""Update step-wise learning rate before optimizer.step."""
self.step_num += 1
lr = self._get_lr()
self._set_lr(lr)
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
def state_dict(self):
return {key: value for key, value in self.__dict__.items() if key != "optimizer"}
def as_tensor(self, start=0, stop=100_000):
"""Returns the scheduler values from start to stop."""
lr_list = []
for _ in range(start, stop):
self.step_num += 1
lr_list.append(self._get_lr())
self.step_num = 0
return torch.tensor(lr_list)
def plot(self, start=0, stop=100_000): # noqa
"""Plot the scheduler values from start to stop."""
import matplotlib.pyplot as plt
all_lr = self.as_tensor(start=start, stop=stop)
plt.plot(all_lr.numpy())
plt.show()
class DPTNetScheduler(BaseScheduler):
"""Dual Path Transformer Scheduler used in [1]
Args:
optimizer (Optimizer): Optimizer instance to apply lr schedule on.
steps_per_epoch (int): Number of steps per epoch.
d_model(int): The number of units in the layer output.
warmup_steps (int): The number of steps in the warmup stage of training.
noam_scale (float): Linear increase rate in first phase.
exp_max (float): Max learning rate in second phase.
exp_base (float): Exp learning rate base in second phase.
Schedule:
This scheduler increases the learning rate linearly for the first
``warmup_steps``, and then decay it by 0.98 for every two epochs.
References
[1]: Jingjing Chen et al. "Dual-Path Transformer Network: Direct Context-
Aware Modeling for End-to-End Monaural Speech Separation" Interspeech 2020.
"""
def __init__(
self,
optimizer,
steps_per_epoch,
d_model,
warmup_steps=4000,
noam_scale=1.0,
exp_max=0.0004,
exp_base=0.98,
):
super().__init__(optimizer)
self.noam_scale = noam_scale
self.d_model = d_model
self.warmup_steps = warmup_steps
self.exp_max = exp_max
self.exp_base = exp_base
self.steps_per_epoch = steps_per_epoch
self.epoch = 0
def _get_lr(self):
if self.step_num % self.steps_per_epoch == 0:
self.epoch += 1
if self.step_num > self.warmup_steps:
# exp decaying
lr = self.exp_max * (self.exp_base ** ((self.epoch - 1) // 2))
else:
# noam
lr = (
self.noam_scale
* self.d_model ** (-0.5)
* min(self.step_num ** (-0.5), self.step_num * self.warmup_steps ** (-1.5))
)
return lr
# Backward compat
_BaseScheduler = BaseScheduler |