Use specialization constants for conv2d params in q8ta shaders

[SS-JIA]

Summary:
Replace Conv2DParams UBO with specialization constants in q8ta_conv2d,
q8ta_conv2d_dw, and q8ta_im2col shaders. This allows the GPU compiler to
treat conv params (kernel size, stride, padding, dilation, groups) as
compile-time constants, enabling loop unrolling, dead code elimination,
and constant folding.

Differential Revision: D96503584

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/18178

• 📄 Preview Python docs built from this PR

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

❌ 3 New Failures, 4 Pending, 1 Unrelated Failure

As of commit b194df5 with merge base 4f900b2 ():

NEW FAILURES - The following jobs have failed:

• Lint / lintrunner (gh)
• pull / unittest / macos / macos-job (gh)
  export/tests/test_target_recipes.py::TestTargetRecipes::test_linear_model
• pull / unittest-editable / macos / macos-job (gh)
  export/tests/test_target_recipes.py::TestTargetRecipes::test_linear_model

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / test-binary-size-linux-gcc / linux-job (gh) (trunk failure)
  /pytorch/executorch/kernels/portable/cpu/op_convolution.cpp:175:41: error: comparison of integer expressions of different signedness: ‘ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Werror=sign-compare]

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

[meta-codesync]

@SS-JIA has exported this pull request. If you are a Meta employee, you can view the originating Diff in D96503584.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.