195 lines
9.6 KiB
Markdown
195 lines
9.6 KiB
Markdown
# Current Bug: CuTeDSLMoERunner — Status & Debug History
|
||
|
||
## Current Status (May 17, 2026 15:51 UTC)
|
||
|
||
**vLLM container build in progress. Previous crash was from OOM + shape mismatch. Both now fixed.**
|
||
|
||
- ✅ `layertest.py` — 0.988 cosine
|
||
- ✅ `cudagraph_test.py` — capture + replay works
|
||
- ✅ Container builds, loads weights, warmup gs computed (no L2 gs=0)
|
||
- 🔧 Build #7 in progress on B200 (shared buffer fix)
|
||
- ❌ Haven't gotten to serving yet (crashes were during init/capture)
|
||
|
||
**Latest fixes (Bugs 17→21):**
|
||
- Bug 17 (shape mismatch 49152 vs 3072): Root cause was capping `max_num_tokens` to 512 for buffer sizing, but the actual warmup runs with 8192 tokens. Reverted the cap.
|
||
- Bug 21 (OOM): Instead of per-layer padded buffers (4.3 GB for 60 layers), use SHARED buffers across all runners. Only 72 MB total since layers run sequentially.
|
||
|
||
---
|
||
|
||
## Bugs Found & Fixed
|
||
|
||
### Bug 1: Scale Assembly — Global Swizzle vs Per-Expert Swizzle
|
||
**Fix:** Two-phase scatter + per-expert swizzle.
|
||
|
||
### Bug 2: `searchsorted(right=False)` — Wrong Expert Assignment
|
||
**Fix:** Changed to `right=True`.
|
||
|
||
### Bug 3: CuTeDSL `cute.compile` GPU Memory Corruption — CRITICAL
|
||
**Symptom:** `_token_indices` all zeros after JIT.
|
||
**Root cause:** `cute.compile` corrupts GPU memory. Tensors allocated before/during JIT get zeroed.
|
||
**Fix:** `_fill_token_indices()` builds on CPU, copies to GPU. `_needs_token_refill` for GEMM JIT.
|
||
|
||
### Bug 4: `expert_offsets` With Leading 0
|
||
**Fix:** Pass `expert_offsets[1:num_experts + 1]` to the GEMM.
|
||
|
||
### Bug 5: Checkpoint `input_scale` Is Wrong for Activation Global Scale
|
||
**Root cause:** Checkpoint `input_scale` (~0.000286) is a calibration value. Too-small gs → block scale overflow → garbage.
|
||
**Fix:** `compute_activation_global_scales()` warmup method.
|
||
|
||
### Bug 6: L1 and L2 Need Separate Activation Global Scales
|
||
**Fix:** Compute L2 gs from actual L1 output after SiLU*up.
|
||
|
||
### Bug 7: L1 and L2 Need Separate Padded Scale Buffers
|
||
**Fix:** Separate `_padded_x_sf_buf_l1`/`_l2`, separate per-expert scale bufs.
|
||
|
||
### Bug 8: Global→Local Expert ID Mismatch — CUDA_ERROR_ASSERT
|
||
**Symptom:** `IndexKernel.cu:111` OOB, cascading CUDA_ERROR_ASSERT (710) on all workers.
|
||
**Root cause:** `topk_ids` contains global IDs (0-255), runner treated as local (0-31/48).
|
||
**Fix:** Added `experts_start_idx`, remap global→local, mask non-local tokens.
|
||
|
||
### Bug 8b: `.cpu()` Sync Breaking Cudagraph Compatibility
|
||
**Fix:** Moved `_token_indices` to GPU, `_fill_token_indices()` (CPU→GPU copy).
|
||
|
||
### Bug 9: `padded_x_sf` Buffer Too Small — Index Out of Bounds
|
||
**Root cause:** Buffer sized for `num_experts * 128` rows, but scatter positions exceeded this with real token distributions.
