""" AgentFile Advanced Resource Manager Goes beyond normal MoE resource management Provides intelligent memory, compute, and quality optimization """ import torch import torch.nn as nn from typing import Dict, List, Optional, Tuple from dataclasses import dataclass from enum import Enum import psutil import threading import time from queue import Queue import numpy as np import logging logger = logging.getLogger(__name__) class ResourcePriority(Enum): """Resource allocation priorities""" CRITICAL = 1 HIGH = 2 NORMAL = 3 LOW = 4 BACKGROUND = 5 @dataclass class ResourceBudget: """Resource budget configuration""" memory_gb: float = 8.0 compute_units: float = 100.0 priority: ResourcePriority = ResourcePriority.NORMAL max_experts: int = 4 quality_threshold: float = 0.8 class IntelligentResourceManager: """Intelligent Resource Manager - Goes beyond normal MoE""" def __init__(self, budget: ResourceBudget): self.budget = budget self.memory_usage = {} self.compute_usage = {} self.quality_metrics = {} # Predictive models self.memory_predictor = self._create_memory_predictor() self.compute_predictor = self._create_compute_predictor() # Resource pools self.memory_pool = Queue() self.compute_pool = Queue() # Monitoring self.monitor_thread = None self.running = False # Statistics self.stats = { 'total_memory_allocated': 0, 'total_compute_allocated': 0, 'avg_quality': 0.0, 'optimization_count': 0 } def _create_memory_predictor(self): """Create a simple memory usage predictor""" class MemoryPredictor: def __init__(self): self.history = [] def predict(self, model_size: int, batch_size: int) -> float: base_memory = model_size * 2 # 2x for forward/backward batch_factor = batch_size * 0.1 # 10% per batch element return base_memory * (1 + batch_factor) return MemoryPredictor() def _create_compute_predictor(self): """Create a compute usage predictor""" class ComputePredictor: def __init__(self): self.flops_per_parameter = 6 # Approximate FLOPs per parameter def predict(self, model_size: int, sequence_length: int) -> float: flops = model_size * self.flops_per_parameter * sequence_length return flops / 1e9 # Convert to GFLOPs return ComputePredictor() def allocate_resources( self, model_size: int, batch_size: int, sequence_length: int ) -> Dict: """Intelligently allocate resources""" # Predict requirements memory_needed = self.memory_predictor.predict(model_size, batch_size) compute_needed = self.compute_predictor.predict(model_size, sequence_length) # Check availability available_memory = self._get_available_memory() available_compute = self._get_available_compute() # Allocate based on availability and priority allocation = { 'memory': min(memory_needed, available_memory * 0.8), 'compute': min(compute_needed, available_compute * 0.8), 'batch_size': batch_size, 'sequence_length': sequence_length, 'optimization_level': self._calculate_optimization_level( memory_needed, available_memory ) } # Update usage tracking self.memory_usage[id(allocation)] = allocation['memory'] self.compute_usage[id(allocation)] = allocation['compute'] return allocation def optimize_allocation( self, current_allocation: Dict, quality_feedback: float ) -> Dict: """Optimize allocation based on quality feedback""" optimized = current_allocation.copy() # If quality is low, increase resources if quality_feedback < self.budget.quality_threshold: optimized['memory'] = min( optimized['memory'] * 1.2, self.budget.memory_gb ) optimized['compute'] = min( optimized['compute'] * 1.15, self.budget.compute_units ) # Reduce batch size if memory constrained if optimized['memory'] >= self.budget.memory_gb * 0.9: optimized['batch_size'] = max(1, optimized['batch_size'] // 2) # If quality is high, we can be more efficient elif quality_feedback > 0.95: optimized['memory'] = optimized['memory'] * 0.9 optimized['batch_size'] = int(optimized['batch_size'] * 1.1) self.stats['optimization_count'] += 1 return optimized def get_optimal_expert_count( self, input_complexity: float, available_resources: Dict ) -> int: """Determine optimal number of experts based on input and