NVFP4 MegaMoE Kernel

Full NVFP4 inference pipeline for DeepSeek-V4 on NVIDIA Blackwell (SM100). The entire model — MoE experts, shared experts, attention projections, and attention compute — runs in native NVFP4 with zero dequantization overhead.

⚠️ READ THIS FIRST — THE #1 RULE

YOU MUST BUILD YOUR OWN KERNELS. ALL OF THEM. DO NOT PATCH vLLM.

Mike was right — we need our own kernels. Not just for the NVFP4 GEMMs, but for the ENTIRE attention pipeline. The current approach of patching individual vLLM functions is a house of cards. Every patch leads to another crash, every workaround reveals three more broken things. FlashMLA, fp8_ds_mla, the fused C++ kernels, the Triton compressor, the indexer — they're all deeply coupled. You cannot swap one piece and expect the rest to work.

THE ONLY PATH FORWARD:

1. Build CuTeDSL kernels for EVERYTHING — attention, KV cache, RoPE, the whole stack
2. Test each kernel standalone on the B200 venv BEFORE touching the container
3. Wire them together into a proper vLLM attention backend
4. THEN and ONLY THEN test in the container

DO NOT:

• ❌ Try to patch vLLM's FlashMLA code to "work" on Blackwell
• ❌ Use pure PyTorch as a "temporary workaround" — it produces garbage
• ❌ Skip the KV cache write and hope for the best
• ❌ Assume you can mix our kernels with vLLM's existing attention backend
• ❌ Touch the container until ALL kernels pass standalone tests

DO:

• ✅ Build CuTeDSL kernels in cutedsl/
• ✅ Test each one in tests/ on the B200 venv
• ✅ Compare against BF16 reference (cosine >= 0.98 or it's broken)
• ✅ Wire them into a proper attention backend class
• ✅ Only test in the container once everything passes standalone

---

What This Is

A native NVFP4 inference stack for DeepSeek-V4:

MoE Experts — CuTeDSL ScaledGroupedGemmKernel ✅:

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 to NVFP4
  L2 GEMM: NVFP4 × NVFP4 → BF16 (down_proj)
  Scatter with routing weights → BF16 output

Attention Projections — CuTeDSL NVFP4 GEMM ✅:

• q_a_proj, q_b_proj, kv_proj, wo_b_proj — native NVFP4, cosine 0.995 vs BF16
• wo_a — BF16 BMM (o_a_proj weights are BF16 in checkpoint)
• All verified with tests/test_full_layer_b200.py

Shared Experts — CuTeDSL NVFP4 GEMM ✅:

• gate_up_proj, down_proj — native NVFP4, cosine 0.990 vs BF16

Attention Pipeline — ✅ Verified standalone, 🔧 vLLM integration blocked by NaN:

• KV cache write (RoPE → fp8 quant → paged cache) — cosine 0.999
• KV cache read (paged cache → fp8 dequant → BF16) — cosine 0.999
• Decode attention (1 query vs N cached KVs) — cosine 0.9998
• Full pipeline (inv RoPE + o_a BMM + o_b) — cosine 0.996–0.999
• All 5 layer types (C128A, C4A, SWA) — cosine ≥0.996

Architecture: DeepSeek-V4-Pro

MegaMoE (384 experts, top-6) with CSA + HCA + mHC:

• CSA (Compress Ratio 4): Compressed Sparse Attention — KV compressed 4x with overlap (coff=2). Indexer finds per-layer top-k.
• HCA (Compress Ratio 128): Heavily Compressed Attention — KV compressed 128x. Top-k indices pre-computed during metadata build.
• mHC: Manifold-Constrained Hyper-Connections — replaces standard residual connections. Learned mixing with Sinkhorn normalization.
• SWA: Sliding Window Attention — local window (compress_ratio=0, last layer only)

Compress Ratios (from config.json):

Layer 0: 128 (HCA)   Layer 1: 128 (HCA)   Layer 2: 4 (CSA)   Layer 3: 128 (HCA)
Layer 4: 4 (CSA)     ...alternating 4/128...                  Layer 60: 0 (SWA)

Expert intermediate size: 3072 (NOT 18432 — that's 6×3072 for top-6)

DeepGEMM MegaMoE: DeepSeek's persistent grouped GEMM for MoE uses TMA tensormap updates per expert with variable block_m (16-192) based on expected tokens per expert. Our CuTeDSL runner uses run_nvfp4_grouped_gemm (simpler, but proven correct in standalone tests).

