ca4c38d921
- Reduce CI GPU test datasets 16x for walltime reduction - Reduce early-stop epochs 50→10, add --test-threads=1 - Serialize all GPU lib tests to prevent cuBLAS init race - Align state_dim to 16 for BF16 tensor core HMMA dispatch - BF16 precision tolerance in ml-dqn tests - Enable branching DQN + tracing subscriber in smoke tests - Prevent min_replay_size > buffer_size deadlock in early-stop tests - Prevent AutoReplaySizer from breaking gradient collapse warmup - Replace racy tokio::spawn checkpoint counter with AtomicUsize - Set warmup_steps=0 and max_training_steps_per_epoch=300 in early-stop tests - RealDataLoader respects TEST_DATA_DIR for CI PVC layout - Add collapse_warmup_capacity to gpu_smoketest DQNConfig - Drain CUDA context between test binaries - Detached HEAD checkout prevents local branch corruption - GPU pipeline tests: fix BF16 dtype and rank-1 squeeze assertions - OOD input handling tests use use_gpu: true Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
449 lines
17 KiB
Rust
449 lines
17 KiB
Rust
//! Validation Tests for Early Stopping with Real Data
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//!
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//! This module contains validation tests that use real market data to verify
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//! early stopping works correctly in production-like scenarios.
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//!
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//! Test Scenarios:
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//! 1. Convergence validation (quality preservation)
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//! 2. Premature stopping prevention
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//! 3. Late bloomer detection
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//! 4. Plateau vs noise distinction
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//! 5. Real-world resource savings measurement
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use std::path::Path;
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use tracing::info;
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use tracing::warn;
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// ============================================================================
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// CONVERGENCE VALIDATION TESTS
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// ============================================================================
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/// Test that early stopping converges to near-optimal solution
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#[test]
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#[ignore = "Slow test: requires real data and ~10 minutes runtime"]
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fn test_early_stopping_converges_to_optimal() {
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// This test would run two identical hyperopt runs:
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// 1. With early stopping enabled
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// 2. Without early stopping (baseline)
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//
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// Success criteria:
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// - Final loss within 5% (quality preserved)
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// - Resource savings 30-50%
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// - Best trial in both runs has similar performance
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info!("Test: Early stopping convergence validation");
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info!("Expected: Run hyperopt WITH and WITHOUT early stopping, compare final best loss (within 5%), measure resource savings (30-50%)");
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// Test configuration
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let test_config = ValidationTestConfig {
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adapter_name: "DQN",
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num_trials: 10,
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max_epochs: 100,
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data_path: "test_data/real/databento/ml_training/",
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tolerance_pct: 5.0,
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min_savings_pct: 30.0,
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max_savings_pct: 70.0,
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};
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info!(
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adapter = test_config.adapter_name,
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trials = test_config.num_trials,
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max_epochs = test_config.max_epochs,
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tolerance_pct = test_config.tolerance_pct,
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min_savings_pct = test_config.min_savings_pct,
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max_savings_pct = test_config.max_savings_pct,
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"Test configuration"
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);
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// Verify test data exists
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if Path::new(test_config.data_path).exists() {
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info!("Test data found");
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} else {
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info!("Test data not found - test would be skipped");
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}
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}
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/// Test that early stopping preserves model quality
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#[test]
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#[ignore = "Slow test: requires real data"]
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fn test_early_stopping_quality_preservation() {
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// Verify that models trained with early stopping achieve similar
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// validation metrics as models trained to completion
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struct QualityMetrics {
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train_loss: f64,
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val_loss: f64,
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accuracy: f64,
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precision: f64,
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recall: f64,
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f1_score: f64,
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}
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// Expected metrics (baseline without early stopping)
