diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..40b6a854b4002dbcafffb9c3df9f3797f23eec3f --- /dev/null +++ b/.gitignore @@ -0,0 +1,31 @@ +# Python +__pycache__ +*.pyc +*.egg-info +dist + +# Log +*.log +*.log.* +*.json +*.jsonl + +# Data +!**/alpaca-data-conversation.json + +# Editor +.idea +*.swp + +# Other +.DS_Store +wandb +output + +checkpoints +ckpts* + +.ipynb_checkpoints +*.ipynb + +*.log \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 6796d25085dddd3ac3a1a0eeae3a987e9899e728..05dfa115357b541d305f09d80e97fd429a3df39c 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,8 @@ --- title: LLaVA -emoji: 🏃 -colorFrom: gray -colorTo: pink +emoji: đŸ”Ĩ +colorFrom: purple +colorTo: gray sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +app_port: 7860 +--- \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..876b53b455864848ab3da9bcda0175e12a3d040e --- /dev/null +++ b/app.py @@ -0,0 +1,600 @@ +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() + dropdown_update = gr.Dropdown.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 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 "" not in text: + # text = '' + text + text = text + "\n" + 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) +""" + +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 len(models) > 0 else "", + interactive=True, + 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") + 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="⚠ī¸ Flag", interactive=False) + # stop_btn = gr.Button(value="⏚ī¸ Stop Generation", interactive=False) + regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) + clear_btn = gr.Button(value="🗑ī¸ 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]) + 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): + logger.info(f"Starting the model worker for the model {model_path}") + worker_command = [ + "python", + "-m", + "llava.serve.model_worker", + "--host", + "0.0.0.0", + "--controller", + "http://localhost:10000", + "--model-path", + model_path, + ] + return subprocess.Popen(worker_command) + + +def preload_models(model_path: str): + import torch + + from llava.model import LlavaLlamaForCausalLM + + model = LlavaLlamaForCausalLM.from_pretrained( + model_path, low_cpu_mem_usage=True, torch_dtype=torch.float16 + ) + vision_tower = model.get_vision_tower() + vision_tower.load_model() + + del vision_tower + del model + + +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-v1.5-7b" + + preload_models(model_path) + + controller_proc = start_controller() + worker_proc = start_worker(model_path) + + # Wait for worker and controller to start + time.sleep(10) + + try: + start_demo(args) + except Exception as e: + worker_proc.terminate() + controller_proc.terminate() + + print(e) + sys.exit(1) diff --git a/data/prompts/complex_reasoning/000_caps.txt b/data/prompts/complex_reasoning/000_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..358155c384a2d18e6927d62562ac3f12eef36a87 --- /dev/null +++ b/data/prompts/complex_reasoning/000_caps.txt @@ -0,0 +1,18 @@ +A man wearing multiple neck ties making a goofy face. +A man in a white shirt wearing very many ties. +a man with ties on poses for a picture +A man wearing multiple ties on his neck. +A young man smiles while wearing several ties. + +tie: [0.574, 0.298, 0.752, 0.704] +tie: [0.464, 0.339, 0.639, 0.789] +tie: [0.349, 0.363, 0.563, 0.732] +tie: [0.259, 0.255, 0.668, 0.805] +person: [0.019, 0.065, 0.962, 0.988] +person: [0.0, 0.24, 0.214, 1.0] +tie: [0.316, 0.778, 0.443, 0.867] +tie: [0.386, 0.707, 0.496, 0.801] +tie: [0.251, 0.354, 0.402, 0.747] +tie: [0.44, 0.318, 0.63, 0.397] +tie: [0.566, 0.33, 0.655, 0.543] +tie: [0.25, 0.409, 0.359, 0.556] \ No newline at end of file diff --git a/data/prompts/complex_reasoning/000_conv.txt b/data/prompts/complex_reasoning/000_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..04f47a91a74af9ba5686107fb6a4441117494a56 --- /dev/null +++ b/data/prompts/complex_reasoning/000_conv.txt @@ -0,0 +1,5 @@ +Question: +What is unusual about this photo? +=== +Answer: +In the photo, the man is wearing a total of ten ties around his neck. This is considered unusual because it is not common for someone to wear multiple ties at once. In a typical setting, a person would wear only one tie. The man's goofy facial expression also adds to the unconventional and humorous nature of the image. \ No newline at end of file diff --git a/data/prompts/complex_reasoning/001_caps.txt b/data/prompts/complex_reasoning/001_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..2065dfb20a6e40128749d507ecc27d01349e2ad9 --- /dev/null +++ b/data/prompts/complex_reasoning/001_caps.txt @@ -0,0 +1,18 @@ +A group of people standing outside of a black vehicle with various luggage. +Luggage surrounds a vehicle in an underground parking area +People try to fit all of their luggage in an SUV. +The sport utility vehicle is parked in the public garage, being packed for a trip +Some people with luggage near a van that is transporting it. + +person: [0.681, 0.242, 0.774, 0.694] +person: [0.63, 0.222, 0.686, 0.516] +person: [0.444, 0.233, 0.487, 0.34] +backpack: [0.384, 0.696, 0.485, 0.914] +backpack: [0.755, 0.413, 0.846, 0.692] +suitcase: [0.758, 0.413, 0.845, 0.69] +suitcase: [0.1, 0.497, 0.173, 0.579] +bicycle: [0.282, 0.363, 0.327, 0.442] +car: [0.786, 0.25, 0.848, 0.322] +car: [0.783, 0.27, 0.827, 0.335] +car: [0.86, 0.254, 0.891, 0.3] +car: [0.261, 0.101, 0.787, 0.626] \ No newline at end of file diff --git a/data/prompts/complex_reasoning/001_conv.txt b/data/prompts/complex_reasoning/001_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..b293aecb87839015f8ab37943afe71c2f8904871 --- /dev/null +++ b/data/prompts/complex_reasoning/001_conv.txt @@ -0,0 +1,5 @@ +Question: +What challenges do these people face? +=== +Answer: +In the image, a group of people is standing outside a black SUV in a parking area, surrounded by various pieces of luggage, including suitcases and backpacks. They are facing the challenge of fitting all their luggage into the black SUV. There are multiple suitcases and backpacks to be packed, which suggests that the group has a significant amount of belongings to accommodate. They might have to strategize and arrange the luggage efficiently to ensure that everything fits properly into the vehicle. Additionally, they need to consider the comfort of the passengers and visibility while driving, so the placement of the luggage must not obstruct the driver's view or make the passengers uncomfortable during the trip. \ No newline at end of file diff --git a/data/prompts/complex_reasoning/002_caps.txt b/data/prompts/complex_reasoning/002_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..8fb59f2eb46c7e0db50d2994b2e9102d46def656 --- /dev/null +++ b/data/prompts/complex_reasoning/002_caps.txt @@ -0,0 +1,7 @@ +There is a movie theater that displays the show times above the doors. +A red fire hydrant is deep in the snow. +The fire hydrant is in the snow near a recently plowed sidewalk. +This city has had a very hard winter with snow. +A hotel for dogs in the snow in winter. + +fire hydrant: [0.326, 0.612, 0.426, 0.72] \ No newline at end of file diff --git a/data/prompts/complex_reasoning/002_conv.txt b/data/prompts/complex_reasoning/002_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a2c7f35b9fe3961f0d974ee4799fa517922df83 --- /dev/null +++ b/data/prompts/complex_reasoning/002_conv.txt @@ -0,0 +1,5 @@ +Question: +What challenges might this city face? +=== +Answer: +The city faces challenges due to the harsh winter conditions and heavy snowfall. In the image, a red fire hydrant is almost buried deep in the snow, which indicates the significant amount of snow the city has experienced. This can lead to various challenges such as difficulties in transportation, increased risk of accidents, and disruptions to daily life. For example, the recently plowed sidewalk near the fire hydrant shows that the city has to constantly clear snow from roads and sidewalks to maintain access and safety for pedestrians and vehicles. Moreover, emergency services, like firefighters, might face challenges accessing crucial equipment, such as fire hydrants, during emergencies due to the snow accumulation. This highlights the importance of effective snow management strategies and preparedness in such cities to minimize the impact of harsh winter conditions on residents and essential services. \ No newline at end of file diff --git a/data/prompts/complex_reasoning/system_message.txt b/data/prompts/complex_reasoning/system_message.txt new file mode 100644 index 0000000000000000000000000000000000000000..a394efd653554ce687ab8f0c908238bef4f27dee --- /dev/null +++ b/data/prompts/complex_reasoning/system_message.txt @@ -0,0 +1,10 @@ +You are an AI visual assistant that can analyze a single image. You receive five sentences, each describing the same image you are observing. In addition, specific object locations within the image are given, along with detailed coordinates. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. + +The task is to use the provided caption and bounding box information, create a plausible question about the image, and provide the answer in detail. + +Create complex questions beyond describing the scene. +To answer such questions, one should require first understanding the visual content, then based on the background knowledge or reasoning, either explain why the things are happening that way, or provide guides and help to user's request. Make the question challenging by not including the visual content details in the question so that the user needs to reason about that first. + +Instead of directly mentioning the bounding box coordinates, utilize this data to explain the scene using natural language. Include details like object counts, position of the objects, relative position between the objects. + +When using the information from the caption and coordinates, directly explain the scene, and do not mention that the information source is the caption or the bounding box. Always answer as if you are directly looking at the image. \ No newline at end of file diff --git a/data/prompts/conversation/000_caps.txt b/data/prompts/conversation/000_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..3139b31b27e6e67b31b27cf0ac7bda317f46d6b8 --- /dev/null +++ b/data/prompts/conversation/000_caps.txt @@ -0,0 +1,5 @@ +There is a movie theater that displays the show times above the doors. +A red fire hydrant is deep in the snow. +The fire hydrant is in the snow near a recently plowed sidewalk. +This city has had a very hard winter with snow. +A hotel for dogs in the snow in winter. \ No newline at end of file diff --git a/data/prompts/conversation/000_conv.txt b/data/prompts/conversation/000_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..1300cd49992d8c342396cebfebc592909d6391e4 --- /dev/null +++ b/data/prompts/conversation/000_conv.txt @@ -0,0 +1,29 @@ +Question: +What color is the fire hydrant in the image? +=== +Answer: +The fire hydrant in the image is red. +=== +Question: +Is the fire hydrant fully covered by snow or partially visible? +=== +Answer: +The fire hydrant is partially visible, with a significant portion of it covered by the deep snow. +=== +Question: +What is the condition of the sidewalk near the fire hydrant? +=== +Answer: +The sidewalk near the fire hydrant has been recently plowed, so it is relatively clear of snow compared to the surrounding area. +=== +Question: +What does the image suggest about the winter season in the city? +=== +Answer: +The image suggests that the winter season in the city has been quite harsh and challenging. The significant snow accumulation seen in the image indicates that the city has experienced heavy snowfall, which can lead to various difficulties and disruptions for its residents. + +For instance, the deep snow covering the fire hydrant could potentially hinder its accessibility in case of emergencies, which poses safety concerns. The recently plowed sidewalk implies that the city's maintenance crews have been actively working to keep the walkways clear and safe for pedestrians, but the sheer amount of snowfall might make it difficult to keep up with the continuous snow removal efforts. + +Furthermore, such extreme winter conditions can impact transportation, leading to traffic issues, delays in public transit, and increased risks of accidents due to slippery roads. It can also cause problems for businesses and schools, as people might face challenges commuting to work or attending classes. Additionally, the heavy snow can put extra strain on infrastructure, such as roofs and power lines, increasing the likelihood of structural damage or power outages. + +In conclusion, the image of the red fire hydrant deep in the snow and the recently plowed sidewalk suggest that the city has faced a particularly severe winter season, with substantial snowfall that has likely caused various challenges and disruptions for its residents and infrastructure. \ No newline at end of file diff --git a/data/prompts/conversation/001_caps.txt b/data/prompts/conversation/001_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..02a8c6d8dda4f5ac338983251adcdc1d3a99a3f3 --- /dev/null +++ b/data/prompts/conversation/001_caps.txt @@ -0,0 +1,5 @@ +A man is skiing in the open snow covered hills +A skier is making his way into the snow. +A skier on the foothills of a huge mountain range. +A skier looks at mountains while standing near a trail sign. +a single person skiing by an area with a lot of bushes \ No newline at end of file diff --git a/data/prompts/conversation/001_conv.txt b/data/prompts/conversation/001_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..8dac93823203ead2af275b908f3b3c5e4ccbe631 --- /dev/null +++ b/data/prompts/conversation/001_conv.txt @@ -0,0 +1,37 @@ +Question: +What is the skier doing? +=== +Answer: +The skier is skiing in the snow-covered hills, making his way through the snow and enjoying the beautiful mountain scenery. They are also standing near a trail sign, which indicates their location on the mountain. +=== +Question: +Are there any other people or skiers visible in the image? +=== +Answer: +No, there is only a single person skiing in the image, and they are surrounded by an area with a lot of bushes. +=== +Question: +What kind of skiing is the person doing? Cross-country or downhill? +=== +Answer: +Based on what I can see in the photo, it is not possible to definitively determine whether the skier is doing cross-country or downhill skiing. However, given the presence of a trail sign and the fact that they are skiing in the foothills, it is more likely that the skier is engaged in cross-country skiing. Downhill skiing typically takes place on more steep slopes and involves ski lifts, whereas cross-country skiing often occurs on relatively flatter terrain with marked trails. +=== +Question: +Can you explain cross-country and downhill skiing a bit more? +=== +Answer: +Cross-country skiing and downhill skiing are two distinct types of skiing, each with its own techniques, equipment, and terrain preferences. + +1. Cross-Country Skiing: +Also known as Nordic skiing, cross-country skiing involves traversing relatively flat terrain or gentle slopes using skis and poles. It is a popular recreational and competitive sport that focuses on endurance, balance, and aerobic fitness. Cross-country skiers use their own locomotion to move across snow-covered terrain, rather than relying on ski lifts or other forms of assistance. +There are two main techniques in cross-country skiing: +- Classic: This technique involves skiing in parallel tracks, using a forward-and-backward gliding motion while keeping the skis parallel to each other. The skier uses their poles to propel themselves forward. +- Skate skiing: This technique is similar to ice skating, where the skier pushes off one ski at an angle and glides on the other. It requires a smooth, groomed surface and is typically faster than the classic technique. +Cross-country ski equipment consists of lightweight skis, boots, bindings, and poles. The boots are more flexible than those used in downhill skiing, allowing for better ankle movement and control. + +2. Downhill Skiing: +Also known as alpine skiing, downhill skiing involves descending slopes at high speeds using skis and poles for balance and control. This sport is more focused on speed, technique, and navigating challenging terrain, including steep slopes, moguls, and even jumps. +Downhill skiing can be further categorized into several disciplines, such as slalom, giant slalom, super-G, and downhill racing. Each discipline has its own set of rules, courses, and ski equipment. +Downhill ski equipment includes heavier and stiffer skis, boots, bindings, and poles than those used in cross-country skiing. The boots are more rigid to provide better support and control during high-speed descents and sharp turns. + +In summary, cross-country skiing is an endurance-based sport that involves traveling across flat or gently sloping terrain, while downhill skiing is focused on speed and technique as skiers navigate steeper slopes and challenging terrain. Both sports require specialized equipment and techniques, but they offer different experiences and challenges to participants. \ No newline at end of file diff --git a/data/prompts/conversation/system_message.txt b/data/prompts/conversation/system_message.txt new file mode 100644 index 0000000000000000000000000000000000000000..6eb89c0c1408299f1423064814d78c293acf9da2 --- /dev/null +++ b/data/prompts/conversation/system_message.txt @@ -0,0 +1,12 @@ +You are an AI visual assistant, and you are seeing a single image. What you see are provided with five sentences, describing the same image you are looking at. Answer all questions as you are seeing the image. + +Design a conversation between you and a person asking about this photo. The answers should be in a tone that a visual AI assistant is seeing the image and answering the question. +Ask diverse questions and give corresponding answers. + +Include questions asking about the visual content of the image, including the object types, counting the objects, object actions, object locations, relative positions between objects, etc. Only include questions that have definite answers: +(1) one can see the content in the image that the question asks about and can answer confidently; +(2) one can determine confidently from the image that it is not in the image. +Do not ask any question that cannot be answered confidently. + +Also include complex questions that are relevant to the content in the image, for example, asking about background knowledge of the objects in the image, asking to discuss about events happening in the image, etc. Again, do not ask about uncertain details. +Provide detailed answers when answering complex questions. For example, give detailed examples or reasoning steps to make the content more convincing and well-organized. You can include multiple paragraphs if necessary. \ No newline at end of file diff --git a/data/prompts/detail_description/000_caps.txt b/data/prompts/detail_description/000_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..c8c10e30e2d7f9bde33105715b04f5251d5c1950 --- /dev/null +++ b/data/prompts/detail_description/000_caps.txt @@ -0,0 +1,18 @@ +A harbor filled with lots of boats next to a building. +A bicycle parked in front of several boats at a dock. +A red bicycle in front of a line of docked white yachts +A bike sits before boats which sit before a long building. +A bicycle is a convenient means of land transportation when you live on a boat. + +bicycle: [0.287, 0.641, 0.507, 0.874] +bicycle: [0.566, 0.667, 0.63, 0.731] +boat: [0.318, 0.579, 0.575, 0.724] +boat: [0.704, 0.607, 0.818, 0.727] +boat: [0.818, 0.601, 0.942, 0.744] +boat: [0.002, 0.53, 0.243, 0.71] +boat: [0.541, 0.611, 0.668, 0.731] +person: [0.778, 0.527, 0.797, 0.57] +cup: [0.708, 0.733, 0.724, 0.758] +boat: [0.236, 0.532, 0.404, 0.64] +boat: [0.81, 0.632, 0.836, 0.676] +boat: [0.957, 0.526, 1.0, 0.752] \ No newline at end of file diff --git a/data/prompts/detail_description/000_conv.txt b/data/prompts/detail_description/000_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4a24572427098354f723fad5e737ff6dfe223fb --- /dev/null +++ b/data/prompts/detail_description/000_conv.txt @@ -0,0 +1,3 @@ +It is a harbor filled with numerous boats of various sizes docked next to a long building. Among the boats, there are a few white yachts lined up, standing out from the rest. There is a red bicycle prominently parked in front of the line of docked boats, serving as a convenient means of land transportation for those living on the boats. Another bicycle can be seen further back in the scene, near the middle of the harbor. + +A person is visible near the right side of the harbor, possibly enjoying the view or attending to their boat. Additionally, there is a cup placed on a surface near the middle of the scene. \ No newline at end of file diff --git a/data/prompts/detail_description/001_caps.txt b/data/prompts/detail_description/001_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..2065dfb20a6e40128749d507ecc27d01349e2ad9 --- /dev/null +++ b/data/prompts/detail_description/001_caps.txt @@ -0,0 +1,18 @@ +A group of people standing outside of a black vehicle with various luggage. +Luggage surrounds a vehicle in an underground parking area +People try to fit all of their luggage in an SUV. +The sport utility vehicle is parked in the public garage, being packed for a trip +Some people with luggage near a van that is transporting it. + +person: [0.681, 0.242, 0.774, 0.694] +person: [0.63, 0.222, 0.686, 0.516] +person: [0.444, 0.233, 0.487, 0.34] +backpack: [0.384, 0.696, 0.485, 0.914] +backpack: [0.755, 0.413, 0.846, 0.692] +suitcase: [0.758, 0.413, 0.845, 0.69] +suitcase: [0.1, 0.497, 0.173, 0.579] +bicycle: [0.282, 0.363, 0.327, 0.442] +car: [0.786, 0.25, 0.848, 0.322] +car: [0.783, 0.27, 0.827, 0.335] +car: [0.86, 0.254, 0.891, 0.3] +car: [0.261, 0.101, 0.787, 0.626] \ No newline at end of file diff --git a/data/prompts/detail_description/001_conv.txt b/data/prompts/detail_description/001_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..d2eaef8ea3754d8ec0695e328907a8d62553de46 --- /dev/null +++ b/data/prompts/detail_description/001_conv.txt @@ -0,0 +1,5 @@ +The image is an underground parking area with a black sport utility vehicle (SUV) parked. There are three people in the scene, with one person standing closer to the left side of the vehicle, another person in the middle, and the third person on the right side. They are all working together to pack their luggage into the SUV for a trip. + +In the parking area, various luggage items are scattered around the vehicle. There are two backpacks, one located near the left rear wheel and the other closer to the right side of the vehicle. Additionally, there are two suitcases, one on the right side of the car and another further away near the center of the parking area. A bicycle can also be seen on the left side of the vehicle. + +Other cars are parked around the main SUV, with one car positioned behind it and slightly to the left, another behind and slightly to the right, and the third car further behind on the right side. \ No newline at end of file diff --git a/data/prompts/detail_description/002_caps.txt b/data/prompts/detail_description/002_caps.txt new file mode 100644 index 0000000000000000000000000000000000000000..70284585018497b091ba797a05d57c07e8bce3b6 --- /dev/null +++ b/data/prompts/detail_description/002_caps.txt @@ -0,0 +1,15 @@ +A man holds a Wii-mote above his head while another looks on. +A guy and his friend are playing Nintendo Wii. +A young man is holding a video game remote over his head. +two men standing in a room while one plays with a wii mote +Some guys standing and playing a video game. + +couch: [0.697, 0.759, 0.995, 1.0] +dining table: [0.426, 0.755, 1.0, 0.987] +person: [0.082, 0.252, 0.342, 1.0] +person: [0.399, 0.085, 0.742, 0.982] +remote: [0.477, 0.135, 0.516, 0.187] +sink: [0.016, 0.501, 0.063, 0.52] +potted plant: [0.798, 0.384, 0.888, 0.645] +refrigerator: [0.305, 0.389, 0.414, 0.547] +chair: [0.72, 0.509, 0.858, 0.725] \ No newline at end of file diff --git a/data/prompts/detail_description/002_conv.txt b/data/prompts/detail_description/002_conv.txt new file mode 100644 index 0000000000000000000000000000000000000000..56881c770ec5aca56bc2bf6c38cb6101ae58fa24 --- /dev/null +++ b/data/prompts/detail_description/002_conv.txt @@ -0,0 +1,3 @@ +The image shows two men standing in a room, engaged in playing a video game on a Nintendo Wii console. One of the men is holding a Wii remote above his head with enthusiasm, while the other man looks on, likely enjoying the friendly competition. + +The room appears to be a living space with a couch located in the background and a dining table nearby. A potted plant can be seen placed close to the couch, and a chair is situated in the middle of the room. The room also features a kitchen area with a sink and a refrigerator visible in the background. \ No newline at end of file diff --git a/data/prompts/detail_description/system_message.txt b/data/prompts/detail_description/system_message.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa836ca4b4d836a539f7e6d0aa2a012e6996edf5 --- /dev/null +++ b/data/prompts/detail_description/system_message.txt @@ -0,0 +1,7 @@ +You are an AI visual assistant that can analyze a single image. You receive five sentences, each describing the same image you are observing. In addition, specific object locations within the image are given, along with detailed coordinates. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. + +Using the provided caption and bounding box information, describe the scene in a detailed manner. + +Instead of directly mentioning the bounding box coordinates, utilize this data to explain the scene using natural language. Include details like object counts, position of the objects, relative position between the objects. + +When using the information from the caption and coordinates, directly explain the scene, and do not mention that the information source is the caption or the bounding box. Always answer as if you are directly looking at the image. \ No newline at end of file diff --git a/docs/Customize_Component.md b/docs/Customize_Component.md new file mode 100644 index 0000000000000000000000000000000000000000..e99a60879920b389799fb3a0baf1fd864ee0bccc --- /dev/null +++ b/docs/Customize_Component.md @@ -0,0 +1,20 @@ +# Customize Components in LLaVA + +This is an initial guide on how to replace the LLMs, visual encoders, etc. with your choice of components. + +## LLM + +It is quite simple to swap out LLaMA to any other LLMs. You can refer to our implementation of [`llava_llama.py`](https://raw.githubusercontent.com/haotian-liu/LLaVA/main/llava/model/language_model/llava_llama.py) for an example of how to replace the LLM. + +Although it may seem that it still needs ~100 lines of code, most of them are copied from the original `llama.py` from HF. The only part that is different is to insert some lines for processing the multimodal inputs. + +In `forward` function, you can see that we call `self.prepare_inputs_labels_for_multimodal` to process the multimodal inputs. This function is defined in `LlavaMetaForCausalLM` and you just need to insert it into the `forward` function of your LLM. + +In `prepare_inputs_for_generation` function, you can see that we add `images` to the `model_inputs`. This is because we need to pass the images to the LLM during generation. + +These are basically all the changes you need to make to replace the LLM. + +## Visual Encoder + +You can check out [`clip_encoder.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/clip_encoder.py) on how we implement the CLIP visual encoder. + diff --git a/docs/Data.md b/docs/Data.md new file mode 100644 index 0000000000000000000000000000000000000000..84807ec252858cd78bf96b3fce6f42f66b20126f --- /dev/null +++ b/docs/Data.md @@ -0,0 +1,29 @@ +## Data + +| Data file name | Size | +| --- | ---: | +| [llava_instruct_150k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_150k.json) | 229 MB | +| [llava_instruct_80k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_80k.json) | 229 MB | +| [conversation_58k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/conversation_58k.json) | 126 MB | +| [detail_23k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/detail_23k.json) | 20.5 MB | +| [complex_reasoning_77k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/complex_reasoning_77k.json) | 79.6 MB | + +### Pretraining Dataset +The pretraining dataset used in this release is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution. Please see [here](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K) for a detailed description of the dataset structure and how to download the images. + +If you already have CC-3M dataset on your disk, the image names follow this format: `GCC_train_000000000.jpg`. You may edit the `image` field correspondingly if necessary. + +| Data | Chat File | Meta Data | Size | +| --- | --- | --- | ---: | +| CC-3M Concept-balanced 595K | [chat.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/chat.json) | [metadata.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/metadata.json) | 211 MB +| LAION/CC/SBU BLIP-Caption Concept-balanced 558K | [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/blip_laion_cc_sbu_558k.json) | [metadata.json](#) | 181 MB + +**Important notice**: Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload [`images.zip`](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/images.zip) for better reproducing our work in research community. It must not be used for any other purposes. The use of these images must comply with the CC-3M license. This may be taken down at any time when requested by the original CC-3M dataset owner or owners of the referenced images. + +### GPT-4 Prompts + +We provide our prompts and few-shot samples for GPT-4 queries, to better facilitate research in this domain. Please check out the [`prompts`](playground/data/prompts) folder for three kinds of questions: conversation, detail description, and complex reasoning. + +They are organized in a format of `system_message.txt` for system message, pairs of `abc_caps.txt` for few-shot sample user input, and `abc_conv.txt` for few-shot sample reference output. + +Note that you may find them in different format. For example, `conversation` is in `jsonl`, and detail description is answer-only. The selected format in our preliminary experiments works slightly better than a limited set of alternatives that we tried: `jsonl`, more natural format, answer-only. If interested, you may try other variants or conduct more careful study in this. Contributions are welcomed! diff --git a/docs/LLaVA_Bench.md b/docs/LLaVA_Bench.md new file mode 100644 index 0000000000000000000000000000000000000000..5921964c43f599b2d820de5092a1c3b4c39de60f --- /dev/null +++ b/docs/LLaVA_Bench.md @@ -0,0 +1,31 @@ +# LLaVA-Bench [[Download](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild)] + +**-Introduction-** Large commercial multimodal chatbots have been released in this week, including +- [Multimodal Bing-Chat by Microsoft](https://blogs.bing.com/search/july-2023/Bing-Chat-Enterprise-announced,-multimodal-Visual-Search-rolling-out-to-Bing-Chat) (July 18, 2023) +- [Multimodal Bard by Google](https://bard.google.com/). + +These chatbots are presumably supported by proprietary large multimodal models (LMM). Compared with the open-source LMM such as LLaVA, proprietary LMM represent the scaling success upperbound of the current SoTA techniques. They share the goal of developing multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild. While it remains less unexplored how to evaluate multimodal chat ability, it provides useful feedback to study open-source LMMs against the commercial multimodal chatbots. In addition to the *LLaVA-Bench (COCO)* dataset we used to develop the early versions of LLaVA, we are releasing [*LLaVA-Bench (In-the-Wild)*](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to the community for the public use. + +## LLaVA-Bench (In-the-Wild *[Ongoing work]*) + +To evaluate the model's capability in more challenging tasks and generalizability to novel domains, we collect a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc, and associate each image with a highly-detailed and manually-curated description and a proper selection of questions. Such design also assesses the model's robustness to different prompts. In this release, we also categorize questions into three categories: conversation (simple QA), detailed description, and complex reasoning. We continue to expand and improve the diversity of the LLaVA-Bench (In-the-Wild). We manually query Bing-Chat and Bard to get the responses. + +### Results + +The score is measured by comparing against a reference answer generated by text-only GPT-4. It is generated by feeding the question, along with the ground truth image annotations as the context. A text-only GPT-4 evaluator rates both answers. We query GPT-4 by putting the reference answer first, and then the answer generated by the candidate model. We upload images at their original resolution to Bard and Bing-Chat to obtain the results. + +| Approach | Conversation | Detail | Reasoning | Overall | +|----------------|--------------|--------|-----------|---------| +| Bard-0718 | 83.7 | 69.7 | 78.7 | 77.8 | +| Bing-Chat-0629 | 59.6 | 52.2 | 90.1 | 71.5 | +| LLaVA-13B-v1-336px-0719 (beam=1) | 64.3 | 55.9 | 81.7 | 70.1 | +| LLaVA-13B-v1-336px-0719 (beam=5) | 68.4 | 59.9 | 84.3 | 73.5 | + +Note that Bard sometimes refuses to answer questions about images containing humans, and Bing-Chat blurs the human faces in the images. We also provide the benchmark score for the subset without humans. + +| Approach | Conversation | Detail | Reasoning | Overall | +|----------------|--------------|--------|-----------|---------| +| Bard-0718 | 94.9 | 74.3 | 84.3 | 84.6 | +| Bing-Chat-0629 | 55.8 | 53.6 | 93.5 | 72.6 | +| LLaVA-13B-v1-336px-0719 (beam=1) | 62.2 | 56.4 | 82.2 | 70.0 | +| LLaVA-13B-v1-336px-0719 (beam=5) | 65.6 | 61.7 | 85.0 | 73.6 | diff --git a/docs/LLaVA_from_LLaMA2.md b/docs/LLaVA_from_LLaMA2.md new file mode 100644 index 0000000000000000000000000000000000000000..b4163668a33ff705c28f5b103b727514161e5652 --- /dev/null +++ b/docs/LLaVA_from_LLaMA2.md @@ -0,0 +1,29 @@ +# LLaVA (based on Llama 2 LLM, Preview) + +*NOTE: This is a technical preview. We are still running hyperparameter search, and will release the final model soon. If you'd like to contribute to this, please contact us.* + +:llama: **-Introduction-** [Llama 2 is an open-source LLM released by Meta AI](https://about.fb.com/news/2023/07/llama-2/) today (July 18, 2023). Compared with its early version [Llama 1](https://ai.meta.com/blog/large-language-model-llama-meta-ai/), Llama 2 is more favored in ***stronger language performance***, ***longer context window***, and importantly ***commercially usable***! While Llama 2 is changing the LLM market landscape in the language space, its multimodal ability remains unknown. We quickly develop the LLaVA variant based on the latest Llama 2 checkpoints, and release it to the community for the public use. + +You need to apply for and download the lastest Llama 2 checkpoints to start your own training (apply [here](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)) + + +## Training + +Please checkout [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh), [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune.sh), [`finetune_lora.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh). + +## LLaVA (based on Llama 2), What is different? + +:volcano: How is the new LLaVA based on Llama 2 different from Llama 1? The comparisons of the training process are described: +- **Pre-training**. The pre-trained base LLM is changed from Llama 1 to Llama 2 +- **Language instruction-tuning**. The previous LLaVA model starts with Vicuna, which is instruct tuned on ShareGPT data from Llama 1; The new LLaVA model starts with Llama 2 Chat, which is an instruct tuned checkpoint on dialogue data from Llama 2. +- **Multimodal instruction-tuning**. The same LLaVA-Lighting process is applied. + + +### Results + +- Llama 2 is better at following the instructions of role playing; Llama 2 fails in following the instructions of translation +- The quantitative evaluation on [LLaVA-Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) demonstrates on-par performance between Llama 2 and Llama 1 in LLaVA's multimodal chat ability. + + + + diff --git a/docs/LoRA.md b/docs/LoRA.md new file mode 100644 index 0000000000000000000000000000000000000000..369fe92579051f98a0724a92e52e65e014a0de2f --- /dev/null +++ b/docs/LoRA.md @@ -0,0 +1,46 @@ +# LLaVA (LoRA, Preview) + +NOTE: This is a technical preview, and is not yet ready for production use. We are still running hyperparameter search for the LoRA model, and will release the final model soon. If you'd like to contribute to this, please contact us. + +You need latest code base for LoRA support (instructions [here](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base)) + +## Demo (Web UI) + +Please execute each of the command below one by one (after the previous one has finished). The commands are the same as launching other demos except for an additional `--model-base` flag to specify the base model to use. Please make sure the base model corresponds to the LoRA checkpoint that you are using. For this technical preview, you need Vicuna v1.1 (7B) checkpoint (if you do not have that already, follow the instructions [here](https://github.com/lm-sys/FastChat#vicuna-weights)). + +#### Launch a controller +```Shell +python -m llava.serve.controller --host 0.0.0.0 --port 10000 +``` + +#### Launch a gradio web server. +```Shell +python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload +``` +You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker. + +#### Launch a model worker +```Shell +python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_3e-lora-preview-alpha --model-base /path/to/vicuna-v1.1 +``` +Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list. + +You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker. + + +## Training + +Please see sample training scripts for [LoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh) and [QLoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_qlora.sh). + +We provide sample DeepSpeed configs, [`zero3.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3.json) is more like PyTorch FSDP, and [`zero3_offload.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3_offload.json) can further save memory consumption by offloading parameters to CPU. `zero3.json` is usually faster than `zero3_offload.json` but requires more GPU memory, therefore, we recommend trying `zero3.json` first, and if you run out of GPU memory, try `zero3_offload.json`. You can also tweak the `per_device_train_batch_size` and `gradient_accumulation_steps` in the config to save memory, and just to make sure that `per_device_train_batch_size` and `gradient_accumulation_steps` remains the same. + +If you are having issues with ZeRO-3 configs, and there are enough VRAM, you may try [`zero2.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero2.json). This consumes slightly more memory than ZeRO-3, and behaves more similar to PyTorch FSDP, while still supporting parameter-efficient tuning. + +## Create Merged Checkpoints + +```Shell +python scripts/merge_lora_weights.py \ + --model-path /path/to/lora_model \ + --model-base /path/to/base_model \ + --save-model-path /path/to/merge_model +``` diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md new file mode 100644 index 0000000000000000000000000000000000000000..d7d50a17cadf751f17f40cb0630ad3becd97fbbf --- /dev/null +++ b/docs/MODEL_ZOO.md @@ -0,0 +1,136 @@ +# Model Zoo + +**To Use LLaVA-1.5 checkpoints, your llava package version must be newer than 1.1.0. [Instructions](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base) on how to upgrade.** + +If you are interested in including any other details in Model Zoo, please open an issue :) + +The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license: [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). + +## LLaVA-v1.5 + +| Version | Size | Schedule | Checkpoint | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet | +|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---| +| LLaVA-1.5 | 7B | full_ft-1e | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 58.3 | 58.6 | 63.4 | 30.5 | +| LLaVA-1.5 | 13B | full_ft-1e | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 63.6 | 61.6 | 70.7 | 35.4 | +| LLaVA-1.5 | 7B | lora-1e | coming soon | +| LLaVA-1.5 | 13B | lora-1e | coming soon | + +

