298cc33fcb
FlashAttention3Config had four flags, all dead or with dead else-branches: - use_sparse_patterns: write-only (sparse_pattern Mask is created unconditionally via create_sparse_mask) - io_aware_tiling: always-true setter; the "else" branch called standard_attention which itself discarded all its QK/scale/mask work and called io_aware.compute_attention — pure dead code - cuda_optimization: load_kernels() gate, always true in practice - standard_attention method + mask parameter on forward(): entirely dead Per user directive "all features enabled" / "should be used": - Deleted 4 fields (use_sparse_patterns, io_aware_tiling, cuda_optimization, sparse_pattern_iterations) — note sparse_pattern (BlockSparsePattern) stays - Collapsed forward() to unconditional io_aware.compute_attention, dropped mask param - Removed 40-LOC standard_attention dead fallback - Dropped AttentionStats.io_aware_enabled field + test assertion - cuda_kernels load unconditionally
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;