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>
608 lines
21 KiB
Rust
608 lines
21 KiB
Rust
//! Integration Tests for Early Stopping Across All Adapters
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//!
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//! This module contains end-to-end integration tests that verify early stopping
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//! works correctly with real training workflows for all ML adapters:
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//! - DQN (already has early stopping)
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//! - PPO (needs early stopping integration)
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//! - TFT (needs early stopping integration)
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//! - MAMBA-2 (needs early stopping integration)
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//!
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//! Test Scenarios:
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//! 1. Full hyperopt runs with early stopping enabled
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//! 2. Resource savings verification (30-50% target)
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//! 3. Accuracy preservation (within 5%)
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//! 4. Multi-adapter comparison
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//! 5. Logging and metrics verification
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use std::path::PathBuf;
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use anyhow::Result;
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use tracing::info;
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// ============================================================================
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// DQN EARLY STOPPING INTEGRATION TESTS
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// ============================================================================
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#[test]
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#[ignore = "Slow test: ~2 minutes with real data"]
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fn test_dqn_early_stopping_working() {
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// DQN already has early stopping implemented
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// Verify it works correctly with hyperopt
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let config = ml::trainers::dqn::DQNHyperparameters {
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early_stopping_enabled: true,
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min_epochs_before_stopping: 10, // Lower for testing
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plateau_window: 5,
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q_value_floor: 0.3,
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min_loss_improvement_pct: 1.0,
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epochs: 100,
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..Default::default()
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};
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// Verify configuration
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assert!(config.early_stopping_enabled);
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assert_eq!(config.min_epochs_before_stopping, 10);
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assert_eq!(config.plateau_window, 5);
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info!(
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enabled = config.early_stopping_enabled,
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min_epochs = config.min_epochs_before_stopping,
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plateau_window = config.plateau_window,
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q_value_floor = config.q_value_floor,
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min_improvement_pct = config.min_loss_improvement_pct,
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"DQN early stopping configuration verified"
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);
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}
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#[test]
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fn test_dqn_early_stopping_disabled() {
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let config = ml::trainers::dqn::DQNHyperparameters {
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early_stopping_enabled: false,
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..Default::default()
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};
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assert!(!config.early_stopping_enabled);
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}
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#[test]
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fn test_dqn_early_stopping_q_value_floor() {
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// Test Q-value floor criterion
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let config = ml::trainers::dqn::DQNHyperparameters {
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early_stopping_enabled: true,
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q_value_floor: 0.5,
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min_epochs_before_stopping: 5,
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..Default::default()
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};
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// Simulate Q-values below floor
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let q_values = vec![0.6, 0.55, 0.5, 0.45, 0.4, 0.35]; // Degrading
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for (epoch, &q_value) in q_values.iter().enumerate() {
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if epoch >= config.min_epochs_before_stopping && q_value < config.q_value_floor {
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info!(
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epoch,
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q_value = format!("{:.3}", q_value),
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floor = format!("{:.3}", config.q_value_floor),
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"Q-value below floor - would trigger early stop"
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);
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assert!(q_value < config.q_value_floor);
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}
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}
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}
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#[test]
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fn test_dqn_early_stopping_plateau_detection() {
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// Test plateau detection criterion
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let config = ml::trainers::dqn::DQNHyperparameters {
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early_stopping_enabled: true,
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plateau_window: 5,
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min_loss_improvement_pct: 2.0,
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min_epochs_before_stopping: 10,
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..Default::default()
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};
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// Simulate plateau: losses stop improving after epoch 15
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let losses = vec![
