Both kernels in `aux_heads_loss_ema_kernel.cu` retrofit in the same commit
per `feedback_no_partial_refactor.md` (single shared cubin, single producer
family).
- Kernel `aux_heads_loss_ema_update`: writes nb_loss/rg_loss scalars to
`producer_step_scratch_buf[41..43)` (slots 41=next-bar, 42=regime).
- Kernel `aux_label_scale_ema_update`: writes mean(|label|) to
`producer_step_scratch_buf[43]`.
- 3 ISV slots wired with Pearls A+D: ISV[113] (Wiener 123..126),
ISV[114] (Wiener 126..129), ISV[117] (Wiener 129..132).
- Launcher `AuxHeadsForwardOps::launch_loss_ema`: drops isv_dev_ptr +
isv_*_index + ema_alpha; takes scratch_dev_ptr + 2 scratch_idx args.
- Launcher `AuxHeadsForwardOps::launch_label_scale_ema`: same retrofit
pattern with a single scratch_idx arg.
- Wrapper `GpuDqnTrainer::launch_aux_heads_loss_ema(_ema_alpha_unused)`:
sync + Pearls A+D loop over both slots.
- `aux_heads_forward` mid-step launch (Step 2b — runs BEFORE
next_bar_loss_reduce + backward consume ISV[117]) gains inline sync +
Pearls A+D update so consumers see the up-to-date scale this step.
Behavior: stationary signals converge to the same value at adaptive rate
(Pearl D's α* derived from per-slot signal-vs-noise variance);
non-stationary signals respond Wiener-optimally faster. Slots stay
semantically identical (next-bar MSE, regime CE, label-scale mean_abs);
only the EMA blending logic changes.
Tests:
- `sp4_aux_heads_loss_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`:
nb_loss=0.5, rg_loss=1.2 stationary, both slots converge within 1%
after 1000 observations.
- `sp4_aux_label_scale_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`:
B=256 mixed-sign ±3.0 labels (mean_abs=3.0), step_obs ∈ ±1e-4 of
analytical mean, Pearl A bootstrap + Pearl D convergence verified.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`,
`feedback_no_partial_refactor.md`. Build: `cargo check -p ml --lib --tests
--offline` clean (11 pre-existing warnings, no new warnings).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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