diff --git a/microservices/model-download/.gitignore b/microservices/model-download/.gitignore new file mode 100644 index 000000000..1666d867b --- /dev/null +++ b/microservices/model-download/.gitignore @@ -0,0 +1,32 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# Ignore environment variables +.env + +# Ignore Python cache files +__pycache__/ +*.py[cod] + +# Ignore virtual environment directories +venv/ +*.venv/ +env/ + +# Ignore log files +*.log + +# Ignore IDE specific files +.vscode/ + +# Ignore pytest cache +.pytest_cache/ + +# Ignore pytest result files +*.pytest_cache + +# pytest related dir +empty_model_dir +mock_model_dir +.coverage +htmlcov \ No newline at end of file diff --git a/microservices/model-download/chart/Chart.yaml b/microservices/model-download/chart/Chart.yaml new file mode 100644 index 000000000..713ce12c4 --- /dev/null +++ b/microservices/model-download/chart/Chart.yaml @@ -0,0 +1,6 @@ +apiVersion: v2 +name: genai-model-download +description: A Helm chart for deploying the model-download FastAPI microservice +type: application +version: 0.1.0 +appVersion: "1.0.0" \ No newline at end of file diff --git a/microservices/model-download/chart/templates/deployment.yaml b/microservices/model-download/chart/templates/deployment.yaml new file mode 100644 index 000000000..58320c77e --- /dev/null +++ b/microservices/model-download/chart/templates/deployment.yaml @@ -0,0 +1,39 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ .Release.Name }}-model-download + labels: + app: {{ .Chart.Name }} +spec: + replicas: {{ .Values.replicaCount }} + selector: + matchLabels: + app: {{ .Chart.Name }} + template: + metadata: + labels: + app: {{ .Chart.Name }} + spec: + containers: + - name: model-download + image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}" + imagePullPolicy: {{ .Values.image.pullPolicy }} + ports: + - containerPort: 8000 + env: + - name: HUGGINGFACE_HUB_CACHE + value: "/root/.cache/huggingface" + - name: HF_HUB_ENABLE_HF_TRANSFER + value: "1" + volumeMounts: + - name: huggingface-cache + mountPath: /root/.cache/huggingface + - name: models + mountPath: /opt/models + volumes: + - name: huggingface-cache + persistentVolumeClaim: + claimName: huggingface-cache-pvc + - name: models + persistentVolumeClaim: + claimName: models-pvc \ No newline at end of file diff --git a/microservices/model-download/chart/templates/pvc.yaml b/microservices/model-download/chart/templates/pvc.yaml new file mode 100644 index 000000000..534a936a1 --- /dev/null +++ b/microservices/model-download/chart/templates/pvc.yaml @@ -0,0 +1,26 @@ +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: huggingface-cache-pvc + labels: + app: {{ .Chart.Name }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 5Gi + +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: models-pvc + labels: + app: {{ .Chart.Name }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 10Gi \ No newline at end of file diff --git a/microservices/model-download/chart/templates/service.yaml b/microservices/model-download/chart/templates/service.yaml new file mode 100644 index 000000000..01187a29b --- /dev/null +++ b/microservices/model-download/chart/templates/service.yaml @@ -0,0 +1,14 @@ +apiVersion: v1 +kind: Service +metadata: + name: {{ .Release.Name }}-model-download + labels: + app: {{ .Chart.Name }} +spec: + type: NodePort + ports: + - port: 8000 + targetPort: 8000 + nodePort: {{ .Values.service.nodePort }} + selector: + app: {{ .Chart.Name }} \ No newline at end of file diff --git a/microservices/model-download/chart/values.yaml b/microservices/model-download/chart/values.yaml new file mode 100644 index 000000000..8069bb827 --- /dev/null +++ b/microservices/model-download/chart/values.yaml @@ -0,0 +1,12 @@ +replicaCount: 1 + +image: + repository: your-dockerhub-username/model-download + tag: latest + pullPolicy: IfNotPresent + +service: + type: NodePort + nodePort: 32004 + +resources: {} \ No newline at end of file diff --git a/microservices/model-download/docker/.dockerignore b/microservices/model-download/docker/.dockerignore new file mode 100644 index 000000000..17657d15d --- /dev/null +++ b/microservices/model-download/docker/.dockerignore @@ -0,0 +1,24 @@ +**/__pycache__ +**/*.pyc +**/*.pyo +**/*.pyd +.Python +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +.git +.gitignore +.pytest_cache +*.egg-info/ +dist/ +build/ +tests/ +docs/ +models/ +**/.cache +**/.coverage +**/.DS_Store diff --git a/microservices/model-download/docker/Dockerfile b/microservices/model-download/docker/Dockerfile new file mode 100644 index 000000000..c43382e64 --- /dev/null +++ b/microservices/model-download/docker/Dockerfile @@ -0,0 +1,76 @@ +# ---- Stage 1: Build dependencies ---- +FROM python:3.11-slim AS python-base + +# Set the working directory in the container +WORKDIR /app + +# Install system dependencies required for Poetry and the application +RUN apt-get update && apt-get install -y \ + libgl1 \ + libsm6 \ + curl \ + git \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Install Poetry +RUN curl -sSL https://install.python-poetry.org | python3 - + +# Add Poetry to PATH +ENV PATH="/root/.local/bin:$PATH" + +# Install Ollama +RUN curl -fsSL https://ollama.com/install.sh | sh + + + +# Copy only the Poetry configuration files first to leverage Docker caching +COPY pyproject.toml poetry.lock /app/ + +# Install dependencies using Poetry +RUN poetry config virtualenvs.create false && poetry install --no-root + +# ---- Stage 2: Final slim image ---- +FROM python:3.11-slim + +ARG UID=1000 +ARG GID=1000 + +RUN apt-get update && apt-get install -y \ + curl \ + libmagic-dev \ + libgl1 \ + libsm6 \ + libglib2.0-0 \ + git \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Create a non-root user +RUN groupadd -g ${GID} appuser && \ + useradd -m -s /bin/bash -u ${UID} -g ${GID} appuser + +# Copy installed dependencies from python-base +COPY --from=python-base /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages +COPY --from=python-base /usr/local/bin /usr/local/bin + + +COPY . /app + +WORKDIR /app + +# Change the user of working directory to non-root user +RUN chown -R appuser:appuser /app + +# Switch to non-root user +USER appuser + +# Expose the port the app runs on +EXPOSE 8000 + +# Set environment variables +ENV PYTHONPATH=/app +ENV MODELS_DIR=/app/models + +# Command to run the application +CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/microservices/model-download/docker/compose.yaml b/microservices/model-download/docker/compose.yaml new file mode 100644 index 000000000..aa0f0c0e4 --- /dev/null +++ b/microservices/model-download/docker/compose.yaml @@ -0,0 +1,17 @@ +services: + model_download_service: + build: + context: .. + dockerfile: docker/Dockerfile + container_name: model_download + environment: + - HF_HUB_ENABLE_HF_TRANSFER=1 + - LOG_LEVEL=INFO + ports: + - "8200:8000" # Map container port 8000 to host port 8200 + volumes: + # Mount volumes for models and caching + - ~/.cache/huggingface:/home/appuser/.cache/huggingface + - ~/models:/app/models + group_add: + - ${USER_GROUP_ID:-1000} \ No newline at end of file diff --git a/microservices/model-download/docs/user-guide/api-docs/openapi.yaml b/microservices/model-download/docs/user-guide/api-docs/openapi.yaml new file mode 100644 index 000000000..ab1e7d84e --- /dev/null +++ b/microservices/model-download/docs/user-guide/api-docs/openapi.yaml @@ -0,0 +1,248 @@ +openapi: 3.0.0 +info: + title: Model Download Service API + description: | + API for downloading from HuggingFace or Ollama and converting models OV_IR format. + This service provides endpoints to download models and optionally convert them to + OpenVINO Model Server (OVMS) format (conversion of Ollama models to OV_IR is not yet supported). + version: 1.0.0 + contact: + name: Intel GenAI Team +servers: + - url: http://Host:8200/api/v1 + description: Base API path + +components: + schemas: + ModelPrecision: + type: string + enum: + - int8 + - fp16 + - fp32 + description: The precision format for model weights + + DeviceType: + type: string + enum: + - CPU + - GPU + description: The target device type for model deployment + + Config: + type: object + properties: + precision: + $ref: '#/components/schemas/ModelPrecision' + default: int8 + device: + $ref: '#/components/schemas/DeviceType' + default: CPU + cache_size: + type: integer + minimum: 1 + description: Cache size for model optimization + description: Configuration for OVMS model conversion + + ModelRequest: + type: object + required: + - name + - hub + properties: + name: + type: string + description: The name/ID of the Hugging Face/Ollama model + hub: + type: string + enum: [huggingface, ollama] + description: The model hub to download from (Options - huggingface or ollama) + type: + type: string + description: The type of the model (e.g., llm, embeddings, rerank) + is_ovms: + type: boolean + default: false + description: Whether to convert the model to OVMS format + revision: + type: string + description: Specific model revision to download + config: + $ref: '#/components/schemas/Config' + description: OVMS-specific configuration + + ModelDownloadRequest: + type: object + required: + - models + properties: + models: + type: array + items: + $ref: '#/components/schemas/ModelRequest' + description: List of models to download + parallel_downloads: + type: boolean + default: false + description: Whether to download models in parallel + + ModelResult: + type: object + required: + - status + - model_name + properties: + status: + type: string + enum: [success, error] + description: The status of the model download/conversion + model_name: + type: string + description: The name of the model + model_path: + type: string + nullable: true + description: Path where the model was downloaded/converted + error: + type: string + nullable: true + description: Error message if status is error + is_ovms: + type: boolean + nullable: true + description: Whether the model was converted to OVMS format + + DownloadResponse: + type: object + properties: + message: + type: string + description: Status message + results: + type: array + items: + $ref: '#/components/schemas/ModelResult' + description: Results for each model download/conversion + model_path: + type: string + description: Path to the downloaded model (for single model requests) + + HealthResponse: + type: object + properties: + status: + type: string + enum: [ok] + description: Health status of the service + + securitySchemes: + ApiKeyAuth: + type: apiKey + in: header + name: Authorization + description: Hugging Face API token for authentication (Not applicable for Ollama models) + +paths: + /health: + get: + summary: Health check endpoint + description: Verify the service is running + operationId: healthCheck + tags: + - Health + responses: + '200': + description: Service is healthy + content: + application/json: + schema: + $ref: '#/components/schemas/HealthResponse' + + /models/download: + post: + summary: Download and optionally convert models + description: | + Downloads one or more models from HuggingFace or Ollama and optionally converts them + to OVMS format (conversion of Ollama models to OVMS is not yet supported). Supports + parallel downloads and custom configurations. + operationId: downloadModels + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: download_path + schema: + type: string + default: null + description: Base path for model downloads + requestBody: + required: true + content: + application/json: + schema: + $ref: '#/components/schemas/ModelDownloadRequest' + example: + models: + - name: microsoft/Phi-3.5-mini-instruct + hub: huggingface + type: llm + is_ovms: true + config: + precision: int8 + device: CPU + cache_size: 10 + parallel_downloads: false + responses: + '200': + description: Successful model download/conversion + content: + application/json: + schema: + $ref: '#/components/schemas/DownloadResponse' + '400': + description: Bad request or model processing error + content: + application/json: + schema: + type: object + properties: + detail: + type: string + '401': + description: Authentication token missing or invalid + content: + application/json: + schema: + type: object + properties: + detail: + type: string + '422': + description: Validation error in request + content: + application/json: + schema: + type: object + properties: + detail: + type: array + items: + type: object + properties: + loc: + type: array + items: + type: string + msg: + type: string + type: + type: string + '500': + description: Internal server error + content: + application/json: + schema: + type: object + properties: + detail: + type: string \ No newline at end of file diff --git a/microservices/model-download/docs/user-guide/get-started.md b/microservices/model-download/docs/user-guide/get-started.md new file mode 100644 index 000000000..987388731 --- /dev/null +++ b/microservices/model-download/docs/user-guide/get-started.md @@ -0,0 +1,171 @@ +# Model Download Service + +The Model Download Service is a microservice that facilitates downloading the models from Hugging Face/Ollama and in case of Hugging Face, converting to OpenVINO Model Server (OVMS) format. This service provides a RESTful API for managing model downloads and conversions. + +## Features + +- Download models from Hugging Face/Ollama model hub +- Convert models to OVMS format (Hugging Face models only) +- Support for various model precisions (INT8, FP16, FP32) +- Support for different device targets (CPU, GPU) +- Parallel download capability +- Configurable model caching +- REST API with OpenAPI documentation + +## Prerequisites + +- Docker and Docker Compose +- Hugging Face API token (not needed for Ollama model hub) +- Sufficient disk space for model storage + +## Quick Start + +1. Clone the repository and navigate to the `model-download` directory: +```bash +cd microservices/model-download +``` + +2. Setup the directory for storing the downloaded models with appropriate permissions +```bash +source scripts/setup_model_path.sh +``` +3. Start the service using Docker Compose: +```bash +docker compose -f docker/compose.yaml up --build +``` + +The service will be available at `http://localhost:8200/api/v1/docs` + +## API Documentation + +### Authentication + +All API endpoints require authentication using a Hugging Face API token. Pass the token in the `Authorize` section: + +```http +HTTPBearer: your_hugging_face_token +``` + +### Endpoints + +#### Download Models + +`POST /api/v1/models/download` + +Downloads one or more models from Hugging Face and optionally converts them to OVMS format. + +**Request Body:** +If the model is present in HuggingFace hub +```json +{ + "models": [ + { + "name": "microsoft/Phi-3.5-mini-instruct", + "hub": "huggingface", + "type": "llm", + "is_ovms": true, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + } + ], + "parallel_downloads": false +} +``` + +If the model is present in Ollama hub (OVMS support not available yet for Ollama models) +```json +{ + "models": [ + { + "name": "tinyllama", + "hub": "ollama", + "type": "llm", + } + ], + "parallel_downloads": false +} +``` + +**Parameters:** +- `name` (required): The name/ID of the Hugging Face/Ollama model +- `hub` (required): The model hub to download from (Options - huggingface or ollama) +- `type`: Model type (e.g., llm, embeddings, rerank) +- `is_ovms`: Whether to convert the model to OVMS format (default: false) +- `config`: Configuration for OVMS conversion + - `precision`: Model precision (int8, fp16, fp32) + - `device`: Target device (CPU, GPU) + - `cache_size`: Cache size for model optimization + +**Response:** +```json +{ + "message": "Model download completed", + "results": [ + { + "status": "success", + "model_name": "microsoft/Phi-3.5-mini-instruct", + "model_path": "/app/models/microsoft_Phi-3.5-mini-instruct", + "is_ovms": true + } + ] +} +``` + +### Configuration + +The service can be configured through environment variables and Docker volumes: + +#### Environment Variables: +- `HF_HUB_ENABLE_HF_TRANSFER`: Enable Hugging Face transfer (default: 1) + +#### Volumes: +- `~/.cache/huggingface:/home/appuser/.cache/huggingface`: Cache Hugging Face models +- `~/models:/app/models`: Persist downloaded models + +## Docker Compose Configuration + +The service can be run using Docker Compose with the following configuration: + +```yaml +services: + model_download_service: + build: + context: .. + dockerfile: docker/Dockerfile + environment: + - HF_HUB_ENABLE_HF_TRANSFER=1 + ports: + - "32004:8000" + volumes: + - ~/.cache/huggingface:/home/appuser/.cache/huggingface + - ~/models:/app/models + group_add: + - ${USER_GROUP_ID:-1000} +``` + +## Error Handling + +The API returns appropriate HTTP status codes: + +- `200`: Successful operation +- `400`: Bad request or model processing error +- `401`: Authentication token missing or invalid +- `422`: Validation error in request + +Error responses include a detail message explaining the error: +```json +{ + "detail": "Error message here" +} +``` + +## Best Practices + +1. Use parallel downloads with caution as they can consume significant resources +2. Configure appropriate cache sizes based on your available memory +3. Choose the appropriate model precision based on your performance requirements +4. Mount volumes for both the Hugging Face cache and model storage to persist data +5. 