Add a utility and synthetic benchmark path for converting FP4 group16 tensors into group32 tensors that the existing SM100 block32 MegaMoE kernels can consume.
Document that this is a requantization path rather than a lossless metadata rewrite, so GLM-5.2 accuracy validation is still required before production use.
Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/utils/math.py tests/test_layout.py tests/test_mega_moe.py
Tested: git diff --check
Not-tested: CUDA build, SM100/B300 runtime, and GLM-5.2 accuracy validation are not available locally.
Allow SM100 FP4 scale layout transforms to accept group16 and thread weight granularity through the MegaMoE Python wrapper, API checks, and synthetic benchmark entrypoint.
Keep fused SM100 MegaMoE compute behind an explicit group16 capability gate until the SFB/TMEM/MMA scale path is updated and validated.
Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/mega/__init__.py tests/test_mega_moe.py tests/generators.py
Tested: git diff --check
Not-tested: CUDA build and SM100/B300 runtime validation are not available locally.
LuminolT