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# coding=utf-8
# Implements parameter-efficient training of a reward model based on ChatGLM.
# This code is inspired by:
# https://github.com/lvwerra/trl/blob/main/examples/summarization/scripts/reward_summarization.py
# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
from utils import (
prepare_args,
prepare_data,
load_pretrained,
preprocess_data,
PairwiseDataCollatorForChatGLM,
PairwiseTrainerForChatGLM,
plot_loss
)
def main():
# prepare pretrained model and dataset
model_args, data_args, training_args, finetuning_args = prepare_args()
dataset = prepare_data(model_args, data_args)
model, tokenizer = load_pretrained(model_args, training_args, finetuning_args, training_args.do_train, stage="rwd")
dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="rwd")
data_collator = PairwiseDataCollatorForChatGLM(
tokenizer=tokenizer,
inference_mode=(not training_args.do_train)
)
training_args.remove_unused_columns = False # Important for pairwise dataset
# Initialize our Trainer
trainer = PairwiseTrainerForChatGLM(
finetuning_args=finetuning_args,
model=model,
args=training_args,
train_dataset=dataset if training_args.do_train else None,
eval_dataset=dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator
)
# Training
if training_args.do_train:
train_result = trainer.train()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
trainer.save_model()
if trainer.is_world_process_zero() and finetuning_args.plot_loss:
plot_loss(training_args)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
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