| | import sys |
| | import time |
| | import inspect |
| |
|
| | from transformers import AutoTokenizer |
| | from typing import Any |
| | import numpy as np |
| | from tqdm import tqdm |
| |
|
| | import json |
| | import argparse |
| | import os |
| |
|
| | def parse_args(): |
| | parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task") |
| | parser.add_argument( |
| | "--source_file", |
| | type=str, |
| | ) |
| | parser.add_argument( |
| | "--max_length", |
| | type=int, |
| | default=512, |
| | ) |
| | parser.add_argument( |
| | "--chunk_size", |
| | type=int, |
| | default=1024, |
| | ) |
| | parser.add_argument( |
| | "--tokenizer_path", |
| | type=str, |
| | ) |
| | args = parser.parse_args() |
| | return args |
| |
|
| |
|
| | def get_tokenizer(tokenizer_path): |
| | tokenizer = tokenizer = AutoTokenizer.from_pretrained( |
| | tokenizer_path, use_fast=not False, trust_remote_code=False |
| | ) |
| | |
| | |
| | return tokenizer |
| |
|
| |
|
| | def convert_data_to_id(tokenizer: AutoTokenizer, data: Any): |
| | input_ids = tokenizer.encode(data) |
| | ids = input_ids |
| | ids = np.array(ids, dtype=np.int32) |
| | return ids |
| |
|
| | args = parse_args() |
| |
|
| | tokenizer = get_tokenizer(args.tokenizer_path) |
| | infile = open(args.source_file, 'r', encoding='utf-8') |
| | file_name, _ = os.path.splitext(os.path.basename(args.source_file)) |
| |
|
| | print("source file - ", args.source_file) |
| | print('############ Start data reading ###########') |
| |
|
| | idx = 0 |
| | max_length = args.max_length |
| | chunk_size = args.chunk_size |
| |
|
| | token_ids = np.array([], dtype=np.int32) |
| |
|
| | with open(file_name+'_streaming_'+str(max_length)+'.jsonl', 'w') as f: |
| | for line in infile: |
| | idx += 1 |
| | if idx % 10000 == 0: |
| | print('Cur idx - ', idx) |
| | try: |
| | line = json.loads(line) |
| | cur_texts = [] |
| | if 'text' in line: |
| | temp = line['text'] + "\n" |
| | elif 'raw_content_lines' in line: |
| | temp = "\n".join(line['raw_content_lines']) + "\n" |
| | else: |
| | print("error") |
| | exit() |
| | try: |
| | token_id = convert_data_to_id(tokenizer, temp) |
| | token_ids = np.concatenate((token_ids, token_id), dtype=np.int32) |
| | except UnicodeDecodeError: |
| | print('Error line - encoding: ', idx) |
| |
|
| | if len(token_ids) > max_length*chunk_size: |
| | while len(token_ids) > max_length: |
| | try: |
| | temp_text = tokenizer.decode(token_ids[: max_length]) |
| | temp_dic = {'text': temp_text} |
| | f.write(json.dumps(temp_dic) + "\n") |
| | token_ids = token_ids[max_length:] |
| | except UnicodeDecodeError: |
| | print('Error line - decoding: ', idx) |
| | token_ids = token_ids[max_length:] |
| | |
| | except: |
| | print("error source file - ", args.source_file) |
| | print('Error line: ', idx) |
| | continue |
| |
|
| | infile.close() |
