mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
# Inspired by: https://github.com/huggingface/transformers/blob/v4.34.1/examples/pytorch/summarization/run_summarization.py
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import os
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from typing import TYPE_CHECKING, List, Optional
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from ...data import get_dataset
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from ...extras.misc import get_logits_processor
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from ...extras.ploting import plot_loss
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from ...model import load_processor, load_model
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from ..utils import create_modelcard_and_push
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from .metric import ComputeMetrics
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from .trainer import CustomSeq2SeqTrainer
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from transformers import DataCollatorForSeq2Seq
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from ...extras.constants import IGNORE_INDEX
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from ...hparams import (
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DataArguments,
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FinetuningArguments,
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GeneratingArguments,
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ModelArguments,
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)
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def run_sft_mm(
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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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processor = load_processor(model_args)
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tokenizer = processor.tokenizer
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dataset = get_dataset(
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tokenizer, model_args, data_args, training_args, "sft", processor
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)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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if getattr(model, "is_quantized", False) and not training_args.do_train:
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setattr(
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model, "_hf_peft_config_loaded", True
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) # hack here: make model compatible with prediction
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train_dataset = dataset
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eval_dataset = dataset
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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer,
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pad_to_multiple_of=(
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8 if tokenizer.padding_side == "right" else None
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), # for shift short attention
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label_pad_token_id=(
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IGNORE_INDEX
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if data_args.ignore_pad_token_for_loss
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else tokenizer.pad_token_id
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),
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)
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# Override the decoding parameters of Seq2SeqTrainer
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training_args.generation_max_length = (
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training_args.generation_max_length or data_args.cutoff_len
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)
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training_args.generation_num_beams = (
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data_args.eval_num_beams or training_args.generation_num_beams
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)
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training_args.remove_unused_columns = False
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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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finetuning_args=finetuning_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=(
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ComputeMetrics(tokenizer) if training_args.predict_with_generate else None
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),
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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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"] = [
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tokenizer.eos_token_id
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] + 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(
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resume_from_checkpoint=training_args.resume_from_checkpoint
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)
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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"])
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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 (
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training_args.predict_with_generate
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): # 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(
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dataset, metric_key_prefix="predict", **gen_kwargs
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)
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if (
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training_args.predict_with_generate
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): # 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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# Create model card
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create_modelcard_and_push(
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trainer, model_args, data_args, training_args, finetuning_args
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)
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