Quantize Industrial LLMs with PEFT and Unsloth Studio for Edge Deployment

                      
                     
"""
Production implementation for Quantizing Industrial LLMs with PEFT and Unsloth Studio.
Provides secure, scalable operations for edge deployment of language models.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')
    max_retries: int = 5
    backoff_factor: float = 0.3

@contextmanager
def connection_pool():
    """Context manager for database connection pooling.
    
    Yields:
        Connection object
    """
    # Simulate connection pooling
    conn = "Database Connection"
    try:
        yield conn
    finally:
        logger.info("Connection closed")

async def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate request data for LLM quantization.
    
    Args:
        data: Input data dictionary to validate.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'quantization_type' not in data:
        raise ValueError('Missing quantization_type')
    return True

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

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data for processing.
    
    Args:
        data: Input data dictionary.
    Returns:
        Dict: Transformed data.
    """
    # Example transformation logic
    transformed_data = {'model_id': data['model_id'], 'quantization': data['quantization_type']}
    return transformed_data

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data for LLM quantization.
    
    Args:
        data: List of data dictionaries to process.
    Returns:
        List: Processed results.
    """
    results = []
    for record in data:
        transformed = await transform_records(record)
        results.append(transformed)
    return results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of processed results.
    Returns:
        Dict: Aggregated metrics.
    """
    metrics = {'total_processed': len(results)}
    return metrics

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the configured API.
    
    Returns:
        List: Fetched data.
    Raises:
        ConnectionError: If the API request fails.
    """
    response = requests.get(Config.api_endpoint)
    if response.status_code != 200:
        raise ConnectionError(f'Failed to fetch data: {response.text}')
    return response.json()

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: List of data dictionaries to save.
    Raises:
        Exception: If saving fails.
    """
    # Simulating database save operation
    logger.info(f"Saving {len(data)} records to the database.")

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call external API for quantization.
    
    Args:
        data: Data dictionary to send.
    Returns:
        Dict: API response.
    Raises:
        Exception: If API call fails.
    """
    response = requests.post(Config.api_endpoint, json=data)
    if response.status_code != 200:
        raise Exception(f'API call failed: {response.text}')
    return response.json()

class LLMQuantizer:
    """Main orchestrator class for LLM quantization.
    
    Attributes:
        config: Configuration object.
    """
    def __init__(self, config: Config):
        self.config = config

    async def run(self) -> None:
        """Execute the main workflow for quantization.
        
        Raises:
            Exception: If any step fails.
        """
        try:
            async with connection_pool() as conn:
                raw_data = await fetch_data()
                await validate_input_data(raw_data)
                sanitized_data = await sanitize_fields(raw_data)
                processed_data = await process_batch([sanitized_data])
                await save_to_db(processed_data)
                metrics = await aggregate_metrics(processed_data)
                logger.info(f'Processing complete with metrics: {metrics}')
        except Exception as e:
            logger.error(f'Error during processing: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    quantizer = LLMQuantizer(config)
    import asyncio
    asyncio.run(quantizer.run())