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import torch
from torch.optim.lr_scheduler import LRScheduler
class LinearSchedulerWithWarmup(LRScheduler):
def __init__(
self,
optimizer: torch.optim.Optimizer,
num_warmup_steps: int,
num_training_steps: int,
last_epoch: int = -1,
verbose: bool = False,
**kwargs,
):
self.num_warmup_steps = num_warmup_steps
self.num_training_steps = num_training_steps
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
def scheduler_fn(current_step):
if current_step < self.num_warmup_steps:
return current_step / max(1, self.num_warmup_steps)
return max(
0.0,
float(self.num_training_steps - current_step)
/ float(max(1, self.num_training_steps - self.num_warmup_steps)),
)
return [base_lr * scheduler_fn(self.last_epoch) for base_lr in self.base_lrs]
class LinearScheduler(LRScheduler):
def __init__(
self,
optimizer: torch.optim.Optimizer,
num_training_steps: int,
last_epoch: int = -1,
verbose: bool = False,
**kwargs,
):
self.num_training_steps = num_training_steps
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
def scheduler_fn(current_step):
# if current_step < self.num_warmup_steps:
# return current_step / max(1, self.num_warmup_steps)
return max(
0.0,
float(self.num_training_steps - current_step)
/ float(max(1, self.num_training_steps)),
)
return [base_lr * scheduler_fn(self.last_epoch) for base_lr in self.base_lrs]
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