532 lines
16 KiB
Rust
532 lines
16 KiB
Rust
//! Evolution Orchestrator - Main evolution loop coordinator
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//!
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//! The orchestrator manages the entire evolution cycle:
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//! 1. Telemetry Analysis → Pattern Mining
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//! 2. Proposal Generation → Multi-objective Optimization
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//! 3. Sandbox Validation → Safety & Performance Testing
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//! 4. Knowledge Graph Learning → Meta-learning Updates
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//! 5. Production Deployment → Safe Rollout
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use crate::{EvolutionError, Result};
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use crate::{KnowledgeGraph, MultiObjectiveOptimizer, SafeSandbox, TelemetryAnalyzer};
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use crate::{ParetoFrontier, PerformancePattern, SandboxConfig};
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use std::collections::HashMap;
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use std::time::{Duration, Instant};
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use tracing::{debug, info, warn};
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use uuid::Uuid;
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/// Configuration for the evolution orchestrator
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#[derive(Debug, Clone)]
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pub struct EvolutionConfig {
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/// How often to analyze telemetry and generate proposals
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pub analysis_interval: Duration,
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/// Maximum time allowed for proposal validation
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pub proposal_timeout: Duration,
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/// Memory limit for sandbox execution (bytes)
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pub sandbox_memory_limit: u64,
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/// Maximum number of concurrent proposals to evaluate
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pub max_concurrent_proposals: usize,
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/// Minimum performance improvement to accept a proposal
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pub success_threshold: f64,
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/// Whether to enable automatic rollback on failures
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pub rollback_enabled: bool,
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}
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impl Default for EvolutionConfig {
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fn default() -> Self {
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Self {
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analysis_interval: Duration::from_secs(300), // 5 minutes
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proposal_timeout: Duration::from_secs(600), // 10 minutes
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sandbox_memory_limit: 2 * 1024 * 1024 * 1024, // 2GB
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max_concurrent_proposals: 3,
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success_threshold: 0.02, // 2% minimum improvement
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rollback_enabled: true,
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}
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}
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}
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impl EvolutionConfig {
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/// Create RTX 5090 optimized configuration
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pub fn rtx5090_optimized() -> Self {
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Self {
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analysis_interval: Duration::from_millis(100), // 10Hz monitoring for real-time optimization
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proposal_timeout: Duration::from_secs(60), // Faster validation cycles
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sandbox_memory_limit: 16 * 1024 * 1024 * 1024, // 16GB - RTX 5090 has large memory
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max_concurrent_proposals: 8, // RTX 5090 can handle more concurrent testing
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success_threshold: 0.01, // 1% minimum improvement - RTX 5090 enables fine-grained optimization
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rollback_enabled: true,
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}
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}
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}
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/// Specification for an optimization proposal
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#[derive(Debug, Clone)]
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pub struct ProposalSpec {
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pub id: Uuid,
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pub description: String,
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pub changes: Vec<Change>,
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pub expected_improvement: f64,
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pub confidence: f64,
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pub risk_level: RiskLevel,
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}
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/// Types of changes that can be proposed
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#[derive(Debug, Clone)]
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pub enum Change {
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KernelParameter {
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kernel: String,
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param: String,
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old_value: i32,
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new_value: i32,
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},
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CompilerFlag {
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flag: String,
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enabled: bool,
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},
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MemoryLayout {
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layout: String,
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},
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AlgorithmSwitch {
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component: String,
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from_algorithm: String,
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to_algorithm: String,
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},
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/// AI-powered code optimization
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CodeOptimization {
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component: String,
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optimization_type: String,
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code_changes: Vec<String>,
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},
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/// RTX 5090 specific optimization
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Rtx5090Optimization {
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optimization_type: String,
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target_feature: String,
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implementation: String,
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},
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/// CUDA 13.0 feature enablement
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Cuda13Feature {
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feature_name: String,
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implementation_code: String,
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performance_target: f64,
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},
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}
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/// Risk level of a proposed change
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#[derive(Debug, Clone, PartialEq)]
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pub enum RiskLevel {
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Low, // Safe changes with minimal impact
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Medium, // Moderate changes requiring validation
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High, // Risky changes requiring careful testing
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}
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/// Result of executing a proposal in the sandbox
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#[derive(Debug, Clone)]
