| 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 |
|
|
|
|
| |
| |
| |
| 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 |
|
|
| |
| |
| |
| class GetInternlmHandle(LocalLLMHandle): |
|
|
| def load_model_info(self): |
| |
| 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): |
| |
| 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 |
|
|
| |
| 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 |
| |
| |
|
|
| 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`." |
| ) |
|
|
| |
| 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) |
| |
| outputs = model( |
| **model_inputs, |
| return_dict=True, |
| output_attentions=False, |
| output_hidden_states=False, |
| ) |
|
|
| next_token_logits = outputs.logits[:, -1, :] |
|
|
| |
| next_token_scores = logits_processor(input_ids, next_token_logits) |
| next_token_scores = logits_warper(input_ids, next_token_scores) |
|
|
| |
| 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) |
|
|
| |
| 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 |
| |
| if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): |
| return |
|
|
| |
| |
| |
| |
| predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetInternlmHandle, model_name) |
