""" Advanced Performance Optimization System for Cyber-LLM GPU optimization, memory management, distributed inference, and scaling Author: Muzan Sano """ import asyncio import json import logging import time import threading from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum import psutil import gc import torch import numpy as np from pathlib import Path from collections import defaultdict, deque import multiprocessing as mp from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor from ..utils.logging_system import CyberLLMLogger, CyberLLMError, ErrorCategory from ..memory.persistent_memory import PersistentMemoryManager, MemoryType class OptimizationTarget(Enum): """Performance optimization targets""" LATENCY = "latency" THROUGHPUT = "throughput" MEMORY_EFFICIENCY = "memory_efficiency" GPU_UTILIZATION = "gpu_utilization" POWER_EFFICIENCY = "power_efficiency" COST_EFFICIENCY = "cost_efficiency" class ResourceType(Enum): """Types of system resources""" CPU = "cpu" GPU = "gpu" MEMORY = "memory" STORAGE = "storage" NETWORK = "network" POWER = "power" @dataclass class PerformanceMetrics: """Performance metrics tracking""" timestamp: datetime # Latency metrics (milliseconds) inference_latency: float = 0.0 preprocessing_latency: float = 0.0 postprocessing_latency: float = 0.0 total_latency: float = 0.0 # Throughput metrics (requests/second) requests_per_second: float = 0.0 tokens_per_second: float = 0.0 # Resource utilization (0-1) cpu_utilization: float = 0.0 gpu_utilization: float = 0.0 memory_utilization: float = 0.0 # Memory metrics (MB) cpu_memory_used: float = 0.0 gpu_memory_used: float = 0.0 gpu_memory_reserved: float = 0.0 # Quality metrics accuracy: float = 0.0 safety_score: float = 0.0 # Cost metrics compute_cost: float = 0.0 power_consumption: float = 0.0 @dataclass class OptimizationConfiguration: """Performance optimization configuration""" optimization_targets: List[OptimizationTarget] = field(default_factory=lambda: [OptimizationTarget.LATENCY]) # Model optimization use_mixed_precision: bool = True use_gradient_checkpointing: bool = False use_model_compilation: bool = True batch_size: int = 8 max_sequence_length: int = 2048 # Memory optimization enable_memory_pooling: bool = True memory_pool_size_mb: int = 4096 enable_garbage_collection: bool = True gc_frequency: int = 100 # requests # Concurrency optimization max_concurrent_requests: int = 32 request_queue_size: int = 128 worker_processes: int = 4 worker_threads_per_process: int = 8 # Caching optimization enable_result_caching: bool = True cache_size_mb: int = 1024 cache_ttl_seconds: int = 3600 # GPU optimization enable_tensor_parallelism: bool = False enable_pipeline_parallelism: bool = False gpu_memory_fraction: float = 0.9 # Monitoring metrics_collection_interval: float = 1.0 # seconds performance_logging_enabled: bool = True class AdvancedPerformanceOptimizer: """Advanced performance optimization system with adaptive tuning""" def __init__(self, config: OptimizationConfiguration = None, memory_manager: Optional[PersistentMemoryManager] = None, logger: Optional[CyberLLMLogger] = None): self.config = config or OptimizationConfiguration() self.memory_manager = memory_manager self.logger = logger or CyberLLMLogger(name="performance_optimizer") # Performance tracking self.metrics_history = deque(maxlen=10000) self.current_metrics = PerformanceMetrics(timestamp=datetime.now()) # Resource management self.resource_monitors = {} self.memory_pool = None self.result_cache = {} # Concurrency management self.request_queue = asyncio.Queue(maxsize=self.config.request_queue_size) self.worker_pool = None self.processing_semaphore = asyncio.Semaphore(self.config.max_concurrent_requests) # Optimization state self.optimization_history = [] self.current_optimization_strategy = {} self.adaptive_tuning_enabled = True # Monitoring and alerting self.performance_alerts = [] self.monitoring_active = False self.monitoring_task = None # Initialize optimizer asyncio.create_task(self._initialize_optimizer()) self.logger.info("Advanced Performance Optimizer initialized") async def _initialize_optimizer(self): """Initialize the performance optimization system""" try: # Initialize GPU optimization if torch.cuda.is_available(): await self._initialize_gpu_optimization() # Initialize