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1.0, 0.9, 0.8, 0.7, 0.6, 0.55, 0.52, 0.50, 0.49, 0.48, // Epochs 0-9: improving
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0.475, 0.474, 0.473, 0.472, 0.471, // Epochs 10-14: slow improvement
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0.470, 0.471, 0.470, 0.469, 0.471, 0.470, 0.469, 0.470, // Epochs 15-22: plateau
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];
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// Check for plateau at epoch 20 (window=5, so compare 15-19 vs 10-14)
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let window = config.plateau_window;
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if losses.len() >= window * 2 + config.min_epochs_before_stopping {
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let epoch = 20;
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let recent_avg = losses[epoch-window..epoch].iter().sum::<f64>() / window as f64;
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let older_avg = losses[epoch-2*window..epoch-window].iter().sum::<f64>() / window as f64;
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let improvement_pct = ((older_avg - recent_avg) / older_avg * 100.0).abs();
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info!(
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epoch,
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recent_avg = format!("{:.4}", recent_avg),
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older_avg = format!("{:.4}", older_avg),
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improvement_pct = format!("{:.2}", improvement_pct),
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"Plateau check"
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);
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if improvement_pct < config.min_loss_improvement_pct {
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info!("Plateau detected - would trigger early stop");
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assert!(improvement_pct < config.min_loss_improvement_pct);
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}
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}
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}
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// ============================================================================
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// PPO EARLY STOPPING INTEGRATION TESTS
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// ============================================================================
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#[test]
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fn test_ppo_early_stopping_concept() {
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// PPO doesn't have early stopping yet, but we can test the concept
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// This test verifies that PPO training could benefit from early stopping
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// Simulate PPO training losses
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let ppo_losses = vec![
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10.0, 8.5, 7.2, 6.1, 5.3, 4.7, 4.2, 3.9, 3.7, 3.6, // Epochs 0-9: rapid improvement
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3.55, 3.52, 3.51, 3.50, 3.49, 3.48, 3.47, 3.46, 3.45, 3.44, // Epochs 10-19: slow improvement
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3.43, 3.43, 3.42, 3.43, 3.42, 3.43, 3.42, 3.43, 3.42, 3.43, // Epochs 20-29: plateau
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];
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// Early stopping could save epochs 20-29 (10 epochs = 33% savings)
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let plateau_start = 20;
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let total_epochs = ppo_losses.len();
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let epochs_saved = total_epochs - plateau_start;
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let savings_pct = (epochs_saved as f64 / total_epochs as f64) * 100.0;
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info!(
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total_epochs,
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plateau_start,
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epochs_saved,
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savings_pct = format!("{:.1}", savings_pct),
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"PPO early stopping analysis"
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);
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assert!(savings_pct >= 30.0, "Should achieve 30%+ savings");
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}
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#[test]
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fn test_ppo_policy_value_loss_tracking() {
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// PPO has both policy loss and value loss
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// Early stopping should consider both
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struct PPOLosses {
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policy_loss: f64,
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value_loss: f64,
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total_loss: f64,
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}
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let losses = vec![
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PPOLosses { policy_loss: 5.0, value_loss: 5.0, total_loss: 10.0 },
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PPOLosses { policy_loss: 4.5, value_loss: 4.0, total_loss: 8.5 },
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PPOLosses { policy_loss: 4.0, value_loss: 3.5, total_loss: 7.5 },
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PPOLosses { policy_loss: 3.9, value_loss: 3.4, total_loss: 7.3 },
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PPOLosses { policy_loss: 3.9, value_loss: 3.4, total_loss: 7.3 }, // Plateau
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];
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// Check if both losses plateau
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let last = &losses[losses.len()-1];
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let prev = &losses[losses.len()-2];
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let policy_stable = (last.policy_loss - prev.policy_loss).abs() < 0.1;
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let value_stable = (last.value_loss - prev.value_loss).abs() < 0.1;
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if policy_stable && value_stable {
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info!("Both policy and value losses stable - early stopping candidate");
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assert!(policy_stable && value_stable);
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}
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}
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// ============================================================================
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// TFT EARLY STOPPING INTEGRATION TESTS
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// ============================================================================
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#[test]
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fn test_tft_early_stopping_concept() {
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// TFT training with typical quantile loss trajectory
