trustformers_debug/performance/

optimization.rs

//! Performance optimization system for production debugging
//!
//! This module provides advanced performance optimizations including low overhead
//! sessions, lazy evaluation, incremental processing, background processing,
//! and selective debugging capabilities for production environments.

use crate::core::session::{DebugConfig, DebugSession};
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Performance configuration for optimized debugging
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceConfig {
    /// Enable low overhead mode
    pub low_overhead_mode: bool,
    /// Enable selective debugging
    pub selective_debugging: bool,
    /// Enable lazy evaluation
    pub lazy_evaluation: bool,
    /// Enable incremental updates
    pub incremental_updates: bool,
    /// Enable background processing
    pub background_processing: bool,
    /// Sampling rate for performance-critical operations
    pub sampling_rate: f32,
    /// Maximum memory usage for debugging (in MB)
    pub max_memory_mb: usize,
    /// Maximum CPU usage percentage for debugging
    pub max_cpu_percentage: f32,
    /// Batch size for background processing
    pub background_batch_size: usize,
    /// Update interval for incremental processing (in milliseconds)
    pub incremental_update_interval_ms: u64,
}

impl Default for PerformanceConfig {
    fn default() -> Self {
        Self {
            low_overhead_mode: false,
            selective_debugging: false,
            lazy_evaluation: true,
            incremental_updates: true,
            background_processing: true,
            sampling_rate: 1.0,
            max_memory_mb: 1024,      // 1GB
            max_cpu_percentage: 25.0, // 25% CPU
            background_batch_size: 100,
            incremental_update_interval_ms: 100,
        }
    }
}

/// Low overhead debugging session optimized for production use
pub struct LowOverheadDebugSession {
    session: DebugSession,
    performance_config: PerformanceConfig,
    selective_components: Vec<DebugComponent>,
    lazy_evaluator: LazyEvaluator,
    incremental_processor: IncrementalProcessor,
    background_processor: Option<BackgroundProcessor>,
}

/// Debug component types for selective debugging
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum DebugComponent {
    TensorInspection,
    GradientDebugging,
    ModelDiagnostics,
    MemoryProfiling,
    ComputationGraphAnalysis,
    AnomalyDetection,
    PerformanceProfiling,
    ArchitectureAnalysis,
    BehaviorAnalysis,
    TrainingDynamics,
}

impl LowOverheadDebugSession {
    /// Create a new low overhead debug session
    pub fn new(
        mut config: DebugConfig,
        performance_config: PerformanceConfig,
        selective_components: Vec<DebugComponent>,
    ) -> Self {
        // Apply low overhead optimizations to config
        if performance_config.low_overhead_mode {
            config = Self::apply_low_overhead_config(config, &performance_config);
        }

        let session = DebugSession::new(config);
        let lazy_evaluator = LazyEvaluator::new();
        let incremental_processor =
            IncrementalProcessor::new(performance_config.incremental_update_interval_ms);

        let background_processor = if performance_config.background_processing {
            Some(BackgroundProcessor::new(
                performance_config.background_batch_size,
            ))
        } else {
            None
        };

        Self {
            session,
            performance_config,
            selective_components,
            lazy_evaluator,
            incremental_processor,
            background_processor,
        }
    }

    /// Apply low overhead configuration
    fn apply_low_overhead_config(
        mut config: DebugConfig,
        perf_config: &PerformanceConfig,
    ) -> DebugConfig {
        config.sampling_rate = perf_config.sampling_rate;
        config.max_tracked_tensors = std::cmp::min(config.max_tracked_tensors, 100);
        config.max_gradient_history = std::cmp::min(config.max_gradient_history, 20);

        // Disable expensive features in low overhead mode
        if perf_config.low_overhead_mode {
            config.enable_visualization = false;
            config.enable_memory_profiling = false;
        }

        config
    }

    /// Start optimized debugging session
    pub async fn start(&mut self) -> Result<()> {
        // Start selective components only
        for component in &self.selective_components {
            match component {
                DebugComponent::TensorInspection
                    if self.session.config().enable_tensor_inspection =>
                {
                    self.session.tensor_inspector_mut().start().await?;
                },
                DebugComponent::GradientDebugging
                    if self.session.config().enable_gradient_debugging =>
                {
                    self.session.gradient_debugger_mut().start().await?;
                },
                DebugComponent::ModelDiagnostics
                    if self.session.config().enable_model_diagnostics =>
                {
                    self.session.model_diagnostics_mut().start().await?;
                },
                DebugComponent::MemoryProfiling => {
                    if let Some(profiler) = self.session.memory_profiler_mut() {
                        profiler.start().await?;
                    }
                },
                DebugComponent::AnomalyDetection => {
                    self.session.anomaly_detector_mut().start().await?;
                },
                DebugComponent::PerformanceProfiling => {
                    self.session.profiler_mut().start().await?;
                },
                _ => {
                    // Other components started on-demand
                },
            }
        }

