notebooks/community/model_garden/model_garden_pytorch_llava.ipynb

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      "source": [
        "# Copyright 2024 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXYOa1odnikj"
      },
      "source": [
        "# Vertex AI Model Garden - LLaVA Deployment\n",
        "\n",
        "<table><tbody><tr>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_llava.ipynb\">\n",
        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llava.ipynb\">\n",
        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</tr></tbody></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbDI9ag4oR4C"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates deploying prebuilt [LLaVA models](https://huggingface.co/LLaVA-hf) with [vLLM](https://github.com/vllm-project/vllm) to improve serving throughput.\n",
        "\n",
        "\n",
        "### Objective\n",
        "\n",
        "- Download and deploy prebuilt LLaVA models\n",
        "- Deploy LLaVA with [vLLM](https://github.com/vllm-project/vllm) to improve serving throughput\n",
        "\n",
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQJWRopioSKT"
      },
      "source": [
        "## Before you begin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "J_jmxcIZoSxU"
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      "outputs": [],
      "source": [
        "# @title Setup Google Cloud project\n",
        "\n",
        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "# @markdown 2. **[Optional]** [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
        "\n",
        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
        "\n",
        "# @markdown 3. **[Optional]** Set region. If not set, the region will be set automatically according to Colab Enterprise environment.\n",
        "\n",
        "REGION = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "import datetime\n",
        "# Import the necessary packages\n",
        "import importlib\n",
        "import os\n",
        "import uuid\n",
        "from typing import Tuple\n",
        "\n",
        "from google.cloud import aiplatform\n",
        "\n",
        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
        "\n",
        "common_util = importlib.import_module(\n",
        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
        ")\n",
        "\n",
        "\n",
        "# Get the default cloud project id.\n",
        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
        "\n",
        "# Get the default region for launching jobs.\n",
        "if not REGION:\n",
        "    REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
        "\n",
        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
        "\n",
        "# Cloud Storage bucket for storing the experiment artifacts.\n",
        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
        "# prefer using your own GCS bucket, change the value yourself below.\n",
        "now = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "\n",
        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
        "else:\n",
        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
        "    bucket_region = shell_output[0].strip().lower()\n",
        "    if bucket_region != REGION:\n",
        "        raise ValueError(\n",
        "            \"Bucket region %s is different from notebook region %s\"\n",
        "            % (bucket_region, REGION)\n",
        "        )\n",
        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
        "\n",
        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"llava\")\n",
        "\n",
        "\n",
        "# Initialize Vertex AI API.\n",
        "print(\"Initializing Vertex AI API.\")\n",
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
        "\n",
        "# Gets the default SERVICE_ACCOUNT.\n",
        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
        "\n",
        "\n",
        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
        "\n",
        "! gcloud config set project $PROJECT_ID\n",
        "! gcloud projects add-iam-policy-binding --no-user-output-enabled {PROJECT_ID} --member=serviceAccount:{SERVICE_ACCOUNT} --role=\"roles/storage.admin\"\n",
        "! gcloud projects add-iam-policy-binding --no-user-output-enabled {PROJECT_ID} --member=serviceAccount:{SERVICE_ACCOUNT} --role=\"roles/aiplatform.user\"\n",
        "\n",
        "models, endpoints = {}, {}\n",
        "\n",
        "# @markdown You must provide a Hugging Face User Access Token (read) to access models from Hugging Face. You can follow the [Hugging Face documentation](https://huggingface.co/docs/hub/en/security-tokens) to create a **read** access token and put it in the `HF_TOKEN` field below.\n",
        "\n",
        "HF_TOKEN = \"\"  # @param {type:\"string\"}\n",
        "assert HF_TOKEN, \"Provide a read HF_TOKEN to load models from Hugging Face.\"\n",
