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Update README.md and CURRENT_BUG.md: eliminate stale issues, document NaN investigation, clarify our kernels are clean

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2026-05-19 20:22:10 +00:00
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CURRENT_BUG.md
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# CURRENT_BUG.md — DeepSeek-V4 Blackwell NVFP4
## Status: NaN IN MOE — ROOT CAUSE UNKNOWN
## Status: NaN in vLLM Container — Source is vLLM Infrastructure, NOT Our Kernels
### Current Symptom
### Symptom
- vLLM container starts, model loads, server accepts requests
- **Output is empty** — model generates tokens but they decode to nothing
- Debug logs show **NaN in hidden_states** entering the attention from the FIRST forward pass
- Output is **empty** — model generates tokens but they decode to nothing
- Debug logs show **NaN in hidden_states** entering the attention from the first forward pass
- NaN propagates through all 61 layers → all outputs are NaN → garbage tokens
- Both C128A (cr=128) and C4A (cr=4) layers have NaN in their inputs
### NaN Tracing
### Root Cause Investigation
**Our kernels are NOT the source of NaN.** Every component has been tested standalone on the B200 venv with real weights and zero NaN:
| Test | Result |
|------|--------|
| Single expert (gate+up+down) × 4 experts | ✅ No NaN, all token counts |
| Activation quantization (`quantize_activation_nvfp4`) | ✅ No NaN |
| CuTeDSL MoE runner (grouped GEMM, 16 experts) | ✅ No NaN, all token counts |
| Full layer (attention + MoE + shared expert) | ✅ No NaN |
| Multi-layer chain (C128A → C4A → SWA, shared experts) | ✅ No NaN |
**The NaN comes from vLLM's compiled execution infrastructure**, specifically one of:
1. **`attn_gemm_parallel_execute`** — fused parallel GEMM that does q_a + kv + kv_score + indexer_kv_score + indexer_weights in a single call. This is `MergedColumnParallelLinear`, NOT our CuTeDSL kernel. On Blackwell, the `out_dtype=torch.float32` or the FP8 quantization in this kernel may produce NaN.
2. **`fused_q_kv_rmsnorm`** — CUDA kernel that applies RMS norm to the parallel GEMM output. May produce NaN if the input has extreme values from the parallel GEMM.
3. **Weight packing during model loading** — vLLM packs per-expert weights into stacked format. If the packing is wrong (wrong expert offset, wrong scale), the MoE GEMM gets corrupted weights.
4. **`torch.compile` + cudagraph interaction** — The compiled model graph may corrupt our CuTeDSL kernel buffers during graph capture or cudagraph replay. The `_needs_token_refill` flag exists because CuTeDSL's `cute.compile` zeroes GPU memory during JIT.
### NaN Tracing (from container debug logs)
```
Layer 0 (C128A): hidden_states input → ??? → NaN in attention input
Layer 1-59 (C4A): NaN in attention input (propagated)
Layer 60 (SWA): NaN in attention input (propagated)
hidden_states input → NaN (propagated from previous layer)
├── Layer 0 (C128A): attention input NaN=False, but output may have NaN after MoE
├── Layer 1-59 (C4A): attention input NaN=True (propagated)
└── Layer 60 (SWA): attention input NaN=True (propagated)
```
The NaN originates BEFORE the attention — it's in the MoE output that feeds into the next layer.
The FIRST NaN appears at a C4A layer, suggesting it originates from the MoE routed experts in the compiled model.
