wehub-resource-sync
35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from torch.optim import Optimizer
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from transformers.trainer import Trainer as HfTrainer
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from .base import OptimizerCallback
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class LorapOptimizerCallback(OptimizerCallback):
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def create_optimizer(self, model=None) -> Optimizer:
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args = self.args
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if model is None:
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model = self.trainer.model
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optimizer_grouped_parameters = None
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if hasattr(model, 'create_optimizer_param_groups'):
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# Lora+ parameter groups
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optimizer_grouped_parameters = model.create_optimizer_param_groups(
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lr=args.learning_rate, weight_decay=args.weight_decay)
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if optimizer_grouped_parameters is None:
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# Default parameter groups
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decay_parameters = HfTrainer.get_decay_parameter_names(None, model)
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optimizer_grouped_parameters = [
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{
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'params': [p for n, p in model.named_parameters() if (n in decay_parameters and p.requires_grad)],
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'weight_decay': args.weight_decay,
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},
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{
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'params':
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[p for n, p in model.named_parameters() if (n not in decay_parameters and p.requires_grad)],
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'weight_decay': 0.0,
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},
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]
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optimizer_cls, optimizer_kwargs = HfTrainer.get_optimizer_cls_and_kwargs(args)
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return optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
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