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# coding=utf-8
# Implements several parameter-efficient supervised fine-tuning method.
# This code is inspired by
# https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
from utils import (
DynamicDataCollatorWithPadding,
Seq2SeqPeftTrainer,
ComputeMetrics,
LogCallback,
load_pretrained,
prepare_args,
prepare_data,
preprocess_data,
get_logits_processor,
plot_loss
)
def main():
# Prepare pretrained model and dataset
model_args, data_args, training_args, finetuning_args = prepare_args(stage="sft")
dataset = prepare_data(model_args, data_args)
model, tokenizer = load_pretrained(model_args, finetuning_args, training_args.do_train, stage="sft")
dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="sft")
data_collator = DynamicDataCollatorWithPadding(tokenizer, data_args.ignore_pad_token_for_loss)
# Override the decoding parameters of Seq2SeqTrainer
training_args.generation_max_length = training_args.generation_max_length if \
training_args.generation_max_length is not None else data_args.max_target_length
training_args.generation_num_beams = data_args.eval_num_beams if \
data_args.eval_num_beams is not None else training_args.generation_num_beams
# Split the dataset
if training_args.do_train:
if data_args.dev_ratio > 1e-6:
dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
else:
trainer_kwargs = {"train_dataset": dataset}
else: # do_eval or do_predict
trainer_kwargs = {"eval_dataset": dataset}
# Initialize our Trainer
trainer = Seq2SeqPeftTrainer(
finetuning_args=finetuning_args,
model=model,
args=training_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[LogCallback()],
compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
**trainer_kwargs
)
# Keyword arguments for `model.generate`
gen_kwargs = {
"do_sample": True,
"top_p": 0.7,
"max_new_tokens": data_args.max_target_length + 1,
"temperature": 0.95,
"logits_processor": get_logits_processor()
}
# Training
if training_args.do_train:
train_result = trainer.train()
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)
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)
trainer.log_metrics("predict", predict_results.metrics)
trainer.save_metrics("predict", predict_results.metrics)
trainer.save_predictions(predict_results, tokenizer)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
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