Fix video temporal padding token estimates

[yinli-systems]

Fixes #47866.

Summary

• Add a shared helper for padding video frame counts to a multiple of temporal_patch_size.
• Replace the legacy num_frames + num_frames % temporal_patch_size formula in Qwen2-VL, GLM4.1V, Kanana-V, Keye, LLaVA-OneVision2, and MiMo V2 Omni token-estimation paths.
• Add a CPU unit test covering temporal_patch_size > 2, including the 17 -> 20 case from the issue.

Root cause

Several _get_vision_info implementations copied the old Qwen2-VL image-processor formula. That formula only happens to work for common temporal_patch_size == 2 cases, but it does not round up to the next valid multiple when temporal_patch_size > 2. For example, 17 + 17 % 4 == 18, which is not divisible by 4.

Duplicate-work check

I checked for overlapping open PRs before opening/updating this PR:

• gh issue view 47866 --repo vllm-project/vllm --comments
• gh pr list --repo vllm-project/vllm --state open --search "47866 in:body"
• gh pr list --repo vllm-project/vllm --state open --search "temporal_patch_size video padding"

The only matching PR for #47866 is this one. The other broad keyword hit, #40116, is about Qwen3-VL torch compile and does not address this padding bug.

Validation

• ruff format --check tests/models/test_utils.py vllm/model_executor/models/utils.py vllm/model_executor/models/qwen2_vl.py vllm/model_executor/models/glm4_1v.py vllm/model_executor/models/kanana_v.py vllm/model_executor/models/keye.py vllm/model_executor/models/llava_onevision2.py vllm/model_executor/models/mimo_v2_omni.py
• ruff check tests/models/test_utils.py vllm/model_executor/models/utils.py vllm/model_executor/models/qwen2_vl.py vllm/model_executor/models/glm4_1v.py vllm/model_executor/models/kanana_v.py vllm/model_executor/models/keye.py vllm/model_executor/models/llava_onevision2.py vllm/model_executor/models/mimo_v2_omni.py
• git diff --check
• grep -RIn "num_frames + num_frames % temporal_patch_size\|effective_frames + effective_frames % temporal_patch_size\|padded_frames = num_frames +" vllm/model_executor/models vllm/models vllm/transformers_utils | head -80

I could not run the full pytest target locally. The local default python3 is broken due to a Python 3.14 framework code-signature error, /usr/bin/python3 -m pytest tests/models/test_utils.py -q lacks tblib, and an isolated uv run cannot resolve this repo's current macOS torch split (torch==2.11.0+cpu). I also initially used /usr/bin/python3 -m py_compile before reading the local AGENTS.md; after reading it, I am not counting that as project-compliant validation.

AI assistance

AI assistance was used to identify the repeated formula, make the mechanical edits, and draft this PR description. I reviewed the changed lines and the issue context before submitting.

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🚀

[wuisabel-gif]

Went through this one carefully. I used Claude Fable 5 to help with the sweep and checked everything myself.

The fix looks right. All six parametrized test cases pass against the real round_up, and I grepped every temporal_patch_size padding site in the tree to check coverage. These six files are the only ones with the old num_frames + num_frames % temporal_patch_size form. qwen3_vl.py, ovis2_5.py and the minimax_m3 preprocessing already round up correctly, and the remaining hits are unrelated spatial padding.

One question about mimo_v2_omni. There, effective_frames = num_frames * tokens_per_second, and tokens_per_second is untyped config data. round_up is ((x + y - 1) // y) * y, which quietly under-rounds floats: round_up(16.5, 4) gives 16.0 where a true round-up is 20. The old -x % y form got that case right. If tokens_per_second is always an int this doesn't matter, but an int(...) cast at that call site would make it safe against fractional-fps configs.

Small nit: qwen3_vl.py already calls round_up(num_frames, temporal_patch_size) directly in its _get_vision_info. You could just import round_up at the six call sites instead of adding the wrapper, or keep the helper and convert qwen3_vl so they match.

[wuisabel-gif]

Following up on the float question with a concrete suggestion. The cleanest fix is one line in vllm/utils/math_utils.py, since your helper delegates to round_up:

def round_up(x: int, y: int) -> int:
    """Round up x to the nearest multiple of y."""
    return cdiv(x, y) * y

cdiv already uses -(a // -b), which rounds up correctly for floats as well as ints. I checked that this returns identical results for all int inputs, including 0 and negatives, and it fixes the float case: round_up(16.5, 4) becomes 20.0 instead of 16.0.

If you'd rather not touch the shared util, the mimo call site alone can be hardened with effective_frames = math.ceil(num_frames * tokens_per_second). That also keeps grid_t an int, which otherwise comes out as a float whenever tokens_per_second is one. Ceil rather than truncate so the estimate can only over-reserve.

To be fair, I could not find a checkpoint that actually ships a fractional tokens_per_second, so this may be purely defensive. Feel free to take it or leave it. Happy to help test either way.

(Same disclosure as above: drafted with Claude Fable 5, verified by hand.)

[labAxiaoming]

This draft has been inactive since July 9. The six call sites can be fixed directly with:

padded_num_frames = num_frames + (-num_frames % temporal_patch_size)

This avoids adding a new helper and changing the shared round_up/cdiv utilities.
Are you planning to continue this PR? If not, I can prepare a smaller replacement PR.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Kevin-Li-2025.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork