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https://github.com/SakanaAI/doc-to-lora.git
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83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from enum import Enum
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from transformers import Seq2SeqTrainer, Trainer
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from transformers.trainer_utils import get_last_checkpoint
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TRAINING_TASK = Enum("TRAINING_TASK", ["CAUSAL_LM", "COMPLETION"])
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def train_model(
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model,
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train_dataset,
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eval_dataset,
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training_args,
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data_collator=None,
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compute_metrics=None,
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):
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# last_checkpoint = None
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# if (
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# os.path.isdir(training_args.output_dir)
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# and not training_args.overwrite_output_dir
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# ):
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# last_checkpoint = get_last_checkpoint(training_args.output_dir)
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# if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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# raise ValueError(
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# f"Output directory ({training_args.output_dir})"
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# " already exists and is not empty. "
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# "Use --overwrite_output_dir to overcome."
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# )
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# elif (
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# last_checkpoint is not None and training_args.resume_from_checkpoint is None
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# ):
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# print(
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# f"Checkpoint detected, resuming training at {last_checkpoint}. "
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# "To avoid this behavior, change "
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# "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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# )
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# if (
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# max(
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# training_args.per_device_train_batch_size,
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# training_args.per_device_eval_batch_size,
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# )
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# == 1
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# ):
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# data_collator = None
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# # print training_args at local_rank 0
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# local_rank = int(os.getenv("LOCAL_RANK", "0"))
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# if local_rank == 0:
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# print(training_args)
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# Seq2SeqTrainer is actually just the same as Trainer
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# (although it uses a different data collator, i.e., explicit prompt/answer separation)
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# it just allows `predict_with_generate`
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# allowing us to compute metrics on the generated outputs
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# no clue why they call this seq2seq...
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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data_collator=data_collator,
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compute_metrics=compute_metrics,
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)
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checkpoint = None
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# if training_args.resume_from_checkpoint is not None:
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# checkpoint = training_args.resume_from_checkpoint
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# elif last_checkpoint is not None:
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# checkpoint = last_checkpoint
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# print(f"Loaded from the checkpoint: {checkpoint}")
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# TODO: save the best model based on eval loss?
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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trainer.log_metrics("train", train_result.metrics)
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metrics = trainer.evaluate()
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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trainer.save_model()
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