[ExecuTorch][WebGPU] Test coverage for the f16-accumulate steel GEMM#20826
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Pull Request resolved: #20801 Adds golden coverage for the f16-accumulate (`pwdqf16acc`) steel GEMM, which runs under `WGPU_BACKEND_STEEL_F16ACC` when `group_size % BK == 0`. Key changes: - `test_quantized_linear.py` / `test_webgpu_native.cpp` — add `pwdqf16acc` (96x2048x256, K=2048) and `pwdqf16acc_down` (128x8192x2048, deep-K worst case) under `#ifdef WGPU_BACKEND_STEEL_F16ACC`, goldened against the fp64 dequant-matmul truth. f16 accumulation error grows with K, so the tolerances are wider than the f16-multiply `steel_f16` (2.3e-4) and the deep-K shape gets the loosest gate; perplexity (the kernel diff) is the primary quality bar and this catches gross bit/index bugs. Co-authored-with: Claude Code. ghstack-source-id: 401515200 @exported-using-ghexport Differential Revision: [D111163651](https://our.internmc.facebook.com/intern/diff/D111163651/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20826
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #20801 by @JCNTH
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/JCNTH/16/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JCNTH/16/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JCNTH/15/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JCNTH/16/orig
@diff-train-skip-merge