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pub struct ExecutionResult {
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pub proposal_id: Uuid,
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pub performance_delta: f64,
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pub memory_delta: f64,
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pub safety_check_passed: bool,
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pub execution_time: Duration,
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pub error_message: Option<String>,
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}
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/// Result of a rollback operation
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#[derive(Debug)]
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pub struct RollbackResult {
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pub rolled_back: bool,
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pub rollback_time: Duration,
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pub restored_state: String,
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}
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/// Statistics about evolution cycles
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#[derive(Debug)]
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pub struct EvolutionStatistics {
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pub total_cycles: u64,
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pub successful_proposals: u64,
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pub failed_proposals: u64,
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pub rollbacks: u64,
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pub average_improvement: f64,
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pub total_runtime: Duration,
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pub last_cycle_time: Option<Instant>,
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}
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/// Main evolution orchestrator coordinating the evolution loop
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pub struct EvolutionOrchestrator {
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config: EvolutionConfig,
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telemetry_analyzer: TelemetryAnalyzer,
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optimizer: MultiObjectiveOptimizer,
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sandbox: SafeSandbox,
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knowledge_graph: KnowledgeGraph,
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statistics: EvolutionStatistics,
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cycle_count: u64,
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start_time: Instant,
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active_proposals: HashMap<Uuid, ProposalSpec>,
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}
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/// Real-time metrics for autonomous optimization
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#[derive(Debug, Clone)]
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pub struct RealTimeMetric {
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pub timestamp: u64,
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pub metric_name: String,
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pub value: f64,
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pub unit: String,
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}
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/// Optimization opportunity detected by AI
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#[derive(Debug, Clone)]
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pub struct OptimizationOpportunity {
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pub confidence: f64,
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pub expected_improvement: f64,
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}
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/// Code pattern for AI analysis
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#[derive(Debug, Clone)]
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pub struct CodePattern {
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pub pattern_type: String,
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pub frequency: f64,
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}
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/// AI-generated optimization proposal
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#[derive(Debug, Clone)]
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pub struct AiProposal {
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pub confidence: f64,
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pub proposal: ProposalSpec,
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}
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/// Performance bottleneck
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#[derive(Debug, Clone)]
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pub struct PerformanceBottleneck {
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pub component: String,
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pub severity: f64,
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}
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/// Bottleneck optimization
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#[derive(Debug, Clone)]
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pub struct BottleneckOptimization {
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pub confidence: f64,
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pub expected_improvement: f64,
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}
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/// Thermal state
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#[derive(Debug, Clone)]
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pub struct ThermalState {
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pub temperature: f64,
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pub power_consumption: f64,
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}
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/// Thermal optimization
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#[derive(Debug, Clone)]
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pub struct ThermalOptimization;
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/// Power optimization
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#[derive(Debug, Clone)]
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pub struct PowerOptimization;
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/// Optimization result
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#[derive(Debug, Clone)]
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pub struct OptimizationResult;
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/// Rollback state
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#[derive(Debug, Clone)]
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pub struct RollbackState;
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impl EvolutionOrchestrator {
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/// Create a new evolution orchestrator
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pub fn new(config: EvolutionConfig) -> Self {
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let sandbox_config = SandboxConfig {
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memory_limit: config.sandbox_memory_limit,
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timeout: config.proposal_timeout,
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isolation_level: IsolationLevel::Full,
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};
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Self {
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telemetry_analyzer: TelemetryAnalyzer::new(),
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optimizer: MultiObjectiveOptimizer::new(),
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sandbox: SafeSandbox::new(sandbox_config),
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knowledge_graph: KnowledgeGraph::new(),
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statistics: EvolutionStatistics {
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total_cycles: 0,
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successful_proposals: 0,
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failed_proposals: 0,
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rollbacks: 0,
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average_improvement: 0.0,
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total_runtime: Duration::ZERO,
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last_cycle_time: None,
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},
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cycle_count: 0,
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start_time: Instant::now(),
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active_proposals: HashMap::new(),
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config,
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}
