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https://github.com/hiyouga/LLaMA-Factory.git
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80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's TRL library.
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# https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/ppo.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING, List, Optional
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from ...data import MultiModalDataCollatorForSeq2Seq, get_dataset, get_template_and_fix_tokenizer
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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 ..callbacks import fix_valuehead_checkpoint
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from ..trainer_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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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, 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 = MultiModalDataCollatorForSeq2Seq(template=template, **tokenizer_module)
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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" = 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,
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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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data_collator=data_collator,
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**dataset_module,
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**tokenizer_module,
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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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