Ground truth: add Kotlin/Native target(s) to skainet-test-groundtruth (jvm/android only today)
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 52/100
Research direction
Start with skainet-test/skainet-test-groundtruth/build.gradle.kts and inspect its kotlin { } targets and the commonMain dependencies, especially whether skainet-io-gguf supports Kotlin/Native. Check whether fixture generation needs changes or only Kotlin consumption. Done when a Kotlin/Native-only module can depend on the ground-truth validator and its fixtures; macosArm64() is the suggested first target.
Written by the indexing model from the issue text.
Description
Problem
skainet-test-groundtruth's kotlin { } block only declares android() and jvm() targets (skainet-test/skainet-test-groundtruth/build.gradle.kts). There's no macosArm64() / iosArm64() / linuxX64() / any Kotlin/Native target at all.
That makes GroundTruthValidator(ops, factory) unusable from any Kotlin/Native-only consumer — for example an out-of-tree GPU backend that only publishes K/N targets (macOS/iOS) and has no JVM target to fall back to. Right now the only way to get any ground-truth coverage for a backend like that is to hand-roll independent fixtures outside the module entirely, which duplicates exactly the hand-written-mapping problem #985/#986/#988 are already trying to avoid on the JVM/Python side.
Ask
Add at least one Kotlin/Native target (macosArm64 would cover the common Apple-Silicon dev case) to skainet-test-groundtruth, so GroundTruthValidator and its fixtures can be consumed by K/N-only modules the same way the skainet-backend-cpu src/goldenTest pattern already works on the JVM side.
Things worth checking first:
- Does GGUF fixture loading (
skainet-io-gguf) already build for K/N targets? - Does anything in
commonMaincurrently reach for ajvm()-only API? - Does the Docker/PyTorch fixture-generation step need to change, or only the Kotlin consumption side?
Context
Ran into this concretely while trying to wire ground-truth validation into an out-of-tree, Kotlin/Native-only MLX/Metal backend module — ended up redefining that task around a small hand-written independent NumPy fixture instead, since the module couldn't be depended on at all from a K/N-only target set.
Related: #984, #985, #988.
- Dominant language
- Kotlin
- Stars
- 52
- Forks
- 15
- Avg merge
- 1d 8h
- Merged PRs (30d)
- 37
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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