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