# NVFP4 MegaMoE Kernel

NVFP4 block-scaled Mixture-of-Experts kernel for DeepSeek-V4 on NVIDIA Blackwell (SM100). Uses CuTeDSL — NVIDIA's Python-based CUTLASS DSL — for a native NVFP4 pipeline that takes full advantage of Blackwell's TMA, MMA, and epilogue overlap.

## What This Is

A fused MoE FFN kernel that runs the entire expert forward pass in NVFP4:

```
BF16 input → quantize to NVFP4
  L1 GEMM: NVFP4 × NVFP4 → BF16 (gate + up)
  SiLU(gate) * up → BF16 (only nonlinear — can't avoid BF16 here)
  Re-quantize → NVFP4
  L2 GEMM: NVFP4 × NVFP4 → BF16 (down_proj)
  Scatter with routing weights → BF16 output
```

Both GEMMs are fully NVFP4: A and B in `float4_e2m1fn_x2`, block scales in `float8_e4m3fn`, global scales in `float32`. BF16 is used only for the SiLU activation and the final scatter — the minimum possible.

## How We Got Here

### The C++ CUTLASS Kernel Was Broken

The original kernel was a C++ `.cu` file using CUTLASS's C++ API directly. It passed all the simple tests (uniform data → exact output, SF remap verifier → 0 errors) but produced **cosine 0.05** with real random data. After weeks of debugging the SF remap (8+ iterations, all producing the same 0.2 cosine against a wrong reference), we discovered:

1. **The BF16 reference comparison was wrong** — our Python dequantization didn't match CUTLASS's internal FP4 handling. A wrong reference is worse than no reference. We chased ghosts through 8+ SF remap rewrites because the 0.2 cosine was never about the remap.

2. **The C++ CUTLASS kernel misinterpreted FP4 data** — even with SF remap verified correct (0 byte errors), the GEMM produced garbage with non-uniform data. The issue was in how CUTLASS's C++ API handles FP4 packing/tiling internally — something we couldn't easily debug or fix.

3. **The checkpoint `input_scale` was a red herring** — we tried using the checkpoint's calibration scale as the activation normalization scale. It saturated all block scales to 448.0 (max float8). The `input_scale` is a calibration constant for alpha computation, not a normalization scale.

### The CuTeDSL Kernel Works

NVIDIA's CuTeDSL approach (Python-based CUTLASS kernels compiled via MLIR → PTX) is what the CUTLASS team recommends for Blackwell. Their official MoE scaled grouped GEMM example (`torch_scaled_grouped_mm.py`) supports NVFP4 out of the box. We adapted it.

**Results with real DeepSeek-V4 layer 0 weights:**
- L1 GEMM alone: cosine 0.995
- Full MoE pipeline (L1→SiLU→L2→scatter): cosine 0.989
- Weight loading: **0% loss** — direct uint8→float4_e2m1fn_x2 view-cast, bit-identical to checkpoint
- Activation quantization: ~1.1% cosine loss (dynamic BF16→NVFP4 — inherent to the format, unavoidable)
- GEMM kernel: 0% loss (CuTeDSL is correct)

The 0.989 cosine is entirely from activation quantization. The weights are bit-identical to the checkpoint — no BF16 round-trip, no precision loss.

### Key Lessons

1. **A wrong reference is worse than no reference** — the 0.2 cosine against a broken BF16 dequant sent us chasing SF remap bugs for weeks
2. **The C++ CUTLASS API is a footgun for FP4** — CuTeDSL handles tensor layouts, tiling, and SF construction correctly by construction
3. **Test with real data early** — uniform tests pass even with broken kernels; random data reveals real bugs
4. **Separate the GEMM from the pipeline** — our `layertest.py` runs without vLLM, Docker, or tensor parallelism. It caught the kernel bug that vLLM's integration layers masked.

