Separate source and destination FP4 scale packing in requant_fp4_to_gran_k so group16-to-group32 conversion always recomputes UE8M0 runtime scales by default.
Make prepare_fp4_weights_for_mega_moe accept raw grouped FP4 weights and scales, then perform optional requantization, DeepGEMM scale layout transform, and MegaMoE UTCCP weight transform internally.
Update the MegaMoE synthetic benchmark so baseline grouped GEMM uses runtime-layout weights while fused MegaMoE uses transformed weights from the same raw source tensors.
Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/__init__.py deep_gemm/mega/__init__.py 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.
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.