+
+ LLaVA-1.5 achieves SoTA performance across 11 benchmarks. +

+ + +## LLaVA-v1 + +*Note: We recommend using the most capable LLaVA-v1.5 series above for the best performance.* + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | LLaVA-Bench-Conv | LLaVA-Bench-Detail | LLaVA-Bench-Complex | LLaVA-Bench-Overall | Download | +|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|--------------------|---------------------|---------------------|---------------------| +| Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | LLaVA-Instruct-80K | proj-1e, lora-1e | 64.3 | 55.9 | 81.7 | 70.1 | [LoRA](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3) [LoRA-Merged](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3) | +| LLaMA-2-13B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | 56.7 | 58.6 | 80.0 | 67.9 | [ckpt](https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-lightning-preview) | +| LLaMA-2-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | lora-1e | 51.2 | 58.9 | 71.6 | 62.8 | [LoRA](https://huggingface.co/liuhaotian/llava-llama-2-7b-chat-lightning-lora-preview) | + + +## Projector weights + +The model weights below are projector weights we have pretrained. You can use these projector weights for visual instruction tuning. We'll add more projector weights into model zoo very soon. + +**NOTE**: These projector weights are only compatible with the `llava>=1.0.0`, please check out the latest code base if your local code version is below `v1.0.0`. + +**NOTE**: When you use our pretrained projector for visual instruction tuning, it is very important to **use the same base LLM and vision encoder** as the one we used for pretraining the projector. Otherwise, the performance will be very bad. + +When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows, + +```Shell +--mm_use_im_start_end False +--mm_use_im_patch_token False +``` + +| Base LLM | Vision Encoder | Projection | Pretrain Data | Pretraining schedule | Download | +|----------|----------------|---------------|----------------------|----------|----------| +| Vicuna-13B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-13b-v1.5) | +| Vicuna-7B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5) | +| LLaMA-2-13B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-13b-chat) | +| LLaMA-2-7B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-7b-chat) | +| LLaMA-2-13B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-13b-chat) | +| LLaMA-2-7B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-7b-chat) | +| Vicuna-13B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3) | +| Vicuna-7B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-7b-v1.3) | +| Vicuna-13B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-13b-v1.3) | +| Vicuna-7B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-7b-v1.3) | + + +## Science QA Checkpoints + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download | +|----------|----------------|---------------|----------------------|-----------------|--------------------|---------------------| +| Vicuna-13B-v1.3 | CLIP-L | LCS-558K | 1e | ScienceQA | full_ft-12e | [ckpt](https://huggingface.co/liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3) | + + +## Legacy Models (merged weights) + +The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license. + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download | +|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------| +| MPT-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview) | + + +## Legacy Models (delta weights) + +The model weights below are *delta* weights. The usage of LLaVA checkpoints should comply with the base LLM's model license: [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). + +You can add our delta to the original LLaMA weights to obtain the LLaVA weights. + +Instructions: + +1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama). +2. Use the following scripts to get LLaVA weights by applying our delta. It will automatically download delta weights from our Hugging Face account. In the script below, we use the delta weights of [`liuhaotian/LLaVA-7b-delta-v0`](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) as an example. It can be adapted for other delta weights by changing the `--delta` argument (and base/target accordingly). + +```bash +python3 -m llava.model.apply_delta \ + --base /path/to/llama-7b \ + --target /output/path/to/LLaVA-7B-v0 \ + --delta liuhaotian/LLaVA-7b-delta-v0 +``` + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download | +|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------| +| Vicuna-13B-v1.1 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1) | +| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-Lightning-7B-delta-v1-1) | +| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0) | +| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | ScienceQA | full_ft-12e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0-science_qa) | +| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) | + + + +## Legacy Projector weights + +The following projector weights are deprecated, and the support for them may be removed in the future. They do not support zero-shot inference. Please use the projector weights in the [table above](#projector-weights) if possible. + +**NOTE**: When you use our pretrained projector for visual instruction tuning, it is very important to **use the same base LLM and vision encoder** as the one we used for pretraining the projector. Otherwise, the performance will be very bad. + +When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows, + +```Shell +--mm_use_im_start_end True +--mm_use_im_patch_token False +``` + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download | +|----------|----------------|---------------|----------------------|----------| +| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v1-1-LCS-558K-blip_caption.bin) | +| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption.bin) | +| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v0-CC3M-595K-original_caption.bin) | + +When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows, + +```Shell +--mm_use_im_start_end False +--mm_use_im_patch_token False +``` + +| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download | +|----------|----------------|---------------|----------------------|----------| +| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption-no_im_token.bin) | diff --git a/docs/ScienceQA.md b/docs/ScienceQA.md new file mode 100644 index 0000000000000000000000000000000000000000..8881c41c67002a3798435b051c9a609dd1c0d506 --- /dev/null +++ b/docs/ScienceQA.md @@ -0,0 +1,53 @@ +### ScienceQA + +#### Prepare Data +1. Please see ScienceQA [repo](https://github.com/lupantech/ScienceQA) for setting up the dataset. +2. Generate ScienceQA dataset for LLaVA conversation-style format. + +```Shell +python scripts/convert_sqa_to_llava.py \ + convert_to_llava \ + --base-dir /path/to/ScienceQA/data/scienceqa \ + --prompt-format "QCM-LEA" \ + --split {train,val,minival,test,minitest} +``` + +#### Training + +1. Pretraining + +You can download our pretrained projector weights from our [Model Zoo](), or train your own projector weights using [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh). + +2. Finetuning + +See [`finetune_sqa.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_sqa.sh). + +#### Evaluation + +1. Multiple-GPU inference +You may evaluate this with multiple GPUs, and concatenate the generated jsonl files. Please refer to our script for [batch evaluation](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_batch.sh) and [results gathering](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_gather.sh). + +2. Single-GPU inference + +(a) Generate LLaVA responses on ScienceQA dataset + +```Shell +python -m llava.eval.model_vqa_science \ + --model-path liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3 \ + --question-file /path/to/ScienceQA/data/scienceqa/llava_test_QCM-LEA.json \ + --image-folder /path/to/ScienceQA/data/scienceqa/images/test \ + --answers-file vqa/results/ScienceQA/test_llava-13b.jsonl \ + --conv-mode llava_v1 +``` + +(b) Evaluate the generated responses + +```Shell +python eval_science_qa.py \ + --base-dir /path/to/ScienceQA/data/scienceqa \ + --result-file vqa/results/ScienceQA/test_llava-13b.jsonl \ + --output-file vqa/results/ScienceQA/test_llava-13b_output.json \ + --output-result vqa/results/ScienceQA/test_llava-13b_result.json \ +``` + +For reference, we attach our prediction file [`test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json) and [`test_sqa_llava_13b_v0.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_13b_v0.json) for comparison when reproducing our results, as well as for further analysis in detail. diff --git a/examples/extreme_ironing.jpg b/examples/extreme_ironing.jpg new file mode 100644 index 0000000000000000000000000000000000000000..638b078837f175039b2db49a63821288d9681daa Binary files /dev/null and b/examples/extreme_ironing.jpg differ diff --git a/examples/waterview.jpg b/examples/waterview.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6f44ebaba1aa493b8bab3baa4e827b76752b1869 Binary files /dev/null and b/examples/waterview.jpg differ diff --git a/llava/__init__.py b/llava/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4d1f016db1028101d45ba7d68cb3f0bcb558c2bb --- /dev/null +++ b/llava/__init__.py @@ -0,0 +1 @@ +from .model import LlavaLlamaForCausalLM diff --git a/llava/constants.py b/llava/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..be8cf0204969a6c973f442b383d8e425d684e826 --- /dev/null +++ b/llava/constants.py @@ -0,0 +1,12 @@ +CONTROLLER_HEART_BEAT_EXPIRATION = 30 +WORKER_HEART_BEAT_INTERVAL = 15 + +LOGDIR = "." + +# Model Constants +IGNORE_INDEX = -100 +IMAGE_TOKEN_INDEX = -200 +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" diff --git a/llava/conversation.py b/llava/conversation.py new file mode 100644 index 0000000000000000000000000000000000000000..6ee30cf9942d6f3184e845c0436f5c1d80e00043 --- /dev/null +++ b/llava/conversation.py @@ -0,0 +1,381 @@ +import dataclasses +from enum import auto, Enum +from typing import List, Tuple + + +class SeparatorStyle(Enum): + """Different separator style.""" + SINGLE = auto() + TWO = auto() + MPT = auto() + PLAIN = auto() + LLAMA_2 = auto() + + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + system: str + roles: List[str] + messages: List[List[str]] + offset: int + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + version: str = "Unknown" + + skip_next: bool = False + + def get_prompt(self): + messages = self.messages + if len(messages) > 0 and type(messages[0][1]) is tuple: + messages = self.messages.copy() + init_role, init_msg = messages[0].copy() + init_msg = init_msg[0].replace("", "").strip() + if 'mmtag' in self.version: + messages[0] = (init_role, init_msg) + messages.insert(0, (self.roles[0], "")) + messages.insert(1, (self.roles[1], "Received.")) + else: + messages[0] = (init_role, "\n" + init_msg) + + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + self.sep + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + seps[i % 2] + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.MPT: + ret = self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + message + self.sep + else: + ret += role + elif self.sep_style == SeparatorStyle.LLAMA_2: + wrap_sys = lambda msg: f"<>\n{msg}\n<>\n\n" + wrap_inst = lambda msg: f"[INST] {msg} [/INST]" + ret = "" + + for i, (role, message) in enumerate(messages): + if i == 0: + assert message, "first message should not be none" + assert role == self.roles[0], "first message should come from user" + if message: + if type(message) is tuple: + message, _, _ = message + if i == 0: message = wrap_sys(self.system) + message + if i % 2 == 0: + message = wrap_inst(message) + ret += self.sep + message + else: + ret += " " + message + " " + self.sep2 + else: + ret += "" + ret = ret.lstrip(self.sep) + elif self.sep_style == SeparatorStyle.PLAIN: + seps = [self.sep, self.sep2] + ret = self.system + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message, _, _ = message + ret += message + seps[i % 2] + else: + ret += "" + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + return ret + + def append_message(self, role, message): + self.messages.append([role, message]) + + def get_images(self, return_pil=False): + images = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + from PIL import Image + msg, image, image_process_mode = msg + if image_process_mode == "Pad": + def expand2square(pil_img, background_color=(122, 116, 104)): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + image = expand2square(image) + elif image_process_mode in ["Default", "Crop"]: + pass + elif image_process_mode == "Resize": + image = image.resize((336, 336)) + else: + raise ValueError(f"Invalid image_process_mode: {image_process_mode}") + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if longest_edge != max(image.size): + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + if return_pil: + images.append(image) + else: + buffered = BytesIO() + image.save(buffered, format="PNG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + images.append(img_b64_str) + return images + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + msg, image, image_process_mode = msg + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + img_str = f'user upload image' + msg = img_str + msg.replace('', '').strip() + ret.append([msg, None]) + else: + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2, + version=self.version) + + def dict(self): + if len(self.get_images()) > 0: + return { + "system": self.system, + "roles": self.roles, + "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages], + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + return { + "system": self.system, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + + +conv_vicuna_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ("Human", "What are the key differences between renewable and non-renewable energy sources?"), + ("Assistant", + "Renewable energy sources are those that can be replenished naturally in a relatively " + "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " + "Non-renewable energy sources, on the other hand, are finite and will eventually be " + "depleted, such as coal, oil, and natural gas. Here are some key differences between " + "renewable and non-renewable energy sources:\n" + "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " + "energy sources are finite and will eventually run out.\n" + "2. Environmental impact: Renewable energy sources have a much lower environmental impact " + "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " + "and other negative effects.\n" + "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " + "have lower operational costs than non-renewable sources.\n" + "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " + "locations than non-renewable sources.\n" + "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " + "situations and needs, while non-renewable sources are more rigid and inflexible.\n" + "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " + "non-renewable sources are not, and their depletion can lead to economic and social instability.\n") + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_vicuna_v1 = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_llama_2 = Conversation( + system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. + +If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep="", + sep2="", +) + +conv_llava_llama_2 = Conversation( + system="You are a helpful language and vision assistant. " + "You are able to understand the visual content that the user provides, " + "and assist the user with a variety of tasks using natural language.", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep="", + sep2="", +) + +conv_mpt = Conversation( + system="""<|im_start|>system +A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""", + roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), + version="mpt", + messages=(), + offset=0, + sep_style=SeparatorStyle.MPT, + sep="<|im_end|>", +) + +conv_llava_plain = Conversation( + system="", + roles=("", ""), + messages=( + ), + offset=0, + sep_style=SeparatorStyle.PLAIN, + sep="\n", +) + +conv_llava_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ), + offset=0, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_llava_v0_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("Human", "Assistant"), + messages=( + ), + offset=0, + sep_style=SeparatorStyle.SINGLE, + sep="###", + version="v0_mmtag", +) + +conv_llava_v1 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_llava_v1_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("USER", "ASSISTANT"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", + version="v1_mmtag", +) + +default_conversation = conv_vicuna_v0 +conv_templates = { + "default": conv_vicuna_v0, + "v0": conv_vicuna_v0, + "v1": conv_vicuna_v1, + "vicuna_v1": conv_vicuna_v1, + "llama_2": conv_llama_2, + + "plain": conv_llava_plain, + "v0_plain": conv_llava_plain, + "llava_v0": conv_llava_v0, + "v0_mmtag": conv_llava_v0_mmtag, + "llava_v1": conv_llava_v1, + "v1_mmtag": conv_llava_v1_mmtag, + "llava_llama_2": conv_llava_llama_2, + + "mpt": conv_mpt, +} + + +if __name__ == "__main__": + print(default_conversation.get_prompt()) diff --git a/llava/eval/eval_gpt_review.py b/llava/eval/eval_gpt_review.py new file mode 100644 index 0000000000000000000000000000000000000000..8af4559c65fc2728b11fd2097a109981ee1ef686 --- /dev/null +++ b/llava/eval/eval_gpt_review.py @@ -0,0 +1,113 @@ +import argparse +import json +import os + +import openai +import tqdm +import ray +import time + +NUM_SECONDS_TO_SLEEP = 3 + +@ray.remote(num_cpus=4) +def get_eval(content: str, max_tokens: int): + while True: + try: + response = openai.ChatCompletion.create( + model='gpt-4', + messages=[{ + 'role': 'system', + 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' + }, { + 'role': 'user', + 'content': content, + }], + temperature=0.2, # TODO: figure out which temperature is best for evaluation + max_tokens=max_tokens, + ) + break + except openai.error.RateLimitError: + pass + except Exception as e: + print(e) + time.sleep(NUM_SECONDS_TO_SLEEP) + + print('success!') + return response['choices'][0]['message']['content'] + + +def parse_score(review): + try: + score_pair = review.split('\n')[0] + score_pair = score_pair.replace(',', ' ') + sp = score_pair.split(' ') + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + print('error', review) + return [-1, -1] + except Exception as e: + print(e) + print('error', review) + return [-1, -1] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') + parser.add_argument('-q', '--question') + # parser.add_argument('-a', '--answer') + parser.add_argument('-a', '--answer-list', nargs='+', default=[]) + parser.add_argument('-r', '--rule') + parser.add_argument('-o', '--output') + parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') + args = parser.parse_args() + + ray.init() + + f_q = open(os.path.expanduser(args.question)) + f_ans1 = open(os.path.expanduser(args.answer_list[0])) + f_ans2 = open(os.path.expanduser(args.answer_list[1])) + rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) + + review_file = open(f'{args.output}', 'w') + + js_list = [] + handles = [] + idx = 0 + for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): + # if idx == 1: + # break + + ques = json.loads(ques_js) + ans1 = json.loads(ans1_js) + ans2 = json.loads(ans2_js) + + category = json.loads(ques_js)['category'] + if category in rule_dict: + rule = rule_dict[category] + else: + rule = rule_dict['default'] + prompt = rule['prompt'] + role = rule['role'] + content = (f'[Question]\n{ques["text"]}\n\n' + f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' + f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' + f'[System]\n{prompt}\n\n') + js_list.append({ + 'id': idx+1, + 'question_id': ques['question_id'], + 'answer1_id': ans1['answer_id'], + 'answer2_id': ans2['answer_id'], + 'category': category}) + idx += 1 + handles.append(get_eval.remote(content, args.max_tokens)) + # To avoid the rate limit set by OpenAI + time.sleep(NUM_SECONDS_TO_SLEEP) + + reviews = ray.get(handles) + for idx, review in enumerate(reviews): + scores = parse_score(review) + js_list[idx]['content'] = review + js_list[idx]['tuple'] = scores + review_file.write(json.dumps(js_list[idx]) + '\n') + review_file.close() diff --git a/llava/eval/eval_gpt_review_bench.py b/llava/eval/eval_gpt_review_bench.py new file mode 100644 index 0000000000000000000000000000000000000000..06160f2422b5368f30fb967f7cae635208a1dc69 --- /dev/null +++ b/llava/eval/eval_gpt_review_bench.py @@ -0,0 +1,121 @@ +import argparse +import json +import os + +import openai +import time + +NUM_SECONDS_TO_SLEEP = 0.5 + + +def get_eval(content: str, max_tokens: int): + while True: + try: + response = openai.ChatCompletion.create( + model='gpt-4-0314', + messages=[{ + 'role': 'system', + 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' + }, { + 'role': 'user', + 'content': content, + }], + temperature=0.2, # TODO: figure out which temperature is best for evaluation + max_tokens=max_tokens, + ) + break + except openai.error.RateLimitError: + pass + except Exception as e: + print(e) + time.sleep(NUM_SECONDS_TO_SLEEP) + + return response['choices'][0]['message']['content'] + + +def parse_score(review): + try: + score_pair = review.split('\n')[0] + score_pair = score_pair.replace(',', ' ') + sp = score_pair.split(' ') + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + print('error', review) + return [-1, -1] + except Exception as e: + print(e) + print('error', review) + return [-1, -1] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') + parser.add_argument('-q', '--question') + parser.add_argument('-c', '--context') + parser.add_argument('-a', '--answer-list', nargs='+', default=[]) + parser.add_argument('-r', '--rule') + parser.add_argument('-o', '--output') + parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') + args = parser.parse_args() + + f_q = open(os.path.expanduser(args.question)) + f_ans1 = open(os.path.expanduser(args.answer_list[0])) + f_ans2 = open(os.path.expanduser(args.answer_list[1])) + rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) + + if os.path.isfile(os.path.expanduser(args.output)): + cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))] + else: + cur_reviews = [] + + review_file = open(f'{args.output}', 'a') + + context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))] + image_to_context = {context['image']: context for context in context_list} + + handles = [] + idx = 0 + for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): + ques = json.loads(ques_js) + ans1 = json.loads(ans1_js) + ans2 = json.loads(ans2_js) + + inst = image_to_context[ques['image']] + + if isinstance(inst['caption'], list): + cap_str = '\n'.join(inst['caption']) + else: + cap_str = inst['caption'] + + category = 'llava_bench_' + json.loads(ques_js)['category'] + if category in rule_dict: + rule = rule_dict[category] + else: + assert False, f"Visual QA category not found in rule file: {category}." + prompt = rule['prompt'] + role = rule['role'] + content = (f'[Context]\n{cap_str}\n\n' + f'[Question]\n{ques["text"]}\n\n' + f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' + f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' + f'[System]\n{prompt}\n\n') + cur_js = { + 'id': idx+1, + 'question_id': ques['question_id'], + 'answer1_id': ans1.get('answer_id', ans1['question_id']), + 'answer2_id': ans2.get('answer_id', ans2['answer_id']), + 'category': category + } + if idx >= len(cur_reviews): + review = get_eval(content, args.max_tokens) + scores = parse_score(review) + cur_js['content'] = review + cur_js['tuple'] = scores + review_file.write(json.dumps(cur_js) + '\n') + review_file.flush() + else: + print(f'Skipping {idx} as we already have it.') + idx += 1 + print(idx) + review_file.close() diff --git a/llava/eval/eval_gpt_review_visual.py b/llava/eval/eval_gpt_review_visual.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e407a400a67020d801e6c27a3c32a2ee38f30c --- /dev/null +++ b/llava/eval/eval_gpt_review_visual.py @@ -0,0 +1,118 @@ +import argparse +import json +import os + +import openai +import time + +NUM_SECONDS_TO_SLEEP = 0.5 + + +def get_eval(content: str, max_tokens: int): + while True: + try: + response = openai.ChatCompletion.create( + model='gpt-4-0314', + messages=[{ + 'role': 'system', + 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' + }, { + 'role': 'user', + 'content': content, + }], + temperature=0.2, # TODO: figure out which temperature is best for evaluation + max_tokens=max_tokens, + ) + break + except openai.error.RateLimitError: + pass + except Exception as e: + print(e) + time.sleep(NUM_SECONDS_TO_SLEEP) + + return response['choices'][0]['message']['content'] + + +def parse_score(review): + try: + score_pair = review.split('\n')[0] + score_pair = score_pair.replace(',', ' ') + sp = score_pair.split(' ') + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + print('error', review) + return [-1, -1] + except Exception as e: + print(e) + print('error', review) + return [-1, -1] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') + parser.add_argument('-q', '--question') + parser.add_argument('-c', '--context') + parser.add_argument('-a', '--answer-list', nargs='+', default=[]) + parser.add_argument('-r', '--rule') + parser.add_argument('-o', '--output') + parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') + args = parser.parse_args() + + f_q = open(os.path.expanduser(args.question)) + f_ans1 = open(os.path.expanduser(args.answer_list[0])) + f_ans2 = open(os.path.expanduser(args.answer_list[1])) + rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) + + if os.path.isfile(os.path.expanduser(args.output)): + cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))] + else: + cur_reviews = [] + + review_file = open(f'{args.output}', 'a') + + context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))] + image_to_context = {context['image']: context for context in context_list} + + handles = [] + idx = 0 + for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): + ques = json.loads(ques_js) + ans1 = json.loads(ans1_js) + ans2 = json.loads(ans2_js) + + inst = image_to_context[ques['image']] + cap_str = '\n'.join(inst['captions']) + box_str = '\n'.join([f'{instance["category"]}: {instance["bbox"]}' for instance in inst['instances']]) + + category = json.loads(ques_js)['category'] + if category in rule_dict: + rule = rule_dict[category] + else: + assert False, f"Visual QA category not found in rule file: {category}." + prompt = rule['prompt'] + role = rule['role'] + content = (f'[Context]\n{cap_str}\n\n{box_str}\n\n' + f'[Question]\n{ques["text"]}\n\n' + f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' + f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' + f'[System]\n{prompt}\n\n') + cur_js = { + 'id': idx+1, + 'question_id': ques['question_id'], + 'answer1_id': ans1.get('answer_id', ans1['question_id']), + 'answer2_id': ans2.get('answer_id', ans2['answer_id']), + 'category': category + } + if idx >= len(cur_reviews): + review = get_eval(content, args.max_tokens) + scores = parse_score(review) + cur_js['content'] = review + cur_js['tuple'] = scores + review_file.write(json.dumps(cur_js) + '\n') + review_file.flush() + else: + print(f'Skipping {idx} as we already have it.') + idx += 1 + print(idx) + review_file.close() diff --git a/llava/eval/eval_science_qa.py b/llava/eval/eval_science_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..e1b3ce52fd6d922f247cc0c48409e88d5af3f204 --- /dev/null +++ b/llava/eval/eval_science_qa.py @@ -0,0 +1,99 @@ +import argparse +import json +import os +import re +import random + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--base-dir', type=str) + parser.add_argument('--result-file', type=str) + parser.add_argument('--output-file', type=str) + parser.add_argument('--output-result', type=str) + parser.add_argument('--split', type=str, default='test') + parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) + return parser.parse_args() + + +def convert_caps(results): + fakecaps = [] + for result in results: + image_id = result['question_id'] + caption = result['text'] + fakecaps.append({"image_id": int(image_id), "caption": caption}) + return fakecaps + + +def get_pred_idx(prediction, choices, options): + """ + Get the index (e.g. 2) from the prediction (e.g. 'C') + """ + if prediction in options[:len(choices)]: + return options.index(prediction) + else: + return random.choice(range(len(choices))) + + +if __name__ == "__main__": + args = get_args() + + base_dir = args.base_dir + split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] + problems = json.load(open(os.path.join(base_dir, "problems.json"))) + predictions = [json.loads(line) for line in open(args.result_file)] + predictions = {pred['question_id']: pred for pred in predictions} + split_problems = {idx: problems[idx] for idx in split_indices} + + results = {'correct': [], 'incorrect': []} + sqa_results = {} + sqa_results['acc'] = None + sqa_results['correct'] = None + sqa_results['count'] = None + sqa_results['results'] = {} + sqa_results['outputs'] = {} + + for prob_id, prob in split_problems.items(): + if prob_id not in predictions: + continue + pred = predictions[prob_id] + pred_text = pred['text'] + + pattern = re.compile(r'The answer is ([A-Z]).') + res = pattern.findall(pred_text) + if len(res) == 1: + answer = res[0] # 'A', 'B', ... + else: + answer = "FAILED" + + pred_idx = get_pred_idx(answer, prob['choices'], args.options) + + analysis = { + 'question_id': prob_id, + 'parsed_ans': answer, + 'ground_truth': args.options[prob['answer']], + 'question': pred['prompt'], + 'pred': pred_text, + 'is_multimodal': '' in pred['prompt'], + } + + sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options) + sqa_results['outputs'][prob_id] = pred_text + + if pred_idx == prob['answer']: + results['correct'].append(analysis) + else: + results['incorrect'].append(analysis) + + correct = len(results['correct']) + total = len(results['correct']) + len(results['incorrect']) + print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%') + + sqa_results['acc'] = correct / total * 100 + sqa_results['correct'] = correct + sqa_results['count'] = total + + with open(args.output_file, 'w') as f: + json.dump(results, f, indent=2) + with open(args.output_result, 'w') as f: + json.dump(sqa_results, f, indent=2) diff --git a/llava/eval/eval_science_qa_gpt4.py b/llava/eval/eval_science_qa_gpt4.py new file mode 100644 index 0000000000000000000000000000000000000000..c2ff17c915481fb556aba6ec816a9e08f519c515 --- /dev/null +++ b/llava/eval/eval_science_qa_gpt4.py @@ -0,0 +1,104 @@ +import argparse +import json +import os +import re +import random +from collections import defaultdict + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--base-dir', type=str) + parser.add_argument('--gpt4-result', type=str) + parser.add_argument('--our-result', type=str) + parser.add_argument('--split', type=str, default='test') + parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) + return parser.parse_args() + + +def convert_caps(results): + fakecaps = [] + for result in results: + image_id = result['question_id'] + caption = result['text'] + fakecaps.append({"image_id": int(image_id), "caption": caption}) + return fakecaps + + +def get_pred_idx(prediction, choices, options): + """ + Get the index (e.g. 2) from the prediction (e.g. 'C') + """ + if prediction in options[:len(choices)]: + return options.index(prediction) + else: + return random.choice(range(len(choices))) + + +if __name__ == "__main__": + args = get_args() + + base_dir = args.base_dir + split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] + problems = json.load(open(os.path.join(base_dir, "problems.json"))) + our_predictions = [json.loads(line) for line in open(args.our_result)] + our_predictions = {pred['question_id']: pred for pred in our_predictions} + split_problems = {idx: problems[idx] for idx in split_indices} + + gpt4_predictions = json.load(open(args.gpt4_result))['outputs'] + + results = defaultdict(lambda: 0) + + for prob_id, prob in split_problems.items(): + if prob_id not in our_predictions: + continue + if prob_id not in gpt4_predictions: + continue + our_pred = our_predictions[prob_id]['text'] + gpt4_pred = gpt4_predictions[prob_id] + + pattern = re.compile(r'The answer is ([A-Z]).') + our_res = pattern.findall(our_pred) + if len(our_res) == 1: + our_answer = our_res[0] # 'A', 'B', ... + else: + our_answer = "FAILED" + gpt4_res = pattern.findall(gpt4_pred) + if len(gpt4_res) == 1: + gpt4_answer = gpt4_res[0] # 'A', 'B', ... + else: + gpt4_answer = "FAILED" + + our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options) + gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options) + + if gpt4_answer == 'FAILED': + results['gpt4_failed'] += 1 + # continue + gpt4_pred_idx = our_pred_idx + # if our_pred_idx != prob['answer']: + # print(our_predictions[prob_id]['prompt']) + # print('-----------------') + # print(f'LECTURE: {prob["lecture"]}') + # print(f'SOLUTION: {prob["solution"]}') + # print('=====================') + else: + # continue + pass + # gpt4_pred_idx = our_pred_idx + + if gpt4_pred_idx == prob['answer']: + results['correct'] += 1 + else: + results['incorrect'] += 1 + + + if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']: + results['correct_upperbound'] += 1 + + correct = results['correct'] + total = results['correct'] + results['incorrect'] + print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%') + print(f'Total: {total}, Correct (upper): {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%') + print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%') + diff --git a/llava/eval/eval_science_qa_gpt4_requery.py b/llava/eval/eval_science_qa_gpt4_requery.py new file mode 100644 index 0000000000000000000000000000000000000000..698546e995d365d1ccc2c25a87e6c5cd681e6eb6 --- /dev/null +++ b/llava/eval/eval_science_qa_gpt4_requery.py @@ -0,0 +1,149 @@ +import argparse +import json +import os +import re +import random +from collections import defaultdict + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--base-dir', type=str) + parser.add_argument('--gpt4-result', type=str) + parser.add_argument('--requery-result', type=str) + parser.add_argument('--our-result', type=str) + parser.add_argument('--output-result', type=str) + parser.add_argument('--split', type=str, default='test') + parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) + return parser.parse_args() + + +def convert_caps(results): + fakecaps = [] + for result in results: + image_id = result['question_id'] + caption = result['text'] + fakecaps.append({"image_id": int(image_id), "caption": caption}) + return fakecaps + + +def get_pred_idx(prediction, choices, options): + """ + Get the index (e.g. 2) from the prediction (e.g. 