180 lines
7.2 KiB
Python
180 lines
7.2 KiB
Python
from transformers import AutoModel, AutoTokenizer
|
||
import time
|
||
import threading
|
||
import importlib
|
||
from toolbox import update_ui, get_conf, Singleton
|
||
from multiprocessing import Process, Pipe
|
||
|
||
def SingletonLocalLLM(cls):
|
||
"""
|
||
一个单实例装饰器
|
||
"""
|
||
_instance = {}
|
||
def _singleton(*args, **kargs):
|
||
if cls not in _instance:
|
||
_instance[cls] = cls(*args, **kargs)
|
||
return _instance[cls]
|
||
elif _instance[cls].corrupted:
|
||
_instance[cls] = cls(*args, **kargs)
|
||
return _instance[cls]
|
||
else:
|
||
return _instance[cls]
|
||
return _singleton
|
||
|
||
class LocalLLMHandle(Process):
|
||
def __init__(self):
|
||
# ⭐主进程执行
|
||
super().__init__(daemon=True)
|
||
self.corrupted = False
|
||
self.load_model_info()
|
||
self.parent, self.child = Pipe()
|
||
self.running = True
|
||
self._model = None
|
||
self._tokenizer = None
|
||
self.info = ""
|
||
self.check_dependency()
|
||
self.start()
|
||
self.threadLock = threading.Lock()
|
||
|
||
def load_model_info(self):
|
||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||
raise NotImplementedError("Method not implemented yet")
|
||
self.model_name = ""
|
||
self.cmd_to_install = ""
|
||
|
||
def load_model_and_tokenizer(self):
|
||
"""
|
||
This function should return the model and the tokenizer
|
||
"""
|
||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||
raise NotImplementedError("Method not implemented yet")
|
||
|
||
def llm_stream_generator(self, **kwargs):
|
||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||
raise NotImplementedError("Method not implemented yet")
|
||
|
||
def try_to_import_special_deps(self, **kwargs):
|
||
"""
|
||
import something that will raise error if the user does not install requirement_*.txt
|
||
"""
|
||
# ⭐主进程执行
|
||
raise NotImplementedError("Method not implemented yet")
|
||
|
||
def check_dependency(self):
|
||
# ⭐主进程执行
|
||
try:
|
||
self.try_to_import_special_deps()
|
||
self.info = "依赖检测通过"
|
||
self.running = True
|
||
except:
|
||
self.info = f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。"
|
||
self.running = False
|
||
|
||
def run(self):
|
||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||
# 第一次运行，加载参数
|
||
try:
|
||
self._model, self._tokenizer = self.load_model_and_tokenizer()
|
||
except:
|
||
self.running = False
|
||
from toolbox import trimmed_format_exc
|
||
self.child.send(f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
|
||
self.child.send('[FinishBad]')
|
||
raise RuntimeError(f"不能正常加载{self.model_name}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_full in self.llm_stream_generator(**kwargs):
|
||
self.child.send(response_full)
|
||
self.child.send('[Finish]')
|
||
# 请求处理结束，开始下一个循环
|
||
except:
|
||
from toolbox import trimmed_format_exc
|
||
self.child.send(f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
|
||
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]':
|
||
break
|
||
if res == '[FinishBad]':
|
||
self.running = False
|
||
self.corrupted = True
|
||
break
|
||
else:
|
||
yield res
|
||
self.threadLock.release()
|
||
|
||
|
||
|
||
def get_local_llm_predict_fns(LLMSingletonClass, model_name):
|
||
load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
|
||
|
||
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
|
||
"""
|
||
_llm_handle = LLMSingletonClass()
|
||
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
|
||
if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
|
||
|
||
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
|
||
history_feedin = []
|
||
history_feedin.append([sys_prompt, "Certainly!"])
|
||
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 _llm_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, ""))
|
||
|
||
_llm_handle = LLMSingletonClass()
|
||
chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
|
||
yield from update_ui(chatbot=chatbot, history=[])
|
||
if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
|
||
|
||
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([system_prompt, "Certainly!"])
|
||
for i in range(len(history)//2):
|
||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||
|
||
# 开始接收回复
|
||
response = f"[Local Message]: 等待{model_name}响应中 ..."
|
||
for response in _llm_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 == f"[Local Message]: 等待{model_name}响应中 ...":
|
||
response = f"[Local Message]: {model_name}响应异常 ..."
|
||
history.extend([inputs, response])
|
||
yield from update_ui(chatbot=chatbot, history=history)
|
||
|
||
return predict_no_ui_long_connection, predict |