541463e48a
Re-applied f32 IS-weights permanently. The f32_to_bf16_cast kernel had a manual RNE rounding overflow bug producing NaN for edge-case values. Fixed cast to use __float2bfloat16 intrinsic, but NaN persists from a DIFFERENT source: done=nan in replay buffer (not IS-weight). Debug printf in MSE loss kernel shows: - done=nan (bf16 replay buffer corruption) - is_weight negative (should be [0,1] — reduce_max atomicMax bug with negative floats) - avg_mse=nan cascading from done=nan through Bellman target Root cause: replay buffer done flag corruption — likely a buffer overwrite from an adjacent allocation. Need compute-sanitizer to find the OOB write. 895/895 unit + 359/359 ml-dqn tests pass. Smoke tests: intermittent (NaN from corrupted done flags in replay buffer). 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;