109 lines
5.0 KiB
Python
109 lines
5.0 KiB
Python
# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py
|
|
import os
|
|
|
|
from typing import TYPE_CHECKING, Optional, List
|
|
from transformers import DataCollatorForSeq2Seq
|
|
|
|
from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
|
|
from llmtuner.extras.constants import IGNORE_INDEX
|
|
from llmtuner.extras.misc import get_logits_processor
|
|
from llmtuner.extras.ploting import plot_loss
|
|
from llmtuner.tuner.core import load_model_and_tokenizer
|
|
from llmtuner.tuner.sft.metric import ComputeMetrics
|
|
from llmtuner.tuner.sft.trainer import Seq2SeqPeftTrainer
|
|
from transformers.trainer_utils import get_last_checkpoint
|
|
from llmtuner.extras.logging import reset_logging, get_logger
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import Seq2SeqTrainingArguments, TrainerCallback
|
|
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
def run_sft(
|
|
model_args: "ModelArguments",
|
|
data_args: "DataArguments",
|
|
training_args: "Seq2SeqTrainingArguments",
|
|
finetuning_args: "FinetuningArguments",
|
|
callbacks: Optional[List["TrainerCallback"]] = None
|
|
):
|
|
dataset = get_dataset(model_args, data_args)
|
|
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
|
|
dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft")
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer=tokenizer,
|
|
label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
|
|
)
|
|
|
|
# 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
|
|
|
|
# Initialize our Trainer
|
|
trainer = Seq2SeqPeftTrainer(
|
|
finetuning_args=finetuning_args,
|
|
model=model,
|
|
args=training_args,
|
|
tokenizer=tokenizer,
|
|
data_collator=data_collator,
|
|
callbacks=callbacks,
|
|
compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
|
|
**split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
|
|
)
|
|
|
|
# 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()
|
|
}
|
|
# Detecting last checkpoint.
|
|
last_checkpoint = None
|
|
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
|
raise ValueError(
|
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
|
"Use --overwrite_output_dir to overcome."
|
|
)
|
|
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
|
logger.info(
|
|
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
|
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
|
)
|
|
# Training
|
|
if training_args.do_train:
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
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)
|
|
if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled
|
|
metrics.pop("eval_loss", None)
|
|
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)
|
|
if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled
|
|
predict_results.metrics.pop("predict_loss", None)
|
|
trainer.log_metrics("predict", predict_results.metrics)
|
|
trainer.save_metrics("predict", predict_results.metrics)
|
|
trainer.save_predictions(predict_results)
|