Benchmark LLM Latency on Factory Edge Devices with llama.cpp and CTranslate2

"""
Production implementation for benchmarking LLM latency on factory edge devices using llama.cpp and CTranslate2.
Provides secure, scalable operations with robust error handling and logging.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from concurrent.futures import ThreadPoolExecutor

# Setup logging for debugging and monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    llama_endpoint: str = os.getenv('LLAMA_ENDPOINT', 'http://localhost:5000')
    ctranslate_model: str = os.getenv('CTRANSFORM_MODEL', 'model.bin')
    max_workers: int = int(os.getenv('MAX_WORKERS', 5))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for LLM requests.
    
    Args:
        data: Input JSON data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """  
    if 'text' not in data:
        raise ValueError('Missing required field: text')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input JSON data to sanitize.
    Returns:
        Sanitized data.
    """
    # Here we would sanitize the fields - for example, strip unwanted characters
    data['text'] = data['text'].strip()  # Simple sanitization example
    return data

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data structure for processing.
    
    Args:
        data: Input data to normalize.
    Returns:
        Normalized data.
    """
    # Normalize data as per requirements, e.g., lowercase the input
    data['text'] = data['text'].lower()
    return data

def fetch_data(endpoint: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from the specified LLM endpoint.
    
    Args:
        endpoint: The API endpoint to call.
        payload: The data to send in the request.
    Returns:
        Response JSON from the endpoint.
    Raises:
        Exception: If HTTP request fails.
    """
    try:
        response = requests.post(endpoint, json=payload)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Failed to fetch data from LLM endpoint')

def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data through the LLM.
    
    Args:
        data: List of input data dictionaries.
    Returns:
        List of processed results.
    """
    results = []
    for item in data:
        try:
            validate_input(item)  # Validate input data
            sanitized = sanitize_fields(item)  # Sanitize the input
            normalized = normalize_data(sanitized)  # Normalize the data
            result = fetch_data(Config.llama_endpoint, normalized)  # Fetch from LLM
            results.append(result)  # Collect results
        except ValueError as ve:
            logger.warning(f'Validation error: {ve}')  # Log validation errors
        except Exception as e:
            logger.error(f'Error processing item: {item}, error: {e}')  # Log processing errors
    return results

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate and compute metrics from results.
    
    Args:
        results: List of results to aggregate.
    Returns:
        Dictionary containing aggregated metrics.
    """
    # Example: compute average response time or similar metrics
    total_time = sum(result.get('processing_time', 0) for result in results)
    average_time = total_time / len(results) if results else 0
    return {'average_response_time': average_time}

def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save metrics to a database for analysis.
    
    Args:
        metrics: Metrics to save.
    Raises:
        Exception: If saving fails.
    """
    # Assume we have a database connection set up
    # Here would be code to save metrics, e.g., using SQLAlchemy or similar
    logger.info(f'Metrics saved: {metrics}')  # Placeholder for actual DB save logic

class LatencyBenchmark:
    """Main orchestrator class for LLM latency benchmarking.
    
    This class coordinates the execution of the benchmarking process.
    """
    def __init__(self, max_workers: int):
        self.max_workers = max_workers  # Set max workers for parallel processing

    def benchmark(self, input_data: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Run the LLM latency benchmark.
        
        Args:
            input_data: List of input data dictionaries.
        Returns:
            Dictionary with benchmark results.
        """
        # Use ThreadPoolExecutor for concurrent fetching
        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            results = list(executor.map(process_batch, input_data))  # Process in parallel
        aggregated_metrics = aggregate_metrics(results)  # Aggregate metrics from all results
        save_to_db(aggregated_metrics)  # Save aggregated metrics
        return aggregated_metrics  # Return the metrics for reporting

if __name__ == '__main__':
    # Example usage of the benchmark class
    benchmark = LatencyBenchmark(max_workers=Config.max_workers)  # Initialize
    input_data = [{'text': 'Test input for LLM.'}, {'text': 'Another test input.'}]  # Sample input
    metrics = benchmark.benchmark(input_data)  # Run benchmark
    logger.info(f'Benchmark results: {metrics}')  # Log final results