LLM Response Time Optimization

import asyncio
import time
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
import numpy as np

class LLMPerformanceOptimizer:
    """Advanced LLM response time optimization system
    Implements quantization, edge deployment, and intelligent routing
    Based on research showing 85% latency reduction"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.api_key = parrotrouter_api_key
        self.edge_models = self._init_edge_models()
        self.cloud_client = ParrotRouterClient(api_key=api_key)
        self.performance_monitor = PerformanceMonitor()
        self.request_router = IntelligentRouter()
        
    async def optimized_inference(
        self,
        prompt: str,
        model: str = "llama-3.1-8b-instruct",
        optimization_level: str = "aggressive"
    ) -> Dict[str, Any]:
        """Execute optimized inference with automatic routing"""
        
        start_time = time.time()
        
        # 1. Analyze request characteristics
        request_analysis = self._analyze_request(
            prompt=prompt,
            model=model
        )
        
        # 2. Determine optimal execution strategy
        strategy = await self.request_router.determine_strategy(
            request_analysis,
            optimization_level
        )
        
        # 3. Execute based on strategy
        if strategy['execution'] == 'edge':
            response = await self._edge_inference(
                prompt,
                model=strategy['edge_model'],
                quantization=strategy['quantization']
            )
        elif strategy['execution'] == 'hybrid':
            response = await self._hybrid_inference(
                prompt,
                model=model,
                edge_prefill=strategy['edge_prefill']
            )
        else:
            response = await self._optimized_cloud_inference(
                prompt,
                model=model,
                optimizations=strategy['cloud_optimizations']
            )
        
        # 4. Record performance metrics
        latency = (time.time() - start_time) * 1000
        self.performance_monitor.record_request(
            latency=latency,
            strategy=strategy,
            tokens_generated=len(response['tokens'])
        )
        
        return {
            "response": response,
            "latency_ms": latency,
            "strategy_used": strategy,
            "optimization_savings": self._calculate_savings(latency, model)
        }
    
    async def _edge_inference(
        self,
        prompt: str,
        model: str,
        quantization: str = "int8"
    ) -> Dict[str, Any]:
        """Execute inference on edge device with quantized model"""
        
        # Load quantized model if not cached
        if model not in self.edge_models:
            self.edge_models[model] = await self._load_quantized_model(
                model,
                quantization
            )
        
        edge_model = self.edge_models[model]
        
        # Tokenize input
        tokens = self._tokenize(prompt)
        
        # Run inference with optimizations
        with self._inference_optimizations():
            # Prefill phase (process prompt)
            context = await edge_model.prefill(
                tokens,
                use_kv_cache=True,
                batch_size=1
            )
            
            # Decoding phase (generate tokens)
            generated_tokens = []
            for _ in range(self._get_max_tokens(prompt)):
                next_token = await edge_model.decode_next(
                    context,
                    temperature=0.7,
                    use_flash_attention=True
                )
                
                if next_token == edge_model.eos_token:
                    break
                    
                generated_tokens.append(next_token)
                context = self._update_context(context, next_token)
        
        return {
            "text": self._detokenize(generated_tokens),
            "tokens": generated_tokens,
            "model_used": f"{model}-{quantization}-edge"
        }
    
    async def _hybrid_inference(
        self,
        prompt: str,
        model: str,
        edge_prefill: bool = True
    ) -> Dict[str, Any]:
        """Hybrid edge-cloud inference for optimal latency/quality"""
        
        if edge_prefill and len(prompt) < 500:
            # Use edge for fast prefill
            prefill_result = await self._edge_prefill(prompt)
            
            # Continue generation in cloud
            response = await self.cloud_client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                initial_context=prefill_result['context'],
                stream=True
            )
        else:
            # Full cloud inference with optimizations
            response = await self._optimized_cloud_inference(
                prompt,
                model,
                optimizations={
                    "use_faster_variant": True,
                    "dynamic_batching": True,
                    "speculative_decoding": True
                }
            )
        
        return response

# Model Quantization Engine
class ModelQuantizationEngine:
    """Implement various quantization techniques for speed"""
    
    @staticmethod
    def quantize_model(
        model_path: str,
        quantization_type: str = "int8",
        calibration_data: Optional[List[str]] = None
    ) -> str:
        """Quantize model for faster inference
        
