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[Bugfix] fix encoder cache hang in Qwen3VL (#32684)

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Signed-off-by: JJJYmmm <92386084+JJJYmmm@users.noreply.github.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Roger Wang <hey@rogerw.io>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
This commit is contained in:
JJJYmmm
2026-01-25 13:17:31 +08:00
committed by GitHub
parent ff6c1da4e6
commit 7e67df5570
3 changed files with 54 additions and 43 deletions
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19
tests/models/multimodal/processing/test_tensor_schema.py
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@@ -48,18 +48,6 @@ VideoInput: TypeAlias = (
AudioInput = list[tuple[np.ndarray, int]]
MM_OPTIONS_OVERRIDES = {
# Qwen3-VL's default profiling video size (64x64) can cause trouble
# after resizing, so we override it here for testing.
"qwen3_vl": dict(
video=VideoDummyOptions(num_frames=128, width=256, height=256),
),
"qwen3_vl_moe": dict(
video=VideoDummyOptions(num_frames=128, width=256, height=256),
),
}
def _resize_data(
_data: Image.Image | np.ndarray, size_factor: float
) -> Image.Image | np.ndarray:
@@ -73,12 +61,12 @@ def _resize_data(
elif is_list_of(_data, Image.Image):
W, H = next(iter(_data)).width, next(iter(_data)).height
T = len(_data)
T, W, H = map(lambda x: max(int(x * size_factor), 1), (T, W, H))
T, W, H = map(lambda x: max(int(x * size_factor), 2), (T, W, H))
return [d.resize((W, H)) for d in _data[:T]]
# Video input with numpy arrays
elif isinstance(_data, np.ndarray) and _data.ndim >= 4:
T, H, W, C = _data.shape[-4:]
T, H, W = map(lambda x: max(int(x * size_factor), 1), (T, H, W))
T, H, W = map(lambda x: max(int(x * size_factor), 2), (T, H, W))
return _data[..., :T, :H, :W, :C]
# Audio input
elif isinstance(_data, np.ndarray) and _data.ndim == 1:
@@ -103,8 +91,6 @@ def create_batched_mm_kwargs(
processor: BaseMultiModalProcessor,
size_factors: tuple[float, ...] = (1.0, 0.5, 0.25),
) -> Iterable[tuple[str, int, BatchedTensorInputs]]:
model_type = model_config.hf_config.model_type
processing_info = processor.info
dummy_inputs = processor.dummy_inputs
supported_mm_limits = processing_info.get_supported_mm_limits()
@@ -115,7 +101,6 @@ def create_batched_mm_kwargs(
processor_inputs = dummy_inputs.get_dummy_processor_inputs(
seq_len=model_config.max_model_len,
mm_counts=mm_counts,
mm_options=MM_OPTIONS_OVERRIDES.get(model_type),
)
mm_data = processor_inputs.mm_data
resized_mm_data = {