Initial commit

crates/training/rtx-evolution/src/knowledge.rs

@@ -0,0 +1,536 @@
+//! Knowledge Graph
+//!
+//! Meta-learning storage, pattern relationships, and success prediction
+
+use crate::{ExecutionResult, ProposalSpec, Result};
+use petgraph::Graph;
+use petgraph::graph::{NodeIndex, UnGraph};
+use std::collections::HashMap;
+use uuid::Uuid;
+
+/// Knowledge graph for meta-learning
+pub struct KnowledgeGraph {
+    graph: UnGraph<Pattern, Relationship>,
+    pattern_index: HashMap<String, NodeIndex>,
+    success_history: Vec<SuccessRecord>,
+    ready: bool,
+}
+
+/// Pattern stored in knowledge graph
+#[derive(Debug, Clone)]
+pub struct Pattern {
+    pub id: String,
+    pub name: String,
+    pub pattern_type: PatternType,
+    pub success_rate: f64,
+    pub average_improvement: f64,
+    pub usage_count: u64,
+    pub confidence: f64,
+}
+
+/// Type of pattern in the knowledge graph
+#[derive(Debug, Clone, PartialEq)]
+pub enum PatternType {
+    OptimizationTechnique,  // General optimization approach
+    ParameterSetting,       // Specific parameter configurations
+    AlgorithmChoice,        // Algorithm selection patterns
+    ResourceUtilization,    // Resource usage patterns
+    PerformanceCorrelation, // Performance correlation patterns
+}
+
+/// Relationship between patterns
+#[derive(Debug, Clone)]
+pub struct Relationship {
+    pub kind: RelationshipKind,
+    pub strength: f64,
+    pub evidence_count: u64,
+}
+
+/// Types of relationships between patterns
+#[derive(Debug, Clone, PartialEq)]
+pub enum RelationshipKind {
+    Synergy,      // Patterns work well together
+    Conflict,     // Patterns interfere with each other
+    Prerequisite, // One pattern requires another
+    Alternative,  // Patterns are alternatives to each other
+    Correlation,  // Patterns often co-occur
+}
+
+/// Record of successful optimizations
+#[derive(Debug, Clone)]
+struct SuccessRecord {
+    proposal_id: Uuid,
+    patterns_used: Vec<String>,
+    performance_improvement: f64,
+    timestamp: std::time::SystemTime,
+}
+
+impl KnowledgeGraph {
+    /// Create new knowledge graph
+    pub fn new() -> Self {
+        let mut kg = Self {
+            graph: Graph::new_undirected(),
+            pattern_index: HashMap::new(),
+            success_history: Vec::new(),
+            ready: true,
+        };
+
+        // Initialize with some basic patterns
+        kg.initialize_base_patterns();
+
+        kg
+    }
+
+    /// Check if knowledge graph is ready
+    pub fn is_ready(&self) -> bool {
+        self.ready
+    }
+
+    /// Update knowledge graph from execution results
+    pub async fn update_from_execution(
+        &mut self,
+        proposal: &ProposalSpec,
+        result: &ExecutionResult,
+    ) -> Result<()> {
+        if result.safety_check_passed && result.performance_delta > 0.0 {
+            // Extract patterns from successful proposal
+            let patterns = self.extract_patterns_from_proposal(proposal).await?;
+
+            // Update pattern success rates
+            for pattern_name in &patterns {
+                self.update_pattern_success(pattern_name, result.performance_delta)
+                    .await?;
+            }
+
+            // Learn relationships between patterns
+            self.learn_pattern_relationships(&patterns, result.performance_delta)
+                .await?;
+
+            // Record successful execution
+            self.record_success(proposal, result, patterns).await?;
+        } else {
+            // Learn from failures too
+            let patterns = self.extract_patterns_from_proposal(proposal).await?;
+            for pattern_name in &patterns {
+                self.update_pattern_failure(pattern_name).await?;
+            }
+        }
+
+        Ok(())
+    }
+
+    /// Initialize knowledge graph with base patterns
+    fn initialize_base_patterns(&mut self) {
+        let base_patterns = vec![
+            Pattern {
+                id: "kernel_tile_optimization".to_string(),
+                name: "Kernel Tile Size Optimization".to_string(),
+                pattern_type: PatternType::ParameterSetting,
+                success_rate: 0.7,
+                average_improvement: 0.12,
+                usage_count: 0,
+                confidence: 0.8,
+            },
+            Pattern {
+                id: "memory_layout_optimization".to_string(),
+                name: "Memory Layout Optimization".to_string(),
+                pattern_type: PatternType::OptimizationTechnique,
+                success_rate: 0.6,
+                average_improvement: 0.08,
+                usage_count: 0,
+                confidence: 0.7,
+            },
+            Pattern {
+                id: "compiler_aggressive_opts".to_string(),
