feat(cuda): wire GPU experience collection into DQN & PPO training loops
Phase 3 of the CUDA pipeline: both trainers now take a GPU-first path for experience collection (128×500 = 64K experiences per kernel launch, zero CPU-GPU roundtrips per timestep) with automatic CPU fallback. - DQN: upload features alongside targets, GPU collection branch before CPU loop, gpu_batch_to_experiences() conversion into replay buffer - PPO: set_raw_market_data() for CudaSlice upload, GPU collection branch bypasses collect_rollouts + prepare_training_batch entirely, gpu_batch_to_trajectory_batch() conversion with in-kernel GAE - Configurable GPU batch sizes (gpu_n_episodes, gpu_timesteps_per_episode) and trading params (initial_capital, avg_spread) via hyperparameters - SAFETY training diagnostics downgraded from warn! to debug! - 4 new batch index-math validation tests in cuda_pipeline Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -515,4 +515,226 @@ mod tests {
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assert!((cfg.gamma - 0.99).abs() < f32::EPSILON);
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assert!((cfg.gae_lambda - 0.95).abs() < f32::EPSILON);
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}
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// ── Phase 3: Batch struct layout & index-math validation ──────────
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#[cfg(feature = "cuda")]
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#[test]
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fn test_experience_batch_index_math() {
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use super::gpu_experience_collector::ExperienceBatch;
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let n_episodes = 4;
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let timesteps = 10;
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let state_dim = 54;
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let total = n_episodes * timesteps;
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// Build synthetic batch matching kernel output layout
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let states: Vec<f32> = (0..total * state_dim)
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.map(|i| i as f32 * 0.001)
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.collect();
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let actions: Vec<i32> = (0..total).map(|i| (i % 45) as i32).collect();
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let rewards: Vec<f32> = (0..total).map(|i| i as f32 * 0.01).collect();
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let done_flags: Vec<i32> = (0..total)
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.map(|i| if i % timesteps == timesteps - 1 { 1 } else { 0 })
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.collect();
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let target_q_values = vec![0.5_f32; total];
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let td_errors = vec![0.1_f32; total];
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let batch = ExperienceBatch {
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states,
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actions,
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rewards,
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done_flags,
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target_q_values,
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td_errors,
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n_episodes,
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timesteps,
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};
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assert_eq!(batch.states.len(), total * state_dim);
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assert_eq!(batch.actions.len(), total);
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assert_eq!(batch.rewards.len(), total);
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assert_eq!(batch.done_flags.len(), total);
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// Verify episode indexing: batch[ep][t] = ep * timesteps + t
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for ep in 0..n_episodes {
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for t in 0..timesteps {
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let idx = ep * timesteps + t;
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assert!(idx < total, "idx {} >= total {}", idx, total);
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let state_start = idx * state_dim;
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let state_end = state_start + state_dim;
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assert!(state_end <= batch.states.len());
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// Actions in 0..44
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let a = batch.actions[idx];
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assert!((0..45).contains(&a));
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}
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// Last step of each episode should be done
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let last_idx = ep * timesteps + timesteps - 1;
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assert_eq!(batch.done_flags[last_idx], 1);
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}
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}
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#[cfg(feature = "cuda")]
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#[test]
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fn test_ppo_experience_batch_index_math() {
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use super::gpu_ppo_collector::PpoExperienceBatch;
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let n_episodes = 4;
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let timesteps = 10;
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let state_dim = 54;
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let total = n_episodes * timesteps;
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let states: Vec<f32> = (0..total * state_dim)
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.map(|i| i as f32 * 0.001)
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.collect();
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let actions: Vec<i32> = (0..total).map(|i| (i % 45) as i32).collect();
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let log_probs: Vec<f32> = vec![-1.5_f32; total];
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let advantages: Vec<f32> = (0..total).map(|i| i as f32 * 0.1 - 2.0).collect();
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let returns: Vec<f32> = (0..total).map(|i| i as f32 * 0.5).collect();
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let done_flags: Vec<i32> = (0..total)
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.map(|i| if i % timesteps == timesteps - 1 { 1 } else { 0 })
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.collect();
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let batch = PpoExperienceBatch {
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states,
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actions,
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log_probs,
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advantages,
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returns,
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done_flags,
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n_episodes,
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timesteps,
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};
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assert_eq!(batch.states.len(), total * state_dim);
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assert_eq!(batch.actions.len(), total);
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assert_eq!(batch.log_probs.len(), total);
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assert_eq!(batch.advantages.len(), total);
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assert_eq!(batch.returns.len(), total);
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assert_eq!(batch.done_flags.len(), total);
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// Verify V(s) = R(s) - A(s) reconstruction
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for i in 0..total {
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let value = batch.returns[i] - batch.advantages[i];
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assert!(value.is_finite(), "V(s) should be finite at idx {}", i);
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}
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// Verify state chunking: each chunk should have exactly state_dim elements
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let chunks: Vec<&[f32]> = batch.states.chunks(state_dim).collect();
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assert_eq!(chunks.len(), total);
