# import spaces import os import re import time import gradio as gr import torch from transformers import AutoModelForCausalLM from transformers import TextIteratorStreamer from threading import Thread import importlib.metadata from importlib import import_module from transformers.utils import is_flash_attn_2_available from packaging import version def check_flash_attention_2_requirements(): # 检查 Flash Attention 2 是否可用 flash_attn_2_available = is_flash_attn_2_available() if not flash_attn_2_available: raise ImportError("Flash Attention 2 is not available.") # 获取已安装的 flash_attn 版本 try: installed_version = importlib.metadata.version("flash_attn") except importlib.metadata.PackageNotFoundError: raise ImportError("flash_attn package is not installed.") # 解析已安装的版本和所需的最低版本 parsed_installed_version = version.parse(installed_version) required_version = version.parse("2.6.3") # 检查版本是否满足要求 if parsed_installed_version < required_version: raise ImportError(f"flash_attn version {installed_version} is installed, but version >= 2.6.3 is required.") print("All requirements for Flash Attention 2 are met.") # 使用 try-except 块来捕获和显示具体的错误 try: check_flash_attention_2_requirements() except ImportError as e: print(f"Error: {e}") print("Using `flash_attention_2` requires having `flash_attn>=2.6.3` installed.") else: print("Flash Attention 2 can be used.") model_name = 'AIDC-AI/Ovis2-16B' # load model model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, multimodal_max_length=8192, trust_remote_code=True).to(device='cuda') text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True) image_placeholder = '' cur_dir = os.path.dirname(os.path.abspath(__file__)) def submit_chat(chatbot, text_input): response = '' chatbot.append((text_input, response)) return chatbot ,'' @spaces.GPU def ovis_chat(chatbot, image_input): # preprocess inputs conversations = [{ "from": "system", "value": "You are Ovis, a multimodal large language model developed by Alibaba International, and your task is to provide reliable and structured responses to users. 你是Ovis，由阿里国际研发的多模态大模型，你的任务是为用户提供可靠、结构化的回复。" }] response = "" text_input = chatbot[-1][0] for query, response in chatbot[:-1]: conversations.append({ "from": "human", "value": query }) conversations.append({ "from": "gpt", "value": response }) text_input = text_input.replace(image_placeholder, '') conversations.append({ "from": "human", "value": text_input }) if image_input is not None: conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"] prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input]) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) if image_input is None: pixel_values = [None] else: pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)] with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=1536, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) response = "" thread = Thread(target=model.generate, kwargs={"inputs": input_ids, "pixel_values": pixel_values, "attention_mask": attention_mask, "streamer": streamer, **gen_kwargs}) thread.start() for new_text in streamer: response += new_text chatbot[-1][1] = response yield chatbot thread.join() # debug print('*'*60) print('*'*60) print('OVIS_CONV_START') for i, (request, answer) in enumerate(chatbot[:-1], 1): print(f'Q{i}:\n {request}') print(f'A{i}:\n {answer}') print('New_Q:\n', text_input) print('New_A:\n', response) print('OVIS_CONV_END') def clear_chat(): return [], None, "" with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file: svg_content = svg_file.read() font_size = "2.5em" svg_content = re.sub(r'(]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content) html = f"""

{svg_content} {model_name.split('/')[-1]}

Ovis has been open-sourced on 😊 Huggingface and 🌟 GitHub. If you find Ovis useful, a like❤️ or a star🌟 would be appreciated.
""" latex_delimiters_set = [{ "left": "\\(", "right": "\\)", "display": True }, { "left": "\\begin{equation}", "right": "\\end{equation}", "display": True }, { "left": "\\begin{align}", "right": "\\end{align}", "display": True }, { "left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True }, { "left": "\\begin{gather}", "right": "\\end{gather}", "display": True }, { "left": "\\begin{CD}", "right": "\\end{CD}", "display": True }, { "left": "\\[", "right": "\\]", "display": True }] text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False) with gr.Blocks(title=model_name.split('/')[-1], theme=gr.themes.Ocean()) as demo: gr.HTML(html) with gr.Row(): with gr.Column(scale=3): image_input = gr.Image(label="image", height=350, type="pil") gr.Examples( examples=[ [f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."], [f"{cur_dir}/examples/case1.png", "explain this model to me."], [f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"], ], inputs=[image_input, text_input] ) with gr.Column(scale=7): chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set) text_input.render() with gr.Row(): send_btn = gr.Button("Send", variant="primary") clear_btn = gr.Button("Clear", variant="secondary") send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot) submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot) clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input]) demo.launch()