Qualcomm AI Engine Direct - VIT Optimization

[winskuo-quic]

Summary

QNN doesn't have much 5D permute optimization, which causes Vit running slower than CPU.

Switched pattern from unsqueeze->permute 5d->squeeze to permute 4d.

Improvements: 150ms/inference -> 4.2ms/inference.

Test plan

Pass Vit UT

[pytorch-bot]

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• pull / unittest / macos / macos-job (gh)
  backends/xnnpack/test/recipes/test_xnnpack_recipes.py::TestXnnpackRecipes::test_int8_static_quant_recipe
• pull / unittest-editable / linux / linux-job (gh)
  backends/xnnpack/test/recipes/test_xnnpack_recipes.py::TestXnnpackRecipes::test_int8_static_quant_recipe
• pull / unittest-editable / macos / macos-job (gh)
  backends/xnnpack/test/recipes/test_xnnpack_recipes.py::TestXnnpackRecipes::test_int8_static_quant_recipe

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• pull / unittest / windows / windows-job (gh) (trunk failure)
  backends/xnnpack/test/ops/test_conv1d.py::TestConv1d::test_qs8_conv1d_batchnorm_seq
• pull / unittest-editable / windows / windows-job (gh) (trunk failure)
  backends/xnnpack/test/ops/test_conv1d.py::TestConv1d::test_qs8_conv1d_batchnorm_seq

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[winskuo-quic]

Hi @cccclai,
This PR is to address the issue you have reported about QNN running slower than CPU for Vit.
I have tested SM8750 with this PR, and inference speed improves from 140ms/inference -> 4.1ms/inference, which should be around 35 times faster.
I have made a source level transformation on the Vit model.
The bottleneck is caused by the 5D permute pattern here: https://github.com/pytorch/pytorch/blob/3cfbf98ea9d937d23f3700168b22706c957308ce/torch/nn/functional.py#L5825.
I did source level transformation because this pattern is a bit specific and it is hard to generalize.
Please have a look and let me know if you have any questions.
Thanks

[cccclai]

It can be a pass ideally, is it correct?

[winskuo-quic]

Yes. This can be a pass. If this pattern occurs frequently in the future, we will see if there's a better way to do this.

[cccclai]

oh just saw that you mentioned the pattern is a bit hard to generalize hence we're doing source transform here