pytorch / pytorch/executorch

How to enable XNN_ENABLE_SPARSE in Executorch

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module: xnnpack
Dominant language
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Description

🚀 The feature, motivation and pitch

I would like to ask if there is any plan to support XNN_ENABLE_SPARSE in Executorch.

I am working on a model that contains a significant amount of sparse operations, and I believe enabling XNN_ENABLE_SPARSE could lead to a substantial performance improvement.

Is this feature currently supported? If not, are there any plans to add this in the future roadmap? Any guidance on how to enable it or potential workarounds would be greatly appreciated.

Alternatives

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Additional context

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RFC (Optional)

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cc @digantdesai @mcr229 @cbilgin

Contributor guide

Open the contributing guide

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.

Research direction

The issue names XNN_ENABLE_SPARSE but mentions no file, test, or entry point. Start by checking whether Executorch currently exposes this option and review the issue discussion for a definitive support status. Done means documenting the supported path, workaround, or a decided implementation scope.

Written by the indexing model from the issue text.

Assessment

Domain
embedded-iot, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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