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[Feature]: StableHLO converter coverage — lower every TensorOps op #1389

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Description

@michalharakal

🧠 D: DOCUMENT — Problem & Opportunity

Exporting a model to StableHLO (StableHloConverter) fails with "No converter found" for any op none of the registered converters lists in supportedOperations. The new StableHLO column of the coverage matrix makes the gap visible: conv3d, convTranspose1d, convTranspose2d, elu, leakyRelu, logSoftmax, powScalar, log2, log10, pad2d, unfold, lt, ge, tril are ❌ today.

Each op is an independent, self-contained converter addition with a sibling to copy from, which makes most of them ideal first contributions.

Summary: one small lane per op group until the StableHLO column is green for every public op.


🔍 A: ASSESS — Feasibility & Impact

  • Feasibility: high; converters are pure text emitters with tests that check the emitted MLIR.
  • Out of scope: matmulWeightTransposed / relayoutPackedWeightForKernels (packed-weight runtime helpers, not graph ops) and convTranspose2d (covered by the TransposedConv2d feature).

📚 R: RESEARCH — What Must Be Understood First?


🛠️ C: CODE — Implementation Plan

  • One lane per op group below

Acceptance Criteria

  • StableHLO column ✅ for every op listed above

Lane breakdown

Only Lane 2 applies; each sub-issue is one converter addition. Review happens in the PR.

New here? Read Getting started as a contributor and the Issue taxonomy. Lanes marked good first issue assume no prior SKaiNET codebase knowledge. The gap markers and the coverage matrix were introduced in #1370.

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    compilerdarcFeature driven through the DARC workflow (Document / Assess / Research / Code)enhancementNew feature or requesttrackingParent/tracking issue with sub-issues

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