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}
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/// Check if orchestrator is ready for operation
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pub fn is_ready(&self) -> bool {
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self.telemetry_analyzer.is_initialized()
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&& self.sandbox.is_available()
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&& self.knowledge_graph.is_ready()
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}
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/// Get the current configuration
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pub fn config(&self) -> &EvolutionConfig {
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&self.config
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}
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/// Run the main evolution loop indefinitely
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pub async fn run(&mut self) -> Result<()> {
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info!("Starting evolution orchestrator");
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loop {
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match self.run_single_cycle().await {
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Ok(()) => {
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info!(
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"Evolution cycle {} completed successfully",
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self.cycle_count
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);
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}
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Err(e) => {
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warn!("Evolution cycle {} failed: {}", self.cycle_count, e);
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// Continue running unless critical error
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if self.is_critical_error(&e) {
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return Err(e);
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}
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}
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}
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// Wait for next analysis interval
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tokio::time::sleep(self.config.analysis_interval).await;
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}
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}
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/// Run a single evolution cycle
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pub async fn run_single_cycle(&mut self) -> Result<()> {
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let cycle_start = Instant::now();
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self.cycle_count += 1;
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self.statistics.total_cycles = self.cycle_count;
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debug!("Starting evolution cycle {}", self.cycle_count);
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// 1. Analyze current system telemetry
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let telemetry_data = self.collect_telemetry().await?;
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let patterns = self.analyze_telemetry(&telemetry_data).await?;
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if patterns.is_empty() {
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debug!(
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"No optimization patterns found in cycle {}",
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self.cycle_count
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);
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return Ok(());
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}
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// 2. Generate optimization proposals
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let proposals = self.generate_proposals().await?;
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if proposals.is_empty() {
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debug!("No proposals generated in cycle {}", self.cycle_count);
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return Ok(());
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}
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// 3. Find Pareto-optimal proposals
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let pareto_frontier = self.find_pareto_optimal(&proposals).await?;
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// 4. Validate top proposals in sandbox
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let mut successful_proposals = Vec::new();
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for proposal in &pareto_frontier.solutions {
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if successful_proposals.len() >= self.config.max_concurrent_proposals {
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break;
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}
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match self.validate_proposal(proposal).await {
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Ok(result) => {
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if result.safety_check_passed
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&& result.performance_delta >= self.config.success_threshold
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{
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successful_proposals.push((proposal.clone(), result));
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self.statistics.successful_proposals += 1;
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} else {
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self.statistics.failed_proposals += 1;
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}
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}
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Err(e) => {
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warn!("Proposal validation failed: {}", e);
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self.statistics.failed_proposals += 1;
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}
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}
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}
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// 5. Learn from results and update knowledge graph
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for (proposal, result) in &successful_proposals {
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self.learn_from_result(proposal, result).await?;
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}
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// 6. Apply successful proposals (in practice, this would deploy to production)
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for (proposal, result) in successful_proposals {
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info!(
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"Would apply proposal {}: {} with {:.2}% improvement",
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proposal.id,
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proposal.description,
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result.performance_delta * 100.0
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);
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}
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// Update statistics
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let cycle_time = cycle_start.elapsed();
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self.statistics.total_runtime += cycle_time;
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self.statistics.last_cycle_time = Some(Instant::now());
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Ok(())
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}
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/// Collect current system telemetry
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async fn collect_telemetry(&self) -> Result<Vec<(&'static str, f64)>> {
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// In a real implementation, this would collect actual metrics
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// For now, simulate some telemetry data
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Ok(vec![
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("gpu_utilization", 0.78),
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("memory_usage", 0.65),
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("kernel_exec_time", 0.023),
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("power_consumption", 0.82),
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("bandwidth_utilization", 0.71),
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])
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}
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/// Analyze telemetry data to identify optimization patterns
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pub async fn analyze_telemetry(
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&self,
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telemetry_data: &[(&str, f64)],
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) -> Result<Vec<PerformancePattern>> {