        // Start background processor if enabled
        if let Some(ref mut bg_processor) = self.background_processor {
            bg_processor.start().await?;
        }

        Ok(())
    }

    /// Add data for lazy evaluation
    pub fn add_lazy_evaluation<T: 'static + Send + Sync>(
        &mut self,
        key: String,
        computation: Box<dyn LazyComputation<T>>,
    ) {
        self.lazy_evaluator.add_computation(key, computation);
    }

    /// Process incremental update
    pub async fn process_incremental_update(&mut self, data: IncrementalData) -> Result<()> {
        self.incremental_processor.process_update(data).await
    }

    /// Submit data for background processing
    pub async fn submit_background_task(&mut self, task: BackgroundTask) -> Result<()> {
        if let Some(ref mut bg_processor) = self.background_processor {
            bg_processor.submit_task(task).await
        } else {
            Err(anyhow::anyhow!("Background processing not enabled"))
        }
    }

    /// Get performance metrics
    pub fn get_performance_metrics(&self) -> PerformanceMetrics {
        PerformanceMetrics {
            memory_usage_mb: self.get_memory_usage_mb(),
            cpu_usage_percentage: self.get_cpu_usage_percentage(),
            lazy_computations_pending: self.lazy_evaluator.pending_count(),
            incremental_updates_processed: self.incremental_processor.processed_count(),
            background_tasks_queued: self
                .background_processor
                .as_ref()
                .map(|p| p.queued_count())
                .unwrap_or(0),
        }
    }

    /// Check if performance limits are exceeded
    pub fn is_within_performance_limits(&self) -> bool {
        let metrics = self.get_performance_metrics();
        metrics.memory_usage_mb <= self.performance_config.max_memory_mb
            && metrics.cpu_usage_percentage <= self.performance_config.max_cpu_percentage
    }

    /// Get current memory usage in MB
    fn get_memory_usage_mb(&self) -> usize {
        // Simplified implementation - would use actual memory monitoring
        0
    }

    /// Get current CPU usage percentage
    fn get_cpu_usage_percentage(&self) -> f32 {
        // Simplified implementation - would use actual CPU monitoring
        0.0
    }
}

/// Lazy evaluation system for expensive computations
pub struct LazyEvaluator {
    computations: HashMap<String, Box<dyn std::any::Any + Send + Sync>>,
    evaluated: HashMap<String, bool>,
}

impl Default for LazyEvaluator {
    fn default() -> Self {
        Self::new()
    }
}

impl LazyEvaluator {
    pub fn new() -> Self {
        Self {
            computations: HashMap::new(),
            evaluated: HashMap::new(),
        }
    }

    /// Add a lazy computation
    pub fn add_computation<T: 'static + Send + Sync>(
        &mut self,
        key: String,
        computation: Box<dyn LazyComputation<T>>,
    ) {
        self.computations.insert(key.clone(), Box::new(computation));
        self.evaluated.insert(key, false);
    }

    /// Evaluate computation on demand
    pub async fn evaluate<T: 'static>(&mut self, key: &str) -> Result<Option<T>> {
        if let Some(computation) = self.computations.remove(key) {
            if let Ok(lazy_comp) = computation.downcast::<Box<dyn LazyComputation<T>>>() {
                let result = lazy_comp.compute().await?;
                self.evaluated.insert(key.to_string(), true);
                return Ok(Some(result));
            }
        }
        Ok(None)
    }

    /// Get number of pending computations
    pub fn pending_count(&self) -> usize {
        self.evaluated.values().filter(|&&v| !v).count()
    }

    /// Clear all computations
    pub fn clear(&mut self) {
        self.computations.clear();
        self.evaluated.clear();
    }
}

