2e37af29d4
Root cause: Wave 4.1b (s1_input_dim 102→103,eb9515e41) introduced `bn_tanh_concat_dd_kernel` which reads `isv[DD_PCT_INDEX=406]` from the collector's `isv_signals_dev_ptr`. Wave 4.1b's atomic refactor docstring claimed "guarded by the captured-graph wiring in training_loop.rs:859 which sets the bus pointer BEFORE the first epoch's forward graph capture" — but ordering audit shows that's wrong: Phase 1c (line 580): init_gpu_experience_collector → collector.isv_signals_dev_ptr = 0 (NULL) Phase 2 (line 730): collect_gpu_experiences_slices → bn_tanh_concat_dd_kernel reads isv[406] = NULL+1624 = 0x658 → ILLEGAL_ADDRESS Phase 4 (line 859+): set_isv_signals_ptr (TOO LATE) The error surfaces as "load sp15_dd_state cubin: ILLEGAL_ADDRESS" via sticky-cascade (the OOB happened in the prior kernel; the next CUDA call — dd_state cubin load — surfaces the cascade). Reproduced locally via compute-sanitizer on test_no_hang_single_epoch (RTX 3050 Ti). Sanitizer pinpointed "Invalid __global__ read of size 4 bytes at bn_tanh_concat_dd_kernel +0x4d0 ... Access at 0x658 is out of bounds" — exactly slot 406 * 4. Fix: move the ISV / SP15 control pointer wiring (set_isv_signals_ptr, set_sp15_alpha_warm_count_ptr, set_sp15_plasticity_target, PER buffer's set_isv_signals_ptr + set_sp15_dd_trajectory_per_env_ptr + set_sp15_per_env_dims) into init_gpu_experience_collector BEFORE the collector is moved into self.gpu_experience_collector. This guarantees the FIRST collect_experiences_gpu call (epoch 0, Phase 2) sees non-NULL pointers — required because cudarc's graph capture bakes the arg values into the captured graph at capture time, so subsequent re-wiring has no effect on the captured replay path. The per-epoch re-wiring at lines 855-957 of the outer epoch loop is now redundant (idempotent setters re-applying the same stable pointers) but kept in place per feedback_no_partial_refactor for explicit per-epoch refresh semantics — these underlying pointers are stable for the trainer's lifetime so re-setting is a no-op, and removing the per-epoch call would scatter the wire-up logic across a non-obvious pre-init / per-epoch split. Verification: * 30/30 SP15 phase 1 oracle tests pass under compute-sanitizer memcheck with 0 errors (unchanged baseline) * test_no_hang_single_epoch under sanitizer: SP15 dd_state OOB is GONE; the first OOB now surfaces at compute_expected_q+0x2480 (denoise_target_q_buf stride 12 vs total_actions=13 — a SEPARATE pre-existing bug previously masked by the SP15 OOB; reported but out of scope for this commit per single-atomic-fix discipline). * compute-sanitizer "Access at 0x658" matches the bn_tanh_concat_dd_kernel isv[406] read path; production training on L40S/H100 should now reach the next-layer issue. Refs: SP15 Wave 4.1b (eb9515e41), feedback_no_partial_refactor, feedback_wire_everything_up, pearl_no_host_branches_in_captured_graph. Co-Authored-By: Claude Opus 4.7 (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;