+                name: "Aggressive Compiler Optimizations".to_string(),
+                pattern_type: PatternType::AlgorithmChoice,
+                success_rate: 0.5,
+                average_improvement: 0.05,
+                usage_count: 0,
+                confidence: 0.6,
+            },
+            Pattern {
+                id: "algorithm_switching".to_string(),
+                name: "Algorithm Switching".to_string(),
+                pattern_type: PatternType::AlgorithmChoice,
+                success_rate: 0.8,
+                average_improvement: 0.15,
+                usage_count: 0,
+                confidence: 0.85,
+            },
+            // AI-powered optimization patterns
+            Pattern {
+                id: "ai_powered_optimization".to_string(),
+                name: "AI-Powered Code Optimization".to_string(),
+                pattern_type: PatternType::OptimizationTechnique,
+                success_rate: 0.85,
+                average_improvement: 0.22,
+                usage_count: 0,
+                confidence: 0.90,
+            },
+            // RTX 5090 hardware-specific patterns
+            Pattern {
+                id: "hardware_specific_optimization".to_string(),
+                name: "Hardware-Specific RTX 5090 Optimization".to_string(),
+                pattern_type: PatternType::OptimizationTechnique,
+                success_rate: 0.90,
+                average_improvement: 0.28,
+                usage_count: 0,
+                confidence: 0.95,
+            },
+            // CUDA 13.0 feature enablement patterns
+            Pattern {
+                id: "cuda_feature_enablement".to_string(),
+                name: "CUDA 13.0 Feature Enablement".to_string(),
+                pattern_type: PatternType::OptimizationTechnique,
+                success_rate: 0.82,
+                average_improvement: 0.20,
+                usage_count: 0,
+                confidence: 0.88,
+            },
+        ];
+
+        for pattern in base_patterns {
+            let node_index = self.graph.add_node(pattern.clone());
+            self.pattern_index.insert(pattern.id, node_index);
+        }
+
+        // Add some initial relationships
+        self.add_initial_relationships();
+    }
+
+    /// Add initial known relationships between patterns
+    fn add_initial_relationships(&mut self) {
+        // Kernel tile optimization and memory layout often work well together (synergy)
+        if let (Some(&tile_idx), Some(&memory_idx)) = (
+            self.pattern_index.get("kernel_tile_optimization"),
+            self.pattern_index.get("memory_layout_optimization"),
+        ) {
+            self.graph.add_edge(
+                tile_idx,
+                memory_idx,
+                Relationship {
+                    kind: RelationshipKind::Synergy,
+                    strength: 0.7,
+                    evidence_count: 5,
+                },
+            );
+        }
+
+        // Aggressive compiler opts and algorithm switching can conflict
+        if let (Some(&compiler_idx), Some(&algorithm_idx)) = (
+            self.pattern_index.get("compiler_aggressive_opts"),
+            self.pattern_index.get("algorithm_switching"),
+        ) {
+            self.graph.add_edge(
+                compiler_idx,
+                algorithm_idx,
+                Relationship {
+                    kind: RelationshipKind::Conflict,
+                    strength: 0.4,
+                    evidence_count: 3,
+                },
+            );
+        }
+    }
+
+    /// Extract patterns from a proposal
+    async fn extract_patterns_from_proposal(&self, proposal: &ProposalSpec) -> Result<Vec<String>> {
+        let mut patterns = Vec::new();
+
+        for change in &proposal.changes {
+            match change {
+                crate::Change::KernelParameter { .. } => {
+                    patterns.push("kernel_tile_optimization".to_string());
+                }
+                crate::Change::MemoryLayout { .. } => {
+                    patterns.push("memory_layout_optimization".to_string());
+                }
+                crate::Change::CompilerFlag { .. } => {
+                    patterns.push("compiler_aggressive_opts".to_string());
+                }
+                crate::Change::AlgorithmSwitch { .. } => {
+                    patterns.push("algorithm_switching".to_string());
+                }
+                crate::Change::CodeOptimization {
+                    optimization_type, ..
+                } => {
+                    patterns.push(format!("ai_code_optimization_{}", optimization_type));
+                    patterns.push("ai_powered_optimization".to_string());
+                }
+                crate::Change::Rtx5090Optimization {
+                    optimization_type, ..
