vllm-project/vllm

[Feature]: Integrate fused `kMoEFinalizeARResidualRMSNorm` from FlashInfer

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#40,544 opened on 2026年4月21日

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feature requesthelp wanted

説明

🚀 The feature, motivation and pitch

Available today via FlashInfer 0.6.8, we can leverage this fused MoE Finalize + ResidualAdd + AllReduce + RMSNorm https://github.com/flashinfer-ai/flashinfer/pull/2982.

This should be added as a new torch.compile custom pass. I think moe_finalize might still be inside the wrapped fused_moe op so this might require pulling that out.

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コントリビューターガイド

技術スタック
pythonpytorchcpp
領域
backendperformance
Issue 種別
feature
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
4
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
3-5 days
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
fresh
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
needs investigation
前提条件
Understanding of Mixture of Experts (MoE) modelsFamiliarity with PyTorch custom opsKnowledge of FlashInfer libraryC++/CUDA programming experience
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
15
調査方針
Begin by studying FlashInfer PR #2982 to understand the fused kernel's interface and semantics. Then examine vLLM's MoE implementation, specifically the `fused moe` op and how `moe finalize` is currently used. The issue suggests that `moe finalize` may be embedded within the wrapped op, so you'll need to locate the relevant code in vLLM's source (likely in `csrc/` or `vllm/model executor/layers/fused moe/`). After understanding the separation, design a new torch.compile custom pass that integrates the FlashInfer kernel, ensuring it handles the finalize + residual add + all reduce + RMSNorm sequence efficiently. Consult the maintainers on the issue to clarify if any intermediate steps or modifications to existing ops are required.
[Feature]: Integrate fused `kMoEFinalizeARResidualRMSNorm` from FlashInfer · vllm-project/vllm#40544 | Good First Issue