94 lines
4.3 KiB
Python
94 lines
4.3 KiB
Python
# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
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import os
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from typing import TYPE_CHECKING, Optional, List
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from transformers import DataCollatorForSeq2Seq
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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 Seq2SeqPeftTrainer
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from transformers.trainer_utils import get_last_checkpoint
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from llmtuner.extras.logging import reset_logging, get_logger
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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logger = get_logger(__name__)
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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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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer,
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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.generation_max_length = training_args.generation_max_length if \
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training_args.generation_max_length is not None else data_args.max_target_length
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training_args.generation_num_beams = data_args.eval_num_beams if \
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data_args.eval_num_beams is not None else training_args.generation_num_beams
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# Initialize our Trainer
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trainer = Seq2SeqPeftTrainer(
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finetuning_args=finetuning_args,
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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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**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"] = list(set([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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checkpoint = None
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if training_args.resume_from_checkpoint is not None:
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checkpoint = training_args.resume_from_checkpoint
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elif last_checkpoint is not None:
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checkpoint = last_checkpoint
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train_result = trainer.train(resume_from_checkpoint=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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