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"^11.2.1" +python = ">=3.10,<=3.13" +pydantic = "^1.10.13" +qwen_vl_utils = "^0.0.11" +sentence_transformers = "3.1.1" +structlog = "25.1.0" +transformers = "^4.51.3" +uvicorn = "^0.23.2" +ultralytics = "^8.3.196" +sentencepiece = "0.2.0" +openai = "^1.0.0" +timm = "1.0.15" +diffusers = "0.33.1" + +[tool.poetry.group.dev.dependencies] +pytest = "^7.4.0" +pytest-cov = "^4.1.0" +pytest-asyncio = "^0.21.1" +httpx = "^0.24.1" +pytest-mock = "^3.11.1" + +[tool.poetry.group.cpu.dependencies] +torch = { version = "^2.7.1", source = "pytorch-cpu" } +torchvision = { version = "^0.23.0", source = "pytorch-cpu" } + +[[tool.poetry.source]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +addopts = "-v --cov=app --cov-report=term-missing" + +[build-system] +requires = ["poetry-core>=1.8.3"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/microservices/model-download/scripts/download_models_ultralytics.sh b/microservices/model-download/scripts/download_models_ultralytics.sh new file mode 100644 index 000000000..9ff9f1e46 --- /dev/null +++ b/microservices/model-download/scripts/download_models_ultralytics.sh @@ -0,0 +1,961 @@ +#!/bin/bash +# ============================================================================== +# Copyright (C) 2021-2025 Intel Corporation +# +# SPDX-License-Identifier: MIT +# ============================================================================== + +MODEL=${1:-"all"} # Supported values listed in SUPPORTED_MODELS below. +QUANTIZE=${2:-""} # Supported values listed in SUPPORTED_MODELS below. + +. /etc/os-release + +# Changing the config dir for the duration of the script to prevent potential conflics with +# previous installations of ultralytics' tools. Quantization datasets could install +# incorrectly without this. +DOWNLOAD_CONFIG_DIR=$(mktemp -d /tmp/tmp.XXXXXXXXXXXXXXXXXXXXXXXXXXX) +QUANTIZE_CONFIG_DIR=$(mktemp -d /tmp/tmp.XXXXXXXXXXXXXXXXXXXXXXXXXXX) +YOLO_CONFIG_DIR=$DOWNLOAD_CONFIG_DIR + +SUPPORTED_MODELS=( + "all" + "yolo_all" + "yolox-tiny" + "yolox_s" + "yolov5n" + "yolov5s" + "yolov5m" + "yolov5l" + "yolov5x" + "yolov5n6" + "yolov5s6" + "yolov5m6" + "yolov5l6" + "yolov5x6" + "yolov5nu" + "yolov5su" + "yolov5mu" + "yolov5lu" + "yolov5xu" + "yolov5n6u" + "yolov5s6u" + "yolov5m6u" + "yolov5l6u" + "yolov5x6u" + "yolov7" + "yolov8n" + "yolov8s" + "yolov8m" + "yolov8l" + "yolov8x" + "yolov8n-obb" + "yolov8s-obb" + "yolov8m-obb" + "yolov8l-obb" + "yolov8x-obb" + "yolov8n-seg" + "yolov8s-seg" + "yolov8m-seg" + "yolov8l-seg" + "yolov8x-seg" + "yolov8_license_plate_detector" + "yolov9t" + "yolov9s" + "yolov9m" + "yolov9c" + "yolov9e" + "yolov10n" + "yolov10s" + "yolov10m" + "yolov10b" + "yolov10l" + "yolov10x" + "yolo11n" + "yolo11s" + "yolo11m" + "yolo11l" + "yolo11x" + "yolo11n-obb" + "yolo11s-obb" + "yolo11m-obb" + "yolo11l-obb" + "yolo11x-obb" + "yolo11n-seg" + "yolo11s-seg" + "yolo11m-seg" + "yolo11l-seg" + "yolo11x-seg" + "yolo11n-pose" + "yolo11s-pose" + "yolo11m-pose" + "yolo11l-pose" + "yolo11x-pose" + "centerface" + "hsemotion" + "deeplabv3" + "clip-vit-large-patch14" + "clip-vit-base-patch16" + "clip-vit-base-patch32" + "ch_PP-OCRv4_rec_infer" # PaddlePaddle OCRv4 multilingual model +) + +# Corresponds to files in 'datasets' directory +declare -A SUPPORTED_QUANTIZATION_DATASETS +SUPPORTED_QUANTIZATION_DATASETS=( + ["coco"]="https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml" + ["coco128"]="https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco128.yaml" +) + +# Function to display text in a given color +echo_color() { + local text="$1" + local color="$2" + local color_code="" + + # Determine the color code based on the color name + case "$color" in + black) color_code="\e[30m" ;; + red) color_code="\e[31m" ;; + green) color_code="\e[32m" ;; + bred) color_code="\e[91m" ;; + bgreen) color_code="\e[92m" ;; + yellow) color_code="\e[33m" ;; + blue) color_code="\e[34m" ;; + magenta) color_code="\e[35m" ;; + cyan) color_code="\e[36m" ;; + white) color_code="\e[37m" ;; + *) echo "Invalid color name"; return 1 ;; + esac + + # Display the text in the chosen color + echo -e "${color_code}${text}\e[0m" +} + +# Function to handle errors +handle_error() { + echo -e "\e[31mError occurred: $1\e[0m" + exit 1 +} + +# Trap errors and call handle_error +trap 'handle_error "- line $LINENO"' ERR + +if ! [[ "${SUPPORTED_MODELS[*]}" =~ $MODEL ]]; then + echo "Unsupported model: $MODEL" >&2 + exit 1 +else + echo "Installing $MODEL..." +fi + +if ! [[ "${!SUPPORTED_QUANTIZATION_DATASETS[*]}" =~ $QUANTIZE ]]; then + echo "Unsupported quantization dataset: $QUANTIZE" >&2 + exit 1 +fi + +set +u # Disable nounset option: treat any unset variable as an empty string +if [ -z "$MODELS_PATH" ]; then + echo "MODELS_PATH is not specified" + echo "Please set MODELS_PATH env variable with target path to download models" + exit 1 +fi + +if [ ! -e "$MODELS_PATH" ]; then + mkdir -p "$MODELS_PATH" || handle_error $LINENO +fi + +set -u # Re-enable nounset option: treat any attempt to use an unset variable as an error + +if [ "$ID" == "fedora" ]; then + export PYTHON_CREATE_VENV=/usr/bin/python3.10 + $PYTHON_CREATE_VENV -m ensurepip --upgrade || handle_error $LINENO +else + export PYTHON_CREATE_VENV=python3 +fi + +# Set the name of the virtual environment directory +VENV_DIR_QUANT="$HOME/.virtualenvs/dlstreamer-quantization" + +# Create a Python virtual environment if it doesn't exist +if [ ! -d "$VENV_DIR_QUANT" ]; then + echo "Creating virtual environment in $VENV_DIR_QUANT..." + $PYTHON_CREATE_VENV -m venv "$VENV_DIR_QUANT" || handle_error $VENV_DIR_QUANT +fi + +# Activate the virtual environment +echo "Activating virtual environment in $VENV_DIR_QUANT..." +source "$VENV_DIR_QUANT/bin/activate" + +# Upgrade pip in the virtual environment +pip install --no-cache-dir --upgrade pip + +# Install OpenVINO module with compatible numpy version +pip install --no-cache-dir "numpy<2.5.0,>=1.16.6" || handle_error $LINENO +pip install --no-cache-dir openvino==2025.2.0 || handle_error $LINENO + +pip install --no-cache-dir onnx || handle_error $LINENO +pip install --no-cache-dir seaborn || handle_error $LINENO +# Install or upgrade NNCF +pip install --no-cache-dir --upgrade nncf || handle_error $LINENO + +# Check and upgrade ultralytics if necessary +if [[ "${MODEL:-}" =~ yolo.* || "${MODEL:-}" == "all" ]]; then + pip install --no-cache-dir --upgrade --extra-index-url https://download.pytorch.org/whl/cpu "ultralytics==8.3.153" "numpy<2.5.0" || handle_error $LINENO +fi + +# Set the name of the virtual environment directory +VENV_DIR="$HOME/.virtualenvs/dlstreamer" + +# Create a Python virtual environment if it doesn't exist +if [ ! -d "$VENV_DIR" ]; then + echo "Creating virtual environment in $VENV_DIR..." + $PYTHON_CREATE_VENV -m venv "$VENV_DIR" || handle_error $LINENO +fi + +# Activate the virtual environment +echo "Activating virtual environment in $VENV_DIR..." +source "$VENV_DIR/bin/activate" + +# Upgrade pip in the virtual environment +pip install --no-cache-dir --upgrade pip + +# Install OpenVINO module with compatible numpy version +pip install --no-cache-dir "numpy<2.0.0,>=1.16.6" || handle_error $LINENO +pip install --no-cache-dir openvino==2024.6.0 || handle_error $LINENO +pip install --no-cache-dir openvino-dev==2024.6.0 || handle_error $LINENO + +pip install --no-cache-dir onnx || handle_error $LINENO +pip install --no-cache-dir seaborn || handle_error $LINENO +# Install or upgrade NNCF +pip install --no-cache-dir --upgrade nncf || handle_error $LINENO + +# Check and upgrade ultralytics if necessary +if [[ "${MODEL:-}" =~ yolo.* || "${MODEL:-}" == "all" ]]; then + pip install --no-cache-dir --upgrade --extra-index-url https://download.pytorch.org/whl/cpu "ultralytics==8.3.153" "numpy<2.0.0" || handle_error $LINENO +fi + +# Install dependencies for CLIP models +if [[ "${MODEL:-}" =~ clip.* || "${MODEL:-}" == "all" ]]; then + pip install --no-cache-dir --upgrade torch torchaudio torchvision || handle_error $LINENO + pip install --no-cache-dir transformers || handle_error $LINENO + pip install --no-cache-dir pillow || handle_error $LINENO +fi + +echo Downloading models to folder "$MODELS_PATH". + +set -euo pipefail +# -------------- YOLOx + + +# Function for quantization of YOLO models +quantize_yolo_model() { + local MODEL_NAME=$1 + MODEL_DIR="$MODELS_PATH/public/$MODEL_NAME" + DST_FILE="$MODEL_DIR/INT8/$MODEL_NAME.xml" + + + if [[ ! -f "$DST_FILE" ]]; then + YOLO_CONFIG_DIR=$QUANTIZE_CONFIG_DIR + export YOLO_CONFIG_DIR + + mkdir -p "$MODELS_PATH/datasets" + local DATASET_MANIFEST="$MODELS_PATH/datasets/$QUANTIZE.yaml" + + curl -L -o "$DATASET_MANIFEST" ${SUPPORTED_QUANTIZATION_DATASETS[$QUANTIZE]} + echo "Quantizing: ${MODEL_DIR}" + mkdir -p "$MODEL_DIR" + + source "$VENV_DIR_QUANT/bin/activate" + cd "$MODELS_PATH" + python3 - < tuple[dict, int, int]: + validator.seen = 0 + validator.jdict = [] + validator.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) + validator.end2end = False + validator.confusion_matrix = ConfusionMatrix(validator.data["nc"]) + compiled_model = ov.compile_model(model, device_name="CPU") + output_layer = compiled_model.output(0) + for batch_i, batch in enumerate(track(data_loader, description="Validating")): + if num_samples is not None and batch_i == num_samples: + break + batch = validator.preprocess(batch) + preds = torch.from_numpy(compiled_model(batch["img"])[output_layer]) + preds = validator.postprocess(preds) + validator.update_metrics(preds, batch) + stats = validator.get_stats() + return stats, validator.seen, validator.nt_per_class.sum() + +def print_statistics(stats: dict[str, float], total_images: int, total_objects: int) -> None: + mp, mr, map50, mean_ap = ( + stats["metrics/precision(B)"], + stats["metrics/recall(B)"], + stats["metrics/mAP50(B)"], + stats["metrics/mAP50-95(B)"], + ) + s = ("%20s" + "%12s" * 6) % ("Class", "Images", "Labels", "Precision", "Recall", "mAP@.5", "mAP@.5:.95") + print(s) + pf = "%20s" + "%12i" * 2 + "%12.3g" * 4 # print format + print(pf % ("all", total_images, total_objects, mp, mr, map50, mean_ap)) + +model_name = sys.argv[1] +dataset_file = sys.argv[2] + + +validator = DetectionValidator() +validator.data = check_det_dataset(dataset_file) +validator.stride = 32 +validator.is_coco = True +validator.class_map = coco80_to_coco91_class + +data_loader = validator.get_dataloader(validator.data["path"], 1) + +def transform_fn(data_item: dict): + input_tensor = validator.preprocess(data_item)["img"].numpy() + return input_tensor + # images, _ = data_item + # return images.numpy() + +calibration_dataset = nncf.Dataset(data_loader, transform_fn) + +model = ov.Core().read_model("./public/" + model_name + "/FP32/" + model_name + ".xml") +quantized_model = nncf.quantize(model, calibration_dataset, subset_size = len(data_loader)) + +# Validate FP32 model +fp_stats, total_images, total_objects = validate(model, data_loader, validator) +print("Floating-point model validation results:") +print_statistics(fp_stats, total_images, total_objects) + +# Validate quantized model +q_stats, total_images, total_objects = validate(quantized_model, data_loader, validator) +print("Quantized model validation results:") +print_statistics(q_stats, total_images, total_objects) + +quantized_model.set_rt_info(ov.get_version(), "Runtime_version") +ov.save_model(quantized_model, "./public/" + model_name + "/INT8/" + model_name + ".xml", compress_to_fp16=False) +EOF + + source "$VENV_DIR/bin/activate" + YOLO_CONFIG_DIR=$DOWNLOAD_CONFIG_DIR + else + echo_color "\nModel already quantized: $MODEL_DIR.\n" "yellow" + fi +} + +# check if model exists in local directory, download as needed +if [ "$MODEL" == "yolox-tiny" ] || [ "$MODEL" == "yolo_all" ] || [ "$MODEL" == "all" ]; then + MODEL_NAME="yolox-tiny" + MODEL_DIR="$MODELS_PATH/public/$MODEL_NAME" + DST_FILE1="$MODEL_DIR/FP16/$MODEL_NAME.xml" + DST_FILE2="$MODEL_DIR/FP32/$MODEL_NAME.xml" + + if [[ ! -f "$DST_FILE1" || ! -f "$DST_FILE2" ]]; then + cd "$MODELS_PATH" + echo "Downloading and converting: ${MODEL_DIR}" + source "$VENV_DIR/bin/activate" + omz_downloader --name "$MODEL_NAME" + omz_converter --name "$MODEL_NAME" + cd "$MODEL_DIR" + rm -rf yolox* + rm -rf models + rm -rf utils + else + echo_color "\nModel already exists: $MODEL_DIR.\n" "yellow" + fi +fi + +if [ "$MODEL" == "yolox_s" ] || [ "$MODEL" == "yolo_all" ] || [ "$MODEL" == "all" ]; then + MODEL_NAME="yolox_s" + MODEL_DIR="$MODELS_PATH/public/$MODEL_NAME" + DST_FILE1="$MODEL_DIR/FP16/$MODEL_NAME.xml" + DST_FILE2="$MODEL_DIR/FP32/$MODEL_NAME.xml" + + if [[ ! -f "$DST_FILE1" || ! -f "$DST_FILE2" ]]; then + mkdir -p "$MODEL_DIR" + mkdir -p "$MODEL_DIR/FP16" + mkdir -p "$MODEL_DIR/FP32" + cd "$MODEL_DIR" + curl -O -L https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx + source "$VENV_DIR/bin/activate" + ovc yolox_s.onnx --compress_to_fp16=True + mv yolox_s.xml "$MODEL_DIR/FP16" + mv yolox_s.bin "$MODEL_DIR/FP16" + ovc yolox_s.onnx --compress_to_fp16=False + mv yolox_s.xml "$MODEL_DIR/FP32" + mv yolox_s.bin "$MODEL_DIR/FP32" + rm -rf yolox_s.onnx + else + echo_color "\nModel already exists: $MODEL_DIR.\n" "yellow" + fi +fi + +# -------------- YOLOv5 (ULTRALYTICS) FP32 & INT8 + +# Function to export YOLOv5 model +export_yolov5_model() { + local model_name=$1 + local model_path="$MODELS_PATH/public/$model_name" + local weights="${model_name::-1}.pt" # Remove the last character from the model name to construct the weights filename + + if [ ! -f "$model_path/FP32/$model_name.xml" ] || [ ! -f "$model_path/FP16/$model_name.xml" ]; then + echo "Downloading and converting: ${model_path}" + mkdir -p "$model_path" + cd "$model_path" + source "$VENV_DIR/bin/activate" + + python3 - < Dict[str, Any]: + """ + Download and optionally convert models. + + Models are downloaded from the specified hub (huggingface, ollama, etc.). + Models will be converted to OpenVINO format if: + 1. is_ovms is set to true in the request for openvino conversion, or + 2. type can be set to 'vlm/llm/embeddings/reranker' in the request + + The config object is optional and used only for conversion. + """ + try: + supported_hubs = set() + for plugin_type in plugin_registry.plugins: + supported_hubs.update(name.lower() for name in plugin_registry.get_plugin_names(plugin_type)) + for model in request.models: + logger.info(f"Requested Model Hub: {model.hub}") + if model.hub.lower() not in supported_hubs: + raise HTTPException( + status_code=400, + detail=f"Unsupported model download/conversion detected. Supported methods are {supported_hubs}.", + ) + + # Authorization for HuggingFace + huggingface_models = any(model.hub == "huggingface" for model in request.models) + if huggingface_models and (not Authorization or not Authorization.credentials): + raise HTTPException( + status_code=401, + detail="Authorization token is required for Hugging Face models", + ) + + logger.info(f"Initiating model download for {len(request.models)} model(s)") + job_ids = [] + + for model in request.models: + # Pass token for HuggingFace + extra_kwargs = model.dict() + needs_conversion = model.is_ovms or (model.type and model.type.lower() == "vlm") + if model.hub.lower() in [hub.value.lower() for hub in ModelHub] and not needs_conversion: + + extra_kwargs["token"] = Authorization.credentials if Authorization else None + download_path = os.path.join( + "models", download_path, model.hub + ) + # First, register download job + download_job_id = model_manager.register_job( + operation_type="download", + model_name=model.name, + hub=model.hub, + output_dir=download_path, + plugin_name=model.hub, + ) + + # Add to job_ids for response + job_ids.append(download_job_id) + + # Start download in background + background_tasks.add_task( + model_manager.process_download, + job_id=download_job_id, + model_name=model.name, + output_dir=download_path, + downloader=model.hub, + **extra_kwargs + ) + + if needs_conversion: + # Get configuration for conversion + extra_kwargs["token"] = Authorization.credentials if Authorization else None + config = model.config.dict() if model.config else {} + + # Create a unique output directory for the converted model + convert_output_dir = os.path.join( "models", + download_path, + "openvino_models", + config['device'], + config['precision'] + ) + + # Register conversion job + convert_job_id = model_manager.register_job( + operation_type="convert", + model_name=model.name, + hub=model.hub, + output_dir=convert_output_dir, + plugin_name="openvino" + ) + + # Add to job_ids for response + job_ids.append(convert_job_id) + + # Start conversion in background + background_tasks.add_task( + model_manager.process_conversion, + job_id=convert_job_id, + #hf_token= + model_path=download_path, + hub=model.hub, + output_dir=convert_output_dir, + converter="openvino", + model_name=model.name, + model_type=model.type, + **config, + hf_token=extra_kwargs["token"] + ) + + # Return response immediately with job IDs + return { + "message": f"Started processing {len(request.models)} model(s)", + "job_ids": job_ids, + "status": "processing" + } + except ValidationError as e: + logger.error(f"Request validation failed: {str(e)}") + raise HTTPException( + status_code=422, detail=f"Invalid request format: {e.errors()}" + ) + except HTTPException: + raise + except Exception as e: + logger.error(f"Unexpected error in model download process: {str(e)}") + raise HTTPException( + status_code=500, + detail=f"Unexpected error in model download process: {str(e)}", + ) + + +@app.get("/models/{model_name}/jobs", tags=["Jobs"]) +async def get_model_jobs(model_name: str): + """ + Get all jobs related to a specific model. + """ + model_jobs = [] + + for job_id, job in model_manager._jobs.items(): + if job.get("model_name") == model_name: + model_jobs.append(job) + + if not model_jobs: + raise HTTPException(status_code=404, detail=f"No jobs found for model {model_name}") + + return {"jobs": model_jobs} + + +@app.get("/jobs/{job_id}", tags=["Jobs"]) +async def get_job_status(job_id: str): + """ + Get the status of a specific job. + """ + if job_id not in model_manager._jobs: + raise HTTPException(status_code=404, detail=f"Job {job_id} not found") + + return model_manager._jobs[job_id] + + +@app.get("/models/results", tags=["Models"]) +async def get_model_results(): + """ + Get completed model downloads and conversions. + """ + completed_jobs = [] + + for job_id, job in model_manager._jobs.items(): + if job.get("status") == "completed": + # Format job as result + result = { + "job_id": job_id, + "model_name": job.get("model_name"), + "hub": job.get("hub"), + "operation_type": job.get("operation_type"), + "status": "success", + "model_path": job.get("output_dir"), + "is_ovms": job.get("operation_type") == "convert", + "completion_time": job.get("completion_time") + } + completed_jobs.append(result) + + return {"results": completed_jobs} + + +@app.get("/jobs", tags=["Jobs"]) +async def list_jobs(): + """ + List all jobs. + """ + return {"jobs": list(model_manager._jobs.values())} + + +@app.get("/plugins", tags=["Plugins"]) +async def list_plugins(): + """ + List all available plugins and their capabilities. + """ + plugins_info = {} + + # Get plugins for each type + for plugin_type in plugin_registry.plugins: + plugins_info[plugin_type] = [] + for plugin_name, plugin in plugin_registry.plugins.get(plugin_type, {}).items(): + # Get plugin capabilities + can_handle_parallel = hasattr(plugin, "get_download_tasks") and callable(getattr(plugin, "get_download_tasks")) + + plugin_info = { + "name": plugin_name, + "type": plugin_type, + "description": getattr(plugin, "__doc__", "No description available").strip(), + "capabilities": { + "supports_parallel_downloads": can_handle_parallel, + } + } + plugins_info[plugin_type].append(plugin_info) + + return { + "available_plugins": plugins_info, + "total_count": sum(len(plugins) for plugins in plugins_info.values()) + } diff --git a/microservices/model-download/src/api/models.py b/microservices/model-download/src/api/models.py new file mode 100644 index 000000000..cbc56c0bf --- /dev/null +++ b/microservices/model-download/src/api/models.py @@ -0,0 +1,51 @@ +from enum import Enum +from typing import List, Optional, TypedDict, Dict, Any +from pydantic import BaseModel, Field + +class ModelPrecision(str, Enum): + INT8 = "int8" + FP16 = "fp16" + FP32 = "fp32" + + +class DeviceType(str, Enum): + CPU = "CPU" + GPU = "GPU" + +class ModelHub(Enum): + HUGGINGFACE = "huggingface" + ULTRALYTICS = "ultralytics" + OLLAMA = "ollama" + +class Config(BaseModel): + precision: ModelPrecision = ModelPrecision.INT8 + device: DeviceType = DeviceType.CPU + cache_size: Optional[int] = Field(None, gt=0) + + +class ModelResult(TypedDict): + status: str # 'success' or 'error' + model_name: str + model_path: Optional[str] + error: Optional[str] + is_ovms: Optional[bool] + + +class DownloadResponse(BaseModel): + message: str + results: List[Dict[str, Any]] + model_path: Optional[str] = None + + +class ModelRequest(BaseModel): + name: str + hub: str + type: Optional[str] = None + is_ovms: bool = False + revision: Optional[str] = None + config: Optional[Config] = None + + +class ModelDownloadRequest(BaseModel): + models: List[ModelRequest] + parallel_downloads: Optional[bool] = False \ No newline at end of file diff --git a/microservices/model-download/src/core/__init__.py b/microservices/model-download/src/core/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/microservices/model-download/src/core/interfaces.py b/microservices/model-download/src/core/interfaces.py new file mode 100644 index 000000000..6139992dc --- /dev/null +++ b/microservices/model-download/src/core/interfaces.py @@ -0,0 +1,61 @@ +from abc import ABC, abstractmethod +from typing import Optional, Dict, Any, List, Callable +from ..api.models import ModelRequest, ModelResult + +class DownloadTask: + """ + Represents a sub-task in a model download process. + Used for parallel downloading of model files. + """ + def __init__(self, file_id: str, url: str, destination: str): + self.file_id = file_id + self.url = url + self.destination = destination + +class ModelDownloadPlugin(ABC): + @property + def plugin_name(self) -> str: + """Return the name of the plugin""" + return self.__class__.__name__.lower() + + @property + def plugin_type(self) -> str: + """Return the type of the plugin (downloader/converter)""" + return "downloader" + + def can_handle(self, model_name: str,hub: str, **kwargs) -> bool: + """ + Check if this plugin can handle the given model name. + Plugins should override this to implement their specific logic. + """ + return False + + def get_download_tasks(self, model_name: str, **kwargs) -> List[DownloadTask]: + """ + Get list of download tasks for a model. + Used for parallel downloading. + """ + raise NotImplementedError("This plugin does not support task-based downloading") + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs) -> str: + """ + Download a single task file. + Used for parallel downloading. + """ + raise NotImplementedError("This plugin does not support task-based downloading") + + def post_process(self, model_name: str, output_dir: str, downloaded_paths: List[str], **kwargs) -> Dict[str, Any]: + """ + Post-process downloaded files. + Called after all files have been downloaded. + """ + # Default implementation just returns basic info + return { + "model_name": model_name, + "download_path": output_dir, + "success": True + } + + @abstractmethod + def download(self, model_name: str, output_dir: str, progress_callback: Optional[Callable] = None, **kwargs) -> Dict[str, Any]: + pass \ No newline at end of file diff --git a/microservices/model-download/src/core/model_manager.py b/microservices/model-download/src/core/model_manager.py new file mode 100644 index 000000000..7581d75ce --- /dev/null +++ b/microservices/model-download/src/core/model_manager.py @@ -0,0 +1,616 @@ +import os +import uuid +import logging +import concurrent.futures +from datetime import datetime +from typing import Dict, Any, Optional, List, Callable + +from .plugin_registry import PluginRegistry +from .interfaces import ModelDownloadPlugin, DownloadTask + +# Configure structured logging +from src.utils.logging import logger + + +class ModelManager: + """ + Core orchestration component that manages model operations. + + Responsibilities: + - Select appropriate plugins for model downloads or conversions + - Create and manage job records + - Coordinate parallel operations + - Track job status and progress + """ + + def __init__(self, plugin_registry: PluginRegistry, default_dir: str = "./models"): + """ + Initialize the ModelManager. + + Args: + plugin_registry: Registry of available plugins + default_dir: Default directory for model downloads and conversions + """ + self.registry = plugin_registry + self.default_dir = os.path.abspath(default_dir) + self._jobs = {} # In-memory job storage + self._executors = {} # Active executor pools by job + os.makedirs(self.default_dir, exist_ok=True) + logger.info("model_manager_initialized", default_dir=self.default_dir) + + def register_job( + self, + operation_type: str, # New parameter to distinguish job types + model_name: str, + hub: str, + output_dir: Optional[str] = None, + plugin_name: Optional[str] = None, + ) -> str: + """ + Register a new job and return its ID. + + Args: + operation_type: Type of operation ('download' or 'convert') + model_name: Name of the model to process + output_dir: Optional custom directory for output + plugin_name: Optional specific plugin to use + + Returns: + Job ID as a string + """ + job_id = str(uuid.uuid4()) + + # Resolve the output directory + if output_dir is None: + # Create a safe directory name from the model name + safe_name = model_name.replace("/", "_") + output_dir = os.path.join(self.default_dir, hub, safe_name) + + output_dir = os.path.abspath(output_dir) + os.makedirs(output_dir, exist_ok=True) + + # Track the job + self._jobs[job_id] = { + "id": job_id, + "operation_type": operation_type, # Store operation type + "model_name": model_name, + "hub": hub, + "output_dir": output_dir, + "status": "queued", + "start_time": datetime.now().isoformat(), + "plugin_name": plugin_name, # Rename from 'downloader' to more generic 'plugin_name' + "progress": {"current": 0, "total": 0, "percentage": 0}, + } + + logger.info( + "job_registered", + job_id=job_id, + operation_type=operation_type, + model_name=model_name, + hub=hub + ) + return job_id + + def update_progress(self, job_id: str, current: int, total: int) -> None: + """Update the progress of a job.""" + if job_id in self._jobs: + percentage = int((current / total) * 100) if total > 0 else 0 + self._jobs[job_id]["progress"] = { + "current": current, + "total": total, + "percentage": percentage, + } + logger.debug( + "job_progress_updated", + job_id=job_id, + current=current, + total=total, + percentage=percentage, + ) + + def process_download( + self, + job_id: str, + model_name: str, + hub: str, + output_dir: Optional[str] = None, + downloader: Optional[str] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Process a download job with parallel execution support. + + Args: + job_id: ID of the job to process + model_name: Name of the model to download + hub: From which hub model needs to be downloaded + output_dir: Directory to save the model + downloader: Specific downloader plugin to use + **kwargs: Additional parameters for the download + + Returns: + Dictionary with job details and status + """ + try: + # Update job status + self._jobs[job_id]["status"] = "downloading" + logger.info(f"Request details: {model_name}, {hub}, {kwargs}") + # Create progress callback + def progress_callback(model_name, current, total): + self.update_progress(job_id, current, total) + logger.info(f"Request details: {model_name}, {hub}, {kwargs}") + # Find appropriate downloader plugin + download_plugin = None + if downloader: + logger.info(f"Request details: {downloader},{model_name}, {hub}, {kwargs}") + # User specifically requested a downloader + download_plugin = self.registry.get_plugin("downloader", downloader) + if not download_plugin: + err_msg = f"Requested downloader '{downloader}' not found" + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = err_msg + logger.error("downloader_not_found", downloader=downloader) + raise ValueError(err_msg) + else: + # Auto-detect appropriate downloader + logger.info(f"Request details: {model_name}, {hub}, {kwargs}") + download_plugin = self.registry.find_plugin_for_model( + "downloader", model_name, hub, **kwargs + ) + + if not download_plugin: + err_msg = f"No suitable downloader found for model '{model_name}'" + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = err_msg + logger.error("no_suitable_downloader", model_name=model_name) + raise ValueError(err_msg) + + # Update job with selected plugin + self._jobs[job_id]["plugin"] = download_plugin.plugin_name + + # Check if the plugin supports parallel downloading via tasks + use_parallel = kwargs.pop("parallel_downloads", True) + max_workers = kwargs.pop("max_workers", 4) + + if use_parallel: + # Try to get downloadable tasks + try: + download_tasks = download_plugin.get_download_tasks( + model_name, **kwargs + ) + + # If we have tasks, proceed with parallel download + if download_tasks: + return self._parallel_download( + job_id=job_id, + plugin=download_plugin, + model_name=model_name, + model_path=output_dir, + tasks=download_tasks, + max_workers=max_workers, + progress_callback=progress_callback, + **kwargs, + ) + except NotImplementedError: + logger.debug( + "plugin_no_task_support", plugin=download_plugin.plugin_name + ) + except Exception as e: + logger.warning( + "task_download_failed", + plugin=download_plugin.plugin_name, + error=str(e), + ) + + # Fall back to the plugin's standard download method + logger.info( + "using_standard_download", + plugin=download_plugin.plugin_name, + model_name=model_name, + ) + + result = download_plugin.download( + model_name, output_dir, progress_callback=progress_callback, **kwargs + ) + + # Update job status + self._jobs[job_id]["status"] = "completed" + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + self._jobs[job_id]["result"] = result + + logger.info("download_completed", job_id=job_id, model_name=model_name) + + return { + "job_id": job_id, + "status": "completed", + "model_name": model_name, + "download_path": output_dir, + "details": result, + } + + except Exception as e: + # Update job status with error + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = str(e) + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + logger.error( + "download_failed", job_id=job_id, model_name=model_name, error=str(e) + ) + return { + "job_id": job_id, + "status": "failed", + "model_name": model_name, + "error": str(e), + } + + def process_conversion( + self, + job_id: str, + model_path: str, + model_name: str, + hub: str, + hf_token: str, + output_dir: Optional[str] = None, + converter: Optional[str] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Process a model conversion job. + + Args: + job_id: ID of the job to process + model_path: Path to the model to convert + hub: Model downloaded from the hub + output_dir: Directory to save the converted model + converter: Specific converter plugin to use + **kwargs: Additional parameters for the conversion + + Returns: + Dictionary with job details and status + """ + try: + # Update job status + self._jobs[job_id]["status"] = "converting" + + # Create progress callback + def progress_callback(model_path, current, total): + self.update_progress(job_id, current, total) + + # Find appropriate converter plugin + convert_plugin = None + if converter: + # User specifically requested a converter + convert_plugin = self.registry.get_plugin("converter", converter) + if not convert_plugin: + err_msg = f"Requested converter '{converter}' not found" + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = err_msg + logger.error("converter_not_found", converter=converter) + raise ValueError(err_msg) + else: + # Auto-detect appropriate converter based on model path and kwargs + convert_plugin = self.registry.find_plugin_for_model( + "converter", model_path, **kwargs + ) + + if not convert_plugin: + err_msg = f"No suitable converter found for model at '{model_path}'" + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = err_msg + logger.error("no_suitable_converter", model_path=model_path) + raise ValueError(err_msg) + + # Update