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# Inspired by:
# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
from typing import TYPE_CHECKING, Optional, List
from transformers import Seq2SeqTrainingArguments
from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
from llmtuner.extras.callbacks import SavePeftModelCallback
from llmtuner.extras.ploting import plot_loss
from llmtuner.tuner.core import load_model_and_tokenizer
from llmtuner.tuner.rm.metric import compute_accuracy
from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding
from llmtuner.tuner.rm.trainer import PairwiseTrainer
if TYPE_CHECKING:
from transformers import TrainerCallback
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
def run_rm(
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None
):
dataset = get_dataset(model_args, data_args)
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
data_collator = PairwiseDataCollatorWithPadding(tokenizer)
training_args_dict = training_args.to_dict()
training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset
training_args = Seq2SeqTrainingArguments(**training_args_dict)
# Initialize our Trainer
trainer = PairwiseTrainer(
model=model,
args=training_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=callbacks + [SavePeftModelCallback()],
compute_metrics=compute_accuracy,
**split_dataset(dataset, data_args, training_args)
)
# 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 model_args.plot_loss:
plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate(metric_key_prefix="eval")
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Predict
if training_args.do_predict:
predict_results = trainer.predict(dataset, metric_key_prefix="predict")
trainer.log_metrics("predict", predict_results.metrics)
trainer.save_metrics("predict", predict_results.metrics)
trainer.save_predictions(predict_results)
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