## Project Structure

```
nvfp4-megamoe-kernel/
├── cutedsl/                          # CuTeDSL kernel + bridge layer
│   ├── bridge.py                     # Tensor layout conversion, quantization, kernel launch
│   ├── moe_pipeline.py              # Full MoE pipeline (L1→SiLU→L2→scatter)
│   └── kernel/moe/                   # NVIDIA's ScaledGroupedGemmKernel (untouched)
│       ├── torch_scaled_grouped_mm.py   # The working kernel (3900 lines)
│       ├── moe_utils.py
│       ├── moe_persistent_scheduler.py
│       └── moe_sched_extension.py
├── src/nvfp4_megamoe_kernel/         # OLD Python pipeline (being replaced)
│   ├── nvfp4_mega_moe.py            # Old pipeline — calls broken C++ kernel
│   └── cutlass_nvfp4_gemm/          # OLD C++ CUTLASS extension (BROKEN)
├── vllm/                             # vLLM integration
│   └── patches/
│       └── deepseek_v4.py           # DeepSeek-V4 model patch
├── tests/
│   ├── layertest.py                 # Layer 0 comparison: CuTeDSL vs BF16 (✅ cosine 0.989)
│   ├── test_cutedsl.py              # Small standalone CuTeDSL test (✅ cosine 0.991)
│   ├── test_uniform_fp4.py          # Uniform data GEMM test
│   ├── test_b_layout.py             # B matrix column layout test
│   └── test_quick_rand.py           # Quick random GEMM sanity check
├── reference/                        # Reference files for study
└── REWRITE_PLAN.md                  # Original rewrite plan
```

## The Bridge Layer (`cutedsl/bridge.py`)

Handles all tensor layout conversion from our pipeline to what the CuTeDSL kernel expects:

| Function | What it does |
|----------|--------------|
| `quantize_to_nvfp4()` | BF16 → float4_e2m1fn_x2 + float8_e4m3fn block scales + float32 global scale |
| `quantize_weight_to_nvfp4()` | Same, but for weight matrices with K as the packed dimension |
| `assemble_scales_2d_side()` | Pad and swizzle activation scale factors (2Dx3D A side) |
| `assemble_scales_3d_side()` | Pad and swizzle weight scale factors (2Dx3D B side) |
| `make_b_k_major()` | Convert B tensor from N-major to K-major strides (required by kernel) |
| `compute_expert_offsets()` | Compute cumulative token offsets for grouped GEMM |
| `run_nvfp4_grouped_gemm()` | Full kernel launch (compile + run) |

## Running Tests

On the B200:

```bash
cd /root/nvfp4-megamoe-kernel/tests
source .venv/bin/activate

# Small standalone test
python3 test_cutedsl.py

# Full layer 0 comparison with real weights
python3 layertest.py
```

## Plan

### Phase 1: Kernel ✅ DONE
- CuTeDSL ScaledGroupedGemmKernel works with NVFP4
- Bridge layer handles all tensor layout conversion
- Full MoE pipeline (L1→SiLU→L2→scatter) produces cosine 0.989 vs BF16

### Phase 2: vLLM Integration (IN PROGRESS)
- Wire `cutedsl/moe_pipeline.py` into the vLLM DeepSeek-V4 model
- Replace `nvfp4_mega_moe_full()` call with `CuTeDSLMoERunner.run()`
- Weight loading: checkpoint uint8 → float4_e2m1fn_x2 view-cast (bit-preserving, no BF16 round-trip)
- Block scales (float8_e4m3fn) and global scales (float32) pass through directly from checkpoint
- L1 dual global scale handling: normalize to max(gate_gs, up_gs), fold ratio into block scales
- Remove C++ CUTLASS extension build from Dockerfile
- Add CuTeDSL dependency to the Docker build

### Phase 3: Optimization
- Explore larger tile sizes for better occupancy
- Profile end-to-end inference on full model

### Phase 4: Production
- Clean up debug artifacts
- Remove old C++ kernel code
- Add proper error handling and logging
- Benchmark vs BF16 baseline