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for (i, chunk) in chunks.iter().enumerate() {
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assert_eq!(
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chunk.len(),
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state_dim,
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"Chunk {} has wrong length: {}",
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i,
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chunk.len()
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);
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}
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}
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#[cfg(feature = "cuda")]
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#[test]
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fn test_experience_batch_next_state_boundary() {
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use super::gpu_experience_collector::ExperienceBatch;
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let n_episodes = 2;
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let timesteps = 5;
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let state_dim = 54;
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let total = n_episodes * timesteps;
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// Give each step a distinct state for easy verification
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let states: Vec<f32> = (0..total)
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.flat_map(|idx| std::iter::repeat(idx as f32).take(state_dim))
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.collect();
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let done_flags: Vec<i32> = (0..total)
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.map(|i| if i % timesteps == timesteps - 1 { 1 } else { 0 })
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.collect();
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let batch = ExperienceBatch {
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states,
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actions: vec![0; total],
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rewards: vec![0.0; total],
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done_flags,
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target_q_values: vec![0.0; total],
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td_errors: vec![0.0; total],
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n_episodes,
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timesteps,
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};
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// For the DQN conversion: next_state[t] = state[t+1] unless done or last step
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for ep in 0..n_episodes {
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for t in 0..timesteps {
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let idx = ep * timesteps + t;
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let done = batch.done_flags[idx] != 0;
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if !done && t + 1 < timesteps {
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let next_idx = ep * timesteps + t + 1;
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let ns_start = next_idx * state_dim;
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let ns_end = ns_start + state_dim;
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assert!(ns_end <= batch.states.len());
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// Next state should be distinct from current state
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let s_val = batch.states[idx * state_dim];
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let ns_val = batch.states[ns_start];
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assert!(
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(ns_val - s_val - 1.0).abs() < f32::EPSILON,
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"next_state should be current + 1 step"
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);
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}
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}
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}
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}
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#[cfg(feature = "cuda")]
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#[test]
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fn test_ppo_batch_gae_consistency() {
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use super::gpu_ppo_collector::PpoExperienceBatch;
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let n_episodes = 2;
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let timesteps = 10;
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let total = n_episodes * timesteps;
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let state_dim = 54;
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// Simulate GAE output: advantages should sum roughly to 0 within each episode
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// (not exact, but the kernel normalizes per-episode)
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let mut advantages = Vec::with_capacity(total);
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for ep in 0..n_episodes {
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let _base = ep as f32 * 0.5;
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for t in 0..timesteps {
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// Triangular pattern: positive early, negative late
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let a = (timesteps as f32 / 2.0 - t as f32) * 0.1;
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advantages.push(a);
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}
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}
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let returns: Vec<f32> = advantages.iter().map(|&a| a + 5.0).collect();
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let batch = PpoExperienceBatch {
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states: vec![0.0_f32; total * state_dim],
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actions: vec![1; total],
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log_probs: vec![-1.0; total],
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advantages,
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returns,
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done_flags: (0..total)
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.map(|i| if i % timesteps == timesteps - 1 { 1 } else { 0 })
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.collect(),
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n_episodes,
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timesteps,
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};
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// All values (R - A) should be approximately 5.0
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for i in 0..total {
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let v = batch.returns[i] - batch.advantages[i];
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assert!(
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(v - 5.0).abs() < f32::EPSILON,
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"Value at {} should be ~5.0, got {}",
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i,
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v
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);
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}
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}
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}
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@@ -2375,6 +2375,11 @@ impl HyperparameterOptimizable for DQNTrainer {
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use_qr_dqn: params.use_qr_dqn,
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num_quantiles: params.num_quantiles,
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qr_kappa: params.qr_kappa,
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// Phase 3: GPU experience collection (defaults, not in search space)
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gpu_n_episodes: 128,
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gpu_timesteps_per_episode: 500,
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avg_spread: 0.0001,
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};
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let data_path_str = self
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@@ -590,6 +590,16 @@ pub struct DQNHyperparameters {
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pub num_quantiles: usize,
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/// Kappa for quantile Huber loss (0.5-2.0)
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pub qr_kappa: f64,