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self.telemetry_analyzer
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.analyze_patterns(telemetry_data)
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.await
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}
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/// Generate optimization proposals based on current analysis
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pub async fn generate_proposals(&self) -> Result<Vec<ProposalSpec>> {
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let mut proposals = Vec::new();
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// Generate diverse proposal types based on knowledge graph insights
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proposals.push(ProposalSpec {
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id: Uuid::new_v4(),
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description: "Increase kernel tile size for better cache utilization".to_string(),
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changes: vec![Change::KernelParameter {
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kernel: "matmul".to_string(),
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param: "tile_size".to_string(),
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old_value: 16,
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new_value: 32,
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}],
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expected_improvement: 0.12,
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confidence: 0.85,
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risk_level: RiskLevel::Low,
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});
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proposals.push(ProposalSpec {
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id: Uuid::new_v4(),
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description: "Switch to more aggressive compiler optimizations".to_string(),
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changes: vec![Change::CompilerFlag {
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flag: "fast-math".to_string(),
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enabled: true,
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}],
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expected_improvement: 0.08,
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confidence: 0.70,
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risk_level: RiskLevel::Medium,
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});
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proposals.push(ProposalSpec {
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id: Uuid::new_v4(),
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description: "Optimize memory layout for sequential access".to_string(),
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changes: vec![Change::MemoryLayout {
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layout: "row_major_optimized".to_string(),
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}],
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expected_improvement: 0.15,
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confidence: 0.75,
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risk_level: RiskLevel::Low,
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});
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// Filter to max concurrent proposals
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proposals.truncate(self.config.max_concurrent_proposals);
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Ok(proposals)
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}
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/// Find Pareto-optimal proposals using multi-objective optimization
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pub async fn find_pareto_optimal(&self, proposals: &[ProposalSpec]) -> Result<ParetoFrontier> {
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self.optimizer.compute_pareto_frontier(proposals).await
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}
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/// Validate a proposal in the safe sandbox environment
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pub async fn validate_proposal(&self, proposal: &ProposalSpec) -> Result<ExecutionResult> {
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info!("Validating proposal: {}", proposal.description);
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self.sandbox.execute_proposal(proposal).await
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}
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/// Learn from execution results and update knowledge graph
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pub async fn learn_from_result(
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&mut self,
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proposal: &ProposalSpec,
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result: &ExecutionResult,
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) -> Result<()> {
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self.knowledge_graph
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.update_from_execution(proposal, result)
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.await
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}
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/// Handle failed proposals with rollback if needed
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pub async fn handle_failure(
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&self,
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proposal: &ProposalSpec,
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result: &ExecutionResult,
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) -> Result<RollbackResult> {
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warn!(
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"Handling failure for proposal {}: {:?}",
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proposal.id, result.error_message
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);
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if self.config.rollback_enabled {
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let rollback_start = Instant::now();
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// Perform rollback operation
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self.sandbox.rollback_changes(proposal).await?;
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Ok(RollbackResult {
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rolled_back: true,
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rollback_time: rollback_start.elapsed(),
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restored_state: "previous_stable_state".to_string(),
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})
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} else {
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Ok(RollbackResult {
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rolled_back: false,
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rollback_time: Duration::ZERO,
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restored_state: "no_rollback".to_string(),
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})
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}
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}
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/// Get the number of cycles executed
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pub fn cycle_count(&self) -> u64 {
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self.cycle_count
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}
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/// Get evolution statistics
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pub fn statistics(&self) -> &EvolutionStatistics {
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&self.statistics
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}
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/// Check if an error is critical and should stop the evolution loop
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fn is_critical_error(&self, error: &EvolutionError) -> bool {
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matches!(
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error,
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EvolutionError::KnowledgeGraph { .. } | EvolutionError::ResourceLimit { .. }
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)
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}
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}
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/// Isolation level for sandbox execution
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#[derive(Debug, Clone)]
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pub enum IsolationLevel {
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None, // No isolation (for testing)
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Partial, // Limited isolation
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Full, // Complete isolation
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}
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