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Running
on
T4
| import argparse | |
| import datetime | |
| import hashlib | |
| import json | |
| import os | |
| import subprocess | |
| import sys | |
| import time | |
| import gradio as gr | |
| import requests | |
| from llava.constants import LOGDIR | |
| from llava.conversation import SeparatorStyle, conv_templates, default_conversation | |
| from llava.utils import ( | |
| build_logger, | |
| moderation_msg, | |
| server_error_msg, | |
| violates_moderation, | |
| ) | |
| logger = build_logger("gradio_web_server", "gradio_web_server.log") | |
| headers = {"User-Agent": "LLaVA Client"} | |
| no_change_btn = gr.Button.update() | |
| enable_btn = gr.Button.update(interactive=True) | |
| disable_btn = gr.Button.update(interactive=False) | |
| priority = { | |
| "vicuna-13b": "aaaaaaa", | |
| "koala-13b": "aaaaaab", | |
| } | |
| 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.Dropdown.update(visible=True) | |
| if "model" in url_params: | |
| model = url_params["model"] | |
| if model in models: | |
| dropdown_update = gr.Dropdown.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() | |
| models_downloaded = True if models else False | |
| model_dropdown_kwargs = { | |
| "choices": [], | |
| "value": "Downloading the models...", | |
| "interactive": models_downloaded, | |
| } | |
| if models_downloaded: | |
| model_dropdown_kwargs["choices"] = models | |
| model_dropdown_kwargs["value"] = models[0] | |
| models_dropdown_update = gr.Dropdown.update(**model_dropdown_kwargs) | |
| send_button_update = gr.Button.update( | |
| interactive=models_downloaded, | |
| ) | |
| return state, models_dropdown_update, send_button_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 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() | |
| 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: | |
| # First round of conversation | |
| if "llava" in model_name.lower(): | |
| if "llama-2" in model_name.lower(): | |
| template_name = "llava_llama_2" | |
| elif "v1" in model_name.lower(): | |
| if "mmtag" in model_name.lower(): | |
| template_name = "v1_mmtag" | |
| elif ( | |
| "plain" in model_name.lower() | |
| and "finetune" not in model_name.lower() | |
| ): | |
| template_name = "v1_mmtag" | |
| else: | |
| template_name = "llava_v1" | |
| elif "mpt" in model_name.lower(): | |
| template_name = "mpt" | |
| else: | |
| if "mmtag" in model_name.lower(): | |
| template_name = "v0_mmtag" | |
| elif ( | |
| "plain" in model_name.lower() | |
| and "finetune" not in model_name.lower() | |
| ): | |
| template_name = "v0_mmtag" | |
| else: | |
| template_name = "llava_v0" | |
| elif "mpt" in model_name: | |
| template_name = "mpt_text" | |
| elif "llama-2" in model_name: | |
| template_name = "llama_2" | |
| else: | |
| template_name = "vicuna_v1" | |
| 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 | |
| # 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(), | |
| disable_btn, | |
| disable_btn, | |
| disable_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": state.sep | |
| if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] | |
| 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() | |
| state.messages[-1][-1] = "▌" | |
| yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5 | |
| try: | |
| # Stream output | |
| response = requests.post( | |
| worker_addr + "/worker_generate_stream", | |
| headers=headers, | |
| json=pload, | |
| stream=True, | |
| timeout=10, | |
| ) | |
| 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()) + ( | |
| disable_btn, | |
| disable_btn, | |
| disable_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()) + ( | |
| disable_btn, | |
| disable_btn, | |
| disable_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(start_tstamp, 4), | |
| "state": state.dict(), | |
| "images": all_image_hash, | |
| "ip": request.client.host, | |
| } | |
| fout.write(json.dumps(data) + "\n") | |
| title_markdown = """ | |
| # 🌋 LLaVA: Large Language and Vision Assistant | |
| [[Project Page]](https://llava-vl.github.io) [[Paper]](https://arxiv.org/abs/2304.08485) [[Code]](https://github.com/haotian-liu/LLaVA) [[Model]](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) | |
| ONLY WORKS WITH GPU! | |
| You can load the model with 4-bit or 8-bit quantization to make it fit in smaller hardwares. Setting the environment variable `bits` to control the quantization. | |
| *Note: 8-bit seems to be slower than both 4-bit/16-bit. Although it has enough VRAM to support 8-bit, until we figure out the inference speed issue, we recommend 4-bit for A10G for the best efficiency.* | |
| Recommended configurations: | |
| | Hardware | T4-Small (16G) | A10G-Small (24G) | A100-Large (40G) | | |
| |-------------------|-----------------|------------------|------------------| | |
| | **Bits** | 4 (default) | 4 | 16 | | |
| """ | |
| 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. | |
| """ | |
| block_css = """ | |
