Update CURRENT_BUG.md: current status, outstanding garbage output issue, hypotheses
This commit is contained in:
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CURRENT_BUG.md
196
CURRENT_BUG.md
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# Current Bug: CuTeDSLMoERunner — Status & Debug History
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## Current Status (May 17, 2026 15:51 UTC)
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## Current Status (May 17, 2026 16:52 UTC)
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**vLLM container build in progress. Previous crash was from OOM + shape mismatch. Both now fixed.**
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**vLLM runs, cudagraph capture succeeds, but model output is empty/invisible tokens (garbage logits). Going back to layer tests to debug GEMM output quality.**
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- ✅ `layertest.py` — 0.988 cosine
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- ✅ `layertest.py` — 0.988 cosine (with dynamic gs reference)
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- ✅ `cudagraph_test.py` — capture + replay works
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- ✅ Container builds, loads weights, warmup gs computed (no L2 gs=0)
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- 🔧 Build #7 in progress on B200 (shared buffer fix)
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- ❌ Haven't gotten to serving yet (crashes were during init/capture)
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- ✅ With `--gpu_memory_utilization=0.9` and `max_cudagraph_capture_size=8`, container starts and serves
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- ❌ Model output is empty content (30 tokens of invisible/BS token) — MoE GEMM output is wrong
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**Latest fixes (Bugs 17→21):**
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- 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.
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- 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.
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**Next step:** Debug WHY the runner produces 0.988 cosine in layertest but garbage in vLLM. Likely issues:
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1. The warmup gs values (computed from random data) don't match real runtime activation magnitudes
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2. The scale assembly layout is subtly wrong for 48 experts vs 3 experts in the test
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3. The padded buffer scatter (clamped_local) is dropping real token data
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**Current vLLM launch config:**
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```
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--gpu_memory_utilization=0.9
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--compilation-config='{"cudagraph_mode": "FULL_DECODE_ONLY", "custom_ops": ["all"], "cudagraph_capture_sizes": [1, 2, 4, 8], "max_cudagraph_capture_size": 8}'
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```
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---
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## Bugs Found & Fixed
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## Bugs Found & Fixed (1–21)
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### Bug 1: Scale Assembly — Global Swizzle vs Per-Expert Swizzle
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### Bug 1: Scale Assembly — Global vs Per-Expert Swizzle
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**Fix:** Two-phase scatter + per-expert swizzle.
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### Bug 2: `searchsorted(right=False)` — Wrong Expert Assignment
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### Bug 2: `searchsorted(right=False)`
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**Fix:** Changed to `right=True`.
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### Bug 3: CuTeDSL `cute.compile` GPU Memory Corruption — CRITICAL
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**Symptom:** `_token_indices` all zeros after JIT.
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**Root cause:** `cute.compile` corrupts GPU memory. Tensors allocated before/during JIT get zeroed.
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**Fix:** `_fill_token_indices()` builds on CPU, copies to GPU. `_needs_token_refill` for GEMM JIT.
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**Root cause:** `cute.compile` corrupts GPU memory.
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**Fix:** `_fill_token_indices()` builds on CPU, copies to GPU. `_needs_token_refill` flag.
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### Bug 4: `expert_offsets` With Leading 0
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**Fix:** Pass `expert_offsets[1:num_experts + 1]` to the GEMM.
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**Fix:** Pass `expert_offsets[1:]` to GEMM.
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### Bug 5: Checkpoint `input_scale` Is Wrong for Activation Global Scale
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**Root cause:** Checkpoint `input_scale` (~0.000286) is a calibration value. Too-small gs → block scale overflow → garbage.
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### Bug 5: Checkpoint `input_scale` Wrong for Runtime gs
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**Root cause:** Calibration value, too-small gs → block scale overflow.
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**Fix:** `compute_activation_global_scales()` warmup method.
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### Bug 6: L1 and L2 Need Separate Activation Global Scales
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**Fix:** Compute L2 gs from actual L1 output after SiLU*up.
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### Bug 6: L1/L2 Need Separate gs
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**Fix:** Compute L2 gs from L1 output after SiLU*up.
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### Bug 7: L1 and L2 Need Separate Padded Scale Buffers
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**Fix:** Separate `_padded_x_sf_buf_l1`/`_l2`, separate per-expert scale bufs.
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### Bug 7: L1/L2 Need Separate Scale Buffers
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**Fix:** Separate `_padded_x_sf_buf_l1`/`_l2`, separate per-expert bufs.