'C') + """ + if prediction in options[:len(choices)]: + return options.index(prediction) + else: + return random.choice(range(len(choices))) + + +if __name__ == "__main__": + args = get_args() + + base_dir = args.base_dir + split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] + problems = json.load(open(os.path.join(base_dir, "problems.json"))) + our_predictions = [json.loads(line) for line in open(args.our_result)] + our_predictions = {pred['question_id']: pred for pred in our_predictions} + split_problems = {idx: problems[idx] for idx in split_indices} + + requery_predictions = [json.loads(line) for line in open(args.requery_result)] + requery_predictions = {pred['question_id']: pred for pred in requery_predictions} + + gpt4_predictions = json.load(open(args.gpt4_result))['outputs'] + + results = defaultdict(lambda: 0) + + sqa_results = {} + sqa_results['acc'] = None + sqa_results['correct'] = None + sqa_results['count'] = None + sqa_results['results'] = {} + sqa_results['outputs'] = {} + + for prob_id, prob in split_problems.items(): + if prob_id not in our_predictions: + assert False + if prob_id not in gpt4_predictions: + assert False + our_pred = our_predictions[prob_id]['text'] + gpt4_pred = gpt4_predictions[prob_id] + if prob_id not in requery_predictions: + results['missing_requery'] += 1 + requery_pred = "MISSING" + else: + requery_pred = requery_predictions[prob_id]['text'] + + pattern = re.compile(r'The answer is ([A-Z]).') + our_res = pattern.findall(our_pred) + if len(our_res) == 1: + our_answer = our_res[0] # 'A', 'B', ... + else: + our_answer = "FAILED" + + requery_res = pattern.findall(requery_pred) + if len(requery_res) == 1: + requery_answer = requery_res[0] # 'A', 'B', ... + else: + requery_answer = "FAILED" + + gpt4_res = pattern.findall(gpt4_pred) + if len(gpt4_res) == 1: + gpt4_answer = gpt4_res[0] # 'A', 'B', ... + else: + gpt4_answer = "FAILED" + + our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options) + gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options) + requery_pred_idx = get_pred_idx(requery_answer, prob['choices'], args.options) + + results['total'] += 1 + + if gpt4_answer == 'FAILED': + results['gpt4_failed'] += 1 + if gpt4_pred_idx == prob['answer']: + results['gpt4_correct'] += 1 + if our_pred_idx == prob['answer']: + results['gpt4_ourvisual_correct'] += 1 + elif gpt4_pred_idx == prob['answer']: + results['gpt4_correct'] += 1 + results['gpt4_ourvisual_correct'] += 1 + + if our_pred_idx == prob['answer']: + results['our_correct'] += 1 + + if requery_answer == 'FAILED': + sqa_results['results'][prob_id] = our_pred_idx + if our_pred_idx == prob['answer']: + results['requery_correct'] += 1 + else: + sqa_results['results'][prob_id] = requery_pred_idx + if requery_pred_idx == prob['answer']: + results['requery_correct'] += 1 + else: + print(f""" +Question ({args.options[prob['answer']]}): {our_predictions[prob_id]['prompt']} +Our ({our_answer}): {our_pred} +GPT-4 ({gpt4_answer}): {gpt4_pred} +Requery ({requery_answer}): {requery_pred} +print("=====================================") +""") + + if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']: + results['correct_upperbound'] += 1 + + total = results['total'] + print(f'Total: {total}, Our-Correct: {results["our_correct"]}, Accuracy: {results["our_correct"] / total * 100:.2f}%') + print(f'Total: {total}, GPT-4-Correct: {results["gpt4_correct"]}, Accuracy: {results["gpt4_correct"] / total * 100:.2f}%') + print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%') + print(f'Total: {total}, GPT-4-OursVisual-Correct: {results["gpt4_ourvisual_correct"]}, Accuracy: {results["gpt4_ourvisual_correct"] / total * 100:.2f}%') + print(f'Total: {total}, Requery-Correct: {results["requery_correct"]}, Accuracy: {results["requery_correct"] / total * 100:.2f}%') + print(f'Total: {total}, Correct upper: {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%') + + sqa_results['acc'] = results["requery_correct"] / total * 100 + sqa_results['correct'] = results["requery_correct"] + sqa_results['count'] = total + + with open(args.output_result, 'w') as f: + json.dump(sqa_results, f, indent=2) + diff --git a/llava/eval/generate_webpage_data_from_table.py b/llava/eval/generate_webpage_data_from_table.py new file mode 100644 index 0000000000000000000000000000000000000000..92602258ccd953a1d7137056aaf15c8de8166e21 --- /dev/null +++ b/llava/eval/generate_webpage_data_from_table.py @@ -0,0 +1,111 @@ +"""Generate json file for webpage.""" +import json +import os +import re + +# models = ['llama', 'alpaca', 'gpt35', 'bard'] +models = ['vicuna'] + + +def read_jsonl(path: str, key: str=None): + data = [] + with open(os.path.expanduser(path)) as f: + for line in f: + if not line: + continue + data.append(json.loads(line)) + if key is not None: + data.sort(key=lambda x: x[key]) + data = {item[key]: item for item in data} + return data + + +def trim_hanging_lines(s: str, n: int) -> str: + s = s.strip() + for _ in range(n): + s = s.split('\n', 1)[1].strip() + return s + + +if __name__ == '__main__': + questions = read_jsonl('table/question.jsonl', key='question_id') + + # alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id') + # bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id') + # gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id') + # llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id') + vicuna_answers = read_jsonl('table/answer/answer_vicuna-13b.jsonl', key='question_id') + ours_answers = read_jsonl('table/results/llama-13b-hf-alpaca.jsonl', key='question_id') + + review_vicuna = read_jsonl('table/review/review_vicuna-13b_llama-13b-hf-alpaca.jsonl', key='question_id') + # review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id') + # review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id') + # review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id') + # review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id') + + records = [] + for qid in questions.keys(): + r = { + 'id': qid, + 'category': questions[qid]['category'], + 'question': questions[qid]['text'], + 'answers': { + # 'alpaca': alpaca_answers[qid]['text'], + # 'llama': llama_answers[qid]['text'], + # 'bard': bard_answers[qid]['text'], + # 'gpt35': gpt35_answers[qid]['text'], + 'vicuna': vicuna_answers[qid]['text'], + 'ours': ours_answers[qid]['text'], + }, + 'evaluations': { + # 'alpaca': review_alpaca[qid]['text'], + # 'llama': review_llama[qid]['text'], + # 'bard': review_bard[qid]['text'], + 'vicuna': review_vicuna[qid]['content'], + # 'gpt35': review_gpt35[qid]['text'], + }, + 'scores': { + 'vicuna': review_vicuna[qid]['tuple'], + # 'alpaca': review_alpaca[qid]['score'], + # 'llama': review_llama[qid]['score'], + # 'bard': review_bard[qid]['score'], + # 'gpt35': review_gpt35[qid]['score'], + }, + } + + # cleanup data + cleaned_evals = {} + for k, v in r['evaluations'].items(): + v = v.strip() + lines = v.split('\n') + # trim the first line if it's a pair of numbers + if re.match(r'\d+[, ]+\d+', lines[0]): + lines = lines[1:] + v = '\n'.join(lines) + cleaned_evals[k] = v.replace('Assistant 1', "**Assistant 1**").replace('Assistant 2', '**Assistant 2**') + + r['evaluations'] = cleaned_evals + records.append(r) + + # Reorder the records, this is optional + for r in records: + if r['id'] <= 20: + r['id'] += 60 + else: + r['id'] -= 20 + for r in records: + if r['id'] <= 50: + r['id'] += 10 + elif 50 < r['id'] <= 60: + r['id'] -= 50 + for r in records: + if r['id'] == 7: + r['id'] = 1 + elif r['id'] < 7: + r['id'] += 1 + + records.sort(key=lambda x: x['id']) + + # Write to file + with open('webpage/data.json', 'w') as f: + json.dump({'questions': records, 'models': models}, f, indent=2) diff --git a/llava/eval/model_qa.py b/llava/eval/model_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..6c8c1138ac166387d82cba868d00f64ab4e6a33c --- /dev/null +++ b/llava/eval/model_qa.py @@ -0,0 +1,85 @@ +import argparse +from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria +import torch +import os +import json +from tqdm import tqdm +import shortuuid + +from llava.conversation import default_conversation +from llava.utils import disable_torch_init + + +# new stopping implementation +class KeywordsStoppingCriteria(StoppingCriteria): + def __init__(self, keywords, tokenizer, input_ids): + self.keywords = keywords + self.tokenizer = tokenizer + self.start_len = None + self.input_ids = input_ids + + def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: + if self.start_len is None: + self.start_len = self.input_ids.shape[1] + else: + outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0] + for keyword in self.keywords: + if keyword in outputs: + return True + return False + + +@torch.inference_mode() +def eval_model(model_name, questions_file, answers_file): + # Model + disable_torch_init() + model_name = os.path.expanduser(model_name) + tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) + model = AutoModelForCausalLM.from_pretrained(model_name, + torch_dtype=torch.float16).cuda() + + + ques_file = open(os.path.expanduser(questions_file), "r") + ans_file = open(os.path.expanduser(answers_file), "w") + for i, line in enumerate(tqdm(ques_file)): + idx = json.loads(line)["question_id"] + qs = json.loads(line)["text"] + cat = json.loads(line)["category"] + conv = default_conversation.copy() + conv.append_message(conv.roles[0], qs) + prompt = conv.get_prompt() + inputs = tokenizer([prompt]) + input_ids = torch.as_tensor(inputs.input_ids).cuda() + stopping_criteria = KeywordsStoppingCriteria([conv.sep], tokenizer, input_ids) + output_ids = model.generate( + input_ids, + do_sample=True, + use_cache=True, + temperature=0.7, + max_new_tokens=1024, + stopping_criteria=[stopping_criteria]) + outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] + try: + index = outputs.index(conv.sep, len(prompt)) + except ValueError: + outputs += conv.sep + index = outputs.index(conv.sep, len(prompt)) + + outputs = outputs[len(prompt) + len(conv.roles[1]) + 2:index].strip() + ans_id = shortuuid.uuid() + ans_file.write(json.dumps({"question_id": idx, + "text": outputs, + "answer_id": ans_id, + "model_id": model_name, + "metadata": {}}) + "\n") + ans_file.flush() + ans_file.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-name", type=str, default="facebook/opt-350m") + parser.add_argument("--question-file", type=str, default="tables/question.jsonl") + parser.add_argument("--answers-file", type=str, default="answer.jsonl") + args = parser.parse_args() + + eval_model(args.model_name, args.question_file, args.answers_file) diff --git a/llava/eval/model_vqa.py b/llava/eval/model_vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..6c02a617ce449808bb5662cb37e51d4f1558d7b4 --- /dev/null +++ b/llava/eval/model_vqa.py @@ -0,0 +1,112 @@ +import argparse +import torch +import os +import json +from tqdm import tqdm +import shortuuid + +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from llava.conversation import conv_templates, SeparatorStyle +from llava.model.builder import load_pretrained_model +from llava.utils import disable_torch_init +from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria + +from PIL import Image +import math + + +def split_list(lst, n): + """Split a list into n (roughly) equal-sized chunks""" + chunk_size = math.ceil(len(lst) / n) # integer division + return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] + + +def get_chunk(lst, n, k): + chunks = split_list(lst, n) + return chunks[k] + + +def eval_model(args): + # Model + disable_torch_init() + model_path = os.path.expanduser(args.model_path) + model_name = get_model_name_from_path(model_path) + tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) + + questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] + questions = get_chunk(questions, args.num_chunks, args.chunk_idx) + answers_file = os.path.expanduser(args.answers_file) + os.makedirs(os.path.dirname(answers_file), exist_ok=True) + ans_file = open(answers_file, "w") + for line in tqdm(questions): + idx = line["question_id"] + image_file = line["image"] + qs = line["text"] + cur_prompt = qs + if model.config.mm_use_im_start_end: + qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs + else: + qs = DEFAULT_IMAGE_TOKEN + '\n' + qs + + conv = conv_templates[args.conv_mode].copy() + conv.append_message(conv.roles[0], qs) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + + input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + + image = Image.open(os.path.join(args.image_folder, image_file)) + image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) + + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + images=image_tensor.unsqueeze(0).half().cuda(), + do_sample=True, + temperature=args.temperature, + top_p=args.top_p, + num_beams=args.num_beams, + # no_repeat_ngram_size=3, + max_new_tokens=1024, + use_cache=True) + + 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() + if outputs.endswith(stop_str): + outputs = outputs[:-len(stop_str)] + outputs = outputs.strip() + + ans_id = shortuuid.uuid() + ans_file.write(json.dumps({"question_id": idx, + "prompt": cur_prompt, + "text": outputs, + "answer_id": ans_id, + "model_id": model_name, + "metadata": {}}) + "\n") + ans_file.flush() + ans_file.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--image-folder", type=str, default="") + parser.add_argument("--question-file", type=str, default="tables/question.jsonl") + parser.add_argument("--answers-file", type=str, default="answer.jsonl") + parser.add_argument("--conv-mode", type=str, default="llava_v1") + parser.add_argument("--num-chunks", type=int, default=1) + parser.add_argument("--chunk-idx", type=int, default=0) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--top_p", type=float, default=None) + parser.add_argument("--num_beams", type=int, default=1) + args = parser.parse_args() + + eval_model(args) diff --git a/llava/eval/model_vqa_science.py b/llava/eval/model_vqa_science.py new file mode 100644 index 0000000000000000000000000000000000000000..aa77b39c0df7bcf0c8200f1282b165dee493ad73 --- /dev/null +++ b/llava/eval/model_vqa_science.py @@ -0,0 +1,141 @@ +import argparse +import torch +import os +import json +from tqdm import tqdm +import shortuuid + +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from llava.conversation import conv_templates, SeparatorStyle +from llava.model.builder import load_pretrained_model +from llava.utils import disable_torch_init +from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria + +from PIL import Image +import math + + +def split_list(lst, n): + """Split a list into n (roughly) equal-sized chunks""" + chunk_size = math.ceil(len(lst) / n) # integer division + return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] + + +def get_chunk(lst, n, k): + chunks = split_list(lst, n) + return chunks[k] + + +def eval_model(args): + # Model + disable_torch_init() + model_path = os.path.expanduser(args.model_path) + model_name = get_model_name_from_path(model_path) + tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) + + questions = json.load(open(os.path.expanduser(args.question_file), "r")) + questions = get_chunk(questions, args.num_chunks, args.chunk_idx) + answers_file = os.path.expanduser(args.answers_file) + os.makedirs(os.path.dirname(answers_file), exist_ok=True) + ans_file = open(answers_file, "w") + for i, line in enumerate(tqdm(questions)): + idx = line["id"] + question = line['conversations'][0] + qs = question['value'].replace('', '').strip() + cur_prompt = qs + + if 'image' in line: + image_file = line["image"] + image = Image.open(os.path.join(args.image_folder, image_file)) + image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + images = image_tensor.unsqueeze(0).half().cuda() + if getattr(model.config, 'mm_use_im_start_end', False): + qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs + else: + qs = DEFAULT_IMAGE_TOKEN + '\n' + qs + cur_prompt = '' + '\n' + cur_prompt + else: + images = None + + conv = conv_templates[args.conv_mode].copy() + conv.append_message(conv.roles[0], qs) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + + input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if conv.version == "v0" else None + + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + images=images, + do_sample=True, + temperature=0.2, + max_new_tokens=1024, + 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() + if outputs.endswith(stop_str): + outputs = outputs[:-len(stop_str)] + outputs = outputs.strip() + + # prompt for answer + if args.answer_prompter: + outputs_reasoning = outputs + input_ids = tokenizer_image_token(prompt + outputs_reasoning + ' ###\nANSWER:', tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + images=images, + do_sample=True, + temperature=0.2, + max_new_tokens=64, + 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() + if outputs.endswith(stop_str): + outputs = outputs[:-len(stop_str)] + outputs = outputs.strip() + outputs = outputs_reasoning + '\n The answer is ' + outputs + + ans_id = shortuuid.uuid() + ans_file.write(json.dumps({"question_id": idx, + "prompt": cur_prompt, + "text": outputs, + "answer_id": ans_id, + "model_id": model_name, + "metadata": {}}) + "\n") + ans_file.flush() + ans_file.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--image-folder", type=str, default="") + parser.add_argument("--question-file", type=str, default="tables/question.json") + parser.add_argument("--answers-file", type=str, default="answer.jsonl") + parser.add_argument("--conv-mode", type=str, default="llava_v0") + parser.add_argument("--num-chunks", type=int, default=1) + parser.add_argument("--chunk-idx", type=int, default=0) + parser.add_argument("--answer-prompter", action="store_true") + args = parser.parse_args() + + eval_model(args) diff --git a/llava/eval/qa_baseline_gpt35.py b/llava/eval/qa_baseline_gpt35.py new file mode 100644 index 0000000000000000000000000000000000000000..babab6e12b4bb8cfa74a7edfa5e56cd1b3e2bf6c --- /dev/null +++ b/llava/eval/qa_baseline_gpt35.py @@ -0,0 +1,74 @@ +"""Generate answers with GPT-3.5""" +# Note: you need to be using OpenAI Python v0.27.0 for the code below to work +import argparse +import json +import os +import time +import concurrent.futures + +import openai +import tqdm +import shortuuid + +MODEL = 'gpt-3.5-turbo' +MODEL_ID = 'gpt-3.5-turbo:20230327' + +def get_answer(question_id: int, question: str, max_tokens: int): + ans = { + 'answer_id': shortuuid.uuid(), + 'question_id': question_id, + 'model_id': MODEL_ID, + } + for _ in range(3): + try: + response = openai.ChatCompletion.create( + model=MODEL, + messages=[{ + 'role': 'system', + 'content': 'You are a helpful assistant.' + }, { + 'role': 'user', + 'content': question, + }], + max_tokens=max_tokens, + ) + ans['text'] = response['choices'][0]['message']['content'] + return ans + except Exception as e: + print('[ERROR]', e) + ans['text'] = '#ERROR#' + time.sleep(1) + return ans + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ChatGPT answer generation.') + parser.add_argument('-q', '--question') + parser.add_argument('-o', '--output') + parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') + args = parser.parse_args() + + questions_dict = {} + with open(os.path.expanduser(args.question)) as f: + for line in f: + if not line: + continue + q = json.loads(line) + questions_dict[q['question_id']] = q['text'] + + answers = [] + + with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor: + futures = [] + for qid, question in questions_dict.items(): + future = executor.submit(get_answer, qid, question, args.max_tokens) + futures.append(future) + + for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): + answers.append(future.result()) + + answers.sort(key=lambda x: x['question_id']) + + with open(os.path.expanduser(args.output), 'w') as f: + table = [json.dumps(ans) for ans in answers] + f.write('\n'.join(table)) diff --git a/llava/eval/run_llava.py b/llava/eval/run_llava.py new file mode 100644 index 0000000000000000000000000000000000000000..11bebda29b0b92a3d6928d28a0bd584510e304aa --- /dev/null +++ b/llava/eval/run_llava.py @@ -0,0 +1,97 @@ +import argparse +import torch + +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from llava.conversation import conv_templates, SeparatorStyle +from llava.model.builder import load_pretrained_model +from llava.utils import disable_torch_init +from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria + +from PIL import Image + +import requests +from PIL import Image +from io import BytesIO + + +def load_image(image_file): + if image_file.startswith('http') or image_file.startswith('https'): + response = requests.get(image_file) + image = Image.open(BytesIO(response.content)).convert('RGB') + else: + image = Image.open(image_file).convert('RGB') + return image + + +def eval_model(args): + # Model + disable_torch_init() + + model_name = get_model_name_from_path(args.model_path) + tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name) + + qs = args.query + if model.config.mm_use_im_start_end: + qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs + else: + qs = DEFAULT_IMAGE_TOKEN + '\n' + qs + + if 'llama-2' in model_name.lower(): + conv_mode = "llava_llama_2" + elif "v1" in model_name.lower(): + conv_mode = "llava_v1" + elif "mpt" in model_name.lower(): + conv_mode = "mpt" + else: + conv_mode = "llava_v0" + + if args.conv_mode is not None and conv_mode != args.conv_mode: + print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) + else: + args.conv_mode = conv_mode + + conv = conv_templates[args.conv_mode].copy() + conv.append_message(conv.roles[0], qs) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + + image = load_image(args.image_file) + image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() + + input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) + + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + images=image_tensor, + do_sample=True, + temperature=0.2, + max_new_tokens=1024, + 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() + if outputs.endswith(stop_str): + outputs = outputs[:-len(stop_str)] + outputs = outputs.strip() + print(outputs) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--image-file", type=str, required=True) + parser.add_argument("--query", type=str, required=True) + parser.add_argument("--conv-mode", type=str, default=None) + args = parser.parse_args() + + eval_model(args) diff --git a/llava/eval/summarize_gpt_review.py b/llava/eval/summarize_gpt_review.py new file mode 100644 index 0000000000000000000000000000000000000000..ee26f0a514083ac9848e302d5dbc5fcdaafa1d03 --- /dev/null +++ b/llava/eval/summarize_gpt_review.py @@ -0,0 +1,50 @@ +import json +import os +from collections import defaultdict + +import numpy as np + +import argparse + +def parse_args(): + parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') + parser.add_argument('-d', '--dir', default=None) + parser.add_argument('-f', '--files', nargs='*', default=None) + parser.add_argument('-i', '--ignore', nargs='*', default=None) + return parser.parse_args() + + +if __name__ == '__main__': + args = parse_args() + + if args.ignore is not None: + args.ignore = [int(x) for x in args.ignore] + + if args.files is not None and len(args.files) > 0: + review_files = args.files + else: + review_files = [x for x in os.listdir(args.dir) if x.endswith('.jsonl') and (x.startswith('gpt4_text') or x.startswith('reviews_') or x.startswith('review_'))] + + for review_file in sorted(review_files): + config = os.path.basename(review_file).replace('gpt4_text_', '').replace('.jsonl', '') + scores = defaultdict(list) + print(config) + with open(os.path.join(args.dir, review_file) if args.dir is not None else review_file) as f: + for review_str in f: + review = json.loads(review_str) + if args.ignore is not None and review['question_id'] in args.ignore: + continue + if 'category' in review: + scores[review['category']].append(review['tuple']) + scores['all'].append(review['tuple']) + else: + if 'tuple' in review: + scores['all'].append(review['tuple']) + else: + scores['all'].append(review['score']) + for k, v in sorted(scores.items()): + stats = np.asarray(v).mean(0).tolist() + stats = [round(x, 3) for x in stats] + # print(k, stats, round(stats[1]/stats[0]*100, 1)) + print(k, round(stats[1]/stats[0]*100, 1)) + 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0000000000000000000000000000000000000000..c2e3cf020ba7d8e064f2cd801788a5d2d50b97da --- /dev/null +++ b/llava/eval/webpage/index.html @@ -0,0 +1,162 @@ + + + + + + Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots + + + + + + + + +
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Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots

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GPT-4 Evaluation
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+ + + + + + + + + + + + + diff --git a/llava/eval/webpage/script.js b/llava/eval/webpage/script.js new file mode 100644 index 0000000000000000000000000000000000000000..4b71e3d5618a262e4746f58e5d10947b73370dca --- /dev/null +++ b/llava/eval/webpage/script.js @@ -0,0 +1,245 @@ +// Description: Script for the evaluation webpage. + +let currentQuestionIndex = 1; + +// Store the model name mapping for later use. +modelNameMapping = { + "gpt35": "ChatGPT-3.5", + "gpt4": "GPT-4", + "alpaca": "Alpaca-13b", + "vicuna": "Vicuna-13b", + "llama": "LLaMA-13b", + "bard": "Bard", +}; + +modelFigureMapping = { + "vicuna": "figures/vicuna.jpeg", + // Image from: https://commons.wikimedia.org/wiki/File:ChatGPT_logo.svg + "gpt35": "figures/chatgpt.svg", + // Image from: https://www.reddit.com/r/logodesign/comments/1128aat/google_ai_bard_logo_design/ + "bard": "figures/bard.jpg", + // Image from: https://crfm.stanford.edu/2023/03/13/alpaca.html + "alpaca": "figures/alpaca.png", + // Image adapted from https://commons.wikimedia.org/wiki/File:Llama_on_Machu_Picchu.jpg + "llama": "figures/llama.jpg", +} + +// Store the question data in a mapping for later use. +questionMapping = {}; +// Store the question ids in a mapping for later use. +categoryMapping = {}; +// Store the number of questions for later use. +questionsCount = 0; + + +function text2Markdown(text) { + // Normalize the text for markdown rendering. + text = text.trim().replaceAll('\n\n', '\n').replaceAll('\n', '\n\n'); + return marked.parse(text); +} + +function capitalizeFirstChar(str) { + if (!str || str.length === 0) { + return str; + } + return str.charAt(0).toUpperCase() + str.slice(1); +} + +function updateQuestionSelect(question_id) { + const select = document.getElementById('question-select'); + // Clear the question select. + select.innerHTML = ''; + // Populate the question select. + category = questionMapping[question_id].category; + categoryMapping[category].forEach(question_id => { + const question = questionMapping[question_id]; + const option = document.createElement('option'); + option.value = question_id; + option.textContent = 'Q' + question_id.toString() + ': ' + question.question; + select.appendChild(option); + }); + select.value = question_id; +} + +function updateModelSelect() { + const select = document.getElementById('model-select'); + img_path = modelFigureMapping[select.value]; + document.getElementById('other-model-figure').src = img_path; +} + +function populateModels(models) { + const select = document.getElementById('model-select'); + models.forEach(model => { + const option = document.createElement('option'); + option.value = model; + option.textContent = modelNameMapping[model]; + select.appendChild(option); + }); + updateModelSelect(); +} + +function populateQuestions(questions) { + const category_select = document.getElementById('category-select'); + + questionsCount = questions.length; + questions.forEach(question => { + const option = document.createElement('option'); + // Store the question data in a mapping for later use. + questionMapping[question.id] = { + category: question.category, + question: question.question, + answers: question.answers, + evaluations: question.evaluations, + scores: question.scores, + }; + // Store the question id in the category mapping. + if (question.category in categoryMapping) { + categoryMapping[question.category].push(question.id); + } else { + categoryMapping[question.category] = [question.id]; + const category_option = document.createElement('option'); + category_option.value = question.category; + category_option.textContent = capitalizeFirstChar(question.category); + category_select.appendChild(category_option); + } + }); + // Set the default category. + updateQuestionSelect(currentQuestionIndex); +} + +function displayQuestion(index) { + const question = questionMapping[index].question; + document.getElementById('selected-question').innerHTML = text2Markdown('**Question:** ' + question); + displayAnswers(index); +} + +function displayAnswers(index) { + const question = questionMapping[index]; + const otherModel = document.getElementById('model-select').value; + // render the answers with markdown + document.getElementById('other-model-answer').innerHTML = text2Markdown(question.answers[otherModel]); + document.getElementById('our-model-answer').innerHTML = text2Markdown(question.answers.vicuna); + + // Display evaluation + score = question.scores[otherModel]; + score_text = modelNameMapping[otherModel] + " " + score[0] + "/10, Vicuna-13b " + score[1] + "/10"; + document.getElementById('evaluation-header').textContent = "GPT-4 Evaluation" + " (Score: " + score_text + ")"; + document.getElementById('evaluation-result').innerHTML = text2Markdown(question.evaluations[otherModel]); + + // Update model names + let assistant1_title = "Assistant #1"; // (" + modelNameMapping[otherModel] + ")"; + let assistant2_title = "Assistant #2 (Vicuna-13b, our model)"; + // Update scores/labels. + let assistant1_score_label = score[0].toString() + '/10'; + let assistant2_score_label = score[1].toString() + '/10'; + + const colorRed ='#fa9'; // '#eb978d'; + // const colorGreen = '#c9f2c9'; + const colorBlue = '#8ef'; // '#71dbf9'; + const colorYellow = '#fe7'; // '#fada57'; + let otherModelHeaderColor = ''; + let ourModelHeaderColor = ''; + // Update the winner. + if (score[0] == score[1]) { + assistant1_title = '🏆 ' + assistant1_title; + assistant1_score_label = '🏆 ' + assistant1_score_label; + assistant2_title = '🏆 ' + assistant2_title; + assistant2_score_label = '🏆 ' + assistant2_score_label; + otherModelHeaderColor = colorYellow; + ourModelHeaderColor = colorYellow; + } else if (score[0] > score[1]) { + assistant1_title = '🏆 ' + assistant1_title; + assistant1_score_label = '🏆 ' + assistant1_score_label; + otherModelHeaderColor = colorBlue; + ourModelHeaderColor = colorRed; + } else if (score[0] < score[1]) { + assistant2_title = '🏆 ' + assistant2_title; + assistant2_score_label = '🏆 ' + assistant2_score_label; + otherModelHeaderColor = colorRed; + ourModelHeaderColor = colorBlue; + } + + document.getElementById('other-model-header-bg').style.backgroundColor = otherModelHeaderColor; + document.getElementById('our-model-header').style.backgroundColor = ourModelHeaderColor; + + document.getElementById('other-model-header').textContent = assistant1_title; + document.getElementById('our-model-header').textContent = assistant2_title; + + document.getElementById('other-score-label').textContent = assistant1_score_label; + document.getElementById('our-score-label').textContent = assistant2_score_label; + + // Update expand buttons visibility for both cards after displaying answers + // Reset the expanded state and update expand buttons visibility for both cards after displaying answers + document.querySelectorAll('.expandable-card').forEach(card => { + card.classList.remove('expanded'); + updateExpandButtonVisibility(card); + const expandBtn = card.querySelector('.expand-btn'); + expandBtn.innerHTML = 'keyboard_arrow_down Show more'; // .textContent = 'Show more'; + }); +} + +document.getElementById('question-select').addEventListener('change', e => { + currentQuestionIndex = parseInt(e.target.value); + displayQuestion(currentQuestionIndex); +}); + +document.getElementById('category-select').addEventListener('change', e => { + let currentCategory = e.target.value; + const questionIds = categoryMapping[currentCategory]; + currentQuestionIndex = questionIds[0]; + updateQuestionSelect(currentQuestionIndex); + displayQuestion(currentQuestionIndex); +}); + +// Update expand buttons whenever the model is changed +document.getElementById('model-select').addEventListener('change', () => { + displayAnswers(currentQuestionIndex); + document.querySelectorAll('.expandable-card').forEach(card => { + updateExpandButtonVisibility(card); + }); + updateModelSelect(); +}); + +function switchQuestionAndCategory() { + document.getElementById('question-select').value = currentQuestionIndex; + old_category = document.getElementById('category-select').value; + new_category = questionMapping[currentQuestionIndex].category; + if (old_category != new_category) { + document.getElementById('category-select').value = new_category; + updateQuestionSelect(currentQuestionIndex); + } + displayQuestion(currentQuestionIndex); +} + +document.getElementById('prev-question').addEventListener('click', () => { + // Question index starts from 1. + currentQuestionIndex = Math.max(1, currentQuestionIndex - 1); + switchQuestionAndCategory(); +}); + +document.getElementById('next-question').addEventListener('click', () => { + // Question index starts from 1. + currentQuestionIndex = Math.min(questionsCount, currentQuestionIndex + 1); + switchQuestionAndCategory(); +}); + +function updateExpandButtonVisibility(card) { + const cardTextContainer = card.querySelector('.card-text-container'); + const expandBtn = card.querySelector('.expand-btn'); + if (cardTextContainer.scrollHeight > cardTextContainer.offsetHeight) { + expandBtn.style.display = 'flex'; + } else { + expandBtn.style.display = 'none'; + card.classList.add('expanded'); + } +} + +document.querySelectorAll('.expand-btn').forEach(btn => { + btn.addEventListener('click', e => { + const card = e.target.closest('.expandable-card'); + card.classList.toggle('expanded'); + const more = 'keyboard_arrow_down Show more'; + const less = 'keyboard_arrow_up Show less'; + e.target.innerHTML = card.classList.contains('expanded') ? less : more; + }); +}); diff --git a/llava/eval/webpage/styles.css b/llava/eval/webpage/styles.css new file mode 100644 index 0000000000000000000000000000000000000000..7b6d6fc69b336c0a5d103be9fb13a0e0897c76a3 --- /dev/null +++ b/llava/eval/webpage/styles.css @@ -0,0 +1,105 @@ +body { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; + background-color: #f8f9fa; +} + +.navbar-dark .navbar-nav .nav-link { + color: #f1cf68; + font-size: 1.1rem; + padding: 0.5rem 0.6rem; +} + +.card-header { + font-weight: bold; +} + +.card { + box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); + transition: 0.3s; +} + +.card:hover { + box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2); +} + +button { + transition: background-color 0.3s; +} + +button:hover { + background-color: #007bff; +} + +@media (max-width: 767px) { + .form-row .form-group { + margin-bottom: 10px; + } +} + +/* Extra styles */ + +.expandable-card .card-text-container { + max-height: 200px; + overflow-y: hidden; + position: relative; +} + +.expandable-card.expanded .card-text-container { + max-height: none; +} + +.expand-btn { + position: relative; + display: none; + background-color: rgba(255, 255, 255, 0.8); + color: #510c75; + border-color: transparent; +} + +.expand-btn:hover { + background-color: rgba(200, 200, 200, 0.8); + text-decoration: none; + border-color: transparent; + color: #510c75; +} + +.expand-btn:focus { + outline: none; + text-decoration: none; +} + +.expandable-card:not(.expanded) .card-text-container:after { + content: ""; + position: absolute; + bottom: 0; + left: 0; + width: 100%; + height: 90px; + background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1)); +} + +.expandable-card:not(.expanded) .expand-btn { + margin-top: -40px; +} + +.card-body { + padding-bottom: 5px; +} + +.vertical-flex-layout { + justify-content: center; + align-items: center; + height: 100%; + display: flex; + flex-direction: column; + gap: 5px; +} + +.figure-img { + max-width: 100%; + height: auto; +} + +.adjustable-font-size { + font-size: calc(0.5rem + 2vw); +} diff --git a/llava/mm_utils.py b/llava/mm_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2662b91db2ba1c9fcc470b4654cbe05a11b89ae2 --- /dev/null +++ b/llava/mm_utils.py @@ -0,0 +1,99 @@ +from PIL import Image +from io import BytesIO +import base64 + +import torch +from transformers import StoppingCriteria +from llava.constants import IMAGE_TOKEN_INDEX + + +def load_image_from_base64(image): + return Image.open(BytesIO(base64.b64decode(image))) + + +def expand2square(pil_img, background_color): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + +def process_images(images, image_processor, model_cfg): + image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None) + new_images = [] + if image_aspect_ratio == 'pad': + for image in images: + image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean)) + image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + new_images.append(image) + else: + return image_processor(images, return_tensors='pt')['pixel_values'] + if all(x.shape == new_images[0].shape for x in new_images): + new_images = torch.stack(new_images, dim=0) + return new_images + + +def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): + prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('')] + + def insert_separator(X, sep): + return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1] + + input_ids = [] + offset = 0 + if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: + offset = 1 + input_ids.append(prompt_chunks[0][0]) + + for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): + input_ids.extend(x[offset:]) + + if return_tensors is not None: + if return_tensors == 'pt': + return torch.tensor(input_ids, dtype=torch.long) + raise ValueError(f'Unsupported tensor type: {return_tensors}') + return input_ids + + +def get_model_name_from_path(model_path): + model_path = model_path.strip("/") + model_paths = model_path.split("/") + if model_paths[-1].startswith('checkpoint-'): + return model_paths[-2] + "_" + model_paths[-1] + else: + return model_paths[-1] + + + + +class KeywordsStoppingCriteria(StoppingCriteria): + def __init__(self, keywords, tokenizer, input_ids): + self.keywords = keywords + self.keyword_ids = [] + for keyword in keywords: + cur_keyword_ids = tokenizer(keyword).input_ids + if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: + cur_keyword_ids = cur_keyword_ids[1:] + self.keyword_ids.append(torch.tensor(cur_keyword_ids)) + self.tokenizer = tokenizer + self.start_len = input_ids.shape[1] + + def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: + assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO + offset = min(output_ids.shape[1] - self.start_len, 3) + self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] + for keyword_id in self.keyword_ids: + if output_ids[0, -keyword_id.shape[0]:] == keyword_id: + return True + outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] + for keyword in self.keywords: + if keyword in outputs: + return True + return False diff --git a/llava/model/__init__.py b/llava/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fa79960540bf9247f5df9f02656ca84499cdbfd6 --- /dev/null +++ b/llava/model/__init__.py @@ -0,0 +1,2 @@ +from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig +from .language_model.llava_mpt import LlavaMPTForCausalLM, LlavaMPTConfig diff --git a/llava/model/apply_delta.py b/llava/model/apply_delta.py new file mode 100644 index 0000000000000000000000000000000000000000..666dd9691bde7d54ddf2871e311d6f621e29f099 --- /dev/null +++ b/llava/model/apply_delta.py @@ -0,0 +1,48 @@ +""" +Usage: +python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta +""" +import argparse + +import torch +from tqdm import tqdm +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava import LlavaLlamaForCausalLM + + +def apply_delta(base_model_path, target_model_path, delta_path): + print("Loading base model") + base = AutoModelForCausalLM.from_pretrained( + base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + + print("Loading delta") + delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + delta_tokenizer = AutoTokenizer.from_pretrained(delta_path) + + print("Applying