[Bugfix] Fix OOB expert_map read in moe_fused_mul_sum with invalid topk_ids#48371
[Bugfix] Fix OOB expert_map read in moe_fused_mul_sum with invalid topk_ids#48371jahnavi-yelamanchi wants to merge 2 commits into
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Could a maintainer approve CI for this PR when you get a chance? The failing pre-run check is just the first-time-contributor gate (this is my first PR here), so the pre-commit job is being skipped rather than reporting an actual failure. I've run |
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Hi @jahnavi-yelamanchi, the pre-commit checks have failed. Please run: uv pip install pre-commit>=4.5.1
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pre-commit run --all-filesThen, commit the changes and push to your branch. For future commits, |
…pk_ids The moe_fused_mul_sum Triton kernel indexed expert_map with topk_ids without guarding against -1 sentinels, which mark invalid token/expert pairs under expert parallelism. Reading expert_map[-1] is an out-of-bounds access, causing CUDA illegal memory access errors when running MoE models with --enable-expert-parallel on the humming backend. Mask the expert_map load on id_val >= 0 so sentinel entries are skipped, and add a regression test covering the expert_map path with and without -1 sentinels. Fixes vllm-project#47281 Signed-off-by: jahnavi-yelamanchi <jahnaviyelamanchi03@gmail.com>
Signed-off-by: Jahnavi Yelamanchi <jahnaviyelamanchi03@gmail.com> Signed-off-by: jahnavi-yelamanchi <jahnaviyelamanchi03@gmail.com>
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Purpose
Fixes #47281. Under expert parallelism,
topk_idscan contain-1sentinels for token/expert pairs that got routed to a different EP rank (documented infused_moe/utils.py). Themoe_fused_mul_sumTriton kernel indexedexpert_mapwith these ids without guarding against-1, so it read out of bounds — sometimes crashing with an illegal memory access (what's reported in the issue), sometimes just silently corrupting output instead. Only shows up with--enable-expert-parallel.Fix masks the
expert_mapload onid_val >= 0.Checked for dupes first and nothing else open touches this kernel or this issue.
Test Plan
New test:
tests/kernels/moe/test_moe_fused_mul_sum.py, run on an L40S:Test Result
Before fix (main): 12/60 cases fail, up to 98% mismatched elements vs. torch reference, e.g.:
After fix:
60 passed in 24.55spre-commit(ruff check/format) clean on changed files.AI assistance (Claude Code) was used for root-cause analysis and the initial patch. I reviewed every line and ran the tests above myself.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.