[MM][CG] Avoid over-padding Qwen2.5-VL encoder cudagraph window metadata

[huanghua1994]

Purpose

This PR follows #40830 .

On B200, the default MM encoder attention kernel is FLASH_ATTN. Enabling MM encoder CUDA graph capture leads to significant performance degrade. On Hopper, we did not observe such performance degrade.

For B200 and FLASH_ATTN backend, we introduce fixes in this PR to address the performance degrade issue. The original max_window_seqs_per_batch upper-bound estimation might be too conservative, and we introduce a new upper bound.

Switching MM encoder attention backend to Flash Infer might also avoid MM encoder CUDA graph performance degrade. Related PR: #42787

Test Plan

Engine command:

export MM_LIMIT='{"image": 1}'
export COMPILATION_CONFIG='{"compile_mm_encoder": true, "cudagraph_mm_encoder": true, "cudagraph_mode": "FULL_AND_PIECEWISE", "max_cudagraph_capture_size": 2048}'
python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-VL-7B-Instruct \
  --profiler-config.profiler cuda \
  --no-enable-prefix-caching \
  --max-model-len 32768 \
  --max-num-seqs 1024 \
  --distributed-executor-backend uni \
  --limit-mm-per-prompt "$MM_LIMIT" \
  --compilation-config "$COMPILATION_CONFIG" \
  --max-num-batched-tokens 2048 \
  --quantization fp8

Benchmark command:

vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --base-url http://127.0.0.1:8000 \
  --profile \
  --metric-percentiles 50,90,99 \
  --dataset-name random-mm \
  --random-mm-base-items-per-request 1 \
  --random-mm-num-mm-items-range-ratio 0 \
  --random-mm-bucket-config "{(512, 512, 1): 1.0}" \
  --random-mm-limit-mm-per-prompt '{"image": 1}' \
  --input-len 512 \
  --output-len 100 \
  --num-warmups 50 \
  --num-prompts 1000 \
  --request-rate 100 

Test Result

On a B200, without this fix:

============ Serving Benchmark Result ============
Successful requests:                     1000
Failed requests:                         0
Request rate configured (RPS):           100.00
Benchmark duration (s):                  58.46
Total input tokens:                      856999
Total generated tokens:                  100000
Request throughput (req/s):              17.11
Output token throughput (tok/s):         1710.57
Peak output token throughput (tok/s):    10299.00
Peak concurrent requests:                1000.00
Total token throughput (tok/s):          16370.09
---------------Time to First Token----------------
Mean TTFT (ms):                          23783.08
Median TTFT (ms):                        23676.70
P50 TTFT (ms):                           23676.70
P90 TTFT (ms):                           43143.44
P99 TTFT (ms):                           47476.18
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          112.64
Median TPOT (ms):                        125.48
P50 TPOT (ms):                           125.48
P90 TPOT (ms):                           125.60
P99 TPOT (ms):                           125.63
---------------Inter-token Latency----------------
Mean ITL (ms):                           111.52
Median ITL (ms):                         124.73
P50 ITL (ms):                            124.73
P90 ITL (ms):                            128.07
P99 ITL (ms):                            131.94
==================================================

With this fix:

============ Serving Benchmark Result ============
Successful requests:                     1000
Failed requests:                         0
Request rate configured (RPS):           100.00
Benchmark duration (s):                  16.97
Total input tokens:                      856999
Total generated tokens:                  100000
Request throughput (req/s):              58.94
Output token throughput (tok/s):         5894.07
Peak output token throughput (tok/s):    13724.00
Peak concurrent requests:                661.00
Total token throughput (tok/s):          56406.22
---------------Time to First Token----------------
Mean TTFT (ms):                          3189.21
Median TTFT (ms):                        3135.42
P50 TTFT (ms):                           3135.42
P90 TTFT (ms):                           5842.89
P99 TTFT (ms):                           6425.74
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          32.43
Median TPOT (ms):                        35.66
P50 TPOT (ms):                           35.66
P90 TPOT (ms):                           35.78
P99 TPOT (ms):                           35.83
---------------Inter-token Latency----------------
Mean ITL (ms):                           32.11
Median ITL (ms):                         35.31
P50 ITL (ms):                            35.31
P90 ITL (ms):                            37.80
P99 ITL (ms):                            52.47
==================================================

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
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[gemini-code-assist]

Code Review

This pull request optimizes CUDA graph replay for the Qwen2.5-VL model by introducing a more precise calculation for the maximum number of window sequences. It adds the get_encoder_cudagraph_max_window_seqs method to provide a tighter bound on cu_window_seqlens, preventing the launch of unnecessary empty FlashAttention CTAs. Additionally, the logic for updating cumulative sequence-length buffers during graph replay was modified to repeat the final offset in padded regions rather than zeroing them, ensuring empty sequences are correctly represented. I have no feedback to provide.

[johncalesp]

@b-mu can you help me review this PR. thanks!

[johncalesp]

@huanghua1994 can you add a benchmark for H100/H200, just to make sure there are no regressions with respect to main branch. Thank you!

[b-mu]

@b-mu can you help me review this PR. thanks!

LGTM

[johncalesp]

@Isotr0py can we put the ready tag on this PR to run the CI. Thx!

[Isotr0py]

Thanks!

[huanghua1994]

@johncalesp I just tested it on H200 with two sets of parameters. It worked as expected and no performance regression w.r.t. main branch.

[huanghua1994]

Hi @Isotr0py , could you please disable the auto-merge of this PR or #42787 ? We probably need to update this PR after merging #42787 , vise versa. Much appreciated for your help!

[Isotr0py]

Let's merge #42787 first becuase its only CI failure is unrelated.

[johncalesp]

Hi @Isotr0py is this PR good to merge too?

[huanghua1994]

Update:

The change in encoder_cudagraph.py fixes how replay metadata is copied for FlashInfer MM encoder attention (after #42787 is merged). Most cu*_seqlens buffers are a single cumulative-offset array, so padding the replay tail by repeating the final offset correctly represents empty sequences. FlashInfer uses a different packed layout, [Q/K/O offsets][V offsets], so the two sections must be copied and tail-padded independently; otherwise the V offsets can be written into the wrong section during CUDA graph replay.

The impact is intentionally narrow: the special path is only used for cu*_seqlens buffers when FlashInfer metadata (sequence_lengths_full/window) is present. Other encoder CUDA graph users keep the existing zero-copy and single-section cu*_seqlens tail-fill behavior.

@Isotr0py @johncalesp @b-mu Please take a look at the new changes if you get a chance. Thank you!

[johncalesp]

Here, we rely on the keys "sequence_lengths_full, sequence_lengths_window" , there might be a chance another backend emits the same fields, also _is_flashinfer_cu_seqlens depends on the shape of the tensors, perhaps we can add another check like is_flashinfer = (self.visual.attn_backend == AttentionBackendEnum.FLASHINFER)
just to be more explicit