docker/intel_code/llama13b/Megatron-DeepSpeed/tools/openwebtext/cleanup_dataset.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.


import ftfy
import json
from langdetect import detect
import numpy as np
import time
import os
import sys

from tokenizer import Tokenizer

MIN_DOCUMENT_LENGHT = 128


def print_progress(prefix, start_time, num_docs, num_fixed_text,
                   num_non_english_docs, chars_non_english_docs,
                   num_small_docs, chars_small_docs):

    string = prefix + ' | '
    string += 'elapsed time: {:.2f} | '.format(time.time() - start_time)
    string += 'documents: {} | '.format(num_docs)
    string += 'fixed text: {} | '.format(num_fixed_text)
    string += 'non-english: {} | '.format(num_non_english_docs)
    string += 'non-english chars: {} | '.format(chars_non_english_docs)
    string += 'small docs: {} | '.format(num_small_docs)
    string += 'small docs chars: {}'.format(chars_small_docs)
    print(string, flush=True)


def filter_corpus(filename, out_filename, print_interval=10000):

    print(' > filtering {}'.format(filename))

    tokenizer = Tokenizer(cache_dir='./cache')

    num_docs = 0
    num_written_docs = 0
    num_small_docs = 0
    num_fixed_text = 0
    num_non_english_docs = 0
    chars_non_english_docs = 0
    chars_small_docs = 0
    start_time = time.time()
    with open(out_filename, 'wb') as f:
        with open(filename, 'r') as fin:
            for line in fin:
                try:
                    num_docs += 1
                    myjson = json.loads(line)
                    # Fix text
                    text = ftfy.fix_text(myjson['text'])
                    if text != myjson['text']:
                        num_fixed_text += 1
                    myjson['text'] = text
                    # Detect language.
                    if detect(text) != 'en':
                        print('[non-english text]', myjson)
                        num_non_english_docs += 1
                        chars_non_english_docs += len(text)
                        continue
                    # On average each token is 5 characters so 8 is an
                    # upper bound.
                    if len(text) < (8 * MIN_DOCUMENT_LENGHT):
                        tokens = tokenizer.tokenize_document(text)
                        if len(tokens) < MIN_DOCUMENT_LENGHT:
                            print('[small document, skipping]:', myjson)
                            num_small_docs += 1
                            chars_small_docs += len(text)
                            continue
                    myjson = json.dumps(myjson, ensure_ascii=False)
                    f.write(myjson.encode('utf-8'))
                    f.write('\n'.encode('utf-8'))
                    num_written_docs += 1
                    if num_docs % print_interval == 0:
                        print_progress('[PROGRESS]', start_time, num_docs,
                                       num_fixed_text, num_non_english_docs,
                                       chars_non_english_docs,
                                       num_small_docs, chars_small_docs)
                except Exception as e:
                    print('    skipping ', line, e)

    print_progress('[FINAL]', start_time, num_docs,
                   num_fixed_text, num_non_english_docs,
                   chars_non_english_docs,
                   num_small_docs, chars_small_docs)


if __name__ == '__main__':

    print('building gpt2 dataset ...')

    input_filename = sys.argv[1]
    output_filename = sys.argv[2]

    print('will be reading {}'.format(input_filename))
    print('and will write the results to {}'.format(output_filename))

    filter_corpus(input_filename, output_filename)