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| 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 | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| # ππ» Local Model Utils | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| def try_to_import_special_deps(): | |
| import sentencepiece | |
| def combine_history(prompt, hist): | |
| user_prompt = "<|User|>:{user}<eoh>\n" | |
| robot_prompt = "<|Bot|>:{robot}<eoa>\n" | |
| cur_query_prompt = "<|User|>:{user}<eoh>\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 | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| # ππ» Local Model | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| class GetInternlmHandle(LocalLLMHandle): | |
| def load_model_info(self): | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| 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): | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| 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 | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| 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 | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| # πββοΈπββοΈπββοΈ 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 | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| # ππ» GPT-Academic Interface | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetInternlmHandle, model_name) |