from transformers import AutoModel, AutoTokenizer import time import os import json import threading import importlib from toolbox import update_ui, get_conf from multiprocessing import Process, Pipe load_message = "ChatGLMFT尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLMFT消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……" def string_to_options(arguments): import argparse import shlex # Create an argparse.ArgumentParser instance parser = argparse.ArgumentParser() # Add command-line arguments parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo") parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='') parser.add_argument("--system_prompt", type=str, help="System prompt", default='') parser.add_argument("--batch", type=int, help="System prompt", default=50) # Parse the arguments args = parser.parse_args(shlex.split(arguments)) return args ################################################################################# class GetGLMFTHandle(Process): def __init__(self): super().__init__(daemon=True) self.parent, self.child = Pipe() self.chatglmft_model = None self.chatglmft_tokenizer = None self.info = "" self.success = True self.check_dependency() self.start() self.threadLock = threading.Lock() def check_dependency(self): try: import sentencepiece self.info = "依赖检测通过" self.success = True except: self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llms/requirements_chatglm.txt`安装ChatGLM的依赖。" self.success = False def ready(self): return self.chatglmft_model is not None def run(self): # 子进程执行 # 第一次运行，加载参数 retry = 0 while True: try: if self.chatglmft_model is None: from transformers import AutoConfig import torch # conf = 'request_llms/current_ptune_model.json' # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息') # with open(conf, 'r', encoding='utf8') as f: # model_args = json.loads(f.read()) CHATGLM_PTUNING_CHECKPOINT = get_conf('CHATGLM_PTUNING_CHECKPOINT') assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点" conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json") with open(conf, 'r', encoding='utf8') as f: model_args = json.loads(f.read()) if 'model_name_or_path' not in model_args: model_args['model_name_or_path'] = model_args['_name_or_path'] self.chatglmft_tokenizer = AutoTokenizer.from_pretrained( model_args['model_name_or_path'], trust_remote_code=True) config = AutoConfig.from_pretrained( model_args['model_name_or_path'], trust_remote_code=True) config.pre_seq_len = model_args['pre_seq_len'] config.prefix_projection = model_args['prefix_projection'] print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}") model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True) prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin")) new_prefix_state_dict = {} for k, v in prefix_state_dict.items(): if k.startswith("transformer.prefix_encoder."): new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) if model_args['quantization_bit'] is not None and model_args['quantization_bit'] != 0: print(f"Quantized to {model_args['quantization_bit']} bit") model = model.quantize(model_args['quantization_bit']) model = model.cuda() if model_args['pre_seq_len'] is not None: # P-tuning v2 model.transformer.prefix_encoder.float() self.chatglmft_model = model.eval() break else: break except Exception as e: retry += 1 if retry > 3: self.child.send('[Local Message] Call ChatGLMFT fail 不能正常加载ChatGLMFT的参数。') raise RuntimeError("不能正常加载ChatGLMFT的参数！") 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.chatglmft_model.stream_chat(self.chatglmft_tokenizer, **kwargs): self.child.send(response) # # 中途接收可能的终止指令（如果有的话） # if self.child.poll(): # command = self.child.recv() # if command == '[Terminate]': break except: from toolbox import trimmed_format_exc self.child.send('[Local Message] Call ChatGLMFT fail.' + '\n```\n' + trimmed_format_exc() + '\n```\n') # 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 glmft_handle glmft_handle = None ################################################################################# def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False): """ 多线程方法 函数的说明请见 request_llms/bridge_all.py """ global glmft_handle if glmft_handle is None: glmft_handle = GetGLMFTHandle() if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + glmft_handle.info if not glmft_handle.success: error = glmft_handle.info glmft_handle = None raise RuntimeError(error) # chatglmft 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 glmft_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_llms/bridge_all.py """ chatbot.append((inputs, "")) global glmft_handle if glmft_handle is None: glmft_handle = GetGLMFTHandle() chatbot[-1] = (inputs, load_message + "\n\n" + glmft_handle.info) yield from update_ui(chatbot=chatbot, history=[]) if not glmft_handle.success: glmft_handle = None return if additional_fn is not None: from core_functional import handle_core_functionality inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) # 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chatglmft的回复 response = "[Local Message] 等待ChatGLMFT响应中 ..." for response in glmft_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ChatGLMFT响应中 ...": response = "[Local Message] ChatGLMFT响应异常 ..." history.extend([inputs, response]) yield from update_ui(chatbot=chatbot, history=history)