NVIDIA-GPU-Bootcamp-Training/ai/Megatron/English/Python/jupyter_notebook/Megatron-LM/tools/openwebtext/cleanup_dataset.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


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)