import json import os from tqdm import tqdm from transformers import AutoModelForCausalLM, AutoTokenizer import re import concurrent.futures from pathlib import Path # 検索するディレクトリのパス file_path = 'wiki_concat_jsonl.jsonl' model_id = "geniacllm/dMoE_8B_math_iter8999" tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False, torch_dtype="auto", device_map="auto") print(" load tokenizer ") # .jsonl ファイルのフルパスを格納するリスト jsonl_files = [] jsonl_datas = [] with open(file_path, 'r', encoding='utf-8') as file: for line in file: # JSON形式のデータをPythonの辞書に変換 data = json.loads(line) jsonl_datas.append(data["text"]) # print("data", gsm8l_datas ) print("data", len(jsonl_datas) ) print("data", jsonl_datas[-1] ) # model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto") # print(model) # inputs = tokenizer("地球温暖化の主な原因となる物質は", return_tensors="pt").to(model.device) # inputs all_data = [] word_num = 0 short_full_document_list =[] complete_full_document_list =[] count = 0 total_items = len(jsonl_datas) # big_list = list(range(total_items)) # サブリストの数 num_chunks = 25 # 各サブリストの長さを計算 items_per_sublist = total_items // num_chunks # サブリストを生成 chunks = [jsonl_datas[i * items_per_sublist:(i + 1) * items_per_sublist] for i in range(num_chunks)] # 最後のサブリストに残りのアイテムを追加する if total_items % num_chunks != 0: remaining_items = jsonl_datas[num_chunks * items_per_sublist:] chunks[-1].extend(remaining_items) # 並列を行う関数 def process_item(sublists): for index, one_data in tqdm( enumerate(sublists)): one_data_token = tokenizer(one_data, return_tensors="pt", add_special_tokens=True) one_data_token_len = one_data_token.input_ids[0].size(0) if one_data_token_len < 50: pass elif one_data_token_len < 1600: short_full_document_list.append( {'length': one_data_token_len, 'text': one_data } ) elif one_data_token_len < 2010: complete_full_document_list.append( {'length': one_data_token_len, 'text': one_data } ) else: one_sentences = one_data.split('。') collect_one_sentences = [] split_one_sentence_len = 0 for index, sentence in enumerate(one_sentences): one_sentence_token = tokenizer(sentence, return_tensors="pt", add_special_tokens=True) one_sentence_token_len = one_sentence_token.input_ids[0].size(0) if split_one_sentence_len + one_sentence_token_len < 2010: split_one_sentence_len = split_one_sentence_len + one_sentence_token_len collect_one_sentences.append( sentence ) elif one_sentence_token_len > 2010: print(" Warning : Over one-sentence token :", one_sentence_token_len ) print( sentence ) split_document = "\n".join(collect_one_sentences) short_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } ) split_one_sentence_len = 0 collect_one_sentences.clear() else : split_document = "\n".join(collect_one_sentences) complete_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } ) split_one_sentence_len = one_sentence_token_len collect_one_sentences.clear() collect_one_sentences.append( sentence ) split_document = "\n".join(collect_one_sentences) if split_one_sentence_len < 1500 : short_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } ) elif split_one_sentence_len < 2015 : complete_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } ) else: print(" Warning : Over split-one-sentence token :", split_one_sentence_len ) print( split_document ) # if index > 96605: # break complete_full_document_sub_list = [] while True: if len(short_full_document_list) == 0: break if len(short_full_document_list)%500 == 0: print( "残り : ", len(short_full_document_list) ) target = short_full_document_list[0] left_len = 2010 - target['length'] if left_len < 0 : print(" Error : Over token target :", target['length'] ) del short_full_document_list[0] # 0番目の要素を削除 if len(short_full_document_list) == 0: complete_full_document_sub_list.append( target ) break else: # ステップ1: 最小差分を計算（デフォルト値を使用） closest_length_diff = min((left_len - rec['length'] for rec in short_full_document_list if rec['length'] <= left_len), default=None) if closest_length_diff is None: complete_full_document_sub_list.append( target ) # print( "complete_full_document_list.append" ) else: # 特定の条件に合致するデータのインデックスを探す index = next((i for i, rec in enumerate(short_full_document_list) if rec['length'] == left_len - closest_length_diff ), None) merge_document = short_full_document_list[index] del short_full_document_list[index] # index番目の要素を削除 # 結果の出力 if index is not None: merge_texts = target['text'] + "" + merge_document['text'] if target['length'] + merge_document['length'] < 1500 : short_full_document_list.append( {'length': target['length'] + merge_document['length'] + 1 , 'text': merge_texts } ) # print( "merge_texts" , merge_texts ) # print( "short_full_document_list" , short_full_document_list[-1] ) elif target['length'] + merge_document['length'] > 2022 : print(" Error : Over token target :", target['length'] , " merge_document : ", merge_document['length'] ) else: complete_full_document_sub_list.append( {'length': target['length'] + merge_document['length'] + 1 , 'text': merge_texts } ) # print( "merge_texts" , merge_texts ) else: print(f"長さが {merge_document['length'] } のデータは見つかりませんでした。") return complete_full_document_sub_list # print( "L110 sublists " , len( chunks) ) # プロセスプールを使用して各チャンクを並列処理 with concurrent.futures.ProcessPoolExecutor() as executor: results = list(executor.map(process_item, chunks)) # print( "L114 results " , len( results) ) # for result in results: # print( "L116 result " , len( result) ) # リスト内包表記を使って平坦化 # sumを使って平坦化 complete_full_document_list.extend( sum(results, [] )) print( "L121 complete_full_document_list " , len( complete_full_document_list) ) length_list = [] collect_length_list = [] file_count = 0 complete_full_document_list_len = len(complete_full_document_list) # 各ファイルに格納する最大ドキュメント数 max_docs_per_file = 9999 # 必要なファイルの数 jsonl_num = complete_full_document_list_len // max_docs_per_file + (1 if complete_full_document_list_len % max_docs_per_file != 0 else 0) # ファイルにデータを分割して保存 for i in range(jsonl_num): start_index = i * max_docs_per_file end_index = start_index + max_docs_per_file # ファイル名を生成し、連番を付ける filename = f"{i}_speech_text.jsonl" # ファイル名形式: "番号_名前.jsonl" # ファイルを開いてデータを書き込む with open(filename, 'w', encoding='utf-8') as file: for document in complete_full_document_list[start_index:end_index]: # 各テキストに対するJSONオブジェクトを作成 json_obj = { "text": document['text'] , "is_rejected": False, "reason": {} } # JSON形式の文字列に変換 json_line = json.dumps(json_obj, ensure_ascii=False) # ファイルに書き込み file.write(json_line + '\n') length_list.append( document['length'] ) document_tokens = tokenizer(document['text'] , return_tensors="pt", add_special_tokens=True) document_tokens_len = document_tokens.input_ids[0].size(0) collect_length_list.append( document_tokens_len ) if document_tokens_len > 2021: print("L209 Error : Over token ", document['length'] , " collect : ", document_tokens_len ) print( "len(complete_full_document_list) " , len(complete_full_document_list) ) # print( "length_list " , length_list )