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### Bug 8: Global→Local Expert ID Mismatch — CUDA_ERROR_ASSERT
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**Symptom:** `IndexKernel.cu:111` OOB, cascading CUDA_ERROR_ASSERT (710) on all workers.
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**Root cause:** `topk_ids` contains global IDs (0-255), runner treated as local (0-31/48).
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**Fix:** Added `experts_start_idx`, remap global→local, mask non-local tokens.
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**Root cause:** `topk_ids` contains global IDs (0-255), runner treated as local.
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**Fix:** `experts_start_idx`, remap global→local, mask non-local tokens.
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### Bug 8b: `.cpu()` Sync Breaking Cudagraph Compatibility
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**Fix:** Moved `_token_indices` to GPU, `_fill_token_indices()` (CPU→GPU copy).
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### Bug 8b: `.cpu()` Sync Breaking Cudagraph
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**Fix:** `_token_indices` on GPU, `_fill_token_indices()` CPU→GPU copy.
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### Bug 9: `padded_x_sf` Buffer Too Small — Index Out of Bounds
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**Root cause:** Buffer sized for `num_experts * 128` rows, but scatter positions exceeded this with real token distributions.
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**Fix:** Iterative — see Bugs 11, 14, 16 for the final solution.
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### Bug 9: `padded_x_sf` Buffer Too Small
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**Fix:** Iterative — see Bugs 14, 16.
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### Bug 10: Wrong `top_k` and `max_num_tokens` Defaults
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**Root cause:** Runner defaulted to `top_k=8`, vLLM uses top_k=6.
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**Fix:** Pass values from `deepseek_v4.py`.
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### Bug 10: Wrong `top_k`/`max_num_tokens` Defaults
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**Fix:** Pass from `deepseek_v4.py`.
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### Bug 11: Full-Buffer Swizzle Produced Wrong GEMM Input
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**Symptom:** L2 gs=0.0 on EP5/EP7.
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**Root cause:** Swizzled entire buffer at once; GEMM expects per-expert swizzled blocks.
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**Fix:** Reverted to per-expert swizzle.
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### Bug 11: Full-Buffer Swizzle Wrong for GEMM
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**Fix:** Per-expert swizzle.
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### Bug 12: `torch.full()` During Cudagraph Capture
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**Symptom:** `cudaErrorStreamCaptureUnsupported` on all 8 workers.
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**Root cause:** `torch.full()` allocates new tensor during stream capture.
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**Fix:** Pre-allocated `_l1_gsa_buf`, `_l2_gsa_buf`, `_output_buf`, `_row_indices_buf`. Use `.fill_()`.
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**Symptom:** `cudaErrorStreamCaptureUnsupported`.
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**Fix:** Pre-allocated buffers, `.fill_()` instead of `torch.full()`.
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### Bug 13: Warmup Passed Global Expert IDs Instead of Local
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### Bug 13: Warmup Passed Global Expert IDs
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**Symptom:** L2 gs=0.0 on EP5/EP7.
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**Root cause:** Warmup passed global IDs (336+) against local range (0..47).
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**Fix:** Pass local IDs (0..num_experts-1).
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**Fix:** Pass local IDs.
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### Bug 14: GEMM Scale Layout Mismatch — Fixed 128-Row vs Variable
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**Symptom:** Model generates BOS token repeatedly (garbage logits).
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**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.
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**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]`.
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### Bug 14: GEMM Scale Layout Mismatch — 128-Row Fixed vs Variable
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**Symptom:** BOS token repeat (garbage logits).
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**Root cause:** Scale assembly at `e*128` offsets, GEMM reads by real `expert_offsets`. Expert with 500 tokens → GEMM reads 500 scale rows but only 128 have data.
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**Fix:** Fixed-layout padding: each expert gets `max_chunks * 128` rows. Pad `slot_hidden`. Pass `padded_expert_offsets` to GEMM. Extract via `l1_out[padded_dst]`.
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### Bug 15: OOM — Padded Buffers Sized for 8192 Tokens (per-layer)
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**Symptom:** `torch.OutOfMemoryError` trying to allocate 1008 MiB.
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**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.
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**Fix (attempt 1 — wrong):** Cap `max_num_tokens` at 512. Caused Bug 17.
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**Fix (attempt 2 — correct):** Shared buffers. See Bug 21.
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### Bug 15: OOM — Per-Layer Padded Buffers (4.3 GB)
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**Root cause:** `padded_hidden_buf` + `padded_activated_buf` at 72 MB × 60 layers.
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**Fix:** Shared buffers (Bug 21).