Current Status

✅ Verified (B200 venv, real weights, zero NaN)

Component	Test	Cosine vs BF16
CuTeDSL NVFP4 Linear (q_a, kv, q_b, wo_b)	test_full_layer_b200.py	0.994+
CuTeDSL NVFP4 MoE (L1 gate+up, SiLU, L2 down)	layertest.py	0.988
FP8 KV quantize/dequant	test_kv_cache_b200.py	0.9997
Paged KV cache read/write	test_kv_cache_b200.py	1.0
CSA sparse attention (cr=4)	test_sparse_attn_b200.py	works, no NaN
HCA sparse attention (cr=128)	test_sparse_attn_b200.py	works, no NaN
Full attention pipeline (all layer types)	test_v4_attention_b200.py	0.981–0.995
KV cache write + decode attention	test_decode_attention_b200.py	0.9998
Decode vs prefill consistency (5 layers)	test_decode_vs_prefill_b200.py	0.996–0.999
E2E 61-layer model (shared experts)	test_e2e_decode_b200.py	healthy logits
MoE runner (grouped GEMM, 16 experts)	test_moe_runner_nan_b200.py	no NaN, all sizes
Full layer (attention + MoE)	test_full_layer_nan_b200.py	no NaN
Multi-layer chain (3 layers)	test_full_layer_nan_b200.py	no NaN

❌ Container — NaN in vLLM compiled execution

The container produces empty/garbage output. Debug logs show NaN in hidden_states from the first forward pass. The NaN is NOT from our kernels — it comes from vLLM's compiled execution infrastructure (see CURRENT_BUG.md for full investigation).

Most likely sources:

1. attn_gemm_parallel_execute — fused parallel GEMM (NOT our CuTeDSL kernel)
2. fused_q_kv_rmsnorm — CUDA kernel that may produce NaN on Blackwell
3. Weight packing during model loading
4. torch.compile + cudagraph interaction with CuTeDSL buffers

❌ Does NOT Work

• NVFP4 Q×K^T GEMM — cosine 0.86, too lossy for attention scores. Keep attention in BF16.
• Patching vLLM's FlashMLA path — house of cards. Don't do it.

Test Files

Test	What it does	Status
tests/test_full_layer_b200.py	All NVFP4 projections vs BF16	✅ 0.994+
tests/layertest.py	MoE layer test	✅ 0.988
tests/cudagraph_test.py	CUDAGraph compatibility	✅ PASS
tests/test_v4_attention_b200.py	All 3 layer types (SWA, C128A, C4A)	✅ 0.981-0.995
tests/test_kv_cache_b200.py	FP8/NVFP4 KV cache + paged cache	✅ 0.9997
tests/test_sparse_attn_b200.py	CSA/HCA sparse + SWA merged	✅ works
tests/test_decode_attention_b200.py	Prefill + decode with KV cache	✅ 0.9998
tests/test_decode_vs_prefill_b200.py	Decode vs prefill consistency	✅ 0.996-0.999
tests/test_e2e_decode_b200.py	61-layer E2E (shared experts)	✅ healthy logits
tests/test_moe_nan_b200.py	Single expert NaN check	✅ no NaN
tests/test_moe_runner_nan_b200.py	MoE grouped GEMM NaN check	✅ no NaN
tests/test_full_layer_nan_b200.py	Full layer + multi-layer NaN check	✅ no NaN

Project Structure

nvfp4-megamoe-kernel/
├── cutedsl/                          # CuTeDSL kernel + bridge layer
│   ├── bridge.py                     # Tensor layout conversion, quantization, kernel launch
│   ├── nvfp4_linear.py              # CuTeDSLNvfp4Linear — NVFP4 GEMM runner
│   ├── runner.py                     # CuTeDSLMoERunner — grouped GEMM MoE
│   ├── blackwell_attention.py        # KV cache + attention (standalone, works)
│   ├── csa_attention.py             # CSA/HCA attention (BF16 SDPA)
│   ├── custom_ops.py                # torch.autograd wrappers
│   └── kernel/moe/                   # NVIDIA's ScaledGroupedGemmKernel
├── vllm/                             # vLLM integration
│   ├── nvfp4_cutedsl.py             # CuTeDSLMoERunner (vLLM wrapper)
│   ├── cutedsl_quant_method.py      # CuTeDSLNvfp4LinearMethod
│   └── patches/
│       ├── deepseek_v4_attention.py # Attention patch (Blackwell dispatch)
│       ├── deepseek_compressor.py   # Compressor patch (skip fused kernel on Blackwell)
│       ├── patch_kv_cache_utils.py  # KV cache page size fix
│       ├── patch_swa_cache.py       # SWA cache alignment fix
│       └── layers/
│           ├── csa_attention.py     # BF16 SDPA + KV cache (our Blackwell path)
│           └── deepseek_compressor.py # Skip fused kernel on Blackwell
├── tests/                            # Standalone tests (run on B200 venv)
├── Dockerfile                        # Container build
├── README.md                         # This file
└── CURRENT_BUG.md                    # Current bug investigation

Plan

Phase 1: MoE Kernel ✅ DONE

Phase 2: NVFP4 Linear Kernels ✅ DONE

Phase 3: Attention Pipeline ✅ DONE (standalone, all tests pass)

Phase 4: vLLM Integration 🔧 IN PROGRESS — blocked by NaN from vLLM infrastructure

Current blocker: NaN in the vLLM container's compiled execution. Our kernels produce zero NaN standalone. The NaN comes from vLLM's attn_gemm_parallel_execute or fused_q_kv_rmsnorm CUDA kernels, weight packing, or torch.compile interaction.

Next:

1. Install vllm in B200 venv, test the exact parallel GEMM path
2. Test with torch.compile disabled in the container
3. Add NaN checks inside the parallel GEMM wrapper
4. If the parallel GEMM is the source, replace it with our CuTeDSL kernels (path of least resistance)

Phase 5: Production

• End-to-end benchmarking
• Optimize tile sizes
• Clean up