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| 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 | |
| LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE') | |
| if LOCAL_MODEL_QUANT == "INT4": # INT4 | |
| _model_name_ = "THUDM/chatglm2-6b-int4" | |
| elif LOCAL_MODEL_QUANT == "INT8": # INT8 | |
| _model_name_ = "THUDM/chatglm2-6b-int8" | |
| else: | |
| _model_name_ = "THUDM/chatglm2-6b" # FP16 | |
| while True: | |
| try: | |
| if self.chatglm_model is None: | |
| self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True) | |
| if device=='cpu': | |
| self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float() | |
| else: | |
| self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().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: | |
| 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(["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) | |