microsoft / microsoft/onnxruntime

[Delivery] Win ARM64 wheels + QNN

Open
#19,162 14 comments 11 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

ep:QNN feature request platform:windows
Dominant language
C++
Stars
21.9k
Forks
4.2k
Avg merge
4d 11h
Merged PRs (30d)
184

Description

Describe the feature request

Creating general issue to track ONNX DX on Windows ARM platform, particularly QNN.

Main problems:

  • Lack of pre-build arm libs/binaries/wheels.

    The necessity of building from source even our own tools create huge obstacles and difficulties for DX. Launching basic SDK demos and examples like this requires a lot of time and mental resources. Days at best, instead of minutes or hours at worse.

  • Necessity to mix/switch x64 and Arm environments to run basic demos.

    This is partially a result of previous problem. Qualcomm SDK and onnxruntime demos for QNN are not arm friendly. Some demos to the point of impossibility of execution on arm machine even with x64 emulation (see lack of AVX instructions emulation).

  • Feels like a development kit rather than a development device.

    Due to the above issues, the DX with Python and ML feels like you are still using an experimental device and requires an additional x64 machine for development. Which is not the case for example for NodeJS/Web development, because everything is cross-platform or arm native already.

Opportunities to improve current state:

  • Fix lack of python wheels for onnx toolkits and runtimes.
  • Make sure our demos are arm ready with no manual build of libraries.
  • Make Olive toolkit available for win arm
    • Connect efforts of Win AI team and Olive team (Olive QNN optimizations)
  • Invest to presence of 3rd party python libs on win arm
  • Improve x64 emulator to support modern CPU instructions
    This can be a backup plan to cover some gaps can't be covered by ourself.
Describe scenario use case

The ability to use Windows ARM64 as main machine for ML development, not only as inference target.

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

Start with the linked onnxruntime issue 19161 and the onnxruntime-inference-examples entry point at c_cxx/QNN_EP/mobilenetv2_classification, then review the linked onnxruntime-extensions and Olive issues. This umbrella issue has no single file or test; completion would require defined ARM64 wheel and demo targets to be agreed and delivered.

Written by the indexing model from the issue text.

Assessment

Tech stack
cpp, python
Domain
build-system, developer-experience, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.