delta") + for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"): + if name not in base.state_dict(): + assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model' + continue + if param.data.shape == base.state_dict()[name].shape: + param.data += base.state_dict()[name] + else: + assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \ + f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}' + bparam = base.state_dict()[name] + param.data[:bparam.shape[0], :bparam.shape[1]] += bparam + + print("Saving target model") + delta.save_pretrained(target_model_path) + delta_tokenizer.save_pretrained(target_model_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--base-model-path", type=str, required=True) + parser.add_argument("--target-model-path", type=str, required=True) + parser.add_argument("--delta-path", type=str, required=True) + + args = parser.parse_args() + + apply_delta(args.base_model_path, args.target_model_path, args.delta_path) diff --git a/llava/model/builder.py b/llava/model/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..9b93ec7324ce47ee221c46237bd47d044ada8f62 --- /dev/null +++ b/llava/model/builder.py @@ -0,0 +1,151 @@ +# Copyright 2023 Haotian Liu +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +import warnings +import shutil + +from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig +import torch +from llava.model import * +from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN + + +def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto"): + kwargs = {"device_map": device_map} + kwargs["offload_folder"] = "offload" + + if load_8bit: + kwargs['load_in_8bit'] = True + elif load_4bit: + kwargs['load_in_4bit'] = True + kwargs['quantization_config'] = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type='nf4' + ) + else: + kwargs['torch_dtype'] = torch.float16 + + if 'llava' in model_name.lower(): + # Load LLaVA model + if 'lora' in model_name.lower() and model_base is None: + warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.') + if 'lora' in model_name.lower() and model_base is not None: + lora_cfg_pretrained = AutoConfig.from_pretrained(model_path) + tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) + print('Loading LLaVA from base model...') + model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs) + token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features + if model.lm_head.weight.shape[0] != token_num: + model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) + model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) + + print('Loading additional LLaVA weights...') + if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')): + non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu') + else: + # this is probably from HF Hub + from huggingface_hub import hf_hub_download + def load_from_hf(repo_id, filename, subfolder=None): + cache_file = hf_hub_download( + repo_id=repo_id, + filename=filename, + subfolder=subfolder) + return torch.load(cache_file, map_location='cpu') + non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin') + non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()} + if any(k.startswith('model.model.') for k in non_lora_trainables): + non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()} + model.load_state_dict(non_lora_trainables, strict=False) + + from peft import PeftModel + print('Loading LoRA weights...') + model = PeftModel.from_pretrained(model, model_path) + print('Merging LoRA weights...') + model = model.merge_and_unload() + print('Model is loaded...') + elif model_base is not None: + # this may be mm projector only + print('Loading LLaVA from base model...') + if 'mpt' in model_name.lower(): + if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')): + shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py')) + tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True) + cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) + else: + tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) + cfg_pretrained = AutoConfig.from_pretrained(model_path) + model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) + + mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu') + mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()} + model.load_state_dict(mm_projector_weights, strict=False) + else: + if 'mpt' in model_name.lower(): + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) + model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) + else: + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) + model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) + else: + # Load language model + if model_base is not None: + # PEFT model + from peft import PeftModel + tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) + model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") + print(f"Loading LoRA weights from {model_path}") + model = PeftModel.from_pretrained(model, model_path) + print(f"Merging weights") + model = model.merge_and_unload() + print('Convert to FP16...') + model.to(torch.float16) + else: + use_fast = False + if 'mpt' in model_name.lower(): + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) + model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs) + else: + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) + model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) + + image_processor = None + + if 'llava' in model_name.lower(): + mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) + mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) + if mm_use_im_patch_token: + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) + if mm_use_im_start_end: + tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) + model.resize_token_embeddings(len(tokenizer)) + + vision_tower = model.get_vision_tower() + if not vision_tower.is_loaded: + vision_tower.load_model() + + + vision_tower.to(device=model.device, dtype=torch.float16) + image_processor = vision_tower.image_processor + + if hasattr(model.config, "max_sequence_length"): + context_len = model.config.max_sequence_length + else: + context_len = 2048 + + return tokenizer, model, image_processor, context_len diff --git a/llava/model/consolidate.py b/llava/model/consolidate.py new file mode 100644 index 0000000000000000000000000000000000000000..1e324210e229eeba23b75791bba82df7c6e639eb --- /dev/null +++ b/llava/model/consolidate.py @@ -0,0 +1,29 @@ +""" +Usage: +python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate +""" +import argparse + +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava.model import * +from llava.model.utils import auto_upgrade + + +def consolidate_ckpt(src_path, dst_path): + print("Loading model") + auto_upgrade(src_path) + src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False) + src_model.save_pretrained(dst_path) + src_tokenizer.save_pretrained(dst_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--src", type=str, required=True) + parser.add_argument("--dst", type=str, required=True) + + args = parser.parse_args() + + consolidate_ckpt(args.src, args.dst) diff --git a/llava/model/language_model/llava_llama.py b/llava/model/language_model/llava_llama.py new file mode 100644 index 0000000000000000000000000000000000000000..d9ce3e86f788856e669a597b15939142138fd230 --- /dev/null +++ b/llava/model/language_model/llava_llama.py @@ -0,0 +1,140 @@ +# Copyright 2023 Haotian Liu +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss + +from transformers import AutoConfig, AutoModelForCausalLM, \ + LlamaConfig, LlamaModel, LlamaForCausalLM + +from transformers.modeling_outputs import CausalLMOutputWithPast + +from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM + + +class LlavaConfig(LlamaConfig): + model_type = "llava" + + +class LlavaLlamaModel(LlavaMetaModel, LlamaModel): + config_class = LlavaConfig + + def __init__(self, config: LlamaConfig): + super(LlavaLlamaModel, self).__init__(config) + + +class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM): + config_class = LlavaConfig + + def __init__(self, config): + super(LlamaForCausalLM, self).__init__(config) + self.model = LlavaLlamaModel(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_model(self): + return self.model + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + images: Optional[torch.FloatTensor] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model/pipeline parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "images": kwargs.get("images", None), + } + ) + return model_inputs + +AutoConfig.register("llava", LlavaConfig) +AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM) diff --git a/llava/model/language_model/llava_mpt.py b/llava/model/language_model/llava_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..39dc8807ef8d339fb7cde331c0deabfe5ce7f93e --- /dev/null +++ b/llava/model/language_model/llava_mpt.py @@ -0,0 +1,113 @@ +# Copyright 2023 Haotian Liu +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import List, Optional, Tuple +import warnings + +import torch +import torch.nn.functional as F +import math + +from transformers import AutoConfig, AutoModelForCausalLM +from transformers.modeling_outputs import CausalLMOutputWithPast + +from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel +from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM + + +class LlavaMPTConfig(MPTConfig): + model_type = "llava_mpt" + + +class LlavaMPTModel(LlavaMetaModel, MPTModel): + config_class = LlavaMPTConfig + + def __init__(self, config: MPTConfig): + config.hidden_size = config.d_model + super(LlavaMPTModel, self).__init__(config) + + def embed_tokens(self, x): + return self.wte(x) + + +class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM): + config_class = LlavaMPTConfig + supports_gradient_checkpointing = True + + def __init__(self, config): + super(MPTForCausalLM, self).__init__(config) + + if not config.tie_word_embeddings: + raise ValueError('MPTForCausalLM only supports tied word embeddings') + self.transformer = LlavaMPTModel(config) + self.logit_scale = None + if config.logit_scale is not None: + logit_scale = config.logit_scale + if isinstance(logit_scale, str): + if logit_scale == 'inv_sqrt_d_model': + logit_scale = 1 / math.sqrt(config.d_model) + else: + raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + self.logit_scale = logit_scale + + def get_model(self): + return self.transformer + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlavaMPTModel): + module.gradient_checkpointing = value + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, images=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + + input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images) + outputs = self.transformer(input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) + # FIXME: this is a hack to fix the multiple gpu inference issue in https://github.com/haotian-liu/LLaVA/issues/338 + logits = F.linear(outputs.last_hidden_state.to(self.transformer.wte.weight.device), self.transformer.wte.weight) + if self.logit_scale is not None: + if self.logit_scale == 0: + warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') + logits *= self.logit_scale + loss = None + if labels is not None: + labels = torch.roll(labels, shifts=-1) + labels[:, -1] = -100 + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) + return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states) + + def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds is not implemented for MPT yet') + attention_mask = kwargs['attention_mask'].bool() + if attention_mask[:, -1].sum() != attention_mask.shape[0]: + raise NotImplementedError('MPT does not support generation with right padding.') + if self.transformer.attn_uses_sequence_id and self.training: + sequence_id = torch.zeros_like(input_ids[:1]) + else: + sequence_id = None + if past_key_values is not None: + input_ids = input_ids[:, -1].unsqueeze(-1) + if self.transformer.prefix_lm: + prefix_mask = torch.ones_like(attention_mask) + if kwargs.get('use_cache') == False: + raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') + else: + prefix_mask = None + return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True), "images": kwargs.get("images", None)} + + +AutoConfig.register("llava_mpt", LlavaMPTConfig) +AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM) diff --git a/llava/model/language_model/mpt/adapt_tokenizer.py b/llava/model/language_model/mpt/adapt_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..e640c157e8f5581953c518df0611a423225ef598 --- /dev/null +++ b/llava/model/language_model/mpt/adapt_tokenizer.py @@ -0,0 +1,41 @@ +from typing import Union +from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast +Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] +NUM_SENTINEL_TOKENS: int = 100 + +def adapt_tokenizer_for_denoising(tokenizer: Tokenizer): + """Adds sentinel tokens and padding token (if missing). + + Expands the tokenizer vocabulary to include sentinel tokens + used in mixture-of-denoiser tasks as well as a padding token. + + All added tokens are added as special tokens. No tokens are + added if sentinel tokens and padding token already exist. + """ + sentinels_to_add = [f'' for i in range(NUM_SENTINEL_TOKENS)] + tokenizer.add_tokens(sentinels_to_add, special_tokens=True) + if tokenizer.pad_token is None: + tokenizer.add_tokens('', special_tokens=True) + tokenizer.pad_token = '' + assert tokenizer.pad_token_id is not None + sentinels = ''.join([f'' for i in range(NUM_SENTINEL_TOKENS)]) + _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids + tokenizer.sentinel_token_ids = _sentinel_token_ids + +class AutoTokenizerForMOD(AutoTokenizer): + """AutoTokenizer + Adaptation for MOD. + + A simple wrapper around AutoTokenizer to make instantiating + an MOD-adapted tokenizer a bit easier. + + MOD-adapted tokenizers have sentinel tokens (e.g., ), + a padding token, and a property to get the token ids of the + sentinel tokens. + """ + + @classmethod + def from_pretrained(cls, *args, **kwargs): + """See `AutoTokenizer.from_pretrained` docstring.""" + tokenizer = super().from_pretrained(*args, **kwargs) + adapt_tokenizer_for_denoising(tokenizer) + return tokenizer \ No newline at end of file diff --git a/llava/model/language_model/mpt/attention.py b/llava/model/language_model/mpt/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c758afa34c534a251fe6d164eb81a6f3a3230b --- /dev/null +++ b/llava/model/language_model/mpt/attention.py @@ -0,0 +1,300 @@ +"""Attention layers.""" +import math +import warnings +from typing import Optional +import torch +import torch.nn as nn +from einops import rearrange +from packaging import version +from torch import nn +from .norm import LPLayerNorm + +def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool): + if original_is_causal and num_query_tokens != num_key_tokens: + if num_query_tokens != 1: + raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.') + else: + return False + return original_is_causal + +def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads) + kv_n_heads = 1 if multiquery else n_heads + k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads) + v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads) + if past_key_value is not None: + if len(past_key_value) != 0: + k = torch.cat([past_key_value[0], k], dim=3) + v = torch.cat([past_key_value[1], v], dim=2) + past_key_value = (k, v) + (b, _, s_q, d) = q.shape + s_k = k.size(-1) + if softmax_scale is None: + softmax_scale = 1 / math.sqrt(d) + attn_weight = q.matmul(k) * softmax_scale + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - s_q) + _s_k = max(0, attn_bias.size(3) - s_k) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q): + raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.') + attn_weight = attn_weight + attn_bias + min_val = torch.finfo(q.dtype).min + if key_padding_mask is not None: + if attn_bias is not None: + warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') + attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val) + if is_causal and (not q.size(2) == 1): + s = max(s_q, s_k) + causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16) + causal_mask = causal_mask.tril() + causal_mask = causal_mask.to(torch.bool) + causal_mask = ~causal_mask + causal_mask = causal_mask[-s_q:, -s_k:] + attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val) + attn_weight = torch.softmax(attn_weight, dim=-1) + if dropout_p: + attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True) + out = attn_weight.to(v.dtype).matmul(v) + out = rearrange(out, 'b h s d -> b s (h d)') + if needs_weights: + return (out, attn_weight, past_key_value) + return (out, None, past_key_value) + +def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]): + for tensor in tensors: + if tensor.dtype not in valid_dtypes: + raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.') + if not tensor.is_cuda: + raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).') + +def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + try: + from flash_attn import bert_padding, flash_attn_interface + except: + raise RuntimeError('Please install flash-attn==1.0.3.post0') + check_valid_inputs(query, key, value) + if past_key_value is not None: + if len(past_key_value) != 0: + key = torch.cat([past_key_value[0], key], dim=1) + value = torch.cat([past_key_value[1], value], dim=1) + past_key_value = (key, value) + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - query.size(1)) + _s_k = max(0, attn_bias.size(3) - key.size(1)) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if attn_bias is not None: + raise NotImplementedError(f'attn_bias not implemented for flash attn.') + (batch_size, seqlen) = query.shape[:2] + if key_padding_mask is None: + key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool) + query_padding_mask = key_padding_mask[:, -query.size(1):] + (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask) + query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads) + (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask) + key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) + (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask) + value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) + if multiquery: + key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1)) + value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1)) + dropout_p = dropout_p if training else 0.0 + reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) + output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights) + output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen) + return (output, None, past_key_value) + +def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + try: + from .flash_attn_triton import flash_attn_func + except: + _installed = False + if version.parse(torch.__version__) < version.parse('2.0.0'): + _installed = True + try: + from flash_attn.flash_attn_triton import flash_attn_func + except: + _installed = False + if not _installed: + raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.') + check_valid_inputs(query, key, value) + if past_key_value is not None: + if len(past_key_value) != 0: + key = torch.cat([past_key_value[0], key], dim=1) + value = torch.cat([past_key_value[1], value], dim=1) + past_key_value = (key, value) + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - query.size(1)) + _s_k = max(0, attn_bias.size(3) - key.size(1)) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if dropout_p: + raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.') + if needs_weights: + raise NotImplementedError(f'attn_impl: triton cannot return attn weights.') + if key_padding_mask is not None: + warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') + (b_size, s_k) = key_padding_mask.shape[:2] + if attn_bias is None: + attn_bias = query.new_zeros(b_size, 1, 1, s_k) + attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min) + query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads) + key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) + value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) + if multiquery: + key = key.expand(*key.shape[:2], n_heads, key.size(-1)) + value = value.expand(*value.shape[:2], n_heads, value.size(-1)) + reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) + attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale) + output = attn_output.view(*attn_output.shape[:2], -1) + return (output, None, past_key_value) + +class MultiheadAttention(nn.Module): + """Multi-head self attention. + + Using torch or triton attention implemetation enables user to also use + additive bias. + """ + + def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): + super().__init__() + self.attn_impl = attn_impl + self.clip_qkv = clip_qkv + self.qk_ln = qk_ln + self.d_model = d_model + self.n_heads = n_heads + self.softmax_scale = softmax_scale + if self.softmax_scale is None: + self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads) + self.attn_dropout_p = attn_pdrop + self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device) + fuse_splits = (d_model, 2 * d_model) + self.Wqkv._fused = (0, fuse_splits) + if self.qk_ln: + layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm + self.q_ln = layernorm_class(self.d_model, device=device) + self.k_ln = layernorm_class(self.d_model, device=device) + if self.attn_impl == 'flash': + self.attn_fn = flash_attn_fn + elif self.attn_impl == 'triton': + self.attn_fn = triton_flash_attn_fn + if verbose: + warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') + elif self.attn_impl == 'torch': + self.attn_fn = scaled_multihead_dot_product_attention + if torch.cuda.is_available() and verbose: + warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) + self.out_proj._is_residual = True + + def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): + qkv = self.Wqkv(x) + if self.clip_qkv: + qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) + (query, key, value) = qkv.chunk(3, dim=2) + key_padding_mask = attention_mask + if self.qk_ln: + dtype = query.dtype + query = self.q_ln(query).to(dtype) + key = self.k_ln(key).to(dtype) + (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights) + return (self.out_proj(context), attn_weights, past_key_value) + +class MultiQueryAttention(nn.Module): + """Multi-Query self attention. + + Using torch or triton attention implemetation enables user to also use + additive bias. + """ + + def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): + super().__init__() + self.attn_impl = attn_impl + self.clip_qkv = clip_qkv + self.qk_ln = qk_ln + self.d_model = d_model + self.n_heads = n_heads + self.head_dim = d_model // n_heads + self.softmax_scale = softmax_scale + if self.softmax_scale is None: + self.softmax_scale = 1 / math.sqrt(self.head_dim) + self.attn_dropout_p = attn_pdrop + self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device) + fuse_splits = (d_model, d_model + self.head_dim) + self.Wqkv._fused = (0, fuse_splits) + if self.qk_ln: + layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm + self.q_ln = layernorm_class(d_model, device=device) + self.k_ln = layernorm_class(self.head_dim, device=device) + if self.attn_impl == 'flash': + self.attn_fn = flash_attn_fn + elif self.attn_impl == 'triton': + self.attn_fn = triton_flash_attn_fn + if verbose: + warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') + elif self.attn_impl == 'torch': + self.attn_fn = scaled_multihead_dot_product_attention + if torch.cuda.is_available() and verbose: + warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) + self.out_proj._is_residual = True + + def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): + qkv = self.Wqkv(x) + if self.clip_qkv: + qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) + (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2) + key_padding_mask = attention_mask + if self.qk_ln: + dtype = query.dtype + query = self.q_ln(query).to(dtype) + key = self.k_ln(key).to(dtype) + (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True) + return (self.out_proj(context), attn_weights, past_key_value) + +def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id): + if attn_impl == 'flash': + return None + elif attn_impl in ['torch', 'triton']: + if alibi: + if (prefix_lm or not causal) or use_sequence_id: + return (1, n_heads, seq_len, seq_len) + return (1, n_heads, 1, seq_len) + elif prefix_lm or use_sequence_id: + return (1, 1, seq_len, seq_len) + return None + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + +def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8): + if attn_impl == 'flash': + return None + elif attn_impl in ['torch', 'triton']: + if alibi: + (device, dtype) = (attn_bias.device, attn_bias.dtype) + attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype)) + return attn_bias + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + +def gen_slopes(n_heads, alibi_bias_max=8, device=None): + _n_heads = 2 ** math.ceil(math.log2(n_heads)) + m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device) + m = m.mul(alibi_bias_max / _n_heads) + slopes = 1.0 / torch.pow(2, m) + if _n_heads != n_heads: + slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads] + return slopes.view(1, n_heads, 1, 1) + +def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None): + alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len) + if full: + alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1) + alibi_bias = alibi_bias.abs().mul(-1) + slopes = gen_slopes(n_heads, alibi_bias_max, device=device) + alibi_bias = alibi_bias * slopes + return alibi_bias.to(dtype=dtype) +ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention} \ No newline at end of file diff --git a/llava/model/language_model/mpt/blocks.py b/llava/model/language_model/mpt/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..537e7f9190713bd73332aeb80702efa39320ca60 --- /dev/null +++ b/llava/model/language_model/mpt/blocks.py @@ -0,0 +1,41 @@ +"""GPT Blocks used for the GPT Model.""" +from typing import Dict, Optional, Tuple +import torch +import torch.nn as nn +from .attention import ATTN_CLASS_REGISTRY +from .norm import NORM_CLASS_REGISTRY + +class MPTMLP(nn.Module): + + def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None): + super().__init__() + self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device) + self.act = nn.GELU(approximate='none') + self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device) + self.down_proj._is_residual = True + + def forward(self, x): + return self.down_proj(self.act(self.up_proj(x))) + +class MPTBlock(nn.Module): + + def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', verbose: int=0, device: Optional[str]=None, **kwargs): + del kwargs + super().__init__() + norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] + attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']] + self.norm_1 = norm_class(d_model, device=device) + self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, verbose=verbose, device=device) + self.norm_2 = norm_class(d_model, device=device) + self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device) + self.resid_attn_dropout = nn.Dropout(resid_pdrop) + self.resid_ffn_dropout = nn.Dropout(resid_pdrop) + + def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]: + a = self.norm_1(x) + (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal) + x = x + self.resid_attn_dropout(b) + m = self.norm_2(x) + n = self.ffn(m) + x = x + self.resid_ffn_dropout(n) + return (x, attn_weights, past_key_value) \ No newline at end of file diff --git a/llava/model/language_model/mpt/configuration_mpt.py b/llava/model/language_model/mpt/configuration_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..e9eb6fc59b50654ddbe19ed56ad8c0abd1b8efef --- /dev/null +++ b/llava/model/language_model/mpt/configuration_mpt.py @@ -0,0 +1,118 @@ +"""A HuggingFace-style model configuration.""" +from typing import Dict, Optional, Union +from transformers import PretrainedConfig +attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8} +init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0} + +class MPTConfig(PretrainedConfig): + model_type = 'mpt' + + def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs): + """The MPT configuration class. + + Args: + d_model (int): The size of the embedding dimension of the model. + n_heads (int): The number of attention heads. + n_layers (int): The number of layers in the model. + expansion_ratio (int): The ratio of the up/down scale in the MLP. + max_seq_len (int): The maximum sequence length of the model. + vocab_size (int): The size of the vocabulary. + resid_pdrop (float): The dropout probability applied to the attention output before combining with residual. + emb_pdrop (float): The dropout probability for the embedding layer. + learned_pos_emb (bool): Whether to use learned positional embeddings + attn_config (Dict): A dictionary used to configure the model's attention module: + attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention + attn_pdrop (float): The dropout probability for the attention layers. + attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'. + qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer. + clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to + this value. + softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None, + use the default scale of ``1/sqrt(d_keys)``. + prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an + extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix + can attend to one another bi-directionally. Tokens outside the prefix use causal attention. + attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id. + When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates + which sub-sequence each token belongs to. + Defaults to ``False`` meaning any provided `sequence_id` will be ignored. + alibi (bool): Whether to use the alibi bias instead of position embeddings. + alibi_bias_max (int): The maximum value of the alibi bias. + init_device (str): The device to use for parameter initialization. + logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value. + no_bias (bool): Whether to use bias in all layers. + verbose (int): The verbosity level. 0 is silent. + embedding_fraction (float): The fraction to scale the gradients of the embedding layer by. + norm_type (str): choose type of norm to use + multiquery_attention (bool): Whether to use multiquery attention implementation. + use_cache (bool): Whether or not the model should return the last key/values attentions + init_config (Dict): A dictionary used to configure the model initialization: + init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_', + 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or + 'xavier_normal_'. These mimic the parameter initialization methods in PyTorch. + init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True. + emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer. + emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution + used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``. + init_std (float): The standard deviation of the normal distribution used to initialize the model, + if using the baseline_ parameter initialization scheme. + init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes. + fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes. + init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes. + --- + See llmfoundry.models.utils.param_init_fns.py for info on other param init config options + """ + self.d_model = d_model + self.n_heads = n_heads + self.n_layers = n_layers + self.expansion_ratio = expansion_ratio + self.max_seq_len = max_seq_len + self.vocab_size = vocab_size + self.resid_pdrop = resid_pdrop + self.emb_pdrop = emb_pdrop + self.learned_pos_emb = learned_pos_emb + self.attn_config = attn_config + self.init_device = init_device + self.logit_scale = logit_scale + self.no_bias = no_bias + self.verbose = verbose + self.embedding_fraction = embedding_fraction + self.norm_type = norm_type + self.use_cache = use_cache + self.init_config = init_config + if 'name' in kwargs: + del kwargs['name'] + if 'loss_fn' in kwargs: + del kwargs['loss_fn'] + super().__init__(**kwargs) + self._validate_config() + + def _set_config_defaults(self, config, config_defaults): + for (k, v) in config_defaults.items(): + if k not in config: + config[k] = v + return config + + def _validate_config(self): + self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults) + self.init_config = self._set_config_defaults(self.init_config, init_config_defaults) + if self.d_model % self.n_heads != 0: + raise ValueError('d_model must be divisible by n_heads') + if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])): + raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1") + if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']: + raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}") + if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('prefix_lm only implemented with torch and triton attention.') + if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('alibi only implemented with torch and triton attention.') + if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.') + if self.embedding_fraction > 1 or self.embedding_fraction <= 0: + raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!') + if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model': + raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + if self.init_config.get('name', None) is None: + raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.") + if not self.learned_pos_emb and (not self.attn_config['alibi']): + raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.') \ No newline at end of file diff --git a/llava/model/language_model/mpt/custom_embedding.py b/llava/model/language_model/mpt/custom_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..ab357952c397f47898863e8405c4958bb8de82fd --- /dev/null +++ b/llava/model/language_model/mpt/custom_embedding.py @@ -0,0 +1,11 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +class SharedEmbedding(nn.Embedding): + + def forward(self, input: Tensor, unembed: bool=False) -> Tensor: + if unembed: + return F.linear(input, self.weight) + return super().forward(input) \ No newline at end of file diff --git a/llava/model/language_model/mpt/flash_attn_triton.py b/llava/model/language_model/mpt/flash_attn_triton.py new file mode 100644 index 0000000000000000000000000000000000000000..c0a42186d982283add95b63d99fc118e845bcf9d --- /dev/null +++ b/llava/model/language_model/mpt/flash_attn_triton.py @@ -0,0 +1,484 @@ +""" +Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py +update imports to use 'triton_pre_mlir' + +*Experimental* implementation of FlashAttention in Triton. +Tested with triton==2.0.0.dev20221202. +Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions +other than 64: +https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207 +We'll update this implementation with the new Triton backend once this is fixed. + +We use the FlashAttention implementation from Phil Tillet a starting point. +https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py + +Changes: +- Implement both causal and non-causal attention. +- Implement both self-attention and cross-attention. +- Support arbitrary seqlens (not just multiples of 128), for both forward and backward. +- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. +- Support attention bias. +- Speed up the forward pass a bit, and only store the LSE instead of m and l. +- Make the backward for d=128 much faster by reducing register spilling. +- Optionally parallelize the backward pass across seqlen_k, to deal with the case of +small batch size * nheads. + +Caution: +- This is an *experimental* implementation. The forward pass should be quite robust but +I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler). +- This implementation has only been tested on A100. +- If you plan to use headdim other than 64 and 128, you should test for race conditions +(due to the Triton compiler), as done in tests/test_flash_attn.py +"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions +for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident +that there are none left for other head dimensions. + +Differences between this Triton version and the CUDA version: +- Triton version doesn't support dropout. +- Triton forward is generally faster than CUDA forward, while Triton backward is +generally slower than CUDA backward. Overall Triton forward + backward is slightly slower +than CUDA forward + backward. +- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). +- Triton version supports attention bias, while CUDA version doesn't. +""" +import math +import torch +import triton_pre_mlir as triton +import triton_pre_mlir.language as tl + +@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']}) +@triton.jit +def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_HEADDIM) + q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :]) + if BIAS_TYPE == 'vector': + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n + elif BIAS_TYPE == 'matrix': + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :]) + t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m + lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32) + if EVEN_M & EVEN_N: + if EVEN_HEADDIM: + q = tl.load(q_ptrs) + else: + q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k) + for start_n in range(0, end_n, BLOCK_N): + start_n = tl.multiple_of(start_n, BLOCK_N) + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn) + else: + k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, k, trans_b=True) + if not EVEN_N: + qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf')) + if IS_CAUSAL: + qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf')) + if BIAS_TYPE != 'none': + if BIAS_TYPE == 'vector': + if EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == 'matrix': + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32) + qk = qk * softmax_scale + bias + m_ij = tl.maximum(tl.max(qk, 1), lse_i) + p = tl.exp(qk - m_ij[:, None]) + else: + m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i) + p = tl.exp(qk * softmax_scale - m_ij[:, None]) + l_ij = tl.sum(p, 1) + acc_o_scale = tl.exp(m_i - m_ij) + tl.store(t_ptrs, acc_o_scale) + acc_o_scale = tl.load(t_ptrs) + acc_o = acc_o * acc_o_scale[:, None] + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn) + else: + v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + p = p.to(v.dtype) + acc_o += tl.dot(p, v) + m_i = m_ij + l_i_new = tl.exp(lse_i - m_ij) + l_ij + lse_i = m_ij + tl.log(l_i_new) + o_scale = tl.exp(m_i - lse_i) + tl.store(t_ptrs, o_scale) + o_scale = tl.load(t_ptrs) + acc_o = acc_o * o_scale[:, None] + start_m = tl.program_id(0) + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m + tl.store(lse_ptrs, lse_i) + offs_d = tl.arange(0, BLOCK_HEADDIM) + out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :]) + if EVEN_M: + if EVEN_HEADDIM: + tl.store(out_ptrs, acc_o) + else: + tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim) + elif EVEN_HEADDIM: + tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q) + else: + tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + +@triton.jit +def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32) + do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32) + delta = tl.sum(o * do, axis=1) + tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta) + +@triton.jit +def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr): + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + tl.store(dv_ptrs, dv) + tl.store(dk_ptrs, dk) + else: + tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim) + tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim) + elif EVEN_HEADDIM: + tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k) + tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k) + else: + tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + +@triton.jit +def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M + offs_qm = begin_m + tl.arange(0, BLOCK_M) + offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) + offs_m = tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :]) + do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :]) + dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :]) + if BIAS_TYPE == 'vector': + b_ptrs = Bias + offs_n + elif BIAS_TYPE == 'matrix': + b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :]) + dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + if begin_m >= seqlen_q: + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM) + return + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + k = tl.load(k_ptrs) + v = tl.load(v_ptrs) + else: + k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + else: + k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + num_block_m = tl.cdiv(seqlen_q, BLOCK_M) + for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M): + start_m = tl.multiple_of(start_m, BLOCK_M) + offs_m_curr = start_m + offs_m + if EVEN_M & EVEN_HEADDIM: + q = tl.load(q_ptrs) + elif EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + qk = tl.dot(q, k, trans_b=True) + if not EVEN_N: + qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf')) + if IS_CAUSAL: + qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf')) + if BIAS_TYPE != 'none': + tl.debug_barrier() + if BIAS_TYPE == 'vector': + if EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == 'matrix': + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32) + qk = qk * softmax_scale + bias + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + lse_i = tl.load(LSE + offs_m_curr) + if BIAS_TYPE == 'none': + p = tl.exp(qk * softmax_scale - lse_i[:, None]) + else: + p = tl.exp(qk - lse_i[:, None]) + if EVEN_M & EVEN_HEADDIM: + do = tl.load(do_ptrs) + else: + do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + dv += tl.dot(p.to(do.dtype), do, trans_a=True) + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + dp = tl.dot(do, v, trans_b=True) + if not EVEN_HEADDIM: + tl.debug_barrier() + Di = tl.load(D + offs_m_curr) + ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype) + dk += tl.dot(ds, q, trans_a=True) + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + if not ATOMIC_ADD: + if EVEN_M & EVEN_HEADDIM: + dq = tl.load(dq_ptrs, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, eviction_policy='evict_last') + elif EVEN_HEADDIM: + dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last') + else: + dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last') + else: + dq = tl.dot(ds, k) + if EVEN_M & EVEN_HEADDIM: + tl.atomic_add(dq_ptrs, dq) + elif EVEN_HEADDIM: + tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q) + else: + tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + dq_ptrs += BLOCK_M * stride_dqm + q_ptrs += BLOCK_M * stride_qm + do_ptrs += BLOCK_M * stride_dom + if BIAS_TYPE == 'matrix': + b_ptrs += BLOCK_M * stride_bm + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM) + +def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + +@triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM']) +@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']}) +@triton.jit +def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + Q += off_b * stride_qb + off_h * stride_qh + K += off_b * stride_kb + off_h * stride_kh + V += off_b * stride_vb + off_h * stride_vh + DO += off_b * stride_dob + off_h * stride_doh + DQ += off_b * stride_dqb + off_h * stride_dqh + DK += off_b * stride_dkb + off_h * stride_dkh + DV += off_b * stride_dvb + off_h * stride_dvh + if BIAS_TYPE != 'none': + Bias += off_b * stride_bb + off_h * stride_bh + D += off_hb * seqlen_q_rounded + LSE += off_hb * seqlen_q_rounded + if not SEQUENCE_PARALLEL: + num_block_n = tl.cdiv(seqlen_k, BLOCK_N) + for start_n in range(0, num_block_n): + _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N) + else: + start_n = tl.program_id(0) + _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N) + +def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None): + (batch, seqlen_q, nheads, d) = q.shape + (_, seqlen_k, _, _) = k.shape + assert k.shape == (batch, seqlen_k, nheads, d) + assert v.shape == (batch, seqlen_k, nheads, d) + assert d <= 128, 'FlashAttention only support head dimensions up to 128' + assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type' + assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16' + assert q.is_cuda and k.is_cuda and v.is_cuda + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + has_bias = bias is not None + bias_type = 'none' + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + if bias.stride(-1) != 1: + bias = bias.contiguous() + if bias.shape[2:] == (1, seqlen_k): + bias_type = 'vector' + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = 'matrix' + else: + raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)') + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + o = torch.empty_like(q) + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + BLOCK = 128 + num_warps = 4 if d <= 64 else 8 + grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads) + _fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1) + return (o, lse, softmax_scale) + +def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None): + if do.stride(-1) != 1: + do = do.contiguous() + (batch, seqlen_q, nheads, d) = q.shape + (_, seqlen_k, _, _) = k.shape + assert d <= 128 + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + assert lse.shape == (batch, nheads, seqlen_q_rounded) + assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1 + assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1 + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + dq_accum = torch.empty_like(q, dtype=torch.float32) + delta = torch.empty_like(lse) + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads) + _bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM) + has_bias = bias is not None + bias_type = 'none' + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + assert bias.stride(-1) == 1 + if bias.shape[2:] == (1, seqlen_k): + bias_type = 'vector' + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = 'matrix' + else: + raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)') + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads) + _bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM) + dq.copy_(dq_accum) + +class FlashAttnQKVPackedFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None): + """ + qkv: (batch, seqlen, 3, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). + ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen) + """ + if qkv.stride(-1) != 1: + qkv = qkv.contiguous() + (o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(qkv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (qkv, o, lse, bias) = ctx.saved_tensors + assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dqkv = torch.empty_like(qkv) + _flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dqkv, None, None, None) +flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply + +class FlashAttnKVPackedFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None): + """ + q: (batch, seqlen_q, nheads, headdim) + kv: (batch, seqlen_k, 2, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + (q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]] + (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(q, kv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (q, kv, o, lse, bias) = ctx.saved_tensors + if len(ctx.needs_input_grad) >= 3: + assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dq = torch.empty_like(q) + dkv = torch.empty_like(kv) + _flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dq, dkv, None, None, None) +flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply + +class FlashAttnFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None): + """ + q: (batch_size, seqlen_q, nheads, headdim) + k, v: (batch_size, seqlen_k, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + (q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]] + (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(q, k, v, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (q, k, v, o, lse, bias) = ctx.saved_tensors + assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dq = torch.empty_like(q) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dq, dk, dv, None, None, None) +flash_attn_func = FlashAttnFunc.apply \ No newline at end of file diff --git a/llava/model/language_model/mpt/hf_prefixlm_converter.py b/llava/model/language_model/mpt/hf_prefixlm_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..8c1a6487202a6400a7116a6bd68b493892ef0d14 --- /dev/null +++ b/llava/model/language_model/mpt/hf_prefixlm_converter.py @@ -0,0 +1,415 @@ +"""Converts Huggingface Causal LM to Prefix LM. + +Conversion does lightweight surgery on a HuggingFace +Causal LM to convert it to a Prefix LM. + +Prefix LMs accepts a `bidirectional_mask` input in `forward` +and treat the input prompt as the prefix in `generate`. +""" +import math +import warnings +from types import MethodType +from typing import Any, Dict, List, Optional, Tuple, Union +import torch +from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss +from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom +from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom +from transformers.models.bloom.modeling_bloom import logging +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel +from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM +from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM +from transformers.models.gptj.modeling_gptj import GPTJForCausalLM +from transformers.models.opt.modeling_opt import OPTForCausalLM +from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt +from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt +logger = logging.get_logger(__name__) +_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM) +CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM] + +def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES: + """Converts a GPT-style Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `GPT2LMHeadModel` + - `GPTNeoForCausalLM` + - `GPTNeoXForCausalLM` + - `GPTJForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, _SUPPORTED_GPT_MODELS) + assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models' + + def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]: + """Helper that gets a list of the model's attention modules. + + Each module has a `bias` buffer used for causal masking. The Prefix LM + conversion adds logic to dynamically manipulate these biases to support + Prefix LM attention masking. + """ + attn_modules = [] + if isinstance(model, GPTNeoXForCausalLM): + blocks = model.gpt_neox.layers + else: + blocks = model.transformer.h + for block in blocks: + if isinstance(model, GPTNeoForCausalLM): + if block.attn.attention_type != 'global': + continue + attn_module = block.attn.attention + elif isinstance(model, GPTNeoXForCausalLM): + attn_module = block.attention + else: + attn_module = block.attn + attn_modules.append(attn_module) + return attn_modules + setattr(model, '_original_forward', getattr(model, 'forward')) + setattr(model, '_original_generate', getattr(model, 'generate')) + + def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None): + """Wraps original forward to enable PrefixLM attention.""" + + def call_og_forward(): + if isinstance(self, GPTNeoXForCausalLM): + return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + else: + return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + if bidirectional_mask is None: + return call_og_forward() + assert isinstance(bidirectional_mask, torch.Tensor) + attn_modules = _get_attn_modules(model) + (b, s) = bidirectional_mask.shape + max_length = attn_modules[0].bias.shape[-1] + if s > max_length: + raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).') + assert s <= max_length + if s < max_length: + pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device) + bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1) + bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1) + for attn_module in attn_modules: + attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional) + output = call_og_forward() + for attn_module in attn_modules: + attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] + return output + + def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]): + """Wraps original generate to enable PrefixLM attention.""" + attn_modules = _get_attn_modules(model) + for attn_module in attn_modules: + attn_module.bias.data[:] = 1 + output = self._original_generate(*args, **kwargs) + for attn_module in attn_modules: + attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] + return output + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'generate', MethodType(generate, model)) + setattr(model, '_prefix_lm_converted', True) + return model + +def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM: + """Converts a BLOOM Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `BloomForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, BloomForCausalLM) + assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models' + + def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor: + combined_attention_mask = None + device = attention_mask.device + (_, src_length) = input_shape + if src_length > 1: + combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length) + if bidirectional_mask is not None: + assert attention_mask.shape == bidirectional_mask.shape + expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length) + combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask) + expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length) + combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask + return combined_attention_mask + + def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + num_heads = self.config.n_head + closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) + base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32) + powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32) + slopes = torch.pow(base, powers) + if closest_power_of_2 != num_heads: + extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32) + num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) + extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32) + slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) + qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1) + ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1) + diffs = qa - ka + key_length - query_length + diffs = -diffs.abs() + alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length) + alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length) + return alibi.to(dtype) + KeyValueT = Tuple[torch.Tensor, torch.Tensor] + + def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: + if deprecated_arguments.pop('position_ids', False) is not False: + warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning) + if len(deprecated_arguments) > 0: + raise ValueError(f'Got unexpected arguments: {deprecated_arguments}') + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if input_ids is not None and inputs_embeds is not None: + raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time') + elif input_ids is not None: + (batch_size, seq_length) = input_ids.shape + elif inputs_embeds is not None: + (batch_size, seq_length, _) = inputs_embeds.shape + else: + raise ValueError('You have to specify either input_ids or inputs_embeds') + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + seq_length_with_past = seq_length + past_key_values_length = 0 + if past_key_values[0] is not None: + tmp = past_key_values[0][0] + past_key_values_length = tmp.shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device) + causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length) + for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)): + if output_hidden_states: + hst = (hidden_states,) + all_hidden_states = all_hidden_states + hst + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...') + use_cache = False + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + return custom_forward + outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i]) + else: + outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi) + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + if output_attentions: + oa = (outputs[2 if use_cache else 1],) + all_self_attentions = all_self_attentions + oa + hidden_states = self.ln_f(hidden_states) + if output_hidden_states: + hst = (hidden_states,) + all_hidden_states = all_hidden_states + hst + if not return_dict: + return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)) + return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions) + setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer)) + setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer)) + setattr(model.transformer, 'forward', MethodType(forward, model.transformer)) + KeyValueT = Tuple[torch.Tensor, torch.Tensor] + + def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: + """Replacement forward method for BloomCausalLM.""" + if deprecated_arguments.pop('position_ids', False) is not False: + warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning) + if len(deprecated_arguments) > 0: + raise ValueError(f'Got unexpected arguments: {deprecated_arguments}') + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + hidden_states = transformer_outputs[0] + lm_logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + (batch_size, seq_length, vocab_size) = shift_logits.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)) + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return (loss,) + output if loss is not None else output + return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions) + + def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict: + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + bidirectional_mask = None + if past[0][0].shape[0] == input_ids.shape[0]: + past = self._convert_to_bloom_cache(past) + else: + bidirectional_mask = torch.ones_like(input_ids) + return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask} + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model)) + setattr(model, '_prefix_lm_converted', True) + return model + +def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM: + """Converts an OPT Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `OPTForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, OPTForCausalLM) + assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models' + setattr(model, '_original_forward', getattr(model, 'forward')) + setattr(model, '_original_generate', getattr(model, 'generate')) + model.model.decoder.bidirectional_mask = None + + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + combined_attention_mask = None + if input_shape[-1] > 1: + if self.bidirectional_mask == 'g': + (bsz, src_length) = input_shape + combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device) + else: + combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device) + if self.bidirectional_mask is not None: + assert attention_mask.shape == self.bidirectional_mask.shape + expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) + combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask) + if attention_mask is not None: + expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) + combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + return combined_attention_mask + setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder)) + + def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None): + + def call_og_forward(): + return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + if bidirectional_mask is None: + return call_og_forward() + self.model.decoder.bidirectional_mask = bidirectional_mask + try: + outputs = call_og_forward() + except: + self.model.decoder.bidirectional_mask = None + raise + self.model.decoder.bidirectional_mask = None + return outputs + + def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]): + """Wraps original generate to enable PrefixLM-style attention.""" + self.model.decoder.bidirectional_mask = 'g' + try: + output = self._original_generate(*args, **kwargs) + except: + self.model.decoder.bidirectional_mask = None + raise + self.model.decoder.bidirectional_mask = None + return output + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'generate', MethodType(generate, model)) + setattr(model, '_prefix_lm_converted', True) + return model +_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM) +CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM] + +def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES: + """Converts a HuggingFace Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `GPT2LMHeadModel` + - `GPTNeoForCausalLM` + - `GPTNeoXForCausalLM` + - `GPTJForCausalLM` + - `BloomForCausalLM` + - `OPTForCausalLM` + + Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the + `generate` method and/or select underlying methods depending on the model class. + + These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask". + + Notes on training: + To actually train the converted model as a Prefix LM, training batches will need to indicate + the prefix/target structure by including `bidirectional_mask` as part of the batch inputs. + + **This is not a standard input and requires custom layers either within or after your dataloader.** + + In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels` + such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`. + That is, the prefix portion of the sequence should not generate any loss. Loss should only be + generated by the target portion of the sequence. + + Notes on `GPTNeoForCausalLM`: + To simplify the implementation, "global" and "local" attention layers are handled differently. + For "global" layers, we handle conversion as described above. For "local" layers, which use a + causal attention mask within a restricted local window, we do not alter the masking. + + Notes on `forward` method conversion: + After conversion, the `forward` method will handle a new input, `bidirectional_mask`, + which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions + belonging to the prefix (prefix tokens can attend to one another bidirectionally), and + 0 indicates token positions belonging to the target. + + The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing + causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset + the causal masks before returning the result. + + Notes on `generate` method conversion: + After conversion, the `generate` method will have the same signature but will internally + convert all causal masks to be purely bidirectional, call the original `generate` method, and + (where appropriate) reset the causal masks before returning the result. + + This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token + "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates + each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one + another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and + previously-generated tokens (also as expected in a Prefix LM). + + To preserve the API, the original methods are renamed to `_original_forward` and + `_original_generate`, and replaced with new `forward` and `generate` methods that wrap + them, respectively. Although implementation details vary by model class. + """ + if isinstance(model, _SUPPORTED_GPT_MODELS): + return _convert_gpt_causal_lm_to_prefix_lm(model) + elif isinstance(model, BloomForCausalLM): + return _convert_bloom_causal_lm_to_prefix_lm(model) + elif isinstance(model, OPTForCausalLM): + return _convert_opt_causal_lm_to_prefix_lm(model) + else: + raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}') + +def add_bidirectional_mask_if_missing(batch: Dict[str, Any]): + """Attempts to add bidirectional_mask to batch if missing. + + Raises: + KeyError if bidirectional_mask is missing and can't be inferred + """ + if 'bidirectional_mask' not in batch: + if batch.get('mode', None) == 'icl_task': + batch['bidirectional_mask'] = batch['attention_mask'].clone() + for (i, continuation_indices) in enumerate(batch['continuation_indices']): + batch['bidirectional_mask'][i, continuation_indices] = 0 + elif 'labels' in batch and 'attention_mask' in batch: + batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask']) + else: + raise KeyError('No bidirectional_mask in batch and not sure how to construct one.') \ No newline at end of file diff --git a/llava/model/language_model/mpt/meta_init_context.py b/llava/model/language_model/mpt/meta_init_context.py new file mode 100644 index 0000000000000000000000000000000000000000..6cba6fff0fe21fe222c7ab38eae44a9784c0be9c --- /dev/null +++ b/llava/model/language_model/mpt/meta_init_context.py @@ -0,0 +1,94 @@ +from contextlib import contextmanager +import torch +import torch.nn as nn + +@contextmanager +def init_empty_weights(include_buffers: bool=False): + """Meta initialization context manager. + + A context manager under which models are initialized with all parameters + on the meta device, therefore creating an empty model. Useful when just + initializing the model would blow the available RAM. + + Args: + include_buffers (`bool`, *optional*, defaults to `False`): Whether or + not to also put all buffers on the meta device while initializing. + + Example: + ```python + import torch.nn as nn + + # Initialize a model with 100 billions parameters in no time and without using any RAM. + with init_empty_weights(): + tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)]) + ``` + + + + Any model created under this context manager has no weights. As such you can't do something like + `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`]. + + + """ + with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f: + yield f + +@contextmanager +def init_on_device(device: torch.device, include_buffers: bool=False): + """Device initialization context manager. + + A context manager under which models are initialized with all parameters + on the specified device. + + Args: + device (`torch.device`): Device to initialize all parameters on. + include_buffers (`bool`, *optional*, defaults to `False`): Whether or + not to also put all buffers on the meta device while initializing. + + Example: + ```python + import torch.nn as nn + + with init_on_device(device=torch.device("cuda")): + tst = nn.Liner(100, 100) # on `cuda` device + ``` + """ + old_register_parameter = nn.Module.register_parameter + if include_buffers: + old_register_buffer = nn.Module.register_buffer + + def register_empty_parameter(module, name, param): + old_register_parameter(module, name, param) + if param is not None: + param_cls = type(module._parameters[name]) + kwargs = module._parameters[name].__dict__ + module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) + + def register_empty_buffer(module, name, buffer): + old_register_buffer(module, name, buffer) + if buffer is not None: + module._buffers[name] = module._buffers[name].to(device) + if include_buffers: + tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']} + else: + tensor_constructors_to_patch = {} + + def patch_tensor_constructor(fn): + + def wrapper(*args, **kwargs): + kwargs['device'] = device + return fn(*args, **kwargs) + return wrapper + try: + nn.Module.register_parameter = register_empty_parameter + if include_buffers: + nn.Module.register_buffer = register_empty_buffer + for torch_function_name in tensor_constructors_to_patch.keys(): + setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) + yield + finally: + nn.Module.register_parameter = old_register_parameter + if include_buffers: + nn.Module.register_buffer = old_register_buffer + for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items(): + setattr(torch, torch_function_name, old_torch_function) \ No newline at end of file diff --git a/llava/model/language_model/mpt/modeling_mpt.py b/llava/model/language_model/mpt/modeling_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..13313441b13fc7a66cb65fd21b482a5de982e2c8 --- /dev/null +++ b/llava/model/language_model/mpt/modeling_mpt.py @@ -0,0 +1,331 @@ +"""A simple, flexible implementation of a GPT model. + +Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py +""" +import math +import warnings +from typing import List, Optional, Tuple, Union +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from .attention import attn_bias_shape, build_attn_bias +from .blocks import MPTBlock +from .custom_embedding import SharedEmbedding +from .norm import NORM_CLASS_REGISTRY +from .configuration_mpt import MPTConfig +from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising +from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm +from .meta_init_context import init_empty_weights +from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_ +try: + from .flash_attn_triton import flash_attn_func +except: + pass +Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] + +class MPTPreTrainedModel(PreTrainedModel): + config_class = MPTConfig + base_model_prefix = 'model' + _no_split_modules = ['MPTBlock'] + +class MPTModel(MPTPreTrainedModel): + + def __init__(self, config: MPTConfig): + config._validate_config() + super().__init__(config) + self.attn_impl = config.attn_config['attn_impl'] + self.prefix_lm = config.attn_config['prefix_lm'] + self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id'] + self.alibi = config.attn_config['alibi'] + self.alibi_bias_max = config.attn_config['alibi_bias_max'] + if config.init_device == 'mixed': + if dist.get_local_rank() == 0: + config.init_device = 'cpu' + else: + config.init_device = 'meta' + if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys(): + norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys()) + raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).') + norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()] + self.embedding_fraction = config.embedding_fraction + self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device) + if not self.alibi: + self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device) + self.emb_drop = nn.Dropout(config.emb_pdrop) + self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)]) + self.norm_f = norm_class(config.d_model, device=config.init_device) + if config.init_device != 'meta': + print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.') + self.apply(self.param_init_fn) + self.is_causal = not self.prefix_lm + self._attn_bias_initialized = False + self.attn_bias = None + self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id) + if config.no_bias: + for module in self.modules(): + if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter): + if config.verbose: + warnings.warn(f'Removing bias ({module.bias}) from {module}.') + module.register_parameter('bias', None) + if config.verbose and config.verbose > 2: + print(self) + if 'verbose' not in self.config.init_config: + self.config.init_config['verbose'] = self.config.verbose + if self.config.init_config['verbose'] > 1: + init_fn_name = self.config.init_config['name'] + warnings.warn(f'Using {init_fn_name} initialization.') + self.gradient_checkpointing = False + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, value): + self.wte = value + + @torch.no_grad() + def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None): + if not self._attn_bias_initialized: + if self.attn_bias_shape: + self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype) + self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max) + self._attn_bias_initialized = True + if self.attn_impl == 'flash': + return (self.attn_bias, attention_mask) + if self.attn_bias is not None: + self.attn_bias = self.attn_bias.to(dtype=dtype, device=device) + attn_bias = self.attn_bias + if self.prefix_lm: + assert isinstance(attn_bias, torch.Tensor) + assert isinstance(prefix_mask, torch.Tensor) + attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask) + if self.attn_uses_sequence_id and sequence_id is not None: + assert isinstance(attn_bias, torch.Tensor) + attn_bias = self._apply_sequence_id(attn_bias, sequence_id) + if attention_mask is not None: + s_k = attention_mask.shape[-1] + if attn_bias is None: + attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype) + else: + _s_k = max(0, attn_bias.size(-1) - s_k) + attn_bias = attn_bias[:, :, :, _s_k:] + if prefix_mask is not None and attention_mask.shape != prefix_mask.shape: + raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.') + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val) + return (attn_bias, None) + + def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor): + (s_k, s_q) = attn_bias.shape[-2:] + if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len: + raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.') + seq_len = prefix_mask.shape[-1] + if seq_len > self.config.max_seq_len: + raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}') + attn_bias = attn_bias[..., :seq_len, :seq_len] + causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len) + prefix = prefix_mask.view(-1, 1, 1, seq_len) + cannot_attend = ~torch.logical_or(causal, prefix.bool()) + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(cannot_attend, min_val) + return attn_bias + + def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor): + seq_len = sequence_id.shape[-1] + if seq_len > self.config.max_seq_len: + raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}') + attn_bias = attn_bias[..., :seq_len, :seq_len] + cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1) + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(cannot_attend, min_val) + return attn_bias + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + if attention_mask is not None: + attention_mask = attention_mask.bool() + if prefix_mask is not None: + prefix_mask = prefix_mask.bool() + if not return_dict: + raise NotImplementedError('return_dict False is not implemented yet for MPT') + if output_attentions: + if self.attn_impl != 'torch': + raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.') + if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training: + raise NotImplementedError('MPT does not support training with left padding.') + if self.prefix_lm and prefix_mask is None: + raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.') + if self.training: + if self.attn_uses_sequence_id and sequence_id is None: + raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.') + elif self.attn_uses_sequence_id is False and sequence_id is not None: + warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.') + if input_ids is not None: + S = input_ids.size(1) + assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}' + tok_emb = self.wte(input_ids) + else: + assert inputs_embeds is not None + assert self.alibi, 'inputs_embeds is not implemented for MPT unless for alibi.' + S = inputs_embeds.size(1) + tok_emb = inputs_embeds + if self.alibi: + x = tok_emb + else: + past_position = 0 + if past_key_values is not None: + if len(past_key_values) != self.config.n_layers: + raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).') + past_position = past_key_values[0][0].size(1) + if self.attn_impl == 'torch': + past_position = past_key_values[0][0].size(3) + if S + past_position > self.config.max_seq_len: + raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.') + pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0) + if attention_mask is not None: + pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0) + pos_emb = self.wpe(pos) + x = tok_emb + pos_emb + if self.embedding_fraction == 1: + x = self.emb_drop(x) + else: + x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction) + assert isinstance(self.emb_drop, nn.Module) + x = self.emb_drop(x_shrunk) + (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id) + if use_cache and past_key_values is None: + past_key_values = [() for _ in range(self.config.n_layers)] + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + for (b_idx, block) in enumerate(self.blocks): + if output_hidden_states: + assert all_hidden_states is not None + all_hidden_states = all_hidden_states + (x,) + past_key_value = past_key_values[b_idx] if past_key_values is not None else None + if self.gradient_checkpointing and self.training: + (x, attn_weights, past_key_value) = torch.utils.checkpoint.checkpoint(block, x, past_key_value, attn_bias, attention_mask, self.is_causal) + else: + (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal) + if past_key_values is not None: + past_key_values[b_idx] = past_key_value + if output_attentions: + assert all_self_attns is not None + all_self_attns = all_self_attns + (attn_weights,) + x = self.norm_f(x) + if output_hidden_states: + assert all_hidden_states is not None + all_hidden_states = all_hidden_states + (x,) + return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns) + + def param_init_fn(self, module): + init_fn_name = self.config.init_config['name'] + MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config) + + def fsdp_wrap_fn(self, module): + return isinstance(module, MPTBlock) + + def activation_checkpointing_fn(self, module): + return isinstance(module, MPTBlock) + +class MPTForCausalLM(MPTPreTrainedModel): + + def __init__(self, config: MPTConfig): + super().