import random import json from tokenizers import ( decoders, models, pre_tokenizers, trainers, Tokenizer, ) import os random.seed(42) def train_tokenizer(): # 读取JSONL文件并提取文本数据 def read_texts_from_jsonl(file_path): with open(file_path, 'r', encoding='utf-8') as f: for line in f: data = json.loads(line) yield data['text'] data_path = '../dataset/pretrain_hq.jsonl' # 初始化tokenizer tokenizer = Tokenizer(models.BPE()) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) # 定义特殊token special_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"] # 设置训练器并添加特殊token trainer = trainers.BpeTrainer( vocab_size=6400, special_tokens=special_tokens, # 确保这三个token被包含 show_progress=True, initial_alphabet=pre_tokenizers.ByteLevel.alphabet() ) # 读取文本数据 texts = read_texts_from_jsonl(data_path) # 训练tokenizer tokenizer.train_from_iterator(texts, trainer=trainer) # 设置解码器 tokenizer.decoder = decoders.ByteLevel() # 检查特殊token的索引 assert tokenizer.token_to_id("<|endoftext|>") == 0 assert tokenizer.token_to_id("<|im_start|>") == 1 assert tokenizer.token_to_id("<|im_end|>") == 2 # 保存tokenizer tokenizer_dir = "../model/" os.makedirs(tokenizer_dir, exist_ok=True) tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json")) tokenizer.model.save("../model/") # 手动创建配置文件 config = { "add_bos_token": False, "add_eos_token": False, "add_prefix_space": False, "added_tokens_decoder": { "0": { "content": "<|endoftext|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True }, "1": { "content": "<|im_start|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True }, "2": { "content": "<|im_end|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True } }, "additional_special_tokens": [], "bos_token": "<|im_start|>", "clean_up_tokenization_spaces": False, "eos_token": "<|im_end|>", "legacy": True, "model_max_length": 32768, "pad_token": "<|endoftext|>", "sp_model_kwargs": {}, "spaces_between_special_tokens": False, "tokenizer_class": "PreTrainedTokenizerFast", "unk_token": "<|endoftext|>", "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' -%}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else -%}\n {{- '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}" } # 保存配置文件 with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as config_file: json.dump(config, config_file, ensure_ascii=False, indent=4) print("Tokenizer training completed and saved.") def eval_tokenizer(): from transformers import AutoTokenizer # 加载预训练的tokenizer tokenizer = AutoTokenizer.from_pretrained("../model/") messages = [ {"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"}, {"role": "user", "content": '你来自哪里？'}, {"role": "assistant", "content": '我来自地球'} ] new_prompt = tokenizer.apply_chat_template( messages, tokenize=False ) print(new_prompt) # 获取实际词汇表长度（包括特殊符号） actual_vocab_size = len(tokenizer) print('tokenizer实际词表长度：', actual_vocab_size) model_inputs = tokenizer(new_prompt) print('encoder长度：', len(model_inputs['input_ids'])) input_ids = model_inputs['input_ids'] response = tokenizer.decode(input_ids, skip_special_tokens=False) print('decoder和原始文本是否一致：', response == new_prompt) def main(): train_tokenizer() eval_tokenizer() if __name__ == '__main__': main()