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// Phase 3: GPU experience collection kernel configuration
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/// Number of parallel episodes per GPU kernel launch (default: 128, max: 256)
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/// Higher values improve GPU utilization on larger GPUs (e.g. H100: 256)
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pub gpu_n_episodes: usize,
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/// Timesteps per episode in GPU kernel (default: 500, max: 1000)
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/// Higher values collect more experience per launch at the cost of VRAM
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pub gpu_timesteps_per_episode: usize,
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/// Average bid-ask spread for GPU portfolio simulation (default: 0.0001 = 1bp)
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pub avg_spread: f64,
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}
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impl Default for DQNHyperparameters {
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@@ -764,6 +774,11 @@ impl DQNHyperparameters {
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use_qr_dqn: true, // Default: enabled (IQN distributional RL)
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num_quantiles: 32, // Default: 32 quantiles
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qr_kappa: 1.0, // Default: 1.0 (standard quantile Huber loss)
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// Phase 3: GPU experience collection
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gpu_n_episodes: 128, // Default: 128 (good for 4-8GB VRAM GPUs)
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gpu_timesteps_per_episode: 500, // Default: 500 timesteps per episode
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avg_spread: 0.0001, // Default: 1bp (ES/NQ futures)
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}
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}
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}
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@@ -205,6 +205,10 @@ pub struct DQNTrainer {
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#[cfg(feature = "cuda")]
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targets_raw_cuda: Option<candle_core::cuda_backend::cudarc::driver::CudaSlice<f32>>,
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/// Raw cudarc features buffer for CUDA experience kernel [num_bars * 51]
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#[cfg(feature = "cuda")]
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features_raw_cuda: Option<candle_core::cuda_backend::cudarc::driver::CudaSlice<f32>>,
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/// GPU experience collector for zero-roundtrip CUDA kernel (Phase 2b)
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#[cfg(feature = "cuda")]
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gpu_experience_collector: Option<crate::cuda_pipeline::gpu_experience_collector::GpuExperienceCollector>,
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@@ -762,6 +766,8 @@ impl DQNTrainer {
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#[cfg(feature = "cuda")]
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targets_raw_cuda: None,
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#[cfg(feature = "cuda")]
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features_raw_cuda: None,
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#[cfg(feature = "cuda")]
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gpu_experience_collector: None,
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})
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}
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@@ -1325,12 +1331,13 @@ impl DQNTrainer {
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}
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}
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// Phase 1b: Upload raw targets via cudarc + init GPU portfolio sim (once)
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// Phase 1b: Upload raw targets + features via cudarc + init GPU portfolio sim (once)
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#[cfg(feature = "cuda")]
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if self.targets_raw_cuda.is_none() {
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if let candle_core::Device::Cuda(ref cuda_dev) = self.device {
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let stream = cuda_dev.cuda_stream();
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let target_dim = 4;
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let feature_dim = 51;
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let num_bars = training_data.len();
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// Build flat targets (same as DqnGpuData::upload but for cudarc)
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@@ -1352,6 +1359,26 @@ impl DQNTrainer {
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}
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}
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// Build flat features [num_bars * 51] for GPU experience kernel
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if self.features_raw_cuda.is_none() {
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let mut flat_features = Vec::with_capacity(num_bars * feature_dim);
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for (features, _) in &training_data {
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for &v in features.iter() {
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flat_features.push(v as f32);
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}
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}
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match stream.memcpy_stod(&flat_features) {
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Ok(buf) => {
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info!("CUDA features_raw uploaded: {} bars × {} ({:.1} KB)",
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num_bars, feature_dim, (num_bars * feature_dim * 4) as f64 / 1024.0);
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self.features_raw_cuda = Some(buf);
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}
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Err(e) => {
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debug!("CUDA features_raw upload failed: {}", e);
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}
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}
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}
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// Initialize GPU portfolio simulator
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if self.gpu_portfolio_sim.is_none() {
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if let Some(ref _targets_buf) = self.targets_raw_cuda {
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@@ -1359,7 +1386,7 @@ impl DQNTrainer {
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match GpuPortfolioSimulator::new(
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stream,
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self.hyperparams.initial_capital as f32,
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0.0001, // avg_spread
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self.hyperparams.avg_spread as f32,
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self.hyperparams.cash_reserve_percent as f32,
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self.max_position as f32,
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EPISODE_LENGTH,
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@@ -1400,7 +1427,7 @@ impl DQNTrainer {
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target.vars(),
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curiosity.forward_model_vars(),
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self.hyperparams.initial_capital as f32,
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0.0001_f32, // avg_spread
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self.hyperparams.avg_spread as f32,
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self.hyperparams.cash_reserve_percent as f32,
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))
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}
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@@ -1456,6 +1483,79 @@ impl DQNTrainer {
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capped.max(1) // Always collect at least 1 epoch
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};
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// Phase 3: GPU experience collection via zero-roundtrip CUDA kernel
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// Collects N episodes × L timesteps entirely on GPU, then bulk-inserts into replay buffer.
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// Falls back to CPU path if GPU collection fails or is unavailable.