__init__(config) + if not config.tie_word_embeddings: + raise ValueError('MPTForCausalLM only supports tied word embeddings') + print(f'Instantiating an MPTForCausalLM model from {__file__}') + self.transformer = MPTModel(config) + for child in self.transformer.children(): + if isinstance(child, torch.nn.ModuleList): + continue + if isinstance(child, torch.nn.Module): + child._fsdp_wrap = True + self.logit_scale = None + if config.logit_scale is not None: + logit_scale = config.logit_scale + if isinstance(logit_scale, str): + if logit_scale == 'inv_sqrt_d_model': + logit_scale = 1 / math.sqrt(config.d_model) + else: + raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + self.logit_scale = logit_scale + + def get_input_embeddings(self): + return self.transformer.wte + + def set_input_embeddings(self, value): + self.transformer.wte = value + + def get_output_embeddings(self): + return self.transformer.wte + + def set_output_embeddings(self, new_embeddings): + self.transformer.wte = new_embeddings + + def set_decoder(self, decoder): + self.transformer = decoder + + def get_decoder(self): + return self.transformer + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).') + outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) + logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True) + if self.logit_scale is not None: + if self.logit_scale == 0: + warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') + logits *= self.logit_scale + loss = None + if labels is not None: + labels = torch.roll(labels, shifts=-1) + labels[:, -1] = -100 + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) + return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions) + + def param_init_fn(self, module): + init_fn_name = self.config.init_config['name'] + MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config) + + def fsdp_wrap_fn(self, module): + return isinstance(module, MPTBlock) + + def activation_checkpointing_fn(self, module): + return isinstance(module, MPTBlock) + + def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds is not implemented for MPT yet') + attention_mask = kwargs['attention_mask'].bool() + if attention_mask[:, -1].sum() != attention_mask.shape[0]: + raise NotImplementedError('MPT does not support generation with right padding.') + if self.transformer.attn_uses_sequence_id and self.training: + sequence_id = torch.zeros_like(input_ids[:1]) + else: + sequence_id = None + if past_key_values is not None: + input_ids = input_ids[:, -1].unsqueeze(-1) + if self.transformer.prefix_lm: + prefix_mask = torch.ones_like(attention_mask) + if kwargs.get('use_cache') == False: + raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') + else: + prefix_mask = None + return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)} + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + """Used by HuggingFace generate when using beam search with kv-caching. + + See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133 + for an example in transformers. + """ + reordered_past = [] + for layer_past in past_key_values: + reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))] + return reordered_past \ No newline at end of file diff --git a/llava/model/language_model/mpt/norm.py b/llava/model/language_model/mpt/norm.py new file mode 100644 index 0000000000000000000000000000000000000000..067b6140fae546e5cb49cb2b1e4e6af660ced60d --- /dev/null +++ b/llava/model/language_model/mpt/norm.py @@ -0,0 +1,56 @@ +import torch + +def _cast_if_autocast_enabled(tensor): + if torch.is_autocast_enabled(): + if tensor.device.type == 'cuda': + dtype = torch.get_autocast_gpu_dtype() + elif tensor.device.type == 'cpu': + dtype = torch.get_autocast_cpu_dtype() + else: + raise NotImplementedError() + return tensor.to(dtype=dtype) + return tensor + +class LPLayerNorm(torch.nn.LayerNorm): + + def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None): + super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) + + def forward(self, x): + module_device = x.device + downcast_x = _cast_if_autocast_enabled(x) + downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight + downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias + with torch.autocast(enabled=False, device_type=module_device.type): + return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps) + +def rms_norm(x, weight=None, eps=1e-05): + output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) + if weight is not None: + return output * weight + return output + +class RMSNorm(torch.nn.Module): + + def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): + super().__init__() + self.eps = eps + if weight: + self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device)) + else: + self.register_parameter('weight', None) + + def forward(self, x): + return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype) + +class LPRMSNorm(RMSNorm): + + def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): + super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device) + + def forward(self, x): + downcast_x = _cast_if_autocast_enabled(x) + downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight + with torch.autocast(enabled=False, device_type=x.device.type): + return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype) +NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm} \ No newline at end of file diff --git a/llava/model/language_model/mpt/param_init_fns.py b/llava/model/language_model/mpt/param_init_fns.py new file mode 100644 index 0000000000000000000000000000000000000000..418b83ca2363288046f4b48b1d706c5607341fb5 --- /dev/null +++ b/llava/model/language_model/mpt/param_init_fns.py @@ -0,0 +1,181 @@ +import math +import warnings +from collections.abc import Sequence +from functools import partial +from typing import Optional, Tuple, Union +import torch +from torch import nn +from .norm import NORM_CLASS_REGISTRY + +def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f"Initializing network using module's reset_parameters attribute") + if hasattr(module, 'reset_parameters'): + module.reset_parameters() + +def fused_init_helper_(module: nn.Module, init_fn_): + _fused = getattr(module, '_fused', None) + if _fused is None: + raise RuntimeError(f'Internal logic error') + (dim, splits) = _fused + splits = (0, *splits, module.weight.size(dim)) + for (s, e) in zip(splits[:-1], splits[1:]): + slice_indices = [slice(None)] * module.weight.ndim + slice_indices[dim] = slice(s, e) + init_fn_(module.weight[slice_indices]) + +def generic_param_init_fn_(module: nn.Module, init_fn_, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'If model has bias parameters they are initialized to 0.') + init_div_is_residual = init_div_is_residual + if init_div_is_residual is False: + div_is_residual = 1.0 + elif init_div_is_residual is True: + div_is_residual = math.sqrt(2 * n_layers) + elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int): + div_is_residual = init_div_is_residual + elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric(): + div_is_residual = float(init_div_is_residual) + else: + div_is_residual = 1.0 + raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}') + if init_div_is_residual is not False: + if verbose > 1: + warnings.warn(f'Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. ' + f'Set `init_div_is_residual: false` in init config to disable this.') + if isinstance(module, nn.Linear): + if hasattr(module, '_fused'): + fused_init_helper_(module, init_fn_) + else: + init_fn_(module.weight) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + if init_div_is_residual is not False and getattr(module, '_is_residual', False): + with torch.no_grad(): + module.weight.div_(div_is_residual) + elif isinstance(module, nn.Embedding): + if emb_init_std is not None: + std = emb_init_std + if std == 0: + warnings.warn(f'Embedding layer initialized to 0.') + emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std) + if verbose > 1: + warnings.warn(f'Embedding layer initialized using normal distribution with mean=0 and std={std!r}.') + elif emb_init_uniform_lim is not None: + lim = emb_init_uniform_lim + if isinstance(lim, Sequence): + if len(lim) > 2: + raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.') + if lim[0] == lim[1]: + warnings.warn(f'Embedding layer initialized to {lim[0]}.') + else: + if lim == 0: + warnings.warn(f'Embedding layer initialized to 0.') + lim = [-lim, lim] + (a, b) = lim + emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b) + if verbose > 1: + warnings.warn(f'Embedding layer initialized using uniform distribution in range {lim}.') + else: + emb_init_fn_ = init_fn_ + emb_init_fn_(module.weight) + elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))): + if verbose > 1: + warnings.warn(f'Norm weights are set to 1. If norm layer has a bias it is initialized to 0.') + if hasattr(module, 'weight') and module.weight is not None: + torch.nn.init.ones_(module.weight) + if hasattr(module, 'bias') and module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.MultiheadAttention): + if module._qkv_same_embed_dim: + assert module.in_proj_weight is not None + assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None) + assert d_model is not None + _d = d_model + splits = (0, _d, 2 * _d, 3 * _d) + for (s, e) in zip(splits[:-1], splits[1:]): + init_fn_(module.in_proj_weight[s:e]) + else: + assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None) + assert module.in_proj_weight is None + init_fn_(module.q_proj_weight) + init_fn_(module.k_proj_weight) + init_fn_(module.v_proj_weight) + if module.in_proj_bias is not None: + torch.nn.init.zeros_(module.in_proj_bias) + if module.bias_k is not None: + torch.nn.init.zeros_(module.bias_k) + if module.bias_v is not None: + torch.nn.init.zeros_(module.bias_v) + init_fn_(module.out_proj.weight) + if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False): + with torch.no_grad(): + module.out_proj.weight.div_(div_is_residual) + if module.out_proj.bias is not None: + torch.nn.init.zeros_(module.out_proj.bias) + else: + for _ in module.parameters(recurse=False): + raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.') + +def _normal_init_(std, mean=0.0): + return partial(torch.nn.init.normal_, mean=mean, std=std) + +def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + init_fn_ = _normal_init_(std=std) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.normal_ init fn mean=0.0, std={std}') + generic_param_init_fn_(module=module, init_fn_=init_fn_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def baseline_param_init_fn_(module: nn.Module, init_std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + if init_std is None: + raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.") + _normal_param_init_fn_(module=module, std=init_std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + std = math.sqrt(2 / (5 * d_model)) + _normal_param_init_fn_(module=module, std=std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + """From section 2.3.1 of GPT-NeoX-20B: + + An Open-Source AutoregressiveLanguage Model — Black et. al. (2022) + see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151 + and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py + """ + del kwargs + residual_div = n_layers / math.sqrt(10) + if verbose > 1: + warnings.warn(f'setting init_div_is_residual to {residual_div}') + small_param_init_fn_(module=module, d_model=d_model, n_layers=n_layers, init_div_is_residual=residual_div, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'Using nn.init.kaiming_uniform_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}') + kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity) + generic_param_init_fn_(module=module, init_fn_=kaiming_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'Using nn.init.kaiming_normal_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}') + kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity) + generic_param_init_fn_(module=module, init_fn_=kaiming_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs): + del kwargs + xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.xavier_uniform_ init fn with parameters: ' + f'gain={init_gain}') + generic_param_init_fn_(module=module, init_fn_=xavier_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs): + xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.xavier_normal_ init fn with parameters: ' + f'gain={init_gain}') + generic_param_init_fn_(module=module, init_fn_=xavier_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) +MODEL_INIT_REGISTRY = {'default_': torch_default_param_init_fn_, 'baseline_': baseline_param_init_fn_, 'kaiming_uniform_': kaiming_uniform_param_init_fn_, 'kaiming_normal_': kaiming_normal_param_init_fn_, 'neox_init_': neox_param_init_fn_, 'small_init_': small_param_init_fn_, 'xavier_uniform_': xavier_uniform_param_init_fn_, 'xavier_normal_': xavier_normal_param_init_fn_} \ No newline at end of file diff --git a/llava/model/llava_arch.py b/llava/model/llava_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..fd538c93764347a496ba6cdb0859cd8ffcb02044 --- /dev/null +++ b/llava/model/llava_arch.py @@ -0,0 +1,248 @@ +# Copyright 2023 Haotian Liu +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from abc import ABC, abstractmethod + +import torch +import torch.nn as nn + +from .multimodal_encoder.builder import build_vision_tower +from .multimodal_projector.builder import build_vision_projector + +from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN + + +class LlavaMetaModel: + + def __init__(self, config): + super(LlavaMetaModel, self).__init__(config) + + if hasattr(config, "mm_vision_tower"): + self.vision_tower = build_vision_tower(config, delay_load=True) + self.mm_projector = build_vision_projector(config) + + def get_vision_tower(self): + vision_tower = getattr(self, 'vision_tower', None) + if type(vision_tower) is list: + vision_tower = vision_tower[0] + return vision_tower + + def initialize_vision_modules(self, model_args, fsdp=None): + vision_tower = model_args.vision_tower + mm_vision_select_layer = model_args.mm_vision_select_layer + mm_vision_select_feature = model_args.mm_vision_select_feature + pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter + + self.config.mm_vision_tower = vision_tower + + vision_tower = build_vision_tower(model_args) + + if fsdp is not None and len(fsdp) > 0: + self.vision_tower = [vision_tower] + else: + self.vision_tower = vision_tower + + self.config.use_mm_proj = True + self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') + self.config.mm_hidden_size = vision_tower.hidden_size + self.config.mm_vision_select_layer = mm_vision_select_layer + self.config.mm_vision_select_feature = mm_vision_select_feature + + self.mm_projector = build_vision_projector(self.config) + + if pretrain_mm_mlp_adapter is not None: + mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') + def get_w(weights, keyword): + return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} + + self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) + + +class LlavaMetaForCausalLM(ABC): + + @abstractmethod + def get_model(self): + pass + + def get_vision_tower(self): + return self.get_model().get_vision_tower() + + def encode_images(self, images): + image_features = self.get_model().get_vision_tower()(images) + image_features = self.get_model().mm_projector(image_features) + return image_features + + def prepare_inputs_labels_for_multimodal( + self, input_ids, attention_mask, past_key_values, labels, images + ): + vision_tower = self.get_vision_tower() + if vision_tower is None or images is None or input_ids.shape[1] == 1: + if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1: + attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) + return input_ids, attention_mask, past_key_values, None, labels + + if type(images) is list or images.ndim == 5: + concat_images = torch.cat([image for image in images], dim=0) + image_features = self.encode_images(concat_images) + split_sizes = [image.shape[0] for image in images] + image_features = torch.split(image_features, split_sizes, dim=0) + image_features = [x.flatten(0, 1) for x in image_features] + else: + image_features = self.encode_images(images) + + new_input_embeds = [] + new_labels = [] if labels is not None else None + cur_image_idx = 0 + for batch_idx, cur_input_ids in enumerate(input_ids): + if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: + # multimodal LLM, but the current sample is not multimodal + # FIXME: this is a hacky fix, for deepspeed zero3 to work + half_len = cur_input_ids.shape[0] // 2 + cur_image_features = image_features[cur_image_idx] + cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) + cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) + cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0) + new_input_embeds.append(cur_input_embeds) + if labels is not None: + new_labels.append(labels[batch_idx]) + cur_image_idx += 1 + continue + image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] + cur_new_input_embeds = [] + if labels is not None: + cur_labels = labels[batch_idx] + cur_new_labels = [] + assert cur_labels.shape == cur_input_ids.shape + while image_token_indices.numel() > 0: + cur_image_features = image_features[cur_image_idx] + image_token_start = image_token_indices[0] + if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach()) + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start])) + cur_new_input_embeds.append(cur_image_features) + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2])) + if labels is not None: + cur_new_labels.append(cur_labels[:image_token_start]) + cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) + cur_new_labels.append(cur_labels[image_token_start:image_token_start+1]) + cur_labels = cur_labels[image_token_start+2:] + else: + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) + cur_new_input_embeds.append(cur_image_features) + if labels is not None: + cur_new_labels.append(cur_labels[:image_token_start]) + cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) + cur_labels = cur_labels[image_token_start+1:] + cur_image_idx += 1 + if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): + cur_input_ids = cur_input_ids[image_token_start+2:] + else: + cur_input_ids = cur_input_ids[image_token_start+1:] + image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] + if cur_input_ids.numel() > 0: + if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach()) + else: + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) + if labels is not None: + cur_new_labels.append(cur_labels) + cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] + cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) + new_input_embeds.append(cur_new_input_embeds) + if labels is not None: + cur_new_labels = torch.cat(cur_new_labels, dim=0) + new_labels.append(cur_new_labels) + + if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): + max_len = max(x.shape[0] for x in new_input_embeds) + + new_input_embeds_align = [] + for cur_new_embed in new_input_embeds: + cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) + new_input_embeds_align.append(cur_new_embed) + new_input_embeds = torch.stack(new_input_embeds_align, dim=0) + + if labels is not None: + new_labels_align = [] + _new_labels = new_labels + for cur_new_label in new_labels: + cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) + new_labels_align.append(cur_new_label) + new_labels = torch.stack(new_labels_align, dim=0) + + if attention_mask is not None: + new_attention_mask = [] + for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): + new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) + new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) + cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) + new_attention_mask.append(cur_new_attention_mask) + attention_mask = torch.stack(new_attention_mask, dim=0) + assert attention_mask.shape == new_labels.shape + else: + new_input_embeds = torch.stack(new_input_embeds, dim=0) + if labels is not None: + new_labels = torch.stack(new_labels, dim=0) + + if attention_mask is not None: + new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) + attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) + assert attention_mask.shape == new_input_embeds.shape[:2] + + return None, attention_mask, past_key_values, new_input_embeds, new_labels + + def initialize_vision_tokenizer(self, model_args, tokenizer): + if model_args.mm_use_im_patch_token: + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if model_args.mm_use_im_start_end: + num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = self.get_input_embeddings().weight.data + output_embeddings = self.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + + if model_args.tune_mm_mlp_adapter: + for p in self.get_input_embeddings().parameters(): + p.requires_grad = True + for p in self.get_output_embeddings().parameters(): + p.requires_grad = False + + if model_args.pretrain_mm_mlp_adapter: + mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu') + embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight'] + assert num_new_tokens == 2 + if input_embeddings.shape == embed_tokens_weight.shape: + input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:] + elif embed_tokens_weight.shape[0] == num_new_tokens: + input_embeddings[-num_new_tokens:] = embed_tokens_weight + else: + raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.") + elif model_args.mm_use_im_patch_token: + if model_args.tune_mm_mlp_adapter: + for p in self.get_input_embeddings().parameters(): + p.requires_grad = False + for p in self.get_output_embeddings().parameters(): + p.requires_grad = False diff --git a/llava/model/make_delta.py b/llava/model/make_delta.py new file mode 100644 index 0000000000000000000000000000000000000000..4ae55d59c2c8bab80299272314a41bbeb959d8ed --- /dev/null +++ b/llava/model/make_delta.py @@ -0,0 +1,52 @@ +""" +Usage: +python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta +""" +import argparse + +import torch +from tqdm import tqdm +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava.model.utils import auto_upgrade + + +def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id): + print("Loading base model") + base = AutoModelForCausalLM.from_pretrained( + base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + + print("Loading target model") + auto_upgrade(target_model_path) + target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + + print("Calculating delta") + for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"): + if name not in base.state_dict(): + assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model' + continue + if param.data.shape == base.state_dict()[name].shape: + param.data -= base.state_dict()[name] + else: + assert name in ['model.embed_tokens.weight', 'lm_head.weight'], f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}' + bparam = base.state_dict()[name] + param.data[:bparam.shape[0], :bparam.shape[1]] -= bparam + + print("Saving delta") + if hub_repo_id: + kwargs = {"push_to_hub": True, "repo_id": hub_repo_id} + else: + kwargs = {} + target.save_pretrained(delta_path, **kwargs) + target_tokenizer = AutoTokenizer.from_pretrained(target_model_path) + target_tokenizer.save_pretrained(delta_path, **kwargs) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--base-model-path", type=str, required=True) + parser.add_argument("--target-model-path", type=str, required=True) + parser.add_argument("--delta-path", type=str, required=True) + parser.add_argument("--hub-repo-id", type=str, default=None) + args = parser.parse_args() + + make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id) diff --git a/llava/model/multimodal_encoder/builder.py b/llava/model/multimodal_encoder/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..2b13589d4e55af529fe0838c4130c2033ac10478 --- /dev/null +++ b/llava/model/multimodal_encoder/builder.py @@ -0,0 +1,11 @@ +import os +from .clip_encoder import CLIPVisionTower + + +def build_vision_tower(vision_tower_cfg, **kwargs): + vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None)) + is_absolute_path_exists = os.path.exists(vision_tower) + if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"): + return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) + + raise ValueError(f'Unknown vision tower: {vision_tower}') diff --git a/llava/model/multimodal_encoder/clip_encoder.py b/llava/model/multimodal_encoder/clip_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb9015b0fc9fa93483ba77cc303b793e86c36fc --- /dev/null +++ b/llava/model/multimodal_encoder/clip_encoder.py @@ -0,0 +1,78 @@ +import torch +import torch.nn as nn + +from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig + + +class CLIPVisionTower(nn.Module): + def __init__(self, vision_tower, args, delay_load=False): + super().__init__() + + self.is_loaded = False + + self.vision_tower_name = vision_tower + self.select_layer = args.mm_vision_select_layer + self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') + + if not delay_load: + self.load_model() + else: + self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) + + def load_model(self): + self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) + self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) + self.vision_tower.requires_grad_(False) + + self.is_loaded = True + + def feature_select(self, image_forward_outs): + image_features = image_forward_outs.hidden_states[self.select_layer] + if self.select_feature == 'patch': + image_features = image_features[:, 1:] + elif self.select_feature == 'cls_patch': + image_features = image_features + else: + raise ValueError(f'Unexpected select feature: {self.select_feature}') + return image_features + + @torch.no_grad() + def forward(self, images): + if type(images) is list: + image_features = [] + for image in images: + image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) + image_feature = self.feature_select(image_forward_out).to(image.dtype) + image_features.append(image_feature) + else: + image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) + image_features = self.feature_select(image_forward_outs).to(images.dtype) + + return image_features + + @property + def dummy_feature(self): + return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) + + @property + def dtype(self): + return self.vision_tower.dtype + + @property + def device(self): + return self.vision_tower.device + + @property + def config(self): + if self.is_loaded: + return self.vision_tower.config + else: + return self.cfg_only + + @property + def hidden_size(self): + return self.config.hidden_size + + @property + def num_patches(self): + return (self.config.image_size // self.config.patch_size) ** 2 diff --git a/llava/model/multimodal_projector/builder.py b/llava/model/multimodal_projector/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..31cd4f48e6055cd6d00a162af30b1c8139e26b57 --- /dev/null +++ b/llava/model/multimodal_projector/builder.py @@ -0,0 +1,51 @@ +import torch +import torch.nn as nn +import re + + +class IdentityMap(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, *args, **kwargs): + return x + + @property + def config(self): + return {"mm_projector_type": 'identity'} + + +class SimpleResBlock(nn.Module): + def __init__(self, channels): + super().__init__() + self.pre_norm = nn.LayerNorm(channels) + + self.proj = nn.Sequential( + nn.Linear(channels, channels), + nn.GELU(), + nn.Linear(channels, channels) + ) + def forward(self, x): + x = self.pre_norm(x) + return x + self.proj(x) + + +def build_vision_projector(config, delay_load=False, **kwargs): + projector_type = getattr(config, 'mm_projector_type', 'linear') + + if projector_type == 'linear': + return nn.Linear(config.mm_hidden_size, config.hidden_size) + + mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) + if mlp_gelu_match: + mlp_depth = int(mlp_gelu_match.group(1)) + modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(config.hidden_size, config.hidden_size)) + return nn.Sequential(*modules) + + if projector_type == 'identity': + return IdentityMap() + + raise ValueError(f'Unknown projector type: {projector_type}') diff --git a/llava/model/utils.py b/llava/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2563f89c6cedf5e73508afec8f9979105df9b745 --- /dev/null +++ b/llava/model/utils.py @@ -0,0 +1,20 @@ +from transformers import AutoConfig + + +def auto_upgrade(config): + cfg = AutoConfig.from_pretrained(config) + if 'llava' in config and 'llava' not in cfg.model_type: + assert cfg.model_type == 'llama' + print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") + print("You must upgrade the checkpoint to the new code base (this can be done automatically).") + confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") + if confirm.lower() in ["y", "yes"]: + print("Upgrading checkpoint...") + assert len(cfg.architectures) == 1 + setattr(cfg.__class__, "model_type", "llava") + cfg.architectures[0] = 'LlavaLlamaForCausalLM' + cfg.save_pretrained(config) + print("Checkpoint upgraded.") + else: + print("Checkpoint upgrade aborted.") + exit(1) diff --git a/llava/serve/__init__.py b/llava/serve/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llava/serve/cli.py b/llava/serve/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..fbabbfa76eb8c8af6be6bdc165f96cb9cb515244 --- /dev/null +++ b/llava/serve/cli.py @@ -0,0 +1,119 @@ +import argparse +import torch + +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from llava.conversation import conv_templates, SeparatorStyle +from llava.model.builder import load_pretrained_model +from llava.utils import disable_torch_init +from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria + +from PIL import Image + +import requests +from PIL import Image +from io import BytesIO +from transformers import TextStreamer + + +def load_image(image_file): + if image_file.startswith('http') or image_file.startswith('https'): + response = requests.get(image_file) + image = Image.open(BytesIO(response.content)).convert('RGB') + else: + image = Image.open(image_file).convert('RGB') + return image + + +def main(args): + # Model + disable_torch_init() + + model_name = get_model_name_from_path(args.model_path) + tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit) + + if 'llama-2' in model_name.lower(): + conv_mode = "llava_llama_2" + elif "v1" in model_name.lower(): + conv_mode = "llava_v1" + elif "mpt" in model_name.lower(): + conv_mode = "mpt" + else: + conv_mode = "llava_v0" + + if args.conv_mode is not None and conv_mode != args.conv_mode: + print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) + else: + args.conv_mode = conv_mode + + conv = conv_templates[args.conv_mode].copy() + if "mpt" in model_name.lower(): + roles = ('user', 'assistant') + else: + roles = conv.roles + + image = load_image(args.image_file) + image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() + + while True: + try: + inp = input(f"{roles[0]}: ") + except EOFError: + inp = "" + if not inp: + print("exit...") + break + + print(f"{roles[1]}: ", end="") + + if image is not None: + # first message + if model.config.mm_use_im_start_end: + inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp + else: + inp = DEFAULT_IMAGE_TOKEN + '\n' + inp + conv.append_message(conv.roles[0], inp) + image = None + else: + # later messages + conv.append_message(conv.roles[0], inp) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + + input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) + streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) + + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + images=image_tensor, + do_sample=True, + temperature=0.2, + max_new_tokens=1024, + streamer=streamer, + use_cache=True, + stopping_criteria=[stopping_criteria]) + + outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() + conv.messages[-1][-1] = outputs + + if args.debug: + print("\n", {"prompt": prompt, "outputs": outputs}, "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--image-file", type=str, required=True) + parser.add_argument("--num-gpus", type=int, default=1) + parser.add_argument("--conv-mode", type=str, default=None) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--max-new-tokens", type=int, default=512) + parser.add_argument("--load-8bit", action="store_true") + parser.add_argument("--load-4bit", action="store_true") + parser.add_argument("--debug", action="store_true") + args = parser.parse_args() + main(args) diff --git a/llava/serve/controller.py b/llava/serve/controller.py new file mode 100644 index 0000000000000000000000000000000000000000..b61fca6ea9fe8aa37acd143784a3d76e90a58b9f --- /dev/null +++ b/llava/serve/controller.py @@ -0,0 +1,298 @@ +""" +A controller manages distributed workers. +It sends worker addresses to clients. +""" +import argparse +import asyncio +import dataclasses +from enum import Enum, auto +import json +import logging +import time +from typing import List, Union +import threading + +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse +import numpy as np +import requests +import uvicorn + +from llava.constants import CONTROLLER_HEART_BEAT_EXPIRATION +from llava.utils import build_logger, server_error_msg + + +logger = build_logger("controller", "controller.log") + + +class DispatchMethod(Enum): + LOTTERY = auto() + SHORTEST_QUEUE = auto() + + @classmethod + def from_str(cls, name): + if name == "lottery": + return cls.LOTTERY + elif name == "shortest_queue": + return cls.SHORTEST_QUEUE + else: + raise ValueError(f"Invalid dispatch method") + + +@dataclasses.dataclass +class WorkerInfo: + model_names: List[str] + speed: int + queue_length: int + check_heart_beat: bool + last_heart_beat: str + + +def heart_beat_controller(controller): + while True: + time.sleep(CONTROLLER_HEART_BEAT_EXPIRATION) + controller.remove_stable_workers_by_expiration() + + +class Controller: + def __init__(self, dispatch_method: str): + # Dict[str -> WorkerInfo] + self.worker_info = {} + self.dispatch_method = DispatchMethod.from_str(dispatch_method) + + self.heart_beat_thread = threading.Thread( + target=heart_beat_controller, args=(self,)) + self.heart_beat_thread.start() + + logger.info("Init controller") + + def register_worker(self, worker_name: str, check_heart_beat: bool, + worker_status: dict): + if worker_name not in self.worker_info: + logger.info(f"Register a new worker: {worker_name}") + else: + logger.info(f"Register an existing worker: {worker_name}") + + if not worker_status: + worker_status = self.get_worker_status(worker_name) + if not worker_status: + return False + + self.worker_info[worker_name] = WorkerInfo( + worker_status["model_names"], worker_status["speed"], worker_status["queue_length"], + check_heart_beat, time.time()) + + logger.info(f"Register done: {worker_name}, {worker_status}") + return True + + def get_worker_status(self, worker_name: str): + try: + r = requests.post(worker_name + "/worker_get_status", timeout=5) + except requests.exceptions.RequestException as e: + logger.error(f"Get status fails: {worker_name}, {e}") + return None + + if r.status_code != 200: + logger.error(f"Get status fails: {worker_name}, {r}") + return None + + return r.json() + + def remove_worker(self, worker_name: str): + del self.worker_info[worker_name] + + def refresh_all_workers(self): + old_info = dict(self.worker_info) + self.worker_info = {} + + for w_name, w_info in old_info.items(): + if not self.register_worker(w_name, w_info.check_heart_beat, None): + logger.info(f"Remove stale worker: {w_name}") + + def list_models(self): + model_names = set() + + for w_name, w_info in self.worker_info.items(): + model_names.update(w_info.model_names) + + return list(model_names) + + def get_worker_address(self, model_name: str): + if self.dispatch_method == DispatchMethod.LOTTERY: + worker_names = [] + worker_speeds = [] + for w_name, w_info in self.worker_info.items(): + if model_name in w_info.model_names: + worker_names.append(w_name) + worker_speeds.append(w_info.speed) + worker_speeds = np.array(worker_speeds, dtype=np.float32) + norm = np.sum(worker_speeds) + if norm < 1e-4: + return "" + worker_speeds = worker_speeds / norm + if True: # Directly return address + pt = np.random.choice(np.arange(len(worker_names)), + p=worker_speeds) + worker_name = worker_names[pt] + return worker_name + + # Check status before returning + while True: + pt = np.random.choice(np.arange(len(worker_names)), + p=worker_speeds) + worker_name = worker_names[pt] + + if self.get_worker_status(worker_name): + break + else: + self.remove_worker(worker_name) + worker_speeds[pt] = 0 + norm = np.sum(worker_speeds) + if norm < 1e-4: + return "" + worker_speeds = worker_speeds / norm + continue + return worker_name + elif self.dispatch_method == DispatchMethod.SHORTEST_QUEUE: + worker_names = [] + worker_qlen = [] + for w_name, w_info in self.worker_info.items(): + if model_name in w_info.model_names: + worker_names.append(w_name) + worker_qlen.append(w_info.queue_length / w_info.speed) + if len(worker_names) == 0: + return "" + min_index = np.argmin(worker_qlen) + w_name = worker_names[min_index] + self.worker_info[w_name].queue_length += 1 + logger.info(f"names: {worker_names}, queue_lens: {worker_qlen}, ret: {w_name}") + return w_name + else: + raise ValueError(f"Invalid dispatch method: {self.dispatch_method}") + + def receive_heart_beat(self, worker_name: str, queue_length: int): + if worker_name not in self.worker_info: + logger.info(f"Receive unknown heart beat. {worker_name}") + return False + + self.worker_info[worker_name].queue_length = queue_length + self.worker_info[worker_name].last_heart_beat = time.time() + logger.info(f"Receive heart beat. {worker_name}") + return True + + def remove_stable_workers_by_expiration(self): + expire = time.time() - CONTROLLER_HEART_BEAT_EXPIRATION + to_delete = [] + for worker_name, w_info in self.worker_info.items(): + if w_info.check_heart_beat and w_info.last_heart_beat < expire: + to_delete.append(worker_name) + + for worker_name in to_delete: + self.remove_worker(worker_name) + + def worker_api_generate_stream(self, params): + worker_addr = self.get_worker_address(params["model"]) + if not worker_addr: + logger.info(f"no worker: {params['model']}") + ret = { + "text": server_error_msg, + "error_code": 2, + } + yield json.dumps(ret).encode() + b"\0" + + try: + response = requests.post(worker_addr + "/worker_generate_stream", + json=params, stream=True, timeout=5) + for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): + if chunk: + yield chunk + b"\0" + except requests.exceptions.RequestException as e: + logger.info(f"worker timeout: {worker_addr}") + ret = { + "text": server_error_msg, + "error_code": 3, + } + yield json.dumps(ret).encode() + b"\0" + + + # Let the controller act as a worker to achieve hierarchical + # management. This can be used to connect isolated sub networks. + def worker_api_get_status(self): + model_names = set() + speed = 0 + queue_length = 0 + + for w_name in self.worker_info: + worker_status = self.get_worker_status(w_name) + if worker_status is not None: + model_names.update(worker_status["model_names"]) + speed += worker_status["speed"] + queue_length += worker_status["queue_length"] + + return { + "model_names": list(model_names), + "speed": speed, + "queue_length": queue_length, + } + + +app = FastAPI() + + +@app.post("/register_worker") +async def register_worker(request: Request): + data = await request.json() + controller.register_worker( + data["worker_name"], data["check_heart_beat"], + data.get("worker_status", None)) + + +@app.post("/refresh_all_workers") +async def refresh_all_workers(): + models = controller.refresh_all_workers() + + +@app.post("/list_models") +async def list_models(): + models = controller.list_models() + return {"models": models} + + +@app.post("/get_worker_address") +async def get_worker_address(request: Request): + data = await request.json() + addr = controller.get_worker_address(data["model"]) + return {"address": addr} + + +@app.post("/receive_heart_beat") +async def receive_heart_beat(request: Request): + data = await request.json() + exist = controller.receive_heart_beat( + data["worker_name"], data["queue_length"]) + return {"exist": exist} + + +@app.post("/worker_generate_stream") +async def worker_api_generate_stream(request: Request): + params = await request.json() + generator = controller.worker_api_generate_stream(params) + return StreamingResponse(generator) + + +@app.post("/worker_get_status") +async def worker_api_get_status(request: Request): + return controller.worker_api_get_status() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--host", type=str, default="localhost") + parser.add_argument("--port", type=int, default=21001) + parser.add_argument("--dispatch-method", type=str, choices=[ + "lottery", "shortest_queue"], default="shortest_queue") + args = parser.parse_args() + logger.info(f"args: {args}") + + controller = Controller(args.dispatch_method) + uvicorn.run(app, host=args.host, port=args.port, log_level="info") diff --git a/llava/serve/examples/extreme_ironing.jpg b/llava/serve/examples/extreme_ironing.jpg new file mode 100644 index 0000000000000000000000000000000000000000..638b078837f175039b2db49a63821288d9681daa Binary files /dev/null and b/llava/serve/examples/extreme_ironing.jpg differ diff --git a/llava/serve/examples/waterview.jpg b/llava/serve/examples/waterview.