5d584dc751
Backward propagates dh_s2_aux through w3/w2/w1 with block-tree-reduce
(no atomicAdd per feedback_no_atomicadd). Critical: kernel set does NOT
write dx_in — encoder gradient remains Q-shaped only. Stop-grad
invariant verified via parameter-list structural enforcement (kernels
literally cannot reference an `dx_in_out` pointer they don't accept) +
kernel source inspection that strips comments and asserts no `dx_in`
write pattern.
Three kernels in aux_trunk_backward_kernel.cu:
- aux_trunk_bwd_dh_pre: per-sample, computes dh_aux2_pre [B, H2] +
dh_aux1_pre [B, H1] using ELU' from POST-activation form
(`(y > 0) ? 1 : (1 + y)` mirrors aux_elu_bwd_from_post in
aux_heads_kernel.cu).
- aux_trunk_bwd_dW_reduce: generic outer-product reduce
`dW[k, j] = sum_b A[b, k] * B[b, j]`. One block per output
element, shmem-tree reduce over batch. Used 3× (dW3, dW2, dW1).
- aux_trunk_bwd_db_reduce: generic batch-reduce `db[j] = sum_b
B[b, j]`. One block per output element. Used 3× (db3, db2, db1).
Memory-efficient: no per-sample partials (avoids B×163,072 floats for
production topology). Per-element reduction means O(P) blocks each
doing O(B) work in shmem.
Rust wrapper AuxTrunkBackwardOps in gpu_aux_trunk.rs orchestrates seven
launches in fixed sequence (capture-friendly, no host branches per
pearl_no_host_branches_in_captured_graph). All three CudaFunction
handles pre-loaded once at construction. Field added to GpuDqnTrainer
alongside aux_trunk_forward_ops; constructor mirrors C.3 pattern.
Tests (all pass on RTX 3050 Ti, sub-ULP forward, 1.33e-2 max rel-err
backward gradient at smallest sampled gradient):
- aux_trunk_forward_matches_numpy_reference (C.3 — preserved).
- aux_trunk_backward_gradient_check (NEW): central-difference
numerical gradient at 16 sampled dW3 indices vs analytic from
backward kernel. Loss = 0.5 * ||h_s2_aux||^2 so dh_s2_aux =
h_s2_aux. EPS=1e-3, B=4, ENC=H1=H2=AUX=32 (33 forwards in ~2s).
REL_TOL = 2e-2 (f32 finite-difference noise floor for
small-gradient tail; production topology is dimension-independent
given runtime args).
- aux_trunk_backward_does_not_write_dx (NEW): reads kernel source,
strips C-style comments (so design-discussion text mentioning
`dx_in` doesn't false-positive), asserts no `dx_in` / `dx_in_out`
symbol survives in code. Complements the structural enforcement
(kernel signatures don't accept `dx_in_out` pointer).
Phase C.4 of SP14 Layer C separate-aux-trunk refactor. Module is
additive — wire-up into collector backward chain + Adam updates lands
in Phase C.5 (atomic).
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;