# Current Bug: CuTeDSLMoERunner — Status & Debug History

## Current Status (May 17, 2026 09:01 UTC)

**Bug 8 fixed. Ready for vLLM container test.**

- ✅ `layertest.py` — 0.988 cosine
- ✅ `cudagraph_test.py` — capture + replay works
- ✅ `test_warmup_gs.py` — warmup gs computation works (test script has a pre-existing NameError in safety margin section, not a runner bug)
- ❌ vLLM server — not yet tested with these fixes

**Fixed in this round:**
- Bug 8: Global→local expert ID remapping (was causing CUDA_ERROR_ASSERT)
- Removed `.cpu()` sync from `run()` — `_token_indices` now on GPU, cudagraph-safe
- Added `_needs_token_refill` flag to handle CuTeDSL JIT GPU memory corruption after first GEMM call

---

## Bugs Found & Fixed

### Bug 1: Scale Assembly — Global Swizzle vs Per-Expert Swizzle

**Symptom:** GEMM produced all zeros even with correct global_scale.

**Root cause:** The original `_assemble_scales_cudagraph_safe` called `pad_and_swizzle_single()` on the ENTIRE padded buffer (all experts concatenated). But the kernel expects each expert's 128-row block to be swizzled independently (matching `assemble_scales_2d_side` which pads+swizzles each expert separately before concatenation).

**Fix:** Two-phase approach:
1. Scatter x_sf rows into 128-aligned positions in a padded buffer (GPU-only, no CPU sync)
2. Per-expert: copy 128 rows from padded buffer, `pad_and_swizzle_single()` each expert's block independently, then concatenate

**Key insight from `torch_scaled_grouped_mm.py` line ~1115:** The kernel computes padded offsets internally when `consistent_token_padding=False`:
```python
padded_size = round_up(offs[expert_idx] - offs[expert_idx-1], pad_granularity)  # 128
```
So the kernel knows each expert's scale data is in a 128-row block.

### Bug 2: `searchsorted(right=False)` — Wrong Expert Assignment

**Symptom:** Scale data in wrong positions after scatter.

**Root cause:** `torch.searchsorted([4, 8, 8], 4, right=False)` returns 0, assigning row 4 (expert 1's first token) to expert 0.

**Fix:** Changed to `right=True`:
```python
expert_assign = torch.searchsorted(expert_offsets[1:], row_indices, right=True)
```

**Verified:** Row 4 → expert 1 (correct), rows 0-3 → expert 0 (correct).

### Bug 3: CuTeDSL `cute.compile` GPU Memory Corruption — CRITICAL

**Symptom:** `_token_indices` was all zeros, making every token map to token 0.

**Root cause:** CuTeDSL's `cute.compile` (JIT compilation) corrupts GPU memory. Tensors allocated on GPU before or during JIT compilation get zeroed. Pre-existing tensors allocated before the JIT survive. This is a bug in the CuTeDSL library.

**Impact:** `_token_indices` (int32 on GPU) was zeroed, causing `hidden_states[sorted_token_ids]` to return `hidden_states[0]` for all 8 slots. Every expert saw the same input.

**Fix:** Allocate `_token_indices` on CPU, keep it there. In `run()` and `compute_activation_global_scales()`, index with `sort_idx.cpu()` then move result to GPU:
```python
sorted_token_ids = token_indices[sort_idx.cpu()].to(device)
```

**Warning:** This introduces a CPU-GPU sync (`.cpu()`) which may interfere with cudagraph capture. Needs verification.

### Bug 4: `expert_offsets` With Leading 0

**Symptom:** GEMM produced wrong output with correct scale data.

**Root cause:** The runner passed `expert_offsets[:num_experts + 1]` = `[0, 4, 8, 8]` (4 elements with leading 0) but the kernel expects `compute_expert_offsets([4, 4, 0], 3)` = `[4, 8, 8]` (3 elements, cumulative sum without leading 0).

**Fix:** Pass `expert_offsets[1:num_experts + 1]` to the GEMM.

### Bug 5: Checkpoint `input_scale` Is Wrong for Activation Global Scale

**Symptom:** Block scales all saturate at float8 max (448), producing garbage quantization.

**Root cause:** The checkpoint's `input_scale` (~0.000286) is a calibration value computed from a different input magnitude (amax ≈ 0.77) than what runtime produces (amax ≈ 8.17). Too-small gs → x/gs has values up to ~13000 → block_amax/6 ≈ 2174 → overflows float8_e4m3fn max of 448 → saturated block scales → garbage.

**Fix:** `compute_activation_global_scales()` warmup method that runs `quantize_to_nvfp4` (dynamic gs with `.max()`) before cudagraph capture to get the exact gs values for L1 and L2.

### Bug 6: L1 and L2 Need Separate Activation Global Scales

**Symptom:** L2 output was garbage even with correct L1 gs.

**Root cause:** After SiLU(gate)*up, the activation has amax ~286. The L1 gs (from input amax ~8) is 30x too small for L2, causing even worse block scale saturation.

**Fix:** `compute_activation_global_scales()` computes L1 gs from the input, runs the L1 GEMM, then computes L2 gs from the actual L1 output (after SiLU*up).

### Bug 7: L1 and L2 Need Separate Padded Scale Buffers

**Symptom:** IndexError when quantizing L2 activation — K_sf differs between L1 (448) and L2 (192).

**Root cause:** `padded_x_sf_buf` was allocated with L1's K_sf (448). When L2's x_sf has K_sf=192, the buffer size mismatch causes issues.

**Fix:** Separate `_padded_x_sf_buf_l1` and `_padded_x_sf_buf_l2`, plus separate `_per_expert_scale_bufs_l1` and `_per_expert_scale_bufs_l2`.

