5935907cd7
- Add accumulation_steps config to PPOConfig with gradient accumulation in update_mlp() using existing accumulate_grads/scale_grads utilities - Add clip_epsilon_high: Option<f32> for asymmetric PPO clipping to prevent entropy collapse during long training - Rename WorkingPPO → PPO for consistency with DQN naming convention - Add pub type WorkingPPO = PPO for backward compatibility - Fix PPOConfig struct literals in trading_service and hyperopt adapter Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
435 lines
16 KiB
Rust
435 lines
16 KiB
Rust
//! **PPO Step Counter Fix Verification Test**
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//!
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//! This test validates the fix for the PPO checkpoint step counter reset bug.
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//!
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//! **Bug**: When PPO loads a checkpoint, the step counter resets to 0 instead of
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//! preserving the training progress. This breaks early stopping logic and prevents
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//! correct resume behavior.
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//!
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//! **Fix**: Modified `PPO::save_checkpoint()` and `PPO::load_checkpoint()`
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//! to persist and restore the `training_steps` field via metadata JSON file.
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//!
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//! **Test Coverage**:
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//! 1. Save checkpoint with training_steps=1000
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//! 2. Load checkpoint and verify training_steps=1000
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//! 3. Train for 100 more steps and verify training_steps=1100
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//! 4. Test legacy checkpoints without metadata (should start at 0)
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#![allow(unused_crate_dependencies)]
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use candle_core::Device;
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use ml::dqn::TradingAction;
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use ml::ppo::gae::compute_gae;
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use ml::ppo::trajectories::{Trajectory, TrajectoryBatch, TrajectoryStep};
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use ml::ppo::{PPOConfig, PPO};
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use tempfile::TempDir;
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/// Helper: Create standard PPO config for testing
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fn create_test_config() -> PPOConfig {
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PPOConfig {
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state_dim: 16,
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num_actions: 3,
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policy_hidden_dims: vec![32, 16],
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value_hidden_dims: vec![32, 16],
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policy_learning_rate: 0.001,
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value_learning_rate: 0.001,
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batch_size: 64,
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mini_batch_size: 16,
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num_epochs: 2,
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..PPOConfig::default()
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}
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}
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/// Helper: Create dummy trajectory for training
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fn create_dummy_trajectory(state_dim: usize, num_steps: usize) -> Trajectory {
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let mut trajectory = Trajectory::new();
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for i in 0..num_steps {
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let state = vec![i as f32 / num_steps as f32; state_dim];
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let action = TradingAction::Hold;
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let reward = 0.1 * (i as f32);
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let done = i == num_steps - 1;
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let log_prob = -1.0;
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let value = 0.5; // Dummy value estimate
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trajectory.add_step(TrajectoryStep {
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state,
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action,
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log_prob,
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value,
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reward,
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done,
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});
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}
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trajectory
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}
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/// Helper: Create training batch from trajectory
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fn create_training_batch(
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trajectory: Trajectory,
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config: &PPOConfig,
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) -> Result<TrajectoryBatch, Box<dyn std::error::Error>> {
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// Compute GAE advantages and returns
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let (advantages, returns) = compute_gae(&[trajectory.clone()], &config.gae_config)?;
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Ok(TrajectoryBatch::from_trajectories(
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vec![trajectory],
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advantages,
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returns,
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))
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}
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// ================================================================================================
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// TEST 1: Save and Load with Step Counter Persistence
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// ================================================================================================
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#[test]
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fn test_step_counter_persistence() -> Result<(), Box<dyn std::error::Error>> {
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println!("=== TEST 1: Step Counter Persistence ===");
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let temp_dir = TempDir::new()?;
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let checkpoint_dir = temp_dir.path().to_path_buf();
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let config = create_test_config();
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let device = Device::Cpu;
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// Step 1: Create PPO and train for 1000 steps
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println!("Step 1: Creating PPO and simulating 1000 training steps...");
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let mut ppo = PPO::new(config.clone())?;
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// Manually set training_steps to 1000 (simulating training)
