Wires the trade-outcome head's forward + loss reduce + backward +
per-sample partial reduce + SAXPY into the trainer's aux_heads_forward
and aux_heads_backward methods. Adam SAXPY for the 4 new weight tensors
at [163..167) extends uniformly via the existing aux_param_specs array
iteration.
Changes to aux_heads_forward:
- Steps 7 + 8 appended after K=2 next-bar head's loss reduce
- K=3 forward reads weights at [163..167) (Phase B1) → writes to
aux_to_* save-for-backward tiles (Phase B2)
- K=3 loss_reduce writes aux_to_loss_scalar_buf + aux_to_valid_count_buf
Changes to aux_heads_backward:
- K=3 backward appended after K=5 regime backward, emits per-sample
partials (dW1, db1, dW2, db2) + per-sample dh_s2_aux
- aux_param_specs array extended from 8 → 12 entries. Per-tensor
reduce + SAXPY loop iterates uniformly, scaling each by aux_weight
- K=3 dh_s2_aux SAXPY appended after K=5's, all three heads'
gradients flow into aux trunk's dh_s2_aux_accum (encoder stop-grad
enforced structurally by aux_trunk_backward's missing dx_in output)
Label semantic (cold-start): aux_to_label_buf is alloc_zeros (all 0
= Profit) until Phase B4b lands replay-buffer label scatter. Model
trains on "predict Profit everywhere" — degraded but well-defined
(no NaN). Mirrors K=2 head's known-degraded state between B1.1a
(forward landed) and B1.1b (label producer wired).
Adam SAXPY: existing global SAXPY iterates 0..NUM_WEIGHT_TENSORS
(now 167) — 4 new weight slots get gradient SAXPYs followed by
Adam m/v updates uniformly. Architectural payoff of Phase B1's
NUM_WEIGHT_TENSORS bump.
Test flake mitigation: added bind_to_thread() to
ensemble::adapters::dqn::tests::shared_device() mirroring the
cuda_stream() test-helper pattern from the fix sweep at ebc1b1502.
test_dqn_checkpoint_round_trip had intermittent CUDA-context-thread-
state flakes under parallel test runs; the bind is idempotent and
forces context current on every test thread accessing the shared
device. Still occasionally non-deterministic at the prediction-
direction level (the test's disable_noise() zeros NoisyLinear
epsilon but may leave other randomness sources untouched; runs
alternate pred1=-1 pred2=1 ↔ pred1=1 pred2=-1). The underlying
NoisyLinear randomness has been flaky since pre-vNext; not a B4
regression.
Verification:
- cargo check -p ml clean (21 warnings, none new).
- cargo test -p ml --lib → 1016 passing / 0 failing.
Phase B4b next: replay-buffer label scatter populating trainer's
aux_to_label_buf from rollout's exp_aux_to_label_buf per (i, t).
Phase B5 (spec's actual "Phase B"): input concat 256→262 with
plan_params.
Audit: docs/dqn-wire-up-audit.md Phase B4 section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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