ci: run Awkward backend tests on GPU

[henryiii]

🤖 AI text below 🤖

Mirrors the GPU CI setup in awkward, which runs on the scikit-hep self-hosted runners we should also have access to.

• .github/workflows/gpu.yml — nightly, push to main, and workflow_dispatch; matrixed over CUDA 12 and 13. Micromamba env with cupy, then run the CUDA tests. Not triggered on pull requests, to keep PRs off the shared runners.
• tests/cuda/test_awkward_cuda.py — vector's Awkward backend already picks lib from the layout's nplike, so a cuda-backed array computes with cupy. The tests run 19 unary ops, 13 binary ops, and all 12 4D coordinate round-trips on both backends and require the results to match, plus a check that results stay on the cuda backend.
• Registered the cuda pytest marker.

The test module importorskips cupy and skips when no device is present, so the regular CI jobs collect and skip it.

Draft because the GPU job has not run yet — it needs a workflow_dispatch from this branch to confirm the runners pick it up. One thing to watch there: filterwarnings = ["error", ...] is repo-wide, so any warning from cupy or awkward's CUDA kernel loading will fail the job.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.72%. Comparing base (e79158a) to head (38cf889).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #733   +/-   ##
=======================================
  Coverage   87.72%   87.72%           
=======================================
  Files          96       96           
  Lines       11194    11194           
=======================================
  Hits         9820     9820           
  Misses       1374     1374           

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[henryiii]

I can't run the test from here, so it needs a merge to be tested.

[ianna]

I can't run the test from here, so it needs a merge to be tested.

would you like me to review it so that you could merge it?

[ianna]

@henryiii - Great! This looks consistent with awkward CUDA workflow. Thanks!

[henryiii]

Okay, let's merge and then followup on failures. Once merged it can be triggered via workflow-dispatch.

[henryiii]

🤖 AI text below 🤖

The GPU workflow ran on the merge to main and passed on real GPUs: run 30124750331.

Job	Result
CUDA 12	34 passed in 5.90s
CUDA 13	34 passed in 6.08s

Both jobs picked up the scikit-hep self-hosted runner, so the runner access assumption held.

Two things I checked specifically, since a green tick alone would not have proved much:

• The tests ran rather than skipped. collected 34 items, all PASSED, zero skips. A missing cupy or a GPU-less machine would have skipped the whole module at import and still exited 0, and the job would have looked identical. It did not; cupy imported and getDeviceCount() found a device.
• The warnings-as-errors concern did not materialize. Nothing from cupy or awkward's CUDA kernel loading tripped filterwarnings = ["error", ...], so no ignore entry is needed for now.

The run takes about a minute per CUDA version, so the nightly cron is cheap.