[Perf] Speed up Mamba chunk metadata computation by ~6x - #48188
samuelkim7 wants to merge 2 commits into
Conversation
Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: samuelkim7 <samuel.kim@goflink.com>
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Hi @tdoublep. Could you take a look when you have a moment? This addresses the TODO (tdoublep): This code could probably be optimized in _compute_chunk_metadata. |
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Hi @heheda12345, this is a small host-side perf improvement in the mamba metadata builder, with a reference-based test that asserts identical outputs. Could you take a look and add the |
Purpose
There is a
# TODO (tdoublep): This code could probably be optimized.inBaseMambaAttentionMetadataBuilder._compute_chunk_metadata. I profiled the function and found that most of the cost comes from per-element.item()calls (3 per prefill request) and a Python loop that runs once per chunk. For example, an 8k-token request with chunk size 256 goes through the loop 32 times.This PR keeps the chunking logic the same. It converts the two input tensors once with
.tolist()instead of calling.item()per element, and emits the remaining chunk start offsets withcu_chunk_seqlen.extend(range(...))instead of the per-chunk loop.Test Plan
The new test uses the original implementation as the reference and checks that the outputs are identical on randomized workloads (chunk sizes 8–2048, batch sizes 1–128, aligned/unaligned computed tokens) and edge cases: new tokens smaller than the realigning chunk, exact chunk boundaries, and empty batch.
Test Result
All 76 tests passed. I also ran 400 additional randomized cases and the outputs are bit-identical. Microbenchmark of the function (pure-Python host-side code):
AI assistance disclosure (per
AGENTS.md): Developed with Claude Code. I refined and refactored every line, and ran all tests locally. Duplicate check: I did not find any open PR addressing this function's overhead. The closest one, #33194 (mamba block-size logic forallmode), does not touch this code path.