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78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# coding=utf-8
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# Implements parameter-efficient training of reward models.
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# This code is inspired by:
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# https://github.com/lvwerra/trl/blob/main/examples/summarization/scripts/reward_summarization.py
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# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
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from utils import (
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PairwiseDataCollatorWithPadding,
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PairwisePeftTrainer,
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LogCallback,
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load_pretrained,
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prepare_args,
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prepare_data,
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preprocess_data,
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compute_accuracy,
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plot_loss
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)
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def main():
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# Prepare pretrained model and dataset
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model_args, data_args, training_args, finetuning_args = prepare_args(stage="rm")
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dataset = prepare_data(model_args, data_args)
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model, tokenizer = load_pretrained(model_args, finetuning_args, training_args.do_train, stage="rm")
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dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="rm")
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data_collator = PairwiseDataCollatorWithPadding(tokenizer)
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Split the dataset
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if training_args.do_train:
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if data_args.dev_ratio > 1e-6:
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dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
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trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
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else:
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trainer_kwargs = {"train_dataset": dataset}
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else: # do_eval or do_predict
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trainer_kwargs = {"eval_dataset": dataset}
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# Initialize our Trainer
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trainer = PairwisePeftTrainer(
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finetuning_args=finetuning_args,
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model=model,
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args=training_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=[LogCallback()],
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compute_metrics=compute_accuracy,
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**trainer_kwargs
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)
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# Training
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if training_args.do_train:
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train_result = trainer.train()
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trainer.log_metrics("train", train_result.metrics)
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trainer.save_metrics("train", train_result.metrics)
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trainer.save_state()
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trainer.save_model()
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if trainer.is_world_process_zero() and model_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
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# Evaluation
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if training_args.do_eval:
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metrics = trainer.evaluate(metric_key_prefix="eval")
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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