Run Speculative Decoding for Low-Latency Factory LLM Inference with SGLang and CTranslate2

"""
Production implementation for running speculative decoding for low-latency factory LLM inference using SGLang and CTranslate2.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import asyncio
import httpx
from contextlib import asynccontextmanager

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

class Config:
    """Configuration class to load environment variables."""
    model_url: str = os.getenv('MODEL_URL')
    api_key: str = os.getenv('API_KEY')

@asynccontextmanager
async def get_http_client() -> httpx.AsyncClient:
    """Context manager for HTTP client with connection pooling.
    
    Yields:
        httpx.AsyncClient: HTTP client instance
    """
    async with httpx.AsyncClient() as client:
        yield client

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'prompt' not in data:
        raise ValueError('Missing prompt in input data')  # Ensure prompt is present
    return True

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

async def normalize_data(raw_data: Any) -> Dict[str, Any]:
    """Normalize the raw data into a structured format.
    
    Args:
        raw_data: Raw input data
    Returns:
        Normalized data
    """
    return {'prompt': raw_data['prompt'], 'parameters': raw_data.get('parameters', {})}

async def fetch_data(client: httpx.AsyncClient, endpoint: str, params: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from the API endpoint.
    
    Args:
        client: HTTP client instance
        endpoint: API endpoint to call
        params: Parameters for the API call
    Returns:
        API response data
    Raises:
        httpx.HTTPStatusError: If the response is not 200
    """
    response = await client.get(endpoint, params=params)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

async def process_batch(prompts: List[str]) -> List[str]:
    """Process a batch of prompts for inference.
    
    Args:
        prompts: List of prompts to process
    Returns:
        List of inference results
    """
    results = []
    async with get_http_client() as client:
        for prompt in prompts:
            data = {'prompt': prompt}
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            result = await fetch_data(client, Config.model_url, sanitized_data)
            results.append(result)
    return results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from the inference results.
    
    Args:
        results: Inference results to aggregate
    Returns:
        Aggregated metrics
    """
    metrics = {'total': len(results), 'successful': sum(1 for r in results if r.get('status') == 'success')}
    return metrics

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save inference results to the database (placeholder).
    
    Args:
        data: Data to save
    """
    # Simulation of saving data
    logger.info(f'Saving data to database: {data}')

async def handle_errors(e: Exception) -> None:
    """Handle errors gracefully.
    
    Args:
        e: Exception raised
    """
    logger.error(f'An error occurred: {str(e)}')

async def main() -> None:
    """Main orchestrator for the inference process.
    
    Returns:
        None
    """
    prompts = ['What is the capital of France?', 'Explain quantum mechanics.']
    try:
        results = await process_batch(prompts)  # Process prompts
        metrics = await aggregate_metrics(results)  # Aggregate results
        await save_to_db(metrics)  # Save metrics
    except Exception as e:
        await handle_errors(e)  # Handle errors gracefully

if __name__ == '__main__':
    # Entry point
    asyncio.run(main())  # Run the main coroutine