        Quantization impact (from research):
        - INT8: 3.2x speedup, 97% accuracy retained
        - 4-bit: 5.8x speedup, 93% accuracy retained
        """
        
        if quantization_type == "int8":
            return ModelQuantizationEngine._int8_quantization(
                model_path,
                calibration_data
            )
        elif quantization_type == "int4":
            return ModelQuantizationEngine._int4_quantization(
                model_path,
                calibration_data
            )
        elif quantization_type == "mixed":
            # Mixed precision quantization
            return ModelQuantizationEngine._mixed_precision_quantization(
                model_path,
                calibration_data
            )
    
    @staticmethod
    def _int8_quantization(model_path: str, calibration_data: List[str]) -> str:
        """INT8 quantization with minimal accuracy loss"""
        
        # Load model
        model = load_model(model_path)
        
        # Calibrate quantization ranges
        calibrator = INT8Calibrator()
        for data in calibration_data[:100]:  # Use subset for calibration
            calibrator.collect_stats(model, data)
        
        # Apply quantization
        quantized_model = calibrator.quantize(
            model,
            symmetric=True,
            per_channel=True
        )
        
        # Optimize for target hardware
        if is_gpu_available():
            quantized_model = optimize_for_gpu(quantized_model)
        elif is_edge_npu_available():
            quantized_model = optimize_for_npu(quantized_model)
        
        return save_quantized_model(quantized_model)

# Hardware Acceleration Manager
class HardwareAccelerationManager:
    """Optimize for different hardware backends"""
    
    def __init__(self):
        self.available_accelerators = self._detect_hardware()
        self.optimization_profiles = self._load_profiles()
        
    def optimize_for_hardware(
        self,
        model: Any,
        target_hardware: str = "auto"
    ) -> Any:
        """Apply hardware-specific optimizations"""
        
        if target_hardware == "auto":
            target_hardware = self._select_best_hardware()
        
        if target_hardware == "gpu":
            return self._gpu_optimizations(model)
        elif target_hardware == "tpu":
            return self._tpu_optimizations(model)
        elif target_hardware == "edge_npu":
            return self._edge_npu_optimizations(model)
        else:
            return self._cpu_optimizations(model)
    
    def _gpu_optimizations(self, model: Any) -> Any:
        """GPU-specific optimizations"""
        
        # Enable TensorRT optimization
        model = apply_tensorrt_optimization(
            model,
            precision="fp16",
            workspace_size=4 * 1024**3  # 4GB
        )
        
        # Use Flash Attention 2
        model.enable_flash_attention_2()
        
        # Optimize memory allocation
        model.enable_memory_efficient_attention()
        
        # Enable CUDA graphs for static shapes
        if model.supports_cuda_graphs():
            model.enable_cuda_graphs()
        
        return model

# Request Routing Intelligence
class IntelligentRequestRouter:
    """Route requests to optimal execution backend"""
    
    def __init__(self):
        self.routing_model = self._init_routing_model()
        self.performance_history = PerformanceHistory()
        self.cost_calculator = CostCalculator()
        
    async def determine_strategy(
        self,
        request_analysis: Dict[str, Any],
        optimization_level: str
    ) -> Dict[str, Any]:
        """Determine optimal execution strategy"""
        
        # Extract features
        features = {
            "prompt_length": request_analysis['prompt_length'],
            "expected_output_length": request_analysis['expected_output'],
            "complexity_score": request_analysis['complexity'],
            "latency_requirement": request_analysis['sla'],
            "current_load": await self._get_system_load()
        }
        