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### Bug 16: `padded_max_slots` Mismatch
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**Root cause:** Computed from `max_tokens*top_k` (3072) but `total_padded_slots` is `num_experts*max_chunks*128` (6144).
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**Fix:** Size for `num_experts * max_chunks * 128`.
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**Root cause:** Sized for `max_tokens*top_k` but needed `num_experts*max_chunks*128`.
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**Fix:** Size correctly.
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### Bug 17: Shape Mismatch — slot_hidden 49152 vs padded_dst 3072
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**Symptom:** `RuntimeError: shape mismatch: [49152, 7168] cannot be broadcast to [3072, 7168]`
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**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.
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**Fix:** Reverted the 512 cap. Use shared buffers (Bug 21) instead.
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### Bug 17: Shape Mismatch (49152 vs 3072)
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**Root cause:** Cap `max_num_tokens` to 512 made buffers too small for 8192-token warmup.
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**Fix:** Reverted cap, use shared buffers (Bug 21).
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### Bug 18: Dynamic Tensor Allocation in Scale Assembly
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**Symptom:** `cudaErrorStreamCaptureInvalidated`.
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**Root cause:** `torch.zeros()` for `padded_expert_offsets` inside `_assemble_scales_cudagraph_safe`.
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**Fix:** Use fixed offsets from Python constants.
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### Bug 19: Variable-Trip `while` Loop in Scale Assembly
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**Symptom:** `cudaErrorStreamCaptureInvalidated`.
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**Root cause:** `while remaining > 0` loop with GPU scalar in condition → CPU sync.
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**Fix:** Fixed `for c in range(max_chunks)` loop.
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### Bug 20: Another `torch.zeros()` in Scale Assembly
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**Fix:** Removed. Use fixed `e * max_chunks * 128 + c * 128` offsets.
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### Bug 18–20: Cudagraph Capture Failures
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**Root cause:** Dynamic tensor allocation (`torch.zeros`), variable-trip loops, GPU scalars in Python control flow.
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**Fix:** Pre-allocate everything, fixed loop counts, Python constants for offsets.
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### Bug 21: OOM (correct fix) — Shared Padded Buffers
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**Symptom:** Same as Bug 15 (4.3 GB for per-layer padded buffers).
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**Root cause:** Per-layer allocation of `padded_hidden_buf` and `padded_activated_buf` at 72 MB × 60 layers.
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**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.
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**Root cause:** Per-layer allocation of padded buffers.
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**Fix:** Class-level shared buffers dict keyed by device. Layers execute sequentially → safe to share. Also shared `padded_x_sf_buf` and `output_buf`. Total ~150 MB instead of ~4.3 GB.
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---
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## vLLM Integration Status
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## Current Architecture: Fixed-Layout Padding
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| Component | Status | Notes |
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|-----------|--------|-------|
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| Weight loading | ✅ | Direct NVFP4 path, no BF16 round-trip |
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| Weight stacking | ✅ | `make_b_k_major` + `assemble_scales_3d_side` |
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| Global→local ID remap | ✅ | `experts_start_idx`, mask non-local tokens |
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| Warmup gs computation | ✅ | Per-layer, local expert IDs, L1+L2 gs |
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| Scale assembly | ✅ | Fixed max_chunks layout, no dynamic allocs |
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| Cudagraph compatibility | ✅ | No dynamic allocs, no CPU syncs, fixed loops |
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| Buffer sizing | ✅ | Shared buffers avoid OOM |
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| Model output | ❓ | Build #7 in progress — never reached serving without crash |
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---
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## Key Architecture: Fixed-Layout Padding
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### Current Design
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```
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Each expert gets max_chunks * 128 rows at fixed offset (e * max_chunks * 128).
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padded_hidden: [exp0_chunk0][exp0_chunk1]...[exp1_chunk0]...
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128 rows 128 rows 128 rows
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Each expert gets max_chunks * 128 rows at offset (e * max_chunks * 128).
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Scatter: padded_dst = expert_assign * max_rows_per_expert + clamped_local_row
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GEMM input: padded_hidden (total = num_experts * max_chunks * 128 rows)
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GEMM offsets: [0, max_rows, 2*max_rows, ...] (fixed, pre-computed in _allocate_buffers)
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GEMM offsets: [0, max_rows, 2*max_rows, ...] (fixed, pre-computed)
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GEMM output: same total rows
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Extract: l1_out[padded_dst] → only real token rows
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@@ -148,36 +116,52 @@ Scale assembly:
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Phase 2: Per-expert, per-chunk swizzle (fixed loop: max_chunks iterations)
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No dynamic tensor allocation, no GPU→CPU syncs
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Shared buffers:
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padded_hidden and padded_activated are class-level (not per-layer).