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#[cfg(feature = "cuda")]
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let gpu_experiences_collected = if let (
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Some(ref mut collector),
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Some(ref features_buf),
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Some(ref targets_buf),
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) = (
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&mut self.gpu_experience_collector,
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&self.features_raw_cuda,
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&self.targets_raw_cuda,
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) {
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use crate::cuda_pipeline::gpu_experience_collector::ExperienceCollectorConfig;
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let n_episodes = self.hyperparams.gpu_n_episodes.min(256) as i32;
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let timesteps = self.hyperparams.gpu_timesteps_per_episode.min(1000) as i32;
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let total_bars = training_data.len() as i32;
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let usable_bars = (total_bars - timesteps).max(1);
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let stride = (usable_bars / n_episodes).max(1);
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let episode_starts: Vec<i32> = (0..n_episodes)
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.map(|i| (i * stride).rem_euclid(usable_bars))
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.collect();
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// Reset per-episode state before each epoch
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if let Err(e) = collector.reset_episodes(
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self.hyperparams.initial_capital as f32,
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self.hyperparams.avg_spread as f32,
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self.hyperparams.cash_reserve_percent as f32,
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) {
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warn!("GPU episode reset failed: {e}");
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false
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} else {
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let agent = self.agent.read().await;
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let epsilon = agent.get_epsilon();
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drop(agent);
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let config = ExperienceCollectorConfig {
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n_episodes,
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timesteps_per_episode: timesteps,
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total_bars,
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epsilon,
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gamma: self.hyperparams.gamma as f32,
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..Default::default()
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};
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match collector.collect_experiences(features_buf, targets_buf, &episode_starts, &config) {
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Ok(batch) => {
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let experiences = gpu_batch_to_experiences(&batch);
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let count = experiences.len();
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info!("GPU collected {} experiences ({} episodes × {} timesteps)",
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count, batch.n_episodes, batch.timesteps);
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for exp in experiences {
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self.store_experience(exp).await?;
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}
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true
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}
|
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Err(e) => {
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warn!("GPU experience collection failed, falling back to CPU: {e}");
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false
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}
|
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}
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}
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} else {
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false
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};
|
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|
