SLM 튜닝예제

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import List, Dict

class SQLTuningSLM(nn.Module):
    def __init__(self, feature_dim, hidden_dim, output_dim):
        super(SQLTuningSLM, self).__init__()
        
        # Feature embedding and processing layers
        self.feature_embedding = nn.Sequential(
            nn.Linear(feature_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.3)
        )
        
        # LSTM for sequential feature processing
        self.lstm = nn.LSTM(hidden_dim, hidden_dim, num_layers=2, 
                            batch_first=True, dropout=0.3)
        
        # Output layers for performance prediction
        self.performance_predictor = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim//2),
            nn.ReLU(),
            nn.Linear(hidden_dim//2, output_dim)
        )
    
    def forward(self, x):
        # Embed input features
        embedded = self.feature_embedding(x)
        
        # Process through LSTM
        lstm_out, _ = self.lstm(embedded.unsqueeze(1))
        
        # Predict performance optimizations
        performance_output = self.performance_predictor(lstm_out.squeeze(1))
        
        return performance_output

class SQLQueryOptimizer:
    def __init__(self, model_path=None):
        # Features to consider for SQL query tuning
        self.features = [
            'index_count', 
            'join_complexity', 
            'table_size', 
            'where_clause_count', 
            'aggregation_complexity',
            'subquery_depth'
        ]
    
    def extract_query_features(self, sql_query: str) -> np.ndarray:
        # Placeholder for feature extraction logic
        # In a real implementation, this would parse the SQL query 
        # and extract relevant performance-related features
        features = np.random.rand(len(self.features))
        return features
    
    def train_optimization_model(self, queries: List[str], performance_targets: List[float]):
        # Prepare training data
        X = np.array([self.extract_query_features(q) for q in queries])
        y = np.array(performance_targets)
        
        # Convert to PyTorch tensors
        X_tensor = torch.FloatTensor(X)
        y_tensor = torch.FloatTensor(y)
        
        # Initialize model
        model = SQLTuningSLM(
            feature_dim=len(self.features), 
            hidden_dim=64, 
            output_dim=1
        )
        
        # Loss and optimizer
        criterion = nn.MSELoss()
        optimizer = optim.Adam(model.parameters(), lr=0.01)
        
        # Training loop
        for epoch in range(100):
            optimizer.zero_grad()
            predictions = model(X_tensor)
            loss = criterion(predictions.squeeze(), y_tensor)
            loss.backward()
            optimizer.step()
            
            if epoch % 10 == 0:
                print(f'Epoch {epoch}, Loss: {loss.item():.4f}')
        
        return model
    
    def suggest_optimizations(self, model, query: str) -> Dict[str, str]:
        # Extract features for the query
        features = self.extract_query_features(query)
        features_tensor = torch.FloatTensor(features)
        
        # Predict potential optimizations
        with torch.no_grad():
            performance_score = model(features_tensor)
        
        # Example optimization suggestions (mock implementation)
        optimizations = {
            'index_recommendation': 'Consider adding an index on join columns',
            'query_restructuring': 'Simplify complex subqueries',
            'performance_score': f'{performance_score.item():.2f}'
        }
        
        return optimizations

# Example usage
def main():
    optimizer = SQLQueryOptimizer()
    
    # Sample training queries and their performance metrics
    queries = [
        "SELECT * FROM large_table WHERE complex_condition",
        "SELECT AVG(column) FROM nested_subquery_table"
    ]
    performance_targets = [100.0, 75.5]  # Lower is better
    
    # Train the optimization model
    tuning_model = optimizer.train_optimization_model(queries, performance_targets)
    
    # Get optimization suggestions for a new query
    new_query = "SELECT COUNT(*) FROM complex_join_table"
    suggestions = optimizer.suggest_optimizations(tuning_model, new_query)
    print("Optimization Suggestions:", suggestions)

if __name__ == "__main__":
    main()