gpt-academicc / request_llm /bridge_jittorllms.py
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尝试加入jittor本地模型
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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 = "jittorllms尚未加载,加载需要一段时间。注意,取决于`config.py`的配置,jittorllms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.jittorllms_model = None
self.info = ""
self.success = True
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def check_dependency(self):
try:
import jittor
from .jittorllms.models import get_model
self.info = "依赖检测通过"
self.success = True
except:
self.info = r"缺少jittorllms的依赖,如果要使用jittorllms,除了基础的pip依赖以外,您还需要运行`pip install -r request_llm/requirements_jittorllms.txt`"+\
r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖(在项目根目录运行这两个指令)。"
self.success = False
def ready(self):
return self.jittorllms_model is not None
def run(self):
# 子进程执行
# 第一次运行,加载参数
def load_model():
import types
try:
if self.jittorllms_model is None:
device, = get_conf('LOCAL_MODEL_DEVICE')
from .jittorllms.models import get_model
# availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
args_dict = {'model': 'chatglm', 'RUN_DEVICE':'cpu'}
self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
except:
self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
raise RuntimeError("不能正常加载jittorllms的参数!")
load_model()
# 进入任务等待状态
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.jittorllms_model.run_web_demo(kwargs['query'], kwargs['history']):
self.child.send(response)
except:
self.child.send('[Local Message] Call jittorllms fail.')
# 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)
# jittorllms 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:
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"]
# 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jittorllms的回复
response = "[Local Message]: 等待jittorllms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jittorllms响应中 ...":
response = "[Local Message]: jittorllms响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)