Compile Industrial LLMs for Multi-Architecture Edge Deployment with MLC-LLM and ONNX Runtime

"""
Production implementation for compiling industrial LLMs for edge deployment using MLC-LLM and ONNX Runtime.
Provides secure, scalable operations for multi-architecture systems.
"""
from typing import Dict, Any, List
import os
import logging
import time
import json
from concurrent.futures import ThreadPoolExecutor

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

class Config:
    """
    Configuration class to handle environment variables.
    """
    model_path: str = os.getenv('MODEL_PATH')
    onnx_runtime_path: str = os.getenv('ONNX_RUNTIME_PATH')

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate the input data for model compilation.
    
    Args:
        data: Input dictionary to validate.
    Returns:
        bool: True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails due to missing fields.
    """
    if 'model_name' not in data:
        raise ValueError('Missing model_name in input data.')
    if 'architecture' not in data:
        raise ValueError('Missing architecture in input data.')
    return True

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

def fetch_data(model_name: str) -> Dict[str, Any]:
    """
    Fetch model configuration and data.
    
    Args:
        model_name: The name of the model to fetch.
    Returns:
        Dict[str, Any]: Model data and configuration.
    """
    # Simulating fetch operation with static return
    logger.info(f'Fetching data for model: {model_name}')
    return {'model_name': model_name, 'architecture': 'x86'}

def transform_records(data: Dict[str, Any]) -> List[str]:
    """
    Transform the model data into a format suitable for ONNX.
    
    Args:
        data: Raw model data to transform.
    Returns:
        List[str]: Transformed model paths.
    """
    transformed = [json.dumps(data)]  # Simulating transformation
    logger.info('Data transformed successfully.')
    return transformed

def compile_model(model_data: Dict[str, Any]) -> str:
    """
    Compile the model using ONNX Runtime.
    
    Args:
        model_data: Data containing model configuration.
    Returns:
        str: Path to the compiled model.
    """
    # Simulating model compilation
    logger.info(f'Compiling model: {model_data.get('model_name')}')
    time.sleep(2)  # Simulating compilation time
    compiled_model_path = f'/path/to/{model_data.get('model_name')}.onnx'
    logger.info(f'Model compiled successfully at {compiled_model_path}')
    return compiled_model_path

def save_to_db(model_path: str) -> None:
    """
    Save the compiled model path to the database.
    
    Args:
        model_path: Path to the compiled model.
    Returns:
        None
    """
    logger.info(f'Saving compiled model path: {model_path} to database.')
    # Simulating DB save operation
    time.sleep(1)  # Simulating database operation
    logger.info('Model path saved successfully.')

class ModelCompiler:
    """
    Orchestrates the compilation process of models for edge deployment.
    """
    def __init__(self, config: Config) -> None:
        self.config = config

    def compile_and_deploy(self, input_data: Dict[str, Any]) -> str:
        """
        Main method to compile and deploy the model.
        
        Args:
            input_data: Dictionary containing input data for compilation.
        Returns:
            str: Path to the compiled model.
        """
        try:
            validate_input(input_data)  # Validate input
            sanitized_data = sanitize_fields(input_data)  # Sanitize input
            model_data = fetch_data(sanitized_data['model_name'])  # Fetch model data
            transformed_data = transform_records(model_data)  # Transform data
            compiled_model_path = compile_model(model_data)  # Compile model
            save_to_db(compiled_model_path)  # Save model path
            return compiled_model_path
        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
        except Exception as e:
            logger.error(f'Error during compilation: {e}')
        return ''  # Return empty on error

if __name__ == '__main__':
    # Example usage
    compiler = ModelCompiler(Config)
    input_data_example = {'model_name': 'Industrial_Model_X', 'architecture': 'x86'}
    compiled_model = compiler.compile_and_deploy(input_data_example)
    logger.info(f'Compiled model path: {compiled_model}')