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Running
on
Zero
| import argparse | |
| import datetime | |
| import json | |
| import os | |
| import time | |
| import gradio as gr | |
| import requests | |
| import hashlib | |
| import pypandoc | |
| import base64 | |
| import sys | |
| import spaces | |
| from io import BytesIO | |
| from serve.conversation import (default_conversation, conv_templates, SeparatorStyle) | |
| from serve.constants import LOGDIR | |
| from serve.utils import (build_logger, server_error_msg, violates_moderation, moderation_msg) | |
| import subprocess | |
| subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| #logger = build_#logger("gradio_web_server", "gradio_web_server.log") | |
| headers = {"User-Agent": "Bunny Client"} | |
| no_change_btn = gr.update() | |
| enable_btn = gr.update(interactive=True) | |
| disable_btn = gr.update(interactive=False) | |
| priority = { | |
| "Bunny": "aaaaaaa", | |
| } | |
| def start_controller(): | |
| print("Starting the controller") | |
| controller_command = [ | |
| sys.executable, | |
| "serve/controller.py", | |
| "--host", | |
| "0.0.0.0", | |
| "--port", | |
| "10000", | |
| ] | |
| print(controller_command) | |
| return subprocess.Popen(controller_command) | |
| def start_worker(model_path: str): | |
| print(f"Starting the model worker for the model {model_path}") | |
| model_path = 'qnguyen3/nanoLLaVA' | |
| worker_command = [ | |
| sys.executable, | |
| "serve/model_worker.py", | |
| "--host", | |
| "0.0.0.0", | |
| "--controller", | |
| "http://localhost:10000", | |
| "--port", | |
| "40000", | |
| "worker", | |
| "http://localhost:40000", | |
| "--model-path", | |
| model_path, | |
| "--model-type", | |
| "qwen1.5-0.5b", | |
| "--use-flash-attn", | |
| ] | |
| print(worker_command) | |
| return subprocess.Popen(worker_command) | |
| def get_conv_log_filename(): | |
| t = datetime.datetime.now() | |
| name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json") | |
| return name | |
| def get_model_list(): | |
| ret = requests.post(args.controller_url + "/refresh_all_workers") | |
| assert ret.status_code == 200 | |
| ret = requests.post(args.controller_url + "/list_models") | |
| models = ret.json()["models"] | |
| models.sort(key=lambda x: priority.get(x, x)) | |
| #logger.info(f"Models: {models}") | |
| return models | |
| get_window_url_params = """ | |
| function() { | |
| const params = new URLSearchParams(window.location.search); | |
| url_params = Object.fromEntries(params); | |
| console.log(url_params); | |
| return url_params; | |
| } | |
| """ | |
| def load_demo(url_params, request: gr.Request): | |
| #logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") | |
| dropdown_update = gr.update(visible=True) | |
| if "model" in url_params: | |
| model = url_params["model"] | |
| if model in models: | |
| dropdown_update = gr.update( | |
| value=model, visible=True) | |
| state = default_conversation.copy() | |
| return state, dropdown_update | |
| def load_demo_refresh_model_list(request: gr.Request): | |
| #logger.info(f"load_demo. ip: {request.client.host}") | |
| models = get_model_list() | |
| state = default_conversation.copy() | |
| dropdown_update = gr.update( | |
| choices=models, | |
| value=models[0] if len(models) > 0 else "" | |
| ) | |
| return state, dropdown_update | |
| def vote_last_response(state, vote_type, model_selector, request: gr.Request): | |
| with open(get_conv_log_filename(), "a") as fout: | |
| data = { | |
| "tstamp": round(time.time(), 4), | |
| "type": vote_type, | |
| "model": model_selector, | |
| "state": state.dict(), | |
| "ip": request.client.host, | |
| } | |
| fout.write(json.dumps(data) + "\n") | |
| def upvote_last_response(state, model_selector, request: gr.Request): | |
| #logger.info(f"upvote. ip: {request.client.host}") | |
| vote_last_response(state, "upvote", model_selector, request) | |
| return ("",) + (disable_btn,) * 3 | |
| def downvote_last_response(state, model_selector, request: gr.Request): | |
| #logger.info(f"downvote. ip: {request.client.host}") | |
| vote_last_response(state, "downvote", model_selector, request) | |
| return ("",) + (disable_btn,) * 3 | |
| def flag_last_response(state, model_selector, request: gr.Request): | |
| #logger.info(f"flag. ip: {request.client.host}") | |
| vote_last_response(state, "flag", model_selector, request) | |