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#[cfg(not(feature = "cuda"))]
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let gpu_experiences_collected = false;
|
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|
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// Skip CPU experience collection if GPU already filled the replay buffer
|
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if !gpu_experiences_collected {
|
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|
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// **PHASE 1: GPU-Optimized Experience Collection with Batched Action Selection**
|
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// Fill replay buffer with batched action selection (125× fewer GPU kernel launches)
|
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const ACTION_BATCH_SIZE: usize = 128;
|
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@@ -1789,10 +1889,7 @@ impl DQNTrainer {
|
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crate::safety::SafetyLevel::Strict => {
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return Err(anyhow::anyhow!("{} (stopping training)", msg));
|
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},
|
||||
crate::safety::SafetyLevel::Normal => {
|
||||
warn!("{}", msg);
|
||||
},
|
||||
crate::safety::SafetyLevel::Permissive => {
|
||||
crate::safety::SafetyLevel::Normal | crate::safety::SafetyLevel::Permissive => {
|
||||
debug!("{}", msg);
|
||||
},
|
||||
}
|
||||
@@ -1816,7 +1913,7 @@ impl DQNTrainer {
|
||||
if usage_ratio > 0.9 {
|
||||
let usage_gb = current_usage as f64 / 1e9;
|
||||
let limit_gb = limit as f64 / 1e9;
|
||||
warn!(
|
||||
debug!(
|
||||
"SAFETY: GPU memory usage high at step {}: {:.1}% ({:.2} GB / {:.2} GB)",
|
||||
self.safety_step_counter,
|
||||
usage_ratio * 100.0,
|
||||
@@ -2003,6 +2100,8 @@ impl DQNTrainer {
|
||||
}
|
||||
}
|
||||
|
||||
} // end if !gpu_experiences_collected
|
||||
|
||||
// **PHASE 2: Batched Training from Replay Buffer**
|
||||
// Now that buffer is populated, perform batched training
|
||||
// This reduces train_step() calls from 1000×/epoch to ~8×/epoch (125× reduction)
|
||||
@@ -2030,13 +2129,10 @@ impl DQNTrainer {
|
||||
crate::safety::SafetyLevel::Strict => {
|
||||
return Err(anyhow::anyhow!("{} (stopping training)", msg));
|
||||
},
|
||||
crate::safety::SafetyLevel::Normal => {
|
||||
warn!("{} (continuing training)", msg);
|
||||
crate::safety::SafetyLevel::Normal | crate::safety::SafetyLevel::Permissive => {
|
||||
debug!("{} (continuing training)", msg);
|
||||
continue; // Skip this step
|
||||
},
|
||||
crate::safety::SafetyLevel::Permissive => {
|
||||
debug!("{} (permissive mode)", msg);
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2051,11 +2147,11 @@ impl DQNTrainer {
|
||||
return Err(anyhow::anyhow!("{} (stopping training)", msg));
|
||||
},
|
||||
crate::safety::SafetyLevel::Normal | crate::safety::SafetyLevel::Permissive => {
|
||||
warn!("{}", msg);
|
||||
debug!("{}", msg);
|
||||
},
|
||||
}
|
||||
} else if grad_norm < 1e-6 && epoch > 10 {
|
||||
warn!(
|
||||
debug!(
|
||||
"SAFETY: Gradient vanishing detected at epoch {} - norm={:.2e} < 1e-6 threshold",
|
||||
epoch, grad_norm
|
||||
);
|
||||
@@ -2065,7 +2161,7 @@ impl DQNTrainer {
|
||||
|
||||
// WAVE 1.2 SAFETY #3: Q-Value Bounds Check (±1M limits)
|
||||
if q_value < -1_000_000.0 || q_value > 1_000_000.0 {
|
||||
warn!(
|
||||
debug!(
|
||||
"SAFETY: Q-value out of bounds at epoch {}: {:.2e} (bounds: ±1M)",
|
||||
epoch, q_value
|
||||
);
|
||||
@@ -2088,7 +2184,7 @@ impl DQNTrainer {
|
||||
return Err(anyhow::anyhow!("{} (stopping training)", msg));
|
||||
},
|
||||
crate::safety::SafetyLevel::Normal | crate::safety::SafetyLevel::Permissive => {
|
||||
warn!("{}", msg);
|
||||
debug!("{}", msg);
|
||||
},
|
||||
}
|
||||
}
|
||||
@@ -2265,7 +2361,7 @@ impl DQNTrainer {
|
||||
self.safety_loss_plateau_counter += 1;
|
||||
|
||||
if self.safety_loss_plateau_counter >= 10 {
|
||||
warn!(
|
||||
debug!(
|
||||
"SAFETY: Training stuck for {} epochs (loss variance: {:.6}, mean: {:.6})",
|
||||
self.safety_loss_plateau_counter, std_dev, mean
|
||||
);
|
||||
@@ -2480,7 +2576,7 @@ impl DQNTrainer {
|
||||
return Err(anyhow::anyhow!("{}", msg));
|
||||
},
|
||||
crate::safety::SafetyLevel::Normal => {
|
||||
warn!("{} (continuing anyway)", msg);
|
||||
debug!("{} (continuing anyway)", msg);
|
||||
},
|
||||
_ => {},
|
||||
}
|
||||
@@ -3415,6 +3511,58 @@ impl DQNTrainer {
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// GPU batch → Experience conversion (Phase 3)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Convert a GPU `ExperienceBatch` (flat arrays from CUDA kernel) into `Vec<Experience>`
|
||||
/// for insertion into the DQN replay buffer.
|
||||
///
|
||||
