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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
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