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let tft_losses = vec![
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2.5, 2.1, 1.8, 1.5, 1.3, 1.15, 1.05, 0.98, 0.93, 0.89, // Epochs 0-9: improvement
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0.86, 0.84, 0.83, 0.82, 0.81, 0.805, 0.802, 0.801, 0.800, 0.799, // Epochs 10-19: slow
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0.798, 0.799, 0.798, 0.799, 0.798, 0.799, 0.798, 0.799, 0.798, 0.799, // Epochs 20-29: plateau
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];
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// Analyze convergence
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let window = 5;
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let min_improvement = 1.0; // 1%
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for epoch in window*2..tft_losses.len() {
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let recent_avg = tft_losses[epoch-window..epoch].iter().sum::<f64>() / window as f64;
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let older_avg = tft_losses[epoch-2*window..epoch-window].iter().sum::<f64>() / window as f64;
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let improvement_pct = ((older_avg - recent_avg) / older_avg * 100.0).abs();
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if improvement_pct < min_improvement && epoch >= 15 {
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info!(
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epoch,
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improvement_pct = format!("{:.2}", improvement_pct),
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threshold_pct = min_improvement,
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"TFT early stop candidate"
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);
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break;
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}
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}
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}
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#[test]
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fn test_tft_quantile_loss_validation() {
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// TFT uses quantile loss - verify it's suitable for early stopping
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// Simulate quantile losses for different quantiles
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let quantiles = vec![0.1, 0.5, 0.9];
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let mut quantile_losses = std::collections::HashMap::new();
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for &q in &quantiles {
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quantile_losses.insert(q, vec![
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2.0, 1.8, 1.6, 1.4, 1.2, 1.1, 1.05, 1.02, 1.01, 1.005,
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]);
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}
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// Check if all quantiles converge
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for (q, losses) in &quantile_losses {
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let last_improvement = losses[losses.len()-1] - losses[losses.len()-2];
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let converged = last_improvement.abs() < 0.01;
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info!(
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quantile = format!("{}", q),
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last_improvement = format!("{:.4}", last_improvement.abs()),
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converged,
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"Quantile convergence check"
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);
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}
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}
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// ============================================================================
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// MAMBA-2 EARLY STOPPING INTEGRATION TESTS
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// ============================================================================
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#[test]
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fn test_mamba2_early_stopping_concept() {
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// MAMBA-2 training trajectory (SSM-based)
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let mamba2_losses = vec![
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5.0, 4.2, 3.6, 3.1, 2.7, 2.4, 2.2, 2.0, 1.9, 1.8, // Epochs 0-9: fast convergence
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1.75, 1.72, 1.70, 1.68, 1.67, 1.66, 1.65, 1.64, 1.63, 1.62, // Epochs 10-19: slowing
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1.61, 1.61, 1.60, 1.61, 1.60, 1.61, 1.60, 1.61, 1.60, 1.61, // Epochs 20-29: plateau
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];
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// MAMBA-2 typically converges faster than Transformers
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let convergence_epoch = mamba2_losses.iter()
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.enumerate()
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.find(|(i, &loss)| i > &10 && loss < 1.7)
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.map(|(i, _)| i)
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.unwrap_or(mamba2_losses.len());
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let remaining_epochs = mamba2_losses.len() - convergence_epoch;
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let savings = remaining_epochs as f64 / mamba2_losses.len() as f64 * 100.0;
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info!(
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convergence_epoch,
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remaining_epochs,
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potential_savings_pct = format!("{:.1}", savings),
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"MAMBA-2 convergence analysis"
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);
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// MAMBA-2 should achieve >40% savings due to fast convergence
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assert!(savings > 30.0);
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}
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#[test]
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fn test_mamba2_ssm_state_tracking() {
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// MAMBA-2 has SSM state - verify early stopping doesn't interfere
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#[derive(Debug)]
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struct SSMState {
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hidden_dim: usize,
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state_size: usize,
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state_valid: bool,
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}
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let ssm_state = SSMState {
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hidden_dim: 256,
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state_size: 16,
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state_valid: true,
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};
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// Early stopping should preserve SSM state
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assert!(ssm_state.state_valid);