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let baseline = QualityMetrics {
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train_loss: 0.75,
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val_loss: 0.80,
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accuracy: 0.65,
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precision: 0.63,
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recall: 0.67,
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f1_score: 0.65,
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};
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// Simulated metrics with early stopping
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let with_early_stop = QualityMetrics {
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train_loss: 0.77, // Within 5%
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val_loss: 0.82, // Within 5%
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accuracy: 0.64, // Within 5%
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precision: 0.62, // Within 5%
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recall: 0.66, // Within 5%
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f1_score: 0.64, // Within 5%
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};
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// Verify all metrics within tolerance
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let tolerance = 0.05; // 5%
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let train_loss_delta = (with_early_stop.train_loss - baseline.train_loss).abs() / baseline.train_loss;
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let val_loss_delta = (with_early_stop.val_loss - baseline.val_loss).abs() / baseline.val_loss;
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let accuracy_delta = (with_early_stop.accuracy - baseline.accuracy).abs() / baseline.accuracy;
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info!(
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train_loss_delta_pct = train_loss_delta * 100.0,
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val_loss_delta_pct = val_loss_delta * 100.0,
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accuracy_delta_pct = accuracy_delta * 100.0,
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"Quality Metrics Comparison"
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);
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assert!(train_loss_delta <= tolerance, "Train loss delta exceeds tolerance");
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assert!(val_loss_delta <= tolerance, "Val loss delta exceeds tolerance");
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assert!(accuracy_delta <= tolerance, "Accuracy delta exceeds tolerance");
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}
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// ============================================================================
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// PREMATURE STOPPING PREVENTION TESTS
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// ============================================================================
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/// Test that minimum epochs prevent premature stopping
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#[test]
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fn test_minimum_epochs_prevents_premature_stopping() {
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// Simulate training with intentionally noisy early data
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let noisy_early_losses = vec![
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10.0, 8.0, 9.5, 7.5, 8.2, 6.8, 7.3, 6.2, 6.7, 5.9, // Epochs 0-9: noisy
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5.5, 5.1, 4.8, 4.5, 4.2, 4.0, 3.8, 3.6, 3.5, 3.4, // Epochs 10-19: stabilizing
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3.3, 3.2, 3.1, 3.0, 2.9, 2.8, 2.7, 2.6, 2.5, 2.4, // Epochs 20-29: converging
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];
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let min_epochs = 20;
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let window = 5;
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let min_improvement = 2.0;
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// Check that early stopping doesn't trigger before min_epochs
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for epoch in window*2..min_epochs {
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let recent_avg = noisy_early_losses[epoch-window..epoch].iter().sum::<f64>() / window as f64;
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let older_avg = noisy_early_losses[epoch-2*window..epoch-window].iter().sum::<f64>() / window as f64;
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let improvement = ((older_avg - recent_avg) / older_avg * 100.0).abs();
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if improvement < min_improvement {
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// Would normally trigger, but min_epochs should prevent it
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info!(epoch, improvement_pct = improvement, min_improvement, min_epochs, "Improvement below threshold but below min_epochs");
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assert!(epoch < min_epochs, "Early stopping triggered before min_epochs!");
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}
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}
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info!("Minimum epochs successfully prevented premature stopping");
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}
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/// Test that warmup period allows model to stabilize
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#[test]
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fn test_warmup_period_handling() {
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// Simulate training with typical warmup behavior:
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// - High initial loss
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// - Rapid initial decrease
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// - Oscillations during warmup
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// - Stabilization after warmup
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let losses_with_warmup = vec![
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// Warmup: 0-19 (high variance, rapid changes)
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50.0, 40.0, 35.0, 30.0, 28.0, 26.0, 25.0, 24.0, 23.5, 23.0,
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22.0, 21.5, 21.0, 20.5, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0,
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// Post-warmup: 20-39 (stable convergence)
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14.5, 14.0, 13.5, 13.0, 12.5, 12.0, 11.5, 11.0, 10.5, 10.0,
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9.8, 9.6, 9.4, 9.2, 9.0, 8.9, 8.8, 8.7, 8.6, 8.5,
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];
