3d17c2a93c
Single-thread-per-(b,c) kernel, no atomicAdd, capture-friendly. CPU-reference test verifies correctness for B=4, K=8, C=256 within 1e-6 tolerance. Test wrapper uses mapped pinned memory exclusively (feedback_no_htod_htoh_only_mapped_pinned.md): allocate MappedF32Buffer, write CPU-side via host_ptr, kernel reads via dev_ptr, output via host_ptr read after stream sync. NO memcpy_stod / memcpy_dtov anywhere. GpuMoeHead struct in crates/ml/src/cuda_pipeline/gpu_moe_head.rs follows existing cuda_pipeline head pattern (cubin loaded once, kernel handles cached). Subsequent kernels (moe_mixture_backward, moe_load_balance_loss, moe_expert_util_ema_update) extend the same struct. Spec: docs/superpowers/specs/2026-04-27-moe-regime-redesign-design.md §6.2. Plan: docs/superpowers/plans/2026-04-27-moe-regime-redesign.md Task 2.1. 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;