from transformers import AutoModel, AutoTokenizer import time import threading import importlib from toolbox import update_ui, get_conf from multiprocessing import Process, Pipe load_message = "ChatGLM尚未加载,加载需要一段时间。注意,取决于`config.py`的配置,ChatGLM消耗大量的内存(CPU)或显存(GPU),也许会导致低配计算机卡死 ……" ################################################################################# class GetGLMHandle(Process): def __init__(self): super().__init__(daemon=True) self.parent, self.child = Pipe() self.chatglm_model = None self.chatglm_tokenizer = None self.info = "" self.success = True self.check_dependency() self.start() self.threadLock = threading.Lock() def check_dependency(self): try: import sentencepiece self.info = "依赖检测通过" self.success = True except: self.info = "缺少ChatGLM的依赖,如果要使用ChatGLM,除了基础的pip依赖以外,您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。" self.success = False def ready(self): return self.chatglm_model is not None def run(self): # 子进程执行 # 第一次运行,加载参数 retry = 0 while True: try: if self.chatglm_model is None: self.chatglm_tokenizer = AutoTokenizer.from_pretrained("fb700/chatglm-fitness-RLHF", trust_remote_code=True) device, = get_conf('LOCAL_MODEL_DEVICE') if device=='cpu': self.chatglm_model = AutoModel.from_pretrained("fb700/chatglm-fitness-RLHF", trust_remote_code=True).float() else: self.chatglm_model = AutoModel.from_pretrained("fb700/chatglm-fitness-RLHF", trust_remote_code=True).half().quantize(8).cuda() self.chatglm_model = self.chatglm_model.eval() break else: break except: retry += 1 if retry > 3: self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。') raise RuntimeError("不能正常加载ChatGLM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, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs): self.child.send(response) # # 中途接收可能的终止指令(如果有的话) # if self.child.poll(): # command = self.child.recv() # if command == '[Terminate]': break except: from toolbox import trimmed_format_exc self.child.send('[Local Message] Call ChatGLM fail.' + '\n```\n' + trimmed_format_exc() + '\n```\n') # 请求处理结束,开始下一个循环 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]': yield res else: break self.threadLock.release() global glm_handle glm_handle = None ################################################################################# 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 """ global glm_handle if glm_handle is None: glm_handle = GetGLMHandle() if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + glm_handle.info if not glm_handle.success: error = glm_handle.info glm_handle = None raise RuntimeError(error) # chatglm 没有 sys_prompt 接口,因此把prompt加入 history history_feedin = [] history_feedin.append(["What can I do?", sys_prompt]) 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 glm_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, "")) global glm_handle if glm_handle is None: glm_handle = GetGLMHandle() chatbot[-1] = (inputs, load_message + "\n\n" + glm_handle.info) yield from update_ui(chatbot=chatbot, history=[]) if not glm_handle.success: glm_handle = None return if additional_fn is not None: import core_functional importlib.reload(core_functional) # 热更新prompt core_functional = core_functional.get_core_functions() if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话) inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"] # 处理历史信息 history_feedin = [] history_feedin.append(["What can I do?", system_prompt] ) for i in range(len(history)//2): history_feedin.append([history[2*i], history[2*i+1]] ) # 开始接收chatglm的回复 response = "[Local Message]: 等待ChatGLM响应中 ..." for response in glm_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 == "[Local Message]: 等待ChatGLM响应中 ...": response = "[Local Message]: ChatGLM响应异常 ..." history.extend([inputs, response]) yield from update_ui(chatbot=chatbot, history=history)