mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
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from typing import TYPE_CHECKING, Optional, List
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from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
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from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
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from llmtuner.extras.constants import IGNORE_INDEX
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from llmtuner.extras.misc import get_logits_processor
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from llmtuner.extras.ploting import plot_loss
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from llmtuner.tuner.core import load_model_and_tokenizer
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from llmtuner.tuner.sft.metric import ComputeMetrics
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from llmtuner.tuner.sft.trainer import CustomSeq2SeqTrainer
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if TYPE_CHECKING:
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from transformers import TrainerCallback
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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def run_sft(
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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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dataset = get_dataset(model_args, data_args)
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
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dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft")
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if training_args.predict_with_generate:
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tokenizer.padding_side = "left" # use left-padding in generation
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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer,
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pad_to_multiple_of=4, # for shift short attention
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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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)
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# Override the decoding parameters of Seq2SeqTrainer
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training_args_dict = training_args.to_dict()
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training_args_dict.update(dict(
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generation_max_length=training_args.generation_max_length or data_args.cutoff_len,
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generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams
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))
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training_args = Seq2SeqTrainingArguments(**training_args_dict)
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# Initialize our Trainer
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trainer = CustomSeq2SeqTrainer(
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model=model,
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args=training_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
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neftune_noise_alpha=finetuning_args.neftune_noise_alpha,
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**split_dataset(dataset, data_args, training_args)
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)
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# Keyword arguments for `model.generate`
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gen_kwargs = generating_args.to_dict()
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gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
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gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
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gen_kwargs["logits_processor"] = get_logits_processor()
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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.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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trainer.save_model()
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if trainer.is_world_process_zero() and model_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
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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", **gen_kwargs)
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if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled
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metrics.pop("eval_loss", None)
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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", **gen_kwargs)
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if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled
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predict_results.metrics.pop("predict_loss", None)
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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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