File size: 2,257 Bytes
3b99abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import torch
from torch.optim.lr_scheduler import _LRScheduler
class Esm2LRScheduler(torch.optim.lr_scheduler._LRScheduler):
"""
This is an implementation of ESM2's learning rate scheduler.
"""
def __init__(self,
optimizer,
last_epoch: int = -1,
verbose: bool = False,
init_lr: float = 0.,
max_lr: float = 4e-4,
final_lr: float = 4e-5,
warmup_steps: int = 2000,
start_decay_after_n_steps: int = 500000,
end_decay_after_n_steps: int = 5000000,
on_use: bool = True,
):
self.init_lr = init_lr
self.max_lr = max_lr
self.final_lr = final_lr
self.warmup_steps = warmup_steps
self.start_decay_after_n_steps = start_decay_after_n_steps
self.end_decay_after_n_steps = end_decay_after_n_steps
self.on_use = on_use
super(Esm2LRScheduler, self).__init__(optimizer, last_epoch, verbose)
def state_dict(self):
state_dict = {k: v for k, v in self.__dict__.items() if k not in ["optimizer"]}
return state_dict
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
def get_lr(self):
if not self._get_lr_called_within_step:
raise RuntimeError(
"To get the last learning rate computed by the scheduler, use "
"get_last_lr()"
)
step_no = self.last_epoch
if not self.on_use:
return [base_lr for base_lr in self.base_lrs]
if step_no <= self.warmup_steps:
lr = self.init_lr + step_no / self.warmup_steps * (self.max_lr - self.init_lr)
elif step_no <= self.start_decay_after_n_steps:
lr = self.max_lr
elif step_no <= self.end_decay_after_n_steps:
portion = (step_no - self.start_decay_after_n_steps) / (self.end_decay_after_n_steps - self.start_decay_after_n_steps)
lr = self.max_lr - portion * (self.max_lr - self.final_lr)
else:
lr = self.final_lr
return [lr for group in self.optimizer.param_groups] |