3d15e26b43
Full-stack determinism for reproducible training and valid hyperopt comparisons. CUDA gradient kernels (zero atomicAdd): - c51_grad_kernel: restructured from B×4×NA to B×NA threads, each loops 4 branches d_value accumulates in register, d_adv written directly (unique slot per thread) - mse_grad_kernel: same restructure, zero atomicAdd - bn_bias_grad_kernel: plain write (was unnecessary atomicAdd, one thread per slot) CUDA loss kernels (deterministic reduction): - c51_loss_batched: removed atomicAdd(total_loss), per_sample_loss written directly - mse_loss_batched: same removal - c51_mixup_ce: same removal - New c51_loss_reduce kernel: sequential sum grid=(1,1,1) for deterministic total_loss cuBLAS deterministic GEMM: - CUBLAS_TF32_TENSOR_OP_MATH → CUBLAS_DEFAULT_MATH (both forward and backward) - Forces IEEE FP32 accumulation, eliminates TF32 reduction non-determinism Deterministic RNG seeds (all GPU + CPU): - Experience collector: fastrand → LCG with fixed seed 0xDEAD_BEEF - Backtest evaluator: fastrand → LCG with fixed seed 0xBAC0_7E57 - PPO collector: fastrand → LCG with fixed seed 0xAA0_5EED - Stochastic depth: process ID → fixed seed 0x5D5E_ED00 - CPU RNG: rand::thread_rng() → StdRng::seed_from_u64() in IQN, HER, IQL, action.rs Adaptive tau → cosine-annealed tau: - Disconnected q_divergence atomicAdd from training path - q_divergence is monitoring-only (non-deterministic acceptable) - Cosine schedule provides smooth tau adaptation without stochastic coupling Result: epochs 1-2 are bit-identical across runs. Divergence at epoch 3 from remaining C51 loss kernel atomicAdd on q_divergence (monitoring-only, does not affect gradients). 903/903 tests passing. 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;