8494853eb0
The TMA inline PTX had three invalid instructions causing CUDA_ERROR_INVALID_PTX at driver JIT time on sm_90 (H100): 1. cp.async.bulk missing required 4th mbarrier operand 2. cp.async.bulk.commit_group — does not exist in any PTX ISA 3. cp.async.bulk.wait_group — does not exist in any PTX ISA These confused two different CUDA instruction families: - cp.async (Ampere, uses commit_group/wait_group, 16B per op) - cp.async.bulk (Hopper TMA, uses mbarrier, up to 256KB per op) Fix: rewrite cooperative_load_tile_tma() with correct Hopper protocol: mbarrier.init → mbarrier.arrive.expect_tx → cp.async.bulk [mbar] → mbarrier.try_wait.parity Also adds 16-byte alignment guard (cp.async.bulk requires size % 16 == 0) with float4 fallback for unaligned bias tiles (e.g. NUM_ACTIONS=5 → 20B). Retains DISABLE_TMA retry in gpu_experience_collector as safety net. Co-Authored-By: Claude Opus 4.6 <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;