31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
from transformers.trainer import *
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class PreTrainer(Trainer):
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def _save(self, output_dir: Optional[str] = None, state_dict=None):
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output_dir = output_dir if output_dir is not None else self.args.output_dir
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os.makedirs(output_dir, exist_ok=True)
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logger.info("Saving model checkpoint to %s", output_dir)
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# Save a trained model and configuration using `save_pretrained()`.
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# They can then be reloaded using `from_pretrained()`
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if not hasattr(self.model, 'save'):
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raise NotImplementedError(
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f'MODEL {self.model.__class__.__name__} '
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f'does not support save interface')
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else:
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self.model.save(output_dir)
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# if self.tokenizer is not None and self.is_world_process_zero():
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# self.tokenizer.save_pretrained(output_dir)
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torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
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def compute_loss(self, model, inputs, return_outputs=False):
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"""
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How the loss is computed by Trainer. By default, all models return the loss in the first element.
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Subclass and override for custom behavior.
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"""
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outputs = model(**inputs)
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loss = outputs.loss
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return (loss, outputs) if return_outputs else loss |