[Bugfix][Multimodal] Fix Qwen3-VL modality-scoped mm_processor_kwargs handling (images_kwargs/videos_kwargs)

[danigarciaoca]

Purpose

Fixes #56363.

Qwen3-VL currently mishandles modality-scoped vision processor kwargs at the boundary between vLLM's modality-resolved view and the kwargs forwarded to Hugging Face processors.

For video inputs, a size override under videos_kwargs can be resolved by vLLM and materialized as a flat size while the original nested value is still present, causing Transformers to reject the duplicated kwarg. For image inputs, a partial size override under images_kwargs can reach the image processor without being completed from the processor defaults.

This PR keeps the modality-resolved view for vLLM-side processing while normalizing values modified by vLLM back into the corresponding HF-style scoped kwargs before the final processor call. It also allows callers that have already merged engine-level mm_processor_kwargs to avoid merging them a second time.

Regression tests cover partial size overrides under images_kwargs and videos_kwargs, as well as preserving already merged multimodal processor kwargs without merging engine-level settings again.

Related issues / PRs

• [Bug]: --mm-processor-kwargs cannot scope an override to one modality: videos_kwargs is ignored by profiling while a flat "size" leaks into the image budget (Qwen3-VL) #52834 reported the original mismatch between HF modality-scoped kwargs and vLLM-side sizing/profiling.
• [Bugfix][Multimodal] Honor modality-scoped mm_processor_kwargs #53808 fixed the sizing/profiling side by introducing modality-aware overlays. The video failure fixed here is a runtime regression exposed by that change.
• [Bugfix][Multimodal] Honor modality-scoped mm_processor_kwargs in every model that reads them #54527 is a related follow-up extending modality-scoped vLLM-side reads to additional models, but it does not modify the affected Qwen3-VL runtime path or InputProcessingContext.call_hf_processor().

AI assistance

This PR was prepared with AI assistance from ChatGPT. I reviewed every changed line, ran the tests above, and am responsible for the final implementation.

Test Plan

pytest -v \
  tests/models/multimodal/processing/test_qwen3_vl.py::test_processor_kwargs_videos_kwargs_partial_size_runtime \
  tests/models/multimodal/processing/test_qwen3_vl.py::test_processor_kwargs_images_kwargs_partial_size_runtime \
  tests/multimodal/test_processing.py::test_hf_processor_call_kwargs_does_not_remerge_merged_kwargs

pre-commit run --files \
  vllm/model_executor/models/qwen3_vl.py \
  vllm/multimodal/processing/context.py \
  tests/models/multimodal/processing/test_qwen3_vl.py \
  tests/multimodal/test_processing.py

Test Result

Before the fix:

• A size override under videos_kwargs containing only longest_edge fails because Transformers receives size both as a flat kwarg and inside videos_kwargs:

  ValueError: Keyword argument size was passed two times: in a dictionary for videos_kwargs and as a **kwarg.
  

• A size override under images_kwargs containing only longest_edge reaches the image processor without being completed from its defaults:

  ValueError: `size` dict must contain 'shortest_edge' and 'longest_edge' keys but got SizeDict(height=None, width=None, longest_edge=16777216, shortest_edge=None, max_height=None, max_width=None, min_pixels=None, max_pixels=None).
  

After the fix:

test_processor_kwargs_videos_kwargs_partial_size_runtime PASSED
test_processor_kwargs_images_kwargs_partial_size_runtime PASSED
test_hf_processor_call_kwargs_does_not_remerge_merged_kwargs PASSED

All applicable pre-commit hooks pass.

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[DarkLight1337]

Shouldn't video_mm_kwargs be exactly what hf_processor_mm_kwargs["videos_kwargs"] resolves to already?

[danigarciaoca]

Hi @DarkLight1337! Not exactly. get_merged_mm_kwargs(..., modality="video") first merges the engine-level and request-level kwargs, and then overlays videos_kwargs onto the flat namespace for vLLM-side video reads.

