113 lines
4.8 KiB
Python
113 lines
4.8 KiB
Python
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from transformers import AutoModel, AutoTokenizer
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import time
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import importlib
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from toolbox import update_ui, get_conf
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from multiprocessing import Process, Pipe
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#################################################################################
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class GetGLMHandle(Process):
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def __init__(self):
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super().__init__(daemon=True)
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self.parent, self.child = Pipe()
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self.chatglm_model = None
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self.chatglm_tokenizer = None
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self.start()
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print('初始化')
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def ready(self):
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return self.chatglm_model is not None
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def run(self):
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while True:
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try:
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if self.chatglm_model is None:
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self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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device, = get_conf('LOCAL_MODEL_DEVICE')
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if device=='cpu':
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self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
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else:
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self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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self.chatglm_model = self.chatglm_model.eval()
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break
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else:
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break
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except:
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pass
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while True:
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kwargs = self.child.recv()
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try:
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for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
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self.child.send(response)
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except:
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self.child.send('[Local Message] Call ChatGLM fail.')
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self.child.send('[Finish]')
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def stream_chat(self, **kwargs):
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self.parent.send(kwargs)
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while True:
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res = self.parent.recv()
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if res != '[Finish]':
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yield res
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else:
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break
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return
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global glm_handle
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glm_handle = None
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#################################################################################
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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多线程方法
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函数的说明请见 request_llm/bridge_all.py
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"""
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global glm_handle
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if glm_handle is None:
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glm_handle = GetGLMHandle()
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observe_window[0] = "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 ……"
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# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
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history_feedin = []
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for i in range(len(history)//2):
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history_feedin.append(["What can I do?", sys_prompt] )
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history_feedin.append([history[2*i], history[2*i+1]] )
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watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
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response = ""
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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']):
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observe_window[0] = response
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if len(observe_window) >= 2:
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if (time.time()-observe_window[1]) > watch_dog_patience:
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raise RuntimeError("程序终止。")
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return response
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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单线程方法
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函数的说明请见 request_llm/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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global glm_handle
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if glm_handle is None:
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glm_handle = GetGLMHandle()
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chatbot[-1] = (inputs, "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 ……")
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yield from update_ui(chatbot=chatbot, history=[])
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if additional_fn is not None:
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import core_functional
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importlib.reload(core_functional) # 热更新prompt
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core_functional = core_functional.get_core_functions()
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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）
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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history_feedin = []
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for i in range(len(history)//2):
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history_feedin.append(["What can I do?", system_prompt] )
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history_feedin.append([history[2*i], history[2*i+1]] )
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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']):
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chatbot[-1] = (inputs, response)
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yield from update_ui(chatbot=chatbot, history=history) |