        "\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEIT7Ol_zs9L"
      },
      "source": [
        "## Deploy prebuilt LLaVA models on vLLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
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      "outputs": [],
      "source": [
        "# @title Deploy\n",
        "\n",
        "# @markdown This section uploads prebuilt LLaVA models to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 to 30 minutes to finish depending on the size of the model.\n",
        "\n",
        "MODEL_ID = \"llava-hf/llava-1.5-7b-hf\"  # @param [\"llava-hf/llava-1.5-7b-hf\", \"llava-hf/llava-1.5-13b-hf\", \"llava-hf/llava-v1.6-mistral-7b-hf\", \"llava-hf/llava-v1.6-vicuna-7b-hf\"]\n",
        "accelerator_type = \"NVIDIA_L4\"  # @param [\"NVIDIA_L4\"]\n",
        "vllm_dtype = \"bfloat16\"\n",
        "gpu_memory_utilization = 0.9\n",
        "accelerator_count = 1\n",
        "machine_type = \"g2-standard-12\"\n",
        "max_model_len = 4096\n",
        "\n",
        "# The pre-built serving docker images.\n",
        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20241007_2233_RC00\"\n",
        "\n",
        "common_util.check_quota(\n",
        "    project_id=PROJECT_ID,\n",
        "    region=REGION,\n",
        "    accelerator_type=accelerator_type,\n",
        "    accelerator_count=accelerator_count,\n",
        "    is_for_training=False,\n",
        ")\n",
        "\n",
        "\n",
        "def deploy_model_vllm(\n",
        "    model_name: str,\n",
        "    model_id: str,\n",
        "    service_account: str,\n",
        "    base_model_id: str = None,\n",
        "    machine_type: str = \"g2-standard-8\",\n",
        "    accelerator_type: str = \"NVIDIA_L4\",\n",
        "    accelerator_count: int = 1,\n",
        "    gpu_memory_utilization: float = 0.9,\n",
        "    max_model_len: int = 4096,\n",
        "    dtype: str = \"auto\",\n",
        "    enable_trust_remote_code: bool = False,\n",
        "    enforce_eager: bool = False,\n",
        "    enable_lora: bool = False,\n",
        "    max_loras: int = 1,\n",
        "    max_cpu_loras: int = 8,\n",
        "    use_dedicated_endpoint: bool = False,\n",
        "    max_num_seqs: int = 256,\n",
        "    model_type: str = None,\n",
        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
        "    endpoint = aiplatform.Endpoint.create(\n",
        "        display_name=f\"{model_name}-endpoint\",\n",
        "        dedicated_endpoint_enabled=use_dedicated_endpoint,\n",
        "    )\n",
        "\n",
        "    if not base_model_id:\n",
        "        base_model_id = model_id\n",
        "\n",
        "    # See https://docs.vllm.ai/en/latest/models/engine_args.html for a list of possible arguments with descriptions.\n",
        "    vllm_args = [\n",
        "        \"python\",\n",
        "        \"-m\",\n",
        "        \"vllm.entrypoints.api_server\",\n",
        "        \"--host=0.0.0.0\",\n",
        "        \"--port=8080\",\n",
        "        f\"--model={model_id}\",\n",
        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
        "        \"--swap-space=16\",\n",
        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
        "        f\"--max-model-len={max_model_len}\",\n",
        "        f\"--dtype={dtype}\",\n",
        "        f\"--max-loras={max_loras}\",\n",
        "        f\"--max-cpu-loras={max_cpu_loras}\",\n",
        "        f\"--max-num-seqs={max_num_seqs}\",\n",
        "        \"--disable-log-stats\",\n",
        "    ]\n",
        "\n",
        "    if enable_trust_remote_code:\n",
        "        vllm_args.append(\"--trust-remote-code\")\n",
        "\n",
        "    if enforce_eager:\n",
        "        vllm_args.append(\"--enforce-eager\")\n",
        "\n",
        "    if enable_lora:\n",
        "        vllm_args.append(\"--enable-lora\")\n",
        "\n",
        "    if model_type:\n",
        "        vllm_args.append(f\"--model-type={model_type}\")\n",
        "\n",
        "    env_vars = {\n",
        "        \"MODEL_ID\": base_model_id,\n",
        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
        "    }\n",
        "\n",
        "    # HF_TOKEN is not a compulsory field and may not be defined.\n",
        "    try:\n",
        "        if HF_TOKEN:\n",
        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
        "    except NameError:\n",
        "        pass\n",
        "\n",
        "    model = aiplatform.Model.upload(\n",
        "        display_name=model_name,\n",
        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
        "        serving_container_args=vllm_args,\n",
        "        serving_container_ports=[8080],\n",
        "        serving_container_predict_route=\"/generate\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=env_vars,\n",
