23e9a1f78c
Pre-existing latent bug surfaced by compute-sanitizer after the SP15 NULL-pointer fix at2e37af29dunblocked the cascade. Threads 60-63 were writing past denoise_target_q_buf's end every batch. Sanitizer evidence (pre-fix on2e37af29d): Invalid __global__ write of size 4 bytes at compute_expected_q+0x2480 by thread (60..63, 0, 0) in block (0, 0, 0) Access at <addr> is out of bounds (45/97/149/201 bytes after nearest allocation of size B*12*4 bytes) Host backtrace: GpuDqnTrainer::compute_denoise_target_q → submit_post_aux_ops → run_full_step Root cause — semantic mismatch between writer stride and buffer size: - compute_expected_q writes q_values[i*total_actions + a] with total_actions = b0+b1+b2+b3 = 4+3+3+3 = 13 (4-direction factored) - denoise_target_q_buf was allocated b*12 (legacy 3+3+3+3 layout) - threads 60..63 wrote slot 12 of samples (60..63 % batch_size) past the buffer end every step The downstream q_denoise_backward + denoise_loss_grad kernels read Q_target[b * D + i] with hardcoded D=12 because the diffusion denoiser MLP itself only has 12 output slots (W2[12,24] + b2[12]) — its 12 are the q_coord_buf's post-cross-branch-attention narrowed output, NOT a clean subset of the 13 raw Q-actions. The exact same fix pattern was already applied to the sibling q_var_buf_trainer allocation in the prior SP4 audit (see comment block at gpu_dqn_trainer.rs:21620-21630 referencing the identical OOB at threads 60..63); denoise_target_q_buf 8 lines below was missed because it was guarded by the SP15 NULL-pointer ILLEGAL_ADDRESS that fault- stopped the cascade before this OOB could fire —2e37af29dremoved the upstream ILLEGAL_ADDRESS, surfacing the latent OOB. Fix architecture (Option A — pad buffer to total_actions, pass stride to consumer; same pattern as q_var_buf_trainer): 1. Widen denoise_target_q_buf from b*12 to b*total_actions (= b*13) to match compute_expected_q's writer stride. 2. Add refined_stride / target_stride / input_stride parameters to q_denoise_backward kernel; the kernel still computes D=12 per- sample (denoiser MLP fixed width) but addresses each input buffer at its own per-sample stride. 3. Add refined_stride / target_stride parameters to denoise_loss_grad kernel (same pattern; used by launch_q_denoise_backward_cublas). 4. Update both Rust launchers (launch_q_denoise_backward, launch_q_denoise_backward_cublas) to pass refined_stride=12 (q_coord and q_input are post-attention narrowed), target_stride=total_actions=13. 5. denoise_q_input_buf STAYS at b*12 — it's a snapshot of q_coord_buf (also b*12) via snapshot_pre_denoise_q's DtoD copy; never written by compute_expected_q. 6. Flat-scan consumers (SP4 target_q_p99_update producer + SP3 slot 46 threshold-check + dqn_clamp_finite_f32) UNCHANGED — they consume the buffer flat via .len(); widening from b*12 to b*13 is monotone (one more valid Q-value per sample in the histogram). Atomic per feedback_no_partial_refactor: 2 kernel signatures + buffer allocation + 2 launch sites + struct doc comments + SP3 slot 46 doc + audit doc — all in one commit. Every consumer of the writer's stride migrates simultaneously. Verification: - SQLX_OFFLINE=true cargo check -p ml --features cuda: clean - compute-sanitizer (RTX 3050 Ti): 0 Invalid __global__ errors at compute_expected_q post-fix (down from 4 per training step in baseline — re-verified on2e37af29dto confirm the diff) - SQLX_OFFLINE=true cargo test -p ml --features cuda --lib: holds parent baseline 947 pass / 12 fail (same 12 pre-existing failures) Files touched: crates/ml/src/cuda_pipeline/experience_kernels.cu — q_denoise_backward + denoise_loss_grad kernel signatures crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs — alloc widen + 2 launcher updates + 4 doc comment updates docs/dqn-wire-up-audit.md — audit entry 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;