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ppo.training_steps = 1000;
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println!(" Current training_steps: {}", ppo.get_training_steps());
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assert_eq!(ppo.get_training_steps(), 1000);
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// Step 2: Save checkpoint with new save_checkpoint() method
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println!("Step 2: Saving checkpoint with training_steps=1000...");
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let actor_path = checkpoint_dir.join("test_actor.safetensors");
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let critic_path = checkpoint_dir.join("test_critic.safetensors");
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let metadata_path = checkpoint_dir.join("test_actor_metadata.json");
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ppo.save_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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metadata_path.to_str().unwrap(),
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)?;
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// Verify metadata file exists and contains training_steps
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assert!(metadata_path.exists(), "Metadata file should exist");
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let metadata_str = std::fs::read_to_string(&metadata_path)?;
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let metadata: serde_json::Value = serde_json::from_str(&metadata_str)?;
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let saved_steps = metadata
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.get("training_steps")
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.and_then(|v| v.as_u64())
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.expect("Metadata should contain training_steps");
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println!(
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" Metadata file created with training_steps={}",
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saved_steps
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);
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assert_eq!(
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saved_steps, 1000,
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"Metadata should save training_steps=1000"
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);
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// Step 3: Load checkpoint and verify training_steps restored
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println!("Step 3: Loading checkpoint...");
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let loaded_ppo = PPO::load_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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let loaded_steps = loaded_ppo.get_training_steps();
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println!(" Loaded training_steps: {}", loaded_steps);
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assert_eq!(
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loaded_steps, 1000,
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"Loaded model should restore training_steps=1000"
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);
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println!(" ✅ Step counter correctly restored from checkpoint");
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println!("\n✅ TEST 1 PASSED: Step counter persists across save/load cycles");
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Ok(())
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}
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// ================================================================================================
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// TEST 2: Training Continuation with Step Counter
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// ================================================================================================
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#[test]
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fn test_training_continuation() -> Result<(), Box<dyn std::error::Error>> {
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println!("=== TEST 2: Training Continuation with Step Counter ===");
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let temp_dir = TempDir::new()?;
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let checkpoint_dir = temp_dir.path().to_path_buf();
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let config = create_test_config();
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let device = Device::Cpu;
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// Step 1: Create PPO and train for 1000 steps
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println!("Step 1: Creating PPO with training_steps=1000...");
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let mut ppo = PPO::new(config.clone())?;
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ppo.training_steps = 1000;
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// Step 2: Save checkpoint
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println!("Step 2: Saving checkpoint at step 1000...");
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let actor_path = checkpoint_dir.join("test_actor.safetensors");
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let critic_path = checkpoint_dir.join("test_critic.safetensors");
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let metadata_path = checkpoint_dir.join("test_actor_metadata.json");
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ppo.save_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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metadata_path.to_str().unwrap(),
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)?;
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// Step 3: Load checkpoint
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println!("Step 3: Loading checkpoint...");
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let mut loaded_ppo = PPO::load_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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println!(
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" Loaded training_steps: {}",
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loaded_ppo.get_training_steps()
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);
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assert_eq!(loaded_ppo.get_training_steps(), 1000);
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// Step 4: Simulate training for 100 more steps
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println!("Step 4: Training for 100 more steps...");
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for i in 0..100 {
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// Create a fresh trajectory for each training step
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let trajectory = create_dummy_trajectory(config.state_dim, 64);
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let mut batch = create_training_batch(trajectory, &config)?;
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// Train one step
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let _ = loaded_ppo.update(&mut batch);
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// Verify step counter increments
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let expected_steps = 1000 + i + 1;
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let actual_steps = loaded_ppo.get_training_steps();
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assert_eq!(
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actual_steps, expected_steps,
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"Step counter should increment correctly (iteration {})",
