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| # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/modules/lr_schedulers.py | |
| # reference: https://github.com/lifeiteng/vall-e | |
| import math | |
| import torch | |
| from matplotlib import pyplot as plt | |
| from torch import nn | |
| from torch.optim import Adam | |
| class WarmupCosineLRSchedule(torch.optim.lr_scheduler._LRScheduler): | |
| """ | |
| Implements Warmup learning rate schedule until 'warmup_steps', going from 'init_lr' to 'peak_lr' for multiple optimizers. | |
| """ | |
| def __init__( | |
| self, | |
| optimizer, | |
| init_lr, | |
| peak_lr, | |
| end_lr, | |
| warmup_steps=10000, | |
| total_steps=400000, | |
| current_step=0, | |
| ): | |
| self.init_lr = init_lr | |
| self.peak_lr = peak_lr | |
| self.end_lr = end_lr | |
| self.optimizer = optimizer | |
| self._warmup_rate = (peak_lr - init_lr) / warmup_steps | |
| self._decay_rate = (end_lr - peak_lr) / (total_steps - warmup_steps) | |
| self._current_step = current_step | |
| self.lr = init_lr | |
| self.warmup_steps = warmup_steps | |
| self.total_steps = total_steps | |
| self._last_lr = [self.lr] | |
| def set_lr(self, lr): | |
| self._last_lr = [g["lr"] for g in self.optimizer.param_groups] | |
| for g in self.optimizer.param_groups: | |
| # g['lr'] = lr | |
| g["lr"] = self.end_lr ###锁定用线性 | |
| def step(self): | |
| if self._current_step < self.warmup_steps: | |
| lr = self.init_lr + self._warmup_rate * self._current_step | |
| elif self._current_step > self.total_steps: | |
| lr = self.end_lr | |
| else: | |
| decay_ratio = (self._current_step - self.warmup_steps) / ( | |
| self.total_steps - self.warmup_steps | |
| ) | |
| if decay_ratio < 0.0 or decay_ratio > 1.0: | |
| raise RuntimeError( | |
| "Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings." | |
| ) | |
| coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) | |
| lr = self.end_lr + coeff * (self.peak_lr - self.end_lr) | |
| self.lr = lr = self.end_lr = 0.002 ###锁定用线性###不听话，直接锁定！ | |
| self.set_lr(lr) | |
| self.lr = lr | |
| self._current_step += 1 | |
| return self.lr | |
| if __name__ == "__main__": | |
| m = nn.Linear(10, 10) | |
| opt = Adam(m.parameters(), lr=1e-4) | |
| s = WarmupCosineLRSchedule( | |
| opt, 1e-6, 2e-4, 1e-6, warmup_steps=2000, total_steps=20000, current_step=0 | |
| ) | |
| lrs = [] | |
| for i in range(25000): | |
| s.step() | |
| lrs.append(s.lr) | |
| print(s.lr) | |
| plt.plot(lrs) | |
| plt.plot(range(0, 25000), lrs) | |
| plt.show() | |