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| # Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py | |
| from typing import TYPE_CHECKING, Optional, List | |
| from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments | |
| from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset | |
| from llmtuner.extras.constants import IGNORE_INDEX | |
| from llmtuner.extras.misc import get_logits_processor | |
| from llmtuner.extras.ploting import plot_loss | |
| from llmtuner.tuner.core import load_model_and_tokenizer | |
| from llmtuner.tuner.sft.metric import ComputeMetrics | |
| from llmtuner.tuner.sft.trainer import CustomSeq2SeqTrainer | |
| if TYPE_CHECKING: | |
| from transformers import TrainerCallback | |
| from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments | |
| def run_sft( | |
| model_args: "ModelArguments", | |
| data_args: "DataArguments", | |
| training_args: "Seq2SeqTrainingArguments", | |
| finetuning_args: "FinetuningArguments", | |
| generating_args: "GeneratingArguments", | |
| 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="sft") | |
| dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft") | |
| if training_args.predict_with_generate: | |
| tokenizer.padding_side = "left" # use left-padding in generation | |
| data_collator = DataCollatorForSeq2Seq( | |
| tokenizer=tokenizer, | |
| label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id | |
| ) | |
| # Override the decoding parameters of Seq2SeqTrainer | |
| training_args_dict = training_args.to_dict() | |
| training_args_dict.update(dict( | |
| generation_max_length=training_args.generation_max_length or data_args.cutoff_len, | |
| generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams | |
| )) | |
| training_args = Seq2SeqTrainingArguments(**training_args_dict) | |
| # Initialize our Trainer | |
| trainer = CustomSeq2SeqTrainer( | |
| model=model, | |
| args=training_args, | |
| tokenizer=tokenizer, | |
| data_collator=data_collator, | |
| callbacks=callbacks, | |
| compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None, | |
| **split_dataset(dataset, data_args, training_args) | |
| ) | |
| # Keyword arguments for `model.generate` | |
| gen_kwargs = generating_args.to_dict() | |
| gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids | |
| gen_kwargs["pad_token_id"] = tokenizer.pad_token_id | |
| gen_kwargs["logits_processor"] = get_logits_processor() | |
| # Training | |
| if training_args.do_train: | |
| train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) | |
| 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", **gen_kwargs) | |
| if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled | |
| metrics.pop("eval_loss", None) | |
| 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", **gen_kwargs) | |
| if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled | |
| predict_results.metrics.pop("predict_loss", None) | |
| trainer.log_metrics("predict", predict_results.metrics) | |
| trainer.save_metrics("predict", predict_results.metrics) | |
| trainer.save_predictions(predict_results) | |