job with selected plugin + self._jobs[job_id]["plugin"] = convert_plugin.plugin_name + + # Execute the conversion + logger.info( + "starting_conversion", + plugin=convert_plugin.plugin_name, + model_path=model_path, + ) + + logger.info(f"Request details: {model_path}, {hub}, {kwargs}") + + result = convert_plugin.convert( + model_name, output_dir, progress_callback=progress_callback,hf_token=hf_token, **kwargs + ) + + # Update job status + self._jobs[job_id]["status"] = "completed" + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + self._jobs[job_id]["result"] = result + + logger.info("conversion_completed", job_id=job_id, model_path=model_path) + + return { + "job_id": job_id, + "status": "completed", + "model_path": model_path, + "conversion_path": output_dir, + "details": result, + } + + except Exception as e: + # Update job status with error + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = str(e) + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + logger.error( + "conversion_failed", job_id=job_id, model_path=model_path, error=str(e) + ) + return { + "job_id": job_id, + "status": "failed", + "model_path": model_path, + "error": str(e), + } + + def _parallel_download( + self, + job_id: str, + plugin: ModelDownloadPlugin, + model_name: str, + model_path: str, + tasks: List[DownloadTask], + max_workers: int, + progress_callback: Optional[Callable] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Perform parallel download of the given tasks. + + Args: + job_id: ID of the job + plugin: Plugin to use for downloading + model_name: Name of the model + model_path: Directory to save the model + tasks: List of download tasks + max_workers: Maximum number of parallel workers + progress_callback: Callback for progress updates + **kwargs: Additional parameters + + Returns: + Dictionary with job details and status + """ + logger.info( + "starting_parallel_download", + model_name=model_name, + tasks=len(tasks), + max_workers=max_workers, + ) + + downloaded_paths = [] + total_tasks = len(tasks) + completed_tasks = 0 + + # Store the executor for potential cancellation + executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) + self._executors[job_id] = executor + + try: + # Define the worker function to download a single task + def download_task_wrapper(task): + nonlocal completed_tasks + try: + # Check if job has been canceled + if self._jobs.get(job_id, {}).get("status") == "canceled": + logger.info("download_task_canceled", task=task.destination) + raise InterruptedError("Download was canceled") + + # Call the plugin to download this task + path = plugin.download_task(task, model_path, **kwargs) + + # Update progress + completed_tasks += 1 + if progress_callback: + progress_callback(model_name, completed_tasks, total_tasks) + + return path + except Exception as e: + logger.error( + "task_download_error", task=task.destination, error=str(e) + ) + raise + + # Submit all tasks to the executor + future_to_task = { + executor.submit(download_task_wrapper, task): task for task in tasks + } + + # Process results as they complete + for future in concurrent.futures.as_completed(future_to_task): + task = future_to_task[future] + try: + path = future.result() + downloaded_paths.append(path) + logger.debug("task_downloaded", file=task.destination, path=path) + except Exception as e: + # If any task fails, we cancel pending tasks and fail the job + if self._jobs[job_id]["status"] != "canceled": + logger.error("task_failure", task=task.destination, error=str(e)) + raise + + # All tasks completed successfully, perform any post-processing + result = plugin.post_process( + model_name, model_path, downloaded_paths, **kwargs + ) + + # Update job status + self._jobs[job_id]["status"] = "completed" + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + self._jobs[job_id]["result"] = result + + # Cleanup + if job_id in self._executors: + del self._executors[job_id] + + logger.info( + "parallel_download_completed", + job_id=job_id, + model_name=model_name, + files=len(downloaded_paths), + ) + + return { + "job_id": job_id, + "status": "completed", + "model_name": model_name, + "download_path": model_path, + "details": result, + } + + except Exception as e: + # Update job status with error if not already canceled + if self._jobs[job_id]["status"] != "canceled": + self._jobs[job_id]["status"] = "failed" + self._jobs[job_id]["error"] = str(e) + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + + # Cleanup + if job_id in self._executors: + del self._executors[job_id] + + logger.error( + "parallel_download_failed", + job_id=job_id, + model_name=model_name, + error=str(e), + ) + + return { + "job_id": job_id, + "status": "failed", + "model_name": model_name, + "error": str(e), + } + + def download_model( + self, + model_name: str, + hub: str, + output_dir: Optional[str] = None, + downloader: Optional[str] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Synchronous model download method. + + For asynchronous downloads, use register_job + process_download. + + Args: + model_name: Name of the model to download + output_dir: Directory to save the model + downloader: Specific downloader plugin to use + **kwargs: Additional parameters for the download + + Returns: + Dictionary with job details and status + """ + job_id = self.register_job("download", model_name, output_dir, downloader) + return self.process_download( + job_id, model_name,hub, output_dir, downloader, **kwargs + ) + + def convert_model( + self, + model_path: str, + output_dir: Optional[str] = None, + converter: Optional[str] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Synchronous model conversion method. + + For asynchronous conversions, use register_job + process_conversion. + + Args: + model_path: Path to the model to convert + output_dir: Directory to save the converted model + converter: Specific converter plugin to use + **kwargs: Additional parameters for the conversion + + Returns: + Dictionary with job details and status + """ + # Extract model name from path for job registration + model_name = os.path.basename(model_path) + job_id = self.register_job("convert", model_name, output_dir, converter) + return self.process_conversion( + job_id=job_id, model_path=model_path, model_name=model_name, output_dir=output_dir, converter=converter, **kwargs + ) + + def get_job_status(self, job_id: str) -> Optional[Dict[str, Any]]: + """Get the status of a specific job.""" + if job_id not in self._jobs: + return None + + job = self._jobs[job_id].copy() # Return a copy to prevent modification + return job + + def list_jobs( + self, limit: int = 100, offset: int = 0, operation_type: Optional[str] = None + ) -> List[Dict[str, Any]]: + """ + List jobs with pagination and optional filtering by operation type. + + Args: + limit: Maximum number of jobs to return + offset: Number of jobs to skip + operation_type: Optional filter by operation type ('download' or 'convert') + + Returns: + List of job details + """ + all_jobs = list(self._jobs.values()) + + # Filter by operation type if specified + if operation_type: + all_jobs = [ + job for job in all_jobs if job.get("operation_type") == operation_type + ] + + # Sort by start time, newest first + sorted_jobs = sorted( + all_jobs, key=lambda j: j.get("start_time", ""), reverse=True + ) + return sorted_jobs[offset : offset + limit] + + def get_available_plugins( + self, plugin_type: Optional[str] = None + ) -> Dict[str, List[str]]: + """ + Get available plugins, optionally filtered by type. + + Args: + plugin_type: Optional filter by plugin type ('downloader' or 'converter') + + Returns: + Dictionary mapping plugin types to lists of plugin names + """ + if plugin_type: + return {plugin_type: self.registry.get_plugins_by_type(plugin_type)} + + result = {} + for p_type in self.registry.get_plugin_types(): + result[p_type] = self.registry.get_plugins_by_type(p_type) + return result + + def cancel_job(self, job_id: str) -> bool: + """Cancel a job if possible.""" + if job_id not in self._jobs: + return False + + if self._jobs[job_id]["status"] in ["queued", "downloading", "converting"]: + self._jobs[job_id]["status"] = "canceled" + self._jobs[job_id]["completion_time"] = datetime.now().isoformat() + + # If there's an active executor for this job, shut it down + if job_id in self._executors: + self._executors[job_id].shutdown(wait=False, cancel_futures=True) + del self._executors[job_id] + + logger.info("job_canceled", job_id=job_id) + return True + + return False diff --git a/microservices/model-download/src/core/plugin_registry.py b/microservices/model-download/src/core/plugin_registry.py new file mode 100644 index 000000000..e9a3f3ff9 --- /dev/null +++ b/microservices/model-download/src/core/plugin_registry.py @@ -0,0 +1,32 @@ + +import importlib +import inspect +from typing import Dict, Type, Any +from .interfaces import ModelDownloadPlugin + +class PluginRegistry: + def __init__(self): + self.plugins = {"downloader": {}, "converter": {}} + + def discover_plugins(self, plugins_package): + for name in dir(plugins_package): + attr = getattr(plugins_package, name) + if inspect.ismodule(attr): + for _, obj in inspect.getmembers(attr, inspect.isclass): + if issubclass(obj, ModelDownloadPlugin) and obj is not ModelDownloadPlugin: + plugin_instance = obj() + plugin_type = getattr(plugin_instance, "plugin_type", "downloader") + plugin_name = getattr(plugin_instance, "plugin_name", obj.__name__.lower()) + self.plugins.setdefault(plugin_type, {})[plugin_name] = plugin_instance + + def get_plugin(self, plugin_type: str, plugin_name: str) -> Any: + return self.plugins.get(plugin_type, {}).get(plugin_name) + + def get_plugin_names(self, plugin_type: str) -> list: + return list(self.plugins.get(plugin_type, {}).keys()) + + def find_plugin_for_model(self, plugin_type: str, model_name: str, hub: str, **kwargs): + for plugin in self.plugins.get(plugin_type, {}).values(): + if hasattr(plugin, "can_handle") and plugin.can_handle(model_name, hub, **kwargs): + return plugin + return None diff --git a/microservices/model-download/src/plugins/__init__.py b/microservices/model-download/src/plugins/__init__.py new file mode 100644 index 000000000..6775bbcf5 --- /dev/null +++ b/microservices/model-download/src/plugins/__init__.py @@ -0,0 +1,6 @@ +# Import all plugins so they can be discovered +from src.plugins.ultralytics_plugin import UltralyticsDownloader +from src.plugins.huggingface_plugin import HuggingFacePlugin +from src.plugins.ollama_plugin import OllamaPlugin +from src.plugins.openvino_plugin import OpenVINOConverter +# Add any other plugins here diff --git a/microservices/model-download/src/plugins/huggingface_plugin.py b/microservices/model-download/src/plugins/huggingface_plugin.py new file mode 100644 index 000000000..f48abd32b --- /dev/null +++ b/microservices/model-download/src/plugins/huggingface_plugin.py @@ -0,0 +1,55 @@ +from huggingface_hub import snapshot_download +from src.core.interfaces import ModelDownloadPlugin, DownloadTask +from src.utils.logging import logger +import os + +class HuggingFacePlugin(ModelDownloadPlugin): + """ + Plugin for downloading models from the HuggingFace Hub. + """ + @property + def plugin_name(self) -> str: + return "huggingface" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, hub: str, **kwargs) -> bool: + return hub.lower() == "huggingface" + + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs) -> dict: + hf_token = kwargs.get("hf_token") + revision = kwargs.get("revision") + model_specific_path = os.path.join(output_dir, model_name.replace("/", "_")) + os.makedirs(model_specific_path, exist_ok=True) + + logger.info(f"Downloading HuggingFace model {model_name} to {model_specific_path}") + model_downloaded_path = snapshot_download( + repo_id=model_name, + token=hf_token, + local_dir=model_specific_path, + revision=revision, + ) + + return { + "model_name": model_name, + "source": "huggingface", + "download_path": model_downloaded_path, + "success": True + } + + def get_download_tasks(self, model_name: str, **kwargs): + raise NotImplementedError("HuggingFace plugin does not support task-based downloading") + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs): + raise NotImplementedError("HuggingFace plugin does not support task-based downloading") + + def post_process(self, model_name: str, output_dir: str, downloaded_paths: list, **kwargs) -> dict: + return { + "model_name": model_name, + "source": "huggingface", + "download_path": output_dir, + "success": True + } \ No newline at end of file diff --git a/microservices/model-download/src/plugins/ollama_plugin.py b/microservices/model-download/src/plugins/ollama_plugin.py new file mode 100644 index 000000000..9b17cc1c1 --- /dev/null +++ b/microservices/model-download/src/plugins/ollama_plugin.py @@ -0,0 +1,97 @@ + +import os +import subprocess +import time +from typing import Optional, Dict, Any, List, Callable +from pathlib import Path +from src.utils.helper import cleanup_model_directory +from src.utils.logging import logger +from src.core.interfaces import ModelDownloadPlugin, DownloadTask + + +class OllamaPlugin(ModelDownloadPlugin): + """Plugin for downloading Ollama models""" + + @property + def plugin_name(self) -> str: + return "ollama" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, hub: str, **kwargs) -> bool: + """Check if this plugin can handle the given model""" + # Case-insensitive check for the hub name + if hub.lower() == "ollama": + return True + return False + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs) -> Dict[str, Any]: + """Download the Ollama model""" + process = None + model_downloaded_path = os.path.join(output_dir, model_name.replace("/", "_")) + + try: + + logger.info(f"Model will be downloaded to: {model_downloaded_path}") + + os.environ["OLLAMA_MODELS"] = model_downloaded_path + + logger.info(f"Directory for Ollama model: {model_downloaded_path}") + try: + os.makedirs(model_downloaded_path, exist_ok=True) + except OSError as e: + logger.error(f"Failed to create directory {model_downloaded_path}: {str(e)}") + raise RuntimeError(f"Failed to create model directory: {str(e)}") + + logger.info("Starting ollama server") + process = subprocess.Popen(["ollama", "serve"]) + + # Sleep for 1 second to allow the server to be start + time.sleep(1) + + logger.info(f"Starting download for Ollama model: {model_name}") + subprocess.run(["ollama", "pull", model_name], check=True) + logger.info(f"Ollama model {model_name} downloaded successfully.") + + return { + "model_name": model_name, + "source": "ollama", + "download_path": model_downloaded_path, + "success": True + } + + except subprocess.CalledProcessError as e: + logger.error(f"Failed to download Ollama model {model_name}: {str(e)}") + raise RuntimeError(f"Failed to download Ollama model: {str(e)}") + finally: + if process is not None: + logger.info("Stopping ollama server") + process.terminate() + + def get_download_tasks(self, model_name: str, **kwargs) -> List[DownloadTask]: + """ + Get list of download tasks for a model. + Ollama does not support task-based downloading. + """ + raise NotImplementedError("Ollama plugin does not support task-based downloading") + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs) -> str: + """ + Download a single task file. + Ollama does not support task-based downloading. + """ + raise NotImplementedError("Ollama plugin does not support task-based downloading") + + def post_process(self, model_name: str, output_dir: str, downloaded_paths: List[str], **kwargs) -> Dict[str, Any]: + """ + Post-process the downloaded files. + For Ollama, this is usually handled by the download process directly. + """ + return { + "model_name": model_name, + "source": "ollama", + "download_path": output_dir, + "success": True + } \ No newline at end of file diff --git a/microservices/model-download/src/plugins/openvino_plugin.py b/microservices/model-download/src/plugins/openvino_plugin.py new file mode 100644 index 000000000..2fcc70c50 --- /dev/null +++ b/microservices/model-download/src/plugins/openvino_plugin.py @@ -0,0 +1,265 @@ +import os +import subprocess +from typing import Dict, Any, Optional, List +from pathlib import Path +from src.api.models import ModelPrecision, DeviceType +from src.core.interfaces import ModelDownloadPlugin, DownloadTask +from src.utils.logging import logger + + +class OpenVINOConverter(ModelDownloadPlugin): + """ + Plugin for converting models to OpenVINO format for deployment with OpenVINO Model Server (OVMS). + Supports converting models from various sources to optimized OpenVINO IR format. + """ + + @property + def plugin_name(self) -> str: + return "openvino" + + @property + def plugin_type(self) -> str: + return "converter" # This is a converter plugin, not a downloader + + def can_handle(self, model_name: str, hub: str, **kwargs) -> bool: + # Check if the hub is openvino or if is_ovms is True + return hub.lower() == "huggingface" or kwargs.get("is_ovms", False) + + def convert(self, model_name: str, output_dir: str, hf_token: str, progress_callback=None, **kwargs) -> Dict[str, Any]: + """ + Convert a model to OpenVINO Model Server (OVMS) format. + This is the main conversion method expected by the model manager. + """ + # Extract parameters from the new payload structure + # Handle both direct parameters and nested config + config = kwargs.get("config", {}) + logger.info(f"Payload {model_name}, {output_dir}, {kwargs}") + logger.info(f"Conversion config: {kwargs.get('config', {})}") + # Extract parameters with fallbacks to maintain backward compatibility + weight_format = config.get("precision", kwargs.get("weight_format", "fp16")) + huggingface_token = hf_token + model_type = kwargs.get("type", kwargs.get("model_type", "llm")) + target_device = config.get("device", kwargs.get("target_device", "CPU")) + cache_size = config.get("cache", kwargs.get("cache_size")) + + try: + # Perform the conversion + result = self.convert_to_ovms_format( + weight_format=weight_format, + huggingface_token=huggingface_token, + model_type=model_type, + target_device=target_device, + model_directory=output_dir, + cache_size=cache_size, + model_name=model_name + ) + + return { + "model_name": model_name, + "source": "openvino", + "conversion_path": output_dir, + "is_ovms": True, + "config": { + "precision": weight_format, + "device": target_device, + "cache": cache_size if cache_size is not None else None + }, + "type": model_type, + "success": True, + "message": "Model successfully converted to OVMS format." + } + except Exception as e: + logger.error(f"Failed to convert model to OVMS format: {str(e)}") + raise RuntimeError(f"Failed to convert model to OVMS format: {str(e)}") + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs) -> Dict[str, Any]: + """ + This plugin is a converter, not a downloader, but implementing this method for compatibility. + Raises NotImplementedError as this plugin does not support direct downloads. + """ + raise NotImplementedError("OpenVINO plugin is a converter, not a downloader. Use the convert method instead.") + + def convert_to_ovms_format( + self, + model_name: str, + weight_format: str, + huggingface_token: Optional[str], + model_type: str, + target_device: str, + model_directory: str, + cache_size: Optional[int] = None, + ): + """ + Convert a downloaded model to OpenVINO Model Server (OVMS) format. + + Args: + model_name (str): The name of the Hugging Face model to download. + weight_format (str): The weight format for the exported model (e.g., "int4", "fp16"). + huggingface_token (str): The Hugging Face API token for authentication. + model_type (str): The type of the model (e.g., "llm", "embeddings", "rerank"). + target_device (str): Target hardware device for optimization (e.g., "CPU", "GPU"). + model_directory (str): Directory to save the converted model. + cache_size (int, optional): Cache size for model optimization. + + Raises: + RuntimeError: If model type is invalid, authentication fails, or model conversion fails + """ + # Map model_type to export type + export_type_map = { + "llm": "text_generation", + "embeddings": "embeddings", + "rerank": "rerank" + } + + # Validate model_type + if model_type not in export_type_map: + raise RuntimeError( + f"Invalid model_type: {model_type}. Must be one of {list(export_type_map.keys())}." + ) + + export_type = export_type_map[model_type] + + # Validate that HF token is provided for OVMS conversion + if not huggingface_token: + raise RuntimeError( + "Hugging Face token is required for OVMS conversion" + ) + + # Step 1: Log in to Hugging Face + logger.info("Logging in to Hugging Face...") + result = subprocess.run(["huggingface-cli", "login", "--token", huggingface_token]) + if result.returncode != 0: + raise RuntimeError( + "Failed to authenticate with Hugging Face. Please check your token." + ) + #logger.info("Starting to install required packages...") + # Step 1: Create a virtual environment if it doesn't exist + #venv_path = os.path.join(os.getcwd(), ".venv") + #venv_python = os.path.join(venv_path, "bin", "python") + #venv_pip = os.path.join(venv_path, "bin", "pip") + + # Delete existing venv if it exists + # if os.path.exists(venv_path): + # logger.info(f"Removing existing virtual environment at {venv_path}...") + # try: + # subprocess.run(["rm", "-rf", venv_path], check=True) + # logger.info("Existing virtual environment removed successfully.") + # except subprocess.CalledProcessError as e: + # raise RuntimeError(f"Failed to remove existing virtual environment: {str(e)}") + + # Create new virtual environment + # logger.info(f"Creating fresh virtual environment at {venv_path}...") + # try: + # subprocess.run(["python3", "-m", "venv", venv_path], check=True) + # logger.info("Virtual environment created successfully.") + # except subprocess.CalledProcessError as e: + # raise RuntimeError(f"Failed to create virtual environment: {str(e)}") + + # Step 2: Install dependencies in the virtual environment + # logger.info("Installing required packages in virtual environment...") + # try: + # requirements_url = "https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/export_models/requirements.txt" + # subprocess.run( + # ["pip3", "install","-r", requirements_url], + # check=True, + # text=True, + # capture_output=True + # ) + + # logger.info("Dependencies installed successfully.") + # except subprocess.CalledProcessError as e: + # raise RuntimeError(f"Failed to install dependencies: {str(e)}") + + # Step 3: Download the export_model.py script + logger.info("Checking for export_model.py script...") + export_script_url = "https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/export_models/export_model.py" + + #export_script_path = os.path.join(model_directory, "export_model.py") + if not os.path.exists("export_model.py"): + logger.info(f"Downloading export_model.py script...") + try: + #os.makedirs(os.path.dirname(export_script_path), exist_ok=True) + subprocess.run(["curl", export_script_url, "-o", "export_model.py"], check=True) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"Failed to download export script: {str(e)}") + else: + logger.info("export_model.py already exists, skipping download.") + + # Step 4: Export the model using the virtual environment's Python + logger.info(f"Exporting model: {model_name} with weight format: {weight_format} and export type: {export_type}...") + + # Ensure models directory exists + os.makedirs(model_directory, exist_ok=True) + + # Build command with Python from the virtual environment + command = [ + "python3", "export_model.py", export_type, + "--source_model", model_name, + "--weight-format", weight_format, + "--config_file_path", f"{model_directory}/config.json", + "--model_repository_path", model_directory, + "--target_device", target_device + ] + + # command_bash = f""" + # curl -o export_model.py https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/export_models/export_model.py && \ + # python3 export_model.py {export_type} --source_model {model_name} --weight-format {weight_format} --config_file_path {model_directory}/config.json --model_repository_path {model_directory} --target_device {target_device} + # """ + # Add optional parameters if provided + if export_type == "text_generation" and cache_size is not None: + command += ["--cache_size", cache_size] + + logger.info(f"Executing command with virtual environment: {command}") + try: + result = subprocess.run(command, check=True, text=True) + logger.info(f"Model conversion output: {result.stdout}") + if result.stderr: + logger.warning(f"Model conversion warnings/errors: {result.stderr}") + except subprocess.CalledProcessError as e: + raise RuntimeError( + f"Model conversion failed: {str(e)}. Check if the model is compatible with the specified format and device." + ) + + logger.info(f"Model successfully converted to OVMS format and saved to {model_directory}") + return {"message": f"Model successfully downloaded, converted, and prepared for OVMS deployment as {export_type}."} + + def get_download_tasks(self, model_name: str, **kwargs) -> List[DownloadTask]: + """ + Get list of download tasks for a model. + OpenVINO converter does not support task-based downloading. + """ + raise NotImplementedError("OpenVINO converter does not support task-based downloading") + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs) -> str: + """ + Download a single task file. + OpenVINO converter does not support task-based downloading. + """ + raise NotImplementedError("OpenVINO converter does not support task-based downloading") + + def post_process(self, model_name: str, output_dir: str, downloaded_paths: List[str], **kwargs) -> Dict[str, Any]: + """ + Post-process the converted files. + For OpenVINO conversion, this is handled by the download/convert method directly. + """ + # Extract parameters to maintain consistent response structure + config = kwargs.get("config", {}) + weight_format = config.get("precision", kwargs.get("weight_format", "fp16")) + model_type = kwargs.get("type", kwargs.get("model_type", "llm")) + target_device = config.get("device", kwargs.get("target_device", "CPU")) + cache_size = config.get("cache", kwargs.get("cache_size")) + + return { + "model_name": model_name, + "source": "openvino", + "conversion_path": output_dir, + "is_ovms": True, + "config": { + "precision": weight_format, + "device": target_device, + "cache": cache_size if cache_size is not None else None + }, + "type": model_type, + "success": True, + "message": "Model conversion completed successfully." + } diff --git a/microservices/model-download/src/plugins/ultralytics_plugin.py b/microservices/model-download/src/plugins/ultralytics_plugin.py new file mode 100644 index 000000000..10ac12c88 --- /dev/null +++ b/microservices/model-download/src/plugins/ultralytics_plugin.py @@ -0,0 +1,175 @@ +import os +import sys +import subprocess +from pathlib import Path +from typing import Dict, Any, Optional, List + +from src.core.interfaces import ModelDownloadPlugin, DownloadTask +from src.utils.logging import logger + +class UltralyticsDownloader(ModelDownloadPlugin): + """Plugin for downloading Ultralytics models""" + + @property + def plugin_name(self) -> str: + return "ultralytics" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, hub: str, **kwargs) -> bool: + """Check if this plugin can handle the given model""" + # Case-insensitive check for the hub name + if hub.lower() == "ultralytics": + return True + + # Check if the model is in the list of supported models + try: + supported_models = self.get_supported_models() + #model_without_prefix = model_name.split(":")[-1] if ":" in model_name else model_name + return model_name in supported_models or model_name == "all" + except: + return False + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs) -> Dict[str, Any]: + """Download the model using the bash script""" + # Remove prefix if present + #model_without_prefix = model_name.split(":")[-1] if ":" in model_name else model_name + + # Extract quantization from kwargs + quantize = kwargs.get("quantize", "") + + # Call the download script + return_code = self._call_bash_script(model=model_name, quantize=quantize, models_path=output_dir) + + if return_code != 0: + raise RuntimeError(f"Failed to download Ultralytics model {model_name}") + + return { + "model_name": model_name, + "source": "ultralytics", + "download_path": output_dir, + "return_code": return_code + } + + def get_supported_models(self) -> List[str]: + """Get list of supported models from the bash script""" + script_path = Path(__file__).parent.parent.parent / "scripts" / "download_models_ultralytics.sh" + + if not script_path.exists(): + raise FileNotFoundError(f"Bash script not found at {script_path}") + + with open(script_path, 'r') as f: + script_content = f.read() + + # Extract SUPPORTED_MODELS section + start = script_content.find("SUPPORTED_MODELS=(") + end = script_content.find(")", start) + models_section = script_content[start:end] + + # Parse models + models = [] + for line in models_section.split('\n'): + line = line.strip() + if line.startswith('"') and line.endswith('"'): + models.append(line.strip('"')) + + return models + + def get_supported_quantization_datasets(self) -> Dict[str, str]: + """Get dict of supported quantization datasets from the bash script""" + script_path = Path(__file__).parent.parent.parent / "scripts" / "download_models_ultralytics.sh" + + if not script_path.exists(): + raise FileNotFoundError(f"Bash script not found at {script_path}") + + with open(script_path, 'r') as f: + script_content = f.read() + + # Extract SUPPORTED_QUANTIZATION_DATASETS section + start = script_content.find("SUPPORTED_QUANTIZATION_DATASETS=(") + end = script_content.find(")", start) + datasets_section = script_content[start:end] + + # Parse datasets + datasets = {} + for line in datasets_section.split('\n'): + line = line.strip() + if '[' in line and ']=' in line: + parts = line.split(']=') + key = parts[0].strip('[" ') + value = parts[1].strip(' "') + if key and value: + datasets[key] = value + + return datasets + + def _call_bash_script(self, model: str = "all", quantize: str = "", models_path: str = "") -> int: + """Call the download_models_ultralytics.sh bash script with arguments""" + # Find script path relative to this file + script_path = str(Path(__file__).parent.parent.parent / "scripts" / "download_models_ultralytics.sh") + + if not os.path.exists(script_path): + raise FileNotFoundError(f"Bash script not found at {script_path}") + + cmd = ["bash", str(script_path), model] + if quantize: + cmd.append(quantize) + + logger.info(f"Executing: {' '.join(cmd)}") + + # Prepare environment with MODELS_PATH + env = os.environ.copy() + env['MODELS_PATH'] = models_path + logger.info(f"Setting MODELS_PATH to {models_path}") + # Execute the bash script and capture output + logger.info(f"Starting download for Ultralytics model: {env}") + logger.info("Command to be executed: " + ' '.join(cmd)) + process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + universal_newlines=True, + text=True, + env=env + ) + + # Stream output in real-time + while True: + stdout_line = process.stdout.readline() if process.stdout else "" + stderr_line = process.stderr.readline() if process.stderr else "" + + if stdout_line: + logger.info(stdout_line.strip()) + if stderr_line: + logger.error(stderr_line.strip()) + + if not stdout_line and not stderr_line and process.poll() is not None: + break + + return_code = process.poll() + if return_code != 0: + logger.error(f"Script execution failed with return code {return_code}") + + return return_code + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs) -> str: + """ + Download a specific file for Ultralytics models. + Note: This method is required for parallel downloading but Ultralytics typically uses + a single script download rather than per-file downloads. + """ + raise NotImplementedError("Ultralytics plugin does not support individual file downloads") + + def post_process(self, model_name: str, output_dir: str, downloaded_paths: List[str], **kwargs) -> Dict[str, Any]: + """ + Post-process the downloaded files. + For Ultralytics, this is usually handled by the download script directly. + """ + return { + "model_name": model_name, + "source": "ultralytics", + "download_path": output_dir, + "success": True + } \ No newline at end of file diff --git a/microservices/model-download/src/utils/__init__.py b/microservices/model-download/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/microservices/model-download/src/utils/helper.py b/microservices/model-download/src/utils/helper.py new file mode 100644 index 000000000..64b246ee1 --- /dev/null +++ b/microservices/model-download/src/utils/helper.py @@ -0,0 +1,19 @@ +import os +import shutil +from .logging import logger + +def cleanup_model_directory(model_dir_path: str): + subdirs = [ + os.path.join(model_dir_path, d) + for d in os.listdir(model_dir_path) + if os.path.isdir(os.path.join(model_dir_path, d)) + ] + if not os.listdir(model_dir_path) or all(not os.listdir(d) for d in subdirs): + try: + logger.warning( + f"No files found in the directory {model_dir_path}. Removing empty directory." + ) + shutil.rmtree(model_dir_path) + + except OSError