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Running
on
Zero
| 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 |