[ExecuTorch][WebGPU] Fix per-op GPU-timestamp inflation on tile-based mobile GPUs

[JCNTH]

Stack from ghstack (oldest at bottom):

• -> [ExecuTorch][WebGPU] Fix per-op GPU-timestamp inflation on tile-based mobile GPUs #21033

The WebGPU per-op profiler (WebGPUQueryPool::extract_results) computed each pass's GPU time as end_of_pass - beginning_of_pass. On tile-based mobile GPUs (e.g. Arm Mali via Dawn -> Vulkan) the beginning-of-pass timestamps resolve at ~command-buffer start rather than per pass, so each (end - begin) measures time-since-start (cumulative) and the summed per-op time inflates by roughly the pass count (~100x on a Llama-1B decode). Desktop backends (Metal) capture beginning-of-pass correctly and are unaffected, which is why it only surfaces on-device.

Fix: compute each pass's duration from consecutive end timestamps by clamping each pass's base up to the previous pass's end (base = max(begin, prev_end)). On a well-behaved backend begin >= prev_end, so base == begin and the per-op numbers are byte-unchanged; on a tile-based GPU the clamp recovers the real per-pass time. The summed duration can no longer exceed the wall span by construction.

Key changes:

• WebGPUQueryPool::fill_shader_durations (new free function) — sort by dispatch index, then per pass base = max(begin, prev_end), duration = end > base ? end - base : 0, tracking prev_end as a running max of end timestamps.
• WebGPUQueryPool::extract_results — calls fill_shader_durations after mapping the readback (extraction makes the math unit-testable without a GPU).
• #include <algorithm> for std::sort / std::max.
• test_webgpu_native.cpp — device-free unit test QueryPoolDeltaMath covering the four regimes.

Entirely within #ifdef WGPU_BACKEND_ENABLE_PROFILING; no change to the non-profiling (production) build.

Differential Revision: D112643054

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21033

• 📄 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 dc7e59e with merge base 44539fd ():
💚 Looks good so far! There are no failures yet. 💚

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

[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.