1389d1c810
Layer A L40S smoke smoke-test-v9kjv revealed CUDA Graph capture failure
at aux_label_scale_ema mid-step host sync inside aux_heads_forward Step 2b
(CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED). Per feedback_no_cpu_compute_strict
and pearl_cold_path_no_exception_to_gpu_drives: CPU compute path is
strictly forbidden — any formula (EMA / reduction / Pearls A+D / adaptive α)
belongs on GPU regardless of frequency.
This commit migrates ALL 11 Pearls A+D applications + 1 inline application
to a single GPU apply_pearls_ad_kernel:
- 5 SP4 producers (target_q, atom_pos×4, param_group_oracle, grad_norm, h_s2)
- 6 A13 retrofits (h_s2_rms, aux_heads_loss, moe_expert_util,
vsn_mask, iqn_quantile, reward_component)
- 1 inline aux_label_scale block in aux_heads_forward Step 2b
Eliminates: stream.synchronize() + host_ptr read_volatile + host arithmetic
+ host_ptr write_volatile pattern from all 11 launchers + 1 inline.
apply_pearls_to_slot host helper deleted. pearls_ad_update kept as
test-only reference implementation (also retained for the post-Adam
pearl_c_post_adam_engagement_check host-side diagnostic which reduces
mapped-pinned i32 counters outside any captured graph). New GPU unit
test asserts kernel output matches reference within fp32 ULP for 5
representative cases plus a multi-slot batch sanity check.
The refactor is graph-capture-compatible by construction: the kernel
runs single-thread in the same stream as the producer kernel; no host
synchronisation needed between producer and applicator.
Build clean (cargo check --workspace), 6 host sp4_wiener_ema tests pass,
13 SP4 GPU tests + 1 new oracle test pass on RTX 3050 Ti. L40S smoke
re-validation deferred to A17 redo.
Refs: smoke-test-v9kjv graph-capture failure (terminated), commit 4c231fa81.
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;