### Bug 8: Global→Local Expert ID Mismatch — CUDA_ERROR_ASSERT

**Symptom:** `IndexKernel.cu:111` assertion `-sizes[i] <= index && index < sizes[i]` failed, cascading into CUDA_ERROR_ASSERT (710) across all workers. vLLM server crash on first inference.

**Root cause:** With expert parallelism (EP=8), `topk_ids` contains **global** expert IDs (0-255), but `CuTeDSLMoERunner` treated them as **local** IDs (0-31). Each rank only owns 32 experts (`num_experts=32`), so tokens assigned to experts 32-255 produced:
1. Wrong `expert_offsets` computation (tokens matched no local expert → zero counts for many experts)
2. Out-of-bounds scatter indices in `_assemble_scales_cudagraph_safe` (`dst_rows` exceeded `padded_x_sf` buffer size)
3. CUDA device-side assert → all subsequent CUDA calls fail with error 710

The layertest never hit this because it uses local expert IDs directly (no EP).

**Fix:** 
1. Added `experts_start_idx` param to `CuTeDSLMoERunner`
2. In `run()`: remap global→local via `local_ids = topk_ids - experts_start_idx`, mask non-local tokens with zero weight, clamp IDs to valid range
3. Pass `experts_start_idx` from `deepseek_v4.py` (which already stores it from EP setup)

### Bug 8b: `.cpu()` Sync Breaking Cudagraph Compatibility

**Symptom:** `sort_idx.cpu()` in `run()` — a CPU-GPU synchronization point that cudagraph cannot capture.

**Root cause:** `_token_indices` was kept on CPU to avoid CuTeDSL JIT GPU memory corruption (Bug 3). But cudagraph requires all ops to be GPU-only.

**Fix:**
1. Moved `_token_indices` to GPU
2. Added `_fill_token_indices()` method to refill the tensor after potential corruption
3. Added `_needs_token_refill` flag — set after `_ensure_stacked()` (weight JIT), checked/cleared after first `run()` call (GEMM JIT). After both JITs have fired, the tensor is stable.

---

## Debug Methodology — How We Got Here

### Step 1: Identified the CuTeDSL kernel works (layertest = 0.988)

The layertest uses `moe_pipeline.run_nvfp4_moe` with `quantize_to_nvfp4` (dynamic gs) and `assemble_scales_2d_side` (per-expert split). This is the reference implementation.

### Step 2: Wrote test_runner_vs_pipeline.py

Compared `runner.run()` vs `run_nvfp4_moe()` with same weights and inputs. Found runner produces all zeros.

### Step 3: Wrote test_scale_assembly.py

Compared `_assemble_scales_cudagraph_safe` vs `assemble_scales_2d_side`. Found data mismatch (global vs per-expert swizzle).

### Step 4: Fixed scale assembly

Rewrote `_assemble_scales_cudagraph_safe` with scatter + per-expert swizzle. Scale data now matches reference.

### Step 5: Found GEMM still produces zeros with correct scales

Isolated the issue: GEMM with the exact same inputs gives cosine 1.0, but runner gives 0.18. The problem was `expert_offsets` format (leading 0).

### Step 6: Fixed expert_offsets, found token_indices corruption

After fixing expert_offsets, cosine improved to 0.35. Traced to `_token_indices` being all zeros (CuTeDSL GPU corruption).

### Step 7: Found and fixed the GPU corruption

Moved `_token_indices` to CPU. Cosine jumped to 0.46 with default gs, 0.97 with warmup gs.

### Step 8: Wrote test_warmup_gs.py

Verified warmup gs computation, tested safety margins, tested different inputs. Found 1.0x safety (no margin) gives best results.

---

## Test Files

| File | Purpose |
|------|---------|
| `tests/layertest.py` | Reference: moe_pipeline with dynamic gs, 3 experts, layer 0. Must pass (≥0.98 cosine). |
| `tests/cudagraph_test.py` | CuTeDSLMoERunner cudagraph capture + replay. Must pass. |
| `tests/test_runner_vs_pipeline.py` | Compare runner.run() vs moe_pipeline. With correct gs should be ≥0.97. |
| `tests/test_scale_assembly.py` | Compare cudagraph-safe vs reference scale assembly. Data must match. |
| `tests/test_warmup_gs.py` | Warmup gs computation, safety margin sweep, different input test. |
| `tests/test_scale_debug.py` | Byte-level scale debug (can be cleaned up). |

**Run order after any code change:**
1. `python3 tests/layertest.py` — must pass
2. `python3 tests/cudagraph_test.py` — must pass
3. `python3 tests/test_warmup_gs.py` — should show ≥0.97 cosine

---

## Files Modified

| File | Changes |
|------|---------|
| `vllm/nvfp4_cutedsl.py` | All 7 bug fixes, `compute_activation_global_scales()` warmup, CPU token_indices |
| `vllm/patches/deepseek_v4.py` | Removed checkpoint `input_scale` → activation global_scale mapping |

---

## Next Steps for vLLM Integration

1. **Add warmup call in `deepseek_v4.py`:** After `finalize_weights()`, call `runner.compute_activation_global_scales()` with a sample input (e.g., 1 token of random data). This must happen before cudagraph capture.

2. **Verify cudagraph compatibility:** The `sort_idx.cpu()` call in `run()` is a CPU-GPU sync. Cudagraph may not support this. If it doesn't, need to find a way to keep `_token_indices` on GPU while avoiding the CuTeDSL corruption.

3. **Test the vLLM container:** Spin up the server and test with a simple prompt. The output should be mostly correct (0.97 cosine ≈ near-perfect output).

4. **Optimize warmup:** The current warmup runs a full forward pass (L1 + L2 GEMM). This is slow (~minutes due to JIT). Consider caching the gs values or computing them more efficiently.