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| import torch | |
| from transformers import TextStreamer | |
| import os | |
| import sys | |
| sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "Evaluation")) | |
| from llava.constants import IMAGE_TOKEN_INDEX | |
| from llava.conversation import conv_templates, SeparatorStyle | |
| from llava.mm_utils import get_model_name_from_path, KeywordsStoppingCriteria, tokenizer_image_token | |
| from llava.model.builder import load_pretrained_model | |
| from llava.utils import disable_torch_init | |
| import shutil | |
| # <a href="https://github.com/SNUMPR/vlm-rlaif.git" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;"> | |
| # <img src="https://z1.ax1x.com/2023/11/07/pil4sqH.png" alt="VLM-RLAIF" style="max-width: 120px; height: auto;"> | |
| # </a> | |
| cur_dir = os.path.dirname(os.path.abspath(__file__)) | |
| title_markdown = (""" | |
| <div style="display: flex; justify-content: center; align-items: center; text-align: center;"> | |
| <img src="/dataset/dcahn/yura/vlm-rlaif/asset/Model.png" alt="VLM-RLAIF" style="max-width: 120px; height: auto;"> | |
| <img src="file:/dataset/dcahn/yura/vlm-rlaif/asset/Model.png" alt="VLM-RLAIF" style="max-width: 120px; height: auto;"> | |
| <div> | |
| <h1 >VLM-RLAIF: Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback (ACL 2024 Oral) </h1> | |
| <h5 style="margin: 0;">If you like our project, please give us a star โจ on Github for the latest update.</h5> | |
| </div> | |
| </div> | |
| <div align="center"> | |
| <div style="display:flex; gap: 0.25rem;" align="center"> | |
| <a href='https://github.com/SNUMPR/vlm-rlaif'><img src='https://img.shields.io/badge/Github-Code-blue'></a> | |
| <a href="https://arxiv.org/abs/2402.03746"><img src="https://img.shields.io/badge/Paper-arxiv-green"></a> | |
| </div> | |
| </div> | |
| """) | |
| # <a href='https://github.com/PKU-YuanGroup/Video-LLaVA/stargazers'><img src='https://img.shields.io/github/stars/PKU-YuanGroup/Video-LLaVA.svg?style=social'></a> # arXiv ๋ฒํผ ์์ ์ถ๊ฐ? | |
| block_css = """ | |
| #buttons button { | |
| min-width: min(120px,100%); | |
| } | |
| """ | |
| tos_markdown = (""" | |
| ### Terms of use | |
| By using this service, users are required to agree to the following terms: | |
| The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. | |
| Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator. | |
| For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality. | |
| """) | |
| learn_more_markdown = (""" | |
| ### License | |
| The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. | |
| """) | |
| class Chat: | |
| def __init__(self, model_path, conv_mode, model_base=None, load_8bit=False, load_4bit=False, device='cuda', cache_dir=None): | |
| # model_base = '/dataset/yura/vlm-rlaif/pretrained/final_models/Video_LLaVA_SFT' | |
| # model_base='/dataset/yura/vlm-rlaif/pretrained/llava-v1.5-7b-lora_w_lora_16_sftv2_short1632_and_then_long_rank32_alpha32_lr1e4_allmodels/SFT_merged' | |
| # model_path = '/dataset/yura/vlm-rlaif/pretrained/LLaVA_Video-RL-Fact-RLHF-7b_SFTv2_RM_13b_v1_40k-v1.5-336-lora-padding/checkpoint-180/adapter_model/lora_policy' | |
| disable_torch_init() | |
| model_name = get_model_name_from_path(model_path) | |
| # self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name, | |
| # load_8bit, load_4bit, | |
| # device=device, cache_dir=cache_dir) | |
| is_rlhf_checkpoint = 'rlhf' in model_path.lower() | |
| print("MODEL_PATH", model_path) | |
| print("RLHF Checkpoint: ", is_rlhf_checkpoint) | |
| if not model_base or model_base == "none": model_base = None | |
| if is_rlhf_checkpoint: | |
| model_name = model_path | |
| print("Config?", os.path.exists(os.path.join(model_path, "config.json"))) | |
| if not os.path.exists(os.path.join(model_path, "config.json")): | |
| print("Copying") | |
| shutil.copy(os.path.join(model_base, "config.json"), os.path.join(model_path, "config.json")) # Copy SFT model's config -> to RLHF folder | |
| print("Listed", os.listdir(model_path)) | |
| print("Copying done") | |
| # return(model_name) | |
| # return | |
| # self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name, load_8bit, load_4bit, device=device) | |
| self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name, False, False, device=device) | |
| self.image_processor = image_processor | |
| # self.image_processor = processor['image'] | |
| # self.video_processor = processor['video'] | |
| self.conv_mode = conv_mode | |
| self.conv = conv_templates[conv_mode].copy() | |
| self.device = self.model.device | |
| print(self.model) | |
| def get_prompt(self, qs, state): | |
| state.append_message(state.roles[0], qs) | |
| state.append_message(state.roles[1], None) | |
| return state | |
| def _get_latest_prompt(self, state): | |
| new_state = state.copy() | |
| new_state.messages = state.messages[-2:] | |
| return new_state | |
| # def generate(self, images_tensor: list, prompt: str, first_run: bool, state): | |
| def generate(self, images_tensor: torch.Tensor, prompt: str, first_run: bool, state): | |
| tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor | |
| state = self.get_prompt(prompt, state) | |
| # prompt = state.get_prompt() | |
| latest_state = self._get_latest_prompt(state) | |
| prompt = latest_state.get_prompt() | |
| # print('\n\n\n') | |
| # print(prompt) | |
| input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device) | |
| temperature = 0.2 | |
| max_new_tokens = 1024 | |
| stop_str = self.conv.sep if self.conv.sep_style != SeparatorStyle.TWO else self.conv.sep2 | |
| keywords = [stop_str] | |
| stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) | |
| streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) | |
| print(prompt, input_ids.shape, images_tensor.shape) | |
| # print(images_tensor) | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=images_tensor, | |
| do_sample=True, | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| streamer=streamer, | |
| use_cache=True, | |
| stopping_criteria=[stopping_criteria]) | |
| input_token_len = input_ids.shape[1] | |
| n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() | |
| if n_diff_input_output > 0: | |
| print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') | |
| outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] | |
| outputs = outputs.strip() | |
| outputs = outputs.replace("QA_GT_caption_based_noisy", "") | |
| if outputs.endswith(stop_str): | |
| outputs = outputs[:-len(stop_str)] | |
| outputs = outputs.strip() | |
| print('response', outputs) | |
| return outputs, state | |