/// Trait for lazy computations
pub trait LazyComputation<T>: Send + Sync {
    fn compute(
        &self,
    ) -> std::pin::Pin<Box<dyn std::future::Future<Output = Result<T>> + Send + '_>>;
}

/// Incremental processing system for efficient updates
pub struct IncrementalProcessor {
    update_interval_ms: u64,
    last_update: std::time::Instant,
    accumulated_data: Vec<IncrementalData>,
    processed_count: usize,
}

impl IncrementalProcessor {
    pub fn new(update_interval_ms: u64) -> Self {
        Self {
            update_interval_ms,
            last_update: std::time::Instant::now(),
            accumulated_data: Vec::new(),
            processed_count: 0,
        }
    }

    /// Process incremental update
    pub async fn process_update(&mut self, data: IncrementalData) -> Result<()> {
        self.accumulated_data.push(data);

        // Check if it's time to process accumulated data
        if self.last_update.elapsed().as_millis() >= self.update_interval_ms as u128 {
            self.process_accumulated_data().await?;
            self.last_update = std::time::Instant::now();
        }

        Ok(())
    }

    /// Force processing of accumulated data
    pub async fn flush(&mut self) -> Result<()> {
        self.process_accumulated_data().await?;
        self.last_update = std::time::Instant::now();
        Ok(())
    }

    /// Process all accumulated data
    async fn process_accumulated_data(&mut self) -> Result<()> {
        if !self.accumulated_data.is_empty() {
            // Process the accumulated data in batch
            let batch_size = self.accumulated_data.len();

            // Simplified processing - would implement actual incremental analysis
            for _data in self.accumulated_data.drain(..) {
                self.processed_count += 1;
            }

            tracing::debug!("Processed {} incremental updates", batch_size);
        }

        Ok(())
    }

    /// Get number of processed updates
    pub fn processed_count(&self) -> usize {
        self.processed_count
    }
}

/// Data for incremental processing
#[derive(Debug, Clone)]
pub enum IncrementalData {
    TensorUpdate {
        tensor_id: String,
        values: Vec<f32>,
    },
    GradientUpdate {
        layer_id: String,
        gradients: Vec<f32>,
    },
    MetricUpdate {
        metric_name: String,
        value: f64,
        timestamp: std::time::Instant,
    },
    PerformanceUpdate {
        operation: String,
        latency_ms: f64,
    },
}

/// Background processing system for non-critical tasks
pub struct BackgroundProcessor {
    batch_size: usize,
    task_queue: Vec<BackgroundTask>,
    #[allow(dead_code)]
    processed_count: usize,
    worker_handle: Option<tokio::task::JoinHandle<()>>,
}

impl BackgroundProcessor {
    pub fn new(batch_size: usize) -> Self {
        Self {
            batch_size,
            task_queue: Vec::new(),
            processed_count: 0,
            worker_handle: None,
        }
    }

    /// Start background processing
    pub async fn start(&mut self) -> Result<()> {
        let (_sender, mut receiver) = tokio::sync::mpsc::channel::<BackgroundTask>(1000);

        // Spawn background worker
        let batch_size = self.batch_size;
        let handle = tokio::spawn(async move {
            let mut batch = Vec::with_capacity(batch_size);

            while let Some(task) = receiver.recv().await {
                batch.push(task);

                if batch.len() >= batch_size {
                    Self::process_batch(&mut batch).await;
                    batch.clear();
                }
            }

            // Process remaining tasks
            if !batch.is_empty() {
                Self::process_batch(&mut batch).await;
            }
        });

        self.worker_handle = Some(handle);
        Ok(())
    }

    /// Submit task for background processing
    pub async fn submit_task(&mut self, task: BackgroundTask) -> Result<()> {
        self.task_queue.push(task);
        Ok(())
    }

    /// Process a batch of background tasks
    async fn process_batch(batch: &mut Vec<BackgroundTask>) {
        for task in batch.drain(..) {
            match task {
                BackgroundTask::ComputeStatistics { data } => {
                    // Compute statistics in background
                    let _stats = Self::compute_statistics(&data).await;
                },
                BackgroundTask::GenerateVisualization { plot_data } => {
                    // Generate visualization in background
                    let _viz = Self::generate_visualization(&plot_data).await;
                },
                BackgroundTask::ExportData { data, format } => {
                    // Export data in background
                    let _result = Self::export_data(&data, &format).await;
                },
                BackgroundTask::CleanupResources { resource_ids } => {
                    // Cleanup resources in background
                    Self::cleanup_resources(&resource_ids).await;
                },
            }
        }
    }

