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TensorDump forward hook + TensorCompare CLI: localise numeric divergence between two runs

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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
kotlin, python
Domain
machine-learning, tooling

Research direction

Start by reading the existing ForwardHooks, ExecutionObserver, and TraceSink APIs to understand how a tensor dump could integrate with current execution. The scope spans a versioned cross-platform format, filtering, comparison metrics and CLI, documentation, and in-tree parity-test use. Done means Android and JVM dumps compare in one command and identify the first tensor over tolerance, with the format documented and a Python reader.

Written by the indexing model from the issue text.

Description

enhancement skill:kotlin-core skill:numerics

Context

The most expensive part of bringing a large model to a phone is not the port, it is finding where numerics diverge between two executions of the same model: FP32 reference vs a quantized or FP16 backend, desktop vs device, eager vs compiled. The downstream Whisper-on-LiteRT case study needed ad-hoc per-block probes to localise an FP16 LayerNorm overflow to block 31 of 32 and an INT8 projection error to the block-0 K projection — all built by hand, twice, in Python and in Kotlin.

SKaiNET has the hooks (ForwardHooks, ExecutionObserver, TraceSink) but no standard artifact that two runs can exchange, and no standard comparison.

Scope

  • TensorDump: a forward hook that writes named intermediate tensors (by module path and op index) to one compact binary file — header with name, shape, dtype/encoding, then payload — appendable, streaming, no heap copy of the whole run. Works on JVM, Android and Kotlin/Native.
  • TensorCompare: a library function + CLI (skainet-plan-style) taking two dumps and reporting per tensor: cosine, RMSE, max abs diff, index of first divergence above a tolerance, and a summary that names the first tensor that crosses the tolerance (the localisation answer).
  • Filters: dump only tensors matching a module-path glob, every N-th op, or a shape predicate, so a 32-layer encoder does not produce gigabytes.
  • Documented recipe in the how-to section: "compare a device run against a desktop reference" with the two commands.
  • Used in-tree at least once: the .vmfb parity acceptance (#1148) or the JNI parity tests read/write this format instead of bespoke arrays.

Acceptance

  • A dump from an Android androidTest run and a dump from a JVM test of the same model compare in one command and point at the first divergent tensor.
  • Format documented and versioned; readable from Python with a short reference reader in ground-truth tooling.

Related

  • #1148 — eager vs compiled parity acceptance
  • #1261 — ULP-level JIT drift, the kind of thing this tool should make a one-liner to see
Dominant language
Kotlin
Stars
52
Forks
15
Avg merge
1d 15h
Merged PRs (30d)
36

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  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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