bcabd29d97
Full backward pass for the 2D bottleneck via cuBLAS chain rule: 1. bn_tanh_backward_kernel: d_bn = d_concat[:,:bn_dim] * (1 - tanh^2) Reads tanh values from forward pass (bn_hidden_buf), applies derivative 2. cast_dx_to_staging: f32 d_bn → bf16 for cuBLAS dW GEMM 3. launch_dw_only: dW_bn[bn_dim, market_dim] += d_bn^T @ states Uses same cuBLAS infrastructure as all other weight gradient GEMMs 4. bn_bias_grad_kernel: db_bn = sum(d_bn, dim=0) backward_full() modified: new s1_dx_output parameter computes d_loss/d_bn_concat when bottleneck is active (was 0 = skip). Gradients flow through entire bottleneck → tanh → GEMM chain. Adam optimizer trains bottleneck weights (tensors 20-21) alongside all other parameters — same flat grad_buf, same spectral norm, same weight decay. No special handling needed. 3 new CUDA kernels: bn_tanh_backward, bn_bias_grad, bn_tanh_concat. 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;