        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
        "        serving_container_deployment_timeout=7200,\n",
        "    )\n",
        "    print(\n",
        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
        "    )\n",
        "    model.deploy(\n",
        "        endpoint=endpoint,\n",
        "        machine_type=machine_type,\n",
        "        accelerator_type=accelerator_type,\n",
        "        accelerator_count=accelerator_count,\n",
        "        deploy_request_timeout=1800,\n",
        "        service_account=service_account,\n",
        "    )\n",
        "    print(\"endpoint_name:\", endpoint.name)\n",
        "\n",
        "    return model, endpoint\n",
        "\n",
        "\n",
        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
        "    model_name=common_util.get_job_name_with_datetime(prefix=MODEL_ID),\n",
        "    model_id=MODEL_ID,\n",
        "    service_account=SERVICE_ACCOUNT,\n",
        "    machine_type=machine_type,\n",
        "    accelerator_type=accelerator_type,\n",
        "    accelerator_count=accelerator_count,\n",
        "    max_model_len=max_model_len,\n",
        "    gpu_memory_utilization=gpu_memory_utilization,\n",
        "    dtype=vllm_dtype,\n",
        ")\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "PSplKtMGilP9"
      },
      "outputs": [],
      "source": [
        "# @title Predict\n",
        "\n",
        "# @markdown This section uses the deployed LLaVA model to answer questions about a given image.\n",
        "# fmt: off\n",
        "image_url = \"https://llava-vl.github.io/static/images/view.jpg\"  # @param {type:\"string\"}\n",
        "# fmt: on\n",
        "\n",
        "image = common_util.download_image(image_url)\n",
        "resized_image = common_util.resize_image(image, 800)\n",
        "image_base64 = common_util.image_to_base64(resized_image)\n",
        "display(resized_image)\n",
        "\n",
        "# Loads an existing endpoint instance using the endpoint name:\n",
        "# - Using `endpoint_name = endpoint.name` allows us to get the\n",
        "#   endpoint name of the endpoint `endpoint` created in the cell\n",
        "#   above.\n",
        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
        "#   an existing endpoint with the ID 1234567890123456789.\n",
        "# You may uncomment the code below to load an existing endpoint.\n",
        "\n",
        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
        "# aip_endpoint_name = (\n",
        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
        "# )\n",
        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
        "\n",
        "raw_prompt = \"What's in the image?\"  # @param {type: \"string\"}\n",
        "prompt = f\"USER: <image>{raw_prompt}ASSISTANT:\"\n",
        "# @markdown If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`, you can reduce the maximum number of output tokens, such as set `max_tokens` as 20.\n",
        "max_tokens = 256  # @param {type:\"integer\"}\n",
        "temperature = 0.8  # @param {type:\"number\"}\n",
        "top_p = 0.95  # @param {type:\"number\"}\n",
        "\n",
        "# Overrides parameters for inferences.\n",
        "instances = [\n",
        "    {\n",
        "        \"prompt\": prompt,\n",
        "        \"multi_modal_data\": {\"image\": image_base64},\n",
        "        \"max_tokens\": max_tokens,\n",
        "        \"temperature\": temperature,\n",
        "        \"top_p\": top_p,\n",
        "    },\n",
        "]\n",
        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
        "\n",
        "for raw_prediction in response.predictions:\n",
        "    prediction = raw_prediction.split(\"Output:\")\n",
        "    print(prediction[1])\n",
        "\n",
        "# @markdown Click \"Show code\" to see more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAelDidov5AW"
      },
      "source": [
        "## Clean up resources"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "8SeZCFo5v7z-"
      },
      "outputs": [],
      "source": [
        "# @title Delete the models and endpoints\n",
        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
        "# @markdown  and avoid unnecessary continuous charges that may incur.\n",
        "\n",
        "# Undeploy model and delete endpoint.\n",
        "for endpoint in endpoints.values():\n",
        "    endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "for model in models.values():\n",
        "    model.delete()\n",
        "\n",
        "delete_bucket = False  # @param {type:\"boolean\"}\n",
        "if delete_bucket:\n",
        "    ! gsutil -m rm -r $BUCKET_NAME"
      ]
    }
  ],
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    "colab": {
      "name": "model_garden_pytorch_llava.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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