biondizzle
c2b752c2fe
- nvfp4_mega_moe_full: drop-in replacement for deep_gemm.mega.fp8_nvfp4_mega_moe - transform_nvfp4_weights_for_mega_moe: weight transformation (tested) - SymmBuffer + get_symm_buffer_for_nvfp4_mega_moe: API-matching stubs - MEGA_MOE_STATIC=1 support for pipeline testing - pyproject.toml for pip install
1.5 KiB
1.5 KiB
NVFP4 Mega MoE Kernel — Mojo Rewrite
Rewrite of the DeepGEMM fp8_nvfp4_mega_moe kernel in Mojo.
Why Mojo?
- Python-like syntax, C-level performance
- Direct GPU programming without PTX inline asm
- Safer than CUDA C++ (ownership, borrowing)
- Better ergonomics for complex kernel development
Architecture
The kernel performs NVFP4 (E2M1 + UE4M3 block16 scales) matrix multiply for MoE (Mixture of Experts) with expert parallelism across NVLink.
Key operations:
- Staging — quantize BF16 activation to FP4 (E2M1) with UE8M0 scales
- TMA load — load packed FP4 weights and UE4M3 scales from global memory
- UMMA —
mxf4nvf4matrix multiply with block scaling - Epilogue — quantize L1 output (BF16 → FP4 + UE4M3 scales for L2)
- NVLink sync — cross-rank barrier and buffer management
NVFP4 specifics (vs MXFP4):
- group_size=16 (UE4M3 block scales), not group_size=32 (UE8M0)
- 2 SF K-columns per BLOCK_K (128/16/4=2), not 1
- Weights are E2M1 packed int8 (2 values per byte)
mxf4nvf4UMMA instruction withscale_vec::4X
Structure
src/
mega_moe.mojo — main kernel entry point
staging.mojo — activation quantization (BF16 → FP4)
tma.mojo — TMA descriptor creation and copy
umma.mojo — UMMA descriptor and MMA operations
epilogue.mojo — output quantization and TMA store
barrier.mojo — NVLink cluster sync and symm buffer
layout.mojo — weight transformation and SF layout
utils.mojo — math helpers, UE4M3 packing