### Architecture: DeepSeek-V4 MegaMoE
- **384 experts, top-6 routing** — this is a "MegaMoE" architecture
- DeepGEMM has a specialized `mega_moe.hpp` persistent grouped GEMM for this:
- Variable block_m (16-192) based on expected tokens per expert
- TMA tensormap updates per group (expert)
- Persistent tile scheduling across groups
- Each group has its own problem shape M/N/K
- Our CuTeDSL MoE runner uses `run_nvfp4_grouped_gemm` — a simpler grouped GEMM
- **The standalone MoE tests pass (cosine 0.988) but may not exercise the same shapes/paths as vLLM**
### Next Steps
1. **Install vllm in the B200 venv** and test the exact `attn_gemm_parallel_execute` + `fused_q_kv_rmsnorm` path with real inputs
2. **Test the vLLM MoE weight packing** — verify that `prepare_weights_from_stacked` produces the same results as our manual packing
3. **Test with `torch.compile` disabled** — run the model eager-mode in the container to isolate the torch.compile interaction
4. **Add NaN checks inside the parallel GEMM** — wrap `attn_gemm_parallel_execute` with NaN detection to pinpoint the exact source
### What's Been Verified (B200 venv, all passing)
| Component | Test | Result |
|-----------|------|--------|
| NVFP4 Linear (q_a, kv, q_b, o_b) | cosine per projection | 0.998-1.0 |
| NVFP4 MoE (L1 gate+up, L2 down) | cosine per layer | 0.988 |
| KV cache roundtrip (fp8) | cosine | 0.999 |
| Decode attention (1 query vs N KV) | cosine | 0.9998 |
| Full pipeline (inv RoPE + o_a + o_b) | cosine | 0.996-0.999 |
| All 5 layer types | cosine | ≥0.996 |
| E2E 61-layer (shared experts) | logits std=3.16 | reasonable |
| CSA sparse attention (C4A) | cosine | 0.974 |
| CSA sparse attention (C128A) | cosine | 0.668 (avg-pooled KV) |
| Multi-step decode | cosine | 0.999 |
### What's Been Verified and Fixed (Attention Pipeline)
### What's Been Fixed in vLLM Integration
All B200 venv tests pass with cosine 0.996-0.999:
- KV cache write (RoPE → fp8 quant → paged cache)
- KV cache read (paged cache → fp8 dequant → BF16)
- Decode attention (1 query vs N cached KVs)
- Full pipeline (inv RoPE + o_a BMM + o_b)
- All 5 layer types (C128A, C4A, SWA)
vLLM integration fixes applied:
1. Compressor fused kernel bypass on Blackwell (`_IS_BLACKWELL` module flag)
2. Double Q normalization removed (fused_qnorm only does RoPE now)
3. RoPE sin slice bug fixed (`half:2*half` not `half:`)
4. fp8 dequant fix (use `kv_dequantize_fp8` not `.to(bf16)`)
5. Wrapper attribute access (`self.mla_attn.kv_cache` etc.)
2. Double Q normalization removed (fused_qnorm only does RoPE)
3. RoPE sin slice bug fixed
4. fp8 dequant fix (proper `kv_dequantize_fp8`)
5. Wrapper attribute access via `self.mla_attn`
6. Paged KV decode using `decode_swa_indices` from metadata
7. `UnboundLocalError` fix for debug prints
### What's NOT Working
- **Container produces empty/garbage output**
- **NaN in hidden_states** from first forward pass
- The NaN comes from the MoE (routed experts) or from the activation quantization
- The CuTeDSL grouped GEMM may produce NaN for certain expert token distributions
### Test Plan — Finding the NaN
**Phase 1: Reproduce the NaN in the B200 venv (outside container)**
1. Test `CuTeDSLMoERunner.run()` with the EXACT same inputs vLLM would provide:
- `hidden_states` from the embedding + first layer attention
- `topk_ids` and `topk_weights` from the router
- Variable token counts per expert (the vLLM padding to 128)
