from dataclasses import dataclass import torch import torch.nn as nn from typing import List, Dict @dataclass class ExpertAllocation: expert_id: int load_factor: float specialization_score: float capacity_available: float class TopologyAwareRouter: def __init__(self): pass class LoadBalancer: def __init__(self): pass class ExpertRoutingSystem: def __init__(self, num_experts: int = 128): self.num_experts = num_experts self.experts = self._initialize_experts() self.router = TopologyAwareRouter() self.load_balancer = LoadBalancer() def allocate_experts(self, input_pattern: torch.Tensor) -> Dict[int, float]: task_requirements = self._analyze_task_requirements(input_pattern) available_experts = self._get_available_experts() return self._optimize_expert_allocation(task_requirements, available_experts) def _analyze_task_requirements(self, input_pattern: torch.Tensor) -> Dict[str, float]: complexity = self._estimate_task_complexity(input_pattern) specialization_needs = self._determine_specialization_needs(input_pattern) return { 'complexity': complexity, 'specialization': specialization_needs, 'resource_requirements': self._estimate_resource_needs(complexity) } def _initialize_experts(self): # Initialize experts return [i for i in range(self.num_experts)] def _get_available_experts(self): # Get available experts return self.experts def _optimize_expert_allocation(self, task_requirements, available_experts): # Optimize expert allocation return {expert: 1.0 for expert in available_experts[:3]} def _estimate_task_complexity(self, input_pattern): # Estimate task complexity return 0.5 def _determine_specialization_needs(self, input_pattern): # Determine specialization needs return 0.7 def _estimate_resource_needs(self, complexity): # Estimate resource needs return complexity * 2.0