as e: + logger.error(f"Failed to remove empty directory {model_dir_path}: {str(e)}") diff --git a/microservices/model-download/src/utils/logging.py b/microservices/model-download/src/utils/logging.py new file mode 100644 index 000000000..b878ec152 --- /dev/null +++ b/microservices/model-download/src/utils/logging.py @@ -0,0 +1,64 @@ +import structlog +import sys + +class Colors: + RESET = "\033[0m" + RED = "\033[31m" + GREEN = "\033[32m" + YELLOW = "\033[33m" + BLUE = "\033[34m" + MAGENTA = "\033[35m" + CYAN = "\033[36m" + WHITE = "\033[37m" + BOLD = "\033[1m" + +# Only use colors when output is to a terminal +USE_COLORS = sys.stdout.isatty() + +def get_colored_message(message, color): + """Apply color to message if output is to a terminal""" + if USE_COLORS: + return f"{color}{message}{Colors.RESET}" + return message + +def log_info(message): + """Log info messages in green""" + if not USE_COLORS or not structlog.is_configured(): + logger.info(message) + else: + logger.info(get_colored_message(message, Colors.GREEN)) + +def log_error(message): + """Log error messages in red""" + if not USE_COLORS or not structlog.is_configured(): + logger.error(message) + else: + logger.error(get_colored_message(message, Colors.RED)) + +def log_warning(message): + """Log warning messages in yellow""" + if not USE_COLORS or not structlog.is_configured(): + logger.warning(message) + else: + logger.warning(get_colored_message(message, Colors.YELLOW)) + +# Configure structlog +processors = [ + structlog.processors.TimeStamper(fmt="iso"), + lambda _, __, event_dict: dict(event_dict, service_name="model_download"), +] + +if USE_COLORS and sys.stdout.isatty(): + # Use ConsoleRenderer with colors for terminal output + processors.append(structlog.dev.ConsoleRenderer(colors=True)) +else: + # Use JSONRenderer for non-terminal output or when colors are disabled + processors.append(structlog.processors.JSONRenderer()) + +structlog.configure( + processors=processors, + cache_logger_on_first_use=True, # Improve performance +) + +# Create logger +logger = structlog.get_logger() diff --git a/microservices/model-download/tests/__init__.py b/microservices/model-download/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/microservices/model-download/tests/conftest.py b/microservices/model-download/tests/conftest.py new file mode 100644 index 000000000..89c9542dd --- /dev/null +++ b/microservices/model-download/tests/conftest.py @@ -0,0 +1,193 @@ +# Keep track of test paths +import os +import pytest +from fastapi.testclient import TestClient +from app.main import app + +# Base path for all test files +TEST_BASE_PATH = os.path.join(os.path.dirname(__file__), "test_data") + +@pytest.fixture(scope="session", autouse=True) +def setup_test_paths(): + """Create and clean up test directories""" + # Create test directories if they don't exist + os.makedirs(TEST_BASE_PATH, exist_ok=True) + yield + +@pytest.fixture +def test_model_path(): + """Fixture to provide model path within test directory""" + path = os.path.join(TEST_BASE_PATH, "models") + os.makedirs(path, exist_ok=True) + return path + +@pytest.fixture +def test_client(): + """TestClient fixture for FastAPI application""" + return TestClient(app) + +@pytest.fixture +def mock_hf_token(): + """Mock Hugging Face token""" + return "mock_hf_token" + +@pytest.fixture +def single_model_request(test_model_path): + """Fixture for a valid single model request with test path""" + return { + "models": [ + { + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": True, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + } + ], + "parallel_downloads": False + } + +@pytest.fixture +def multi_model_request(): + """Fixture for a valid multi-model request""" + return { + "models": [ + { + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": True, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + }, + { + "name": "BAAI/bge-small-en-v1.5", + "hub": "huggingface", + "type": "embeddings", + "is_ovms": True, + "config": { + "precision": "fp16", + "device": "GPU", + "cache_size": 20 + } + } + ], + "parallel_downloads": True + } + +@pytest.fixture +def invalid_model_requests(): + """Fixture for various invalid request scenarios""" + return { + "empty_models": {"models": []}, + "missing_name": { + "models": [{"hub": "huggingface", "type": "llm", "is_ovms": True}] + }, + "invalid_type": { + "models": [{ + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "invalid", + "is_ovms": True + }] + }, + "invalid_config": { + "models": [{ + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": True, + "config": { + "precision": "invalid", + "device": "invalid", + "cache_size": -1 + } + }] + }, + "invalid_revision": { + "models": [{ + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "revision": 123, # Should be string + "is_ovms": True + }] + } + } + +@pytest.fixture +def vlm_compression_request(): + """Fixture for a valid VLM compression request""" + return { + "model_name": "microsoft/Phi-3.5-mini-instruct", + "weight_format": "int8", + "hf_token": "test_hf_token", + "model_path": "/test/model/path" + } + +@pytest.fixture +def vlm_compression_request_no_token(): + """Fixture for VLM compression request without HF token""" + return { + "model_name": "microsoft/Phi-3.5-mini-instruct", + "weight_format": "fp16", + "hf_token": None, + "model_path": "/test/model/path" + } + +@pytest.fixture +def vlm_model_request(test_model_path): + """Fixture for a valid VLM model request""" + return { + "models": [ + { + "name": "microsoft/Phi-3.5-mini-instruct", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + } + ], + "parallel_downloads": False + } + +@pytest.fixture +def multi_vlm_model_request(): + """Fixture for multiple VLM models request""" + return { + "models": [ + { + "name": "Qwen/Qwen2.5-VL-7B-Instruct", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + }, + { + "name": "microsoft/Phi-3.5-vision-instruct", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False, + "config": { + "precision": "fp16", + "device": "GPU", + "cache_size": 20 + } + } + ], + "parallel_downloads": True + } diff --git a/microservices/model-download/tests/unit/test_convert_ovms.py b/microservices/model-download/tests/unit/test_convert_ovms.py new file mode 100644 index 000000000..5cbc99321 --- /dev/null +++ b/microservices/model-download/tests/unit/test_convert_ovms.py @@ -0,0 +1,268 @@ +import os +import pytest +from unittest.mock import patch, MagicMock +from fastapi import HTTPException +from app.main import convert_to_ovms_format + +class TestConvertToOvmsFormat: + """Test the convert_to_ovms_format function""" + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_successful_conversion(self, mock_makedirs, mock_exists, mock_subprocess): + """Test successful model conversion to OVMS format""" + # Setup mocks + mock_exists.return_value = True # Script already exists + mock_subprocess.return_value = MagicMock(returncode=0) + + # Call function + result = convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir", + cache_size=10 + ) + + # Verify + assert "successfully downloaded, converted, and prepared for OVMS deployment" in result["message"] + mock_makedirs.assert_called_with("/test/model/dir", exist_ok=True) + assert mock_subprocess.call_count >= 2 # Login + export commands + + def test_invalid_model_type(self): + """Test conversion with invalid model type""" + with pytest.raises(HTTPException) as exc_info: + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="invalid_type", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert exc_info.value.status_code == 400 + assert "Invalid model_type: invalid_type" in exc_info.value.detail + + def test_missing_huggingface_token(self): + """Test conversion without Hugging Face token""" + with pytest.raises(HTTPException) as exc_info: + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token=None, + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert exc_info.value.status_code == 401 + assert "Hugging Face token is required" in exc_info.value.detail + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_huggingface_login_failure(self, mock_makedirs, mock_exists, mock_subprocess): + """Test handling of Hugging Face login failure""" + mock_exists.return_value = True + mock_subprocess.return_value = MagicMock(returncode=1) # Login fails + + with pytest.raises(HTTPException) as exc_info: + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="invalid_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert exc_info.value.status_code == 401 + assert "Failed to authenticate with Hugging Face" in exc_info.value.detail + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_script_download_failure(self, mock_makedirs, mock_exists, mock_subprocess): + """Test handling of export script download failure""" + mock_exists.return_value = False # Script doesn't exist + + def subprocess_side_effect(*args, **kwargs): + if "huggingface-cli" in args[0]: + return MagicMock(returncode=0) # Login succeeds + elif "curl" in args[0]: + from subprocess import CalledProcessError + raise CalledProcessError(1, "curl") # Download fails + return MagicMock(returncode=0) + + mock_subprocess.side_effect = subprocess_side_effect + + with pytest.raises(HTTPException) as exc_info: + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert exc_info.value.status_code == 500 + assert "Failed to download export script" in exc_info.value.detail + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_model_export_failure(self, mock_makedirs, mock_exists, mock_subprocess): + """Test handling of model export failure""" + mock_exists.return_value = True + + def subprocess_side_effect(*args, **kwargs): + if "huggingface-cli" in args[0]: + return MagicMock(returncode=0) # Login succeeds + elif "python3" in args[0] and "export_model.py" in args[0]: + from subprocess import CalledProcessError + raise CalledProcessError(1, "export_model.py") # Export fails + return MagicMock(returncode=0) + + mock_subprocess.side_effect = subprocess_side_effect + + with pytest.raises(HTTPException) as exc_info: + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert exc_info.value.status_code == 500 + assert "Model conversion failed" in exc_info.value.detail + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_different_model_types(self, mock_makedirs, mock_exists, mock_subprocess): + """Test conversion with different valid model types""" + mock_exists.return_value = True + mock_subprocess.return_value = MagicMock(returncode=0) + + model_types = ["llm", "embeddings", "rerank"] + + for model_type in model_types: + result = convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type=model_type, + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert "successfully downloaded, converted, and prepared for OVMS deployment" in result["message"] + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_script_download_when_missing(self, mock_makedirs, mock_exists, mock_subprocess): + """Test that script is downloaded when missing""" + mock_exists.return_value = False # Script doesn't exist + mock_subprocess.return_value = MagicMock(returncode=0) + + convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + # Verify curl command was called to download script + curl_calls = [call for call in mock_subprocess.call_args_list if "curl" in str(call)] + assert len(curl_calls) > 0 + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_conversion_with_cache_size(self, mock_makedirs, mock_exists, mock_subprocess): + """Test conversion with cache size parameter""" + mock_exists.return_value = True + mock_subprocess.return_value = MagicMock(returncode=0) + + result = convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="fp16", + huggingface_token="test_token", + model_type="llm", # Use text_generation type for cache_size + target_device="GPU", + model_directory="/test/model/dir", + cache_size=20 + ) + + assert "successfully downloaded, converted, and prepared for OVMS deployment" in result["message"] + + # Verify cache size was included in the command + export_calls = [call for call in mock_subprocess.call_args_list + if "export_model.py" in str(call)] + assert len(export_calls) > 0 + # Check that cache size argument is in the command + assert any("--cache_size" in str(call) and "20" in str(call) for call in export_calls) + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_different_weight_formats(self, mock_makedirs, mock_exists, mock_subprocess): + """Test conversion with different weight formats""" + mock_exists.return_value = True + mock_subprocess.return_value = MagicMock(returncode=0) + + weight_formats = ["int8", "fp16", "fp32"] + + for weight_format in weight_formats: + result = convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format=weight_format, + huggingface_token="test_token", + model_type="llm", + target_device="CPU", + model_directory="/test/model/dir" + ) + + assert "successfully downloaded, converted, and prepared for OVMS deployment" in result["message"] + + # Verify weight format was used in command + export_calls = [call for call in mock_subprocess.call_args_list + if "export_model.py" in str(call)] + assert any(weight_format in str(call) for call in export_calls) + + @patch("subprocess.run") + @patch("os.path.exists") + @patch("os.makedirs") + def test_different_target_devices(self, mock_makedirs, mock_exists, mock_subprocess): + """Test conversion with different target devices""" + mock_exists.return_value = True + mock_subprocess.return_value = MagicMock(returncode=0) + + devices = ["CPU", "GPU"] + + for device in devices: + result = convert_to_ovms_format( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + huggingface_token="test_token", + model_type="llm", + target_device=device, + model_directory="/test/model/dir" + ) + + assert "successfully downloaded, converted, and prepared for OVMS deployment" in result["message"] + + # Verify device was used in command + export_calls = [call for call in mock_subprocess.call_args_list + if "export_model.py" in str(call)] + assert any(device in str(call) for call in export_calls) diff --git a/microservices/model-download/tests/unit/test_model_download.py b/microservices/model-download/tests/unit/test_model_download.py new file mode 100644 index 000000000..682d10534 --- /dev/null +++ b/microservices/model-download/tests/unit/test_model_download.py @@ -0,0 +1,576 @@ +import os +from unittest.mock import patch, MagicMock, ANY +import pytest +from fastapi import HTTPException + +class TestHealth: + def test_health_check(self, test_client): + """Test health check endpoint""" + response = test_client.get("/health") + assert response.status_code == 200 + assert response.json() == {"status": "ok"} + +class TestAuthentication: + def test_model_download_missing_token(self, test_client): + """Test that API returns 401 when authorization token is missing""" + request = { + "models": [ + { + "name": "test-model", + "hub": "huggingface", + "type": "llm", + "is_ovms": False + } + ] + } + response = test_client.post("/models/download", json=request) + assert response.status_code == 401 + assert "Authorization token is required" in response.json()["detail"] + + def test_model_download_empty_token(self, test_client): + """Test that API returns 401 when authorization token is empty""" + request = { + "models": [ + { + "name": "test-model", + "hub": "huggingface", + "type": "llm", + "is_ovms": False + } + ] + } + response = test_client.post("/models/download", json=request, headers={"Authorization": ""}) + assert response.status_code == 401 + assert "Authorization token is required" in response.json()["detail"] + +class TestSingleModelDownload: + @patch("app.main.cleanup_model_directory") + @patch("app.main.snapshot_download") + @patch("app.main.convert_to_ovms_format") + def test_successful_single_model_download(self, mock_convert, mock_download, mock_cleanup, + test_client, mock_hf_token, single_model_request, test_model_path): + """Test successful single model download and conversion""" + mock_download.return_value = os.path.join(test_model_path, "model") + mock_convert.return_value = {"message": "Model converted successfully"} + + response = test_client.post( + "/models/download", + json=single_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert "Model downloaded successfully" in data["message"] + assert data["model_path"] == os.path.join(test_model_path, "model") + assert len(data["results"]) == 1 + assert data["results"][0]["status"] == "success" + + mock_download.assert_called_once() + mock_convert.assert_called_once() + + @patch("app.main.cleanup_model_directory") + @patch("app.main.snapshot_download") + def test_single_model_download_without_ovms(self, mock_download, mock_cleanup, test_client, mock_hf_token, test_model_path): + """Test single model download without OVMS conversion""" + mock_download.return_value = os.path.join(test_model_path, "model") + request = { + "models": [ + { + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": False + } + ] + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert data["results"][0]["is_ovms"] is False + mock_download.assert_called_once() + + @patch("app.main.snapshot_download") + def test_single_model_download_failure(self, mock_download, test_client, mock_hf_token): + """Test single model download failure handling""" + mock_download.side_effect = Exception("Download failed") + request = { + "models": [ + { + "name": "nonexistent-model", + "hub": "huggingface", + "type": "llm", + "is_ovms": False + } + ] + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 400 + assert "Download failed" in response.json()["detail"] + +class TestMultiModelDownload: + @patch("app.main.cleanup_model_directory") + @patch("app.main.snapshot_download") + @patch("app.main.convert_to_ovms_format") + def test_successful_multi_model_download(self, mock_convert, mock_download, mock_cleanup, + test_client, mock_hf_token, multi_model_request, test_model_path): + """Test successful multiple model download and conversion""" + mock_download.return_value = os.path.join(test_model_path, "model") + mock_convert.return_value = {"message": "Model converted successfully"} + + response = test_client.post( + "/models/download", + json=multi_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert len(data["results"]) == 2 + assert all(result["status"] == "success" for result in data["results"]) + assert mock_download.call_count == 2 + assert mock_convert.call_count == 2 + + @patch("app.main.cleanup_model_directory") + @patch("app.main.snapshot_download") + def test_partial_download_failure(self, mock_download, mock_cleanup, test_client, mock_hf_token): + """Test handling of partial failure in multiple model download""" + def mock_download_effect(repo_id, **kwargs): + if repo_id == "successful-model": + return "/mock/path/success" + raise Exception("Download failed for second model") + + mock_download.side_effect = mock_download_effect + request = { + "models": [ + {"name": "successful-model", "hub": "huggingface", "type": "llm", "is_ovms": False}, + {"name": "failing-model", "hub": "huggingface", "type": "llm", "is_ovms": False} + ], + "parallel_downloads": True + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert len(data["results"]) == 2 + assert any(result["status"] == "success" for result in data["results"]) + assert any(result["status"] == "error" for result in data["results"]) + + def test_parallel_processing(self, test_client, mock_hf_token): + """Test that parallel processing flag is respected""" + with patch("app.main.ThreadPoolExecutor") as mock_executor: + mock_instance = MagicMock() + mock_executor.return_value.__enter__.return_value = mock_instance + + request = { + "models": [ + {"name": "model1", "hub": "huggingface", "type": "llm", "is_ovms": False}, + {"name": "model2", "hub": "huggingface", "type": "llm", "is_ovms": False} + ], + "parallel_downloads": True + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + mock_executor.assert_called_once_with(max_workers=2) + +class TestValidation: + @pytest.mark.parametrize("scenario", [ + "empty_models", + "missing_name", + "invalid_type", + "invalid_config", + "invalid_revision" + ]) + def test_invalid_requests(self, test_client, mock_hf_token, invalid_model_requests, scenario): + """Test various invalid request scenarios""" + response = test_client.post( + "/models/download", + json=invalid_model_requests[scenario], + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + if scenario == "empty_models": + # Empty models list should succeed but with no downloads + assert response.status_code == 200 + assert response.json()["message"] == "Model download completed" + assert len(response.json()["results"]) == 0 + else: + assert response.status_code in [400, 422] + + def test_unsupported_hub(self, test_client, mock_hf_token): + """Test unsupported model hub""" + request = { + "models": [ + { + "name": "test-model", + "hub": "unsupported_hub", + "type": "llm", + "is_ovms": False + } + ] + } + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + assert response.status_code == 400 + assert "Unsupported model hub" in response.json()["detail"] + + def test_ollama_hub_no_auth_required(self, test_client): + """Test that Ollama models don't require authentication""" + request = { + "models": [ + { + "name": "test-model", + "hub": "ollama", + "type": "llm", + "is_ovms": False + } + ] + } + with patch("app.main.download_ollama_model") as mock_download: + mock_download.return_value = { + "status": "success", + "model_name": "test-model", + "model_path": "/test/path", + "error": None, + "is_ovms": False + } + response = test_client.post("/models/download", json=request) + assert response.status_code == 200 + + def test_request_validation_error(self, test_client, mock_hf_token): + """Test pydantic validation errors""" + invalid_request = { + "models": [ + { + "name": 123, # Should be string + "hub": "huggingface", + "type": "llm" + } + ] + } + response = test_client.post( + "/models/download", + json=invalid_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + # The API converts integers to strings, so this will result in a 400 error + # during processing, not a validation error + assert response.status_code == 400 + + def test_unexpected_exception_handling(self, test_client, mock_hf_token): + """Test handling of unexpected exceptions""" + with patch("app.main.download_and_process_model") as mock_download: + mock_download.side_effect = RuntimeError("Unexpected error") + request = { + "models": [ + { + "name": "test-model", + "hub": "huggingface", + "type": "llm", + "is_ovms": False + } + ] + } + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + assert response.status_code == 500 + assert "Failed to execute model downloads: Unexpected error" in response.json()["detail"] + + @patch("app.main.cleanup_model_directory") + @patch("app.main.convert_to_ovms_format") + @patch("app.main.snapshot_download") + def test_ovms_config_override(self, mock_download, mock_convert, mock_cleanup, + test_client, mock_hf_token): + """Test OVMS configuration override behavior""" + mock_download.return_value = "/mock/path/model" + mock_convert.return_value = {"message": "Model converted successfully"} + mock_cleanup.return_value = None + request = { + "models": [ + { + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": True, + "config": { + "precision": "fp16", + "device": "GPU", + "cache_size": 20 + } + } + ] + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + mock_convert.assert_called_once() + call_args = mock_convert.call_args[1] + assert call_args["weight_format"] == "fp16" + assert call_args["target_device"] == "GPU" + assert call_args["cache_size"] == 20 + +class TestEnvironment: + @patch("app.main.cleanup_model_directory") + @patch("app.main.convert_to_ovms_format") + @patch("app.main.snapshot_download") + def test_environment_variable_setting(self, mock_download, mock_convert, mock_cleanup, + test_client, mock_hf_token, single_model_request): + """Test that functions are called with correct parameters""" + mock_download.return_value = "/mock/path/model" + mock_convert.return_value = {"message": "Model converted successfully"} + + response = test_client.post( + "/models/download", + json=single_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + # Verify the token was passed to the functions correctly + mock_download.assert_called_once() + mock_convert.assert_called_once() + + @patch("app.main.cleanup_model_directory") + @patch("app.main.convert_to_ovms_format") + @patch("app.main.snapshot_download") + @patch("app.main.os.makedirs") + def test_directory_creation(self, mock_makedirs, mock_download, mock_convert, mock_cleanup, + test_client, mock_hf_token, single_model_request): + """Test that necessary directories are created""" + mock_download.return_value = "/mock/path/model" + mock_convert.return_value = {"message": "Model converted successfully"} + + response = test_client.post( + "/models/download", + json=single_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + mock_makedirs.assert_called_with(ANY, exist_ok=True) + + +class TestVLMIntegration: + """Test VLM model integration in the main API""" + + @patch("app.main.cleanup_model_directory") + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_vlm_model_compression_success(self, mock_download, mock_vlm_compress, mock_cleanup, + test_client, mock_hf_token, vlm_model_request, test_model_path): + """Test successful VLM model compression through API""" + mock_download.return_value = os.path.join(test_model_path, "vlm_model") + mock_vlm_compress.return_value = {"status": "success", "output": "VLM compression completed"} + + response = test_client.post( + "/models/download", + json=vlm_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert "Model downloaded successfully" in data["message"] + assert len(data["results"]) == 1 + assert data["results"][0]["status"] == "success" + + # Verify VLM compression was called + mock_vlm_compress.assert_called_once() + call_args = mock_vlm_compress.call_args[1]["req"] + assert call_args.model_name == "microsoft/Phi-3.5-mini-instruct" + assert call_args.weight_format == "int8" + assert call_args.hf_token == mock_hf_token + + @patch("app.main.cleanup_model_directory") + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_vlm_model_compression_failure(self, mock_download, mock_vlm_compress, mock_cleanup, + test_client, mock_hf_token, vlm_model_request, test_model_path): + """Test VLM model compression failure handling""" + mock_download.return_value = os.path.join(test_model_path, "vlm_model") + mock_vlm_compress.side_effect = HTTPException(status_code=500, detail="VLM compression failed") + mock_cleanup.return_value = None + + response = test_client.post( + "/models/download", + json=vlm_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + # The API should handle the VLM compression error gracefully + assert response.status_code == 400 + + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_multiple_vlm_models_compression(self, mock_download, mock_vlm_compress, + test_client, mock_hf_token, multi_vlm_model_request, test_model_path): + """Test compression of multiple VLM models""" + mock_download.return_value = test_model_path + mock_vlm_compress.return_value = {"status": "success", "output": "VLM compression completed"} + + response = test_client.post( + "/models/download", + json=multi_vlm_model_request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert len(data["results"]) == 2 + + # Verify VLM compression was called for each model + assert mock_vlm_compress.call_count == 2 + + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_vlm_model_with_different_weight_formats(self, mock_download, mock_vlm_compress, + test_client, mock_hf_token, test_model_path): + """Test VLM models with different weight formats""" + mock_download.return_value = test_model_path + mock_vlm_compress.return_value = {"status": "success", "output": "VLM compression completed"} + + weight_formats = ["int8", "fp16", "fp32"] + + for weight_format in weight_formats: + request = { + "models": [ + { + "name": "test/vlm-model", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False, + "config": { + "precision": weight_format, + "device": "CPU", + "cache_size": 10 + } + } + ], + "parallel_downloads": False + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + + # Verify correct weight format was used + call_args = mock_vlm_compress.call_args[1]["req"] + assert call_args.weight_format == weight_format + + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_vlm_model_without_config(self, mock_download, mock_vlm_compress, + test_client, mock_hf_token, test_model_path): + """Test VLM model without config (should use default weight format)""" + mock_download.return_value = test_model_path + mock_vlm_compress.return_value = {"status": "success", "output": "VLM compression completed"} + + request = { + "models": [ + { + "name": "test/vlm-model", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False + } + ], + "parallel_downloads": False + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + + # Verify default weight format was used + call_args = mock_vlm_compress.call_args[1]["req"] + assert call_args.weight_format == "int8" # Default format + + @patch("app.main.vlm_compress_model") + @patch("app.main.snapshot_download") + def test_mixed_model_types_with_vlm(self, mock_download, mock_vlm_compress, + test_client, mock_hf_token, test_model_path): + """Test mixed model types including VLM models""" + mock_download.return_value = test_model_path + mock_vlm_compress.return_value = {"status": "success", "output": "VLM compression completed"} + + request = { + "models": [ + { + "name": "Intel/neural-chat-7b-v3-3", + "hub": "huggingface", + "type": "llm", + "is_ovms": True, + "config": { + "precision": "int8", + "device": "CPU", + "cache_size": 10 + } + }, + { + "name": "test/vlm-model", + "hub": "huggingface", + "type": "vlm", + "is_ovms": False, + "config": { + "precision": "fp16", + "device": "GPU", + "cache_size": 20 + } + } + ], + "parallel_downloads": False + } + + response = test_client.post( + "/models/download", + json=request, + headers={"Authorization": f"Bearer {mock_hf_token}"} + ) + + assert response.status_code == 200 + data = response.json() + assert len(data["results"]) == 2 + + # Verify VLM compression was called only once (for VLM model) + mock_vlm_compress.assert_called_once() diff --git a/microservices/model-download/tests/unit/test_model_manager.py b/microservices/model-download/tests/unit/test_model_manager.py new file mode 100644 index 000000000..fcab06fc5 --- /dev/null +++ b/microservices/model-download/tests/unit/test_model_manager.py @@ -0,0 +1,319 @@ +import os +import pytest +import tempfile +import time +from unittest.mock import MagicMock, patch + +from src.core.model_manager import ModelManager +from src.core.plugin_registry import PluginRegistry +from src.core.interfaces import ModelDownloadPlugin, DownloadTask + +# Mock plugins for testing +class MockSuccessPlugin(ModelDownloadPlugin): + @property + def plugin_name(self) -> str: + return "mock_success" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, **kwargs) -> bool: + return model_name.startswith("mock:") + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs): + # Simulate download with progress updates + if progress_callback: + for i in range(1, 6): + progress_callback(model_name, i, 5) + time.sleep(0.01) # Small delay to simulate work + + # Create a dummy file to simulate download + with open(os.path.join(output_dir, "mock_model.bin"), "w") as f: + f.write("mock model content") + + return { + "model_name": model_name, + "source": "mock", + "download_path": output_dir + } + +class MockParallelPlugin(ModelDownloadPlugin): + @property + def plugin_name(self) -> str: + return "mock_parallel" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, **kwargs) -> bool: + return model_name.startswith("parallel:") + + def get_download_tasks(self, model_name: str, **kwargs): + # Create 5 mock tasks + tasks = [] + for i in range(5): + task = DownloadTask( + model_name=model_name, + file_path=f"file_{i}.bin", + size=100, + metadata={"index": i} + ) + tasks.append(task) + return tasks + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs): + # Simulate individual task download + time.sleep(0.01) # Small delay + + # Create the file + file_path = os.path.join(output_dir, task.file_path) + with open(file_path, "w") as f: + f.write(f"content for {task.file_path}") + + return file_path + + def post_process(self, model_name: str, output_dir: str, downloaded_files: list, **kwargs): + return { + "model_name": model_name, + "source": "mock_parallel", + "download_path": output_dir, + "files": len(downloaded_files) + } + +class MockFailPlugin(ModelDownloadPlugin): + @property + def plugin_name(self) -> str: + return "mock_fail" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, **kwargs) -> bool: + return model_name.startswith("fail:") + + def download(self, model_name: str, output_dir: str, progress_callback=None, **kwargs): + # Simulate a failed download + raise ValueError("Simulated download failure") + +class MockParallelFailPlugin(ModelDownloadPlugin): + @property + def plugin_name(self) -> str: + return "mock_parallel_fail" + + @property + def