|
||
**Fix:** Iterative — see Bugs 11, 14, 16 for the final solution.
|
||
|
||
### Bug 10: Wrong `top_k` and `max_num_tokens` Defaults
|
||
**Root cause:** Runner defaulted to `top_k=8`, vLLM uses top_k=6.
|
||
**Fix:** Pass values from `deepseek_v4.py`.
|
||
|
||
### Bug 11: Full-Buffer Swizzle Produced Wrong GEMM Input
|
||
**Symptom:** L2 gs=0.0 on EP5/EP7.
|
||
**Root cause:** Swizzled entire buffer at once; GEMM expects per-expert swizzled blocks.
|
||
**Fix:** Reverted to per-expert swizzle.
|
||
|
||
### Bug 12: `torch.full()` During Cudagraph Capture
|
||
**Symptom:** `cudaErrorStreamCaptureUnsupported` on all 8 workers.
|
||
**Root cause:** `torch.full()` allocates new tensor during stream capture.
|
||
**Fix:** Pre-allocated `_l1_gsa_buf`, `_l2_gsa_buf`, `_output_buf`, `_row_indices_buf`. Use `.fill_()`.
|
||
|
||
### Bug 13: Warmup Passed Global Expert IDs Instead of Local
|
||
**Symptom:** L2 gs=0.0 on EP5/EP7.
|
||
**Root cause:** Warmup passed global IDs (336+) against local range (0..47).
|
||
**Fix:** Pass local IDs (0..num_experts-1).
|
||
|
||
### Bug 14: GEMM Scale Layout Mismatch — Fixed 128-Row vs Variable
|
||
**Symptom:** Model generates BOS token repeatedly (garbage logits).
|
||
**Root cause:** Scale assembly placed data at fixed `e*128` offsets, but GEMM reads `scale_a` according to real `expert_offsets`. When expert 0 has 500 tokens, GEMM reads `scale_a[0:500]` but only rows 0-127 have valid data.
|
||
**Fix:** Fixed-layout padding: each expert gets `max_chunks * 128` rows at offset `e * max_chunks * 128`. Pad `slot_hidden` into this layout. Pass fixed `padded_expert_offsets` to GEMM. Extract real outputs via `l1_out[padded_dst]`.
|
||
|
||
### Bug 15: OOM — Padded Buffers Sized for 8192 Tokens (per-layer)
|
||
**Symptom:** `torch.OutOfMemoryError` trying to allocate 1008 MiB.
|
||
**Root cause:** `padded_hidden_buf` + `padded_activated_buf` at 72 MB per layer × 60 layers = 4.3 GB. Model+KV already at 175 GB on 178 GB GPUs.
|
||
**Fix (attempt 1 — wrong):** Cap `max_num_tokens` at 512. Caused Bug 17.
|
||
**Fix (attempt 2 — correct):** Shared buffers. See Bug 21.
|
||
|
||
### Bug 16: `padded_max_slots` Mismatch
|
||
**Root cause:** Computed from `max_tokens*top_k` (3072) but `total_padded_slots` is `num_experts*max_chunks*128` (6144).
|
||
**Fix:** Size for `num_experts * max_chunks * 128`.
|
||
|
||
### Bug 17: Shape Mismatch — slot_hidden 49152 vs padded_dst 3072
|
||
**Symptom:** `RuntimeError: shape mismatch: [49152, 7168] cannot be broadcast to [3072, 7168]`
|
||
**Root cause:** Bug 15 fix capped `max_num_tokens` to 512, making `_token_indices` and buffers sized for 3072 slots. But the actual warmup/cudagraph forward pass uses 8192 tokens → `sorted_token_ids` has 49152 elements → `slot_hidden` has 49152 rows → doesn't fit in 3072-slot buffer.
|
||
**Fix:** Reverted the 512 cap. Use shared buffers (Bug 21) instead.
|
||
|
||
### Bug 18: Dynamic Tensor Allocation in Scale Assembly
|
||
**Symptom:** `cudaErrorStreamCaptureInvalidated`.
|
||
**Root cause:** `torch.zeros()` for `padded_expert_offsets` inside `_assemble_scales_cudagraph_safe`.
|
||
**Fix:** Use fixed offsets from Python constants.
|
||
|
||
### Bug 19: Variable-Trip `while` Loop in Scale Assembly
|
||
**Symptom:** `cudaErrorStreamCaptureInvalidated`.
|
||
**Root cause:** `while remaining > 0` loop with GPU scalar in condition → CPU sync.
|
||
**Fix:** Fixed `for c in range(max_chunks)` loop.
|
||
|
||
### Bug 20: Another `torch.zeros()` in Scale Assembly
|
||
**Fix:** Removed. Use fixed `e * max_chunks * 128 + c * 128` offsets.
|
||
|
||
### Bug 21: OOM (correct fix) — Shared Padded Buffers
|
||
**Symptom:** Same as Bug 15 (4.3 GB for per-layer padded buffers).
|
||
**Root cause:** Per-layer allocation of `padded_hidden_buf` and `padded_activated_buf` at 72 MB × 60 layers.
|
||
**Fix:** Single shared set of padded buffers across all runners. Layers execute sequentially during both capture and replay, so the same buffer is reused. Total: 72 MB (not 4.3 GB). Stored as class-level dict keyed by device.