| return ("",) + (disable_btn,) * 3 | |
| def regenerate(state, image_process_mode, request: gr.Request): | |
| #logger.info(f"regenerate. ip: {request.client.host}") | |
| state.messages[-1][-1] = None | |
| prev_human_msg = state.messages[-2] | |
| if type(prev_human_msg[1]) in (tuple, list): | |
| prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode) | |
| state.skip_next = False | |
| return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 | |
| def clear_history(request: gr.Request): | |
| #logger.info(f"clear_history. ip: {request.client.host}") | |
| state = default_conversation.copy() | |
| return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 | |
| def save_conversation(conversation): | |
| print("save_conversation_wrapper is called") | |
| html_content = "<html><body>" | |
| for role, message in conversation.messages: | |
| if isinstance(message, str): # only text | |
| html_content += f"<p><b>{role}</b>: {message}</p>" | |
| elif isinstance(message, tuple): # text+image | |
| text, image_obj, _ = message | |
| # add text | |
| if text: | |
| html_content += f"<p><b>{role}</b>: {text}</p>" | |
| # add image | |
| buffered = BytesIO() | |
| image_obj.save(buffered, format="PNG") | |
| encoded_image = base64.b64encode(buffered.getvalue()).decode() | |
| html_content += f'<img src="data:image/png;base64,{encoded_image}" /><br>' | |
| html_content += "</body></html>" | |
| doc_path = "./conversation.docx" | |
| pypandoc.convert_text(html_content, 'docx', format='html', outputfile=doc_path, | |
| extra_args=["-M2GB", "+RTS", "-K64m", "-RTS"]) | |
| return doc_path | |
| def add_text(state, text, image, image_process_mode, request: gr.Request): | |
| #logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}") | |
| if len(text) <= 0 and image is None: | |
| state.skip_next = True | |
| return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5 | |
| if args.moderate: | |
| flagged = violates_moderation(text) | |
| if flagged: | |
| state.skip_next = True | |
| return (state, state.to_gradio_chatbot(), moderation_msg, None) + ( | |
| no_change_btn,) * 5 | |
| text = text[:1536] # Hard cut-off | |
| if image is not None: | |
| text = text[:1200] # Hard cut-off for images | |
| if '<image>' not in text: | |
| # text = '<Image><image></Image>' + text | |
| text = text + '\n<image>' | |
| text = (text, image, image_process_mode) | |
| if len(state.get_images(return_pil=True)) > 0: | |
| state = default_conversation.copy() | |
| #logger.info(f"Input Text: {text}") | |
| state.append_message(state.roles[0], text) | |
| state.append_message(state.roles[1], None) | |
| state.skip_next = False | |
| return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 | |
| def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request): | |
| #logger.info(f"http_bot. ip: {request.client.host}") | |
| start_tstamp = time.time() | |
| model_name = model_selector | |
| if state.skip_next: | |
| # This generate call is skipped due to invalid inputs | |
| yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5 | |
| return | |
| if len(state.messages) == state.offset + 2: | |
| template_name = "bunny" | |
| new_state = conv_templates[template_name].copy() | |
| new_state.append_message(new_state.roles[0], state.messages[-2][1]) | |
| new_state.append_message(new_state.roles[1], None) | |
| state = new_state | |
| #logger.info(f"Processed Input Text: {state.messages[-2][1]}") | |
| # Query worker address | |
| controller_url = args.controller_url | |
| ret = requests.post(controller_url + "/get_worker_address", | |
| json={"model": model_name}) | |
| worker_addr = ret.json()["address"] | |
| #logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}") | |
| # No available worker | |
| if worker_addr == "": | |
| state.messages[-1][-1] = server_error_msg | |
| yield (state, state.to_gradio_chatbot(), enable_btn, enable_btn, enable_btn) | |
| return | |
| # Construct prompt | |
| prompt = state.get_prompt() | |
| all_images = state.get_images(return_pil=True) | |
| all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images] | |
| for image, hash in zip(all_images, all_image_hash): | |
| t = datetime.datetime.now() | |
| filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg") | |
| if not os.path.isfile(filename): | |
| os.makedirs(os.path.dirname(filename), exist_ok=True) | |
| image.save(filename) | |
| # Make requests | |
| pload = { | |
