Production Transformer Architecture

class ProductionTransformerServer:
    def __init__(self, config):
        self.config = config
        self.model = self.load_optimized_model()
        self.request_queue = RequestQueue(max_size=config.queue_size)
        self.batch_processor = BatchProcessor(config.batch_config)
        self.metrics = MetricsCollector()
        
        # Performance optimizations
        self.setup_optimizations()
        
        # Start processing loops
        asyncio.create_task(self.batch_processing_loop())
        asyncio.create_task(self.monitoring_loop())
    
    def load_optimized_model(self):
        """Load model with production optimizations"""
        
        # Load base model
        model = AutoModelForCausalLM.from_pretrained(
            self.config.model_name,
            torch_dtype=self.config.precision,  # fp16 or bf16
            device_map=self.config.device_map,
            trust_remote_code=True
        )
        
        # Apply optimizations
        if self.config.compile_model:
            model = torch.compile(
                model, 
                mode="reduce-overhead",
                fullgraph=True
            )
        
        # Enable memory optimizations
        if hasattr(model, 'enable_memory_efficient_attention'):
            model.enable_memory_efficient_attention()
            
        # Quantization if enabled
        if self.config.quantization:
            model = self.apply_quantization(model)
        
        # Set to evaluation mode
        model.eval()
        
        return model
    
    async def generate_response(self, 
                              request: GenerationRequest) -> GenerationResponse:
        """Handle single generation request with batching"""
        
        start_time = time.time()
        
        # Add to processing queue
        result_future = asyncio.Future()
        await self.request_queue.put(QueueItem(request, result_future))
        
        # Wait for batch processing
        try:
            result = await asyncio.wait_for(
                result_future, 
                timeout=self.config.timeout_seconds
            )
            
            # Record metrics
            total_time = time.time() - start_time
            await self.metrics.record_generation(
                tokens_generated=len(result.tokens),
                total_time=total_time,
                queue_time=result.queue_time,
                generation_time=result.generation_time
            )
            
            return result
            
        except asyncio.TimeoutError:
            await self.metrics.record_timeout()
            raise TimeoutError("Generation request timed out")
    
    async def batch_processing_loop(self):
        """Main batch processing loop"""
        while True:
            try:
                # Collect batch from queue
                batch_items = await self.collect_batch()
                
                if not batch_items:
                    await asyncio.sleep(0.01)
                    continue
                
                # Process batch
                batch_results = await self.process_batch(batch_items)
                
                # Return results
                for item, result in zip(batch_items, batch_results):
                    item.result_future.set_result(result)
                    
            except Exception as e:
                logger.error(f"Batch processing error: {e}")
                # Return errors to waiting requests
                for item in batch_items:
                    item.result_future.set_exception(e)
    
    async def collect_batch(self) -> List[QueueItem]:
        """Intelligently collect batch considering constraints"""
        batch = []
        max_batch_size = self.config.max_batch_size
        max_wait_time = self.config.max_batch_wait_ms / 1000.0
        
        # Wait for at least one item
        first_item = await self.request_queue.get()
        batch.append(first_item)
        batch_start_time = time.time()
        
        # Collect additional items
        while (len(batch) < max_batch_size and 
               time.time() - batch_start_time < max_wait_time):
            try:
                # Non-blocking queue get with short timeout
                item = await asyncio.wait_for(
                    self.request_queue.get(), 
                    timeout=0.001
                )
                
                # Check if compatible with current batch
                if self.is_batch_compatible(batch[0].request, item.request):
                    batch.append(item)
                else:
                    # Put back incompatible item
                    await self.request_queue.put(item)
                    break
                    
            except asyncio.TimeoutError:
                break
        
        return batch
    
    async def process_batch(self, batch_items: List[QueueItem]) -> List[GenerationResponse]:
        """Process batch of requests efficiently"""
        
        requests = [item.request for item in batch_items]
        batch_start_time = time.time()
        
        # Prepare batch inputs
        batch_inputs = self.prepare_batch_inputs(requests)
        
        # Generate responses
        with torch.inference_mode():
            generated_tokens = self.model.generate(
                input_ids=batch_inputs['input_ids'],
                attention_mask=batch_inputs['attention_mask'],
                max_new_tokens=batch_inputs['max_new_tokens'],
                do_sample=batch_inputs.get('do_sample', True),
                temperature=batch_inputs.get('temperature', 0.7),
                top_p=batch_inputs.get('top_p', 0.9),
                pad_token_id=self.model.config.eos_token_id,
                use_cache=True
            )
        
        generation_time = time.time() - batch_start_time
        
        # Decode and format responses
        responses = []
        for i, (request, tokens) in enumerate(zip(requests, generated_tokens)):
            # Extract only new tokens
            input_length = len(batch_inputs['input_ids'][i])
            new_tokens = tokens[input_length:]
            
            # Decode response
            response_text = self.model.tokenizer.decode(
                new_tokens, 
                skip_special_tokens=True
            )
            
            responses.append(GenerationResponse(
                text=response_text,
                tokens=new_tokens.tolist(),
                generation_time=generation_time / len(batch_items),
                queue_time=batch_start_time - item.request.timestamp
            ))
        
        return responses
    
    def setup_optimizations(self):
        """Setup various production optimizations"""
        
        # Memory optimization
        if self.config.enable_attention_slicing:
            # Reduce memory usage for attention computation
            self.model.enable_attention_slicing("auto")
        
        # CPU offloading for large models
        if self.config.enable_cpu_offload:
            self.model.enable_model_cpu_offload()
        
        # Flash Attention if available
        try:
            from flash_attn import flash_attn_func
            self.model.enable_flash_attention()
        except ImportError:
            logger.warning("Flash Attention not available")
        
        # Warm up model with dummy inputs
        self.warmup_model()
    
    def warmup_model(self):
        """Warm up model to avoid cold start latency"""
        dummy_input = torch.randint(
            0, self.model.config.vocab_size, 
            (1, 10), 
            device=self.model.device
        )
        
        with torch.inference_mode():
            for _ in range(3):
                _ = self.model.generate(
                    dummy_input, 
                    max_new_tokens=1,
                    do_sample=False
                )
        
        # Clear GPU cache after warmup
        torch.cuda.empty_cache()