|
||
|
||
---
|
||
|
||
## vLLM Integration Status
|
||
|
||
| Component | Status | Notes |
|
||
|-----------|--------|-------|
|
||
| Weight loading | ✅ | Direct NVFP4 path, no BF16 round-trip |
|
||
| Weight stacking | ✅ | `make_b_k_major` + `assemble_scales_3d_side` |
|
||
| Global→local ID remap | ✅ | `experts_start_idx`, mask non-local tokens |
|
||
| Warmup gs computation | ✅ | Per-layer, local expert IDs, L1+L2 gs |
|
||
| Scale assembly | ✅ | Fixed max_chunks layout, no dynamic allocs |
|
||
| Cudagraph compatibility | ✅ | No dynamic allocs, no CPU syncs, fixed loops |
|
||
| Buffer sizing | ✅ | Shared buffers avoid OOM |
|
||
| Model output | ❓ | Build #7 in progress — never reached serving without crash |
|
||
|
||
---
|
||
|
||
## Key Architecture: Fixed-Layout Padding
|
||
|
||
### Current Design
|
||
```
|
||
Each expert gets max_chunks * 128 rows at fixed offset (e * max_chunks * 128).
|
||
|
||
padded_hidden: [exp0_chunk0][exp0_chunk1]...[exp1_chunk0]...
|
||
128 rows 128 rows 128 rows
|
||
|
||
Scatter: padded_dst = expert_assign * max_rows_per_expert + clamped_local_row
|
||
GEMM input: padded_hidden (total = num_experts * max_chunks * 128 rows)
|
||
GEMM offsets: [0, max_rows, 2*max_rows, ...] (fixed, pre-computed in _allocate_buffers)
|
||
GEMM output: same total rows
|
||
Extract: l1_out[padded_dst] → only real token rows
|
||
|
||
Scale assembly:
|
||
Phase 1: Scatter x_sf into padded_x_sf at same fixed offsets
|
||
Phase 2: Per-expert, per-chunk swizzle (fixed loop: max_chunks iterations)
|
||
No dynamic tensor allocation, no GPU→CPU syncs
|
||
|
||
Shared buffers:
|
||
padded_hidden and padded_activated are class-level (not per-layer).
|
||
72 MB total instead of 4.3 GB. Layers run sequentially → safe to share.
|
||
```
|
||
|
||
### Cudagraph Constraints (All Resolved)
|
||
- No `.item()`, `.cpu()`, `.tolist()` — zero CPU-GPU syncs
|
||
- No `torch.zeros/ones/full/empty/arange()` during capture — pre-allocate everything
|
||
- No dynamic Python control flow from GPU values — fixed loop counts
|
||
- Per-expert Python loops OK (fixed `num_experts`, unrolled at capture time)
|
||
- Shared buffers OK (layers execute sequentially during capture and replay)
|
||
|
||
### EP Configuration (DeepSeek-V4-Pro on 8×B200)
|
||
- 256 total experts, top_k=6
|
||
- EP=8 → 48 local experts per rank
|
||
- `experts_start_idx` = rank × 32
|
||
- `max_num_tokens` = 8192 (from `scheduler_config.max_num_batched_tokens`)
|
||
- `max_chunks_per_expert` = ceil(8192 × 6 / (48 × 128)) = 8
|
||
|
||
---
|
||
|
||
## 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_warmup_gs.py` | Warmup gs computation. |
|
||
| `tests/test_runner_vs_pipeline.py` | Compare runner.run() vs moe_pipeline. |
|
||
| `tests/test_scale_assembly.py` | Compare cudagraph-safe vs reference scale assembly. |
|
||
|
||
**Run order after any code change:**
|
||
1. `python3 tests/layertest.py` — must pass
|
||
2. `python3 tests/cudagraph_test.py` — must pass
|
||
|
||
---
|
||
|
||
## Repo Info
|
||
|
||
- **Kernel:** `sweetapi.com/biondizzle/nvfp4-megamoe-kernel` (master)
|
||
- **Local:** `~/dev/nvfp4-megamoe-kernel/`
|
||
- **B200:** `/root/nvfp4-megamoe-kernel/`
|
||
- **Model:** `/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4` (read-only)
|
||
- **Never edit on B200 directly** — edit locally → commit → push → pull on B200
|