Comprehensive comparison for Quantization technology in AI applications

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from awq import AutoAWQForCausalLM
import logging
from typing import Optional, Dict, Any
import time

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

class ProductRecommendationService:
    """
    Production-ready service for generating product recommendations
    using AWQ quantized language models for efficient inference.
    """
    
    def __init__(self, model_path: str, quantized_model_path: str):
        self.model_path = model_path
        self.quantized_model_path = quantized_model_path
        self.model = None
        self.tokenizer = None
        
    def quantize_model(self, w_bit: int = 4, q_group_size: int = 128) -> None:
        """
        Quantize the model using AWQ for optimized inference.
        """
        try:
            logger.info(f"Loading base model from {self.model_path}")
            model = AutoAWQForCausalLM.from_pretrained(self.model_path)
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
            
            # Configure quantization settings
            quant_config = {
                "zero_point": True,
                "q_group_size": q_group_size,
                "w_bit": w_bit,
                "version": "GEMM"
            }
            
            logger.info("Starting AWQ quantization process...")
            # Quantize the model with calibration data
            model.quantize(self.tokenizer, quant_config=quant_config)
            
            # Save quantized model
            model.save_quantized(self.quantized_model_path)
            self.tokenizer.save_pretrained(self.quantized_model_path)
            logger.info(f"Model quantized and saved to {self.quantized_model_path}")
            
        except Exception as e:
            logger.error(f"Quantization failed: {str(e)}")
            raise
    
    def load_quantized_model(self) -> None:
        """
        Load pre-quantized AWQ model for inference.
        """
        try:
            logger.info(f"Loading quantized model from {self.quantized_model_path}")
            self.model = AutoAWQForCausalLM.from_quantized(
                self.quantized_model_path,
                fuse_layers=True,
                batch_size=1
            )
            self.tokenizer = AutoTokenizer.from_pretrained(self.quantized_model_path)
            logger.info("Quantized model loaded successfully")
            
        except FileNotFoundError:
            logger.error(f"Quantized model not found at {self.quantized_model_path}")
            raise
        except Exception as e:
            logger.error(f"Failed to load quantized model: {str(e)}")
            raise
    
    def generate_recommendation(self, user_query: str, max_tokens: int = 150) -> Optional[Dict[str, Any]]:
        """
        Generate product recommendations using the quantized model.
        """
        if self.model is None or self.tokenizer is None:
            logger.error("Model not loaded. Call load_quantized_model() first.")
            return None
        
        try:
            start_time = time.time()
            
            # Prepare input with proper prompt formatting
            prompt = f"Based on the user query, provide product recommendations:\nQuery: {user_query}\nRecommendations:"
            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
            
            # Generate response with optimized settings
            with torch.no_grad():
                outputs = self.model.generate(
                    **inputs,
                    max_new_tokens=max_tokens,
                    temperature=0.7,
                    top_p=0.9,
                    do_sample=True,
                    pad_token_id=self.tokenizer.eos_token_id
                )
            
            response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            inference_time = time.time() - start_time
            
            logger.info(f"Inference completed in {inference_time:.2f}s")
            
            return {
                "query": user_query,
                "recommendations": response,
                "inference_time_seconds": inference_time,
                "model_type": "AWQ-4bit"
            }
            
        except torch.cuda.OutOfMemoryError:
            logger.error("GPU out of memory during inference")
            return None
        except Exception as e:
            logger.error(f"Inference failed: {str(e)}")
            return None

# Production usage example
if __name__ == "__main__":
    service = ProductRecommendationService(
        model_path="meta-llama/Llama-2-7b-chat-hf",
        quantized_model_path="./models/llama-2-7b-awq"
    )
    
    # Load pre-quantized model for inference
    service.load_quantized_model()
    
    # Generate recommendations
    result = service.generate_recommendation(
        user_query="I need a laptop for video editing and gaming"
    )
    
    if result:
        print(f"Recommendations generated in {result['inference_time_seconds']:.2f}s")
        print(result['recommendations'])