113 lines
4.7 KiB
Python
113 lines
4.7 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# This code is inspired by CarperAI's trlx library.
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# https://github.com/CarperAI/trlx/blob/v0.7.0/examples/summarize_rlhf/reward_model/train_reward_model_gptj.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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#
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# MIT License
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#
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# Copyright (c) 2022 CarperAI
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from typing import TYPE_CHECKING, List, Optional
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from ...data import PairwiseDataCollatorWithPadding, get_dataset, split_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 ..trainer_utils import create_modelcard_and_push
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from .metric import compute_accuracy
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from .trainer import PairwiseTrainer
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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, ModelArguments
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def run_rm(
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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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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="rm", **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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data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
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# Update arguments
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Initialize our Trainer
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trainer = PairwiseTrainer(
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model=model,
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args=training_args,
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finetuning_args=finetuning_args,
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data_collator=data_collator,
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callbacks=callbacks + [FixValueHeadModelCallback()],
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compute_metrics=compute_accuracy,
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**tokenizer_module,
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**split_dataset(dataset, data_args, training_args),
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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(resume_from_checkpoint=training_args.resume_from_checkpoint)
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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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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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if trainer.is_world_process_zero() and finetuning_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "eval_accuracy"])
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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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# Predict
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if training_args.do_predict:
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predict_results = trainer.predict(dataset, metric_key_prefix="predict")
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trainer.log_metrics("predict", predict_results.metrics)
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trainer.save_metrics("predict", predict_results.metrics)
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trainer.save_predictions(predict_results)
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# Create model card
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create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)
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