+                } => {
+                    patterns.push(format!("rtx5090_optimization_{}", optimization_type));
+                    patterns.push("hardware_specific_optimization".to_string());
+                }
+                crate::Change::Cuda13Feature { feature_name, .. } => {
+                    patterns.push(format!("cuda13_feature_{}", feature_name));
+                    patterns.push("cuda_feature_enablement".to_string());
+                }
+            }
+        }
+
+        Ok(patterns)
+    }
+
+    /// Update pattern success statistics
+    async fn update_pattern_success(&mut self, pattern_name: &str, improvement: f64) -> Result<()> {
+        if let Some(&node_idx) = self.pattern_index.get(pattern_name) {
+            if let Some(pattern) = self.graph.node_weight_mut(node_idx) {
+                // Update success rate using exponential moving average
+                let alpha = 0.1; // Learning rate
+                pattern.success_rate = (1.0 - alpha) * pattern.success_rate + alpha * 1.0;
+
+                // Update average improvement
+                pattern.average_improvement =
+                    (1.0 - alpha) * pattern.average_improvement + alpha * improvement;
+
+                // Increment usage count
+                pattern.usage_count += 1;
+
+                // Update confidence based on usage count
+                pattern.confidence =
+                    (pattern.usage_count as f64 / (pattern.usage_count as f64 + 10.0)).min(0.95);
+            }
+        }
+
+        Ok(())
+    }
+
+    /// Update pattern failure statistics
+    async fn update_pattern_failure(&mut self, pattern_name: &str) -> Result<()> {
+        if let Some(&node_idx) = self.pattern_index.get(pattern_name) {
+            if let Some(pattern) = self.graph.node_weight_mut(node_idx) {
+                // Update success rate with failure
+                let alpha = 0.1;
+                pattern.success_rate = (1.0 - alpha) * pattern.success_rate + alpha * 0.0;
+
+                // Increment usage count
+                pattern.usage_count += 1;
+
+                // Adjust confidence down slightly for failures
+                pattern.confidence *= 0.98;
+            }
+        }
+
+        Ok(())
+    }
+
+    /// Learn relationships between patterns based on co-occurrence
+    async fn learn_pattern_relationships(
+        &mut self,
+        patterns: &[String],
+        improvement: f64,
+    ) -> Result<()> {
+        // Learn relationships between all pairs of patterns used together
+        for i in 0..patterns.len() {
+            for j in (i + 1)..patterns.len() {
+                let pattern_a = &patterns[i];
+                let pattern_b = &patterns[j];
+
+                if let (Some(&idx_a), Some(&idx_b)) = (
+                    self.pattern_index.get(pattern_a),
+                    self.pattern_index.get(pattern_b),
+                ) {
+                    self.update_relationship(idx_a, idx_b, improvement).await?;
+                }
+            }
+        }
+
+        Ok(())
+    }
+
+    /// Update relationship strength between two patterns
+    async fn update_relationship(
+        &mut self,
+        idx_a: NodeIndex,
+        idx_b: NodeIndex,
+        improvement: f64,
+    ) -> Result<()> {
+        // Find existing edge or create new one
+        let edge_idx = self.graph.find_edge(idx_a, idx_b);
+
+        match edge_idx {
+            Some(edge_idx) => {
+                // Update existing relationship
+                if let Some(relationship) = self.graph.edge_weight_mut(edge_idx) {
+                    relationship.evidence_count += 1;
+
+                    // Determine relationship kind based on improvement
+                    let relationship_kind = if improvement > 0.1 {
+                        RelationshipKind::Synergy
+                    } else if improvement < 0.0 {
+                        RelationshipKind::Conflict
+                    } else {
+                        RelationshipKind::Correlation
+                    };
+
+                    // Update strength using exponential moving average
+                    let alpha = 0.2;
+                    let new_strength = improvement.abs();
+                    relationship.strength =
+                        (1.0 - alpha) * relationship.strength + alpha * new_strength;
+
+                    // Update relationship kind if evidence is strong
+                    if relationship.evidence_count >= 3 {
+                        relationship.kind = relationship_kind;
+                    }
+                }
+            }
+            None => {
+                // Create new relationship
+                let relationship_kind = if improvement > 0.1 {
+                    RelationshipKind::Synergy
+                } else if improvement < 0.0 {
+                    RelationshipKind::Conflict
+                } else {
+                    RelationshipKind::Correlation
+                };
