[ExecuTorch][WebGPU] Op-tests for q8ta_conv2d_dw#21202
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21202
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ❌ 46 New Failures, 3 Unrelated FailuresAs of commit 5ad3c1e with merge base 266e0dc ( NEW FAILURES - The following jobs have failed:
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This was referenced Jul 22, 2026
This was referenced Jul 22, 2026
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Approving full WebGPU stack
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Stack from ghstack (oldest at bottom):
Problem: The new
q8ta_conv2d_dwint8 depthwise conv needs golden coverage of the full quantized-conv subgraph across kernel-geometry, bias, stride, dilation, and batch regimes.Solution:
make_q8ta_conv2d_dw_moduleruns a depthwisenn.Conv2d(groups == channels) through XNNPACK static PT2E (per-channel weight, static per-tensor activation) in-process and returns the converted module, so the op-test framework goldens the WebGPU output against the converted eager (fp32, the fake-quant reference) e2e throughquantize_per_tensor -> q8ta_conv2d_dw -> dequantize_per_tensor(C0 + the new op) — no external model, no hand-written golden.Implementation:
cases.pyregistersk3(3x3, stride 1, pad 1),no_bias,stride2,dil2(dilation 2 — exercises thedil_h/dil_wwindow taps), andbatch2(N=2 — exercises the batch decomposition) overC % 4 == 0/W_out % 4 == 0shapes at atol=rtol=1e-3. XNNPACK-static calibration yields non-zero and negative activation zero-points, so the per-element zero-point-subtraction / requant path is exercised.test_q8ta_conv2d_dw.pycarries the delegation smoke test (asserts theVulkanBackenddelegate containsq8ta_conv2d_dw).Differential Revision: D112257617