memory management await self._initialize_memory_management() # Initialize resource monitoring await self._initialize_resource_monitoring() # Initialize worker pools await self._initialize_worker_pools() # Start performance monitoring await self._start_performance_monitoring() self.logger.info("Performance optimizer initialization completed") except Exception as e: self.logger.error("Failed to initialize performance optimizer", error=str(e)) raise CyberLLMError("Performance optimizer initialization failed", ErrorCategory.SYSTEM) async def _initialize_gpu_optimization(self): """Initialize GPU performance optimizations""" if not torch.cuda.is_available(): self.logger.warning("CUDA not available, skipping GPU optimization") return try: # Set memory fraction torch.cuda.set_per_process_memory_fraction(self.config.gpu_memory_fraction) # Enable mixed precision if supported if self.config.use_mixed_precision and torch.cuda.is_bf16_supported(): torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True torch.backends.cudnn.allow_tf32 = True # Initialize memory pool if self.config.enable_memory_pooling: torch.cuda.empty_cache() # Custom memory pool (simplified) self.memory_pool = { "allocated": 0, "reserved": 0, "max_reserved": self.config.memory_pool_size_mb * 1024 * 1024 } # Initialize tensor parallelism if enabled if self.config.enable_tensor_parallelism: await self._setup_tensor_parallelism() self.logger.info("GPU optimization initialized", devices=torch.cuda.device_count(), memory_fraction=self.config.gpu_memory_fraction) except Exception as e: self.logger.error("GPU optimization initialization failed", error=str(e)) async def _initialize_memory_management(self): """Initialize advanced memory management""" try: # Configure garbage collection if self.config.enable_garbage_collection: gc.set_threshold(700, 10, 10) # Aggressive GC for memory efficiency # Initialize result cache if self.config.enable_result_caching: self.result_cache = { "cache": {}, "access_times": {}, "max_size_mb": self.config.cache_size_mb, "ttl_seconds": self.config.cache_ttl_seconds, "current_size_mb": 0 } # Set memory optimization flags if hasattr(torch.backends, 'opt_einsum'): torch.backends.opt_einsum.enabled = True self.logger.info("Memory management initialized") except Exception as e: self.logger.error("Memory management initialization failed", error=str(e)) async def _initialize_resource_monitoring(self): """Initialize comprehensive resource monitoring""" try: # CPU monitoring self.resource_monitors['cpu'] = { 'utilization_history': deque(maxlen=100), 'temperature_history': deque(maxlen=100), 'frequency_history': deque(maxlen=100) } # Memory monitoring self.resource_monitors['memory'] = { 'usage_history': deque(maxlen=100), 'swap_history': deque(maxlen=100), 'cache_history': deque(maxlen=100) } # GPU monitoring (if available) if torch.cuda.is_available(): self.resource_monitors['gpu'] = { 'utilization_history': deque(maxlen=100), 'memory_history': deque(maxlen=100), 'temperature_history': deque(maxlen=100), 'power_history': deque(maxlen=100) } # Network monitoring self.resource_monitors['network'] = { 'throughput_history': deque(maxlen=100), 'latency_history': deque(maxlen=100), 'error_history': deque(maxlen=100) } self.logger.info("Resource monitoring initialized") except Exception as e: self.logger.error("Resource monitoring initialization failed", error=str(e)) async def _initialize_worker_pools(self): """Initialize worker pools for concurrent processing""" try: # Process pool for CPU-intensive tasks self.worker_pool = { 'process_pool': ProcessPoolExecutor(max_workers=self.config.worker_processes), 'thread_pool': ThreadPoolExecutor(max_workers=self.config.worker_threads_per_process * self.config.worker_processes) } self.logger.info("Worker pools initialized", processes=self.config.worker_processes, threads=self.config.worker_threads_per_process * self.config.worker_processes) except Exception as e: self.logger.error("Worker pool initialization failed", error=str(e)) async def _start_performance_monitoring(self): """Start continuous performance monitoring""" self.monitoring_active = True self.monitoring_task = asyncio.create_task(self._performance_monitoring_loop()) self.logger.info("Performance monitoring started") async def _performance_monitoring_loop(self): """Continuous performance monitoring loop""" while self.monitoring_active: try: # Collect current metrics current_metrics = await self._collect_performance_metrics() # Update metrics history self.metrics_history.append(current_metrics) self.current_metrics = current_metrics # Check for performance alerts await self._check_performance_alerts(current_metrics) # Adaptive optimization if self.adaptive_tuning_enabled: await self._adaptive_performance_tuning(current_metrics) # Store metrics in memory system if available if self.memory_manager: await self.memory_manager.store_memory( memory_type=MemoryType.PROCEDURAL, content=current_metrics.__dict__, importance=0.3, context_tags=["performance_metrics", "monitoring"], agent_id="performance_optimizer" ) await asyncio.sleep(self.config.metrics_collection_interval) except Exception as e: self.logger.error("Performance monitoring error", error=str(e)) await asyncio.sleep(5) # Error recovery delay async def _collect_performance_metrics(self) -> PerformanceMetrics: """Collect comprehensive performance metrics""" metrics = PerformanceMetrics(timestamp=datetime.now()) try: # CPU metrics cpu_percent = psutil.cpu_percent(interval=None) metrics.cpu_utilization = cpu_percent / 100.0 # Memory metrics memory_info = psutil.virtual_memory() metrics.memory_utilization = memory_info.percent / 100.0 metrics.cpu_memory_used = memory_info.used / (1024 * 1024) # MB # GPU metrics (if available) if torch.cuda.is_available(): gpu_memory = torch.cuda.memory_stats() metrics.gpu_memory_used = gpu_memory.get('allocated_bytes.all.current', 0) / (1024 * 1024) metrics.gpu_memory_reserved = gpu_memory.get('reserved_bytes.all.current', 0) / (1024 * 1024) # GPU utilization (approximated) metrics.gpu_utilization = min(1.0, metrics.gpu_memory_used / (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))) # Network metrics (simplified) network_io = psutil.net_io_counters() if hasattr(self, '_last_network_io'): bytes_sent_diff = network_io.bytes_sent - self._last_network_io.bytes_sent bytes_recv_diff = network_io.bytes_recv - self._last_network_io.bytes_recv time_diff = time.time() - self._last_network_time if time_diff > 0: network_throughput = (bytes_sent_diff + bytes_recv_diff) / time_diff / (1024 * 1024) # MB/s self._last_network_io = network_io self._last_network_time = time.time() # Update resource monitors self.resource_monitors['cpu']['utilization_history'].append(cpu_percent) self.resource_monitors['memory']['usage_history'].append(memory_info.percent) if torch.cuda.is_available(): self.resource_monitors['gpu']['memory_history'].append(metrics.gpu_memory_used) self.resource_monitors['gpu']['utilization_history'].append(metrics.gpu_utilization * 100) except Exception as e: self.logger.error("Failed to collect performance metrics", error=str(e)) return metrics async def optimize_inference_request(self, request_data: Dict[str, Any], priority: int = 5) -> Dict[str, Any]: """Optimize and process an inference request""" request_id = request_data.get('request_id', f"req_{int(time.time())}") start_time = time.time() try: # Acquire processing semaphore async with self.processing_semaphore: # Check result cache first if self.config.enable_result_caching: cached_result = await self._check_result_cache(request_data) if cached_result: self.logger.debug(f"Returning cached result for request: {request_id}") return cached_result # Preprocess request preprocessing_start = time.time() preprocessed_data = await self._optimize_preprocessing(request_data) preprocessing_time = time.time() - preprocessing_start # Execute inference with optimizations inference_start = time.time() inference_result = await self._execute_optimized_inference(preprocessed_data) inference_time = time.time() - inference_start # Postprocess result postprocessing_start = time.time() final_result = await self._optimize_postprocessing(inference_result, request_data) postprocessing_time = time.time() - postprocessing_start total_time = time.time() - start_time # Update performance metrics await self._update_request_metrics( total_time, preprocessing_time, inference_time, postprocessing_time ) # Cache result if enabled if self.config.enable_result_caching: await self._cache_result(request_data, final_result) # Add performance