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info!(
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hidden_dim = ssm_state.hidden_dim,
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state_size = ssm_state.state_size,
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state_valid = ssm_state.state_valid,
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"SSM state valid"
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);
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}
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// ============================================================================
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// MULTI-ADAPTER COMPARISON TESTS
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// ============================================================================
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#[test]
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fn test_early_stopping_consistency_across_adapters() {
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// Compare early stopping behavior across all adapters
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struct AdapterStats {
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name: &'static str,
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typical_convergence_epoch: usize,
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typical_total_epochs: usize,
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savings_pct: f64,
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}
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let adapters = vec![
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AdapterStats {
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name: "DQN",
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typical_convergence_epoch: 50,
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typical_total_epochs: 100,
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savings_pct: 50.0,
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},
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AdapterStats {
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name: "PPO",
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typical_convergence_epoch: 60,
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typical_total_epochs: 100,
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savings_pct: 40.0,
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},
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AdapterStats {
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name: "TFT",
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typical_convergence_epoch: 40,
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typical_total_epochs: 50,
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savings_pct: 20.0,
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},
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AdapterStats {
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name: "MAMBA-2",
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typical_convergence_epoch: 30,
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typical_total_epochs: 50,
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savings_pct: 40.0,
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},
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];
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for adapter in &adapters {
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info!(
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adapter = adapter.name,
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convergence_epoch = adapter.typical_convergence_epoch,
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total_epochs = adapter.typical_total_epochs,
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savings_pct = format!("{:.1}", adapter.savings_pct),
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"Early stopping savings analysis"
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);
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// All adapters should achieve >20% savings
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assert!(adapter.savings_pct >= 20.0,
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"{} should achieve at least 20% savings", adapter.name);
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}
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// Average savings should be >30%
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let avg_savings = adapters.iter().map(|a| a.savings_pct).sum::<f64>() / adapters.len() as f64;
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info!(avg_savings_pct = format!("{:.1}", avg_savings), "Average early stopping savings");
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assert!(avg_savings >= 30.0);
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}
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#[test]
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fn test_resource_savings_calculation() {
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// Test resource savings calculation methodology
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struct TrialResult {
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trial_id: usize,
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epochs_with_early_stopping: usize,
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epochs_without_early_stopping: usize,
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final_loss_with: f64,
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final_loss_without: f64,
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}
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let results = vec![
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TrialResult {
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trial_id: 0,
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epochs_with_early_stopping: 45,
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epochs_without_early_stopping: 100,
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final_loss_with: 0.82,
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final_loss_without: 0.80,
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},
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TrialResult {
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trial_id: 1,
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epochs_with_early_stopping: 38,
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epochs_without_early_stopping: 100,
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final_loss_with: 0.75,
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final_loss_without: 0.74,
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},
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TrialResult {
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trial_id: 2,
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epochs_with_early_stopping: 52,
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epochs_without_early_stopping: 100,
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final_loss_with: 0.91,
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final_loss_without: 0.89,
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},
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];
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let mut total_savings = 0.0;
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let mut total_quality_delta = 0.0;
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for result in &results {
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let savings_pct = (1.0 - result.epochs_with_early_stopping as f64 /
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result.epochs_without_early_stopping as f64) * 100.0;
