[Perf][DSV4] Optimize sparse top-k metadata kernels for higher prefill throughput - #52084
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…ughput Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com>
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…ughput Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com>
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…l throughput (vllm-project#52084) Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com> (cherry picked from commit 836aac9)
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…l throughput (vllm-project#52084) Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com> Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com>
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flashinfer's sparse_mla_sm120_paged_attention asserts eidx.IsContiguous(); the vllm-project#52084 triton index-conversion can return a strided view, killing every rank at decode warmup on GB10 TP16 (tvm InternalError -> RecreateGroup loop). Make the caller contiguous-ize; copy cost is negligible.
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Purpose
Optimize sparse top-k metadata kernels for higher prefill throughput
Test
Test Plan
Paired A/B/A Serving Results
The second optimized run improved output throughput by 1.56% and reduced
mean TPOT by 2.79% relative to the paired 128-worker baseline.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.