04fcbd27cf
Replaces the kernel's hardcoded `alpha` with the shared `pearls_ad_update`
host-side helper. 9 ISV slots retrofit: 8 per-expert util + 1 gate entropy.
- Kernel `moe_expert_util_ema_update` signature: drops `(isv,
isv_util_base, isv_entropy_index, alpha)` for `(scratch_buf,
scratch_idx_util_base=44, scratch_idx_entropy=52)`.
- Single-block single-thread; computes per-expert col_mean in a single
forward pass (collapses prior dual-pass), writes each to scratch slots
[44..52) and Shannon entropy to slot 52.
- 9 ISV slots wired with Pearls A+D: ISV[118..126) (per-expert util,
Wiener offsets 132..156) + ISV[126] (gate entropy, Wiener offset 156).
- Launcher `gpu_moe_head.rs::launch_expert_util_ema`: drops alpha + isv
args; takes scratch_dev_ptr + 2 scratch_idx args.
- Wrapper `GpuDqnTrainer::launch_moe_expert_util_ema(_ema_alpha_unused)`:
sync + Pearls A+D loop over 9 slots via `apply_pearls(scratch_idx,
isv_idx)` closure.
- `debug_assert_eq!(MOE_NUM_EXPERTS, 8)` guards against K changes
silently breaking the contiguous scratch layout.
Behavior: stationary signals converge to the same value at adaptive rate.
The 9 slots stay semantically identical (per-expert col_mean, gate
entropy); HEALTH_DIAG mirror + the adaptive λ controller
`moe_lambda_eff_update` (which reads ISV[MOE_GATE_ENTROPY_EMA_INDEX])
continue to consume them unchanged.
Tests: `sp4_moe_expert_util_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`
drives kernel with B=128 K=8 perfect-uniform gate → analytical col_mean=1/8,
entropy=ln(8)≈2.0794. Asserts slot values ∈ ±1e-6/1e-5, non-target slots
remain 0; verifies Pearl A bootstrap + Pearl D convergence.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`. Build: `cargo check -p ml
--lib --tests --offline` clean (11 pre-existing warnings, no new warnings).
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;