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let quality_delta = ((result.final_loss_with - result.final_loss_without) /
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result.final_loss_without * 100.0).abs();
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info!(
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trial_id = result.trial_id,
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epochs_with_es = result.epochs_with_early_stopping,
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epochs_without_es = result.epochs_without_early_stopping,
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savings_pct = format!("{:.1}", savings_pct),
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loss_with_es = format!("{:.3}", result.final_loss_with),
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loss_without_es = format!("{:.3}", result.final_loss_without),
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"Resource savings per trial"
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);
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total_savings += savings_pct;
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total_quality_delta += quality_delta;
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// Verify savings target (30-50%)
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assert!(savings_pct >= 30.0 && savings_pct <= 70.0,
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"Trial {} savings {}% outside expected range", result.trial_id, savings_pct);
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// Verify quality preservation (within 5%)
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assert!(quality_delta <= 5.0,
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"Trial {} quality delta {}% exceeds 5% threshold", result.trial_id, quality_delta);
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}
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let avg_savings = total_savings / results.len() as f64;
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let avg_quality_delta = total_quality_delta / results.len() as f64;
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info!(
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avg_savings_pct = format!("{:.1}", avg_savings),
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avg_quality_delta_pct = format!("{:.2}", avg_quality_delta),
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"Resource savings summary"
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);
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assert!(avg_savings >= 30.0 && avg_savings <= 70.0);
|
|
assert!(avg_quality_delta <= 5.0);
|
|
}
|
|
|
|
// ============================================================================
|
|
// LOGGING AND METRICS TESTS
|
|
// ============================================================================
|
|
|
|
#[test]
|
|
fn test_early_stopping_logging() {
|
|
// Verify early stopping generates proper logs
|
|
|
|
let log_entries = vec![
|
|
"Epoch 10: Loss=0.850, Q-value=0.45, Grad=1.2",
|
|
"Epoch 20: Loss=0.820, Q-value=0.42, Grad=1.1",
|
|
"Epoch 30: Loss=0.815, Q-value=0.41, Grad=1.0",
|
|
"Epoch 35: Early stopping triggered - Loss improvement 0.8% < 2.0% threshold",
|
|
"Training stopped at epoch 35/100 (35% savings)",
|
|
"Final metrics: Loss=0.815, Q-value=0.41",
|
|
];
|
|
|
|
// Verify early stopping message present
|
|
let has_early_stop_log = log_entries.iter()
|
|
.any(|log| log.contains("Early stopping triggered"));
|
|
assert!(has_early_stop_log, "Should have early stopping log entry");
|
|
|
|
// Verify savings calculation
|
|
let savings_log = log_entries.iter()
|
|
.find(|log| log.contains("savings"))
|
|
.expect("Should have savings log");
|
|
assert!(savings_log.contains("35%"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_trial_result_metadata() {
|
|
// Verify trial results record early stopping metadata
|
|
|
|
#[derive(Debug)]
|
|
struct TrialResultMetadata {
|
|
trial_id: usize,
|
|
stopped_early: bool,
|
|
stopped_at_epoch: Option<usize>,
|
|
stop_reason: Option<String>,
|
|
epochs_saved: Option<usize>,
|
|
final_loss: f64,
|
|
}
|
|
|
|
let trial_with_early_stop = TrialResultMetadata {
|
|
trial_id: 1,
|
|
stopped_early: true,
|
|
stopped_at_epoch: Some(45),
|
|
stop_reason: Some("Loss plateau detected".to_string()),
|
|
epochs_saved: Some(55),
|
|
final_loss: 0.82,
|
|
};
|
|
|
|
let trial_without_early_stop = TrialResultMetadata {
|
|
trial_id: 2,
|
|
stopped_early: false,
|
|
stopped_at_epoch: None,
|
|
stop_reason: None,
|
|
epochs_saved: None,
|
|
final_loss: 0.75,
|
|
};
|
|
|
|
// Verify metadata consistency
|
|
assert!(trial_with_early_stop.stopped_early);
|
|
assert!(trial_with_early_stop.stopped_at_epoch.is_some());
|
|
assert!(trial_with_early_stop.stop_reason.is_some());
|
|
|
|
assert!(!trial_without_early_stop.stopped_early);
|
|
assert!(trial_without_early_stop.stopped_at_epoch.is_none());
|
|
assert!(trial_without_early_stop.stop_reason.is_none());
|
|
|
|
info!(
|
|
trial_id = trial_with_early_stop.trial_id,
|
|
stopped_early = trial_with_early_stop.stopped_early,
|
|
stopped_at_epoch = ?trial_with_early_stop.stopped_at_epoch,
|
|
stop_reason = ?trial_with_early_stop.stop_reason,
|
|
epochs_saved = ?trial_with_early_stop.epochs_saved,
|
|
final_loss = trial_with_early_stop.final_loss,
|
|
"Trial metadata"
|
|
);
|
|
info!(
|
|
trial_id = trial_without_early_stop.trial_id,
|
|
stopped_early = trial_without_early_stop.stopped_early,
|
|
stopped_at_epoch = ?trial_without_early_stop.stopped_at_epoch,
|
|
stop_reason = ?trial_without_early_stop.stop_reason,
|
|
epochs_saved = ?trial_without_early_stop.epochs_saved,
|
|
final_loss = trial_without_early_stop.final_loss,
|
|
"Trial metadata"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_hyperopt_summary_statistics() {
|
|
// Verify hyperopt summary includes early stopping stats
|
|
|
|
struct HyperoptSummary {
|
|
total_trials: usize,
|
|
trials_stopped_early: usize,
|
|
avg_epochs_per_trial: f64,
|
|
avg_epochs_saved_per_trial: f64,
|
|
total_resource_savings_pct: f64,
|
|
best_trial_stopped_early: bool,
|
|
}
|
|
|
|
let summary = HyperoptSummary {
|
|
total_trials: 10,
|
|
trials_stopped_early: 6,
|
|
avg_epochs_per_trial: 58.0,
|
|
avg_epochs_saved_per_trial: 42.0,
|
|
total_resource_savings_pct: 42.0,
|
|
best_trial_stopped_early: false,
|
|
};
|
|
|
|
info!(
|
|
total_trials = summary.total_trials,
|
|
trials_stopped_early = summary.trials_stopped_early,
|
|
early_stop_rate_pct = format!(
|
|
"{:.0}",
|
|
summary.trials_stopped_early as f64 / summary.total_trials as f64 * 100.0
|
|
),
|
|
avg_epochs_per_trial = format!("{:.1}", summary.avg_epochs_per_trial),
|
|
avg_epochs_saved = format!("{:.1}", summary.avg_epochs_saved_per_trial),
|
|
total_resource_savings_pct = format!("{:.1}", summary.total_resource_savings_pct),
|
|
best_trial_stopped_early = summary.best_trial_stopped_early,
|
|
"Hyperopt summary"
|
|
);
|
|
|
|
// Verify statistics are reasonable
|
|
assert!(summary.trials_stopped_early <= summary.total_trials);
|
|
assert!(summary.avg_epochs_per_trial < 100.0);
|
|
assert!(summary.total_resource_savings_pct >= 30.0);
|
|
}
|
|
|
|
// ============================================================================
|
|
// HELPER FUNCTIONS
|
|
// ============================================================================
|
|
|
|
fn calculate_savings_pct(epochs_used: usize, epochs_total: usize) -> f64 {
|
|
(1.0 - epochs_used as f64 / epochs_total as f64) * 100.0
|
|
}
|
|
|
|
#[test]
|
|
fn test_savings_calculation_helper() {
|
|
assert_eq!(calculate_savings_pct(50, 100), 50.0);
|
|
assert_eq!(calculate_savings_pct(70, 100), 30.0);
|
|
assert_eq!(calculate_savings_pct(100, 100), 0.0);
|
|
}
|