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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,请避免混用多种jittor模型,否则可能导致显存溢出而造成卡顿,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.local_history = [] | |
self.success = True | |
self.check_dependency() | |
self.start() | |
self.threadLock = threading.Lock() | |
def check_dependency(self): | |
try: | |
import pandas | |
self.info = "依赖检测通过" | |
self.success = True | |
except: | |
from toolbox import trimmed_format_exc | |
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 -i https://pypi.jittor.org/simple -I`"+\ | |
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)。" +\ | |
r"警告:安装jittorllms依赖后将完全破坏现有的pytorch环境,建议使用docker环境!" + trimmed_format_exc() | |
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 validate_path(): | |
import os, sys | |
dir_name = os.path.dirname(__file__) | |
env = os.environ.get("PATH", "") | |
os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') | |
root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') | |
os.chdir(root_dir_assume + '/request_llm/jittorllms') | |
sys.path.append(root_dir_assume + '/request_llm/jittorllms') | |
validate_path() # validate path so you can run from base directory | |
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': 'pangualpha'} | |
print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))') | |
self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict)) | |
print('done get model') | |
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!") | |
print('load_model') | |
load_model() | |
# 进入任务等待状态 | |
print('进入任务等待状态') | |
while True: | |
# 进入任务等待状态 | |
kwargs = self.child.recv() | |
query = kwargs['query'] | |
history = kwargs['history'] | |
# 是否重置 | |
if len(self.local_history) > 0 and len(history)==0: | |
print('触发重置') | |
self.jittorllms_model.reset() | |
self.local_history.append(query) | |
print('收到消息,开始请求') | |
try: | |
for response in self.jittorllms_model.stream_chat(query, history): | |
print(response) | |
self.child.send(response) | |
except: | |
from toolbox import trimmed_format_exc | |
print(trimmed_format_exc()) | |
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 pangu_glm_handle | |
pangu_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 pangu_glm_handle | |
if pangu_glm_handle is None: | |
pangu_glm_handle = GetGLMHandle() | |
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + pangu_glm_handle.info | |
if not pangu_glm_handle.success: | |
error = pangu_glm_handle.info | |
pangu_glm_handle = None | |
raise RuntimeError(error) | |
# jittorllms 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([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 pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): | |
print(response) | |
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 pangu_glm_handle | |
if pangu_glm_handle is None: | |
pangu_glm_handle = GetGLMHandle() | |
chatbot[-1] = (inputs, load_message + "\n\n" + pangu_glm_handle.info) | |
yield from update_ui(chatbot=chatbot, history=[]) | |
if not pangu_glm_handle.success: | |
pangu_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 = [] | |
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 pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, 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) | |