cb80b74ce9
Phase 3b builds on Phase 3a (2e4c7ebf6). Adds the α infrastructure to the trainer: 2 new CUBIN statics + 7 new struct fields (W weight + Adam moments + grad accumulator + 3 kernel handles), and the new() constructor's alloc + kernel-load block. α kernels are loaded but NEVER launched yet. Phase 3b is functionally equivalent to Phase 3a at runtime — the loaded kernels are dead code until the captured-graph integration (Phase 3c) lands. Architectural finding deferred to Phase 3c ────────────────────────────────────────── The trainer's dueling head doesn't have a separate Q_dir buffer. The `mag_concat_qdir` kernel (gpu_dqn_trainer.rs:9884) computes Q_dir INTERNALLY from on_v_logits_buf + on_b_logits_buf (V + A dueling combine: Q[a] = V + (A[a] - mean(A)) per atom), then immediately concatenates the result with h_s2 in one fused pass. There's no intermediate buffer between "Q_dir computed" and "Q_dir consumed" where α could inject as a parallel skip connection. Two options for α integration (Phase 3c will pick): 1. Modify mag_concat_qdir to take W_aux + state_121 args and apply the α bias to its internal Q_dir computation before concat. Invasive — changes a load-bearing kernel. 2. Add a NEW α-precompute kernel: write Q_dir into a dedicated buffer (V + A combine), then mag_concat_qdir reads from that buffer instead of doing the combine inline. Refactors the dueling head's forward — cleaner separation, bigger change. Phase 3b commits the α infrastructure so Phase 3c can focus solely on the captured-graph integration design choice without also needing to allocate buffers + load kernels. Files ───── - crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs: + pub(crate) static SP22_AUX_TO_Q_DIR_BIAS_CUBIN + pub(crate) static SP22_AUX_TO_Q_DIR_BIAS_BWD_CUBIN + 7 struct fields: w_aux_to_q_dir, adam_m_w_aux, adam_v_w_aux, dw_aux_buf, aux_to_q_dir_bias_kernel, aux_to_q_dir_bias_backward_dw_kernel, aux_to_q_dir_bias_backward_dstate_kernel + new() block: alloc 4 zero-init f32 buffers (b0_size=4) for W + Adam moments + grad accumulator; load 2 cubins, 3 function handles. + Struct construction list extended with 7 new fields. - docs/dqn-wire-up-audit.md: Phase 3b entry documenting the architectural finding + Phase 3c scope. Verification ──────────── - cargo check -p ml --features cuda: 0 errors, 21 pre-existing warnings (Phase 2/3a baseline parity). - nvcc cubins unchanged (kernels built in Phase A). - Runtime equivalent to Phase 3a: α kernels never launched. Phase 3c scope (remaining for full α activation) ──────────────────────────────────────────────── - Pick option 1 or 2 for mag_concat_qdir integration. - Wire α forward in training captured forward graph (Step 7). - Wire α backward kernels in captured backward graph (Step 8). - Wire α Adam-step update (Step 9). - C1: α forward in collector's rollout-time captured graph. - D1-D7: A2 eval-side aux trunk + α + state-gather wiring. - B6: SP11 controller extension for non-zero scale_β. - B7/B10/B11: HEALTH_DIAG telemetry extensions. - E + F: verification gates + atomic Phase F commit + smoke + verdict. Estimated remaining: ~20-30 hr engineering + ~37 min smoke wall-clock. 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) -464bc5f7a(Phase A foundation) -2e4c7ebf6(Phase 3a — 7-component contract + β producer) - pearl_no_partial_refactor (Phase 3b is additive struct fields) 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;