464bc5f7a4
Phase A of the SP22 H6 Phase 3 implementation runbook
(docs/plans/2026-05-13-sp22-h6-phase3-alpha-beta-runbook.md). Purely
additive: new ISV slots + new kernel files + build.rs registration
+ state_reset_registry entries. Nothing in the running training or
eval pipeline consumes this infrastructure yet — the existing
6-component contract is untouched. Phase A is committable as a clean
WIP foundation; Phases B-F (7-component contract migration, α
plumbing, A2 eval-side wiring, verification, smoke) resume in a
future session.
Files
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- crates/ml/src/cuda_pipeline/sp22_isv_slots.rs (NEW)
REWARD_AUX_ALIGN_EMA_INDEX = 536 — EMA of 7th reward component
SP22_AUX_ALIGN_SCALE_INDEX = 537 — SP11-controller scale_β
- crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
ISV_TOTAL_DIM bumped 536 → 538 (SP22 H6 Phase 3 +2 slots)
Layout fingerprint extended with SLOT_536 + SLOT_537 entries
- crates/ml/src/cuda_pipeline/mod.rs
pub mod sp22_isv_slots
- crates/ml/src/trainers/dqn/state_reset_registry.rs
sp22_reward_aux_align_ema (FoldReset, sentinel 0)
sp22_aux_align_scale (FoldReset, sentinel 0)
- crates/ml/src/cuda_pipeline/aux_to_q_dir_bias_kernel.cu (NEW)
α forward: Q_dir[b, a] += W_aux[a] * state_121[b]
Reads state_121 from encoder INPUT (batch_states), bypassing
the cold encoder weight for slot 121. No atomicAdd, no host
branches, capture-safe.
- crates/ml/src/cuda_pipeline/aux_to_q_dir_bias_backward_kernel.cu (NEW)
Two kernels in one .cu:
- aux_to_q_dir_bias_backward_dw: per-action block tree-reduce
computing dW[a] = Σ_b state_121[b] * dq_dir[b, a]
- aux_to_q_dir_bias_backward_dstate: per-sample sum
dstate_121[b] += Σ_a W[a] * dq_dir[b, a] (option-(i) gradient
routing: both α and encoder paths get signal)
- crates/ml/build.rs
Both new cubins registered in kernels_with_common (compiled
successfully by nvcc; cubins exist at OUT_DIR/aux_to_q_dir_bias_*.cubin)
- docs/dqn-wire-up-audit.md
Phase A entry documenting the checkpoint + remaining-work
breakdown for next session.
Verification
────────────
- cargo check -p ml --features cuda: 0 errors, 21 pre-existing
warnings (Phase 2 baseline parity)
- nvcc compiles both new cubins (3.6 KB fwd, 10.6 KB bwd)
- No runtime impact: no consumer wires the new infrastructure yet
Resumes in
──────────
Future session executes Phases B-F per the runbook:
- B: 11 tasks — 7-component contract migration cascade
- C: 1 task — α collector rollout-side wiring
- D: 7 tasks — A2 eval-side aux trunk + α + state-gather
- E: 7 tasks — verification gates
- F: 5 tasks — audit doc append + atomic commit (incl. this Phase A
+ Phase B-F changes) + push + smoke + verdict
Estimated remaining: ~28-43 hr engineering + ~37 min smoke wall-clock.
Phase B partial work (300-line diff for experience_kernels.cu + reward_component_ema_kernel.cu — 7-stride migration + β producer + preamble update) saved at /tmp/sp22-h6-phase3-b-partial.patch (local-only).
Refs
────
- docs/plans/2026-05-12-sp22-h6-phase3-alpha-beta.md (spec)
- docs/plans/2026-05-13-sp22-h6-phase3-alpha-beta-runbook.md (runbook)
- pearl_no_partial_refactor (Phase A is additive; safe to commit alone)
- pearl_no_atomicadd (backward block tree-reduce, no atomics)
- pearl_no_host_branches_in_captured_graph (kernel capture-safety)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;