metadata final_result['performance'] = { 'total_latency_ms': total_time * 1000, 'preprocessing_latency_ms': preprocessing_time * 1000, 'inference_latency_ms': inference_time * 1000, 'postprocessing_latency_ms': postprocessing_time * 1000, 'cache_hit': False, 'optimization_applied': True } return final_result except Exception as e: self.logger.error(f"Request optimization failed: {request_id}", error=str(e)) raise CyberLLMError("Request optimization failed", ErrorCategory.PROCESSING) async def _optimize_preprocessing(self, request_data: Dict[str, Any]) -> Dict[str, Any]: """Optimize preprocessing with batching and parallelization""" try: # Batch similar requests for efficiency if 'batch_processing' in request_data: return await self._batch_preprocess(request_data) # Standard preprocessing with optimizations optimized_data = { 'processed_input': request_data.get('input', ''), 'max_length': min(request_data.get('max_length', 512), self.config.max_sequence_length), 'optimization_flags': { 'use_mixed_precision': self.config.use_mixed_precision, 'gradient_checkpointing': self.config.use_gradient_checkpointing } } return optimized_data except Exception as e: self.logger.error("Preprocessing optimization failed", error=str(e)) return request_data async def _execute_optimized_inference(self, preprocessed_data: Dict[str, Any]) -> Dict[str, Any]: """Execute inference with performance optimizations""" try: # Apply memory optimizations if self.config.enable_garbage_collection and hasattr(self, '_request_count'): self._request_count += 1 if self._request_count % self.config.gc_frequency == 0: gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() # Simulate optimized inference (in production, would call actual model) await asyncio.sleep(0.01) # Simulate inference time result = { 'output': f"Optimized inference result for: {preprocessed_data.get('processed_input', '')[:50]}...", 'confidence': 0.95, 'optimization_metrics': { 'memory_efficient': True, 'gpu_accelerated': torch.cuda.is_available(), 'mixed_precision': self.config.use_mixed_precision } } return result except Exception as e: self.logger.error("Optimized inference failed", error=str(e)) raise async def _optimize_postprocessing(self, inference_result: Dict[str, Any], original_request: Dict[str, Any]) -> Dict[str, Any]: """Optimize postprocessing with parallel operations""" try: # Parallel postprocessing tasks tasks = [] # Format output tasks.append(self._format_output(inference_result)) # Apply safety filters tasks.append(self._apply_safety_filters(inference_result)) # Generate metadata tasks.append(self._generate_response_metadata(inference_result, original_request)) # Execute tasks in parallel formatted_output, safety_filtered, metadata = await asyncio.gather(*tasks) final_result = { 'response': formatted_output, 'safety_score': safety_filtered.get('safety_score', 1.0), 'metadata': metadata, 'request_id': original_request.get('request_id'), 'timestamp': datetime.now().isoformat() } return final_result except Exception as e: self.logger.error("Postprocessing optimization failed", error=str(e)) return inference_result async def _adaptive_performance_tuning(self, current_metrics: PerformanceMetrics): """Adaptive performance tuning based on current metrics""" try: # Analyze performance trends if len(self.metrics_history) < 10: return # Need more data for adaptive tuning recent_metrics = list(self.metrics_history)[-10:] # Calculate performance trends latency_trend = self._calculate_trend([m.total_latency for m in recent_metrics]) memory_trend = self._calculate_trend([m.memory_utilization for m in recent_metrics]) gpu_trend = self._calculate_trend([m.gpu_utilization for m in recent_metrics]) adaptations = [] # Latency optimization if latency_trend > 0.1: # Latency increasing if current_metrics.memory_utilization < 0.7: # Increase batch size for better throughput new_batch_size = min(self.config.batch_size * 2, 32) if new_batch_size != self.config.batch_size: self.config.batch_size = new_batch_size adaptations.append(f"Increased batch size to {new_batch_size}") if not self.config.use_mixed_precision and torch.cuda.is_available(): self.config.use_mixed_precision = True adaptations.append("Enabled mixed precision training") # Memory optimization if memory_trend > 0.1 and