ColossalAI/applications/Colossal-LLaMA-2/prepare_pretrain_dataset.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Prepare dataset for continual pre-training
"""

import argparse
import json
import math
import os
import time
from multiprocessing import cpu_count

from colossal_llama2.dataset.spliced_and_tokenized_dataset import (
    ClosedToConstantLengthSplicedDataset,
    supervised_tokenize_pretrain,
)
from datasets import dataset_dict, load_dataset
from transformers.models.llama.tokenization_llama import LlamaTokenizer

from colossalai.logging import get_dist_logger

logger = get_dist_logger()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--data_input_dirs",
        type=str,
        required=True,
        default=None,
        help="Comma(i.e., ',') separated list of all data directories containing `.jsonl` data files.",
    )
    parser.add_argument(
        "--tokenizer_dir", type=str, required=True, default=None, help="A directory containing the tokenizer"
    )
    parser.add_argument("--data_cache_dir", type=str, default="cache", help="Data cache directory")
    parser.add_argument(
        "--data_jsonl_output_dir",
        type=str,
        default="jsonl_output",
        help="Output directory of spliced dataset with jsonl format",
    )
    parser.add_argument(
        "--data_arrow_output_dir",
        type=str,
        default="arrow_output",
        help="Output directory of spliced dataset with arrow format",
    )
    parser.add_argument("--max_length", type=int, default=4096, help="Max length of each spliced tokenized sequence")
    parser.add_argument("--num_spliced_dataset_bins", type=int, default=10, help="Number of spliced dataset bins")
    args = parser.parse_args()

    if args.num_spliced_dataset_bins >= 100000:
        raise ValueError("Too many spliced divisions, must be smaller than 100000")

    assert not os.path.exists(args.data_cache_dir), f"Find existed data cache dir {args.data_cache_dir}"
    assert not os.path.exists(
        args.data_jsonl_output_dir
    ), f"Find existed jsonl data output dir {args.data_jsonl_output_dir}"
    assert not os.path.exists(
        args.data_arrow_output_dir
    ), f"Find existed arrow data output dir {args.data_arrow_output_dir}"
    os.makedirs(args.data_jsonl_output_dir)
    os.makedirs(args.data_arrow_output_dir)

    # Prepare to all input datasets
    input_data_paths = []
    input_data_dirs = args.data_input_dirs.split(",")
    for ds_dir in input_data_dirs:
        ds_dir = os.path.abspath(ds_dir)
        assert os.path.exists(ds_dir), f"Not find data dir {ds_dir}"
        ds_files = [name for name in os.listdir(ds_dir) if name.endswith(".jsonl")]
        ds_paths = [os.path.join(ds_dir, name) for name in ds_files]
        input_data_paths.extend(ds_paths)

    # Prepare to data splitting.
    train_splits = []
    split_interval = math.ceil(100 / args.num_spliced_dataset_bins)
    for i in range(0, 100, split_interval):
        start = i
        end = i + split_interval
        if end > 100:
            end = 100
        train_splits.append(f"train[{start}%:{end}%]")

    # Prepare to the tokenizer.
    tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_dir)
    tokenizer.add_bos_token = False
    tokenizer.add_eos_token = False
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.unk_token

    list_dataset = load_dataset(
        path="json",
        data_files=input_data_paths,
        cache_dir=os.path.join(args.data_cache_dir, "raw"),
        keep_in_memory=False,
        split=train_splits,
        num_proc=cpu_count(),
    )
    for index, dataset in enumerate(list_dataset):
        assert isinstance(dataset, dataset_dict.Dataset)
        logger.info(f"Start to process part-{index}/{len(list_dataset)} of all original datasets.")
        dataset = dataset.map(
            function=supervised_tokenize_pretrain,
            fn_kwargs={"tokenizer": tokenizer, "max_length": args.max_length},
            keep_in_memory=False,
            num_proc=min(len(dataset), cpu_count()),
        )
        dataset = dataset.remove_columns(column_names=["source", "target", "category"])
        dataset = dataset.sort(column_names=("seq_category", "seq_length"), reverse=False, keep_in_memory=False)
        dataset = dataset.remove_columns(column_names=["seq_category", "seq_length"])
        spliced_dataset = ClosedToConstantLengthSplicedDataset(
            dataset=dataset, tokenizer=tokenizer, max_length=args.max_length, error_strict=False
        )
        # Save each jsonl spliced dataset.
        output_index = "0" * (5 - len(str(index))) + str(index)
        output_name = f"part-{output_index}"
        output_jsonl_path = os.path.join(args.data_jsonl_output_dir, output_name + ".jsonl")
        st = time.time()
        with open(file=output_jsonl_path, mode="w", encoding="utf-8") as fp_writer:
            spliced_count = 0
            for spliced_data_point in spliced_dataset:
                if spliced_count % 500 == 0:
                    logger.info(f"processing {spliced_count} spliced data points for {fp_writer.name}")
                spliced_count += 1
                fp_writer.write(json.dumps(spliced_data_point, ensure_ascii=False) + "\n")
        logger.info(
            f"Current file {fp_writer.name}; "
            f"Data size: {len(spliced_dataset)}; "
            f"Spliced data size: {spliced_dataset.current_size}; "
            f"Splicing compression rate: {round(spliced_dataset.current_size / len(spliced_dataset), 6)}; "
            f"Time cost: {round((time.time() - st) / 60, 6)} minutes."
        )

        # Save each arrow spliced dataset
        output_arrow_path = os.path.join(args.data_arrow_output_dir, output_name)
        logger.info(f"Start to save {output_arrow_path}")
        spliced_dataset = load_dataset(
            path="json",
            data_files=[output_jsonl_path],
            cache_dir=os.path.join(args.data_cache_dir, "spliced_and_tokenized"),
            keep_in_memory=False,
            num_proc=cpu_count(),
            split="train",
        )
        spliced_dataset.save_to_disk(dataset_path=output_arrow_path, num_proc=min(len(spliced_dataset), cpu_count()))


if __name__ == "__main__":
    main()