Use weight cache for quantized tensor scale data

[pytorchbot]

Summary:
When enabling the XNNPACK weight cache and running a model with qb4 or qc8-quantized linear weights, it triggers an assertion that is intended to make sure all data is in the weight cache. This can be reproduced by running the XNNPACK backend linear op tests with weight cache enabled.

The root cause appears to be that tensor scale data is bypassing the weight cache - likely an oversight. This isn't a correctness issue, but does cause the aforementioned assert to fail and uses marginally more memory than it otherwise needs to.

This PR updates the XNNPACK compileModel call to use the weight cache for scale data (instead of putting it in the unpacked_buffers list). With this change, the linear op tests pass with weight cache enabled.

Differential Revision: D82862629

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14455

• 📄 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 32fe0bf with merge base e0dda90 ():
💚 Looks good so far! There are no failures yet. 💚

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