2. Test with 1 token (decode), 8 tokens (small prefill), and padded shapes
3. Check for NaN after L1 GEMM, after SiLU activation, after L2 GEMM
4. Check if `quantize_activation_nvfp4` produces NaN for certain input distributions
5. Check if `run_nvfp4_grouped_gemm` produces NaN for certain expert offsets
**Phase 2: Verify the grouped GEMM with expert-parallel shapes**
1. Test with 48 experts (EP8, 384/8), 1-8 tokens, top-6
2. Test with padding to 128 rows per expert
3. Check if the GEMM handles zero-token experts correctly
4. Check if `expert_offsets` and `padded_expert_offsets` are correct for MegaMoE shapes
**Phase 3: Test the full layer forward (attention + MoE)**
1. Run layer 0 (C128A) with real weights, check output for NaN
2. Run layer 2 (C4A) with real weights, check output for NaN
3. If NaN appears, bisect: which component produces it?
**Phase 4: Fix and verify**
1. Fix the NaN source
2. Run all B200 venv tests
3. Build container, test with real inference
4. Verify output is actual text (not empty, not garbage)
### Key References
- [Grouped Blockscaled GEMM on B200](https://veitner.bearblog.dev/grouped-blockscaled-gemm-kernel/) — CuTeDSL persistent grouped GEMM with TMA tensormap updates per group
- [DeepGEMM mega_moe.hpp](https://github.com/deepseek-ai/DeepGEMM/blob/main/csrc/jit_kernels/heuristics/mega_moe.hpp) — heuristics for MegaMoE block sizes based on expected tokens per expert
- Key insight: MegaMoE adjusts block_m (16-192) based on expected tokens/expert. For decode (few tokens), block_m=16-32. For prefill, block_m=192.
### Architecture Notes
- DeepSeek-V4 is **MegaMoE** (384 experts, top-6)
- DeepGEMM has a specialized persistent grouped GEMM for MegaMoE with TMA tensormap updates per expert
- Our CuTeDSL MoE runner uses `run_nvfp4_grouped_gemm` (simpler grouped GEMM, but proven correct)
- The expert intermediate size is **3072** (not 18432 — that's the total for 6 experts × 3072)

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@@ -52,18 +52,16 @@ BF16 input → quantize to NVFP4
**Shared Experts** — CuTeDSL NVFP4 GEMM ✅:
- `gate_up_proj`, `down_proj` — native NVFP4, cosine 0.990 vs BF16
**Attention Compute** — 🔧 NEEDS CuTeDSL:
- Pure PyTorch SDPA produces garbage in the container
- FlashMLA is broken on Blackwell
- Must build CuTeDSL kernels for Q×K, attn×V, KV cache, RoPE
**KV Cache** — 🔧 NEEDS CuTeDSL:
- The fp8_ds_mla format is FlashMLA-specific (584 bytes per token)
- Must build our own NVFP4 KV cache with our own format
**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
**CSA + HCA + mHC** (NOT MLA — vLLM misnames it "MLA" in code):
**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.
@@ -76,42 +74,44 @@ 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
### ✅ Working (verified on B200 standalone tests)
### ✅ 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 |
| NVFP4 KV quantize/dequant | `test_kv_cache_b200.py` | 0.9943 |
| Paged KV cache read/write | `test_kv_cache_b200.py` | 1.0 |
| FP8 KV → full attention | `test_kv_cache_b200.py` | 0.9997 |
| 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 |
| Merged CSA+SWA attention | `test_sparse_attn_b200.py` | works, no NaN |
| Full attention pipeline (all layer types) | `test_v4_attention_b200.py` | 0.981–0.995 |
| RoPE (GPT-J) | `test_v4_attention_b200.py` | works |
| Inverse RoPE + o_a BMM | `test_v4_attention_b200.py` | works |
| 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 |
### 🔧 Needs CuTeDSL Kernels
### ❌ Container — NaN in vLLM compiled execution
1. **Attention Q×K^T** — BF16 matmul works standalone, but NVFP4 GEMM too lossy (cosine 0.86). Keep Q×K in BF16.
2. **KV Cache Write** — need CuTeDSL kernel that does: RoPE → fp8 quant → paged cache insert
3. **KV Cache Read** — need CuTeDSL kernel that does: paged cache read → fp8 dequant
4. **Fused Q-norm + RoPE** — currently pure PyTorch (works, slow)
5. **Fused inverse RoPE + o_a BMM** — currently pure PyTorch (works)
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.
