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Native fused LayerNorm / GELU / softmax / conv1d kernels for the JNI and FFM tiers

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
c, kotlin
Domain
machine-learning, performance

Research direction

Start with the existing matmul kernels in skainet-backend-native-cpu/native/src/, then trace the scalar Kotlin implementations and the JNI and FFM registration patterns. Add on-device parity tests for the four operations, checking the stated precision requirements. Done means the kernels are registered for both tiers and simpleperf measurements compare a whisper-tiny encoder step before and after.

Written by the indexing model from the issue text.

Description

compute-backend enhancement skill:android skill:native

Context

Companion to the dense FP32 JNI matmul issue. #949 measured that ~83% of Android eager decode time was non-matmul overhead and fixed the boxed per-element paths by hoisting the primitive FloatArray loops into shared code. That closed the boxing problem; the ops are still scalar Kotlin loops on ART, where there is no Vector API.

For a Whisper-class encoder that matters: per layer there are two LayerNorms over [1500,1280], a GELU over [1500,5120], 20 softmaxes over [1500,1500], and the stem is two conv1d layers (k=3, stride 1 and 2) over [128,3000]. skainet-backend-native-cpu/native/src/ contains matmul kernels only.

Scope

  • Native C kernels (NEON bodies gated on __ARM_NEON, scalar fallback) for: layerNorm (fused mean/var/normalize/affine, FP32 accumulation), gelu (tanh approximation and erf variants, matching Fp32 reference to 1 ULP where the DSL promises exactness), row softmax (max-subtract, exp, normalize), and conv1d (direct or im2col + the FP32 matmul kernel).
  • JNI entries + JniKernelProvider registrations; FFM bindings on the JVM tier for parity with the existing pattern.
  • Parity tests against the scalar Kotlin implementations on device.
  • simpleperf before/after on a whisper-tiny encoder step, the way #949 did for decode, so the remaining overhead is named.

Acceptance

  • Results match the scalar path within 1 ULP (GELU/softmax) or bit-identically (LayerNorm with FP32 accumulation, conv1d).
  • Combined with the dense FP32 matmul issue: whisper-tiny encoder ≥ 5× faster than 0.54.0 on a Pixel-class phone.

Related

  • #949 — boxed element access on Android (closed; this is the SIMD follow-up)
  • #910 — vectorized eager kernels for Kotlin/Native targets (same kernels, different binding)
Dominant language
Kotlin
Stars
52
Forks
15
Avg merge
1d 15h
Merged PRs (30d)
36

Getting set up

First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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