model_name = "InternLM" cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`" from transformers import AutoModel, AutoTokenizer import time import threading import importlib from toolbox import update_ui, get_conf, ProxyNetworkActivate from multiprocessing import Process, Pipe from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns # ------------------------------------------------------------------------------------------------------------------------ # πŸ”ŒπŸ’» Local Model Utils # ------------------------------------------------------------------------------------------------------------------------ def try_to_import_special_deps(): import sentencepiece def combine_history(prompt, hist): user_prompt = "<|User|>:{user}\n" robot_prompt = "<|Bot|>:{robot}\n" cur_query_prompt = "<|User|>:{user}\n<|Bot|>:" 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 # ------------------------------------------------------------------------------------------------------------------------ # πŸ”ŒπŸ’» Local Model # ------------------------------------------------------------------------------------------------------------------------ class GetInternlmHandle(LocalLLMHandle): def load_model_info(self): # πŸƒβ€β™‚οΈπŸƒβ€β™‚οΈπŸƒβ€β™‚οΈ ε­θΏ›η¨‹ζ‰§θ‘Œ self.model_name = model_name self.cmd_to_install = cmd_to_install def try_to_import_special_deps(self, **kwargs): """ import something that will raise error if the user does not install requirement_*.txt """ import sentencepiece def load_model_and_tokenizer(self): # πŸƒβ€β™‚οΈπŸƒβ€β™‚οΈπŸƒβ€β™‚οΈ ε­θΏ›η¨‹ζ‰§θ‘Œ import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = get_conf('LOCAL_MODEL_DEVICE') with ProxyNetworkActivate('Download_LLM'): 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 # πŸƒβ€β™‚οΈπŸƒβ€β™‚οΈπŸƒβ€β™‚οΈ ε­θΏ›η¨‹ζ‰§θ‘Œ 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 # πŸƒβ€β™‚οΈπŸƒβ€β™‚οΈπŸƒβ€β™‚οΈ ε­θΏ›η¨‹ζ‰§θ‘Œ # πŸƒβ€β™‚οΈπŸƒβ€β™‚οΈπŸƒβ€β™‚οΈ 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]) device = get_conf('LOCAL_MODEL_DEVICE') for k, v in inputs.items(): inputs[k] = v.to(device) 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 # ------------------------------------------------------------------------------------------------------------------------ # πŸ”ŒπŸ’» GPT-Academic Interface # ------------------------------------------------------------------------------------------------------------------------ predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetInternlmHandle, model_name)