resources""" base_experts = int(input_complexity * self.budget.max_experts) memory_factor = available_resources.get('memory', 0) / self.budget.memory_gb compute_factor = available_resources.get('compute', 0) / self.budget.compute_units resource_factor = (memory_factor + compute_factor) / 2 optimal_experts = int(base_experts * resource_factor) return max(1, min(optimal_experts, self.budget.max_experts)) def _get_available_memory(self) -> float: """Get available memory in GB""" try: if torch.cuda.is_available(): return torch.cuda.get_device_properties(0).total_mem / 1e9 else: return psutil.virtual_memory().available / 1e9 except: return 8.0 def _get_available_compute(self) -> float: """Get available compute units""" try: return psutil.cpu_percent() / 100 except: return 100.0 def _calculate_optimization_level(self, needed: float, available: float) -> str: """Calculate optimization level needed""" ratio = needed / available if available > 0 else 1.0 if ratio < 0.5: return "none" elif ratio < 0.7: return "light" elif ratio < 0.9: return "moderate" else: return "aggressive" def start_monitoring(self): """Start resource monitoring thread""" self.running = True self.monitor_thread = threading.Thread(target=self._monitor_loop) self.monitor_thread.daemon = True self.monitor_thread.start() logger.info("Resource monitoring started") def stop_monitoring(self): """Stop resource monitoring""" self.running = False if self.monitor_thread: self.monitor_thread.join() logger.info("Resource monitoring stopped") def _monitor_loop(self): """Main monitoring loop""" while self.running: self._update_stats() self._check_resource_leaks() self._auto_optimize() time.sleep(1.0) def _update_stats(self): """Update resource statistics""" total_memory = sum(self.memory_usage.values()) total_compute = sum(self.compute_usage.values()) self.stats['total_memory_allocated'] = total_memory self.stats['total_compute_allocated'] = total_compute if self.quality_metrics: self.stats['avg_quality'] = np.mean(list(self.quality_metrics.values())) def _check_resource_leaks(self): """Check for and clean up resource leaks""" for alloc_id, memory in list(self.memory_usage.items()): # In real implementation, you'd track timestamps pass def _auto_optimize(self): """Automatically optimize resource allocation""" if self.stats['total_memory_allocated'] > self.budget.memory_gb * 0.9: self._reduce_memory_usage() if self.stats['total_compute_allocated'] < self.budget.compute_units * 0.5: self._increase_compute_usage() def _reduce_memory_usage(self): """Reduce memory usage""" for alloc_id in list(self.memory_usage.keys()): # In real implementation, you'd modify the actual allocations pass def _increase_compute_usage(self): """Increase compute usage for better throughput""" pass def get_status(self) -> Dict: """Get current resource status""" return { 'memory_usage': self.memory_usage, 'compute_usage': self.compute_usage, 'quality_metrics': self.quality_metrics, 'stats': self.stats, 'budget': { 'memory_gb': self.budget.memory_gb, 'compute_units': self.budget.compute_units, 'max_experts': self.budget.max_experts } } class AdaptiveBatchScheduler: """Adaptive Batch Scheduler - Dynamically adjusts batch sizes""" def __init__(self, resource_manager: IntelligentResourceManager): self.resource_manager = resource_manager self.batch_history = [] self.quality_history = [] def get_optimal_batch_size( self, current_batch_size: int, quality_feedback: float ) -> int: """Get optimal batch size based on feedback""" self.batch_history.append(current_batch_size) self.quality_history.append(quality_feedback) if len(self.batch_history) > 100: self.batch_history = self.batch_history[-100:] self.quality_history = self.quality_history[-100:] if len(self.quality_history) > 10: recent_quality = np.mean(self.quality_history[-10:]) older_quality = np.mean(self.quality_history[-20:-10]) if len(self.quality_history) > 20 else recent_quality quality_trend = recent_quality - older_quality else: quality_trend = 0 if quality_trend > 0.05: new_batch_size = int(current_batch_size * 1.1) elif quality_trend < -0.05: new_batch_size = int(current_batch_size * 0.9) else: new_batch_size = current_batch_size new_batch_size = max(1, min(new_batch_size, 32)) return new_batch_size class QualityAwareRouter: """Quality-Aware Router - Routes based on quality requirements""" def __init__(self, num_experts: int, quality_threshold: float = 0.8): self.num_experts = num_experts self.quality_threshold = quality_threshold # Expert quality scores self.expert_quality_scores = [0.5] * num_experts # Input complexity analyzer self.complexity_analyzer = self._create_complexity_analyzer() def _create_complexity_analyzer(self): """Create input complexity analyzer""" class ComplexityAnalyzer: def analyze(self, input_ids: torch.Tensor) -> float: unique_tokens = len(torch.unique(input_ids)) total_tokens = input_ids.numel() return unique_tokens / total_tokens return ComplexityAnalyzer() def route( self, input_ids: torch.Tensor, available_experts: List[int] ) -> List[Tuple[int, float]]: """Route input to experts based on quality requirements""" complexity = self.complexity_analyzer.analyze(input_ids) selected_experts = [] for expert_id in available_experts: quality_score = self.expert_quality_scores[expert_id] if quality_score >= self.quality_threshold: weight = self._calculate_expert_weight(expert_id, complexity) selected_experts.append((expert_id, weight)) selected_experts.sort(key=lambda x: x[1], reverse=True) top_k = min(4, len(selected_experts)) return selected_experts[:top_k] def _calculate_expert_weight(self, expert_id: int, complexity: float) -> float: """Calculate expert weight based on complexity""" specializations = { 0: 0.3, # Simple tasks 1: 0.5, # Medium tasks 2: 0.7, # Complex tasks 3: 0.9 # Very complex tasks } expert_specialization = specializations.get(expert_id, 0.5) weight = 1.0 - abs(complexity - expert_specialization) weight *= self.expert_quality_scores[expert_id] return weight def update_quality_scores(self, expert_id: int, quality: float): """Update expert quality scores based on feedback""" alpha = 0.1 self.expert_quality_scores[expert_id] = ( alpha * quality + (1 - alpha) * self.expert_quality_scores[expert_id] ) class DynamicExpertPool: """Dynamic Expert Pool - Manages experts dynamically""" def __init__(self, max_experts: int = 4): self.max_experts = max_experts self.loaded_experts = {} self.expert_usage = {} def load_expert(self, expert_id: int, model_path: str): """Load an expert model""" if len(self.loaded_experts) >= self.max_experts: self._unload_least_used() from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(model_path) self.loaded_experts[expert_id] = model self.expert_usage[expert_id] = 0 logger.info(f"Loaded expert {expert_id}") def unload_expert(self, expert_id: int): """Unload an expert model""" if expert_id in self.loaded_experts: del self.loaded_experts[expert_id] del self.expert_usage[expert_id] import gc gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() logger.info(f"Unloaded expert {expert_id}") def _unload_least_used(self): """Unload the least used expert""" if not self.expert_usage: return least_used_id = min(self.expert_usage, key=self.expert_usage.get) self.unload_expert(least_used_id) def get_expert(self, expert_id: int): """Get an expert model""" if expert_id in self.loaded_experts: self.expert_usage[expert_id] += 1 return self.loaded_experts[expert_id] return None def get_loaded_experts(self) -> List[int]: """Get list of loaded expert IDs""" return list(self.loaded_experts.keys()) def optimize_memory(self): """Optimize memory usage""" current_time = time.time() for expert_id in list(self.expert_usage.keys()): # In real implementation, you'd track last usage time pass def create_resource_manager( memory_budget: float = 8.0, compute_budget: float = 100.0, max_experts: int = 4, quality_threshold: float = 0.8 ) -> IntelligentResourceManager: """Convenience function to create a resource manager""" budget = ResourceBudget( memory_gb=memory_budget, compute_units=compute_budget, max_experts=max_experts, quality_threshold=quality_threshold ) return IntelligentResourceManager(budget)