Infer the FP4 weight scale group size from the loaded scale tensor instead of hard-coding K/32.
This keeps upstream-style FP4 expert layouts working while allowing GLM/ModelOpt NVFP4 layouts that use K/16 scale columns to build MegaMoE sidecar weights.
Constraint: preserve the existing runner layout and only change MegaMoE sidecar metadata/recipe.
Feature-flag: --moe-a2a-backend=megamoe.
Conflict-hotspots: python/sglang/srt/layers/moe/mega_moe.py.
Scope-risk: actual DeepGEMM recipe support still needs target GPU runtime validation.
Tested: PYTHONPYCACHEPREFIX=/private/tmp/sglang_pycache python3 -m py_compile python/sglang/srt/layers/moe/mega_moe.py test/registered/unit/moe/test_glm_megamoe.py.
Tested: git diff --check.
Not-tested: GLM 5.2 MegaMoE GPU e2e; local environment lacks target runtime and hardware.
c00630088c