use crate::core::session::{DebugConfig, DebugSession};
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceConfig {
pub low_overhead_mode: bool,
pub selective_debugging: bool,
pub lazy_evaluation: bool,
pub incremental_updates: bool,
pub background_processing: bool,
pub sampling_rate: f32,
pub max_memory_mb: usize,
pub max_cpu_percentage: f32,
pub background_batch_size: usize,
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, max_cpu_percentage: 25.0, background_batch_size: 100,
incremental_update_interval_ms: 100,
}
}
}
pub struct LowOverheadDebugSession {
session: DebugSession,
performance_config: PerformanceConfig,
selective_components: Vec<DebugComponent>,
lazy_evaluator: LazyEvaluator,
incremental_processor: IncrementalProcessor,
background_processor: Option<BackgroundProcessor>,
}
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum DebugComponent {
TensorInspection,
GradientDebugging,
ModelDiagnostics,
MemoryProfiling,
ComputationGraphAnalysis,
AnomalyDetection,
PerformanceProfiling,
ArchitectureAnalysis,
BehaviorAnalysis,
TrainingDynamics,
}
impl LowOverheadDebugSession {
pub fn new(
mut config: DebugConfig,
performance_config: PerformanceConfig,
selective_components: Vec<DebugComponent>,
) -> Self {
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,
}
}
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);
if perf_config.low_overhead_mode {
config.enable_visualization = false;
config.enable_memory_profiling = false;
}
config
}
pub async fn start(&mut self) -> Result<()> {
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?;
},
_ => {
},
}
}
if let Some(ref mut bg_processor) = self.background_processor {
bg_processor.start().await?;
}
Ok(())
}
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);
}
pub async fn process_incremental_update(&mut self, data: IncrementalData) -> Result<()> {
self.incremental_processor.process_update(data).await
}
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"))
}
}
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),
}
}
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
}
fn get_memory_usage_mb(&self) -> usize {
0
}
fn get_cpu_usage_percentage(&self) -> f32 {
0.0
}
}
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(),
}
}
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);
}
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)
}
pub fn pending_count(&self) -> usize {
self.evaluated.values().filter(|&&v| !v).count()
}
pub fn clear(&mut self) {
self.computations.clear();
self.evaluated.clear();
}
}
pub trait LazyComputation<T>: Send + Sync {
fn compute(
&self,
) -> std::pin::Pin<Box<dyn std::future::Future<Output = Result<T>> + Send + '_>>;
}
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,
}
}
pub async fn process_update(&mut self, data: IncrementalData) -> Result<()> {
self.accumulated_data.push(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(())
}
pub async fn flush(&mut self) -> Result<()> {
self.process_accumulated_data().await?;
self.last_update = std::time::Instant::now();
Ok(())
}
async fn process_accumulated_data(&mut self) -> Result<()> {
if !self.accumulated_data.is_empty() {
let batch_size = self.accumulated_data.len();
for _data in self.accumulated_data.drain(..) {
self.processed_count += 1;
}
tracing::debug!("Processed {} incremental updates", batch_size);
}
Ok(())
}
pub fn processed_count(&self) -> usize {
self.processed_count
}
}
#[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,
},
}
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,
}
}
pub async fn start(&mut self) -> Result<()> {
let (_sender, mut receiver) = tokio::sync::mpsc::channel::<BackgroundTask>(1000);
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();
}
}
if !batch.is_empty() {
Self::process_batch(&mut batch).await;
}
});
self.worker_handle = Some(handle);
Ok(())
}
pub async fn submit_task(&mut self, task: BackgroundTask) -> Result<()> {
self.task_queue.push(task);
Ok(())
}
async fn process_batch(batch: &mut Vec<BackgroundTask>) {
for task in batch.drain(..) {
match task {
BackgroundTask::ComputeStatistics { data } => {
let _stats = Self::compute_statistics(&data).await;
},
BackgroundTask::GenerateVisualization { plot_data } => {
let _viz = Self::generate_visualization(&plot_data).await;
},
BackgroundTask::ExportData { data, format } => {
let _result = Self::export_data(&data, &format).await;
},
BackgroundTask::CleanupResources { resource_ids } => {
Self::cleanup_resources(&resource_ids).await;
},
}
}
}
async fn compute_statistics(data: &[f32]) -> Vec<f64> {
tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;
vec![data.iter().map(|&x| x as f64).sum()]
}
async fn generate_visualization(plot_data: &PlotData) -> String {
tokio::time::sleep(tokio::time::Duration::from_millis(50)).await;
format!(
"Generated visualization for {} data points",
plot_data.points.len()
)
}
async fn export_data(data: &ExportData, format: &str) -> Result<String> {
tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
Ok(format!(
"Exported {} items in {} format",
data.items.len(),
format
))
}
async fn cleanup_resources(resource_ids: &[String]) {
tokio::time::sleep(tokio::time::Duration::from_millis(20)).await;
tracing::debug!("Cleaned up {} resources", resource_ids.len());
}
pub fn queued_count(&self) -> usize {
self.task_queue.len()
}