plugin_type(self) -> str: + return "downloader" + + def can_handle(self, model_name: str, **kwargs) -> bool: + return model_name.startswith("parallel_fail:") + + def get_download_tasks(self, model_name: str, **kwargs): + return [ + DownloadTask(model_name, "success.bin", 100, {}), + DownloadTask(model_name, "fail.bin", 100, {}) + ] + + def download_task(self, task: DownloadTask, output_dir: str, **kwargs): + if task.file_path == "fail.bin": + raise ValueError(f"Simulated task failure: {task.file_path}") + + # Create the file for success case + file_path = os.path.join(output_dir, task.file_path) + with open(file_path, "w") as f: + f.write(f"content for {task.file_path}") + + return file_path + + +@pytest.fixture +def mock_registry(): + """Create a mock plugin registry with test plugins""" + registry = PluginRegistry() + + # Add our mock plugins + registry.register_plugin(MockSuccessPlugin()) + registry.register_plugin(MockParallelPlugin()) + registry.register_plugin(MockFailPlugin()) + registry.register_plugin(MockParallelFailPlugin()) + + return registry + +@pytest.fixture +def model_manager(mock_registry): + """Create a ModelManager instance with temp directory and mock registry""" + with tempfile.TemporaryDirectory() as temp_dir: + manager = ModelManager(mock_registry, default_dir=temp_dir) + yield manager + +class TestModelManager: + + def test_register_job(self, model_manager): + """Test job registration creates a job with the correct properties""" + job_id = model_manager.register_job("mock:model") + + # Verify job was created + assert job_id in model_manager._jobs + + job = model_manager._jobs[job_id] + assert job["model_name"] == "mock:model" + assert job["status"] == "queued" + assert "start_time" in job + assert "progress" in job + + # Test with custom output dir + custom_dir = "/tmp/custom_dir" + job_id2 = model_manager.register_job("mock:model2", output_dir=custom_dir) + assert model_manager._jobs[job_id2]["output_dir"] == custom_dir + + def test_update_progress(self, model_manager): + """Test progress updates are correctly tracked""" + job_id = model_manager.register_job("mock:model") + + # Update progress + model_manager.update_progress(job_id, 3, 10) + + # Verify progress was updated + progress = model_manager._jobs[job_id]["progress"] + assert progress["current"] == 3 + assert progress["total"] == 10 + assert progress["percentage"] == 30 # 3/10 * 100 = 30% + + # Test edge case with total=0 + model_manager.update_progress(job_id, 5, 0) + assert model_manager._jobs[job_id]["progress"]["percentage"] == 0 + + def test_download_model_success(self, model_manager): + """Test successful model download""" + result = model_manager.download_model("mock:test_model") + + # Verify successful download + assert result["status"] == "completed" + assert result["model_name"] == "mock:test_model" + + # Check that job record was updated + job_id = result["job_id"] + job = model_manager._jobs[job_id] + assert job["status"] == "completed" + assert "completion_time" in job + assert "result" in job + + # Verify file was created + model_path = os.path.join(job["output_dir"], "mock_model.bin") + assert os.path.exists(model_path) + + def test_parallel_download(self, model_manager): + """Test parallel download functionality""" + result = model_manager.download_model("parallel:test_model", use_parallel=True, max_workers=2) + + # Verify successful download + assert result["status"] == "completed" + + # Check that job record was updated + job_id = result["job_id"] + job = model_manager._jobs[job_id] + assert job["status"] == "completed" + + # Verify files were created (5 tasks) + for i in range(5): + file_path = os.path.join(job["output_dir"], f"file_{i}.bin") + assert os.path.exists(file_path) + + def test_download_failure(self, model_manager): + """Test handling of download failures""" + result = model_manager.download_model("fail:test_model") + + # Verify failure status + assert result["status"] == "failed" + assert "error" in result + + # Check job record + job_id = result["job_id"] + job = model_manager._jobs[job_id] + assert job["status"] == "failed" + assert "error" in job + assert "Simulated download failure" in job["error"] + + def test_parallel_download_failure(self, model_manager): + """Test handling of failures in parallel downloads""" + result = model_manager.download_model("parallel_fail:test_model", use_parallel=True) + + # Verify failure status + assert result["status"] == "failed" + assert "error" in result + + # Check job record + job_id = result["job_id"] + job = model_manager._jobs[job_id] + assert job["status"] == "failed" + assert "error" in job + assert "Simulated task failure" in job["error"] + + def test_get_job_status(self, model_manager): + """Test retrieving job status""" + # Create a job + job_id = model_manager.register_job("mock:model") + + # Get status + status = model_manager.get_job_status(job_id) + + # Verify correct status returned + assert status["id"] == job_id + assert status["status"] == "queued" + + # Test non-existent job + assert model_manager.get_job_status("non-existent-id") is None + + def test_list_jobs(self, model_manager): + """Test listing jobs with pagination""" + # Create multiple jobs + job_ids = [ + model_manager.register_job(f"mock:model_{i}") + for i in range(5) + ] + + # List all jobs + jobs = model_manager.list_jobs() + assert len(jobs) == 5 + + # Test pagination + jobs_paginated = model_manager.list_jobs(limit=2, offset=1) + assert len(jobs_paginated) == 2 + + def test_cancel_job(self, model_manager): + """Test job cancellation""" + # Create a job and start a long download to ensure we can cancel + with patch.object(MockSuccessPlugin, 'download', + side_effect=lambda *args, **kwargs: time.sleep(10000)): + + # Register job + job_id = model_manager.register_job("mock:model") + + # Update status to simulate download in progress + model_manager._jobs[job_id]["status"] = "downloading" + + # Cancel the job + result = model_manager.cancel_job(job_id) + assert result is True + + # Verify job was canceled + job = model_manager._jobs[job_id] + assert job["status"] == "canceled" + + # Test canceling completed job (should fail) + completed_job_id = model_manager.register_job("mock:completed") + model_manager._jobs[completed_job_id]["status"] = "completed" + assert model_manager.cancel_job(completed_job_id) is False + + # Test canceling non-existent job + assert model_manager.cancel_job("non-existent-id") is False + + def test_get_available_plugins(self, model_manager): + """Test retrieving available plugins""" + plugins = model_manager.get_available_plugins() + + # Verify we get the correct plugins + assert "downloader" in plugins + assert set(plugins["downloader"]) == {"mock_success", "mock_parallel", "mock_fail", "mock_parallel_fail"} \ No newline at end of file diff --git a/microservices/model-download/tests/unit/test_vlm_utils.py b/microservices/model-download/tests/unit/test_vlm_utils.py new file mode 100644 index 000000000..958727b8f --- /dev/null +++ b/microservices/model-download/tests/unit/test_vlm_utils.py @@ -0,0 +1,319 @@ +import os +import pytest +from unittest.mock import patch, MagicMock +from fastapi import HTTPException +from app.vlm_utils import vlm_compress_model, CompressionRequest + + +class TestCompressionRequest: + """Test cases for CompressionRequest model validation""" + + def test_compression_request_valid_data(self): + """Test CompressionRequest with valid data""" + req = CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + hf_token="hf_token_123", + model_path="/path/to/model" + ) + + assert req.model_name == "microsoft/Phi-3.5-mini-instruct" + assert req.weight_format == "int8" + assert req.hf_token == "hf_token_123" + assert req.model_path == "/path/to/model" + + def test_compression_request_without_token(self): + """Test CompressionRequest without HF token (optional field)""" + req = CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="fp16", + model_path="/path/to/model" + ) + + assert req.model_name == "microsoft/Phi-3.5-mini-instruct" + assert req.weight_format == "fp16" + assert req.hf_token is None + assert req.model_path == "/path/to/model" + + def test_compression_request_missing_required_fields(self): + """Test CompressionRequest with missing required fields""" + with pytest.raises(ValueError): + CompressionRequest( + weight_format="int8", + model_path="/path/to/model" + # Missing model_name + ) + + with pytest.raises(ValueError): + CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + model_path="/path/to/model" + # Missing weight_format + ) + + with pytest.raises(ValueError): + CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8" + # Missing model_path + ) + + +class TestVLMCompressModel: + """Test cases for vlm_compress_model function""" + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_success(self, mock_subprocess): + """Test successful VLM model compression""" + # Setup mock + mock_result = MagicMock() + mock_result.stdout = "Model compression completed successfully" + mock_subprocess.return_value = mock_result + + # Create request + req = CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + hf_token="hf_token_123", + model_path="/path/to/model" + ) + + # Call function + result = vlm_compress_model(req) + + # Assertions + assert result["status"] == "success" + assert result["output"] == "Model compression completed successfully" + + # Verify subprocess was called with correct arguments + mock_subprocess.assert_called_once() + call_args = mock_subprocess.call_args[0][0] + assert call_args[0] == "bash" + assert "vlm_compress_model.sh" in call_args[1] + assert call_args[2] == "microsoft/Phi-3.5-mini-instruct" + assert call_args[3] == "int8" + assert call_args[4] == "hf_token_123" + assert call_args[5] == "/path/to/model" + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_success_without_token(self, mock_subprocess): + """Test successful VLM model compression without HF token""" + # Setup mock + mock_result = MagicMock() + mock_result.stdout = "Model compression completed successfully" + mock_subprocess.return_value = mock_result + + # Create request without token + req = CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="fp16", + hf_token=None, + model_path="/path/to/model" + ) + + # Call function + result = vlm_compress_model(req) + + # Assertions + assert result["status"] == "success" + assert result["output"] == "Model compression completed successfully" + + # Verify subprocess was called with empty token + mock_subprocess.assert_called_once() + call_args = mock_subprocess.call_args[0][0] + assert call_args[4] == "" # Empty token + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_script_failure(self, mock_subprocess): + """Test VLM model compression when script fails""" + # Setup mock to raise CalledProcessError + from subprocess import CalledProcessError + mock_subprocess.side_effect = CalledProcessError( + returncode=1, + cmd="bash vlm_compress_model.sh", + stderr="Script execution failed" + ) + + # Create request + req = CompressionRequest( + model_name="microsoft/Phi-3.5-mini-instruct", + weight_format="int8", + hf_token="hf_token_123", + model_path="/path/to/model" + ) + + # Call function and expect HTTPException + with pytest.raises(HTTPException) as exc_info: + vlm_compress_model(req) + + assert exc_info.value.status_code == 500 + assert "Script execution failed" in str(exc_info.value.detail) + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_different_weight_formats(self, mock_subprocess): + """Test VLM model compression with different weight formats""" + mock_result = MagicMock() + mock_result.stdout = "Compression successful" + mock_subprocess.return_value = mock_result + + weight_formats = ["int8", "fp16", "fp32"] + + for weight_format in weight_formats: + req = CompressionRequest( + model_name="test/model", + weight_format=weight_format, + hf_token="token", + model_path="/test/path" + ) + + result = vlm_compress_model(req) + assert result["status"] == "success" + + # Verify correct weight format was passed + call_args = mock_subprocess.call_args[0][0] + assert call_args[3] == weight_format + + @patch('app.vlm_utils.subprocess.run') + @patch('app.vlm_utils.os.path.dirname') + @patch('app.vlm_utils.os.path.join') + def test_vlm_compress_model_script_path_construction(self, mock_join, mock_dirname, mock_subprocess): + """Test that the script path is constructed correctly""" + # Setup mocks + mock_dirname.return_value = "/app" + mock_join.return_value = "/app/../scripts/vlm_compress_model.sh" + + mock_result = MagicMock() + mock_result.stdout = "Success" + mock_subprocess.return_value = mock_result + + # Create request + req = CompressionRequest( + model_name="test/model", + weight_format="int8", + model_path="/test/path" + ) + + # Call function + vlm_compress_model(req) + + # Verify path construction + mock_dirname.assert_called_once() + mock_join.assert_called_once_with("/app", "../scripts/vlm_compress_model.sh") + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_subprocess_parameters(self, mock_subprocess): + """Test that subprocess is called with correct parameters""" + import subprocess + + mock_result = MagicMock() + mock_result.stdout = "Success" + mock_subprocess.return_value = mock_result + + req = CompressionRequest( + model_name="test/model", + weight_format="int8", + model_path="/test/path" + ) + + vlm_compress_model(req) + + # Verify subprocess parameters + mock_subprocess.assert_called_once() + call_kwargs = mock_subprocess.call_args[1] + assert call_kwargs["stdout"] == subprocess.PIPE + assert call_kwargs["stderr"] == subprocess.STDOUT + assert call_kwargs["text"] is True + assert call_kwargs["check"] is True + + @patch('app.vlm_utils.subprocess.run') + @patch('builtins.print') + def test_vlm_compress_model_prints_output(self, mock_print, mock_subprocess): + """Test that function prints the subprocess output""" + mock_result = MagicMock() + mock_result.stdout = "Detailed compression output" + mock_subprocess.return_value = mock_result + + req = CompressionRequest( + model_name="test/model", + weight_format="int8", + model_path="/test/path" + ) + + vlm_compress_model(req) + + # Verify print was called with the output + mock_print.assert_called_once_with("Detailed compression output") + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_empty_output(self, mock_subprocess): + """Test VLM model compression with empty stdout""" + mock_result = MagicMock() + mock_result.stdout = "" + mock_subprocess.return_value = mock_result + + req = CompressionRequest( + model_name="test/model", + weight_format="int8", + model_path="/test/path" + ) + + result = vlm_compress_model(req) + + assert result["status"] == "success" + assert result["output"] == "" + + @patch('app.vlm_utils.subprocess.run') + def test_vlm_compress_model_special_characters_in_paths(self, mock_subprocess): + """Test VLM model compression with special characters in paths and names""" + mock_result = MagicMock() + mock_result.stdout = "Success" + mock_subprocess.return_value = mock_result + + req = CompressionRequest( + model_name="test/model-with-hyphens_and_underscores", + weight_format="int8", + hf_token="hf_token-with-special_chars123", + model_path="/path/with spaces/and-special_chars" + ) + + result = vlm_compress_model(req) + + assert result["status"] == "success" + + # Verify the arguments were passed correctly + call_args = mock_subprocess.call_args[0][0] + assert call_args[2] == "test/model-with-hyphens_and_underscores" + assert call_args[4] == "hf_token-with-special_chars123" + assert call_args[5] == "/path/with spaces/and-special_chars" + + +class TestVLMUtilsIntegration: + """Integration tests for VLM utilities""" + + def test_compression_request_to_vlm_compress_model_flow(self): + """Test the complete flow from request creation to function call""" + with patch('app.vlm_utils.subprocess.run') as mock_subprocess: + mock_result = MagicMock() + mock_result.stdout = "Integration test success" + mock_subprocess.return_value = mock_result + + # Create and use request + req = CompressionRequest( + model_name="integration/test-model", + weight_format="fp16", + hf_token="integration_token", + model_path="/integration/test/path" + ) + + result = vlm_compress_model(req) + + # Verify end-to-end flow + assert result["status"] == "success" + assert result["output"] == "Integration test success" + + # Verify all request fields were used + call_args = mock_subprocess.call_args[0][0] + assert "integration/test-model" in call_args + assert "fp16" in call_args + assert "integration_token" in call_args + assert "/integration/test/path" in call_args