Quantize and Export Factory LLM Families for Multi-Platform Edge Deployment with torchao and ONNX Runtime

"""
Production implementation for quantizing and exporting factory LLM families for edge deployment.
Provides secure, scalable operations leveraging torchao and ONNX Runtime.
"""

from typing import Dict, Any, List
import os
import logging
import time
import torch
from torchao import quantization
import onnx
import onnxruntime

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

class Config:
    """
    Configuration class to load environment variables.
    """
    model_dir: str = os.getenv('MODEL_DIR', 'models/')
    output_dir: str = os.getenv('OUTPUT_DIR', 'output/')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for model quantization.
    
    Args:
        data: Input data containing model parameters
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_name' not in data:
        raise ValueError('Missing required field: model_name')
    if 'precision' not in data:
        raise ValueError('Missing required field: precision')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

def quantize_model(model_name: str, precision: str) -> str:
    """Quantizes the model based on the provided precision.
    
    Args:
        model_name: The name of the model to quantize
        precision: The precision level (e.g., 'int8')
    Returns:
        Path to the quantized model
    Raises:
        RuntimeError: If quantization fails
    """
    try:
        logger.info(f'Quantizing model: {model_name} with precision: {precision}')
        model_path = os.path.join(Config.model_dir, model_name)
        quantized_model = quantization.quantize(model_path, precision)
        quantized_model_path = os.path.join(Config.output_dir, f'quantized_{model_name}')
        torch.save(quantized_model, quantized_model_path)
        return quantized_model_path
    except Exception as e:
        logger.error(f'Error during model quantization: {e}')
        raise RuntimeError('Quantization failed')

def create_onnx_model(quantized_model_path: str) -> str:
    """Exports the quantized model to ONNX format.
    
    Args:
        quantized_model_path: Path to the quantized model
    Returns:
        Path to the exported ONNX model
    Raises:
        RuntimeError: If export fails
    """
    try:
        logger.info(f'Exporting quantized model to ONNX: {quantized_model_path}')
        onnx_model_path = quantized_model_path.replace('.pt', '.onnx')
        model = torch.load(quantized_model_path)
        onnx.export(model, torch.zeros(1, 3, 224, 224), onnx_model_path)
        return onnx_model_path
    except Exception as e:
        logger.error(f'Error during ONNX export: {e}')
        raise RuntimeError('Export to ONNX failed')

def load_model(onnx_model_path: str) -> onnxruntime.InferenceSession:
    """Loads the ONNX model for inference.
    
    Args:
        onnx_model_path: Path to the ONNX model
    Returns:
        ONNX Runtime InferenceSession
    Raises:
        RuntimeError: If loading fails
    """
    try:
        logger.info(f'Loading ONNX model: {onnx_model_path}')
        session = onnxruntime.InferenceSession(onnx_model_path)
        return session
    except Exception as e:
        logger.error(f'Error loading ONNX model: {e}')
        raise RuntimeError('Model loading failed')

def infer_model(session: onnxruntime.InferenceSession, input_data: List[float]) -> Any:
    """Runs inference on the model with given input data.
    
    Args:
        session: Loaded ONNX Runtime session
        input_data: Input data for inference
    Returns:
        Inference results
    Raises:
        RuntimeError: If inference fails
    """
    try:
        logger.info('Running inference')
        inputs = {session.get_inputs()[0].name: input_data}
        results = session.run(None, inputs)
        return results
    except Exception as e:
        logger.error(f'Error during inference: {e}')
        raise RuntimeError('Inference failed')

def main(data: Dict[str, Any]) -> None:
    """Main orchestration function to quantize and export models.
    
    Args:
        data: Input data for model quantization and export
    """
    try:
        # Validate and sanitize input
        validate_input(data)
        sanitized_data = sanitize_fields(data)
        # Quantize model
        quantized_model_path = quantize_model(sanitized_data['model_name'], sanitized_data['precision'])
        # Export to ONNX
        onnx_model_path = create_onnx_model(quantized_model_path)
        # Load and run inference
        session = load_model(onnx_model_path)
        dummy_input = [0.0] * 3 * 224 * 224  # Example input shape
        results = infer_model(session, dummy_input)
        logger.info(f'Inference results: {results}')
    except Exception as e:
        logger.error(f'Error in main workflow: {e}')

if __name__ == '__main__':
    # Example usage
    input_data = {'model_name': 'my_model.pt', 'precision': 'int8'}
    main(input_data)