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97 lines
3.4 KiB
97 lines
3.4 KiB
# coding=utf-8
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# Implements several parameter-efficient supervised fine-tuning method.
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# This code is inspired by
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# https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
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from utils import (
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DynamicDataCollatorWithPadding,
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Seq2SeqPeftTrainer,
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ComputeMetrics,
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LogCallback,
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load_pretrained,
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prepare_args,
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prepare_data,
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preprocess_data,
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get_logits_processor,
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plot_loss
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)
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def main():
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# Prepare pretrained model and dataset
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model_args, data_args, training_args, finetuning_args = prepare_args(stage="sft")
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dataset = prepare_data(model_args, data_args)
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model, tokenizer = load_pretrained(model_args, finetuning_args, training_args.do_train, stage="sft")
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dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="sft")
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data_collator = DynamicDataCollatorWithPadding(tokenizer, data_args.ignore_pad_token_for_loss)
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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.num_beams if \
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data_args.num_beams is not None else training_args.generation_num_beams
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# Split the dataset
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if training_args.do_train:
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if data_args.dev_ratio > 1e-6:
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dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
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trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
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else:
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trainer_kwargs = {"train_dataset": dataset}
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else: # do_eval or do_predict
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trainer_kwargs = {"eval_dataset": dataset}
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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=[LogCallback()],
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compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
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**trainer_kwargs
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)
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# Keyword arguments for `model.generate`
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gen_kwargs = {
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"do_sample": True,
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"top_p": 0.7,
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"max_new_tokens": data_args.max_target_length + 1,
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"temperature": 0.95,
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"logits_processor": get_logits_processor()
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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()
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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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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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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, tokenizer)
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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