refactor: eliminate prefetch pattern — sample one batch, train, repeat
The old PREFETCH_K loop pre-allocated all batches into Vec<BatchSample> before training any of them. With 488 steps this meant 976 GPU buffer lock/sample/alloc cycles upfront, causing multi-minute stalls on H100. New loop: sample 1 batch from GPU PER, train it, sample next. Zero prefetch, zero Vec accumulation, natural CPU/GPU interleaving. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -1347,23 +1347,11 @@ impl DQNTrainer {
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self.gradient_logging_step as u64 > ws
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};
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// Batch pre-sampling: sample batches in chunks under one READ lock.
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// usize::MAX caused 488 batches × 8192 samples = 4M PER traversals
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// in one go from a 24.7M sum tree — minutes of CPU work on H100.
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const PREFETCH_K: usize = 16;
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let mut sample_total_us = 0_u64;
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let mut fused_total_us = 0_u64;
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let mut guard_total_us = 0_u64;
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// Task 15: capture regime-biased sampling params before the borrow
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let regime_decay = self.regime_replay_decay_override;
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let regime_id = self.fold_dominant_regime;
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let use_regime_bias = regime_decay < 1.0;
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// Wait for experience collection GPU kernels to complete before sampling.
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// The event was recorded at the end of collect_gpu_experiences — all CPU-side
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// setup above (can_train, ensure_fused_ctx, guard init, variable capture)
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// overlaps with the tail of experience collection on the GPU.
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if let Some(event) = self.experience_done_event.take() {
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if !event.is_complete() {
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event.synchronize()
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@@ -1371,81 +1359,45 @@ impl DQNTrainer {
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}
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}
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eprintln!("[DEBUG] run_training_steps: num_steps={}, batch_size={}, prefetch_k={}", num_training_steps, batch_size, PREFETCH_K);
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for chunk_start in (0..num_training_steps).step_by(PREFETCH_K) {
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let chunk_end = (chunk_start + PREFETCH_K).min(num_training_steps);
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eprintln!("[DEBUG] run_training_steps: num_steps={}, batch_size={}", num_training_steps, batch_size);
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// Sample-train loop: one batch at a time, zero prefetch.
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// GPU PER sampling (seg_tree_sample kernel) + GPU training (graph replay)
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// interleave naturally. No CPU-side Vec<BatchSample> accumulation.
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for _step in 0..num_training_steps {
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// Sample one batch (GPU-native PER for GpuPrioritized)
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let sample_start = std::time::Instant::now();
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let (batches, vaccine_batches) = {
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let (batch, vaccine_batch) = {
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let agent = self.agent.read().await;
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let buffer = agent.memory();
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let can = buffer.can_sample(self.current_batch_size);
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let buf_len = buffer.len();
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tracing::debug!(
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can_sample = can,
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buffer_len = buf_len,
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batch_size = self.current_batch_size,
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chunk = chunk_start,
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regime_biased = use_regime_bias,
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"Pre-sampling replay buffer"
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);
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let mut b = Vec::with_capacity(chunk_end - chunk_start);
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// #32 Gradient Vaccine: always sample a second batch for validation
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let mut vaccine_batches = Vec::with_capacity(chunk_end - chunk_start);
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for _ in chunk_start..chunk_end {
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b.push(buffer.can_sample(self.current_batch_size).then(|| {
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// Task 15: Use regime-biased sampling when adaptive decay < 1.0
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if use_regime_bias {
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buffer
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.sample_regime_biased(self.current_batch_size, regime_id, regime_decay)
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.map_err(|e| anyhow::anyhow!("Regime-biased pre-sample: {e}"))
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} else {
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buffer
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.sample(self.current_batch_size)
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.map_err(|e| anyhow::anyhow!("Pre-sample: {e}"))
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}
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}).transpose()?);
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// Sample separate vaccine batch (different indices from PER)
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// Vaccine uses standard PER (unbiased) for gradient vaccine diversity
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if buffer.can_sample(self.current_batch_size) {
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vaccine_batches.push(buffer.sample(self.current_batch_size)
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.map_err(|e| anyhow::anyhow!("Vaccine sample: {e}")).ok());
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} else {
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vaccine_batches.push(None);
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}