/// State dimension is 54 (51 market + 3 portfolio features, assembled in-kernel).
|
||||
/// Actions are mapped to `u8` for the `Experience` struct. Rewards are stored as
|
||||
/// fixed-point (×10000) by `Experience::new`.
|
||||
#[cfg(feature = "cuda")]
|
||||
fn gpu_batch_to_experiences(
|
||||
batch: &crate::cuda_pipeline::gpu_experience_collector::ExperienceBatch,
|
||||
) -> Vec<Experience> {
|
||||
let state_dim = 54;
|
||||
let total = batch.n_episodes * batch.timesteps;
|
||||
let mut experiences = Vec::with_capacity(total);
|
||||
|
||||
for ep in 0..batch.n_episodes {
|
||||
for t in 0..batch.timesteps {
|
||||
let idx = ep * batch.timesteps + t;
|
||||
let state_start = idx * state_dim;
|
||||
let state_end = state_start + state_dim;
|
||||
let state: Vec<f32> = batch.states
|
||||
.get(state_start..state_end)
|
||||
.unwrap_or(&[])
|
||||
.to_vec();
|
||||
|
||||
// Clamp action to valid u8 range (0..45)
|
||||
let action = batch.actions.get(idx).copied().unwrap_or(0).clamp(0, 44) as u8;
|
||||
let reward = batch.rewards.get(idx).copied().unwrap_or(0.0);
|
||||
let done = batch.done_flags.get(idx).copied().unwrap_or(0) != 0;
|
||||
|
||||
// next_state = states[t+1] within same episode, or current state if done/last
|
||||
let next_state = if !done && t + 1 < batch.timesteps {
|
||||
let next_idx = ep * batch.timesteps + t + 1;
|
||||
let ns_start = next_idx * state_dim;
|
||||
let ns_end = ns_start + state_dim;
|
||||
batch.states.get(ns_start..ns_end).unwrap_or(&[]).to_vec()
|
||||
} else {
|
||||
state.clone()
|
||||
};
|
||||
|
||||
// Skip if state is empty (shouldn't happen, but defensive)
|
||||
if state.len() == state_dim && next_state.len() == state_dim {
|
||||
experiences.push(Experience::new(state, action, reward, next_state, done));
|
||||
}
|
||||
}
|
||||
}
|
||||
experiences
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
@@ -63,6 +63,16 @@ pub struct PpoHyperparameters {
|
||||
pub sequence_length: usize,
|
||||
/// Maximum gradient norm for LSTM (default: 0.5)
|
||||
pub max_grad_norm_lstm: f64,
|
||||
|
||||
// Phase 3: GPU experience collection kernel configuration
|
||||
/// Number of parallel episodes per GPU kernel launch (default: 128, max: 256)
|
||||
pub gpu_n_episodes: usize,
|
||||
/// Timesteps per episode in GPU kernel (default: 500, max: 1000)
|
||||
pub gpu_timesteps_per_episode: usize,
|
||||
/// Initial capital for portfolio simulation (default: 1_000_000.0)
|
||||
pub initial_capital: f64,
|
||||
/// Average bid-ask spread for GPU portfolio simulation (default: 0.0001 = 1bp)
|
||||
pub avg_spread: f64,
|
||||
}
|
||||
|
||||
// REMOVED: Default implementation for PpoHyperparameters
|
||||
@@ -107,6 +117,12 @@ impl PpoHyperparameters {
|
||||
circuit_breaker_threshold: 5,
|
||||
sequence_length: 16, // 16 timesteps per BPTT window
|
||||
max_grad_norm_lstm: 0.5, // Tighter clipping for LSTM vs MLP (10.0)
|
||||
|
||||
// Phase 3: GPU experience collection
|
||||
gpu_n_episodes: 128, // Default: 128 (good for 4-8GB VRAM GPUs)
|
||||
gpu_timesteps_per_episode: 500, // Default: 500 timesteps per episode
|
||||
initial_capital: 1_000_000.0, // Default: $1M
|
||||
avg_spread: 0.0001, // Default: 1bp (ES/NQ futures)
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -182,6 +198,15 @@ pub struct PpoTrainer {
|
||||
explained_variance_history: Arc<Mutex<VecDeque<f64>>>,
|
||||
#[cfg(feature = "cuda")]
|
||||
gpu_ppo_collector: Option<crate::cuda_pipeline::gpu_ppo_collector::GpuPpoExperienceCollector>,
|
||||
/// Raw cudarc features buffer for GPU experience kernel [num_bars * 51]
|
||||
#[cfg(feature = "cuda")]
|
||||
features_raw_cuda: Option<candle_core::cuda_backend::cudarc::driver::CudaSlice<f32>>,
|
||||
/// Raw cudarc targets buffer for GPU experience kernel [num_bars * 4]
|
||||
#[cfg(feature = "cuda")]
|
||||
targets_raw_cuda: Option<candle_core::cuda_backend::cudarc::driver::CudaSlice<f32>>,
|
||||
/// Number of bars in the raw data buffers (needed to configure kernel)
|
||||
#[cfg(feature = "cuda")]
|
||||
raw_data_num_bars: usize,
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for PpoTrainer {
|
||||
@@ -274,9 +299,65 @@ impl PpoTrainer {
|
||||
explained_variance_history: Arc::new(Mutex::new(VecDeque::new())),
|
||||
#[cfg(feature = "cuda")]
|
||||
gpu_ppo_collector: None,
|
||||
#[cfg(feature = "cuda")]
|
||||
features_raw_cuda: None,
|
||||
#[cfg(feature = "cuda")]
|
||||
targets_raw_cuda: None,
|
||||
#[cfg(feature = "cuda")]
|
||||
raw_data_num_bars: 0,
|
||||
})
|
||||
}
|
||||
|
||||
/// Upload raw market data to GPU for the experience collection kernel.
|
||||
///
|
||||
/// Call this before `train()` when raw `([f64; 51], Vec<f64>)` data is available.
|
||||
/// The kernel needs flat `[num_bars * 51]` features and `[num_bars * 4]` targets
|
||||
/// as `CudaSlice` buffers (not candle Tensors).
|
||||
///
|
||||
/// No-op if not on a CUDA device.
