10d88e1b2a
mamba2_scan_backward kernel reads d_h_enriched [B, SH2] but was passed self.grad_buf [TOTAL_PARAMS] — wrong buffer, wrong size. At batch_size=4096 the kernel read 1M floats from a 582K buffer → 2749 OOB reads. Fixed: use self.bw_d_h_s2 [B, SH2] which is the actual trunk activation gradient from the cuBLAS backward pass. Also increased smoke test batch_size to 4096 to catch scale-dependent OOB. compute-sanitizer: 0 errors at batch_size=4096. Co-Authored-By: Claude Opus 4.6 (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;