| "model": model_name, | |
| "prompt": prompt, | |
| "temperature": float(temperature), | |
| "top_p": float(top_p), | |
| "max_new_tokens": min(int(max_new_tokens), 1536), | |
| "stop": '<|im_end|>', #state.sep if state.sep_style in [SeparatorStyle.PLAIN, ] else state.sep2, | |
| "images": f'List of {len(state.get_images())} images: {all_image_hash}', | |
| } | |
| #logger.info(f"==== request ====\n{pload}") | |
| pload['images'] = state.get_images() | |
| print('=========> get_images') | |
| state.messages[-1][-1] = "▌" | |
| yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5 | |
| print('=========> state', state.messages[-1][-1]) | |
| try: | |
| # Stream output | |
| response = requests.post(worker_addr + "/worker_generate_stream", | |
| headers=headers, json=pload, stream=True, timeout=1000) | |
| print("====> response ok") | |
| print("====> response dir", dir(response)) | |
| print("====> response", response) | |
| for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): | |
| if chunk: | |
| data = json.loads(chunk.decode()) | |
| if data["error_code"] == 0: | |
| output = data["text"][len(prompt):].strip() | |
| state.messages[-1][-1] = output + "▌" | |
| yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5 | |
| else: | |
| output = data["text"] + f" (error_code: {data['error_code']})" | |
| state.messages[-1][-1] = output | |
| yield (state, state.to_gradio_chatbot()) + (enable_btn, enable_btn, enable_btn) | |
| return | |
| time.sleep(0.03) | |
| except requests.exceptions.RequestException as e: | |
| state.messages[-1][-1] = server_error_msg | |
| yield (state, state.to_gradio_chatbot()) + (enable_btn, enable_btn, enable_btn) | |
| return | |
| state.messages[-1][-1] = state.messages[-1][-1][:-1] | |
| yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5 | |
| finish_tstamp = time.time() | |
| #logger.info(f"{output}") | |
| with open(get_conv_log_filename(), "a") as fout: | |
| data = { | |
| "tstamp": round(finish_tstamp, 4), | |
| "type": "chat", | |
| "model": model_name, | |
| "start": round(start_tstamp, 4), | |
| "finish": round(finish_tstamp, 4), | |
| "state": state.dict(), | |
| "images": all_image_hash, | |
| "ip": request.client.host, | |
| } | |
| fout.write(json.dumps(data) + "\n") | |
| title_markdown = (""" | |
| # 🐰 Bunny: A family of lightweight multimodal models | |
| [📖[Technical report](https://arxiv.org/abs/2402.11530)] | [🏠[Code](https://github.com/BAAI-DCAI/Bunny)] | [🤗[Model](https://huggingface.co/BAAI/Bunny-v1_0-3B)] | |
| """) | |
| 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 | |
| This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0. | |
| """) | |
| block_css = """ | |
| .centered { | |
| text-align: center; | |
| } | |
| #buttons button { | |
| min-width: min(120px,100%); | |
| } | |
| #file-downloader { | |
| min-width: min(120px,100%); | |
| height: 50px; | |
| } | |
| """ | |
| def trigger_download(doc_path): | |
| return doc_path | |
| def build_demo(embed_mode): | |
| textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) | |
| with gr.Blocks(title="Bunny", theme=gr.themes.Default(primary_hue="blue", secondary_hue="lime"), | |
| css=block_css) as demo: | |
| state = gr.State() | |
| if not embed_mode: | |
| gr.Markdown(title_markdown) | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| with gr.Row(elem_id="model_selector_row"): | |
| model_selector = gr.Dropdown( | |
| choices=models, | |
| value=models[0] if len(models) > 0 else "", | |
| interactive=True, | |
| show_label=False, | |
| container=False, | |
| allow_custom_value=True | |
| ) | |
| imagebox = gr.Image(type="pil") | |
| image_process_mode = gr.Radio( | |
| ["Crop", "Resize", "Pad", "Default"], | |
| value="Default", | |
| label="Preprocess for non-square image", visible=False) | |
| cur_dir = os.path.dirname(os.path.abspath(__file__)) | |
| gr.Examples(examples=[ | |
| [f"{cur_dir}/examples/example_1.png", "What is the astronaut holding in his hand?"], | |
| [f"{cur_dir}/examples/example_2.png", "Why is the image funny?"], | |
| ], inputs=[imagebox, textbox]) | |
| with gr.Accordion("Parameters", open=False) as parameter_row: | |
| temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, | |
| label="Temperature", ) | |
| top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P", ) | |
| max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, | |
| label="Max output tokens", ) | |