For example, given:

hf_processor_mm_kwargs = {
    "num_frames": 32,
    "foo": 1,
    "videos_kwargs": {
        "size": {"longest_edge": 100},
    },
}

then:

video_mm_kwargs = self.info.ctx.get_merged_mm_kwargs(
    hf_processor_mm_kwargs, modality="video"
)

resolves conceptually to:

video_mm_kwargs = {
    "num_frames": 32,
    "foo": 1,
    "videos_kwargs": {
        "size": {"longest_edge": 100},
    },
    "size": {"longest_edge": 100},
}

rather than just:

{
    "size": {"longest_edge": 100},
}

In other words, passing modality="video" does not filter the result down to hf_processor_mm_kwargs["videos_kwargs"]: it keeps the full merged namespace and overlays the video-scoped kwargs onto the flat keys so existing vLLM-side code can read them without changing its flat-key assumptions.

So the two dicts have different purposes:

• video_mm_kwargs is the full merged/effective namespace used for vLLM-side video processing. It contains the shared/flat kwargs plus the videos_kwargs overlay.
• video_scoped_kwargs is the modality-scoped representation that we build back under videos_kwargs before calling the HF processor.

And regarding the video_mm_kwargs name, it follows the existing code, where it was initialized as the mutable copy of the processor kwargs used for per-video adjustments:

vllm/vllm/model_executor/models/qwen3_vl.py

Lines 1368 to 1373 in a2bc2ff

	# NOTE: a copy of is created to update do_sample_frames,
	# otherwise mm_hash for the object will be incorrect.
	video_mm_kwargs = dict(**hf_processor_mm_kwargs)
	merged = self.info.ctx.get_merged_mm_kwargs(
	hf_processor_mm_kwargs, modality="video"
	)

and was later passed directly to both get_hf_processor() and call_hf_processor() after applying the per-video adjustments:

vllm/vllm/model_executor/models/qwen3_vl.py

Lines 1423 to 1430 in a2bc2ff

	video_outputs = self.info.ctx.call_hf_processor(
	self.info.get_hf_processor(**video_mm_kwargs),
	dict(
	text="<|vision_start|><|video_pad|><|vision_end|>",
	**video_mm_data,
	),
	video_mm_kwargs,
	)

This PR keeps that role for video_mm_kwargs, while video_scoped_kwargs is specifically the HF-style videos_kwargs representation rebuilt before the processor call.

[DarkLight1337]

cc @Prudhvivuda I think this isn't an ideal solution, as we would need to pass mm_kwargs_are_merged in a lot of places as a workaround. Perhaps we should make overlay_modality_mm_kwargs return a dict subclass, and introduce a method (e.g. get_kwarg(modality=...)), called by vLLM code, that resolves the correct kwarg according to the modality explicitly. Meanwhile, the raw dict should be passed to HF unchanged,

[danigarciaoca]

@DarkLight1337 thanks for the quick reply and for taking a look at this!

One clarification on mm_kwargs_are_merged first: it defaults to False, so existing call sites keep exactly the current behavior and do not need to be changed. The True case is only for callers that have already merged the engine/request mm_processor_kwargs and then intentionally transformed that merged result before passing it to HF.

I see the motivation behind using a modality-aware accessor such as get_kwarg(..., modality=...), but I don't think that approach by itself would solve the issue that requires the flag here. The problem is not only how vLLM resolves a modality-scoped value; it is also that the same kwargs are merged again later.

Let me explain.

The problem

There are implicit merges at multiple layers. Once a caller has already merged the kwargs and then intentionally removes or moves a key, merging the startup kwargs again can reintroduce values that were deliberately removed. At that point, the merge is no longer idempotent. This is what happens, for instance, with a flat size configured at startup.

For example, suppose the server is started with:

mm_processor_kwargs = {
    "size": X,
}

For the video call, after resolving the effective video kwargs, the vLLM implementation in qwen3_vl.py prepares:

{
    "videos_kwargs": {
        "size": Y,
    },
}

with the flat size removed, because Transformers rejects receiving the same processor kwarg both flat and under videos_kwargs. This is the key point: the flat key has to be removed before the HF call, and the problem starts when a later merge brings it back.

