"""This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers. We mainly modified part of the code logic to adapt to the generation of our model. Please refer to these links below for more information: 1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps 2. chatglm2: https://github.com/THUDM/ChatGLM2-6B 3. transformers: https://github.com/huggingface/transformers Please run with the command `streamlit run path/to/web_demo.py --server.address=0.0.0.0 --server.port 7860`. Using `python path/to/web_demo.py` may cause unknown problems. """ # isort: skip_file import copy import warnings from dataclasses import asdict, dataclass from typing import Callable, List, Optional import streamlit as st import torch from torch import nn from transformers.generation.utils import (LogitsProcessorList, StoppingCriteriaList) from transformers.utils import logging from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip logger = logging.get_logger(__name__) model_name_or_path="internlm/internlm2_5-7b-chat-4bit" @dataclass class GenerationConfig: # this config is used for chat to provide more diversity max_length: int = 32768 top_p: float = 0.8 temperature: float = 0.8 do_sample: bool = True repetition_penalty: float = 1.005 @torch.inference_mode() def generate_interactive( model, tokenizer, prompt, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, additional_eos_token_id: Optional[int] = None, **kwargs, ): 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'] _, 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 = ( # noqa: F841 # pylint: disable=W0612 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 \ ({repr(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: logger.warn( # pylint: disable=W4902 f"Both 'max_new_tokens' (={generation_config.max_new_tokens}) " f"and 'max_length'(={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' logger.warning( f'Input length of {input_ids_string} is {input_ids_seq_length}, ' f"but 'max_length' is set to {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): break def on_btn_click(): del st.session_state.messages @st.cache_resource def load_model(): # model = (AutoModelForCausalLM.from_pretrained( # model_name_or_path, # trust_remote_code=True).to(torch.bfloat16).cuda()) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) return model, tokenizer def prepare_generation_config(): with st.sidebar: max_length = st.slider('Max Length', min_value=8, max_value=32768, value=32768) top_p = st.slider('Top P', 0.0, 1.0, 0.8, step=0.01) temperature = st.slider('Temperature', 0.0, 1.0, 0.7, step=0.01) st.button('Clear Chat History', on_click=on_btn_click) generation_config = GenerationConfig(max_length=max_length, top_p=top_p, temperature=temperature) return generation_config user_prompt = '<|im_start|>user\n{user}<|im_end|>\n' robot_prompt = '<|im_start|>assistant\n{robot}<|im_end|>\n' cur_query_prompt = '<|im_start|>user\n{user}<|im_end|>\n\ <|im_start|>assistant\n' def combine_history(prompt): messages = st.session_state.messages meta_instruction = ('You are a helpful, honest, ' 'and harmless AI assistant.') total_prompt = f'<|im_start|>system\n{meta_instruction}<|im_end|>\n' for message in messages: cur_content = message['content'] if message['role'] == 'user': cur_prompt = user_prompt.format(user=cur_content) elif message['role'] == 'robot': cur_prompt = robot_prompt.format(robot=cur_content) else: raise RuntimeError total_prompt += cur_prompt total_prompt = total_prompt + cur_query_prompt.format(user=prompt) return total_prompt def main(): st.title('internlm2_5-7b-chat-assistant') # torch.cuda.empty_cache() print('load model begin.') model, tokenizer = load_model() print('load model end.') generation_config = prepare_generation_config() # Initialize chat history if 'messages' not in st.session_state: st.session_state.messages = [] # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message['role'], avatar=message.get('avatar')): st.markdown(message['content']) # Accept user input if prompt := st.chat_input('What is up?'): # Display user message in chat message container with st.chat_message('user', avatar='user'): st.markdown(prompt) real_prompt = combine_history(prompt) # Add user message to chat history st.session_state.messages.append({ 'role': 'user', 'content': prompt, 'avatar': 'user' }) with st.chat_message('robot', avatar='assistant'): message_placeholder = st.empty() for cur_response in generate_interactive( model=model, tokenizer=tokenizer, prompt=real_prompt, additional_eos_token_id=92542, device='cuda:0', **asdict(generation_config), ): # Display robot response in chat message container message_placeholder.markdown(cur_response + '▌') message_placeholder.markdown(cur_response) # Add robot response to chat history st.session_state.messages.append({ 'role': 'robot', 'content': cur_response, # pylint: disable=undefined-loop-variable 'avatar': 'assistant', }) torch.cuda.empty_cache() if __name__ == '__main__': main()