Quantize compatible node + activation patterns as one block

[Tessil]

Annotate conv1d/conv2d/linear followed by relu/relu6 patterns as one block and fuse the activation into its parent.

The activation will then be implicitly done in the tosa.rescale node that will have a -128 zero-point.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/7555

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit b60972b with merge base 08770b7 ():
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[swolchok]

This PR caused a type-checking failure:

executorch/backends/arm/_passes/fuse_quantized_activation_pass.py:27:48 Uninitialized local [61]: Local variable `zp` is undefined, or not always defined.
executorch/backends/arm/_passes/fuse_quantized_activation_pass.py:27:54 Uninitialized local [61]: Local variable `qmin` is undefined, or not always defined.

[freddan80]

@Tessil will you have a look?

[Tessil]

@swolchok Thanks for the info. What would be the best way to fix it as the PR has already gone through? Raise a new one with a fix?

Note that the zp and qmin will theoretically never be evaluated if they are uninitialized with the short-circuit and as they are initialized in any case when is_quantized is true and evaluated only if is_quantized is true. Though still best to fix the type-checking issue.

[swolchok]

Raise a new one with a fix?

yep!

[Tessil]

@swolchok Fix: #7671
Thanks