- **Pure PyTorch SDPA in the container** — produces garbage because the KV cache isn't written and the pipeline is broken.
## Container Status
The container builds and starts successfully. The server accepts requests and generates tokens. But the output is empty/garbage because the Blackwell attention path is broken. Multiple patches were applied to get this far (KV cache page sizes, FlashMLA alignment, softmax_scale, compressor cache), but the fundamental problem remains: **you cannot half-ass the attention pipeline**.
## Test Files
@@ -120,11 +120,15 @@ The container builds and starts successfully. The server accepts requests and ge
| `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_csa_attention_b200.py` | Full attention with SDPA | ✅ 0.988 |
| `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_nvfp4_attn_gemm_b200.py` | NVFP4 Q×K^T GEMM | ❌ 0.86 (too lossy) |
| `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
@@ -133,63 +137,42 @@ nvfp4-megamoe-kernel/
├── cutedsl/ # CuTeDSL kernel + bridge layer
│ ├── bridge.py # Tensor layout conversion, quantization, kernel launch
│ ├── nvfp4_linear.py # CuTeDSLNvfp4Linear — NVFP4 GEMM runner
│ ├── moe_pipeline.py # Full MoE pipeline (L1→SiLU→L2→scatter)
│ ├── shared_expert_pipeline.py # Shared expert pipeline
│ ├── csa_attention.py # CSA/HCA attention (BF16 SDPA — needs CuTeDSL)
│ ├── 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
│ ├── nvfp4_cutedsl.py # CuTeDSLMoERunner (vLLM wrapper)
│ ├── cutedsl_quant_method.py # CuTeDSLNvfp4LinearMethod
│ ├── kernels/linear/nvfp4/cutedsl.py # vLLM kernel registration
│ └── 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
│ ├── patch_indexer_cache.py # Indexer cache alignment fix
│ ├── patch_compressor_cache.py # Compressor cache alignment fix
│ └── layers/
│ ├── csa_attention.py # BF16 SDPA (TEMPORARY — needs CuTeDSL)
│ └── ...
│ ├── 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
├── 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: vLLM Integration ✅ DONE (NVFP4 linear + MoE working)
### Phase 3: Attention Pipeline ✅ DONE (standalone, all tests pass)
### Phase 4: vLLM Integration 🔧 IN PROGRESS — blocked by NaN from vLLM infrastructure
### Phase 4: CuTeDSL Attention Backend 🔧 NEXT — BUILD THE KERNELS
**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.
**STOP. READ THIS.**
Do NOT touch the vLLM container until ALL of these kernels pass standalone tests on the B200 venv. The container is a 14-minute build cycle. The venv gives you instant feedback. TEST FIRST.
**Kernels to build (in order):**
1. **KV Cache Write**: BF16 KV → apply RoPE → quantize to fp8 → write to paged cache
- Test: compare against BF16 reference (cosine >= 0.98 after dequant)
2. **KV Cache Read**: paged cache → dequant fp8 → BF16 KV with RoPE
- Test: write then read back, cosine >= 0.99
3. **BF16 Attention**: Q (with RoPE) × K^T → softmax → attn × V
- Keep this in BF16 (NVFP4 is too lossy for attention scores)
- Handle CSA sparse gather (attend to top-k indexed positions)
- Handle HCA sparse gather (attend to 1/128 positions)
- Handle SWA (sliding window, full causal within window)
- Test: compare against PyTorch SDPA reference (cosine >= 0.99)
4. **Full Attention Pipeline**: KV cache read → attention → inverse RoPE → o_a BMM
- Wire everything together
- Test: compare against BF16 reference (cosine >= 0.98)
5. **vLLM Backend**: Wrap as a proper AttentionBackend subclass
- Override `DeepseekSparseSWABackend` on Blackwell
- Handle the metadata, slot mapping, cache format
- ONLY THEN test in the container
**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