import time import threading from toolbox import update_ui, Singleton from multiprocessing import Process, Pipe from contextlib import redirect_stdout from request_llms.queued_pipe import create_queue_pipe class ThreadLock(object): def __init__(self): self._lock = threading.Lock() def acquire(self): # print("acquiring", self) #traceback.print_tb self._lock.acquire() # print("acquired", self) def release(self): # print("released", self) #traceback.print_tb self._lock.release() def __enter__(self): self.acquire() def __exit__(self, type, value, traceback): self.release() @Singleton class GetSingletonHandle(): def __init__(self): self.llm_model_already_running = {} def get_llm_model_instance(self, cls, *args, **kargs): if cls not in self.llm_model_already_running: self.llm_model_already_running[cls] = cls(*args, **kargs) return self.llm_model_already_running[cls] elif self.llm_model_already_running[cls].corrupted: self.llm_model_already_running[cls] = cls(*args, **kargs) return self.llm_model_already_running[cls] else: return self.llm_model_already_running[cls] def reset_tqdm_output(): import sys, tqdm def status_printer(self, file): fp = file if fp in (sys.stderr, sys.stdout): getattr(sys.stderr, 'flush', lambda: None)() getattr(sys.stdout, 'flush', lambda: None)() def fp_write(s): print(s) last_len = [0] def print_status(s): from tqdm.utils import disp_len len_s = disp_len(s) fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0))) last_len[0] = len_s return print_status tqdm.tqdm.status_printer = status_printer class LocalLLMHandle(Process): def __init__(self): # ⭐run in main process super().__init__(daemon=True) self.is_main_process = True # init self.corrupted = False self.load_model_info() self.parent, self.child = create_queue_pipe() self.parent_state, self.child_state = create_queue_pipe() # allow redirect_stdout self.std_tag = "[Subprocess Message] " self.running = True self._model = None self._tokenizer = None self.state = "" self.check_dependency() self.is_main_process = False # state wrap for child process self.start() self.is_main_process = True # state wrap for child process self.threadLock = ThreadLock() def get_state(self): # ⭐run in main process while self.parent_state.poll(): self.state = self.parent_state.recv() return self.state def set_state(self, new_state): # ⭐run in main process or 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process if self.is_main_process: self.state = new_state else: self.child_state.send(new_state) def load_model_info(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process 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 """ # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process raise NotImplementedError("Method not implemented yet") def llm_stream_generator(self, **kwargs): # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process 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 """ # ⭐run in main process raise NotImplementedError("Method not implemented yet") def check_dependency(self): # ⭐run in main process try: self.try_to_import_special_deps() self.set_state("`依赖检测通过`") self.running = True except: self.set_state(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): # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process # 第一次运行，加载参数 self.child.flush = lambda *args: None self.child.write = lambda x: self.child.send(self.std_tag + x) reset_tqdm_output() self.set_state("`尝试加载模型`") try: with redirect_stdout(self.child): self._model, self._tokenizer = self.load_model_and_tokenizer() except: self.set_state("`加载模型失败`") 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！") self.set_state("`准备就绪`") 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) # print('debug' + 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 clear_pending_messages(self): # ⭐run in main process while True: if self.parent.poll(): self.parent.recv() continue for _ in range(5): time.sleep(0.5) if self.parent.poll(): r = self.parent.recv() continue break return def stream_chat(self, **kwargs): # ⭐run in main process if self.get_state() == "`准备就绪`": yield "`正在等待线程锁，排队中请稍后 ...`" with self.threadLock: if self.parent.poll(): yield "`排队中请稍后 ...`" self.clear_pending_messages() self.parent.send(kwargs) std_out = "" std_out_clip_len = 4096 while True: res = self.parent.recv() # pipe_watch_dog.feed() if res.startswith(self.std_tag): new_output = res[len(self.std_tag):] std_out = std_out[:std_out_clip_len] print(new_output, end='') std_out = new_output + std_out yield self.std_tag + '\n```\n' + std_out + '\n```\n' elif res == '[Finish]': break elif res == '[FinishBad]': self.running = False self.corrupted = True break else: std_out = "" yield res def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'): 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): """ refer to request_llms/bridge_all.py """ _llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass) if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.get_state() if not _llm_handle.running: raise RuntimeError(_llm_handle.get_state()) if history_format == 'classic': # 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]]) elif history_format == 'chatglm3': # 有 sys_prompt 接口 conversation_cnt = len(history) // 2 history_feedin = [{"role": "system", "content": sys_prompt}] if conversation_cnt: for index in range(0, 2*conversation_cnt, 2): what_i_have_asked = {} what_i_have_asked["role"] = "user" what_i_have_asked["content"] = history[index] what_gpt_answer = {} what_gpt_answer["role"] = "assistant" what_gpt_answer["content"] = history[index+1] if what_i_have_asked["content"] != "": if what_gpt_answer["content"] == "": continue history_feedin.append(what_i_have_asked) history_feedin.append(what_gpt_answer) else: history_feedin[-1]['content'] = what_gpt_answer['content'] 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): """ refer to request_llms/bridge_all.py """ chatbot.append((inputs, "")) _llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass) chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.get_state()) yield from update_ui(chatbot=chatbot, history=[]) if not _llm_handle.running: raise RuntimeError(_llm_handle.get_state()) 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 if history_format == 'classic': # 没有 sys_prompt 接口，因此把prompt加入 history 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]]) elif history_format == 'chatglm3': # 有 sys_prompt 接口 conversation_cnt = len(history) // 2 history_feedin = [{"role": "system", "content": system_prompt}] if conversation_cnt: for index in range(0, 2*conversation_cnt, 2): what_i_have_asked = {} what_i_have_asked["role"] = "user" what_i_have_asked["content"] = history[index] what_gpt_answer = {} what_gpt_answer["role"] = "assistant" what_gpt_answer["content"] = history[index+1] if what_i_have_asked["content"] != "": if what_gpt_answer["content"] == "": continue history_feedin.append(what_i_have_asked) history_feedin.append(what_gpt_answer) else: history_feedin[-1]['content'] = what_gpt_answer['content'] # 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