If call_hf_processor() then merges the startup kwargs again, the merge is effectively:

startup_kwargs | prepared_video_kwargs

and the result becomes:

{
    "size": X,
    "videos_kwargs": {
        "size": Y,
    },
}

The flat startup-level size has now been reintroduced, so we recreate the exact duplicate-kwarg failure we were trying to avoid.

A modality-aware accessor such as:

get_kwarg("size", modality="video")

would change how vLLM reads the effective value, but it would not change this later merge. The startup-level flat size would still be reintroduced after the caller had intentionally removed it.

Why using only videos_kwargs is not enough

With the current API, top-level kwargs are also part of the effective video configuration.

For example:

{
    "num_frames": 32,
    "foo": 1,
    "videos_kwargs": {
        "size": Y,
    },
}

has an effective video configuration containing num_frames, foo, and the scoped size.

Reducing that to:

hf_processor_mm_kwargs["videos_kwargs"]

would give only:

{
    "size": Y,
}

and would drop the valid shared/top-level kwargs.

This is why get_merged_mm_kwargs(..., modality="video") currently represents the full effective namespace for the video side, rather than only the contents of videos_kwargs.

Why the flag is scoped

mm_kwargs_are_merged is not intended to be passed by every modality-aware caller.

It represents a different state of the kwargs:

• False: the kwargs still need the normal engine/request merge.
• True: the kwargs have already gone through that merge and have subsequently been prepared for a specific HF call.

Keeping the default as False preserves the existing behavior everywhere else.

The important distinction is that re-merging is harmless as long as an overridden value remains under the same top-level key. The problem appears when vLLM has to move a value from its flat representation into a modality-scoped one before calling HF: a later merge no longer finds the same top-level key in the prepared kwargs, so it can reintroduce the flat startup value.

Alternative

If we do not want the API to expose the distinction between unmerged kwargs and already-merged/prepared kwargs, I think the alternative needs to be more fundamental: reduce the number of valid representations.

• One option would be to require modality-specific processor kwargs to use the HF-style images_kwargs / videos_kwargs / audio_kwargs representation exclusively.
• Another possibility would be to define a set of modality-specific keys, such as size, min_pixels, max_pixels, fps, or num_frames, that must always live under their corresponding modality-scoped dict. They could either be normalized into that representation as soon as they enter vLLM, or rejected when provided flat.

The important part would be that the same semantic option cannot remain valid both as a flat key and as a modality-scoped key. Otherwise a later shallow merge cannot know that, for example, flat size and videos_kwargs["size"] represent the same setting for the video path.

That would remove the ambiguity at the source, but it would be a broader API/compatibility change rather than a local fix to the current merge behavior.

[DarkLight1337]

How about replace overlay_modality_mm_kwargs with something like parse_mm_kwargs that gathers the kwargs that exist for each modality? That way we could conveniently query the kwargs inside both vLLM while still being able to pass them to HF.

[danigarciaoca]

parsing the kwargs by modality is not the main issue here.

In Qwen3-VL, the prepared video kwargs are passed to get_hf_processor() here:

vllm/vllm/model_executor/models/qwen3_vl.py

Lines 1423 to 1430 in 7d8d71e

	video_outputs = self.info.ctx.call_hf_processor(
	self.info.get_hf_processor(**video_mm_kwargs),
	dict(
	text="<|vision_start|><|video_pad|><|vision_end|>",
	**video_mm_data,
	),
	video_mm_kwargs,
	)

That internally goes through Qwen3-VL's get_hf_processor():

vllm/vllm/model_executor/models/qwen3_vl.py

Lines 914 to 919 in 7d8d71e

	def get_hf_processor(self, **kwargs: object) -> Qwen3VLProcessor:
	return self.ctx.get_hf_processor(
	Qwen3VLProcessor,
	use_fast=kwargs.pop("use_fast", True),
	**kwargs,
	)

and then InputProcessingContext.get_hf_processor() merges the engine-level mm_processor_kwargs again:

vllm/vllm/multimodal/processing/context.py

Line 241 in 7d8d71e

merged_kwargs = self.get_merged_mm_kwargs(kwargs)

For example, suppose the engine was started with:

vllm serve Qwen/Qwen3-VL-4B-Instruct \
    --mm-processor-kwargs '{"size": {"longest_edge": 25165824}}'

and the request provides a modality-scoped video override:

{
    "mm_processor_kwargs": {
        "videos_kwargs": {
            "size": {
                "longest_edge": Y,
            }
        }
    }
}

After resolving the effective video kwargs, vLLM prepares the kwargs for HF as:

{
    "videos_kwargs": {
        "size": Y,
    },
}

with the flat size removed because HF rejects receiving the same processor kwarg both flat and under videos_kwargs.

