Accelerate Industrial LLM Inference on Intel Xeon Edge Servers with IPEX-LLM and OpenVINO

"""
Production implementation for accelerating LLM inference on Intel Xeon Edge Servers.
Utilizes IPEX-LLM and OpenVINO for optimized performance and resource management.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests

# Logger setup to track the flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')  # Default to in-memory database
    model_path: str = os.getenv('MODEL_PATH', './model')  # Model storage path

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for inference.
    
    Args:
        data (Dict[str, Any]): Input data to validate.
    Returns:
        bool: True if valid, else raises ValueError.
    Raises:
        ValueError: If validation fails.
    """
    if 'input' not in data:
        raise ValueError('Missing required field: input')
    logger.info('Input data validated successfully.')
    return True


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


def normalize_data(data: Dict[str, Any]) -> List[float]:
    """Normalize the input data for inference.
    
    Args:
        data (Dict[str, Any]): Input data to normalize.
    Returns:
        List[float]: Normalized data as a list.
    """
    normalized = [float(x) / 255.0 for x in data['input']]
    logger.info('Data normalization completed.')
    return normalized


def transform_records(data: List[float]) -> List[float]:
    """Transform input records for model compatibility.
    
    Args:
        data (List[float]): Normalized data.
    Returns:
        List[float]: Transformed data.
    """
    transformed = [x * 1.0 for x in data]  # Example transformation
    logger.info('Records transformed successfully.')
    return transformed


def process_batch(batch: List[float]) -> Any:
    """Process a batch of data through the model.
    
    Args:
        batch (List[float]): Transformed data batch.
    Returns:
        Any: Model inference result.
    """
    # Simulate model inference
    result = {'output': sum(batch) / len(batch)}  # Replace with actual model call
    logger.info('Batch processed successfully.')
    return result


def aggregate_metrics(results: List[Any]) -> Dict[str, float]:
    """Aggregate metrics from inference results.
    
    Args:
        results (List[Any]): List of results from model inference.
    Returns:
        Dict[str, float]: Aggregated metrics.
    """
    aggregated = {'average': sum(res['output'] for res in results) / len(results)}
    logger.info('Metrics aggregated successfully.')
    return aggregated


def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch input data from an external API.
    
    Args:
        url (str): URL to fetch data from.
    Returns:
        Dict[str, Any]: Data fetched from the API.
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Data fetched successfully from %s', url)
        return response.json()
    except requests.RequestException as e:
        logger.error('Error fetching data: %s', e)
        raise RuntimeError('Data fetch failed') from e


def save_to_db(data: Dict[str, Any]) -> None:
    """Save inference results to the database.
    
    Args:
        data (Dict[str, Any]): Data to save.
    """
    logger.info('Saving data to database: %s', data)
    # Simulate database save
    # Connection pooling and actual DB logic would go here


def handle_errors(e: Exception) -> None:
    """Handle exceptions gracefully.
    
    Args:
        e (Exception): Exception to handle.
    """
    logger.error('An error occurred: %s', e)

class InferenceService:
    """Main orchestrator for the inference service."""

    def __init__(self, config: Config):
        self.config = config

    def run(self, data: Dict[str, Any]):
        """Main method to run inference workflow.
        
        Args:
            data (Dict[str, Any]): Input data for inference.
        """
        try:
            validate_input(data)  # Validate input
            sanitized_data = sanitize_fields(data)  # Sanitize input fields
            normalized_data = normalize_data(sanitized_data)  # Normalize input
            transformed_data = transform_records(normalized_data)  # Transform for model
            result = process_batch(transformed_data)  # Process through model
            save_to_db(result)  # Save results to database
            logger.info('Inference workflow completed successfully.')
        except Exception as e:
            handle_errors(e)  # Handle errors gracefully

if __name__ == '__main__':
    config = Config()  # Initialize configuration
    service = InferenceService(config)  # Create InferenceService instance
    example_data = {'input': [100, 200, 300]}  # Example input data
    service.run(example_data)  # Run inference