current_metrics.memory_utilization > 0.8: # Increase garbage collection frequency self.config.gc_frequency = max(self.config.gc_frequency // 2, 10) adaptations.append(f"Increased GC frequency to {self.config.gc_frequency}") # Reduce batch size if memory pressure if self.config.batch_size > 1: self.config.batch_size = max(self.config.batch_size // 2, 1) adaptations.append(f"Reduced batch size to {self.config.batch_size}") # GPU optimization if torch.cuda.is_available() and gpu_trend < -0.1: # GPU underutilized if current_metrics.gpu_utilization < 0.5: # Increase concurrent requests new_max_concurrent = min(self.config.max_concurrent_requests + 8, 64) if new_max_concurrent != self.config.max_concurrent_requests: self.config.max_concurrent_requests = new_max_concurrent self.processing_semaphore = asyncio.Semaphore(new_max_concurrent) adaptations.append(f"Increased max concurrent requests to {new_max_concurrent}") # Log adaptations if adaptations: adaptation_record = { 'timestamp': datetime.now().isoformat(), 'adaptations': adaptations, 'trigger_metrics': { 'latency_trend': latency_trend, 'memory_trend': memory_trend, 'gpu_trend': gpu_trend } } self.optimization_history.append(adaptation_record) if self.memory_manager: await self.memory_manager.store_memory( memory_type=MemoryType.PROCEDURAL, content=adaptation_record, importance=0.7, context_tags=["adaptive_tuning", "performance_optimization"], agent_id="performance_optimizer" ) self.logger.info("Applied adaptive optimizations", adaptations=adaptations) except Exception as e: self.logger.error("Adaptive tuning failed", error=str(e)) def _calculate_trend(self, values: List[float]) -> float: """Calculate trend in performance metrics""" if len(values) < 2: return 0.0 # Simple linear trend calculation x = np.arange(len(values)) y = np.array(values) if np.std(y) == 0: return 0.0 correlation = np.corrcoef(x, y)[0, 1] return correlation if not np.isnan(correlation) else 0.0 def get_performance_dashboard(self) -> Dict[str, Any]: """Get comprehensive performance dashboard data""" if not self.metrics_history: return {"status": "No metrics available"} recent_metrics = list(self.metrics_history)[-100:] # Last 100 measurements return { "current_metrics": self.current_metrics.__dict__, "performance_trends": { "latency_trend": self._calculate_trend([m.total_latency for m in recent_metrics]), "throughput_trend": self._calculate_trend([m.requests_per_second for m in recent_metrics]), "memory_trend": self._calculate_trend([m.memory_utilization for m in recent_metrics]), "gpu_trend": self._calculate_trend([m.gpu_utilization for m in recent_metrics]) }, "optimization_config": { "batch_size": self.config.batch_size, "max_concurrent_requests": self.config.max_concurrent_requests, "gc_frequency": self.config.gc_frequency, "mixed_precision_enabled": self.config.use_mixed_precision, "caching_enabled": self.config.enable_result_caching }, "system_resources": { "cpu_count": mp.cpu_count(), "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0, "total_memory_gb": psutil.virtual_memory().total / (1024**3), "available_memory_gb": psutil.virtual_memory().available / (1024**3) }, "recent_adaptations": self.optimization_history[-10:] if self.optimization_history else [], "monitoring_status": { "active": self.monitoring_active, "metrics_collected": len(self.metrics_history), "adaptive_tuning_enabled": self.adaptive_tuning_enabled } } async def shutdown(self): """Graceful shutdown of performance optimizer""" self.logger.info("Shutting down performance optimizer") # Stop monitoring self.monitoring_active = False if self.monitoring_task: self.monitoring_task.cancel() try: await self.monitoring_task except asyncio.CancelledError: pass # Shutdown worker pools if self.worker_pool: self.worker_pool['process_pool'].shutdown(wait=True) self.worker_pool['thread_pool'].shutdown(wait=True) # Clear GPU memory if torch.cuda.is_available(): torch.cuda.empty_cache() # Final garbage collection gc.collect() self.logger.info("Performance optimizer shutdown completed") # Factory function def create_performance_optimizer(config: OptimizationConfiguration = None, **kwargs) -> AdvancedPerformanceOptimizer: """Create advanced performance optimizer with configuration""" return AdvancedPerformanceOptimizer(config, **kwargs)