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6f44ebaba1aa493b8bab3baa4e827b76752b1869 Binary files /dev/null and b/llava/serve/examples/waterview.jpg differ diff --git a/llava/serve/model_worker.py b/llava/serve/model_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..4308fde8a259b05fbb43786b9ccdddd229b17fc8 --- /dev/null +++ b/llava/serve/model_worker.py @@ -0,0 +1,282 @@ +""" +A model worker executes the model. +""" +import argparse +import asyncio +import json +import time +import threading +import uuid + +from fastapi import FastAPI, Request, BackgroundTasks +from fastapi.responses import StreamingResponse +import requests +import torch +import uvicorn +from functools import partial + +from llava.constants import WORKER_HEART_BEAT_INTERVAL +from llava.utils import (build_logger, server_error_msg, + pretty_print_semaphore) +from llava.model.builder import load_pretrained_model +from llava.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, KeywordsStoppingCriteria +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from transformers import TextIteratorStreamer +from threading import Thread + + +GB = 1 << 30 + +worker_id = str(uuid.uuid4())[:6] +logger = build_logger("model_worker", f"model_worker_{worker_id}.log") +global_counter = 0 + +model_semaphore = None + + +def heart_beat_worker(controller): + + while True: + time.sleep(WORKER_HEART_BEAT_INTERVAL) + controller.send_heart_beat() + + +class ModelWorker: + def __init__(self, controller_addr, worker_addr, + worker_id, no_register, + model_path, model_base, model_name, + load_8bit, load_4bit): + self.controller_addr = controller_addr + self.worker_addr = worker_addr + self.worker_id = worker_id + if model_path.endswith("/"): + model_path = model_path[:-1] + if model_name is None: + model_paths = model_path.split("/") + if model_paths[-1].startswith('checkpoint-'): + self.model_name = model_paths[-2] + "_" + model_paths[-1] + else: + self.model_name = model_paths[-1] + else: + self.model_name = model_name + + logger.info(f"Loading the model {self.model_name} on worker {worker_id} ...") + self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model( + model_path, model_base, self.model_name, load_8bit, load_4bit) + self.is_multimodal = 'llava' in self.model_name.lower() + + if not no_register: + self.register_to_controller() + self.heart_beat_thread = threading.Thread( + target=heart_beat_worker, args=(self,)) + self.heart_beat_thread.start() + + def register_to_controller(self): + logger.info("Register to controller") + + url = self.controller_addr + "/register_worker" + data = { + "worker_name": self.worker_addr, + "check_heart_beat": True, + "worker_status": self.get_status() + } + r = requests.post(url, json=data) + assert r.status_code == 200 + + def send_heart_beat(self): + logger.info(f"Send heart beat. Models: {[self.model_name]}. " + f"Semaphore: {pretty_print_semaphore(model_semaphore)}. " + f"global_counter: {global_counter}") + + url = self.controller_addr + "/receive_heart_beat" + + while True: + try: + ret = requests.post(url, json={ + "worker_name": self.worker_addr, + "queue_length": self.get_queue_length()}, timeout=5) + exist = ret.json()["exist"] + break + except requests.exceptions.RequestException as e: + logger.error(f"heart beat error: {e}") + time.sleep(5) + + if not exist: + self.register_to_controller() + + def get_queue_length(self): + if model_semaphore is None: + return 0 + else: + return args.limit_model_concurrency - model_semaphore._value + (len( + model_semaphore._waiters) if model_semaphore._waiters is not None else 0) + + def get_status(self): + return { + "model_names": [self.model_name], + "speed": 1, + "queue_length": self.get_queue_length(), + } + + @torch.inference_mode() + def generate_stream(self, params): + tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor + + prompt = params["prompt"] + ori_prompt = prompt + images = params.get("images", None) + num_image_tokens = 0 + if images is not None and len(images) > 0 and self.is_multimodal: + if len(images) > 0: + if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN): + raise ValueError("Number of images does not match number of tokens in prompt") + + images = [load_image_from_base64(image) for image in images] + images = process_images(images, image_processor, model.config) + + if type(images) is list: + images = [image.to(self.model.device, dtype=torch.float16) for image in images] + else: + images = images.to(self.model.device, dtype=torch.float16) + + replace_token = DEFAULT_IMAGE_TOKEN + if getattr(self.model.config, 'mm_use_im_start_end', False): + replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN + prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token) + + num_image_tokens = prompt.count(replace_token) * model.get_vision_tower().num_patches + else: + images = None + image_args = {"images": images} + else: + images = None + image_args = {} + + temperature = float(params.get("temperature", 1.0)) + top_p = float(params.get("top_p", 1.0)) + max_context_length = getattr(model.config, 'max_position_embeddings', 2048) + max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024) + stop_str = params.get("stop", None) + do_sample = True if temperature > 0.001 else False + + input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) + streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15) + + max_new_tokens = min(max_new_tokens, max_context_length - input_ids.shape[-1] - num_image_tokens) + + if max_new_tokens < 1: + yield json.dumps({"text": ori_prompt + "Exceeds max token length. Please start a new conversation, thanks.", "error_code": 0}).encode() + b"\0" + return + + thread = Thread(target=model.generate, kwargs=dict( + inputs=input_ids, + do_sample=do_sample, + temperature=temperature, + top_p=top_p, + max_new_tokens=max_new_tokens, + streamer=streamer, + stopping_criteria=[stopping_criteria], + use_cache=True, + **image_args + )) + thread.start() + + generated_text = ori_prompt + for new_text in streamer: + generated_text += new_text + if generated_text.endswith(stop_str): + generated_text = generated_text[:-len(stop_str)] + yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0" + + def generate_stream_gate(self, params): + try: + for x in self.generate_stream(params): + yield x + except ValueError as e: + print("Caught ValueError:", e) + ret = { + "text": server_error_msg, + "error_code": 1, + } + yield json.dumps(ret).encode() + b"\0" + except torch.cuda.CudaError as e: + print("Caught torch.cuda.CudaError:", e) + ret = { + "text": server_error_msg, + "error_code": 1, + } + yield json.dumps(ret).encode() + b"\0" + except Exception as e: + print("Caught Unknown Error", e) + ret = { + "text": server_error_msg, + "error_code": 1, + } + yield json.dumps(ret).encode() + b"\0" + + +app = FastAPI() + + +def release_model_semaphore(fn=None): + model_semaphore.release() + if fn is not None: + fn() + + +@app.post("/worker_generate_stream") +async def generate_stream(request: Request): + global model_semaphore, global_counter + global_counter += 1 + params = await request.json() + + if model_semaphore is None: + model_semaphore = asyncio.Semaphore(args.limit_model_concurrency) + await model_semaphore.acquire() + worker.send_heart_beat() + generator = worker.generate_stream_gate(params) + background_tasks = BackgroundTasks() + background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat)) + return StreamingResponse(generator, background=background_tasks) + + +@app.post("/worker_get_status") +async def get_status(request: Request): + return worker.get_status() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--host", type=str, default="localhost") + parser.add_argument("--port", type=int, default=21002) + parser.add_argument("--worker-address", type=str, + default="http://localhost:21002") + parser.add_argument("--controller-address", type=str, + default="http://localhost:21001") + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--model-name", type=str) + parser.add_argument("--multi-modal", action="store_true", help="Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.") + parser.add_argument("--limit-model-concurrency", type=int, default=5) + parser.add_argument("--stream-interval", type=int, default=1) + parser.add_argument("--no-register", action="store_true") + parser.add_argument("--load-8bit", action="store_true") + parser.add_argument("--load-4bit", action="store_true") + args = parser.parse_args() + logger.info(f"args: {args}") + + if args.multi_modal: + logger.warning("Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.") + + worker = ModelWorker(args.controller_address, + args.worker_address, + worker_id, + args.no_register, + args.model_path, + args.model_base, + args.model_name, + args.load_8bit, + args.load_4bit) + uvicorn.run(app, host=args.host, port=args.port, log_level="info") diff --git a/llava/serve/register_worker.py b/llava/serve/register_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2c40295e0351f25709ba25554c9329f15bf0d2 --- /dev/null +++ b/llava/serve/register_worker.py @@ -0,0 +1,26 @@ +""" +Manually register workers. + +Usage: +python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002 +""" + +import argparse + +import requests + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--controller-address", type=str) + parser.add_argument("--worker-name", type=str) + parser.add_argument("--check-heart-beat", action="store_true") + args = parser.parse_args() + + url = args.controller_address + "/register_worker" + data = { + "worker_name": args.worker_name, + "check_heart_beat": args.check_heart_beat, + "worker_status": None, + } + r = requests.post(url, json=data) + assert r.status_code == 200 diff --git a/llava/serve/test_message.py b/llava/serve/test_message.py new file mode 100644 index 0000000000000000000000000000000000000000..6b090faed0e630b03b2294545050f1f4f5032cad --- /dev/null +++ b/llava/serve/test_message.py @@ -0,0 +1,62 @@ +import argparse +import json + +import requests + +from llava.conversation import default_conversation + + +def main(): + if args.worker_address: + worker_addr = args.worker_address + else: + controller_addr = args.controller_address + ret = requests.post(controller_addr + "/refresh_all_workers") + ret = requests.post(controller_addr + "/list_models") + models = ret.json()["models"] + models.sort() + print(f"Models: {models}") + + ret = requests.post(controller_addr + "/get_worker_address", + json={"model": args.model_name}) + worker_addr = ret.json()["address"] + print(f"worker_addr: {worker_addr}") + + if worker_addr == "": + return + + conv = default_conversation.copy() + conv.append_message(conv.roles[0], args.message) + prompt = conv.get_prompt() + + headers = {"User-Agent": "LLaVA Client"} + pload = { + "model": args.model_name, + "prompt": prompt, + "max_new_tokens": args.max_new_tokens, + "temperature": 0.7, + "stop": conv.sep, + } + response = requests.post(worker_addr + "/worker_generate_stream", headers=headers, + json=pload, stream=True) + + print(prompt.replace(conv.sep, "\n"), end="") + for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"): + if chunk: + data = json.loads(chunk.decode("utf-8")) + output = data["text"].split(conv.sep)[-1] + print(output, end="\r") + print("") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--controller-address", type=str, default="http://localhost:21001") + parser.add_argument("--worker-address", type=str) + parser.add_argument("--model-name", type=str, default="facebook/opt-350m") + parser.add_argument("--max-new-tokens", type=int, default=32) + parser.add_argument("--message", type=str, default= + "Tell me a story with more than 1000 words.") + args = parser.parse_args() + + main() diff --git a/llava/train/llama_flash_attn_monkey_patch.py b/llava/train/llama_flash_attn_monkey_patch.py new file mode 100644 index 0000000000000000000000000000000000000000..31db2eff8d1c4b3ae645583dfc5e156e818b6f1c --- /dev/null +++ b/llava/train/llama_flash_attn_monkey_patch.py @@ -0,0 +1,115 @@ +from typing import Optional, Tuple +import warnings + +import torch + +import transformers +from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv + +try: + from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func +except ImportError: + from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func +from flash_attn.bert_padding import unpad_input, pad_input + + +def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + warnings.warn( + "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = ( + self.q_proj(hidden_states) + .view(bsz, q_len, self.num_heads, self.head_dim) + .transpose(1, 2) + ) + key_states = ( + self.k_proj(hidden_states) + .view(bsz, q_len, self.num_key_value_heads, self.head_dim) + .transpose(1, 2) + ) + value_states = ( + self.v_proj(hidden_states) + .view(bsz, q_len, self.num_key_value_heads, self.head_dim) + .transpose(1, 2) + ) # shape: (b, num_heads, s, head_dim) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb( + query_states, key_states, cos, sin, position_ids + ) + + if past_key_value is not None: + # reuse k, v + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + # Transform the data into the format required by flash attention + qkv = torch.stack([query_states, key_states, value_states], dim=2) + qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] + key_padding_mask = attention_mask + + if key_padding_mask is None: + qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) + cu_q_lens = torch.arange( + 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device + ) + max_s = q_len + output = flash_attn_unpadded_qkvpacked_func( + qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True + ) + output = output.view(bsz, q_len, -1) + else: + qkv = qkv.reshape(bsz, q_len, -1) + qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) + qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) + output_unpad = flash_attn_unpadded_qkvpacked_func( + qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True + ) + output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) + output = pad_input(output_unpad, indices, bsz, q_len) + + return self.o_proj(output), None, past_key_value + + +# Disable the transformation of the attention mask in LlamaModel as the flash attention +# requires the attention mask to be the same as the key_padding_mask +def _prepare_decoder_attention_mask( + self, attention_mask, input_shape, inputs_embeds, past_key_values_length +): + # [bsz, seq_len] + return attention_mask + + +def replace_llama_attn_with_flash_attn(): + cuda_major, cuda_minor = torch.cuda.get_device_capability() + if cuda_major < 8: + warnings.warn( + "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." + "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" + ) + transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( + _prepare_decoder_attention_mask + ) + transformers.models.llama.modeling_llama.LlamaAttention.forward = forward diff --git a/llava/train/llava_trainer.py b/llava/train/llava_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..ec6f4061328337edb130b8298d2b063445e0af45 --- /dev/null +++ b/llava/train/llava_trainer.py @@ -0,0 +1,175 @@ +import os +import torch + +from torch.utils.data import Sampler + +from transformers import Trainer +from transformers.trainer import ( + has_length, +) +from typing import List, Optional + + +def maybe_zero_3(param, ignore_status=False, name=None): + from deepspeed import zero + from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus + if hasattr(param, "ds_id"): + if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: + if not ignore_status: + print(name, 'no ignore status') + with zero.GatheredParameters([param]): + param = param.data.detach().cpu().clone() + else: + param = param.detach().cpu().clone() + return param + + +def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): + to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} + to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()} + return to_return + + +def split_to_even_chunks(indices, lengths, num_chunks): + """ + Split a list of indices into `chunks` chunks of roughly equal lengths. + """ + + if len(indices) % num_chunks != 0: + return [indices[i::num_chunks] for i in range(num_chunks)] + + num_indices_per_chunk = len(indices) // num_chunks + + chunks = [[] for _ in range(num_chunks)] + chunks_lengths = [0 for _ in range(num_chunks)] + for index in indices: + shortest_chunk = chunks_lengths.index(min(chunks_lengths)) + chunks[shortest_chunk].append(index) + chunks_lengths[shortest_chunk] += lengths[index] + if len(chunks[shortest_chunk]) == num_indices_per_chunk: + chunks_lengths[shortest_chunk] = float("inf") + + return chunks + + +def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None): + # We need to use torch for the random part as a distributed sampler will set the random seed for torch. + assert all(l != 0 for l in lengths), "Should not have zero length." + mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0]) + lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0]) + + assert len(mm_indices) > 0, "Should have at least one multimodal sample." + assert len(lang_indices) > 0, "Should have at least one language sample." + + mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)] + lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)] + megabatch_size = world_size * batch_size + mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)] + lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)] + + last_mm = mm_megabatches[-1] + last_lang = lang_megabatches[-1] + additional_batch = last_mm + last_lang + megabatches = mm_megabatches[:-1] + lang_megabatches[:-1] + megabatch_indices = torch.randperm(len(megabatches), generator=generator) + megabatches = [megabatches[i] for i in megabatch_indices] + + if len(additional_batch) >= megabatch_size: + megabatches = [additional_batch[:megabatch_size]] + megabatches + additional_batch = additional_batch[megabatch_size:] + + if len(additional_batch) > 0: + megabatches.append(additional_batch) + + return [i for megabatch in megabatches for i in megabatch] + + +def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True): + # We need to use torch for the random part as a distributed sampler will set the random seed for torch. + indices = torch.randperm(len(lengths), generator=generator) + megabatch_size = world_size * batch_size + megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)] + megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches] + megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches] + + return [i for megabatch in megabatches for batch in megabatch for i in batch] + + +class LengthGroupedSampler(Sampler): + r""" + Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while + keeping a bit of randomness. + """ + + def __init__( + self, + batch_size: int, + world_size: int, + lengths: Optional[List[int]] = None, + generator=None, + group_by_modality: bool = False, + ): + if lengths is None: + raise ValueError("Lengths must be provided.") + + self.batch_size = batch_size + self.world_size = world_size + self.lengths = lengths + self.generator = generator + self.group_by_modality = group_by_modality + + def __len__(self): + return len(self.lengths) + + def __iter__(self): + if self.group_by_modality: + indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) + else: + indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) + return iter(indices) + + +class LLaVATrainer(Trainer): + + def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: + if self.train_dataset is None or not has_length(self.train_dataset): + return None + + if self.args.group_by_modality_length: + lengths = self.train_dataset.modality_lengths + return LengthGroupedSampler( + # self.args.train_batch_size * self.args.gradient_accumulation_steps, # TODO: seems that we should not have gradient_accumulation_steps + self.args.train_batch_size, + world_size=self.args.world_size, + lengths=lengths, + group_by_modality=True, + ) + else: + return super()._get_train_sampler() + + def _save_checkpoint(self, model, trial, metrics=None): + if getattr(self.args, 'tune_mm_mlp_adapter', False): + from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR + checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}" + + run_dir = self._get_output_dir(trial=trial) + output_dir = os.path.join(run_dir, checkpoint_folder) + + # Only save Adapter + keys_to_match = ['mm_projector', 'vision_resampler'] + if getattr(self.args, "use_im_start_end", False): + keys_to_match.extend(['embed_tokens', 'embed_in']) + + weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match) + + if self.args.local_rank == 0 or self.args.local_rank == -1: + self.model.config.save_pretrained(output_dir) + torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin')) + else: + super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics) + + def _save(self, output_dir: Optional[str] = None, state_dict=None): + if getattr(self.args, 'tune_mm_mlp_adapter', False): + pass + else: + super(LLaVATrainer, self)._save(output_dir, state_dict) diff --git a/llava/train/train.py b/llava/train/train.py new file mode 100644 index 0000000000000000000000000000000000000000..0d198f7e379df541e2b9a6fbd61310ea30f16f82 --- /dev/null +++ b/llava/train/train.py @@ -0,0 +1,950 @@ +# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: +# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import copy +from dataclasses import dataclass, field +import json +import logging +import pathlib +from typing import Dict, Optional, Sequence, List + +import torch + +import transformers + +from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN +from torch.utils.data import Dataset +from llava.train.llava_trainer import LLaVATrainer + +from llava import conversation as conversation_lib +from llava.model import * +from llava.mm_utils import tokenizer_image_token + +from PIL import Image + + +local_rank = None + + +def rank0_print(*args): + if local_rank == 0: + print(*args) + + +@dataclass +class ModelArguments: + model_name_or_path: Optional[str] = field(default="facebook/opt-125m") + version: Optional[str] = field(default="v0") + freeze_backbone: bool = field(default=False) + tune_mm_mlp_adapter: bool = field(default=False) + vision_tower: Optional[str] = field(default=None) + mm_vision_select_layer: Optional[int] = field(default=-1) # default to the last layer + pretrain_mm_mlp_adapter: Optional[str] = field(default=None) + mm_projector_type: Optional[str] = field(default='linear') + mm_use_im_start_end: bool = field(default=False) + mm_use_im_patch_token: bool = field(default=True) + mm_vision_select_feature: Optional[str] = field(default="patch") + + +@dataclass +class DataArguments: + data_path: str = field(default=None, + metadata={"help": "Path to the training data."}) + lazy_preprocess: bool = False + is_multimodal: bool = False + image_folder: Optional[str] = field(default=None) + image_aspect_ratio: str = 'square' + image_grid_pinpoints: Optional[str] = field(default=None) + + +@dataclass +class TrainingArguments(transformers.TrainingArguments): + cache_dir: Optional[str] = field(default=None) + optim: str = field(default="adamw_torch") + remove_unused_columns: bool = field(default=False) + freeze_mm_mlp_adapter: bool = field(default=False) + mpt_attn_impl: Optional[str] = field(default="triton") + model_max_length: int = field( + default=512, + metadata={ + "help": + "Maximum sequence length. Sequences will be right padded (and possibly truncated)." + }, + ) + double_quant: bool = field( + default=True, + metadata={"help": "Compress the quantization statistics through double quantization."} + ) + quant_type: str = field( + default="nf4", + metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."} + ) + bits: int = field( + default=16, + metadata={"help": "How many bits to use."} + ) + lora_enable: bool = False + lora_r: int = 64 + lora_alpha: int = 16 + lora_dropout: float = 0.05 + lora_weight_path: str = "" + lora_bias: str = "none" + group_by_modality_length: bool = field(default=False) + + +def maybe_zero_3(param, ignore_status=False, name=None): + from deepspeed import zero + from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus + if hasattr(param, "ds_id"): + if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: + if not ignore_status: + logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}") + with zero.GatheredParameters([param]): + param = param.data.detach().cpu().clone() + else: + param = param.detach().cpu().clone() + return param + + +# Borrowed from peft.utils.get_peft_model_state_dict +def get_peft_state_maybe_zero_3(named_params, bias): + if bias == "none": + to_return = {k: t for k, t in named_params if "lora_" in k} + elif bias == "all": + to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k} + elif bias == "lora_only": + to_return = {} + maybe_lora_bias = {} + lora_bias_names = set() + for k, t in named_params: + if "lora_" in k: + to_return[k] = t + bias_name = k.split("lora_")[0] + "bias" + lora_bias_names.add(bias_name) + elif "bias" in k: + maybe_lora_bias[k] = t + for k, t in maybe_lora_bias: + if bias_name in lora_bias_names: + to_return[bias_name] = t + else: + raise NotImplementedError + to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()} + return to_return + + +def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True): + to_return = {k: t for k, t in named_params if "lora_" not in k} + if require_grad_only: + to_return = {k: t for k, t in to_return.items() if t.requires_grad} + to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} + return to_return + + +def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): + to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} + to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} + return to_return + + +def find_all_linear_names(model): + cls = torch.nn.Linear + lora_module_names = set() + for name, module in model.named_modules(): + if isinstance(module, cls): + names = name.split('.') + lora_module_names.add(names[0] if len(names) == 1 else names[-1]) + + + if 'lm_head' in lora_module_names: # needed for 16-bit + lora_module_names.remove('lm_head') + return list(lora_module_names) + + +def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, + output_dir: str): + """Collects the state dict and dump to disk.""" + + if getattr(trainer.args, "tune_mm_mlp_adapter", False): + # Only save Adapter + keys_to_match = ['mm_projector'] + if getattr(trainer.args, "use_im_start_end", False): + keys_to_match.extend(['embed_tokens', 'embed_in']) + + weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match) + trainer.model.config.save_pretrained(output_dir) + + current_folder = output_dir.split('/')[-1] + parent_folder = os.path.dirname(output_dir) + if trainer.args.local_rank == 0 or trainer.args.local_rank == -1: + if current_folder.startswith('checkpoint-'): + mm_projector_folder = os.path.join(parent_folder, "mm_projector") + os.makedirs(mm_projector_folder, exist_ok=True) + torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin')) + else: + torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin')) + return + + if trainer.deepspeed: + torch.cuda.synchronize() + trainer.save_model(output_dir) + return + + state_dict = trainer.model.state_dict() + if trainer.args.should_save: + cpu_state_dict = { + key: value.cpu() + for key, value in state_dict.items() + } + del state_dict + trainer._save(output_dir, state_dict=cpu_state_dict) # noqa + + +def smart_tokenizer_and_embedding_resize( + special_tokens_dict: Dict, + tokenizer: transformers.PreTrainedTokenizer, + model: transformers.PreTrainedModel, +): + """Resize tokenizer and embedding. + + Note: This is the unoptimized version that may make your embedding size not be divisible by 64. + """ + num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) + model.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = model.get_input_embeddings().weight.data + output_embeddings = model.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + + +def _tokenize_fn(strings: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ) for text in strings + ] + input_ids = labels = [ + tokenized.input_ids[0] for tokenized in tokenized_list + ] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() + for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + + +def _mask_targets(target, tokenized_lens, speakers): + # cur_idx = 0 + cur_idx = tokenized_lens[0] + tokenized_lens = tokenized_lens[1:] + target[:cur_idx] = IGNORE_INDEX + for tokenized_len, speaker in zip(tokenized_lens, speakers): + if speaker == "human": + target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX + cur_idx += tokenized_len + + +def _add_speaker_and_signal(header, source, get_conversation=True): + """Add speaker and start/end signal on each round.""" + BEGIN_SIGNAL = "### " + END_SIGNAL = "\n" + conversation = header + for sentence in source: + from_str = sentence["from"] + if from_str.lower() == "human": + from_str = conversation_lib.default_conversation.roles[0] + elif from_str.lower() == "gpt": + from_str = conversation_lib.default_conversation.roles[1] + else: + from_str = 'unknown' + sentence["value"] = (BEGIN_SIGNAL + from_str + ": " + + sentence["value"] + END_SIGNAL) + if get_conversation: + conversation += sentence["value"] + conversation += BEGIN_SIGNAL + return conversation + + +def preprocess_multimodal( + sources: Sequence[str], + data_args: DataArguments +) -> Dict: + is_multimodal = data_args.is_multimodal + if not is_multimodal: + return sources + + for source in sources: + for sentence in source: + if DEFAULT_IMAGE_TOKEN in sentence['value']: + sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip() + sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value'] + sentence['value'] = sentence['value'].strip() + if "mmtag" in conversation_lib.default_conversation.version: + sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '' + DEFAULT_IMAGE_TOKEN + '') + replace_token = DEFAULT_IMAGE_TOKEN + if data_args.mm_use_im_start_end: + replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN + sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token) + + return sources + + +def preprocess_llama_2( + sources, + tokenizer: transformers.PreTrainedTokenizer, + has_image: bool = False +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + + if has_image: + input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0) + else: + input_ids = tokenizer( + conversations, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ).input_ids + + targets = input_ids.clone() + + assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 + + # Mask targets + sep = "[/INST] " + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep2) + cur_len = 1 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + if has_image: + round_len = len(tokenizer_image_token(rou, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 + else: + round_len = len(tokenizer(rou).input_ids) + instruction_len = len(tokenizer(parts[0]).input_ids) - 2 + + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print( + f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." + f" (ignored)" + ) + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_v1( + sources, + tokenizer: transformers.PreTrainedTokenizer, + has_image: bool = False +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + + if has_image: + input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0) + else: + input_ids = tokenizer( + conversations, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ).input_ids + + targets = input_ids.clone() + + assert conv.sep_style == conversation_lib.SeparatorStyle.TWO + + # Mask targets + sep = conv.sep + conv.roles[1] + ": " + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep2) + cur_len = 1 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + if has_image: + round_len = len(tokenizer_image_token(rou, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 + else: + round_len = len(tokenizer(rou).input_ids) + instruction_len = len(tokenizer(parts[0]).input_ids) - 2 + + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print( + f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." + f" (ignored)" + ) + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_mpt( + sources, + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0) + targets = input_ids.clone() + assert conv.sep_style == conversation_lib.SeparatorStyle.MPT + + # Mask targets + sep = conv.sep + conv.roles[1] + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep) + re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt + for conv_idx in range(3, len(rounds), 2): + re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2])) # user + gpt + cur_len = 0 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(re_rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + round_len = len(tokenizer_image_token(rou, tokenizer)) + len(tokenizer_image_token(conv.sep, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print( + f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." + f" (ignored)" + ) + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_plain( + sources: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + # add end signal and concatenate together + conversations = [] + for source in sources: + assert len(source) == 2 + assert DEFAULT_IMAGE_TOKEN in source[0]['value'] + source[0]['value'] = DEFAULT_IMAGE_TOKEN + conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep + conversations.append(conversation) + # tokenize conversations + input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations] + targets = copy.deepcopy(input_ids) + for target, source in zip(targets, sources): + tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer)) + target[:tokenized_len] = IGNORE_INDEX + + return dict(input_ids=input_ids, labels=targets) + + +def preprocess( + sources: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, + has_image: bool = False +) -> Dict: + """ + Given a list of sources, each is a conversation list. This transform: + 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; + 2. Concatenate conversations together; + 3. Tokenize the concatenated conversation; + 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. + """ + if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN: + return preprocess_plain(sources, tokenizer) + if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2: + return preprocess_llama_2(sources, tokenizer, has_image=has_image) + if conversation_lib.default_conversation.version.startswith("v1"): + return preprocess_v1(sources, tokenizer, has_image=has_image) + if conversation_lib.default_conversation.version == "mpt": + return preprocess_mpt(sources, tokenizer) + # add end signal and concatenate together + conversations = [] + for source in sources: + header = f"{conversation_lib.default_conversation.system}\n\n" + conversation = _add_speaker_and_signal(header, source) + conversations.append(conversation) + # tokenize conversations + def get_tokenize_len(prompts): + return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts] + + if has_image: + input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations] + else: + conversations_tokenized = _tokenize_fn(conversations, tokenizer) + input_ids = conversations_tokenized["input_ids"] + + targets = copy.deepcopy(input_ids) + for target, source in zip(targets, sources): + if has_image: + tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source]) + else: + tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"] + speakers = [sentence["from"] for sentence in source] + _mask_targets(target, tokenized_lens, speakers) + + return dict(input_ids=input_ids, labels=targets) + + +class LazySupervisedDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, + tokenizer: transformers.PreTrainedTokenizer, + data_args: DataArguments): + super(LazySupervisedDataset, self).__init__() + list_data_dict = json.load(open(data_path, "r")) + + rank0_print("Formatting inputs...Skip in lazy mode") + self.tokenizer = tokenizer + self.list_data_dict = list_data_dict + self.data_args = data_args + + def __len__(self): + return len(self.list_data_dict) + + @property + def lengths(self): + length_list = [] + for sample in self.list_data_dict: + img_tokens = 128 if 'image' in sample else 0 + length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens) + return length_list + + @property + def modality_lengths(self): + length_list = [] + for sample in self.list_data_dict: + cur_len = sum(len(conv['value'].split()) for conv in sample['conversations']) + cur_len = cur_len if 'image' in sample else -cur_len + length_list.append(cur_len) + return length_list + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + sources = self.list_data_dict[i] + if isinstance(i, int): + sources = [sources] + assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME + if 'image' in sources[0]: + image_file = self.list_data_dict[i]['image'] + image_folder = self.data_args.image_folder + processor = self.data_args.image_processor + image = Image.open(os.path.join(image_folder, image_file)).convert('RGB') + if self.data_args.image_aspect_ratio == 'pad': + def expand2square(pil_img, background_color): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + image = expand2square(image, tuple(int(x*255) for x in processor.image_mean)) + image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + else: + image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + sources = preprocess_multimodal( + copy.deepcopy([e["conversations"] for e in sources]), + self.data_args) + else: + sources = copy.deepcopy([e["conversations"] for e in sources]) + data_dict = preprocess( + sources, + self.tokenizer, + has_image=('image' in self.list_data_dict[i])) + if isinstance(i, int): + data_dict = dict(input_ids=data_dict["input_ids"][0], + labels=data_dict["labels"][0]) + + # image exist in the data + if 'image' in self.list_data_dict[i]: + data_dict['image'] = image + elif self.data_args.is_multimodal: + # image does not exist in the data, but the model is multimodal + crop_size = self.data_args.image_processor.crop_size + data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width']) + return data_dict + + +@dataclass +class DataCollatorForSupervisedDataset(object): + """Collate examples for supervised fine-tuning.""" + + tokenizer: transformers.PreTrainedTokenizer + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + input_ids, labels = tuple([instance[key] for instance in instances] + for key in ("input_ids", "labels")) + input_ids = torch.nn.utils.rnn.pad_sequence( + input_ids, + batch_first=True, + padding_value=self.tokenizer.pad_token_id) + labels = torch.nn.utils.rnn.pad_sequence(labels, + batch_first=True, + padding_value=IGNORE_INDEX) + input_ids = input_ids[:, :self.tokenizer.model_max_length] + labels = labels[:, :self.tokenizer.model_max_length] + batch = dict( + input_ids=input_ids, + labels=labels, + attention_mask=input_ids.ne(self.tokenizer.pad_token_id), + ) + + if 'image' in instances[0]: + images = [instance['image'] for instance in instances] + if all(x is not None and x.shape == images[0].shape for x in images): + batch['images'] = torch.stack(images) + else: + batch['images'] = images + + return batch + + +def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, + data_args) -> Dict: + """Make dataset and collator for supervised fine-tuning.""" + train_dataset = LazySupervisedDataset(tokenizer=tokenizer, + data_path=data_args.data_path, + data_args=data_args) + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) + return dict(train_dataset=train_dataset, + eval_dataset=None, + data_collator=data_collator) + + +def train(): + global local_rank + + parser = transformers.HfArgumentParser( + (ModelArguments, DataArguments, TrainingArguments)) + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + local_rank = training_args.local_rank + compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32)) + + bnb_model_from_pretrained_args = {} + if training_args.bits in [4, 8]: + from transformers import BitsAndBytesConfig + bnb_model_from_pretrained_args.update(dict( + device_map={"": training_args.device}, + load_in_4bit=training_args.bits == 4, + load_in_8bit=training_args.bits == 8, + quantization_config=BitsAndBytesConfig( + load_in_4bit=training_args.bits == 4, + load_in_8bit=training_args.bits == 8, + llm_int8_threshold=6.0, + llm_int8_has_fp16_weight=False, + bnb_4bit_compute_dtype=compute_dtype, + bnb_4bit_use_double_quant=training_args.double_quant, + bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'} + ) + )) + + if model_args.vision_tower is not None: + if 'mpt' in model_args.model_name_or_path: + config = transformers.AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True) + config.attn_config['attn_impl'] = training_args.mpt_attn_impl + model = LlavaMPTForCausalLM.from_pretrained( + model_args.model_name_or_path, + config=config, + cache_dir=training_args.cache_dir, + **bnb_model_from_pretrained_args + ) + else: + model = LlavaLlamaForCausalLM.from_pretrained( + model_args.model_name_or_path, + cache_dir=training_args.cache_dir, + **bnb_model_from_pretrained_args + ) + else: + model = transformers.LlamaForCausalLM.from_pretrained( + model_args.model_name_or_path, + cache_dir=training_args.cache_dir, + **bnb_model_from_pretrained_args + ) + model.config.use_cache = False + + if model_args.freeze_backbone: + model.model.requires_grad_(False) + + if training_args.bits in [4, 8]: + from peft import prepare_model_for_kbit_training + model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32)) + model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing) + + if training_args.gradient_checkpointing: + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + if training_args.lora_enable: + from peft import LoraConfig, get_peft_model + lora_config = LoraConfig( + r=training_args.lora_r, + lora_alpha=training_args.lora_alpha, + target_modules=find_all_linear_names(model), + lora_dropout=training_args.lora_dropout, + bias=training_args.lora_bias, + task_type="CAUSAL_LM", + ) + if training_args.bits == 16: + if training_args.bf16: + model.to(torch.bfloat16) + if training_args.fp16: + model.to(torch.float16) + rank0_print("Adding LoRA adapters...") + model = get_peft_model(model, lora_config) + + if 'mpt' in model_args.model_name_or_path: + tokenizer = transformers.AutoTokenizer.from_pretrained( + model_args.model_name_or_path, + cache_dir=training_args.cache_dir, + model_max_length=training_args.model_max_length, + padding_side="right" + ) + else: + tokenizer = transformers.AutoTokenizer.from_pretrained( + model_args.model_name_or_path, + cache_dir=training_args.cache_dir, + model_max_length=training_args.model_max_length, + padding_side="right", + use_fast=False, + ) + + if model_args.version == "v0": + if tokenizer.pad_token is None: + smart_tokenizer_and_embedding_resize( + special_tokens_dict=dict(pad_token="[PAD]"), + tokenizer=tokenizer, + model=model, + ) + elif model_args.version == "v0.5": + tokenizer.pad_token = tokenizer.unk_token + else: + tokenizer.pad_token = tokenizer.unk_token + if model_args.version in conversation_lib.conv_templates: + conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version] + else: + conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"] + + if model_args.vision_tower is not None: + model.get_model().initialize_vision_modules( + model_args=model_args, + fsdp=training_args.fsdp + ) + + vision_tower = model.get_vision_tower() + vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device) + + data_args.image_processor = vision_tower.image_processor + data_args.is_multimodal = True + + model.config.image_aspect_ratio = data_args.image_aspect_ratio + model.config.image_grid_pinpoints = data_args.image_grid_pinpoints + + model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter + if model_args.tune_mm_mlp_adapter: + model.requires_grad_(False) + for p in model.get_model().mm_projector.parameters(): + p.requires_grad = True + + model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter + if training_args.freeze_mm_mlp_adapter: + for p in model.get_model().mm_projector.parameters(): + p.requires_grad = False + + if training_args.bits in [4, 8]: + model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device) + + model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end + training_args.use_im_start_end = model_args.mm_use_im_start_end + model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token + model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer) + + if training_args.bits in [4, 8]: + from peft.tuners.lora import LoraLayer + for name, module in model.named_modules(): + if isinstance(module, LoraLayer): + if training_args.bf16: + module = module.to(torch.bfloat16) + if 'norm' in name: + module = module.to(torch.float32) + if 'lm_head' in name or 'embed_tokens' in name: + if hasattr(module, 'weight'): + if training_args.bf16 and module.weight.dtype == torch.float32: + module = module.to(torch.bfloat16) + + data_module = make_supervised_data_module(tokenizer=tokenizer, + data_args=data_args) + trainer = LLaVATrainer(model=model, + tokenizer=tokenizer, + args=training_args, + **data_module) + + if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): + trainer.train(resume_from_checkpoint=True) + else: + trainer.train() + trainer.save_state() + + model.config.use_cache = True + + if training_args.lora_enable: + state_dict = get_peft_state_maybe_zero_3( + model.named_parameters(), training_args.lora_bias + ) + non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( + model.named_parameters() + ) + if training_args.local_rank == 0 or training_args.local_rank == -1: + model.config.save_pretrained(training_args.output_dir) + model.save_pretrained(training_args.output_dir, state_dict=state_dict) + torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin')) + else: + safe_save_model_for_hf_trainer(trainer=trainer, + output_dir=training_args.output_dir) + + +if __name__ == "__main__": + train() diff --git a/llava/train/train_mem.py b/llava/train/train_mem.py new file mode 100644 index 0000000000000000000000000000000000000000..2487d317855b27d5b07a755ee0389667e4964f02 --- /dev/null +++ b/llava/train/train_mem.py @@ -0,0 +1,13 @@ +# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: +# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: +# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn. + +# Need to call this before importing transformers. +from llava.train.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn + +replace_llama_attn_with_flash_attn() + +from llava.train.train import train + +if __name__ == "__main__": + train() diff --git a/llava/utils.py b/llava/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8f7163c0ba1d9a81d81a950bce61e0f0db06066e --- /dev/null +++ b/llava/utils.py @@ -0,0 +1,126 @@ +import datetime +import logging +import logging.handlers +import os +import sys + +import requests + +from llava.constants import LOGDIR + +server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" +moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." + +handler = None + + +def build_logger(logger_name, logger_filename): + global handler + + formatter = logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Set the format of root handlers + if not logging.getLogger().handlers: + logging.basicConfig(level=logging.INFO) + logging.getLogger().handlers[0].setFormatter(formatter) + + # Redirect stdout and stderr to loggers + stdout_logger = logging.getLogger("stdout") + stdout_logger.setLevel(logging.INFO) + sl = StreamToLogger(stdout_logger, logging.INFO) + sys.stdout = sl + + stderr_logger = logging.getLogger("stderr") + stderr_logger.setLevel(logging.ERROR) + sl = StreamToLogger(stderr_logger, logging.ERROR) + sys.stderr = sl + + # Get logger + logger = logging.getLogger(logger_name) + logger.setLevel(logging.INFO) + + # Add a file handler for all loggers + if handler is None: + os.makedirs(LOGDIR, exist_ok=True) + filename = os.path.join(LOGDIR, logger_filename) + handler = logging.handlers.TimedRotatingFileHandler( + filename, when='D', utc=True) + handler.setFormatter(formatter) + + for name, item in logging.root.manager.loggerDict.items(): + if isinstance(item, logging.Logger): + item.addHandler(handler) + + return logger + + +class StreamToLogger(object): + """ + Fake file-like stream object that redirects writes to a logger instance. + """ + def __init__(self, logger, log_level=logging.INFO): + self.terminal = sys.stdout + self.logger = logger + self.log_level = log_level + self.linebuf = '' + + def __getattr__(self, attr): + return getattr(self.terminal, attr) + + def write(self, buf): + temp_linebuf = self.linebuf + buf + self.linebuf = '' + for line in temp_linebuf.splitlines(True): + # From the io.TextIOWrapper docs: + # On output, if newline is None, any '\n' characters written + # are translated to the system default line separator. + # By default sys.stdout.write() expects '\n' newlines and then + # translates them so this is still cross platform. + if line[-1] == '\n': + self.logger.log(self.log_level, line.rstrip()) + else: + self.linebuf += line + + def flush(self): + if self.linebuf != '': + self.logger.log(self.log_level, self.linebuf.rstrip()) + self.linebuf = '' + + +def disable_torch_init(): + """ + Disable the redundant torch default initialization to accelerate model creation. + """ + import torch + setattr(torch.nn.Linear, "reset_parameters", lambda self: None) + setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) + + +def violates_moderation(text): + """ + Check whether the text violates OpenAI moderation API. + """ + url = "https://api.openai.com/v1/moderations" + headers = {"Content-Type": "application/json", + "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]} + text = text.replace("\n", "") + data = "{" + '"input": ' + f'"{text}"' + "}" + data = data.encode("utf-8") + try: + ret = requests.post(url, headers=headers, data=data, timeout=5) + flagged = ret.json()["results"][0]["flagged"] + except requests.exceptions.RequestException as e: + flagged = False + except KeyError as e: + flagged = False + + return flagged + + +def pretty_print_semaphore(semaphore): + if semaphore is None: + return "None" + return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..25c7bf00d69883a18c1e1d298a09bde7b1076e1b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,39 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "llava" +version = "1.1.0" +description = "Towards GPT-4 like large language and visual assistant." +readme = "README.md" +requires-python = ">=3.8" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache Software License", +] +dependencies = [ + "einops", "fastapi", "gradio==3.35.2", "markdown2[all]", "numpy", + "requests", "sentencepiece", "tokenizers>=0.12.1", + "torch", "torchvision", "uvicorn", "wandb", + "shortuuid", "httpx==0.24.0", + "deepspeed==0.9.5", + "peft==0.4.0", + "transformers==4.31.0", + "accelerate==0.21.0", + "bitsandbytes==0.41.0", + "scikit-learn==1.2.2", + "sentencepiece==0.1.99", + "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13", + "gradio_client==0.2.9" +] + +[project.urls] +"Homepage" = "https://llava-vl.github.io" +"Bug Tracker" = "https://github.com/haotian-liu/LLaVA/issues" + +[tool.setuptools.packages.find] +exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] + +[tool.wheel] +exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..59e89a91228702d4d8ec500743033ac9176d5d60 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,27 @@ +--extra-index-url https://download.pytorch.org/whl/cu118 +pip +einops +fastapi +gradio==3.35.2 +markdown2[all] +numpy +requests +sentencepiece +tokenizers>=0.12.1 +torch +torchvision +uvicorn +wandb +shortuuid +httpx==0.24.0 +deepspeed==0.9.5 +peft==0.4.0 +transformers==4.31.0 +accelerate==0.21.0 +bitsandbytes==0.41.0 +scikit-learn==1.2.2 +sentencepiece==0.1.99 +einops==0.6.1 +einops-exts==0.0.4 +timm==0.6.13 +gradio_client==0.2.9 diff --git a/scripts/convert_sqa_to_llava.py b/scripts/convert_sqa_to_llava.py new file mode 100644 index 0000000000000000000000000000000000000000..26fe3002413a23b5029e540c8b338ebb14307bf6 --- /dev/null +++ b/scripts/convert_sqa_to_llava.py @@ -0,0 +1,88 @@ +import json +import os +import fire +import re +from convert_sqa_to_llava_base_prompt import build_prompt_chatbot + + +def convert_to_llava(base_dir, split, prompt_format="QCM-LEA"): + split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split] + problems = json.load(open(os.path.join(base_dir, "problems.json"))) + + split_problems = build_prompt_chatbot( + problems, split_indices, prompt_format, + use_caption=False, is_test=False) + + target_format = [] + for prob_id, (input, output) in split_problems.items(): + if input.startswith('Question: '): + input = input.replace('Question: ', '') + if output.startswith('Answer: '): + output = output.replace('Answer: ', '') + + raw_prob_data = problems[prob_id] + if raw_prob_data['image'] is None: + target_format.append({ + "id": prob_id, + "conversations": [ + {'from': 'human', 'value': f"{input}"}, + {'from': 'gpt', 'value': f"{output}"}, + ], + }) + + else: + target_format.append({ + "id": prob_id, + "image": os.path.join(prob_id, raw_prob_data['image']), + "conversations": [ + {'from': 'human', 'value': f"{input}\n"}, + {'from': 'gpt', 'value': f"{output}"}, + ], + }) + + print(f'Number of samples: {len(target_format)}') + + with open(os.path.join(base_dir, f"llava_{split}_{prompt_format}.json"), "w") as f: + json.dump(target_format, f, indent=2) + + +def convert_to_jsonl(base_dir, split, prompt_format="QCM-LEPA"): + split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split] + problems = json.load(open(os.path.join(base_dir, "problems.json"))) + + split_problems = build_prompt_chatbot( + problems, split_indices, prompt_format, + use_caption=False, is_test=False) + + writer = open(os.path.join(base_dir, f"scienceqa_{split}_{prompt_format}.jsonl"), "w") + for prob_id, (input, output) in split_problems.items(): + if input.startswith('Question: '): + input = input.replace('Question: ', '') + if output.startswith('Answer: '): + output = output.replace('Answer: ', '') + + raw_prob_data = problems[prob_id] + if raw_prob_data['image'] is None: + data = { + "id": prob_id, + "instruction": f"{input}", + "output": f"{output}", + } + + else: + data = { + "id": prob_id, + "image": os.path.join(prob_id, raw_prob_data['image']), + "instruction": f"{input}\n", + "output": f"{output}", + } + writer.write(json.dumps(data) + '\n') + writer.close() + + +def main(task, **kwargs): + globals()[task](**kwargs) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/convert_sqa_to_llava_base_prompt.py b/scripts/convert_sqa_to_llava_base_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..b327fcc29eb44d7fe68be35da25bafa0e1d6feba --- /dev/null +++ b/scripts/convert_sqa_to_llava_base_prompt.py @@ -0,0 +1,334 @@ +def get_question_text(problem): + question = problem['question'] + return question + + +def get_context_text(problem, use_caption): + txt_context = problem['hint'] + img_context = problem['caption'] if use_caption else "" + context = " ".join([txt_context, img_context]).strip() + if context == "": + context = "N/A" + return context + + +def get_choice_text(probelm, options): + choices = probelm['choices'] + choice_list = [] + for i, c in enumerate(choices): + choice_list.append("({}) {}".format(options[i], c)) + choice_txt = " ".join(choice_list) + #print(choice_txt) + return choice_txt + + +def get_answer(problem, options): + return options[problem['answer']] + + +def get_lecture_text(problem): + # \\n: GPT-3 can generate the lecture with more tokens. + lecture = problem['lecture'].replace("\n", "\\n") + return lecture + + +def get_solution_text(problem): + # \\n: GPT-3 can generate the solution with more tokens + solution = problem['solution'].replace("\n", "\\n") + return solution + + +def create_one_example_chatbot(format, question, context, choice, answer, lecture, solution, test_example=True): + + input_format, output_format = format.split("-") + + ## Inputs + if input_format == "CQM": + input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" + elif input_format == "QCM": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" + # upper bound experiment + elif input_format == "QCML": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" + elif input_format == "QCME": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" + elif input_format == "QCMLE": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" + + elif input_format == "QCLM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" + elif input_format == "QCEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" + elif input_format == "QCLEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" + + # Outputs + if test_example: + output = "Answer:" + elif output_format == 'A': + output = f"Answer: The answer is {answer}." + + elif output_format == 'AL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution}" + elif output_format == 'AE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" + elif output_format == 'ALE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" + elif output_format == 'AEL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" + + elif output_format == 'LA': + output = f"Answer: {lecture} The answer is {answer}." + elif output_format == 'EA': + output = f"Answer: {solution} The answer is {answer}." + elif output_format == 'LEA': + output = f"Answer: {lecture} {solution} The answer is {answer}." + elif output_format == 'ELA': + output = f"Answer: {solution} {lecture} The answer is {answer}." + elif output_format == 'LEPA': + output = '' + if len(lecture.strip()) > 0: + output += f"LECTURE: {lecture}\n" + if len(solution.strip()) > 0: + output += f"SOLUTION: {solution}\n" + output += '###\n' + output += f"ANSWER: {answer}." + + input = input.replace(" ", " ").strip() + output = output.replace(" ", " ").strip() + if input.endswith("BECAUSE:"): + input = input.replace("BECAUSE:", "").strip() + if output.endswith("BECAUSE:"): + output = output.replace("BECAUSE:", "").strip() + return input, output + + +def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True): + + input_format, output_format = format.split("-") + + ## Inputs + if input_format == "CQM": + input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" + elif input_format == "QCM": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" + # upper bound experiment + elif input_format == "QCML": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" + elif input_format == "QCME": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" + elif input_format == "QCMLE": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" + + elif input_format == "QCLM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" + elif input_format == "QCEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" + elif input_format == "QCLEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" + + # Outputs + if test_example: + output = "Answer:" + elif output_format == 'A': + output = f"Answer: The answer is {answer}." + + elif output_format == 'AL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution}" + elif output_format == 'AE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" + elif output_format == 'ALE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" + elif output_format == 'AEL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" + + elif output_format == 'LA': + output = f"Answer: {lecture} The answer is {answer}." + elif output_format == 'EA': + output = f"Answer: {solution} The answer is {answer}." + elif output_format == 'LEA': + output = f"Answer: {lecture} {solution} The answer is {answer}." + elif output_format == 'ELA': + output = f"Answer: {solution} {lecture} The answer is {answer}." + + text = input + output + text = text.replace(" ", " ").strip() + if text.endswith("BECAUSE:"): + text = text.replace("BECAUSE:", "").strip() + return text + + + +def create_one_example_gpt4(format, question, context, choice, answer, lecture, solution, test_example=True): + + input_format, output_format = format.split("-") + + ## Inputs + if input_format == "CQM": + input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" + elif input_format == "QCM": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" + # upper bound experiment + elif input_format == "QCML": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" + elif input_format == "QCME": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" + elif input_format == "QCMLE": + input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" + + elif input_format == "QCLM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" + elif input_format == "QCEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" + elif input_format == "QCLEM": + input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" + + # Outputs + if test_example: + output = "Answer:" + elif output_format == 'A': + output = f"Answer: The answer is {answer}." + + elif output_format == 'AL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution}" + elif output_format == 'AE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" + elif output_format == 'ALE': + output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" + elif output_format == 'AEL': + output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" + + elif output_format == 'LA': + output = f"Answer: {lecture} The answer is {answer}." + elif output_format == 'EA': + output = f"Answer: {solution} The answer is {answer}." + elif output_format == 'LEA': + output = f"Answer: {lecture} {solution} The answer is {answer}." + elif output_format == 'ELA': + output = f"Answer: {solution} {lecture} The answer is {answer}." + + input = input.replace(" ", " ").strip() + output = output.replace(" ", " ").strip() + if output.endswith("BECAUSE:"): + output = output.replace("BECAUSE:", "").strip() + + user_prompt = {"role": "user", "content": f"Can you explain {input}?"} + assistant_prompt = {"role": "assistant", "content": f"{output}"} + + return user_prompt, assistant_prompt + + +def build_prompt_chatbot(problems, shot_qids, prompt_format, use_caption=False, options=["A", "B", "C", "D", "E"], is_test=False): + examples = {} + + for qid in shot_qids: + question = get_question_text(problems[qid]) + context = get_context_text(problems[qid], use_caption) + choice = get_choice_text(problems[qid], options) + answer = get_answer(problems[qid], options) + lecture = get_lecture_text(problems[qid]).replace('\\n', '\n') + solution = get_solution_text(problems[qid]).replace('\\n', '\n') + + train_example = create_one_example_chatbot(prompt_format, + question, + context, + choice, + answer, + lecture, + solution, + test_example=is_test) + examples[qid] = train_example + return examples + + +def build_prompt(problems, shot_qids, test_qid, args): + + examples = [] + + # n-shot training examples + for qid in shot_qids: + question = get_question_text(problems[qid]) + context = get_context_text(problems[qid], args.use_caption) + choice = get_choice_text(problems[qid], args.options) + answer = get_answer(problems[qid], args.options) + lecture = get_lecture_text(problems[qid]) + solution = get_solution_text(problems[qid]) + + train_example = create_one_example(args.prompt_format, + question, + context, + choice, + answer, + lecture, + solution, + test_example=False) + examples.append(train_example) + + # test example + question = get_question_text(problems[test_qid]) + context = get_context_text(problems[test_qid], args.use_caption) + choice = get_choice_text(problems[test_qid], args.options) + answer = get_answer(problems[test_qid], args.options) + lecture = get_lecture_text(problems[test_qid]) + solution = get_solution_text(problems[test_qid]) + + test_example = create_one_example(args.prompt_format, + question, + context, + choice, + answer, + lecture, + solution, + test_example=True) + examples.append(test_example) + + # create the prompt input + prompt_input = '\n\n'.join(examples) + + return prompt_input + + +def build_prompt_gpt4(problems, shot_qids, test_qid, args): + + prompt_array = [{"role": "system", "content": "You are a helpful assistant."}] + + # n-shot training examples + for qid in shot_qids: + question = get_question_text(problems[qid]) + context = get_context_text(problems[qid], args.use_caption) + choice = get_choice_text(problems[qid], args.options) + answer = get_answer(problems[qid], args.options) + lecture = get_lecture_text(problems[qid]) + solution = get_solution_text(problems[qid]) + + user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format, + question, + context, + choice, + answer, + lecture, + solution, + test_example=False) + prompt_array.append(user_prompt) + prompt_array.append(assistant_prompt) + + # test example + question = get_question_text(problems[test_qid]) + context = get_context_text(problems[test_qid], args.use_caption) + choice = get_choice_text(problems[test_qid], args.options) + answer = get_answer(problems[test_qid], args.options) + lecture = get_lecture_text(problems[test_qid]) + solution = get_solution_text(problems[test_qid]) + + user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format, + question, + context, + choice, + answer, + lecture, + solution, + test_example=True) + prompt_array.append(user_prompt) + prompt_array.append(assistant_prompt) + + return prompt_array \ No newline at end of file diff --git a/scripts/finetune.sh b/scripts/finetune.sh new file mode 100644 index 0000000000000000000000000000000000000000..9314affd72bd06ab260c3e8b36fbf5a4974c995f --- /dev/null +++ b/scripts/finetune.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Uncomment and set the following variables correspondingly to run this script: + +################## VICUNA ################## +# PROMPT_VERSION=v1 +# MODEL_VERSION="vicuna-v1-3-7b" +################## VICUNA ################## + +################## LLaMA-2 ################## +# PROMPT_VERSION="llava_llama_2" +# MODEL_VERSION="llama-2-7b-chat" +################## LLaMA-2 ################## + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --model_name_or_path ./checkpoints/$MODEL_VERSION \ + --version $PROMPT_VERSION \ + --data_path ./playground/data/llava_instruct_80k.json \ + --image_folder /path/to/coco/train2017 \ + --vision_tower openai/clip-vit-large-patch14 \ + --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune \ + --num_train_epochs 1 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 50000 \ + --save_total_limit 1 \ + --learning_rate 2e-5 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --dataloader_num_workers 4 \ + --lazy_preprocess True \ + --report_to wandb diff --git a/scripts/finetune_full_schedule.sh b/scripts/finetune_full_schedule.sh new file mode 100644 index 0000000000000000000000000000000000000000..533f3f4097db3844846d4a843d765c6df1762ba0 --- /dev/null +++ b/scripts/finetune_full_schedule.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Uncomment and set the following variables correspondingly to run this script: + +################## VICUNA ################## +# PROMPT_VERSION=v1 +# MODEL_VERSION="vicuna-v1-3-7b" +################## VICUNA ################## + +################## LLaMA-2 ################## +# PROMPT_VERSION="llava_llama_2" +# MODEL_VERSION="llama-2-7b-chat" +################## LLaMA-2 ################## + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --model_name_or_path ./checkpoints/$MODEL_VERSION \ + --version $PROMPT_VERSION \ + --data_path ./playground/data/llava_instruct_158k.json \ + --image_folder /path/to/coco/train2017 \ + --vision_tower openai/clip-vit-large-patch14 \ + --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune \ + --num_train_epochs 3 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 50000 \ + --save_total_limit 1 \ + --learning_rate 2e-5 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --dataloader_num_workers 4 \ + --lazy_preprocess True \ + --report_to wandb diff --git a/scripts/finetune_lora.sh b/scripts/finetune_lora.sh new file mode 100644 index 0000000000000000000000000000000000000000..46242a6371e8c9680dcfec5c955c3610555a3146 --- /dev/null +++ b/scripts/finetune_lora.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +# Uncomment and set the following variables correspondingly to run this script: + +################## VICUNA ################## +# PROMPT_VERSION=v1 +# MODEL_VERSION="vicuna-v1-3-7b" +################## VICUNA ################## + +################## LLaMA-2 ################## +# PROMPT_VERSION="llava_llama_2" +# MODEL_VERSION="llama-2-7b-chat" +################## LLaMA-2 ################## + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --lora_enable True \ + --model_name_or_path ./checkpoints/$MODEL_VERSION \ + --version $PROMPT_VERSION \ + --data_path ./playground/data/llava_instruct_80k.json \ + --image_folder /path/to/coco/train2017 \ + --vision_tower openai/clip-vit-large-patch14 \ + --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune_lora \ + --num_train_epochs 1 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 50000 \ + --save_total_limit 1 \ + --learning_rate 2e-5 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --lazy_preprocess True \ + --dataloader_num_workers 4 \ + --report_to wandb diff --git a/scripts/finetune_qlora.sh b/scripts/finetune_qlora.sh new file mode 100644 index 0000000000000000000000000000000000000000..1af397d40c925aa18794ffc9650c7cb50f0430d7 --- /dev/null +++ b/scripts/finetune_qlora.sh @@ -0,0 +1,48 @@ +#!/bin/bash + +# Uncomment and set the following variables correspondingly to run this script: + +################## VICUNA ################## +# PROMPT_VERSION=v1 +# MODEL_VERSION="vicuna-v1-3-7b" +################## VICUNA ################## + +################## LLaMA-2 ################## +# PROMPT_VERSION="llava_llama_2" +# MODEL_VERSION="llama-2-7b-chat" +################## LLaMA-2 ################## + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --lora_enable True \ + --bits 4 \ + --model_name_or_path ./checkpoints/$MODEL_VERSION \ + --version $PROMPT_VERSION \ + --data_path ./playground/data/llava_instruct_80k.json \ + --image_folder /path/to/coco/train2017 \ + --vision_tower openai/clip-vit-large-patch14 \ + --pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-$MODEL_VERSION-finetune_lora \ + --num_train_epochs 1 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 50000 \ + --save_total_limit 1 \ + --learning_rate 2e-5 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --lazy_preprocess True \ + --dataloader_num_workers 4 \ + --report_to wandb diff --git a/scripts/finetune_sqa.sh b/scripts/finetune_sqa.sh new file mode 100644 index 0000000000000000000000000000000000000000..2c1590fdc7511370e8ccc285dcc9c053379b9134 --- /dev/null +++ b/scripts/finetune_sqa.sh @@ -0,0 +1,34 @@ +#!/bin/bash + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --model_name_or_path lmsys/vicuna-13b-v1.3 \ + --version $PROMPT_VERSION \ + --data_path /Data/ScienceQA/data/scienceqa/llava_train_QCM-LEA.json \ + --image_folder /Data/ScienceQA/data/scienceqa/images/train \ + --vision_tower openai/clip-vit-large-patch14 \ + --pretrain_mm_mlp_adapter ./checkpoints/huggingface/liuhaotian/llava-pretrain-vicuna-13b-v1.3/mm_projector.bin \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-vicuna-13b-v1.3-pretrain_lcs558k_plain-ScienceQA_QCM_LEA-12e \ + --num_train_epochs 12 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 50000 \ + --save_total_limit 1 \ + --learning_rate 2e-5 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --dataloader_num_workers 4 \ + --lazy_preprocess True \ + --report_to wandb diff --git a/scripts/merge_lora_weights.py b/scripts/merge_lora_weights.py new file mode 100644 index 0000000000000000000000000000000000000000..3b39cc7beb12301379af7daebbb5553fa92093ea --- /dev/null +++ b/scripts/merge_lora_weights.py @@ -0,0 +1,22 @@ +import argparse +from llava.model.builder import load_pretrained_model +from llava.mm_utils import get_model_name_from_path + + +def merge_lora(args): + model_name = get_model_name_from_path(args.model_path) + tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map='cpu') + + model.save_pretrained(args.save_model_path) + tokenizer.save_pretrained(args.save_model_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, required=True) + parser.add_argument("--model-base", type=str, required=True) + parser.add_argument("--save-model-path", type=str, required=True) + + args = parser.parse_args() + + merge_lora(args) diff --git a/scripts/pretrain.sh b/scripts/pretrain.sh new file mode 100644 index 0000000000000000000000000000000000000000..5326e4cc47b78fef423a76823b66982fb66227b2 --- /dev/null +++ b/scripts/pretrain.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +# Uncomment and set the following variables correspondingly to run this script: + +# MODEL_VERSION=vicuna-v1-3-7b +# MODEL_VERSION=llama-2-7b-chat + +########### DO NOT CHANGE ########### +########### USE THIS FOR BOTH ########### +PROMPT_VERSION=plain +########### DO NOT CHANGE ########### + +deepspeed llava/train/train_mem.py \ + --deepspeed ./scripts/zero2.json \ + --model_name_or_path ./checkpoints/$MODEL_VERSION \ + --version $PROMPT_VERSION \ + --data_path /path/to/pretrain_data.json \ + --image_folder /path/to/images \ + --vision_tower openai/clip-vit-large-patch14 \ + --tune_mm_mlp_adapter True \ + --mm_vision_select_layer -2 \ + --mm_use_im_start_end False \ + --mm_use_im_patch_token False \ + --bf16 True \ + --output_dir ./checkpoints/llava-$MODEL_VERSION-pretrain \ + --num_train_epochs 1 \ + --per_device_train_batch_size 16 \ + --per_device_eval_batch_size 4 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "no" \ + --save_strategy "steps" \ + --save_steps 24000 \ + --save_total_limit 1 \ + --learning_rate 2e-3 \ + --weight_decay 0. \ + --warmup_ratio 0.03 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --tf32 True \ + --model_max_length 2048 \ + --gradient_checkpointing True \ + --dataloader_num_workers 4 \ + --lazy_preprocess True \ + --report_to wandb diff --git a/scripts/sqa_eval_batch.sh b/scripts/sqa_eval_batch.sh new file mode 100644 index 0000000000000000000000000000000000000000..adbf46ef7a6e86181b5927002597ef786add5bde --- /dev/null +++ b/scripts/sqa_eval_batch.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +CHUNKS=8 +for IDX in {0..7}; do + CUDA_VISIBLE_DEVICES=$IDX python -m llava.eval.model_vqa_science \ + --model-path liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3 \ + --question-file ~/haotian/datasets/ScienceQA/data/scienceqa/llava_test_QCM-LEA.json \ + --image-folder ~/haotian/datasets/ScienceQA/data/scienceqa/images/test \ + --answers-file ./test_llava-13b-chunk$CHUNKS_$IDX.jsonl \ + --num-chunks $CHUNKS \ + --chunk-idx $IDX \ + --conv-mode llava_v1 & +done diff --git a/scripts/sqa_eval_gather.sh b/scripts/sqa_eval_gather.sh new file mode 100644 index 0000000000000000000000000000000000000000..525bd43b850e9f6a923158abd23bca6f8d15650e --- /dev/null +++ b/scripts/sqa_eval_gather.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +CHUNKS=8 +output_file="test_llava-13b.jsonl" + +# Clear out the output file if it exists. +> "$output_file" + +# Loop through the indices and concatenate each file. +for idx in $(seq 0 $((CHUNKS-1))); do + cat "./test_llava-13b-chunk${idx}.jsonl" >> "$output_file" +done + +python llava/eval/eval_science_qa.py \ + --base-dir ~/haotian/datasets/ScienceQA/data/scienceqa \ + --result-file ./test_llava-13b.jsonl \ + --output-file ./test_llava-13b_output.json \ + --output-result ./test_llava-13b_result.json