+
+                let new_relationship = Relationship {
+                    kind: relationship_kind,
+                    strength: improvement.abs(),
+                    evidence_count: 1,
+                };
+
+                self.graph.add_edge(idx_a, idx_b, new_relationship);
+            }
+        }
+
+        Ok(())
+    }
+
+    /// Record successful execution in history
+    async fn record_success(
+        &mut self,
+        proposal: &ProposalSpec,
+        result: &ExecutionResult,
+        patterns: Vec<String>,
+    ) -> Result<()> {
+        let success_record = SuccessRecord {
+            proposal_id: proposal.id,
+            patterns_used: patterns,
+            performance_improvement: result.performance_delta,
+            timestamp: std::time::SystemTime::now(),
+        };
+
+        // In a real implementation, this would be mutable
+        // For now, we simulate recording the success
+        tracing::info!(
+            "Recording success: proposal {} with {:.2}% improvement using {} patterns",
+            proposal.id,
+            result.performance_delta * 100.0,
+            success_record.patterns_used.len()
+        );
+
+        Ok(())
+    }
+
+    /// Get recommendations for optimization patterns
+    pub async fn get_recommendations(
+        &self,
+        context: &OptimizationContext,
+    ) -> Result<Vec<PatternRecommendation>> {
+        let mut recommendations = Vec::new();
+
+        // Analyze all patterns and rank by potential success
+        for (pattern_id, &node_idx) in &self.pattern_index {
+            if let Some(pattern) = self.graph.node_weight(node_idx) {
+                let score = self
+                    .calculate_recommendation_score(pattern, context)
+                    .await?;
+
+                if score > 0.3 {
+                    // Threshold for recommendations
+                    recommendations.push(PatternRecommendation {
+                        pattern_id: pattern_id.clone(),
+                        pattern_name: pattern.name.clone(),
+                        confidence: pattern.confidence,
+                        expected_improvement: pattern.average_improvement,
+                        score,
+                        rationale: self.generate_rationale(pattern, context).await?,
+                    });
+                }
+            }
+        }
+
+        // Sort by score (highest first)
+        recommendations.sort_by(|a, b| b.score.total_cmp(&a.score));
+
+        // Limit to top 5 recommendations
+        recommendations.truncate(5);
+
+        Ok(recommendations)
+    }
+
+    /// Calculate recommendation score for a pattern given context
+    async fn calculate_recommendation_score(
+        &self,
+        pattern: &Pattern,
+        context: &OptimizationContext,
+    ) -> Result<f64> {
+        let base_score = pattern.success_rate * pattern.confidence;
+
+        // Adjust based on context
+        let context_multiplier = match (&pattern.pattern_type, &context.optimization_target) {
+            (PatternType::ParameterSetting, OptimizationTarget::Performance) => 1.2,
+            (PatternType::AlgorithmChoice, OptimizationTarget::Performance) => 1.5,
+            (PatternType::OptimizationTechnique, OptimizationTarget::Memory) => 1.3,
+            (PatternType::ResourceUtilization, OptimizationTarget::Power) => 1.4,
+            _ => 1.0,
+        };
+
+        let final_score = base_score * context_multiplier;
+        Ok(final_score.max(0.0).min(1.0))
+    }
+
+    /// Generate rationale for why a pattern is recommended
+    async fn generate_rationale(
+        &self,
+        pattern: &Pattern,
+        context: &OptimizationContext,
+    ) -> Result<String> {
+        let success_percentage = (pattern.success_rate * 100.0) as i32;
+        let avg_improvement_percentage = (pattern.average_improvement * 100.0) as i32;
+
+        let rationale = format!(
+            "Pattern '{}' has {:.0}% success rate with average {:.0}% improvement. Used successfully {} times. Confidence: {:.0}%",
+            pattern.name,
+            success_percentage,
+            avg_improvement_percentage,
+            pattern.usage_count,
+            pattern.confidence * 100.0
+        );
+
+        Ok(rationale)
+    }
+}
+
+/// Context for optimization recommendations
+#[derive(Debug, Clone)]
+pub struct OptimizationContext {
+    pub optimization_target: OptimizationTarget,
+    pub current_metrics: HashMap<String, f64>,
+    pub constraints: Vec<String>,
+}
+
+/// Target for optimization
+#[derive(Debug, Clone, PartialEq)]
+pub enum OptimizationTarget {
+    Performance,
+    Memory,
+    Power,
+    Latency,
+}
+
+/// Pattern recommendation from knowledge graph
+#[derive(Debug, Clone)]
+pub struct PatternRecommendation {
+    pub pattern_id: String,
+    pub pattern_name: String,
+    pub confidence: f64,
+    pub expected_improvement: f64,
+    pub score: f64,
+    pub rationale: String,
+}