pub async fn stop(&mut self) -> Result<()> {
if let Some(handle) = self.worker_handle.take() {
handle.abort();
}
Ok(())
}
}
#[derive(Debug, Clone)]
pub enum BackgroundTask {
ComputeStatistics { data: Vec<f32> },
GenerateVisualization { plot_data: PlotData },
ExportData { data: ExportData, format: String },
CleanupResources { resource_ids: Vec<String> },
}
#[derive(Debug, Clone)]
pub struct PlotData {
pub points: Vec<(f64, f64)>,
pub title: String,
pub x_label: String,
pub y_label: String,
}
#[derive(Debug, Clone)]
pub struct ExportData {
pub items: Vec<String>,
pub metadata: HashMap<String, String>,
}
#[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,
}
#[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,
}
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum DebugPriority {
Low,
Medium,
High,
Critical,
}
#[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 {
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,
},
}
}
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,
},
}
}
}
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,
)
}
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct InterpretabilityConfig;
#[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>> {
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,
{
Ok(vec![0.1, 0.2, 0.3])
}
}
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct InterpretabilityReport;
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)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_performance_config_default() {
let cfg = PerformanceConfig::default();
assert!(!cfg.low_overhead_mode);
assert!(!cfg.selective_debugging);
assert!(cfg.lazy_evaluation);
assert!(cfg.incremental_updates);
assert!(cfg.background_processing);
assert!((cfg.sampling_rate - 1.0).abs() < 1e-6);
assert!(cfg.max_memory_mb > 0);
assert!(cfg.max_cpu_percentage > 0.0);
assert!(cfg.background_batch_size > 0);
assert!(cfg.incremental_update_interval_ms > 0);
}
#[test]
fn test_performance_config_low_overhead() {
let cfg = PerformanceConfig {
low_overhead_mode: true,
selective_debugging: true,
sampling_rate: 0.01,
max_memory_mb: 100,
max_cpu_percentage: 5.0,
..PerformanceConfig::default()
};
assert!(cfg.low_overhead_mode);
assert!((cfg.sampling_rate - 0.01).abs() < 1e-6);
}
#[test]
fn test_debug_component_variants() {
let components = [
DebugComponent::TensorInspection,
DebugComponent::GradientDebugging,
DebugComponent::ModelDiagnostics,
DebugComponent::MemoryProfiling,
DebugComponent::ComputationGraphAnalysis,
DebugComponent::AnomalyDetection,
DebugComponent::PerformanceProfiling,
DebugComponent::ArchitectureAnalysis,
DebugComponent::BehaviorAnalysis,
DebugComponent::TrainingDynamics,
];
for c in &components {
assert!(!format!("{:?}", c).is_empty());
}
}
#[test]
fn test_debug_component_equality() {
assert_eq!(
DebugComponent::TensorInspection,
DebugComponent::TensorInspection
);
assert_ne!(
DebugComponent::TensorInspection,
DebugComponent::GradientDebugging
);
}
#[test]
fn test_debug_priority_variants() {
let priorities = [
DebugPriority::Low,
DebugPriority::Medium,
DebugPriority::High,
DebugPriority::Critical,
];
for p in &priorities {
assert!(!format!("{:?}", p).is_empty());
}
}
#[test]
fn test_production_monitoring_config() {
let cfg = SelectiveDebugConfig::production_monitoring();
assert!(cfg.components.contains(&DebugComponent::AnomalyDetection));
assert!(!cfg.sampling_rules.is_empty());
assert!(!cfg.priority_rules.is_empty());
}
#[test]
fn test_development_debugging_config() {
let cfg = SelectiveDebugConfig::development_debugging();
assert!(cfg.components.contains(&DebugComponent::GradientDebugging));
assert!(cfg.components.contains(&DebugComponent::ModelDiagnostics));
assert!(cfg.resource_limits.max_memory_per_component_mb > 0);
}
#[test]
fn test_resource_limits_in_production_config() {
let cfg = SelectiveDebugConfig::production_monitoring();
let limits = &cfg.resource_limits;
assert!(limits.max_memory_per_component_mb > 0);
assert!(limits.max_cpu_per_component_percentage > 0.0);
assert!(limits.max_concurrent_operations > 0);
}
#[test]
fn test_optimized_debug_session_creation() {
let selective_cfg = SelectiveDebugConfig::production_monitoring();
let perf_cfg = PerformanceConfig::default();
let _session = optimized_debug_session(selective_cfg, perf_cfg);
}
#[test]
fn test_low_overhead_session_creation() {
let selective_cfg = SelectiveDebugConfig::production_monitoring();
let perf_cfg = PerformanceConfig {
low_overhead_mode: true,
..PerformanceConfig::default()
};
let _session = optimized_debug_session(selective_cfg, perf_cfg);
}
#[test]
fn test_ultra_low_overhead_session_creation() {
let _session = ultra_low_overhead_session();
}
#[tokio::test]
async fn test_interpretability_analyzer_generate_report() {
let analyzer = InterpretabilityAnalyzer::new(InterpretabilityConfig);
let report = analyzer.generate_report().await.expect("should succeed");
let _ = format!("{:?}", report);
}
#[tokio::test]
async fn test_interpretability_analyzer_shap() {
let analyzer = InterpretabilityAnalyzer::new(InterpretabilityConfig);
let mut instance = HashMap::new();
instance.insert("f0".to_string(), 1.0_f64);
instance.insert("f1".to_string(), 2.0_f64);
let result = analyzer
.analyze_shap(&instance, &[0.5, 0.7], &[])
.await
.expect("should succeed");
assert!(!result.is_empty());
}
}