docs: spec for GPU regime + H100 training hang fix
Three issues in one spec: 1. H100 hang: cuBLAS set_stream() inside CUDA graph capture → deadlock 2. Bug: train_walk_forward calls non-stratified generate_folds() 3. GPU regime: classification kernel + prefix sum for O(1) range queries Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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jgrusewski
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# GPU Regime Stratification + H100 Training Hang Fix
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**Status**: Design approved
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**Priority**: Critical — blocks H100 production training
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**Date**: 2026-04-05
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---
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## Problem Statement
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Three issues block production H100 training:
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1. **H100 training hang**: Process freezes after CUDA graph capture with `gpu_n_episodes=4096`. Root cause: `cuBLAS::set_stream()` and event synchronization calls execute during CUDA graph capture but are NOT recorded in the graph. On replay, the cuBLAS handle is in an undefined stream state → deadlock.
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2. **Regime stratification bug**: `train_walk_forward()` at `trainer/mod.rs:570` calls `generate_folds()` (non-stratified) instead of `generate_folds_stratified()`. Walk-forward folds are not regime-balanced.
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3. **CPU regime computation**: Regime classification and distribution computation run on CPU. For 1.1M bars this is fast, but the architecture should be GPU-native for scaling to 10M+ bars with real-time MBP-10 feeds.
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---
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## Fix 1: Dual-Stream CUDA Graph Architecture
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### Root Cause
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In `gpu_dqn_trainer.rs`, `submit_forward_ops()` is called INSIDE `stream.begin_capture()` / `stream.end_capture()`. This function contains:
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- **Line 4702**: `cublas.set_stream(&self.double_dqn_stream)` — executes but NOT captured
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- **Line 4712**: `cublas.set_stream(&self.stream)` — executes but NOT captured
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- **Line 4683**: `self.pass1_event.record(&self.stream)` — events disallowed in capture
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- **Line 4687**: `self.double_dqn_stream.wait(&self.pass1_event)` — not captured
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On graph replay, the cuBLAS handle's internal stream pointer is stale. The multi-stream event synchronization is missing. Result: deadlock.
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### Design
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Split the single `graph_forward` into two independent graphs:
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**`graph_forward_main`**: Captures Pass 1 (online forward on states) + Pass 2 (target forward on next_states) on the main stream. No stream switching inside.
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**`graph_forward_ddqn`**: Captures Pass 3 (online forward on next_states for Double DQN action selection) on `double_dqn_stream`. Uses a separate cuBLAS handle already bound to that stream.
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**Replay sequence** (ungraphed orchestration):
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```
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1. graph_forward_main.launch() // main stream
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2. pass1_event.record(main_stream) // ungraphed event
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3. double_dqn_stream.wait(pass1_event) // ungraphed sync
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4. graph_forward_ddqn.launch() // double_dqn_stream
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5. pass3_event.record(double_dqn_stream) // ungraphed event
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6. main_stream.wait(pass3_event) // ungraphed sync — join before loss kernels
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7. Loss + gradient kernels (main stream, part of graph_forward_main or separate)
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```
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Pass 2 and Pass 3 execute concurrently on separate SM partitions (H100 has 132 SMs, each GEMM uses ~30-40 SMs).
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### Changes
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| File | Change |
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|------|--------|
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| `gpu_dqn_trainer.rs` | Split `submit_forward_ops()` into `submit_pass1_pass2()` and `submit_pass3_ddqn()` |
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| `gpu_dqn_trainer.rs` | `capture_training_graphs()` captures two forward graphs instead of one |
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| `gpu_dqn_trainer.rs` | `replay_forward()` orchestrates both graphs with ungraphed event sync |
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| `gpu_dqn_trainer.rs` | Loss/gradient kernels stay on main stream (after join) |
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| `gpu_dqn_trainer.rs` | Remove `set_stream()` calls from inside any captured function |
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| `gpu_dqn_trainer.rs` | Create second `CublasForward` instance for `double_dqn_stream` (currently shares one handle via `set_stream`) |
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| `batched_forward.rs` | No changes — `CublasForward::new()` already takes a stream and creates an independent handle |
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### Constraints
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- No `cuBLAS::set_stream()` inside ANY function called during graph capture
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- No CUDA event operations inside graph capture
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- Each graph uses exactly ONE stream with ONE cuBLAS handle
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- The `double_dqn_stream` cuBLAS handle must be a SEPARATE instance (not shared)
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- Event synchronization happens ONLY in ungraphed replay orchestration code
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### Validation
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- `test_no_nan_after_graph_capture`: 200/200 passes (existing)
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- `test_no_hang_single_epoch`: passes with `gpu_n_episodes=4096` (new parameter override)
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- H100 Argo workflow completes epoch 1 within 60 seconds
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---
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## Fix 2: Regime Stratification Bug
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### Root Cause
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`trainer/mod.rs` line 570:
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```rust
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let folds = wf_config.generate_folds(features.len());
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```
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Should be:
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```rust
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let folds = wf_config.generate_folds_stratified(training_data);
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```
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The non-stratified `generate_folds()` ignores regime distribution entirely. Validation sets may be dominated by a single regime (e.g., 90% trending), making walk-forward evaluation unreliable.
