Files
foxhunt/crates/ml-alpha
jgrusewski 9ddde632b2 feat(ml-alpha): projection kernel (128->8 + layer-norm)
Single-block 8-thread kernel; thread j computes its own 128-dim dot
product, then thread 0 computes block-wide mean/var, then each thread
applies the per-output affine layer-norm. No atomicAdd; reductions are
single-thread (8 elements — negligible cost).

Tests (5/5 on sm_86) assert:
  - layer-norm zero-mean output under identity gain
  - layer-norm unit-variance output under identity gain
  - ln_bias shifts mean uniformly
  - ln_gain scales variance (var = gain^2)
  - finite output under zero input (variance clamp activates)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-16 21:52:57 +02:00
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