from transformers import AutoModel, AutoTokenizer import time import threading import importlib from toolbox import update_ui, get_conf, Singleton from multiprocessing import Process, Pipe def SingletonLocalLLM(cls): """ 一个单实例装饰器 """ _instance = {} def _singleton(*args, **kargs): if cls not in _instance: _instance[cls] = cls(*args, **kargs) return _instance[cls] elif _instance[cls].corrupted: _instance[cls] = cls(*args, **kargs) return _instance[cls] else: return _instance[cls] return _singleton class LocalLLMHandle(Process): def __init__(self): # ⭐主进程执行 super().__init__(daemon=True) self.corrupted = False self.load_model_info() self.parent, self.child = Pipe() self.running = True self._model = None self._tokenizer = None self.info = "" self.check_dependency() self.start() self.threadLock = threading.Lock() def load_model_info(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 raise NotImplementedError("Method not implemented yet") self.model_name = "" self.cmd_to_install = "" def load_model_and_tokenizer(self): """ This function should return the model and the tokenizer """ # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 raise NotImplementedError("Method not implemented yet") def llm_stream_generator(self, **kwargs): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 raise NotImplementedError("Method not implemented yet") def try_to_import_special_deps(self, **kwargs): """ import something that will raise error if the user does not install requirement_*.txt """ # ⭐主进程执行 raise NotImplementedError("Method not implemented yet") def check_dependency(self): # ⭐主进程执行 try: self.try_to_import_special_deps() self.info = "依赖检测通过" self.running = True except: self.info = f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。" self.running = False def run(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 # 第一次运行，加载参数 try: self._model, self._tokenizer = self.load_model_and_tokenizer() except: self.running = False from toolbox import trimmed_format_exc self.child.send(f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n') self.child.send('[FinishBad]') raise RuntimeError(f"不能正常加载{self.model_name}的参数！") while True: # 进入任务等待状态 kwargs = self.child.recv() # 收到消息，开始请求 try: for response_full in self.llm_stream_generator(**kwargs): self.child.send(response_full) self.child.send('[Finish]') # 请求处理结束，开始下一个循环 except: from toolbox import trimmed_format_exc self.child.send(f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n') self.child.send('[Finish]') def stream_chat(self, **kwargs): # ⭐主进程执行 self.threadLock.acquire() self.parent.send(kwargs) while True: res = self.parent.recv() if res == '[Finish]': break if res == '[FinishBad]': self.running = False self.corrupted = True break else: yield res self.threadLock.release() def get_local_llm_predict_fns(LLMSingletonClass, model_name): load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……" def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): """ ⭐多线程方法 函数的说明请见 request_llm/bridge_all.py """ _llm_handle = LLMSingletonClass() if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info if not _llm_handle.running: raise RuntimeError(_llm_handle.info) # chatglm 没有 sys_prompt 接口，因此把prompt加入 history history_feedin = [] history_feedin.append([sys_prompt, "Certainly!"]) for i in range(len(history)//2): history_feedin.append([history[2*i], history[2*i+1]] ) watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可 response = "" for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): if len(observe_window) >= 1: observe_window[0] = response if len(observe_window) >= 2: if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。") return response def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): """ ⭐单线程方法 函数的说明请见 request_llm/bridge_all.py """ chatbot.append((inputs, "")) _llm_handle = LLMSingletonClass() chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info) yield from update_ui(chatbot=chatbot, history=[]) if not _llm_handle.running: raise RuntimeError(_llm_handle.info) if additional_fn is not None: from core_functional import handle_core_functionality inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) # 处理历史信息 history_feedin = [] history_feedin.append([system_prompt, "Certainly!"]) for i in range(len(history)//2): history_feedin.append([history[2*i], history[2*i+1]] ) # 开始接收回复 response = f"[Local Message]: 等待{model_name}响应中 ..." for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): chatbot[-1] = (inputs, response) yield from update_ui(chatbot=chatbot, history=history) # 总结输出 if response == f"[Local Message]: 等待{model_name}响应中 ...": response = f"[Local Message]: {model_name}响应异常 ..." history.extend([inputs, response]) yield from update_ui(chatbot=chatbot, history=history) return predict_no_ui_long_connection, predict