ColossalAI/applications/Colossal-LLaMA/prepare_pretrain_dataset.py

143 lines
5.8 KiB
Python

#!/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_llama.dataset.spliced_and_tokenized_dataset import (
ClosedToConstantLengthSplicedDataset,
supervised_tokenize_pretrain,
)
from datasets import dataset_dict, load_dataset
from transformers import AutoTokenizer
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_output_dirs", type=str, default="data_output_dirs", help="Data output directory")
parser.add_argument("--max_length", type=int, default=8192, 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")
args.data_cache_dir = os.path.join(args.data_output_dirs, "cache")
args.data_jsonl_output_dir = os.path.join(args.data_output_dirs, "jsonl")
args.data_arrow_output_dir = os.path.join(args.data_output_dirs, "arrow")
if not os.path.exists(args.data_cache_dir):
os.makedirs(args.data_cache_dir)
if not os.path.exists(args.data_jsonl_output_dir):
os.makedirs(args.data_jsonl_output_dir)
if not os.path.exists(args.data_arrow_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 = AutoTokenizer.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()