    /// Compute statistics for background task
    async fn compute_statistics(data: &[f32]) -> Vec<f64> {
        // Simplified implementation
        tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;
        vec![data.iter().map(|&x| x as f64).sum()]
    }

    /// Generate visualization for background task
    async fn generate_visualization(plot_data: &PlotData) -> String {
        // Simplified implementation
        tokio::time::sleep(tokio::time::Duration::from_millis(50)).await;
        format!(
            "Generated visualization for {} data points",
            plot_data.points.len()
        )
    }

    /// Export data for background task
    async fn export_data(data: &ExportData, format: &str) -> Result<String> {
        // Simplified implementation
        tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
        Ok(format!(
            "Exported {} items in {} format",
            data.items.len(),
            format
        ))
    }

    /// Cleanup resources for background task
    async fn cleanup_resources(resource_ids: &[String]) {
        // Simplified implementation
        tokio::time::sleep(tokio::time::Duration::from_millis(20)).await;
        tracing::debug!("Cleaned up {} resources", resource_ids.len());
    }

    /// Get number of queued tasks
    pub fn queued_count(&self) -> usize {
        self.task_queue.len()
    }

    /// Stop background processing
    pub async fn stop(&mut self) -> Result<()> {
        if let Some(handle) = self.worker_handle.take() {
            handle.abort();
        }
        Ok(())
    }
}

/// Background task types
#[derive(Debug, Clone)]
pub enum BackgroundTask {
    ComputeStatistics { data: Vec<f32> },
    GenerateVisualization { plot_data: PlotData },
    ExportData { data: ExportData, format: String },
    CleanupResources { resource_ids: Vec<String> },
}

/// Plot data for background visualization
#[derive(Debug, Clone)]
pub struct PlotData {
    pub points: Vec<(f64, f64)>,
    pub title: String,
    pub x_label: String,
    pub y_label: String,
}

/// Export data for background processing
#[derive(Debug, Clone)]
pub struct ExportData {
    pub items: Vec<String>,
    pub metadata: HashMap<String, String>,
}

/// Performance metrics for monitoring
#[derive(Debug, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub memory_usage_mb: usize,
    pub cpu_usage_percentage: f32,
    pub lazy_computations_pending: usize,
    pub incremental_updates_processed: usize,
    pub background_tasks_queued: usize,
}

/// Selective debugging configuration
#[derive(Debug, Clone)]
pub struct SelectiveDebugConfig {
    pub components: Vec<DebugComponent>,
    pub sampling_rules: HashMap<DebugComponent, f32>,
    pub priority_rules: HashMap<DebugComponent, DebugPriority>,
    pub resource_limits: ResourceLimits,
}

/// Debug priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum DebugPriority {
    Low,
    Medium,
    High,
    Critical,
}

/// Resource limits for selective debugging
#[derive(Debug, Clone)]
pub struct ResourceLimits {
    pub max_memory_per_component_mb: usize,
    pub max_cpu_per_component_percentage: f32,
    pub max_concurrent_operations: usize,
}

impl SelectiveDebugConfig {
    /// Create config for production monitoring
    pub fn production_monitoring() -> Self {
        let mut sampling_rules = HashMap::new();
        sampling_rules.insert(DebugComponent::AnomalyDetection, 1.0);
        sampling_rules.insert(DebugComponent::PerformanceProfiling, 0.1);
        sampling_rules.insert(DebugComponent::MemoryProfiling, 0.05);

        let mut priority_rules = HashMap::new();
        priority_rules.insert(DebugComponent::AnomalyDetection, DebugPriority::Critical);
        priority_rules.insert(DebugComponent::PerformanceProfiling, DebugPriority::Medium);

        Self {
            components: vec![
                DebugComponent::AnomalyDetection,
                DebugComponent::PerformanceProfiling,
            ],
            sampling_rules,
            priority_rules,
            resource_limits: ResourceLimits {
                max_memory_per_component_mb: 50,
                max_cpu_per_component_percentage: 5.0,
                max_concurrent_operations: 2,
            },
        }
    }