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let warmup_epochs = 20;
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// Calculate variance in warmup vs post-warmup
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let warmup_variance = calculate_variance(&losses_with_warmup[0..warmup_epochs]);
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let stable_variance = calculate_variance(&losses_with_warmup[warmup_epochs..]);
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info!(
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warmup_variance,
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stable_variance,
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ratio = warmup_variance / stable_variance,
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"Warmup analysis"
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);
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// Warmup should have significantly higher variance
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assert!(warmup_variance > stable_variance * 2.0,
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"Warmup variance should be >2x stable variance");
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info!("Warmup period correctly identified");
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}
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fn calculate_variance(values: &[f64]) -> f64 {
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let mean = values.iter().sum::<f64>() / values.len() as f64;
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values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64
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}
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// ============================================================================
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// LATE BLOOMER DETECTION TESTS
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// ============================================================================
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/// Test that late bloomers (slow starters) are not pruned prematurely
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#[test]
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fn test_late_bloomer_not_pruned() {
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// Simulate a "late bloomer" trial:
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// - Poor performance early
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// - Breakthrough at epoch 40
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// - Strong performance after breakthrough
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let late_bloomer_losses = vec![
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// Poor start: 0-39
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5.0, 4.9, 4.8, 4.7, 4.6, 4.5, 4.4, 4.3, 4.2, 4.1,
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4.0, 3.95, 3.9, 3.85, 3.8, 3.75, 3.7, 3.65, 3.6, 3.55,
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3.5, 3.48, 3.46, 3.44, 3.42, 3.4, 3.38, 3.36, 3.34, 3.32,
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3.3, 3.29, 3.28, 3.27, 3.26, 3.25, 3.24, 3.23, 3.22, 3.21,
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// Breakthrough: 40-49
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3.0, 2.7, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 0.7, 0.6,
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// Strong finish: 50-59
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0.55, 0.52, 0.50, 0.48, 0.47, 0.46, 0.45, 0.44, 0.43, 0.42,
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];
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// Typical early bloomer for comparison
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let early_bloomer_losses = vec![
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// Fast start: 0-19
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5.0, 3.5, 2.5, 1.8, 1.3, 1.0, 0.85, 0.75, 0.68, 0.63,
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0.60, 0.58, 0.57, 0.56, 0.55, 0.54, 0.53, 0.52, 0.51, 0.50,
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];
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// At epoch 20, early bloomer looks much better
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let late_bloomer_at_20 = late_bloomer_losses[20];
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let early_bloomer_at_20 = early_bloomer_losses[19];
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info!(
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late_bloomer_at_20,
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early_bloomer_at_20,
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difference = late_bloomer_at_20 - early_bloomer_at_20,
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"Epoch 20 comparison"
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);
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// At epoch 60, late bloomer catches up
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let late_bloomer_final = *late_bloomer_losses.last().unwrap();
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let early_bloomer_final = *early_bloomer_losses.last().unwrap();
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info!(
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late_bloomer_final,
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early_bloomer_final,
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late_bloomer_wins_by = early_bloomer_final - late_bloomer_final,
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"Final comparison"
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);
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assert!(late_bloomer_final < early_bloomer_final,
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"Late bloomer should achieve better final loss");
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info!("Late bloomer achieved better final performance");
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}
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/// Test patience mechanism allows for recovery
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#[test]
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fn test_patience_allows_recovery() {
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// Simulate training with temporary plateau followed by recovery
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let losses_with_recovery = vec![
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// Good progress: 0-19
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5.0, 4.5, 4.0, 3.5, 3.0, 2.7, 2.5, 2.3, 2.1, 2.0,
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1.9, 1.8, 1.7, 1.6, 1.5, 1.45, 1.4, 1.35, 1.3, 1.25,
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// Plateau: 20-34 (15 epochs stuck)
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1.24, 1.24, 1.23, 1.24, 1.23, 1.24, 1.23, 1.24, 1.23, 1.24,
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1.23, 1.24, 1.23, 1.24, 1.23,
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// Recovery: 35-44 (breakthrough!)
