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ae1e90a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | #!/usr/bin/env python3
"""
Expert Router for LEGO-MoE
Routes queries to appropriate experts based on ISL decisions
"""
import torch
import re
from pathlib import Path
import sys
sys.path.append(str(Path(__file__).parent / 'training' / 'distillation'))
from models import ClassificationExpert
class ExpertRouter:
"""Routes queries to appropriate micro-experts"""
def __init__(self):
print("Loading expert router...")
self.isl = self.load_isl_expert()
self.experts = self.load_all_experts()
print(f"✓ Router loaded with {len(self.experts)} experts")
def load_isl_expert(self):
"""Load ISL meta-governor"""
try:
expert = ClassificationExpert(
input_dim=256,
hidden_dim=256,
num_layers=3,
num_classes=3,
dropout=0.1
)
expert.load_state_dict(torch.load("models/isl_meta-governor_expert.pt", map_location='cpu'))
expert.eval()
return expert
except Exception as e:
print(f"⚠️ Could not load ISL expert: {e}")
return None
def load_all_experts(self):
"""Load all available micro-experts"""
experts = {}
expert_configs = [
("grammar", 2),
("mathematical_reasoning", 2),
("question_answering", 2),
("semantic_understanding", 2),
]
for name, num_classes in expert_configs:
try:
expert = ClassificationExpert(
input_dim=256,
hidden_dim=256,
num_layers=3,
num_classes=num_classes,
dropout=0.1
)
expert.load_state_dict(torch.load(f"models/{name}_expert.pt", map_location='cpu'))
expert.eval()
experts[name] = expert
except:
continue
return experts
def encode_query(self, query: str) -> torch.Tensor:
"""Simple query encoding"""
features = torch.zeros(1, 256)
# Set features based on query patterns
query_lower = query.lower()
# Math indicators
if any(word in query_lower for word in ['calculate', 'compute', 'solve', 'math']):
features[0, 0] = 1.0
if any(char in query for char in ['+', '-', '*', '/', '=', '%']):
features[0, 1] = 1.0
if re.search(r'\d+', query):
features[0, 2] = 1.0
# Grammar indicators
if any(word in query_lower for word in ['grammar', 'correct', 'fix', 'spell']):
features[0, 3] = 1.0
# Question indicators
if query.strip().endswith('?'):
features[0, 4] = 1.0
if any(word in query_lower for word in ['what', 'why', 'how', 'when', 'where', 'who']):
features[0, 5] = 1.0
# Code indicators
if any(word in query_lower for word in ['code', 'function', 'python', 'javascript']):
features[0, 6] = 1.0
if any(char in query for char in ['(', ')', '{', '}', '[', ']', ';']):
features[0, 7] = 1.0
return features
def isl_decision(self, query: str) -> str:
"""ISL expert decides complexity"""
if not self.isl:
return "ESCALATE_FULL" # Default if ISL not available
embedding = self.encode_query(query)
with torch.no_grad():
_, confidence, logits = self.isl(embedding)
prediction = torch.argmax(logits, dim=1).item()
decisions = ["TERMINATE_SAFE", "ESCALATE_FULL", "ESCALATE_PARTIAL"]
decision = decisions[prediction] if prediction < 3 else "ESCALATE_FULL"
return decision
def select_experts(self, query: str) -> list:
"""Select which experts to run"""
selected = []
query_lower = query.lower()
# Math expert
if any(word in query_lower for word in ['calculate', 'compute', 'solve', 'math', 'multiply', 'add', 'subtract']):
if "mathematical_reasoning" in self.experts:
selected.append("mathematical_reasoning")
if re.search(r'\d+\s*[\+\-\*\/]\s*\d+', query):
if "mathematical_reasoning" in self.experts:
selected.append("mathematical_reasoning")
# Grammar expert
if any(word in query_lower for word in ['grammar', 'correct', 'fix', 'spell', 'punctuation']):
if "grammar" in self.experts:
selected.append("grammar")
# QA expert
if query.strip().endswith('?'):
if "question_answering" in self.experts:
selected.append("question_answering")
# Semantic understanding for complex queries
if len(query.split()) > 10:
if "semantic_understanding" in self.experts:
selected.append("semantic_understanding")
return list(set(selected)) # Remove duplicates
def route(self, query: str) -> dict:
"""
Route query to appropriate experts
Returns:
{
'decision': ISL decision,
'experts': list of expert names to run,
'direct_to_llm': bool
}
"""
# ISL decision
decision = self.isl_decision(query)
# If terminate safe, go direct to LLM
if decision == "TERMINATE_SAFE":
return {
'decision': decision,
'experts': [],
'direct_to_llm': True
}
# Select experts
experts = self.select_experts(query)
return {
'decision': decision,
'experts': experts,
'direct_to_llm': len(experts) == 0
}
def test_router():
"""Test expert router"""
print("="*70)
print("Testing Expert Router")
print("="*70)
router = ExpertRouter()
test_queries = [
"What is 15 * 23?",
"Fix this sentence: I goes to school yesterday",
"What is the capital of France?",
"Hello, how are you?",
]
for query in test_queries:
print(f"\nQuery: {query}")
result = router.route(query)
print(f" ISL Decision: {result['decision']}")
print(f" Selected Experts: {result['experts']}")
print(f" Direct to LLM: {result['direct_to_llm']}")
print("\n" + "="*70)
print("✓ Router working!")
print("="*70)
if __name__ == "__main__":
test_router()
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