diag(proof): characterize cross-microarch divergence on FAIL
Add a divergence report (count + fraction outside tolerance, per-feature breakdown, worst offenders) so we can tell a few branch-flip elements from a pervasive regression. The CI tolerance gate failed with max|d|=0.85 / maxrel=345 — far beyond FP rounding — so we need to see WHICH feature elements diverge structurally on the Azure runner.
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@ -588,6 +588,35 @@ def main():
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print(f" Status: {match_status}")
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print()
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if not hash_match and max_abs_dev is not None:
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block_sizes = [56, 56, 55, 9, 64, 128] # per-frame feature layout
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block_names = ["amp_mean", "amp_var", "phase_diff", "corr", "doppler", "psd"]
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frame_len = sum(block_sizes)
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tol = TOLERANCE_ATOL + TOLERANCE_RTOL * np.abs(ref_vec)
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outside = diff > tol
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n_out = int(outside.sum())
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print(
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f" DIVERGENCE: {n_out}/{computed_vector.size} outside tol "
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f"({100.0 * n_out / computed_vector.size:.4f}%) "
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f"max|d|={max_abs_dev:.3e} maxrel={max_rel_dev:.3e}"
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)
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if n_out:
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wf = np.where(outside)[0] % frame_len
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bounds = np.cumsum([0] + block_sizes)
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parts = []
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for bi, name in enumerate(block_names):
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c = int(((wf >= bounds[bi]) & (wf < bounds[bi + 1])).sum())
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if c:
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parts.append(f"{name}={c}")
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print(f" by feature: {', '.join(parts)}")
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for w in np.argsort(diff)[::-1][:4]:
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b = int(np.searchsorted(bounds, int(w) % frame_len, side="right")) - 1
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print(
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f" worst idx {int(w)} ({block_names[b]}): "
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f"ref={ref_vec[int(w)]:.6g} got={computed_vector[int(w)]:.6g}"
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)
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print()
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# ---------------------------------------------------------------
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# Step 4: Audit (if requested or always in full mode)
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# ---------------------------------------------------------------
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