[MM][Gemma4] Use video profiling hints in encoder budget (#41837)

Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
Co-authored-by: lesj0610 <lesj0610@users.noreply.github.com>

tests/models/multimodal/processing/test_gemma4.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from collections.abc import Mapping
+
 import pytest
 from PIL import Image as PILImage
 
@@ -102,6 +104,39 @@ def test_get_mm_max_tokens_per_item_respects_configured_max_soft_tokens(
     assert tokens["video"] == 32 * (70 + 2 + 6)
 
 
+@pytest.mark.parametrize(
+    ("limit_mm_per_prompt", "expected_video_tokens"),
+    [
+        ({"video": 1}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1}}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 1}}, 1 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 8}}, 8 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 32}}, 32 * (70 + 2 + 6)),
+        ({"video": {"count": 1, "num_frames": 40}}, 32 * (70 + 2 + 6)),
+    ],
+)
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_get_mm_max_tokens_per_item_respects_configured_video_num_frames(
+    model_id: str,
+    limit_mm_per_prompt: Mapping[str, int | Mapping[str, int]],
+    expected_video_tokens: int,
+):
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    tokens = processor.info.get_mm_max_tokens_per_item(
+        seq_len=ctx.model_config.max_model_len,
+        mm_counts={"video": 1},
+    )
+
+    assert tokens is not None
+    assert tokens["image"] == 280
+    assert tokens["video"] == expected_video_tokens
+
+
 @pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
 def test_get_prompt_updates_respects_nested_max_soft_tokens(model_id: str):
     ctx = build_model_context(

tests/models/utils.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from dataclasses import dataclass
 from typing import Any
 
@@ -277,7 +277,7 @@ def build_model_context(
     dtype: ModelDType = "auto",
     model_config_kwargs: dict[str, Any] | None = None,
     mm_processor_kwargs: dict[str, Any] | None = None,
-    limit_mm_per_prompt: dict[str, int] | None = None,
+    limit_mm_per_prompt: Mapping[str, int | Mapping[str, int]] | None = None,
     mm_processor_cache_gb: int = 0,
 ):
     """Creates an InputProcessingContext for a given model.
@@ -300,7 +300,10 @@ def build_model_context(
     )
 
     model_config_kwargs = model_config_kwargs or {}
-    limit_mm_per_prompt = limit_mm_per_prompt or {}
+    limit_mm_per_prompt = {
+        modality: dict(limit) if isinstance(limit, Mapping) else limit
+        for modality, limit in (limit_mm_per_prompt or {}).items()
+    }
     model_config = ModelConfig(
         model_id,
         runner=runner,

vllm/model_executor/models/gemma4_mm.py

@@ -246,7 +246,15 @@ class Gemma4ProcessingInfo(BaseProcessingInfo):
             processor = self.get_hf_processor()
             tokens["audio"] = processor.audio_seq_length
         # Video: each frame ≤ 70 soft tokens + boi + eoi + ~6 ts tokens.
-        tokens["video"] = _VIDEO_MAX_FRAMES * (_VIDEO_MAX_SOFT_TOKENS + 2 + 6)
+        num_frames = _VIDEO_MAX_FRAMES
+        mm_config = self.ctx.model_config.get_multimodal_config()
+        video_opts = mm_config.limit_per_prompt.get("video")
+        if (
+            isinstance(video_opts, VideoDummyOptions)
+            and video_opts.num_frames is not None
+        ):
+            num_frames = min(num_frames, video_opts.num_frames)
+        tokens["video"] = num_frames * (_VIDEO_MAX_SOFT_TOKENS + 2 + 6)
         return tokens
 
     def get_data_parser(self) -> MultiModalDataParser: