llava-finetune.ipynb

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    "  <a href=\"https://discord.gg/NVDyv7TUgJ\" style=\"color: #06b6d4;\">Discord</a>\n",
    "</center>\n",
    "\n",
    "# Fine-tune and deploy the multimodal LLaVA model with DeepSpeed🤙\n",
    "\n",
    "Hi everyone!\n",
    "\n",
    "In this notebook we'll fine-tune the LLaVA model. LLaVA is multimodal which means it can ingest and understand images along with text! LLaVA comes from a research paper titled [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) and introduces the Large Language and Vision Assistant methodology. In order to process images, LLaVA relies on the pre-trained CLIP visual encoder ViT-L/14 which maps images and text into the same latent space. \n",
    "\n",
    "Help us make this tutorial better! Please provide feedback on the [Discord channel](https://discord.gg/RN2a436M73) or on [X](https://x.com/brevdev)."
   ]
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   "source": [
    "## Table of contents \n",
    "\n",
    "1. Data Preprocessing\n",
    "2. LLaVA Installation\n",
    "3. DeepSpeed configuration\n",
    "4. Weights and Biases\n",
    "5. Finetuning flow\n",
    "6. Deployment via gradio interface"
   ]
  },
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    "## Data Preprocessing \n",
    "\n",
    "LLaVA requires data to be in a very specific format. Below we use a [helper function](https://wandb.ai/byyoung3/ml-news/reports/How-to-Fine-Tune-LLaVA-on-a-Custom-Dataset--Vmlldzo2NjUwNTc1) to format the OKV-QA dataset. This dataset teaches the model to respond to an image in short phrases without any preamble or extra verbiage. "
   ]
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    "### Optional: create your own dataset using GPT-4o\n",
    "\n",
    "The guide to creating your own dataset is relatively simple! Here's a simple script that you could use that leverages GPT4o's multimodal capabilities to quickly create a dataset that can be used in the dataset creator function that we write below!\n",
    " \n",
    "\n",
    "```python\n",
    "import os\n",
    "import base64\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# OpenAI API Key\n",
    "api_key = \"<enter key here>\"\n",
    "\n",
    "# Function to encode the image\n",
    "def encode_image(image_path):\n",
    "    with open(image_path, \"rb\") as image_file:\n",
    "        return base64.b64encode(image_file.read()).decode('utf-8')\n",
    "\n",
    "# Path to your images folder\n",
    "folder_path = \"<enter image folder path here>\"\n",
    "\n",
    "headers = {\n",
    "    \"Content-Type\": \"application/json\",\n",
    "    \"Authorization\": f\"Bearer {api_key}\"\n",
    "}\n",
    "\n",
    "# Question to ask for each image\n",
    "question = \"Generate a detailed description about this image\" #change this depending on your use case\n",
    "\n",
    "# Function to process each image and get the description\n",
    "def process_image(image_path, image_name):\n",
    "    base64_image = encode_image(image_path)\n",
    "    \n",
    "    payload = {\n",
    "        \"model\": \"gpt-4o\",\n",
    "        \"messages\": [\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\n",
    "                        \"type\": \"text\",\n",
    "                        \"text\": question\n",
    "                    },\n",
    "                    {\n",
    "                        \"type\": \"image_url\",\n",
    "                        \"image_url\": {\n",
    "                            \"url\": f\"data:image/jpeg;base64,{base64_image}\"\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        ],\n",
    "        \"max_tokens\": 300\n",
    "    }\n",
    "\n",
    "    response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=payload)\n",
    "    response_json = response.json()\n",
    "    return response_json['choices'][0]['message']['content']\n",
    "\n",
    "# List to store all JSON data\n",
    "all_json_data = []\n",
    "\n",
    "# Process each image in the folder\n",
    "for image_name in os.listdir(folder_path):\n",
    "    if image_name.endswith((\".jpg\", \".jpeg\", \".png\")):\n",
    "        image_path = os.path.join(folder_path, image_name)\n",
    "        formatted_answers = process_image(image_path, image_name)\n",
    "        \n",
    "        json_data = {\n",
    "            \"id\": image_name.split('.')[0],\n",
    "            \"image\": image_name,\n",
    "            \"conversations\": [\n",
    "                {\n",
    "                    \"from\": \"human\",\n",
    "                    \"value\": question\n",
    "                },\n",
    "                {\n",
    "                    \"from\": \"gpt\",\n",
    "                    \"value\": formatted_answers\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "        \n",
    "        all_json_data.append(json_data)\n",
    "\n",
    "# Save the results to a JSON file\n",
    "output_file = \"output.json\"\n",
    "with open(output_file, \"w\") as outfile:\n",
    "    json.dump(all_json_data, outfile, indent=4)\n",
    "\n",
    "print(f\"Data has been saved to {output_file}\")\n",
    "\n",
    "```\n",
    "How to use this script\n",
    "1. Create a folder called dataset. Inside of this folder, create a subfolder called images.\n",
    "2. Place all your images in a directory and specify that path as folder_path.\n",
    "3. Outputs are saved in a JSON file in the specified output_folder, pairing each image file with its generated description. \n",
    "4. After the script is run, create another folder inside dataset called train and move the output.json file into this folder."
   ]
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   "source": [
    "## Back to it!"
   ]
  },
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   "source": [
    "# Install preprocessing libraries\n",
    "!pip install datasets\n",
    "!pip install --upgrade --force-reinstall Pillow"
   ]
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    "# Imports\n",
    "from datasets import load_dataset\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import requests\n",
    "import os\n",
    "import json\n",
    "import uuid\n",
    "\n",
    "# Check PIL import\n",
    "import PIL.Image\n",
    "\n",
    "# Define preprocessing functions\n",
    "def process_and_save(dataset, output_folder, subset_name):\n",
    "    # Define image subfolder within output folder\n",
    "    subset_folder = os.path.join(output_folder, subset_name)\n",
    "    image_subfolder = os.path.join(output_folder, 'images')\n",
    "\n",
    "    if not os.path.exists(image_subfolder):\n",
    "        os.makedirs(image_subfolder)\n",
    "\n",
    "    if not os.path.exists(subset_folder):\n",
    "        os.makedirs(subset_folder)\n",
    "\n",
    "    # Initialize list to hold all JSON data\n",
    "    json_data_list = []\n",
    "\n",
    "    # Process and save images and labels\n",
    "    for item in dataset:\n",
    "        # Load image if it's a URL or a file path\n",
    "        if isinstance(item['image'], str):\n",
    "            response = requests.get(item['image'])\n",
    "            image = Image.open(BytesIO(response.content))\n",
    "        else:\n",
    "            image = item['image']  # Assuming it's a PIL.Image object\n",
    "\n",
    "        # Create a unique ID for each image\n",
    "        unique_id = str(uuid.uuid4())\n",
    "\n",
    "        # Define image path\n",
    "        image_path = os.path.join(image_subfolder, f\"{unique_id}.jpg\")\n",
    "\n",
    "        # Save image\n",
    "        image.save(image_path)\n",
    "\n",
    "        # Remove duplicates and format answers\n",
    "        answers = item['answers']\n",
    "        unique_answers = list(set(answers))\n",
    "        formatted_answers = \", \".join(unique_answers)\n",
    "\n",
    "        # Structure for LLaVA JSON\n",
    "        json_data = {\n",
    "            \"id\": unique_id,\n",
    "            \"image\": f\"{unique_id}.jpg\",\n",
    "            \"conversations\": [\n",
    "                {\n",
    "                    \"from\": \"human\",\n",
    "                    \"value\": item['question']\n",
    "                },\n",
    "                {\n",
    "                    \"from\": \"gpt\",\n",
    "                    \"value\": formatted_answers\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "\n",
    "        # Append to list\n",
    "        json_data_list.append(json_data)\n",
    "\n",
    "    # Save the JSON data list to a file\n",
    "    json_output_path = os.path.join(output_folder, subset_name, 'dataset.json')\n",
    "    with open(json_output_path, 'w') as json_file:\n",
    "        json.dump(json_data_list, json_file, indent=4)\n",
    "\n",
    "def save_dataset(dataset_name, output_folder, class_name, subset_name, val_samples=None):\n",
    "    # Load the dataset from Hugging Face\n",
    "    dataset = load_dataset(dataset_name, split=subset_name)\n",
    "\n",
    "    # Filter for images with the specified class in 'question_type'\n",
    "    filtered_dataset = [item for item in dataset if item['question_type'] == class_name]\n",
    "\n",
    "    # Determine the split for training and validation\n",
    "    if val_samples is not None and subset_name == 'train':\n",
    "        train_dataset = filtered_dataset[val_samples:]\n",
    "        val_dataset = filtered_dataset[:val_samples]\n",
    "    else:\n",
    "        train_dataset = filtered_dataset\n",
    "        val_dataset = []\n",
    "\n",
    "    # Process and save the datasets\n",
    "    for subset, data in [('train', train_dataset), ('validation', val_dataset)]:\n",
    "        if data:\n",
    "            process_and_save(data, output_folder, subset)"
   ]
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    "# Create dataset\n",
    "output_folder = 'dataset'\n",
    "class_name = 'other'\n",
    "val_samples = 300\n",
    "save_dataset('Multimodal-Fatima/OK-VQA_train', output_folder, class_name, 'train', val_samples)\n",
    "save_dataset('Multimodal-Fatima/OK-VQA_test', output_folder, class_name, 'test')"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Install LLaVA\n",
    "\n",
    "To install the functions needed to use the model, we have to clone the original LLaVA repository and and install it in editable mode. This lets us access all functions and helper methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0387008-d4ad-4c58-bb9d-d77f71637257",
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   "source": [
    "# The pip install -e . lets us install the repository in editable mode\n",
    "!git clone https://github.com/haotian-liu/LLaVA.git\n",
    "!cd LLaVA && pip install --upgrade pip && pip install -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa03b91-85a8-434a-81f6-105dfa87ecb2",
   "metadata": {},
   "source": [
    "## DeepSpeed\n",
    "\n",
    "Microsoft DeepSpeed is a deep learning optimization library designed to enhance the training speed and scalability of large-scale artificial intelligence (AI) models. Developed by Microsoft, this open-source tool specifically addresses the challenges associated with training very large models, allowing for reduced computational times and resource usage. By optimizing memory management and introducing novel parallelism techniques, DeepSpeed enables developers and researchers to train models with billions of parameters efficiently, even on limited hardware setups.DeepSpeed API is a lightweight wrapper on PyTorch. DeepSpeed manages all of the boilerplate training techniques, such as distributed training, mixed precision, gradient accumulation, and checkpoints and allows you to just focus on model development. To learn more about DeepSpeed and how it performs the magic, check out this [article](https://www.deepspeed.ai/2021/03/07/zero3-offload.html) on DeepSpeed and ZeRO.\n",
    "\n",
    "Using deepspeed is extremely simple - you simply pip install it! The LLaVA respository contains the setup scripts and configuration files needed to finetune in different ways. "
   ]
  },
  {
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   "id": "55721ad3-f88f-4e03-9d99-d193c276bd0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!cd LLaVA && pip install -e \".[train]\"\n",
    "!pip install flash-attn --no-build-isolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38d16e09-a3ec-461b-8a95-78c3a4e22379",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install deepspeed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772ac37a-10ad-46b9-84f7-ef289a29bbbb",
   "metadata": {},
   "source": [
    "## Weights and Biases\n",
    "\n",
    "Weights and Biases is an industry standard MLOps tool to used to monitor and evaluate training jobs. At Brev, we use Weights and Biases to track all of our finetuning jobs! Its extremely easy to setup and plugs into the DeepSpeed training loop. You simply create an account and use the cells below to log in!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ae96a8-f281-471c-aeb9-dac6bc7f5bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "330f3d47-8daa-480a-acd7-6517bac50c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb\n",
    "\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc74b4e-6c88-4e9e-92bd-610a54fa01a3",
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   "source": [
    "## Finetuning job\n",
    "\n",
    "Below we start the DeepSpeed training run for 5 epochs. It will automatically recognize multiple GPUs and parallelize across them. Most of the input flags are standard but you can adjust your training run with the `num_train_epochs` and `per_device_train_batch_size` flags!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!deepspeed LLaVA/llava/train/train_mem.py \\\n",
    "    --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 \\\n",
    "    --deepspeed LLaVA/scripts/zero3.json \\\n",
    "    --model_name_or_path liuhaotian/llava-v1.5-13b \\\n",
    "    --version v1 \\\n",
    "    --data_path ./dataset/train/dataset.json \\\n",
    "    --image_folder ./dataset/images \\\n",
    "    --vision_tower openai/clip-vit-large-patch14-336 \\\n",
    "    --mm_projector_type mlp2x_gelu \\\n",
    "    --mm_vision_select_layer -2 \\\n",
    "    --mm_use_im_start_end False \\\n",
    "    --mm_use_im_patch_token False \\\n",
    "    --image_aspect_ratio pad \\\n",
    "    --group_by_modality_length True \\\n",
    "    --bf16 True \\\n",
    "    --output_dir ./checkpoints/llava-v1.5-13b-task-lora \\\n",
    "    --num_train_epochs 1 \\\n",
    "    --per_device_train_batch_size 16 \\\n",
    "    --per_device_eval_batch_size 4 \\\n",
    "    --gradient_accumulation_steps 1 \\\n",
    "    --evaluation_strategy \"no\" \\\n",
    "    --save_strategy \"steps\" \\\n",
    "    --save_steps 50000 \\\n",
    "    --save_total_limit 1 \\\n",
    "    --learning_rate 2e-4 \\\n",
    "    --weight_decay 0. \\\n",
    "    --warmup_ratio 0.03 \\\n",
    "    --lr_scheduler_type \"cosine\" \\\n",
    "    --logging_steps 1 \\\n",
    "    --tf32 True \\\n",
    "    --model_max_length 2048 \\\n",
    "    --gradient_checkpointing True \\\n",
    "    --dataloader_num_workers 4 \\\n",
    "    --lazy_preprocess True \\\n",
    "    --report_to wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0922ab44-5b91-4f64-9e9f-e52e54894597",
   "metadata": {},
   "source": [
    "Here's an excerpt from my WandB run!"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e85526d8-eba3-4cfa-9354-c4906225b9b6",
   "metadata": {},
   "source": [
    "![Screenshot 2024-04-17 at 11.23.31 AM.jpg](attachment:da73b948-7a81-4864-97fb-3ea3f892d67f.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "657dca5d-8014-4c28-a922-c24187d9db2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge the LoRA weights with the full model\n",
    "!python LLaVA/scripts/merge_lora_weights.py --model-path checkpoints/llava-v1.5-13b-task-lora --model-base liuhaotian/llava-v1.5-13b --save-model-path llava-ftmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e52c548-a1b4-477a-ad68-1d9ac153fd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bump transformers down for gradio/deployment inference if needed\n",
    "!pip install transformers==4.37.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7f3d67-3c9c-4413-b86d-0a4ec2883df8",
   "metadata": {},
   "source": [
    "## Deployment\n",
    "\n",
    "LLaVA gives us 2 ways to deploy the model - via CLI or Gradio UI. We suggest using the Gradio UI for interactivity as you can compare two models and see the finetuning effect compared to the original model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34046faf-3e2c-4726-9e41-6f4bb3028dd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Uncomment the lines below to run the CLI. You need to pass in a JPG image URL to use the multimodal capabilities\n",
    "\n",
    "# !python -m llava.serve.cli \\\n",
    "#     --model-path llava-ftmodel \\\n",
    "#     --image-file \"https://llava-vl.github.io/static/images/view.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "549a9a5a-d386-4b9b-a9f6-808217d6f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the model runner\n",
    "!wget -L https://raw.githubusercontent.com/brevdev/notebooks/main/assets/llava-deploy.sh "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9550ef4-e881-49d7-a237-a997234d179e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run inference! Use the public link provided in the output to test\n",
    "!chmod +x llava-deploy.sh && ./llava-deploy.sh"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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