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### Change
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One line in `trainer/mod.rs:570`. The `generate_folds_stratified()` function already exists in `gpu_walk_forward.rs` and is tested.
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### Validation
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- Existing walk-forward tests pass
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- Log output shows regime distribution per fold with max deviation < 10%
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---
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## Fix 3: GPU Regime Classification + Prefix Sum
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### Current State
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`gpu_walk_forward.rs:322-338` classifies regimes on CPU by reading features[40] (ADX) and features[41] (CUSUM) for each bar. The boundary adjustment search at lines 240-264 recomputes regime distribution per shift candidate at O(val_size) per candidate.
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### Design
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**CUDA kernel: `regime_classify_kernel`**
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```cuda
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// One thread per bar. Reads features[40] and features[41], writes u8 regime label.
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// Input: features [total_bars, 42] f64 (host) or bf16 (GPU)
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// Output: regimes [total_bars] u8
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extern "C" __global__ void regime_classify_kernel(
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const double* features, // [total_bars × 42]
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unsigned char* regimes, // [total_bars]
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int total_bars,
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int feat_stride // 42
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) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i >= total_bars) return;
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double adx = features[i * feat_stride + 40];
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double cusum = features[i * feat_stride + 41];
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unsigned char regime;
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if (adx > 0.25) regime = 0; // Trending
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else if (fabs(cusum) > 0.7) regime = 2; // Volatile
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else regime = 1; // Ranging
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regimes[i] = regime;
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}
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```
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**CUDA kernel: `regime_prefix_sum_kernel`**
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Compute per-regime cumulative counts using parallel prefix sum (scan). Output: three arrays of length `total_bars`, where `prefix[r][i]` = count of regime `r` in bars `[0..i)`.
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With prefix sums, any range query `regime_count(r, start, end) = prefix[r][end] - prefix[r][start]` is O(1). The boundary adjustment search becomes O(num_shifts) instead of O(num_shifts × val_size).
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### Data Flow
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```
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fxcache features [1.1M, 42] f64
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↓ upload to GPU as f64
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regime_classify_kernel → regimes [1.1M] u8 (GPU)
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↓
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regime_prefix_sum_kernel → prefix_counts [3, 1.1M] u32 (GPU)
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↓ DtoH transfer (3 × 4.4MB = 13.2MB)
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CPU boundary search using O(1) range queries
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↓
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fold_ranges: Vec<FoldRange>
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```
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### Changes
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| File | Change |
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|------|--------|
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| `gpu_walk_forward.rs` | Add `regime_classify_kernel` and `regime_prefix_sum_kernel` CUDA source |
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| `gpu_walk_forward.rs` | New `classify_regimes_gpu()` function: upload features, run kernel, return GPU buffer |
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| `gpu_walk_forward.rs` | New `compute_regime_prefix_sums_gpu()`: run prefix sum, transfer to CPU |
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| `gpu_walk_forward.rs` | Update `generate_folds_stratified()` to use GPU prefix sums for O(1) range queries |
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| `gpu_walk_forward.rs` | `regime_distribution_from_labels()` replaced by prefix sum lookup |
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### Performance
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| Operation | CPU (current) | GPU (new) |
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|-----------|--------------|-----------|
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| Classify 1.1M bars | 2ms | 0.05ms |
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| Distribution per range query | O(val_size) = 0.5ms | O(1) = 0.001ms |
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| Boundary search (4 folds × 500 shifts) | 1000ms | 2ms |
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| Prefix sum computation | N/A | 0.5ms |
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| DtoH transfer | N/A | 0.1ms |
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| **Total** | **~1000ms** | **~3ms** |
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### Validation
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- `generate_folds_stratified()` produces identical fold ranges as CPU version on same input
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- Unit test: prefix sum correctness on known regime sequence
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- Walk-forward tests pass with GPU regime path
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---
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## Files Modified (Summary)
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| File | Changes |
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|------|---------|
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| `crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs` | Split forward graphs, fix stream binding, dual-graph replay |
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| `crates/ml/src/cuda_pipeline/gpu_walk_forward.rs` | GPU regime kernels, prefix sum, updated stratification |
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| `crates/ml/src/trainers/dqn/trainer/mod.rs` | Fix `generate_folds()` → `generate_folds_stratified()` |
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| `crates/ml/src/trainers/dqn/smoke_tests/regression.rs` | Add gpu_n_episodes=4096 hang regression test |
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## Success Criteria
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1. H100 training completes 200 epochs without hang
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2. Zero NaN (existing 200/200 regression holds)
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3. Walk-forward folds are regime-stratified (max deviation < 10%)
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4. 19/19 smoke tests pass
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5. Epoch time on H100 ≤ 30s (1.1M bars, batch=8192)
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