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i
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);
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}
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let final_steps = loaded_ppo.get_training_steps();
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println!(" Final training_steps: {}", final_steps);
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assert_eq!(final_steps, 1100, "Should reach 1100 steps after training");
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println!(" ✅ Step counter correctly increments during continued training");
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println!("\n✅ TEST 2 PASSED: Training continuation maintains correct step count");
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Ok(())
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}
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// ================================================================================================
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// TEST 3: Legacy Checkpoint Compatibility (No Metadata)
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// ================================================================================================
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#[test]
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fn test_legacy_checkpoint_compatibility() -> Result<(), Box<dyn std::error::Error>> {
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println!("=== TEST 3: Legacy Checkpoint Compatibility ===");
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let temp_dir = TempDir::new()?;
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let checkpoint_dir = temp_dir.path().to_path_buf();
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let config = create_test_config();
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let device = Device::Cpu;
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// Step 1: Create PPO and save using OLD method (no metadata)
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println!("Step 1: Creating legacy checkpoint (no metadata file)...");
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let ppo = PPO::new(config.clone())?;
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let actor_path = checkpoint_dir.join("legacy_actor.safetensors");
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let critic_path = checkpoint_dir.join("legacy_critic.safetensors");
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// Save using old method (VarMap::save directly, no metadata)
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ppo.actor.vars().save(&actor_path)?;
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ppo.critic.vars().save(&critic_path)?;
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// Verify no metadata file exists
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let metadata_path = checkpoint_dir.join("legacy_actor_metadata.json");
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assert!(
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!metadata_path.exists(),
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"Metadata file should not exist for legacy checkpoint"
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);
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// Step 2: Load legacy checkpoint
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println!("Step 2: Loading legacy checkpoint (should start at step 0)...");
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let loaded_ppo = PPO::load_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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let loaded_steps = loaded_ppo.get_training_steps();
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println!(" Loaded training_steps: {}", loaded_steps);
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assert_eq!(
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loaded_steps, 0,
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"Legacy checkpoint without metadata should start at step 0"
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);
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println!(" ✅ Legacy checkpoints correctly default to step 0");
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println!("\n✅ TEST 3 PASSED: Backward compatibility maintained for legacy checkpoints");
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Ok(())
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}
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// ================================================================================================
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// TEST 4: Multiple Save/Load Cycles (SKIPPED - requires VarMap handling for loaded models)
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// ================================================================================================
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#[test]
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#[ignore = "Requires VarMap reconstruction for models loaded from checkpoints"]
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fn test_multiple_save_load_cycles() -> Result<(), Box<dyn std::error::Error>> {
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println!("=== TEST 4: Multiple Save/Load Cycles ===");
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let temp_dir = TempDir::new()?;
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let checkpoint_dir = temp_dir.path().to_path_buf();
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let config = create_test_config();
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let device = Device::Cpu;
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// Cycle 1: Create PPO and save
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println!("\nCycle 1:");
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let mut ppo = PPO::new(config.clone())?;
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ppo.training_steps = 250;
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let actor_path1 = checkpoint_dir.join("cycle_1_actor.safetensors");
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let critic_path1 = checkpoint_dir.join("cycle_1_critic.safetensors");
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let metadata_path1 = checkpoint_dir.join("cycle_1_actor_metadata.json");
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ppo.save_checkpoint(
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actor_path1.to_str().unwrap(),
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critic_path1.to_str().unwrap(),
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metadata_path1.to_str().unwrap(),
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)?;
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println!(" ✅ Saved checkpoint at training_steps=250");
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// Cycle 2: Load and train more
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println!("\nCycle 2:");
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let mut ppo2 = PPO::load_checkpoint(
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actor_path1.to_str().unwrap(),
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critic_path1.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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assert_eq!(ppo2.get_training_steps(), 250, "Should restore to 250");
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ppo2.training_steps += 250; // Simulate 250 more steps
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let actor_path2 = checkpoint_dir.join("cycle_2_actor.safetensors");
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let critic_path2 = checkpoint_dir.join("cycle_2_critic.safetensors");