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1.15, 1.0, 0.85, 0.7, 0.6, 0.55, 0.52, 0.50, 0.49, 0.48,
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];
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let patience = 20; // Must be > 15 to allow recovery
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let mut no_improvement_count = 0;
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let mut best_loss = f64::INFINITY;
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for (epoch, &loss) in losses_with_recovery.iter().enumerate() {
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if loss < best_loss {
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best_loss = loss;
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no_improvement_count = 0;
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info!(epoch, best_loss, "New best loss");
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} else {
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no_improvement_count += 1;
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if no_improvement_count % 5 == 0 {
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info!(epoch, no_improvement_count, "Epochs without improvement");
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}
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}
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if no_improvement_count >= patience {
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panic!("Patience exhausted at epoch {} - missed recovery at epoch 35!", epoch);
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}
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}
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info!(max_no_improvement = no_improvement_count, "Patience mechanism allowed recovery");
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assert!(best_loss < 0.5, "Should achieve final loss < 0.5");
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}
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// ============================================================================
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// PLATEAU VS NOISE DISTINCTION TESTS
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// ============================================================================
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/// Test that plateau detection is robust to noise
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#[test]
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fn test_plateau_detection_robust_to_noise() {
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// Test Case 1: Real plateau with noise
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let noisy_plateau = vec![
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1.0, 1.01, 0.99, 1.02, 0.98, 1.01, 0.99, 1.00,
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1.01, 0.99, 1.02, 0.98, 1.01, 0.99, 1.00, 1.01,
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];
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let window = 4;
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let min_improvement = 2.0;
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let recent_avg = noisy_plateau[12..16].iter().sum::<f64>() / 4.0;
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let older_avg = noisy_plateau[8..12].iter().sum::<f64>() / 4.0;
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let improvement = ((older_avg - recent_avg) / older_avg * 100.0).abs();
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info!(recent_avg, older_avg, improvement_pct = improvement, "Noisy plateau analysis");
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// Should detect as plateau despite noise
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assert!(improvement < min_improvement, "Should detect plateau with noise");
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// Test Case 2: Consistent improvement with noise
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let noisy_improvement = vec![
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1.0, 0.98, 1.01, 0.95, 0.99, 0.92, 0.96, 0.89,
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0.93, 0.86, 0.90, 0.83, 0.87, 0.80, 0.84, 0.77,
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];
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let recent_avg2 = noisy_improvement[12..16].iter().sum::<f64>() / 4.0;
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let older_avg2 = noisy_improvement[8..12].iter().sum::<f64>() / 4.0;
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let improvement2 = ((older_avg2 - recent_avg2) / older_avg2 * 100.0).abs();
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info!(recent_avg = recent_avg2, older_avg = older_avg2, improvement_pct = improvement2, "Noisy improvement analysis");
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// Should NOT detect as plateau (real improvement)
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assert!(improvement2 >= min_improvement, "Should detect continued improvement");
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}
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/// Test signal-to-noise ratio in loss trajectory
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#[test]
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fn test_signal_to_noise_ratio_analysis() {
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// Calculate SNR for different training phases
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struct PhaseAnalysis {
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phase_name: &'static str,
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losses: Vec<f64>,
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mean: f64,
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variance: f64,
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snr_db: f64,
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}
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let early_phase = vec![5.0, 4.8, 4.9, 4.5, 4.7, 4.3, 4.5, 4.1, 4.3, 3.9];
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let late_phase = vec![1.0, 1.01, 0.99, 1.02, 0.98, 1.01, 0.99, 1.00, 1.01, 0.99];
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let early_mean = early_phase.iter().sum::<f64>() / early_phase.len() as f64;
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let late_mean = late_phase.iter().sum::<f64>() / late_phase.len() as f64;
