[Perf] Use 1024 threads per block in the prefill top-k kernel - #55363
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topKPerRowPrefill runs one block per row with 512 threads. Both the histogram (kNumBins) and the final-sort buffer (kNumFinalItems) hold 2048 entries, so 1024 threads halve the rounds per block and the per-thread item count in cub::BlockRadixSort. Measured on MI355X (gfx950) through torch.ops._C.top_k_per_row_prefill, 15 shapes (512-32768 rows x 8k/32k/128k KV), median of 15 runs each, same build with only the launch config differing: topK=2048 mean -12.9 % 14 of 15 shapes faster topK=1024 mean -13.4 % 14 of 15 shapes faster topK= 512 mean -13.4 % 14 of 15 shapes faster The gain is independent of topK, so this is a constant rather than a topK-dependent policy. Every shape was verified against torch.topk: exact index match, max value delta 0. The single regressing shape is 8192 rows x 8192 KV (+3 to +4 %, i.e. 3.6 us on a 103 us kernel) -- a chunk as long as the whole context, which does not occur with chunked prefill on long sequences. At 128k KV the same row count gains 17 %. Signed-off-by: Asan Stefanski <asan.stefanski.claude@gmail.com>
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WalkthroughChangesPrefill top-k kernel
Estimated code review effort: 1 (Trivial) | ~2 minutes Merge Risk: ⚪ Minimal · up to The prefill top-k kernel now uses 1024 threads per block to reduce sorting work and improve performance. Correctness coverage indicates unchanged results, with no current merge-blocking risk. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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sylvesterkaczmarek
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The buffer geometry makes 1024 threads a defensible fixed point: both 2048-entry phases halve their per-thread work without changing algorithmic state. The benchmark also checks three top-K regimes and calls out the one regression rather than averaging it away. No code-level concern in this launch change.
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2 participants
Purpose
topKPerRowPrefilllaunches one block per row with 512 threads. Both thehistogram (
kNumBins) and the final-sort buffer (kNumFinalItems) hold 2048entries, so 1024 threads halve the rounds per block (2 instead of 4) and the
per-thread item count in
cub::BlockRadixSort. The 512 has been there since thekernel was added, without a measurement attached to it.
Test Plan
Called
torch.ops._C.top_k_per_row_prefilldirectly on MI355X (gfx950), 15shapes (512–32768 rows x 8k/32k/128k KV), median of 15 runs each, causal
cu_seqlen_ks/keas in a real prefill. Same build throughout — the launch configwas switched at runtime, so the two arms cannot differ by anything else. Every
result checked against
torch.topkon the longest row.Repeated at three values of topK, because if the gain were topK-dependent this
would belong in a policy rather than a constant.
Test Result
Selected shapes at topK=1024:
Correctness: exact index match (2048/2048 where topK=2048) and max value delta
0.00e+00on every shape in all three runs.The one regression
8192 rows x 8192 KVis 3–4 % slower — 3.6 µs on a 103 µs kernel. That is achunk as long as the entire context, which does not arise with chunked prefill on
long sequences; at 128k KV the same row count gains 17 %. If you would rather not
take any regression at all, I can gate on
numRows * 16 < numColumnsor similar,but that seemed like more machinery than the case warrants.
I only have gfx950 to measure on. If someone can run the same sweep on H100/B200
before this lands, that would be worth having — the reasoning (2048-entry buffers
divide evenly by 1024) is not ROCm-specific, but the measurement is.