Then, when get_hf_processor() is called, the engine-level kwargs are merged again, giving us:

{
    "size": {
        "longest_edge": 25165824,
    },
    "videos_kwargs": {
        "size": Y,
    },
}

so both representations are present again.

A parse_mm_kwargs helper could make querying the effective kwargs for each modality cleaner, but it would not by itself prevent this later merge from reintroducing the flat engine-level value. This is the real problem.

[DarkLight1337]

We have recently introduced overlay_modality_mm_kwargs to more models in #53808, so I think we need a more comprehensive solution for this, instead of just fixing the problem for one model.

[danigarciaoca]

This bug is not exclusive to Qwen3-VL. The same processing pattern exists in other image/video multimodal models: modality-specific kwargs are prepared and then passed through get_hf_processor(**video_mm_kwargs).

For example, this is the corresponding path in Gemma4:

vllm/vllm/model_executor/models/gemma4_mm.py

Lines 636 to 640 in 7d8d71e

	frame_outputs = self.info.ctx.call_hf_processor(
	self.info.get_hf_processor(**video_mm_kwargs),
	dict(text=dummy_prompt, **{"images": frames}),
	video_mm_kwargs,
	)

So I think the underlying problem is structural. vLLM is currently trying to support the same semantic processor option both as a flat mm_processor_kwargs value and in the HF-style modality-scoped representation (images_kwargs / videos_kwargs / audio_kwargs). With the current repeated shallow merges, those two representations cannot be handled safely as if they were interchangeable, because the merge only sees different top-level keys.

I think the comprehensive solution should therefore happen at the common mm_processor_kwargs handling layer: either normalize modality-specific kwargs into a single representation from the beginning, or define which keys must be modality-scoped and reject/normalize them when they are provided flat. That removes the ambiguity at its source.

In the meantime, the mm_kwargs_are_merged flag I introduced in InputProcessingContext.call_hf_processor() does not change the behavior of any existing caller. It defaults to False, so all other models continue following the current merge path unless a caller explicitly opts into the already-merged behavior. The tests added in this PR cover that distinction.

[DarkLight1337]

either normalize modality-specific kwargs into a single representation from the beginning, or define which keys must be modality-scoped and reject/normalize them when they are provided flat. That removes the ambiguity at its source.

Yes this is what I mean by my proposed parse_mm_kwargs, which should normalize all applicable kwargs into images_kwargs, videos_kwargs, etc. Then vLLM should explicitly get kwargs arguments by accessing parsed_mm_kwargs["images_kwargs"]["size"], for example.

[danigarciaoca]

Now I think that understand your proposal.

Would the idea be to replace the modality-specific overlay in get_merged_mm_kwargs() with something along these lines?

def get_merged_mm_kwargs(
    self,
    kwargs: Mapping[str, object],
    ...
) -> dict[str, Any]:
    mm_config = self.model_config.get_multimodal_config()
    merged = mm_config.merge_mm_processor_kwargs(kwargs)
    return parse_mm_kwargs(merged, ...)

So there would no longer be a modality argument, and parse_mm_kwargs would normalize all applicable flat kwargs into their corresponding images_kwargs, videos_kwargs, audio_kwargs, etc.

For that, I assume parse_mm_kwargs would need access to the kwargs supported by each scope, e.g. through the processor's ProcessingKwargs / valid_processor_kwargs annotations, so it can determine something conceptually like:

image processor kwargs:
    size
    min_pixels
    max_pixels
    ...

video processor kwargs:
    size
    min_pixels
    max_pixels
    fps
    num_frames
    do_sample_frames
    ...

and preserve the current precedence:

explicit scoped value > applicable flat value > absent

The precedence could be implemented conceptually as:

video_kwargs = {}

# Flat/shared kwargs provide defaults for every applicable scope.
for key, value in flat_kwargs.items():
    if key in video_supported_kwargs:
        video_kwargs[key] = value

# Explicitly scoped kwargs override the flat defaults.
video_kwargs.update(kwargs.get("videos_kwargs", {}))

Putting that together, an input like:

{
    "size": X,
    "videos_kwargs": {
        "size": Y,
    },
}

would normalize to something like:

{
    "images_kwargs": {
        "size": X,
    },
    "videos_kwargs": {
        "size": Y,
    },
}

with the normalized flat size no longer remaining at the top level.

Is that what you have in mind?

[DarkLight1337]

Yeah that would work

[danigarciaoca]

Cool, thanks! Who do you think should take this forward then?

I’m asking because overlay_modality_mm_kwargs is already being extended to more models in:

#54527 --> f2b7ae1

so I’m not sure whether this broader normalization should be handled as part of that work or separately here.

[DarkLight1337]

Let's fix the problem first before merging that other PR

[Hotragn]

Nice write-up in #56363 -- the reproductions are precise enough to check against
neighbouring code, and the images_kwargs half reproduces one file over on a
model this PR does not touch.

Qwen2_5OmniThinkerMultiModalProcessor._call_hf_processor carries a near-copy of
the video size block you are rewriting (qwen2_5_omni_thinker.py:848-863 on
main), and then hands the request kwargs straight into a combined HF call:

hf_inputs = self.info.ctx.call_hf_processor(
    self.info.get_hf_processor(**hf_processor_mm_kwargs),
    dict(text=prompt_text, **mm_data),
    hf_processor_mm_kwargs,   # nested images_kwargs / videos_kwargs, unresolved
)

Qwen2_5OmniThinkerProcessingInfo derives from Qwen2_5_VLProcessingInfo
(qwen2_5_omni_thinker.py:356), so its image processor is the same
Qwen2VLImageProcessor whose resize raises
`size` dict must contain 'shortest_edge' and 'longest_edge' keys
in your second traceback. Your reproduction #2 payload --
{"images_kwargs": {"size": {"longest_edge": 16_777_216}}} -- lands there for
exactly the same reason: nothing resolves the partial size against the
processor defaults. Qwen3OmniMoeThinkerMultiModalProcessor
(qwen3_omni_moe_thinker.py:1219) inherits _call_hf_processor unchanged, so
both models sit on that path.

Your duplicate-kwarg case (#1) reaches the same block, but only in a mixed form
there: the merge on :848 is deliberately called without modality, so a
nested-only override never trips
merged.keys() & {"size", "min_pixels", "max_pixels"}. It takes a flat
max_pixels/min_pixels plus a nested videos_kwargs["size"] -- then the
block writes a flat size while the nested dict survives into the same call.

One thing worth knowing before porting anything there, because I already tripped
on it in #54527: I originally threaded modality="video" into that Omni block by
analogy with qwen3_vl.py:1371, and it was strictly worse than main. Omni does
not split the call -- it pops only audios, so images and videos go through
one hf_processor(...), and the flat size that block synthesizes from the
video processor defaults then lands on the images too. I reverted it and left a
comment at that site recording why.

Your approach sidesteps that: resolving the size and writing the result back
under videos_kwargs / images_kwargs keeps it modality-scoped, which is safe in
a combined call in a way a flat size is not.

Not asking you to widen this PR -- happy to pick the Omni site up as a follow-up
if you would rather keep this one to Qwen3-VL. Flagging it mainly because
@DarkLight1337 has asked more than once that this class of fix land in one go
rather than one CI run per model, so it is probably worth a decision either way.

[Hotragn]

Data for the parse_mm_kwargs design, from having threaded modality= through the
vLLM-side read sites in #54527. To be clear up front: I am not opening a competing PR.
#54527 rebases onto whatever lands here, and I am happy to convert its read sites to the
new accessor afterwards so this PR does not have to carry them.

