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| from transformers import AutoModel, AutoTokenizer | |
| import time | |
| 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() | |
| 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): | |
| # 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("THUDM/chatglm-6b", trust_remote_code=True) | |
| device, = get_conf('LOCAL_MODEL_DEVICE') | |
| if device=='cpu': | |
| self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float() | |
| else: | |
| self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", 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！") | |
| # 进入任务等待状态 | |
| while True: | |
| kwargs = self.child.recv() | |
| try: | |
| for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs): | |
| self.child.send(response) | |
| except: | |
| self.child.send('[Local Message] Call ChatGLM fail.') | |
| self.child.send('[Finish]') | |
| def stream_chat(self, **kwargs): | |
| self.parent.send(kwargs) | |
| while True: | |
| res = self.parent.recv() | |
| if res != '[Finish]': | |
| yield res | |
| else: | |
| break | |
| return | |
| global glm_handle | |
| glm_handle = None | |
| ################################################################################# | |
| def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, 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() | |
| 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 = [] | |
| for i in range(len(history)//2): | |
| history_feedin.append(["What can I do?", sys_prompt] ) | |
| 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']): | |
| 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"] | |
| history_feedin = [] | |
| for i in range(len(history)//2): | |
| history_feedin.append(["What can I do?", system_prompt] ) | |
| history_feedin.append([history[2*i], history[2*i+1]] ) | |
| 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) |