| #buttons button { | |
| min-width: min(120px,100%); | |
| } | |
| """ | |
| def build_demo(embed_mode): | |
| models = get_model_list() | |
| textbox = gr.Textbox( | |
| show_label=False, placeholder="Enter text and press ENTER", container=False | |
| ) | |
| with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo: | |
| state = gr.State(default_conversation.copy()) | |
| if not embed_mode: | |
| gr.Markdown(title_markdown) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| with gr.Row(elem_id="model_selector_row"): | |
| model_selector = gr.Dropdown( | |
| choices=models, | |
| value=models[0] if models else "Downloading the models...", | |
| interactive=True if models else False, | |
| show_label=False, | |
| container=False, | |
| ) | |
| 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/extreme_ironing.jpg", | |
| "What is unusual about this image?", | |
| ], | |
| [ | |
| f"{cur_dir}/examples/waterview.jpg", | |
| "What are the things I should be cautious about when I visit here?", | |
| ], | |
| ], | |
| 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", | |
| ) | |
| with gr.Column(scale=8): | |
| chatbot = gr.Chatbot( | |
| elem_id="chatbot", label="LLaVA Chatbot", 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", interactive=False | |
| ) | |
| 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) | |
| flag_btn = gr.Button(value="⚠️ Flag", 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 history", 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, flag_btn, regenerate_btn, clear_btn] | |
| upvote_btn.click( | |
| upvote_last_response, | |
| [state, model_selector], | |
| [textbox, upvote_btn, downvote_btn, flag_btn], | |
| ) | |
| downvote_btn.click( | |
| downvote_last_response, | |
| [state, model_selector], | |
| [textbox, upvote_btn, downvote_btn, flag_btn], | |
| ) | |
| flag_btn.click( | |
| flag_last_response, | |
| [state, model_selector], | |
| [textbox, upvote_btn, downvote_btn, flag_btn], | |
| ) | |
| regenerate_btn.click( | |
| regenerate, | |
| [state, image_process_mode], | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| ).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 | |
| ) | |
| textbox.submit( | |
| add_text, | |
| [state, textbox, imagebox, image_process_mode], | |
| [state, chatbot, textbox, imagebox] + btn_list, | |
| ).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, | |
| ).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, | |
| ) | |
| elif args.model_list_mode == "reload": | |
| demo.load( | |
| load_demo_refresh_model_list, None, [state, model_selector, submit_btn] | |
| ) | |
| else: | |
| raise ValueError(f"Unknown model list mode: {args.model_list_mode}") | |
| return demo | |
| def start_controller(): | |
| logger.info("Starting the controller") | |
| controller_command = [ | |
| "python", | |
| "-m", | |
| "llava.serve.controller", | |
| "--host", | |
| "0.0.0.0", | |
| "--port", | |
| "10000", | |
| ] | |
| return subprocess.Popen(controller_command) | |
| def start_worker(model_path: str, bits=16): | |
| logger.info(f"Starting the model worker for the model {model_path}") | |
| model_name = model_path.strip("/").split("/")[-1] | |
| assert bits in [4, 8, 16], "It can be only loaded with 16-bit, 8-bit, and 4-bit." | |
| if bits != 16: | |
| model_name += f"-{bits}bit" | |
| worker_command = [ | |
| "python", | |
| "-m", | |
| "llava.serve.model_worker", | |
| "--host", | |
| "0.0.0.0", | |
| "--controller", | |
| "http://localhost:10000", | |
| "--model-path", | |
| model_path, | |
| "--model-name", | |
| model_name, | |
| ] | |
| if bits != 16: | |
| worker_command += [f"--load-{bits}bit"] | |
| return subprocess.Popen(worker_command) | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--host", type=str, default="0.0.0.0") | |
| parser.add_argument("--port", type=int) | |
| parser.add_argument("--controller-url", type=str, default="http://localhost:10000") | |
| parser.add_argument("--concurrency-count", type=int, default=8) | |
| parser.add_argument( | |
| "--model-list-mode", type=str, default="reload", choices=["once", "reload"] | |
| ) | |
| 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() | |
| return args | |
| def start_demo(args): | |
| demo = build_demo(args.embed) | |
| demo.queue( | |
| concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False | |
| ).launch(server_name=args.host, server_port=args.port, share=args.share) | |
| if __name__ == "__main__": | |
| args = get_args() | |
| logger.info(f"args: {args}") | |
| model_path = "liuhaotian/llava-v1.5-13b" | |
| bits = int(os.getenv("bits", 8)) | |
| controller_proc = start_controller() | |
| worker_proc = start_worker(model_path, bits=bits) | |
| # Wait for worker and controller to start | |
| time.sleep(10) | |
| exit_status = 0 | |
| try: | |
| start_demo(args) | |
| except Exception as e: | |
| print(e) | |
| exit_status = 1 | |
| finally: | |
| worker_proc.kill() | |
| controller_proc.kill() | |
| sys.exit(exit_status) | |