Three constraints I checked against transformers 5.16.1 and current main (b6e2aa748b).

1. The per-modality key set is not knowable from the processor class.

ProcessorMixin._merge_kwargs builds it as
ModelProcessorKwargs.__annotations__[modality].__annotations__ unioned with
getattr(self.image_processor / self.video_processor, "valid_kwargs").__annotations__.
The source comment says why: "Some preprocessors define a set of accepted valid_kwargs
(currently only vision). In those cases, we don't declare a ModalityKwargs attribute in
the TypedDict."

Qwen3-VL is exactly that case:

>>> list(Qwen3VLProcessorKwargs.__annotations__["images_kwargs"].__annotations__)
['do_convert_rgb', 'do_resize', 'size', 'default_to_square', 'crop_size', 'resample',
 'do_rescale', 'rescale_factor', 'do_normalize', 'image_mean', 'image_std', 'do_pad',
 'pad_size', 'do_center_crop', 'data_format', 'input_data_format', 'device',
 'return_tensors', 'disable_grouping', 'image_seq_length']

Qwen3VLProcessorKwargs declares only _defaults, so that is the base ImagesKwargs
verbatim — and min_pixels / max_pixels are not in it. They are declared on the
image processor's own valid_kwargs
(transformers/models/qwen2_vl/image_processing_qwen2_vl.py:55-56). A normalizer keyed
off the processor class would silently fail to route precisely the keys this thread is
about.

That constrains placement more than naming: InputProcessingContext.get_hf_processor
calls get_merged_mm_kwargs in order to construct the processor
(context.py:241 -> cached_processor_from_config(**merged_kwargs)), so a
parse_mm_kwargs that needs an instantiated processor cannot run at that call site.
Either it normalizes earlier against a default-built processor, or construction sites keep
passing raw kwargs and only the read sites parse.

2. Normalization has to fan out, not move.

17 of the 20 base ImagesKwargs keys are also VideosKwargs keys — size, do_resize,
resample, do_normalize, do_rescale, crop_size, do_pad, image_mean, image_std,
data_format, ... HF gets flat keys rather than popping them, with the comment
"modality-specific processors can have overlapping kwargs". There is a concrete vLLM
case: MultiModalConfig.merge_mm_processor_kwargs injects a flat do_normalize=False /
do_rescale=False when mm_device_do_normalize is set
(multimodal.py:503-505).
Under normalization those have to land in both scoped dicts, not be assigned to one.

3. The merge that would become the canonical one is shallow.

multimodal.py:506
is return kwargs | dict(inference_kwargs), so a request-level videos_kwargs replaces
the engine-level one wholesale:

vllm serve ... --mm-processor-kwargs '{"videos_kwargs": {"fps": 2}}'
request:                              {"videos_kwargs": {"size": {...}}}
merged:                               {"videos_kwargs": {"size": {...}}}   # fps silently dropped

That is already true today, but it only bites people who are already using the nested form
at both levels. Once nesting is the only representation it becomes the main path, so the
design needs a per-modality two-level merge.

One behaviour that already matches HF and is worth preserving. When a scoped dict is
present but does not carry a key, _merge_kwargs falls back to the flat kwarg per key
(if kwarg_value == "__empty__" and modality_key in non_modality_kwargs) — a scoped dict
does not shadow the flat namespace wholesale. That is exactly what
overlay_modality_mm_kwargs does with merged | dict(scoped). Whatever the accessor ends
up being called, keeping those semantics is what keeps vLLM's budget reads agreeing with
what HF will actually compute.

Scope, for sizing the change. 44 get_merged_mm_kwargs call sites across 30 files on
b6e2aa748b. The split that decides the API is whether the merged dict is consumed as a
value inside vLLM (token / patch / pixel budgets — 12 of them, the ones #54527 scopes) or
handed to HF (processor construction or __call__ — those must pass the nested dicts
through untouched). #54527's description has the per-model table if that is useful here.

AI assistance was used to research and draft this comment; I verified each claim against
the code before posting.