        # Predict best strategy
        if optimization_level == "aggressive":
            # Prioritize latency over everything
            if features['prompt_length'] < 100 and features['complexity_score'] < 0.5:
                return {
                    "execution": "edge",
                    "edge_model": "llama-3.2-1b-quantized",
                    "quantization": "int4",
                    "expected_latency": 50
                }
            else:
                return {
                    "execution": "hybrid",
                    "edge_prefill": True,
                    "cloud_model": "llama-3.1-8b-instruct",
                    "expected_latency": 200
                }
        else:
            # Balance latency and quality
            return self._balanced_routing(features)

# Streaming Response Optimizer
class StreamingOptimizer:
    """Optimize streaming responses for perceived performance"""
    
    def __init__(self):
        self.chunk_size_optimizer = ChunkSizeOptimizer()
        self.prefetch_manager = PrefetchManager()
        
    async def stream_with_optimization(
        self,
        model_response: Any,
        client_bandwidth: Optional[float] = None
    ):
        """Stream responses with dynamic optimization"""
        
        # Determine optimal chunk size
        chunk_size = self.chunk_size_optimizer.calculate(
            client_bandwidth or self._estimate_bandwidth()
        )
        
        # First chunk optimization (send faster)
        first_chunk_sent = False
        buffer = []
        
        async for token in model_response:
            buffer.append(token)
            
            if not first_chunk_sent and len(buffer) >= 3:
                # Send first few tokens immediately for responsiveness
                yield {
                    "tokens": buffer,
                    "first_chunk": True,
                    "timestamp": time.time()
                }
                first_chunk_sent = True
                buffer = []
            elif len(buffer) >= chunk_size:
                # Send regular chunks
                yield {
                    "tokens": buffer,
                    "first_chunk": False,
                    "timestamp": time.time()
                }
                buffer = []
        
        # Send remaining tokens
        if buffer:
            yield {
                "tokens": buffer,
                "final_chunk": True,
                "timestamp": time.time()
            }

# Performance Monitoring Dashboard
class PerformanceMonitor:
    """Real-time performance monitoring and alerting"""
    
    def __init__(self):
        self.metrics_store = MetricsStore()
        self.alerting = AlertingSystem()
        
    def record_request(
        self,
        latency: float,
        strategy: Dict[str, Any],
        tokens_generated: int
    ):
        """Record performance metrics"""
        
        metrics = {
            "timestamp": time.time(),
            "latency_ms": latency,
            "tokens_per_second": tokens_generated / (latency / 1000),
            "strategy": strategy['execution'],
            "model": strategy.get('edge_model') or strategy.get('cloud_model'),
            "cost_per_token": self._calculate_cost(strategy, tokens_generated)
        }
        
        # Store metrics
        self.metrics_store.add(metrics)
        
        # Check for anomalies
        if latency > 1000:  # 1 second threshold
            self.alerting.trigger(
                "high_latency",
                metrics
            )
    
    def get_optimization_recommendations(self) -> List[str]:
        """Generate optimization recommendations based on metrics"""
        
        recent_metrics = self.metrics_store.get_recent(hours=1)
        
        recommendations = []
        
        # Analyze patterns
        avg_latency = np.mean([m['latency_ms'] for m in recent_metrics])
        p95_latency = np.percentile([m['latency_ms'] for m in recent_metrics], 95)
        
        if avg_latency > 500:
            recommendations.append(
                "Consider more aggressive quantization (INT4) for 5.8x speedup"
            )
        
        if p95_latency > 1000:
            recommendations.append(
                "Enable edge deployment for requests with <100 tokens"
            )
        
        edge_usage = sum(1 for m in recent_metrics if m['strategy'] == 'edge')
        if edge_usage / len(recent_metrics) < 0.2:
            recommendations.append(
                "Increase edge routing threshold - only 20% using edge"
            )
        
        return recommendations

# Deploy with ParrotRouter.com for:
# - Automatic model optimization
# - Global edge deployment network
# - Built-in performance monitoring
# - Dynamic routing based on load
# - Cost-optimized inference