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72 MB total instead of 4.3 GB. Layers run sequentially → safe to share.
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Shared buffers (class-level):
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padded_hidden, padded_activated, padded_xsf_l1, padded_xsf_l2, output
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~150 MB total (not per-layer)
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```
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### Cudagraph Constraints (All Resolved)
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- No `.item()`, `.cpu()`, `.tolist()` — zero CPU-GPU syncs
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- No `torch.zeros/ones/full/empty/arange()` during capture — pre-allocate everything
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- No dynamic Python control flow from GPU values — fixed loop counts
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- Per-expert Python loops OK (fixed `num_experts`, unrolled at capture time)
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- Shared buffers OK (layers execute sequentially during capture and replay)
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- No `.item()`, `.cpu()`, `.tolist()`
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- No `torch.zeros/ones/full/empty/arange()` during capture
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- No dynamic Python control flow from GPU values
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- Per-expert Python loops OK (fixed `num_experts`)
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- Shared buffers OK (layers sequential during capture and replay)
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### EP Configuration (DeepSeek-V4-Pro on 8×B200)
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- 256 total experts, top_k=6
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- EP=8 → 48 local experts per rank
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- `experts_start_idx` = rank × 32
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- `max_num_tokens` = 8192 (from `scheduler_config.max_num_batched_tokens`)
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- `max_num_tokens` = 8192
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- `max_chunks_per_expert` = ceil(8192 × 6 / (48 × 128)) = 8
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---
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## Outstanding Issue: Garbage Model Output
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**Symptom:** Model generates 30 tokens of empty/invisible content (BOS or thinking token). Not meaningful text.
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**What works:** layertest gives 0.988 cosine with 3 experts, 8 tokens, top_k=8.
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**What doesn't:** vLLM with 48 experts, variable tokens, top_k=6 produces garbage.
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**Hypotheses to investigate:**
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1. **Warmup gs from random data ≠ real activation magnitudes.** The warmup uses `torch.randn` (amax ~3) but real activations have amax ~8-10. The gs values would be wrong, causing quantization errors.
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2. **Scale assembly with 48 experts × 8 chunks.** With max_chunks=8 and 48 experts, there are 384 swizzle blocks. The fixed-layout scatter with `clamped_local` may be dropping tokens that overflow the expert's max_rows section.
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3. **`clamped_local = local_row.clamp(max=max_rows_per_expert - 1)`.** If an expert has more than `max_chunks*128` real tokens, overflow tokens all map to the same row, overwriting each other. This silently drops data.
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4. **The `_needs_token_refill` path.** After GEMM JIT, `_token_indices` may get corrupted. The refill happens AFTER the first run, but the first run already used corrupted indices.
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---
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## Test Files
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| File | Purpose |
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|------|---------|
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| `tests/layertest.py` | Reference: moe_pipeline with dynamic gs, 3 experts, layer 0. Must pass (≥0.98 cosine). |
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| `tests/cudagraph_test.py` | CuTeDSLMoERunner cudagraph capture + replay. Must pass. |
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| `tests/layertest.py` | Reference vs runner, 3 experts. Must pass ≥0.98 cosine. |
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| `tests/cudagraph_test.py` | Cudagraph capture + replay. Must pass. |
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| `tests/test_runner_vs_pipeline.py` | Runner vs pipeline comparison. |
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| `tests/test_scale_assembly.py` | Scale assembly comparison. |
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| `tests/test_warmup_gs.py` | Warmup gs computation. |
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| `tests/test_runner_vs_pipeline.py` | Compare runner.run() vs moe_pipeline. |
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| `tests/test_scale_assembly.py` | Compare cudagraph-safe vs reference scale assembly. |
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**Run order after any code change:**
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1. `python3 tests/layertest.py` — must pass
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@@ -17,7 +17,8 @@ services:
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- --tensor-parallel-size=8
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#- --enforce-eager
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- --compilation-config
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- '{"cudagraph_mode": "FULL_DECODE_ONLY", "custom_ops": ["all"], "cudagraph_capture_sizes": [1, 2, 4, 8], "max_cudagraph_capture_size": 8}'
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#- '{"cudagraph_mode": "NONE", "custom_ops": ["all"]}'
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- '{"cudagraph_mode": "FULL_DECODE_ONLY", "custom_ops": ["all"], "cudagraph_capture_sizes": [1, 2, 4, 8], "max_cudagraph_capture_size": 8}' # This is what is runing right now
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#- '{"cudagraph_mode":"FULL_AND_PIECEWISE", "custom_ops":["all"]}'
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#- --moe-backend=deep_gemm_mega_moe
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- --tokenizer-mode=deepseek_v4
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70
tests/debug_output.py
Normal file
70
tests/debug_output.py
Normal file
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"""Debug: Compare runner output vs reference pipeline output.
