176 lines
7.6 KiB
Python
176 lines
7.6 KiB
Python
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from transformers import AutoModel, AutoTokenizer
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import time
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import threading
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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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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 ……"
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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.jittorllms_model = None
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self.info = ""
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self.local_history = []
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self.success = True
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self.check_dependency()
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self.start()
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self.threadLock = threading.Lock()
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def check_dependency(self):
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try:
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import pandas
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self.info = "依赖检测通过"
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self.success = True
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except:
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from toolbox import trimmed_format_exc
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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_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
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r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llms/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）。" +\
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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()
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self.success = False
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def ready(self):
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return self.jittorllms_model is not None
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def run(self):
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# 子进程执行
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# 第一次运行，加载参数
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def validate_path():
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import os, sys
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dir_name = os.path.dirname(__file__)
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env = os.environ.get("PATH", "")
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os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
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root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
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os.chdir(root_dir_assume + '/request_llms/jittorllms')
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sys.path.append(root_dir_assume + '/request_llms/jittorllms')
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validate_path() # validate path so you can run from base directory
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def load_model():
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import types
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try:
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if self.jittorllms_model is None:
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device, = get_conf('LOCAL_MODEL_DEVICE')
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from .jittorllms.models import get_model
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# availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
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args_dict = {'model': 'pangualpha'}
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print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
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self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
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print('done get model')
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except:
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self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
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raise RuntimeError("不能正常加载jittorllms的参数！")
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print('load_model')
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load_model()
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# 进入任务等待状态
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print('进入任务等待状态')
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while True:
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# 进入任务等待状态
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kwargs = self.child.recv()
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query = kwargs['query']
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history = kwargs['history']
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# 是否重置
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if len(self.local_history) > 0 and len(history)==0:
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print('触发重置')
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self.jittorllms_model.reset()
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self.local_history.append(query)
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print('收到消息，开始请求')
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try:
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for response in self.jittorllms_model.stream_chat(query, history):
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print(response)
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self.child.send(response)
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except:
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from toolbox import trimmed_format_exc
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print(trimmed_format_exc())
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self.child.send('[Local Message] Call jittorllms fail.')
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# 请求处理结束，开始下一个循环
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self.child.send('[Finish]')
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def stream_chat(self, **kwargs):
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# 主进程执行
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self.threadLock.acquire()
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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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self.threadLock.release()
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global pangu_glm_handle
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pangu_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=[], 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_llms/bridge_all.py
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"""
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global pangu_glm_handle
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if pangu_glm_handle is None:
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pangu_glm_handle = GetGLMHandle()
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if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + pangu_glm_handle.info
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if not pangu_glm_handle.success:
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error = pangu_glm_handle.info
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pangu_glm_handle = None
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raise RuntimeError(error)
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# jittorllms 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([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 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']):
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print(response)
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if len(observe_window) >= 1: 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_llms/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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global pangu_glm_handle
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if pangu_glm_handle is None:
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pangu_glm_handle = GetGLMHandle()
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chatbot[-1] = (inputs, load_message + "\n\n" + pangu_glm_handle.info)
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yield from update_ui(chatbot=chatbot, history=[])
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if not pangu_glm_handle.success:
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pangu_glm_handle = None
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return
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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# 处理历史信息
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history_feedin = []
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for i in range(len(history)//2):
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history_feedin.append([history[2*i], history[2*i+1]] )
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# 开始接收jittorllms的回复
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response = "[Local Message]: 等待jittorllms响应中 ..."
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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']):
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chatbot[-1] = (inputs, response)
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yield from update_ui(chatbot=chatbot, history=history)
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# 总结输出
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if response == "[Local Message]: 等待jittorllms响应中 ...":
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response = "[Local Message]: jittorllms响应异常 ..."
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history.extend([inputs, response])
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yield from update_ui(chatbot=chatbot, history=history)
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