74f63f80b2
Same optimization pattern as IQN: increase block size from 32 (1 warp) to 256 (8 warps) for all attention and IQL kernels. Attention (forward + backward): - Loop strides 32→256, shared_concat dynamic shmem - Added bf16_block_max/bf16_block_sum for 8-warp reduction - Replaces warp-only shuffle with shared memory cross-warp reduce IQL (forward+loss, backward, forward-only): - Loop strides 32→256, added iql_bf16_block_sum - grad_norm + adam kernels already at 256, unchanged Expected: ~15-20ms/step savings from attention+IQL combined. 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;