937bbabb84
Batch upload: - Added indirect pad_states + indirect copy kernels to cubin - Batch source pointers uploaded as 8 x u64 via async HtoD to batch_ptr_buf - Kernels read source addresses from device indirection buffer - upload_batch_gpu replaced with upload_batch_ptrs → graph-captured indirect kernels - 8 ungraphed DtoD/kernel launches → 0 (captured in graph_forward) HER relabel (Random strategy): - Captured as graph_her (random_donors + inplace_relabel) - Uses stable addresses: donor_indices (pre-allocated), batch_ptr_buf[1] (indirect) - 2 ungraphed launches → 1 graph replay PER priority update: - Captured in graph_adam (after regime_scale) - Kernel changed to read indices/priorities from batch_ptr_buf[6..8] (indirect) - PER pointers uploaded via upload_per_ptrs before graph_adam replay - 1 ungraphed launch → 0 (captured in graph_adam) New CUDA kernels: - pad_states_indirect_kernel: reads src ptr from device buffer - indirect_copy_f32_kernel: f32 copy with indirect src - indirect_copy_i32_kernel: i32 copy with indirect src - per_update_priorities_kernel: changed to indirect indices/priorities Per-step operation count: 9 graph replays (forward, adam, ema, attention, iql, iqn, her) + 1 async HtoD (8 batch pointers, 64 bytes) + 1 async HtoD (tau, 4 bytes) + 1 async HtoD (adam_step, 4 bytes) + 1 async HtoD (iqn tau, 4 bytes) + 1 async HtoD (per ptrs, 16 bytes) = 9 graph replays + 5 async HtoD (92 bytes total) Zero ungraphed kernel launches in the per-step hot path. 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;