eb9515e41c
Atomic consumer migration that flips production callers of bn_tanh_concat_kernel
over to Wave 4.1a's bn_tanh_concat_dd_kernel and bumps s1_input_dim from 102 to
103 across the entire trunk forward + backward path. Eliminates the documented
Wave 4.1a transient orphan.
Changes:
- s1_input_dim formula bump (bn_dim + portfolio_dim → bn_dim + portfolio_dim + 1)
at compute_param_sizes, trainer ctor's CublasGemmSet, xavier_init_params_buf,
the experience collector's CublasGemmSet, and CublasBackwardSet::new for the
backward gemm cache. cuBLAS gemm caches re-key automatically (fresh HashMap).
- GRN w_a_h_s1[0] / w_residual_h_s1[4] reshape [shared_h1, 102] → [shared_h1, 103]
via compute_param_sizes + xavier_init's fan_dims. Xavier-uniform init covers
the new dd_pct column (bounded [0,1] — Xavier's small-magnitude assumption is
appropriate; differs from SP14's aux_softmax_diff zero-init which was driven
by the bidirectional ±1 range).
- bn_concat_dim() accessor +1 (TLOB backward row stride).
- 5 concat_dim local-var bumps (1 alloc + 3 forward + 2 backward + 1 in
experience collector).
- 4 forward-call migrations to launch_sp15_bn_concat_dd: DDQN argmax pass
(~27158), online forward (~27467), target forward (~27666), experience
collector forward (~3853). Each takes self.isv_signals_dev_ptr; the kernel
reads ISV[DD_PCT_INDEX=406] on-device and broadcasts.
- 3 GRN backward sites (main, ensemble, CQL) flow through encoder_backward_chain
which uses s1_input_dim — bumped automatically. dd_pct column gradient is
silently discarded by vsn_d_gated_state_portfolio_pad_kernel (reads
[bn_dim..bn_dim+portfolio_dim) only) and bn_tanh_backward_kernel (reads
[0..bn_dim) only). Correct: dd_pct sources from ISV bus, no learnable input.
- Legacy bn_tanh_concat_kernel field DELETED from trainer struct alongside its
loader, tuple-element, destructuring, assignment (5 mechanical sites for the
one dead field). Function tuple shrinks 44→43 elements. Kernel symbol stays
in the cubin source for SP15 oracle parity tests.
- mag_concat / OFI concat audit verdict: DECOUPLED from s1_input_dim. They
widen shared_h2, not the trunk INPUT dim.
- test_gpu_backtest_evaluator_state_dim_calculation migrated to assert
STATE_DIM == 128 (was 96, stale per feedback_trust_code_not_docs).
Atomic per feedback_no_partial_refactor: every consumer of s1_input_dim and
bn_concat_buf row-stride migrated together. Eliminates Wave 4.1a transient-
orphan launcher per feedback_wire_everything_up. Legacy field deleted per
feedback_no_legacy_aliases.
Tests: cargo check clean (18 pre-existing unrelated warnings). Wave 4.1a
oracle parity test (bn_tanh_concat_dd_kernel_writes_dd_pct_column) still
passes. ML lib suite went from 945 pass / 14 fail (pre-Wave-4.1b baseline) to
947 pass / 12 fail post-Wave-4.1b — improved by +2 (state_dim_calculation
migration + ensemble checkpoint round-trip flake resolved).
Refs: SP15 Wave 4.1a (a8da1cb9c), pearl_no_host_branches_in_captured_graph,
feedback_no_partial_refactor, feedback_wire_everything_up,
feedback_no_legacy_aliases, feedback_isv_for_adaptive_bounds,
feedback_trust_code_not_docs.
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;