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https://github.com/hiyouga/LLaMA-Factory.git
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98 lines
3.8 KiB
Python
98 lines
3.8 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/kto.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 KTODataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.ploting import plot_loss
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from ...hparams import ModelArguments
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from ...model import load_model, load_tokenizer
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from ..trainer_utils import create_modelcard_and_push, create_ref_model
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from .trainer import CustomKTOTrainer
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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
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def run_kto(
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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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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="kto", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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data_collator = KTODataCollatorWithPadding(
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template=template,
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pad_to_multiple_of=8,
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label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
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**tokenizer_module,
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)
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# Create reference model
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if finetuning_args.ref_model is None and (not training_args.do_train): # use the model itself
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ref_model = model
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else:
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ref_model = create_ref_model(model_args, finetuning_args)
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# Update arguments
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training_args.remove_unused_columns = False # important for multimodal and pairwise dataset
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# Initialize our Trainer
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trainer = CustomKTOTrainer(
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model=model,
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ref_model=ref_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,
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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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train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
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trainer.save_model()
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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", "train/rewards/chosen"])
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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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if id(model) == id(ref_model): # unable to compute rewards without a reference model
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remove_keys = [key for key in metrics.keys() if "rewards" in key]
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for key in remove_keys:
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metrics.pop(key)
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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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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