[ROCm] [bugfix] Chunked prefill paged decode masked load perf - #50017
dllehr-amd merged 11 commits into
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Signed-off-by: Andy Friedrich <afriedri@amd.com>
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/ci run |
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✅ CI is already running for this commit: https://buildkite.com/vllm/ci/builds/82207 |
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✅ @afriedri, CI is now available for this PR.
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✅ Triggered Buildkite CI #83441 for commit |
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Thanks @afriedri! This looks good. Keeping the mask off until the last block looks like it should work!
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Confirmed, this fixes mentioned regression on RDNA GPUs as well. |
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/ci run |
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✅ Triggered Buildkite CI #83592 for commit |
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@afriedri Did you launch a full CI build on AMD? |
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/ci retry |
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✅ Queued 13 failed job(s) for retry in Buildkite CI #83592. |
6 participants
Purpose
Observed 10-15% latency performance regression for serving Qwen/Qwen3-30B-A3B-Thinking-2507; trace revealed that
kernel_paged_attention_2dwas the culprit, taking 1.33x as long on average on v0.25.0 vs v0.24.0. #47305 fixed correctness but introduced performance drop because of universally applied masking.Edited masking so that it is only enacted during last block, when token id can be greater than sequence length.
Test Plan
Latency
Ensure that fixes solve benchmark latency issue.
VLLM_ROCM_USE_AITER=1 vllm serve Qwen/Qwen3-30B-A3B-Thinking-2507 --dtype float16 -tp 1 --no-enable-prefix-caching --disable-uvicorn-access-logvllm bench serve --model Qwen/Qwen3-30B-A3B-Thinking-2507 --percentile-metrics tpot,ttft,itl,e2el --dataset-name random --ignore-eos --temperature 0 --max-concurrency 1 --num-prompts 10 --random-input-len 1024 --random-output-len 1024Accuracy
Ensure accuracy remains using gsm8k benchmark.
lm_eval --model local-completions
--model_args model=Qwen/Qwen3-30B-A3B-Thinking-2507,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=256,max_retries=10,max_gen_toks=2048
--batch_size auto --tasks gsm8k --num_fewshot 5 --limit 1000
Test Result
Latency
Accuracy
gsm8k, 5-shot,
--limit 1000, same serve config:Deltas (+1.2pt flexible, −0.3pt strict) are within stderr.
No other PRs address this performance regression.
This PR was prepared with AI assistance (Claude). All changes were reviewed and the
test/eval commands were run by Andy.