| file_output = gr.components.File(label="Download Document", visible=True, elem_id="file-downloader") | |
| with gr.Column(scale=8): | |
| chatbot = gr.Chatbot(elem_id="chatbot", label="Bunny Chatbot", | |
| avatar_images=[f"{cur_dir}/examples/user.png", f"{cur_dir}/examples/icon.jpg"], | |
| height=550) | |
| with gr.Row(): | |
| with gr.Column(scale=8): | |
| textbox.render() | |
| with gr.Column(scale=1, min_width=50): | |
| submit_btn = gr.Button(value="Send", variant="primary") | |
| with gr.Row(elem_id="buttons") as button_row: | |
| upvote_btn = gr.Button(value="👍 Upvote", interactive=False) | |
| downvote_btn = gr.Button(value="👎 Downvote", interactive=False) | |
| # stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False) | |
| regenerate_btn = gr.Button(value="🔁 Regenerate", interactive=False) | |
| clear_btn = gr.Button(value="🚮 Clear", interactive=False) | |
| save_conversation_btn = gr.Button(value="🗃️ Save", interactive=False) | |
| if not embed_mode: | |
| gr.Markdown(tos_markdown) | |
| gr.Markdown(learn_more_markdown) | |
| url_params = gr.JSON(visible=False) | |
| # Register listeners | |
| btn_list = [upvote_btn, downvote_btn, regenerate_btn, clear_btn, save_conversation_btn] | |
| upvote_btn.click( | |
| upvote_last_response, | |
| [state, model_selector], | |
| [textbox, upvote_btn, downvote_btn] | |
| ) | |
| downvote_btn.click( | |
| downvote_last_response, | |
| [state, model_selector], | |
| [textbox, upvote_btn, downvote_btn] | |
| ) | |
| regenerate_btn.click( | |
| regenerate, | |
| [state, image_process_mode], | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| queue=False | |
| ).then( | |
| http_bot, | |
| [state, model_selector, temperature, top_p, max_output_tokens], | |
| [state, chatbot] + btn_list | |
| ) | |
| clear_btn.click( | |
| clear_history, | |
| None, | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| queue=False | |
| ) | |
| save_conversation_btn.click( | |
| save_conversation, | |
| inputs=[state], | |
| outputs=file_output | |
| ) | |
| textbox.submit( | |
| add_text, | |
| [state, textbox, imagebox, image_process_mode], | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| queue=False | |
| ).then( | |
| http_bot, | |
| [state, model_selector, temperature, top_p, max_output_tokens], | |
| [state, chatbot] + btn_list | |
| ) | |
| submit_btn.click( | |
| add_text, | |
| [state, textbox, imagebox, image_process_mode], | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| queue=False | |
| ).then( | |
| http_bot, | |
| [state, model_selector, temperature, top_p, max_output_tokens], | |
| [state, chatbot] + btn_list | |
| ) | |
| if args.model_list_mode == "once": | |
| demo.load( | |
| load_demo, | |
| [url_params], | |
| [state, model_selector], | |
| _js=get_window_url_params, | |
| queue=False | |
| ) | |
| elif args.model_list_mode == "reload": | |
| demo.load( | |
| load_demo_refresh_model_list, | |
| None, | |
| [state, model_selector], | |
| queue=False | |
| ) | |
| else: | |
| raise ValueError(f"Unknown model list mode: {args.model_list_mode}") | |
| return demo | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--host", type=str, default="127.0.0.1") | |
| parser.add_argument("--port", type=int) | |
| parser.add_argument("--concurrency-count", type=int, default=10) | |
| parser.add_argument("--model-list-mode", type=str, default="once", | |
| choices=["once", "reload"]) | |
| parser.add_argument("--controller-url", type=str, default="http://localhost:10000") | |
| parser.add_argument("--share", action="store_true") | |
| parser.add_argument("--moderate", action="store_true") | |
| parser.add_argument("--embed", action="store_true") | |
| args = parser.parse_args() | |
| #logger.info(f"args: {args}") | |
| models = get_model_list() | |
| #logger.info(args) | |
| concurrency_count = int(os.getenv("concurrency_count", 5)) | |
| controller_proc = start_controller() | |
| model_path = 'qnguyen3/nanoLLaVA' | |
| worker_proc = start_worker(model_path) | |
| time.sleep(10) | |
| exit_status = 0 | |
| demo = build_demo(args.embed) | |
| demo.launch( | |
| server_name=args.host, | |
| server_port=args.port, | |
| share=args.share, | |
| debug=True, | |
| max_threads=10) | |
| # ) | |
| # except Exception as e: | |
| # print(e) | |
| # exit_status = 1 | |
| # finally: | |
| # worker_proc.kill() | |
| # controller_proc.kill() | |
| # sys.exit(exit_status) |