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from typing import List
import torch
import transformers
from torch.optim import AdamW
class AdamWWithWarmupOptimizer:
def __init__(
self,
lr: float,
warmup_steps: int,
total_steps: int,
weight_decay: float,
no_decay_params: List[str],
):
self.lr = lr
self.warmup_steps = warmup_steps
self.total_steps = total_steps
self.weight_decay = weight_decay
self.no_decay_params = no_decay_params
def group_params(self, module: torch.nn.Module) -> list:
if self.no_decay_params is not None:
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in module.named_parameters()
if not any(nd in n for nd in self.no_decay_params)
],
"weight_decay": self.weight_decay,
},
{
"params": [
p
for n, p in module.named_parameters()
if any(nd in n for nd in self.no_decay_params)
],
"weight_decay": 0.0,
},
]
else:
optimizer_grouped_parameters = [
{"params": module.parameters(), "weight_decay": self.weight_decay}
]
return optimizer_grouped_parameters
def __call__(self, module: torch.nn.Module):
optimizer_grouped_parameters = self.group_params(module)
optimizer = AdamW(
optimizer_grouped_parameters, lr=self.lr, weight_decay=self.weight_decay
)
scheduler = transformers.get_linear_schedule_with_warmup(
optimizer, self.warmup_steps, self.total_steps
)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"interval": "step",
"frequency": 1,
},
}
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