mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py
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from typing import TYPE_CHECKING, List, Optional
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from transformers import DataCollatorWithPadding
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from ...data import get_dataset
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from ...extras.callbacks import FixValueHeadModelCallback
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from ...extras.misc import fix_valuehead_checkpoint
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..utils import create_ref_model, create_reward_model
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from .trainer import CustomPPOTrainer
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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def run_ppo(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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generating_args: "GeneratingArguments",
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callbacks: Optional[List["TrainerCallback"]] = None,
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
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tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
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# Create reference model and reward model
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ref_model = create_ref_model(model_args, finetuning_args, add_valuehead=True)
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reward_model = create_reward_model(model, model_args, finetuning_args)
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# Initialize our Trainer
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ppo_trainer = CustomPPOTrainer(
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model_args=model_args,
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training_args=training_args,
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finetuning_args=finetuning_args,
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generating_args=generating_args,
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callbacks=callbacks + [FixValueHeadModelCallback()],
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model=model,
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reward_model=reward_model,
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ref_model=ref_model,
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tokenizer=tokenizer,
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dataset=dataset,
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data_collator=data_collator,
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)
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# Training
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if training_args.do_train:
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ppo_trainer.ppo_train(resume_from_checkpoint=training_args.resume_from_checkpoint)
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ppo_trainer.save_model()
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if training_args.should_save:
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fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
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ppo_trainer.save_state() # must be called after save_model to have a folder
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if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "reward"])
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