f388a4a9ce
Replace per-sample CUDA kernels (multihead_feature_attention forward,
attention_backward_kernel + tiled weight_grad_reduce) with 12 cached
cublasLtMatmul descriptors for Q/K/V/O projections.
Forward: 4 cuBLAS GEMMs + bias_add + attn_sdp_fwd cubin + attn_layer_norm_fwd cubin
Backward: 8 cuBLAS GEMMs (4 dW + 4 dX) + attn_sdp_bwd + attn_layer_norm_bwd cubins
+ 4 bias_grad_reduce kernels
Eliminates: d_params_per_sample tiling buffer (was 26MB at B=16384),
per-sample backward kernel, weight_grad_reduce kernel, forward_kernel,
backward_kernel, saved_qkv buffer.
Adds: 15 intermediate D*B buffers for projection/gradient flow, SDP scores,
LayerNorm save state. Constructor takes Arc<SharedCublasHandle> instead of
Arc<CudaStream>. CUBLAS_COMPUTE_32F_FAST_TF32. Zero atomicAdd.
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;