from transformers import AutoModel, AutoTokenizer import time import threading import importlib from toolbox import update_ui, get_conf, Singleton from multiprocessing import Process, Pipe model_name = "InternLM" cmd_to_install = "`pip install ???`" load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}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 try_to_import_special_deps(): import sentencepiece user_prompt = "<|User|>:{user}\n" robot_prompt = "<|Bot|>:{robot}\n" cur_query_prompt = "<|User|>:{user}\n<|Bot|>:" def combine_history(prompt, hist): messages = hist total_prompt = "" for message in messages: cur_content = message cur_prompt = user_prompt.replace("{user}", cur_content[0]) total_prompt += cur_prompt cur_prompt = robot_prompt.replace("{robot}", cur_content[1]) total_prompt += cur_prompt total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt) return total_prompt @Singleton class GetInternlmHandle(Process): def __init__(self): # ⭐主进程执行 super().__init__(daemon=True) self.parent, self.child = Pipe() self._model = None self._tokenizer = None self.info = "" self.success = True self.check_dependency() self.start() self.threadLock = threading.Lock() def ready(self): # ⭐主进程执行 return self._model is not None def load_model_and_tokenizer(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 import torch from transformers import AutoModelForCausalLM, AutoTokenizer device, = get_conf('LOCAL_MODEL_DEVICE') if self._model is None: tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True) if device=='cpu': model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16) else: model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda() model = model.eval() return model, tokenizer def llm_stream_generator(self, **kwargs): import torch import logging import copy import warnings import torch.nn as nn from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 def adaptor(): model = self._model tokenizer = self._tokenizer prompt = kwargs['query'] max_length = kwargs['max_length'] top_p = kwargs['top_p'] temperature = kwargs['temperature'] history = kwargs['history'] real_prompt = combine_history(prompt, history) return model, tokenizer, real_prompt, max_length, top_p, temperature model, tokenizer, prompt, max_length, top_p, temperature = adaptor() prefix_allowed_tokens_fn = None logits_processor = None stopping_criteria = None additional_eos_token_id = 103028 generation_config = None # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 # 🏃‍♂️🏃‍♂️🏃‍♂️ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25 inputs = tokenizer([prompt], padding=True, return_tensors="pt") input_length = len(inputs["input_ids"][0]) for k, v in inputs.items(): inputs[k] = v.cuda() input_ids = inputs["input_ids"] batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] if generation_config is None: generation_config = model.generation_config generation_config = copy.deepcopy(generation_config) model_kwargs = generation_config.update(**kwargs) bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id if isinstance(eos_token_id, int): eos_token_id = [eos_token_id] if additional_eos_token_id is not None: eos_token_id.append(additional_eos_token_id) has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None if has_default_max_length and generation_config.max_new_tokens is None: warnings.warn( f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" " recommend using `max_new_tokens` to control the maximum length of the generation.", UserWarning, ) elif generation_config.max_new_tokens is not None: generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length if not has_default_max_length: logging.warn( f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " "Please refer to the documentation for more information. " "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", UserWarning, ) if input_ids_seq_length >= generation_config.max_length: input_ids_string = "input_ids" logging.warning( f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" " increasing `max_new_tokens`." ) # 2. Set generation parameters if not already defined logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() logits_processor = model._get_logits_processor( generation_config=generation_config, input_ids_seq_length=input_ids_seq_length, encoder_input_ids=input_ids, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, logits_processor=logits_processor, ) stopping_criteria = model._get_stopping_criteria( generation_config=generation_config, stopping_criteria=stopping_criteria ) logits_warper = model._get_logits_warper(generation_config) unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) scores = None while True: model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs) # forward pass to get next token outputs = model( **model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False, ) next_token_logits = outputs.logits[:, -1, :] # pre-process distribution next_token_scores = logits_processor(input_ids, next_token_logits) next_token_scores = logits_warper(input_ids, next_token_scores) # sample probs = nn.functional.softmax(next_token_scores, dim=-1) if generation_config.do_sample: next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) else: next_tokens = torch.argmax(probs, dim=-1) # update generated ids, model inputs, and length for next step input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) model_kwargs = model._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder=False ) unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long()) output_token_ids = input_ids[0].cpu().tolist() output_token_ids = output_token_ids[input_length:] for each_eos_token_id in eos_token_id: if output_token_ids[-1] == each_eos_token_id: output_token_ids = output_token_ids[:-1] response = tokenizer.decode(output_token_ids) yield response # stop when each sentence is finished, or if we exceed the maximum length if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): return def check_dependency(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 try: try_to_import_special_deps() self.info = "依赖检测通过" self.success = True except: self.info = f"缺少{model_name}的依赖，如果要使用{model_name}，除了基础的pip依赖以外，您还需要运行{cmd_to_install}安装{model_name}的依赖。" self.success = False def run(self): # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行 # 第一次运行，加载参数 try: self._model, self._tokenizer = self.load_model_and_tokenizer() except: from toolbox import trimmed_format_exc self.child.send(f'[Local Message] 不能正常加载{model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n') raise RuntimeError(f"不能正常加载{model_name}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_full in self.llm_stream_generator(**kwargs): self.child.send(response_full) except: from toolbox import trimmed_format_exc self.child.send(f'[Local Message] 调用{model_name}失败.' + '\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() # ------------------------------------------------------------------------------------------------------------------------ # 🔌💻 GPT-Academic # ------------------------------------------------------------------------------------------------------------------------ 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 """ _llm_handle = GetInternlmHandle() if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info if not _llm_handle.success: error = _llm_handle.info _llm_handle = None raise RuntimeError(error) # chatglm 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 _llm_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_llm/bridge_all.py """ chatbot.append((inputs, "")) _llm_handle = GetInternlmHandle() chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info) yield from update_ui(chatbot=chatbot, history=[]) if not _llm_handle.success: _llm_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 = [] 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chatglm的回复 response = f"[Local Message]: 等待{model_name}响应中 ..." for response in _llm_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 == f"[Local Message]: 等待{model_name}响应中 ...": response = f"[Local Message]: {model_name}响应异常 ..." history.extend([inputs, response]) yield from update_ui(chatbot=chatbot, history=history)