020e9ba460
Replace warp-reduced atomicAdd gradient accumulation in the curiosity forward model with a two-kernel deterministic pipeline: 1. curiosity_fwd_bwd_per_block: each block reduces its threads' gradient contributions via shared memory tree reduction, writes one partial gradient vector [CUR_TOTAL_PARAMS] per block. 2. curiosity_grad_reduce: one thread per parameter sums block partials in fixed order (block 0, 1, 2, ...). Fully deterministic. Deleted kernels: curiosity_forward_backward (atomicAdd path), curiosity_fused_zero_fwd_bwd_adam (grid-wide atomic barrier), curiosity_adam_step_fused (dead code), warp_sum_cur helper. Rust side: removed block_counter, adam_fused_func, fused_zero_fwd_bwd_adam_func. Added partial_grads buffer [max_blocks * CUR_TOTAL_PARAMS] (~2.8 MB for 64 blocks). 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;