133 lines
4.6 KiB
Python
133 lines
4.6 KiB
Python
import logging
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import os
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from pathlib import Path
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from transformers import AutoConfig, AutoTokenizer
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from transformers import (
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HfArgumentParser,
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set_seed,
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)
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from .arguments import ModelArguments, DataArguments, \
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RetrieverTrainingArguments as TrainingArguments
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from .data import TrainDatasetForReranker, RerankCollator
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from .modeling import BiEncoderModel
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from .trainer import BiTrainer
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from .load_model import get_model
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logger = logging.getLogger(__name__)
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def main():
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parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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model_args: ModelArguments
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data_args: DataArguments
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training_args: TrainingArguments
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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
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)
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
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)
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logger.warning(
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"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
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training_args.local_rank,
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training_args.device,
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training_args.n_gpu,
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bool(training_args.local_rank != -1),
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training_args.fp16,
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)
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logger.info("Training/evaluation parameters %s", training_args)
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logger.info("Model parameters %s", model_args)
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logger.info("Data parameters %s", data_args)
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# Set seed
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set_seed(training_args.seed)
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num_labels = 1
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=False,
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trust_remote_code=True,
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add_eos_token=True,
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)
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if tokenizer.pad_token_id is None:
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if tokenizer.unk_token_id is not None:
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tokenizer.pad_token_id = tokenizer.unk_token_id
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elif tokenizer.eod_id is not None:
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tokenizer.pad_token_id = tokenizer.eod_id
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tokenizer.bos_token_id = tokenizer.im_start_id
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tokenizer.eos_token_id = tokenizer.im_end_id
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if 'mistral' in model_args.model_name_or_path.lower():
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tokenizer.padding_side = 'left'
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base_model = get_model(model_args, training_args, tokenizer('Yes', add_special_tokens=False)['input_ids'][-1])
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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num_labels=num_labels,
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cache_dir=model_args.cache_dir,
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trust_remote_code=True,
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)
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logger.info('Config: %s', config)
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model = BiEncoderModel(model=base_model,
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tokenizer=tokenizer,
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train_batch_size=training_args.per_device_train_batch_size,
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start_layer=model_args.start_layer)
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# model = base_model
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if training_args.gradient_checkpointing:
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model.enable_input_require_grads()
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train_dataset = TrainDatasetForReranker(args=data_args, tokenizer=tokenizer)
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trainer = BiTrainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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data_collator=RerankCollator(
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tokenizer=tokenizer,
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query_max_len=data_args.query_max_len,
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passage_max_len=data_args.passage_max_len,
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pad_to_multiple_of=8,
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return_tensors="pt",
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padding=True
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),
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tokenizer=tokenizer,
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)
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trainer.use_lora = model_args.use_lora
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Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)
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# Training
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trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
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trainer.save_model()
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if not model_args.use_lora:
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checkpoint_dir = os.path.join(training_args.output_dir, "checkpoint-final")
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trainer.deepspeed.save_checkpoint(checkpoint_dir)
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# For convenience, we also re-save the tokenizer to the same directory,
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# so that you can share your model easily on huggingface.co/models =)
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if trainer.is_world_process_zero():
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tokenizer.save_pretrained(training_args.output_dir)
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model.model.config.save_pretrained(training_args.output_dir)
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if __name__ == "__main__":
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main() |