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if !buffer.can_sample(self.current_batch_size) {
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break;
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}
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(b, vaccine_batches)
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let b = buffer.sample(self.current_batch_size)
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.map_err(|e| anyhow::anyhow!("PER sample: {e}"))?;
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let vb = buffer.sample(self.current_batch_size).ok();
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(b, vb)
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};
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sample_total_us += sample_start.elapsed().as_micros() as u64;
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// GPU train steps (single WRITE lock)
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// Train one step
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{
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let mut agent = self.agent.write().await;
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let accum_steps = self.hyperparams.gradient_accumulation_steps;
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if accum_steps <= 1 {
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let mut vaccine_iter = vaccine_batches.into_iter();
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for explicit_batch in batches {
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let fused_start = std::time::Instant::now();
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let _gpu_result = if let Some(ref mut fused) = self.fused_ctx {
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let batch_data = explicit_batch.as_ref().ok_or_else(|| {
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anyhow::anyhow!("No batch data for fused training step")
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})?;
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// #32 Set vaccine batch for this step (consumed by run_full_step)
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if let Some(Some(vb)) = vaccine_iter.next() {
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fused.pending_vaccine_batch = vb.gpu_batch;
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}
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let result = fused.run_full_step(batch_data, &mut *agent, &self.device)
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.map_err(|e| { eprintln!("!!! FUSED STEP ERROR: {:#}", e); e })
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.context("Fused CUDA training step failed")?;
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if train_step_count % 50 == 0 {
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}
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result
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} else {
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unreachable!("Fused CUDA training is the only production path — fused_ctx must be initialized")
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};
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let fused_start = std::time::Instant::now();
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let _gpu_result = if let Some(ref mut fused) = self.fused_ctx {
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if let Some(vb) = vaccine_batch {
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fused.pending_vaccine_batch = vb.gpu_batch;
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}
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let result = fused.run_full_step(&batch, &mut *agent, &self.device)
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.map_err(|e| { eprintln!("!!! FUSED STEP ERROR: {:#}", e); e })
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.context("Fused CUDA training step failed")?;
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result
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} else {
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unreachable!("Fused CUDA training is the only production path")
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};
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fused_total_us += fused_start.elapsed().as_micros() as u64;
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fused_total_us += fused_start.elapsed().as_micros() as u64;
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let guard_start = std::time::Instant::now();
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if let Some(ref mut guard) = self.training_guard {
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@@ -1480,22 +1432,20 @@ impl DQNTrainer {
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})?;
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}
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}
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guard_total_us += guard_start.elapsed().as_micros() as u64;
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guard_total_us += guard_start.elapsed().as_micros() as u64;
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// Q-value stats: reduce from training batch every 50 steps
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if train_step_count % 50 == 0 {
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if let Some(ref mut fused) = self.fused_ctx {
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if let Ok(stats) = fused.reduce_current_q_stats() {
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self.cached_avg_q = stats.q_mean as f64;
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if stats.q_min < self.epoch_q_min { self.epoch_q_min = stats.q_min; }
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if stats.q_max > self.epoch_q_max { self.epoch_q_max = stats.q_max; }
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}
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}
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// Q-value stats: reduce from training batch every 50 steps
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if train_step_count % 50 == 0 {
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if let Some(ref mut fused) = self.fused_ctx {
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if let Ok(stats) = fused.reduce_current_q_stats() {
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self.cached_avg_q = stats.q_mean as f64;
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if stats.q_min < self.epoch_q_min { self.epoch_q_min = stats.q_min; }
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if stats.q_max > self.epoch_q_max { self.epoch_q_max = stats.q_max; }
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}
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train_step_count += 1;
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self.gradient_logging_step += 1;
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}
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}
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train_step_count += 1;
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self.gradient_logging_step += 1;
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}
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}
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