Fold LpNormalization
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- Dominant language
- C++
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Description
Why?
Pytorch onnx unfolds L2* normalization as multiple operators, as opposed to use LpNormalization in onnx.
What?
I feel that it's within the scope of this package to fold multiple operators into a single, no?
Relevance
Some hardware partners, e.g., Qualcomm:QNN/SNPE support LpNormalization. In such cases, model performance might suffer due to an arbitrary choice of Pytorch.
Kindly provide an example to accomplish such task.
*Possibly L1 and others
Contributor guide
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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.
Research direction
No files, tests, or entry points are named. Start by reading the linked ONNX LpNormalization specification and inspecting the PyTorch-exported operator graph, then locate the optimizer pass and its existing tests. Done means a normalization pattern is folded into LpNormalization, with any supported L1 or other cases explicitly covered.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- backend, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100