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| 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 |