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Focus on whether the scale assembly + GEMM produces correct values."""
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import torch
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import sys
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sys.path.insert(0, '/root/nvfp4-megamoe-kernel/cutedsl')
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sys.path.insert(0, '/root/nvfp4-megamoe-kernel/vllm')
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from cutedsl.reference.moe_pipeline import moe_pipeline
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from vllm.nvfp4_cutedsl import CuTeDSLMoERunner
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torch.cuda.set_device(0)
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# Load real model weights for layer 0
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from cutedsl.weight_loader import load_layer_weights
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weights = load_layer_weights(layer_idx=0, num_experts=3)
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# Run reference pipeline with dynamic gs
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ref_out = moe_pipeline(
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hidden_states=torch.randn(4, 256, dtype=torch.bfloat16, device='cuda'),
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topk_weights=torch.ones(4, 2, dtype=torch.float32, device='cuda') / 2,
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topk_ids=torch.tensor([[0,1],[0,1],[0,1],[0,1]], dtype=torch.int64, device='cuda'),
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l1_fp4=weights['l1_fp4'],
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l1_sf=weights['l1_sf'],
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l1_gs=weights['l1_gs'],
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l2_fp4=weights['l2_fp4'],
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l2_sf=weights['l2_sf'],
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l2_gs=weights['l2_gs'],
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num_experts=3,
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hidden_size=256,
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intermediate_size=512,
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)
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print(f"Reference output: amax={ref_out.amax().item():.4f} mean={ref_out.mean().item():.4f}")
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# Run runner with warmup gs
|
||||
runner = CuTeDSLMoERunner(
|
||||
num_experts=3, hidden_size=256, intermediate_size=512,
|
||||
max_num_tokens=4, top_k=2, device='cuda'
|
||||
)
|
||||
# Set weights directly
|
||||
runner.l1_fp4 = weights['l1_fp4']
|
||||
runner.l1_sf = weights['l1_sf']
|
||||
runner.l1_gs = weights['l1_gs']
|
||||
runner.l2_fp4 = weights['l2_fp4']
|
||||
runner.l2_sf = weights['l2_sf']
|
||||
runner.l2_gs = weights['l2_gs']
|
||||
|
||||
# Compute warmup gs
|
||||
hs = torch.randn(4, 256, dtype=torch.bfloat16, device='cuda')
|
||||
tw = torch.ones(4, 2, dtype=torch.float32, device='cuda') / 2
|
||||
ti = torch.tensor([[0,1],[0,1],[0,1],[0,1]], dtype=torch.int64, device='cuda')
|
||||
runner.compute_activation_global_scales(hs, tw, ti)
|
||||
print(f"Warmup gs: L1={runner._l1_activation_global_scale} L2={runner._l2_activation_global_scale}")
|
||||
|
||||
# Run with same input as reference
|
||||
runner_out = runner.run(hs, tw, ti)
|
||||
print(f"Runner output: amax={runner_out.amax().item():.4f} mean={runner_out.mean().item():.4f}")
|
||||
|
||||
# Cosine similarity
|
||||
cos = torch.nn.functional.cosine_similarity(ref_out.flatten().unsqueeze(0), runner_out.flatten().unsqueeze(0)).item()
|
||||
print(f"Cosine similarity: {cos:.6f}")
|
||||
|
||||
# Check for NaN/Inf
|
||||
print(f"Runner NaN: {torch.isnan(runner_out).any().item()} Inf: {torch.isinf(runner_out).any().item()}")
|
||||
print(f"Ref NaN: {torch.isnan(ref_out).any().item()} Inf: {torch.isinf(ref_out).any().item()}")
|
||||
|
||||
# Per-token comparison
|
||||
for i in range(4):
|
||||
cos_i = torch.nn.functional.cosine_similarity(ref_out[i].unsqueeze(0), runner_out[i].unsqueeze(0)).item()
|
||||
print(f" Token {i}: cosine={cos_i:.6f} ref_max={ref_out[i].amax().item():.4f} run_max={runner_out[i].amax().item():.4f}")
|
||||
Reference in New Issue
Block a user