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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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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_llm/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_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）。" +\ |
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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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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_llm/jittorllms') |
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sys.path.append(root_dir_assume + '/request_llm/jittorllms') |
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validate_path() |
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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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args_dict = {'model': 'llama'} |
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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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print('进入任务等待状态') |
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while True: |
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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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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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self.child.send('[Finish]') |
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def stream_chat(self, **kwargs): |
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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 llama_glm_handle |
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llama_glm_handle = None |
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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_llm/bridge_all.py |
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""" |
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global llama_glm_handle |
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if llama_glm_handle is None: |
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llama_glm_handle = GetGLMHandle() |
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if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + llama_glm_handle.info |
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if not llama_glm_handle.success: |
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error = llama_glm_handle.info |
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llama_glm_handle = None |
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raise RuntimeError(error) |
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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 |
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response = "" |
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for response in llama_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_llm/bridge_all.py |
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""" |
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chatbot.append((inputs, "")) |
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global llama_glm_handle |
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if llama_glm_handle is None: |
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llama_glm_handle = GetGLMHandle() |
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chatbot[-1] = (inputs, load_message + "\n\n" + llama_glm_handle.info) |
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yield from update_ui(chatbot=chatbot, history=[]) |
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if not llama_glm_handle.success: |
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llama_glm_handle = None |
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return |
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if additional_fn is not None: |
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import core_functional |
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importlib.reload(core_functional) |
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core_functional = core_functional.get_core_functions() |
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if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) |
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"] |
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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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response = "[Local Message]: 等待jittorllms响应中 ..." |
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for response in llama_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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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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