Quantize and Deploy Industrial LLMs with torchao and ExecuTorch

"""
Production implementation for deploying and quantizing industrial LLMs.
This script orchestrates the entire workflow from loading models,
quantizing them, and deploying for inference.
"""
import os
import logging
import torch
from typing import Dict, Any, List, Tuple
from torchao import Quantizer
from execurotch import ModelDeployer

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    model_path: str = os.getenv('MODEL_PATH')
    deploy_url: str = os.getenv('DEPLOY_URL')
    quantization_method: str = os.getenv('QUANTIZATION_METHOD', 'dynamic')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input configuration for model deployment.
    Args:
        data (Dict[str, Any]): Configuration parameters for deployment.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'model_path' not in data:
        raise ValueError('Missing model_path in configuration')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    Args:
        data (Dict[str, Any]): The input data to sanitize.
    Returns:
        Dict[str, Any]: Sanitized data.
    """
    return {k: str(v).strip() for k, v in data.items()}

async def load_model(model_path: str) -> Any:
    """Load the model from the specified path.
    Args:
        model_path (str): Path to the model file.
    Returns:
        Any: Loaded model object.
    Raises:
        FileNotFoundError: If the model file does not exist.
    """
    if not os.path.exists(model_path):
        raise FileNotFoundError(f'Model file not found: {model_path}')
    model = torch.load(model_path)
    logger.info('Model loaded successfully from %s', model_path)
    return model

async def quantize_model(model: Any, method: str) -> Any:
    """Quantize the model using the specified method.
    Args:
        model (Any): The model to quantize.
        method (str): The quantization method to use.
    Returns:
        Any: The quantized model.
    """
    quantizer = Quantizer(method)
    quantized_model = quantizer.quantize(model)
    logger.info('Model quantized using method: %s', method)
    return quantized_model

async def deploy_model(model: Any, deploy_url: str) -> None:
    """Deploy the model to the specified URL.
    Args:
        model (Any): The model to deploy.
        deploy_url (str): The URL to deploy the model to.
    Raises:
        RuntimeError: If deployment fails.
    """
    deployer = ModelDeployer(deploy_url)
    success = deployer.deploy(model)
    if not success:
        raise RuntimeError('Failed to deploy model')
    logger.info('Model deployed successfully to %s', deploy_url)

async def process_deployment(config: Dict[str, Any]) -> None:
    """Main orchestration function for the deployment process.
    Args:
        config (Dict[str, Any]): Configuration parameters.
    """
    await validate_input(config)
    sanitized_config = await sanitize_fields(config)
    model = await load_model(sanitized_config['model_path'])
    quantized_model = await quantize_model(model, sanitized_config['quantization_method'])
    await deploy_model(quantized_model, sanitized_config['deploy_url'])

if __name__ == '__main__':
    # Example usage
    deployment_config = {
        'model_path': Config.model_path,
        'deploy_url': Config.deploy_url,
        'quantization_method': Config.quantization_method,
    }
    try:
        import asyncio
        asyncio.run(process_deployment(deployment_config))
    except Exception as e:
        logger.error('Error during deployment: %s', str(e))
        raise