[Bugfix] Detect unloaded NVFP4 weight scales with a NaN sentinel - #52501
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This pull request has merge conflicts that must be resolved before it can be |
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Rebased onto current main — the conflict was because |
…ght_scale check count_nonzero()==0 relied on torch.empty() happening to return zero-filled memory, which is an allocator implementation detail, not a guarantee. Follow the sentinel pattern from vllm-project#45320: initialize weight_scale to NaN at registration (register_params already supports init=), and check for NaN survival after loading instead. A value a loader legitimately writes is never NaN, so this has no false positives and no dependence on allocator behavior. Reported by @Jie-Fang on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
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@Jie-Fang good catch, thanks — |
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Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. 📝 SummarySummary by CodeRabbit
WalkthroughNVFP4 per-block weight scales now initialize to ChangesNVFP4 Scale Validation
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to NVFP4 models with unloaded weight scales now fail during loading with actionable guidance instead of producing later NaN outputs. No active merge-blocking risk remains. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Inline comments:
In `@vllm/model_executor/layers/quantization/modelopt.py`:
- Line 1916: Update the NaN check on layer.weight_scale to cast the FP8 tensor
to torch.float32 before calling torch.isnan, while preserving the existing
RuntimeError behavior when a NaN is detected.
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vllm/model_executor/layers/quantization/modelopt.py
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…l check torch.isnan() on a raw float8_e4m3fn tensor works on CUDA (verified on GB10), but isn't guaranteed portable across every backend vLLM supports (ROCm/XPU/CPU fp8 kernels aren't required to implement isnan directly). Casting to float32 first is a no-op in outcome (a NaN bit pattern stays NaN after upcast) and removes the backend dependency. Reported by CodeRabbit on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
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Fixed — cast to |
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LGTM, thanks |
jeejeelee
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/ci run |
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✅ Triggered Buildkite CI #87467 for commit |
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Hi @pavelzak, the pre-commit checks have failed. Please run: uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-filesThen, commit the changes and push to your branch. For future commits, |
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…ght_scale check count_nonzero()==0 relied on torch.empty() happening to return zero-filled memory, which is an allocator implementation detail, not a guarantee. Follow the sentinel pattern from vllm-project#45320: initialize weight_scale to NaN at registration (register_params already supports init=), and check for NaN survival after loading instead. A value a loader legitimately writes is never NaN, so this has no false positives and no dependence on allocator behavior. Reported by @Jie-Fang on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
…l check torch.isnan() on a raw float8_e4m3fn tensor works on CUDA (verified on GB10), but isn't guaranteed portable across every backend vLLM supports (ROCm/XPU/CPU fp8 kernels aren't required to implement isnan directly). Casting to float32 first is a no-op in outcome (a NaN bit pattern stays NaN after upcast) and removes the backend dependency. Reported by CodeRabbit on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
pavelzak
changed the title
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/ci run |
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Note GitHub couldn't provide a complete incremental comparison for this pull request, so CodeRabbit is performing a full review instead. This review may take a little longer. |
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✅ Triggered Buildkite CI #87502 for commit |
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/ci retry |
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✅ Queued 2 failed job(s) for retry in Buildkite CI #87502. |
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/ci run |
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✅ Triggered Buildkite CI #88470 for commit |
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All-zeros means the FP4 weights were never loaded (the checkpoint stores the layer as BF16 and the weight loader silently skipped it). Fail fast with an actionable message instead of producing NaN at runtime. Rebased onto the KNvfp4Static/QuantKeyScheme recipe refactor: the original check lived in the old monolithic ModelOptNvFp4LinearMethod.process_weights_after_loading, which has since been split by role. The WEIGHT-role successor is KNvfp4Static.process(), where this now lives. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
…ght_scale check count_nonzero()==0 relied on torch.empty() happening to return zero-filled memory, which is an allocator implementation detail, not a guarantee. Follow the sentinel pattern from vllm-project#45320: initialize weight_scale to NaN at registration (register_params already supports init=), and check for NaN survival after loading instead. A value a loader legitimately writes is never NaN, so this has no false positives and no dependence on allocator behavior. Reported by @Jie-Fang on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
…l check torch.isnan() on a raw float8_e4m3fn tensor works on CUDA (verified on GB10), but isn't guaranteed portable across every backend vLLM supports (ROCm/XPU/CPU fp8 kernels aren't required to implement isnan directly). Casting to float32 first is a no-op in outcome (a NaN bit pattern stays NaN after upcast) and removes the backend dependency. Reported by CodeRabbit on vllm-project#52501. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C7YveGPhnT8v6qrAEucmVW Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
Head branch was pushed to by a user without write access
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/ci run |
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✅ Triggered Buildkite CI #88577 for commit |
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/ci run |
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@jeejeelee could you please re-enable squash auto-merge on this PR? It was disabled after the rebase, and the approval is still present. Buildkite #88577 passed all executed jobs on commit 820b6db, including all four previously failing CuTeDSL tests. Main has since been merged into the branch, and I have requested fresh CI for the current head, ead7688. Thanks! |
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✅ Triggered Buildkite CI #88600 for commit |
Include the upstream MRV2 warmup rollback (vllm-project#56654) before rerunning CI. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: pavelzak <pavel.zakharov@gmail.com>
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/ci run |
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✅ Triggered Buildkite CI #88642 for commit |
3 participants
Purpose
Fail during loading when a ModelOpt NVFP4 linear layer's
weight_scalewas not fully populated, instead of producing NaN outputs later. This can happen when a mixed-precision checkpoint stores the layer as BF16 but the layer is not excluded from NVFP4 quantization.Initialize per-block
weight_scalevalues to NaN, then check for remaining NaNs inKNvfp4Static.process()after casting to FP32 for backend compatibility. If any remain, raise aRuntimeErroridentifying the layer and suggesting that it be excluded from quantization. This detects skipped or partial scale loading without relying on the contents oftorch.empty()memory.This complements #45320, which checks missing per-expert scales in the MoE path. This PR covers per-block scales in the linear path. #42601 addresses activation-scale clamping for dead experts, a separate NaN source.
Validation
.venv/bin/python -m pre_commit run --all-files --hook-stage manual --show-diff-on-failure— all hooks passed, including Ruff, clang-format, and mypy for Python 3.10–3.13.git range-diffconfirms all three reviewed patches are unchanged by the rebase onto6fbb00b18.The rebase includes upstream #55630, which fixes the unrelated CUDA comment formatting that failed the previous pre-commit run.
AI assistance was used to prepare and rebase this PR. The hardware and model validation above was previously reported by the submitter.