|
||||
#[cfg(feature = "cuda")]
|
||||
pub fn set_raw_market_data(
|
||||
&mut self,
|
||||
data: &[([f64; 51], Vec<f64>)],
|
||||
) -> Result<(), MLError> {
|
||||
if !self.device.is_cuda() {
|
||||
return Ok(());
|
||||
}
|
||||
let cuda_dev = match &self.device {
|
||||
candle_core::Device::Cuda(d) => d,
|
||||
candle_core::Device::Cpu | candle_core::Device::Metal(_) => return Ok(()),
|
||||
};
|
||||
let stream = cuda_dev.cuda_stream();
|
||||
let num_bars = data.len();
|
||||
|
||||
// Upload features [num_bars * 51]
|
||||
let mut flat_features = Vec::with_capacity(num_bars * 51);
|
||||
for (features, _) in data {
|
||||
for &v in features.iter() {
|
||||
flat_features.push(v as f32);
|
||||
}
|
||||
}
|
||||
let features_buf = stream.memcpy_stod(&flat_features)
|
||||
.map_err(|e| MLError::ModelError(format!("CUDA features upload: {e}")))?;
|
||||
self.features_raw_cuda = Some(features_buf);
|
||||
|
||||
// Upload targets [num_bars * 4]
|
||||
let mut flat_targets = Vec::with_capacity(num_bars * 4);
|
||||
for (_, targets) in data {
|
||||
for i in 0..4 {
|
||||
flat_targets.push(targets.get(i).copied().unwrap_or(0.0) as f32);
|
||||
}
|
||||
}
|
||||
let targets_buf = stream.memcpy_stod(&flat_targets)
|
||||
.map_err(|e| MLError::ModelError(format!("CUDA targets upload: {e}")))?;
|
||||
self.targets_raw_cuda = Some(targets_buf);
|
||||
self.raw_data_num_bars = num_bars;
|
||||
|
||||
info!("PPO raw market data uploaded: {} bars ({:.1} MB)",
|
||||
num_bars, (num_bars * 55 * 4) as f64 / 1_048_576.0);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Train PPO model
|
||||
///
|
||||
/// # Arguments
|
||||
@@ -333,6 +414,9 @@ impl PpoTrainer {
|
||||
let model = self.model.lock().await;
|
||||
let actor_vars = model.actor.vars();
|
||||
let critic_vars = model.critic.vars();
|
||||
let initial_capital = self.hyperparams.initial_capital as f32;
|
||||
let avg_spread = self.hyperparams.avg_spread as f32;
|
||||
let cash_reserve_pct = self.hyperparams.cash_reserve_pct as f32 / 100.0;
|
||||
match (|| -> Result<_, MLError> {
|
||||
let cuda_device = match &self.device {
|
||||
candle_core::Device::Cuda(d) => d,
|
||||
@@ -344,9 +428,9 @@ impl PpoTrainer {
|
||||
actor_vars,
|
||||
critic_vars,
|
||||
&candle_nn::VarMap::new(), // curiosity placeholder — PPO curiosity integration is future work
|
||||
1_000_000.0, // initial_capital
|
||||
0.0001, // avg_spread
|
||||
0.20, // cash_reserve_pct
|
||||
initial_capital,
|
||||
avg_spread,
|
||||
cash_reserve_pct,
|
||||
)
|
||||
})() {
|
||||
Ok(collector) => {
|
||||
@@ -365,19 +449,80 @@ impl PpoTrainer {
|
||||
for epoch in 0..self.hyperparams.epochs {
|
||||
debug!("Training epoch {}/{}", epoch + 1, self.hyperparams.epochs);
|
||||
|
||||
// Step 1: Collect rollouts (trajectories)
|
||||
let trajectories = if let Some(n) = self.num_envs {
|
||||
if n > 1 {
|
||||
self.collect_vectorized_rollouts(&market_data).await?
|
||||
// Phase 3: GPU experience collection — bypasses CPU collect_rollouts + prepare_training_batch
|
||||
#[cfg(feature = "cuda")]
|
||||
let gpu_batch_result: Option<TrajectoryBatch> = if let (
|
||||
Some(ref mut collector),
|
||||
Some(ref features_buf),
|
||||
Some(ref targets_buf),
|
||||
) = (
|
||||
&mut gpu_ppo_collector,
|
||||
&self.features_raw_cuda,
|
||||
&self.targets_raw_cuda,
|
||||
) {
|
||||
use crate::cuda_pipeline::gpu_ppo_collector::PpoCollectorConfig;
|
||||
|
||||
let n_episodes = self.hyperparams.gpu_n_episodes.min(256) as i32;
|
||||
let timesteps = self.hyperparams.gpu_timesteps_per_episode.min(1000) as i32;
|
||||
let total_bars = self.raw_data_num_bars as i32;
|
||||
let usable_bars = (total_bars - timesteps).max(1);
|
||||
let stride = (usable_bars / n_episodes).max(1);
|
||||
let episode_starts: Vec<i32> = (0..n_episodes)
|
||||
.map(|i| (i * stride).rem_euclid(usable_bars))
|
||||
.collect();
|
||||
|
||||
let config = PpoCollectorConfig {
|
||||
n_episodes,
|
||||
timesteps_per_episode: timesteps,
|
||||
total_bars,
|
||||
gamma: self.hyperparams.gamma as f32,
|
||||
gae_lambda: self.hyperparams.gae_lambda,
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
// Reset per-episode state before each epoch
|
||||
let initial_capital = self.hyperparams.initial_capital as f32;
|
||||
let avg_spread = self.hyperparams.avg_spread as f32;
|
||||
let cash_reserve_pct = self.hyperparams.cash_reserve_pct as f32 / 100.0;
|
||||
|
||||
if let Err(e) = collector.reset_episodes(initial_capital, avg_spread, cash_reserve_pct) {
|
||||
warn!("GPU PPO episode reset failed: {e}");
|
||||
None
|
||||
} else {
|
||||
self.collect_rollouts(&market_data).await?