    /// Create config for development debugging
    pub fn development_debugging() -> Self {
        let mut sampling_rules = HashMap::new();
        sampling_rules.insert(DebugComponent::TensorInspection, 0.5);
        sampling_rules.insert(DebugComponent::GradientDebugging, 1.0);
        sampling_rules.insert(DebugComponent::ModelDiagnostics, 1.0);
        sampling_rules.insert(DebugComponent::AnomalyDetection, 1.0);

        let mut priority_rules = HashMap::new();
        priority_rules.insert(DebugComponent::GradientDebugging, DebugPriority::High);
        priority_rules.insert(DebugComponent::AnomalyDetection, DebugPriority::Critical);
        priority_rules.insert(DebugComponent::ModelDiagnostics, DebugPriority::Medium);

        Self {
            components: vec![
                DebugComponent::TensorInspection,
                DebugComponent::GradientDebugging,
                DebugComponent::ModelDiagnostics,
                DebugComponent::AnomalyDetection,
            ],
            sampling_rules,
            priority_rules,
            resource_limits: ResourceLimits {
                max_memory_per_component_mb: 200,
                max_cpu_per_component_percentage: 15.0,
                max_concurrent_operations: 6,
            },
        }
    }
}

/// Create optimized debug session for production use
pub fn optimized_debug_session(
    selective_config: SelectiveDebugConfig,
    performance_config: PerformanceConfig,
) -> LowOverheadDebugSession {
    let debug_config = DebugConfig {
        enable_tensor_inspection: selective_config
            .components
            .contains(&DebugComponent::TensorInspection),
        enable_gradient_debugging: selective_config
            .components
            .contains(&DebugComponent::GradientDebugging),
        enable_model_diagnostics: selective_config
            .components
            .contains(&DebugComponent::ModelDiagnostics),
        enable_memory_profiling: selective_config
            .components
            .contains(&DebugComponent::MemoryProfiling),
        enable_computation_graph_analysis: selective_config
            .components
            .contains(&DebugComponent::ComputationGraphAnalysis),
        sampling_rate: performance_config.sampling_rate,
        max_tracked_tensors: if performance_config.low_overhead_mode { 50 } else { 500 },
        max_gradient_history: if performance_config.low_overhead_mode { 10 } else { 50 },
        ..Default::default()
    };

    LowOverheadDebugSession::new(
        debug_config,
        performance_config,
        selective_config.components,
    )
}

/// Placeholder configuration for interpretability analysis
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct InterpretabilityConfig;

/// Placeholder interpretability analyzer
#[derive(Debug)]
pub struct InterpretabilityAnalyzer;

impl InterpretabilityAnalyzer {
    pub fn new(_config: InterpretabilityConfig) -> Self {
        Self
    }

    pub async fn generate_report(&self) -> anyhow::Result<InterpretabilityReport> {
        Ok(InterpretabilityReport)
    }

    pub async fn analyze_shap(
        &self,
        _instance: &HashMap<String, f64>,
        _model_predictions: &[f64],
        _background_data: &[HashMap<String, f64>],
    ) -> anyhow::Result<Vec<f64>> {
        // Placeholder implementation
        Ok(vec![0.1, 0.2, 0.3])
    }

    pub async fn analyze_lime<F>(
        &self,
        _instance: &HashMap<String, f64>,
        _model_fn: F,
    ) -> anyhow::Result<Vec<f64>>
    where
        F: Fn(&HashMap<String, f64>) -> f64 + Send + 'static,
    {
        // Placeholder implementation
        Ok(vec![0.1, 0.2, 0.3])
    }
}

/// Placeholder interpretability report
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct InterpretabilityReport;

/// Create ultra-low overhead session for production monitoring
pub fn ultra_low_overhead_session() -> LowOverheadDebugSession {
    let selective_config = SelectiveDebugConfig::production_monitoring();
    let performance_config = PerformanceConfig {
        low_overhead_mode: true,
        selective_debugging: true,
        lazy_evaluation: true,
        incremental_updates: true,
        background_processing: true,
        sampling_rate: 0.01,
        max_memory_mb: 100,
        max_cpu_percentage: 5.0,
        background_batch_size: 50,
        incremental_update_interval_ms: 1000,
    };

    optimized_debug_session(selective_config, performance_config)
}