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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) |