|
||||
match collector.collect_experiences(features_buf, targets_buf, &episode_starts, &config) {
|
||||
Ok(batch) => {
|
||||
info!("GPU PPO collected {} experiences ({} episodes × {} timesteps)",
|
||||
batch.n_episodes * batch.timesteps, batch.n_episodes, batch.timesteps);
|
||||
Some(gpu_batch_to_trajectory_batch(&batch))
|
||||
}
|
||||
Err(e) => {
|
||||
warn!("GPU PPO collection failed, CPU fallback: {e}");
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
} else {
|
||||
self.collect_rollouts(&market_data).await?
|
||||
None
|
||||
};
|
||||
|
||||
// Step 2: Prepare training batch with GAE
|
||||
let mut training_batch = self.prepare_training_batch(trajectories)?;
|
||||
#[cfg(not(feature = "cuda"))]
|
||||
let gpu_batch_result: Option<TrajectoryBatch> = None;
|
||||
|
||||
// Step 1+2: Use GPU batch if available, otherwise CPU collect_rollouts + prepare_training_batch
|
||||
let mut training_batch = if let Some(batch) = gpu_batch_result {
|
||||
batch
|
||||
} else {
|
||||
let trajectories = if let Some(n) = self.num_envs {
|
||||
if n > 1 {
|
||||
self.collect_vectorized_rollouts(&market_data).await?
|
||||
} else {
|
||||
self.collect_rollouts(&market_data).await?
|
||||
}
|
||||
} else {
|
||||
self.collect_rollouts(&market_data).await?
|
||||
};
|
||||
self.prepare_training_batch(trajectories)?
|
||||
};
|
||||
|
||||
// Step 2.5: Pre-train value network (first 10 epochs only)
|
||||
if epoch < 10 {
|
||||
@@ -1129,6 +1274,68 @@ impl PpoTrainer {
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert GPU-collected PPO experience batch to TrajectoryBatch for model.update().
|
||||
///
|
||||
/// The GPU kernel computes advantages and returns in-kernel via GAE, so this
|
||||
/// bypasses the CPU collect_rollouts() + prepare_training_batch() pipeline entirely.
|
||||
/// The `trajectories` field is left empty since `update()` only uses flattened fields.
|
||||
#[cfg(feature = "cuda")]
|
||||
fn gpu_batch_to_trajectory_batch(
|
||||
batch: &crate::cuda_pipeline::gpu_ppo_collector::PpoExperienceBatch,
|
||||
) -> TrajectoryBatch {
|
||||
let state_dim = 54;
|
||||
let total = batch.n_episodes * batch.timesteps;
|
||||
|
||||
let states: Vec<Vec<f32>> = batch
|
||||
.states
|
||||
.chunks(state_dim)
|
||||
.take(total)
|
||||
.map(|c| c.to_vec())
|
||||
.collect();
|
||||
|
||||
let actions: Vec<TradingAction> = batch
|
||||
.actions
|
||||
.iter()
|
||||
.take(total)
|
||||
.map(|&a| {
|
||||
TradingAction::from_int(a.clamp(0, 44) as u8).unwrap_or(TradingAction::Hold)
|
||||
})
|
||||
.collect();
|
||||
|
||||
let log_probs: Vec<f32> = batch.log_probs.iter().take(total).copied().collect();
|
||||
|
||||
// V(s) = R(s) - A(s), since the kernel stores R = A + V
|
||||
let values: Vec<f32> = batch
|
||||
.returns
|
||||
.iter()
|
||||
.zip(&batch.advantages)
|
||||
.take(total)
|
||||
.map(|(r, a)| r - a)
|
||||
.collect();
|
||||
|
||||
let dones: Vec<bool> = batch
|
||||
.done_flags
|
||||
.iter()
|
||||
.take(total)
|
||||
.map(|&d| d != 0)
|
||||
.collect();
|
||||
|
||||
// Rewards not needed — advantages/returns already computed by kernel
|
||||
let rewards = vec![0.0_f32; states.len()];
|
||||
|
||||
TrajectoryBatch {
|
||||
trajectories: vec![],
|
||||
states,
|
||||
actions,
|
||||
log_probs,
|
||||
values,
|
||||
rewards,
|
||||
dones,
|
||||
advantages: batch.advantages.iter().take(total).copied().collect(),
|
||||
returns: batch.returns.iter().take(total).copied().collect(),
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
@@ -96,6 +96,10 @@ fn create_test_hyperparams(_use_lstm: bool, sequence_length: usize) -> PpoHyperp
|
||||
circuit_breaker_threshold: 5,
|
||||
sequence_length,
|
||||
max_grad_norm_lstm: 0.5,
|
||||
gpu_n_episodes: 128,
|
||||
gpu_timesteps_per_episode: 500,
|
||||
initial_capital: 1_000_000.0,
|
||||
avg_spread: 0.0001,
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user