| |
| import numpy as np |
| from transformers import TrainerControl, TrainerState |
| from typing import TYPE_CHECKING |
|
|
| from swift.utils import get_logger |
| from .base import TrainerCallback |
|
|
| if TYPE_CHECKING: |
| from swift.trainers import Trainer, TrainingArguments |
|
|
| logger = get_logger() |
|
|
|
|
| class EarlyStopCallback(TrainerCallback): |
| """An early stop implementation""" |
|
|
| def __init__(self, args: 'TrainingArguments', trainer: 'Trainer'): |
| super().__init__(args, trainer) |
| self.best_metric = None |
| self.interval = 0 |
| self.total_interval = args.early_stop_interval |
|
|
| def on_save(self, args: 'TrainingArguments', state: TrainerState, control: TrainerControl, **kwargs): |
| operator = np.greater if args.greater_is_better else np.less |
| if self.best_metric is None or operator(state.best_metric, self.best_metric): |
| self.best_metric = state.best_metric |
| self.interval = 0 |
| else: |
| self.interval += 1 |
|
|
| if self.interval >= self.total_interval: |
| logger.info(f'Training stop because of eval metric is stable at step {state.global_step}') |
| control.should_training_stop = True |
|
|
