train_colbert.py

import logging
import os
import sys
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer
from transformers import (
    HfArgumentParser,
    set_seed,
)
import ir_datasets
import torch

from tevatron.arguments import (
    ModelArguments,
    DataArguments,
    TevatronTrainingArguments as TrainingArguments,
)
from tevatron.data import TrainDataset, QPCollator
from tevatron.modeling import ColbertModel
from tevatron.trainer import TevatronTrainer as Trainer, GCTrainer
from tevatron.datasets import HFTrainDataset


from denserr.train.perturbed_train_dataset import (
    PerturbedTrainDataset,
)
from denserr.train.colbert_train_dataset import (
    ColBERTPerturbedHFTrainDataset,
)
from denserr.model.colbert_train import load_train_colbert

logger = logging.getLogger(__name__)


def main() -> None:
    parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        model_args, data_args, training_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1])
        )
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
        model_args: ModelArguments
        data_args: DataArguments
        training_args: TrainingArguments

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)
    logger.info("MODEL parameters %s", model_args)

    set_seed(training_args.seed)

    num_labels = 1

    # model = AnceForTrain.from_pretrained(model_args.model_name_or_path)
    model, _, query_tokenizer, doc_tokenizer = load_train_colbert()

    train_dataset = ColBERTPerturbedHFTrainDataset(
        query_tokenizer=query_tokenizer,
        doc_tokenizer=doc_tokenizer,
        data_args=data_args,
        cache_dir=data_args.data_cache_dir or model_args.cache_dir,
    )
    tokenizer = doc_tokenizer.tok

    if training_args.local_rank > 0:
        print("Waiting for main process to perform the mapping")
        torch.distributed.barrier()
    train_dataset = PerturbedTrainDataset(data_args, train_dataset.process(), tokenizer)
    if training_args.local_rank == 0:
        print("Loading results from main process")
        torch.distributed.barrier()

    trainer_cls = GCTrainer if training_args.grad_cache else Trainer
    trainer = trainer_cls(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        data_collator=QPCollator(
            tokenizer, max_p_len=data_args.p_max_len, max_q_len=data_args.q_max_len
        ),
    )
    train_dataset.trainer = trainer

    trainer.train()
    trainer.save_model()
    if trainer.is_world_process_zero():
        tokenizer.save_pretrained(training_args.output_dir)


if __name__ == "__main__":
    main()