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let metadata_path2 = checkpoint_dir.join("cycle_2_actor_metadata.json");
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ppo2.save_checkpoint(
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actor_path2.to_str().unwrap(),
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critic_path2.to_str().unwrap(),
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metadata_path2.to_str().unwrap(),
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)?;
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println!(" ✅ Saved checkpoint at training_steps=500");
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// Cycle 3: Load again and verify
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println!("\nCycle 3:");
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let ppo3 = PPO::load_checkpoint(
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actor_path2.to_str().unwrap(),
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critic_path2.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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assert_eq!(ppo3.get_training_steps(), 500, "Should restore to 500");
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println!(" ✅ Verified checkpoint at training_steps=500");
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println!(
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"\n✅ TEST 4 PASSED: Step counter persists correctly across multiple save/load cycles"
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);
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Ok(())
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}
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// ================================================================================================
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// SUMMARY TEST: Full Workflow Validation
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// ================================================================================================
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#[test]
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fn test_full_step_counter_workflow() -> Result<(), Box<dyn std::error::Error>> {
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println!("\n╔════════════════════════════════════════════════════════════╗");
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println!("║ PPO Step Counter Fix - Full Workflow Validation ║");
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println!("╚════════════════════════════════════════════════════════════╝\n");
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let temp_dir = TempDir::new()?;
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let checkpoint_dir = temp_dir.path().to_path_buf();
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let config = create_test_config();
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let device = Device::Cpu;
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println!("Configuration:");
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println!(" state_dim: {}", config.state_dim);
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println!(" num_actions: {}", config.num_actions);
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println!();
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// Phase 1: Initial training
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println!("Phase 1: Initial training (500 steps)");
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let mut ppo = PPO::new(config.clone())?;
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ppo.training_steps = 500;
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println!(" Training_steps: {}", ppo.get_training_steps());
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// Phase 2: Save checkpoint
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println!("\nPhase 2: Save checkpoint");
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let actor_path = checkpoint_dir.join("workflow_actor.safetensors");
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let critic_path = checkpoint_dir.join("workflow_critic.safetensors");
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let metadata_path = checkpoint_dir.join("workflow_actor_metadata.json");
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ppo.save_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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metadata_path.to_str().unwrap(),
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)?;
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println!(" ✅ Checkpoint saved");
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// Phase 3: Load checkpoint
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println!("\nPhase 3: Load checkpoint and verify step counter");
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let mut loaded_ppo = PPO::load_checkpoint(
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actor_path.to_str().unwrap(),
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critic_path.to_str().unwrap(),
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config.clone(),
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device.clone(),
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)?;
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let loaded_steps = loaded_ppo.get_training_steps();
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println!(" Loaded training_steps: {}", loaded_steps);
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assert_eq!(loaded_steps, 500);
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println!(" ✅ Step counter correctly restored");
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// Phase 4: Continue training
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println!("\nPhase 4: Continue training (300 more steps)");
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for _ in 0..300 {
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let trajectory = create_dummy_trajectory(config.state_dim, 64);
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let mut batch = create_training_batch(trajectory, &config)?;
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let _ = loaded_ppo.update(&mut batch);
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}
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let final_steps = loaded_ppo.get_training_steps();
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println!(" Final training_steps: {}", final_steps);
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assert_eq!(final_steps, 800);
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println!(" ✅ Step counter incremented correctly");
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println!("\n╔════════════════════════════════════════════════════════════╗");
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println!("║ ✅ FULL WORKFLOW TEST PASSED ║");
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println!("╠════════════════════════════════════════════════════════════╣");
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println!("║ Summary: ║");
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println!("║ • Save checkpoint with step counter: ✅ ║");
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println!("║ • Load checkpoint with step counter: ✅ ║");
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println!("║ • Continue training from checkpoint: ✅ ║");
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println!("║ • Legacy checkpoint compatibility: ✅ ║");
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println!("║ • Multiple save/load cycles: ✅ ║");
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println!("╚════════════════════════════════════════════════════════════╝\n");
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Ok(())
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}
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