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let early_var = calculate_variance(&early_phase);
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let late_var = calculate_variance(&late_phase);
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let early_snr = 10.0 * (early_mean / early_var.sqrt()).log10();
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let late_snr = 10.0 * (late_mean / late_var.sqrt()).log10();
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info!(early_mean, early_var, early_snr_db = early_snr, late_mean, late_var, late_snr_db = late_snr, "Signal-to-Noise Ratio Analysis");
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// Late phase should have lower variance (more stable)
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assert!(late_var < early_var, "Late phase should be more stable");
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}
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// ============================================================================
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// REAL-WORLD RESOURCE SAVINGS TESTS
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// ============================================================================
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/// Test resource savings measurement methodology
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#[test]
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#[ignore = "Slow test: requires real hyperopt run"]
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fn test_real_world_resource_savings_measurement() {
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// This test would run a real hyperopt and measure:
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// 1. Wall-clock time saved
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// 2. Epochs saved
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// 3. GPU memory-hours saved
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// 4. Cost saved (RunPod pricing)
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struct ResourceSavings {
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trials_total: usize,
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trials_stopped_early: usize,
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epochs_without_es: usize,
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epochs_with_es: usize,
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time_without_es_mins: f64,
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time_with_es_mins: f64,
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cost_without_es_usd: f64,
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cost_with_es_usd: f64,
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}
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// Simulated results (would come from real run)
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let savings = ResourceSavings {
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trials_total: 20,
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trials_stopped_early: 14,
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epochs_without_es: 2000, // 20 trials × 100 epochs
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epochs_with_es: 1180, // Average 59 epochs/trial
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time_without_es_mins: 60.0,
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time_with_es_mins: 35.4,
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cost_without_es_usd: 0.25, // RTX A4000 @ $0.25/hr
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cost_with_es_usd: 0.15,
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};
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let epoch_savings_pct = (1.0 - savings.epochs_with_es as f64 / savings.epochs_without_es as f64) * 100.0;
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let time_savings_pct = (1.0 - savings.time_with_es_mins / savings.time_without_es_mins) * 100.0;
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let cost_savings_pct = (1.0 - savings.cost_with_es_usd / savings.cost_without_es_usd) * 100.0;
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info!(
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trials_total = savings.trials_total,
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trials_stopped_early = savings.trials_stopped_early,
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stopped_early_pct = savings.trials_stopped_early as f64 / savings.trials_total as f64 * 100.0,
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epochs_without_es = savings.epochs_without_es,
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epochs_with_es = savings.epochs_with_es,
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epochs_saved = savings.epochs_without_es - savings.epochs_with_es,
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epoch_savings_pct,
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time_without_es_mins = savings.time_without_es_mins,
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time_with_es_mins = savings.time_with_es_mins,
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time_savings_pct,
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cost_without_es_usd = savings.cost_without_es_usd,
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cost_with_es_usd = savings.cost_with_es_usd,
|
||
cost_savings_pct,
|
||
"Resource Savings Analysis"
|
||
);
|
||
|
||
// Verify savings targets
|
||
assert!(epoch_savings_pct >= 30.0 && epoch_savings_pct <= 70.0,
|
||
"Epoch savings {}% outside 30-70% range", epoch_savings_pct);
|
||
assert!(time_savings_pct >= 30.0 && time_savings_pct <= 70.0,
|
||
"Time savings {}% outside 30-70% range", time_savings_pct);
|
||
assert!(cost_savings_pct >= 30.0 && cost_savings_pct <= 70.0,
|
||
"Cost savings {}% outside 30-70% range", cost_savings_pct);
|
||
}
|
||
|
||
// ============================================================================
|
||
// HELPER STRUCTURES
|
||
// ============================================================================
|
||
|
||
struct ValidationTestConfig {
|
||
adapter_name: &'static str,
|
||
num_trials: usize,
|
||
max_epochs: usize,
|
||
data_path: &'static str,
|
||
tolerance_pct: f64,
|
||
min_savings_pct: f64,
|
||
max_savings_pct: f64,
|
||
}
|