Spaces:
Running
Running
| from transformers import AutoModel, AutoTokenizer | |
| import time | |
| import importlib | |
| from toolbox import update_ui, get_conf | |
| global chatglm_model, chatglm_tokenizer | |
| chatglm_model = None | |
| chatglm_tokenizer = None | |
| def model_loader(): | |
| global chatglm_model, chatglm_tokenizer | |
| if chatglm_tokenizer is None: | |
| chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) | |
| if chatglm_model is None: # 尚未加载 | |
| device, = get_conf('LOCAL_MODEL_DEVICE') | |
| if device=='cpu': | |
| chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float() | |
| else: | |
| chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() | |
| chatglm_model = chatglm_model.eval() | |
| chatglm_model = chatglm_model.eval() | |
| def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False): | |
| """ | |
| 函数的说明请见 request_llm/bridge_all.py | |
| """ | |
| global chatglm_model, chatglm_tokenizer | |
| if chatglm_model is None: | |
| observe_window[0] = "ChatGLM尚未加载，加载需要一段时间 ……" | |
| model_loader() | |
| # 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, history in chatglm_model.stream_chat(chatglm_tokenizer, inputs, history=history_feedin, max_length=llm_kwargs['max_length'], | |
| top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): | |
| # 观测窗，把已经获取的数据显示出去 | |
| observe_window[0] = response | |
| # 看门狗 (watchdog)，如果超过期限没有喂狗，则终止 | |
| if len(observe_window) >= 2: | |
| if (time.time()-observe_window[1]) > watch_dog_patience: | |
| raise RuntimeError("程序终止。") | |
| # if not console_slience: | |
| # print(response) | |
| return response | |
| def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): | |
| """ | |
| 函数的说明请见 request_llm/bridge_all.py | |
| """ | |
| global chatglm_model, chatglm_tokenizer | |
| chatbot.append((inputs, "")) | |
| if chatglm_model is None: | |
| chatbot[-1] = (inputs, "ChatGLM尚未加载，加载需要一段时间 ……") | |
| yield from update_ui(chatbot=chatbot, history=[]) | |
| model_loader() | |
| 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, history in chatglm_model.stream_chat(chatglm_tokenizer, 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) |