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[CI] Fix the MultiModalKwargsItem to align with vllm changes #1568

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Merged
jcyang43 merged 1 commit into from
Jan 29, 2026
Merged

[CI] Fix the MultiModalKwargsItem to align with vllm changes #1568

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36 changes: 18 additions & 18 deletions tests/runner/test_multimodal_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,12 +98,12 @@ def test_execute_mm_encoder_single_image(self):
# Mock request state
dummy_pixel_values = torch.randn(3, 224, 224, dtype=torch.bfloat16)
dummy_grid_thw = torch.tensor([[1, 1, 1]], dtype=torch.int64)
mm_item = MultiModalKwargsItem.from_elems([
MultiModalFieldElem("image", "pixel_values", dummy_pixel_values,
MultiModalBatchedField()),
MultiModalFieldElem("image", "image_grid_thw", dummy_grid_thw,
MultiModalBatchedField())
])
mm_item = MultiModalKwargsItem({
"pixel_values":
MultiModalFieldElem(dummy_pixel_values, MultiModalBatchedField()),
"image_grid_thw":
MultiModalFieldElem(dummy_grid_thw, MultiModalBatchedField())
})

req_state = CachedRequestState(
req_id="req-1",
Expand Down Expand Up @@ -183,12 +183,12 @@ def test_execute_mm_encoder_multiple_images(self):
px_1 = torch.randn(3, 224, 224, dtype=torch.bfloat16)
grid_1 = torch.tensor([[1, 1, 1]], dtype=torch.int64)

mm_item_1 = MultiModalKwargsItem.from_elems([
MultiModalFieldElem("image", "pixel_values", px_1,
MultiModalBatchedField()),
MultiModalFieldElem("image", "image_grid_thw", grid_1,
MultiModalBatchedField())
])
mm_item_1 = MultiModalKwargsItem({
"pixel_values":
MultiModalFieldElem(px_1, MultiModalBatchedField()),
"image_grid_thw":
MultiModalFieldElem(grid_1, MultiModalBatchedField())
})

req_state_1 = CachedRequestState(
req_id="req-1",
Expand All @@ -210,12 +210,12 @@ def test_execute_mm_encoder_multiple_images(self):

px_2 = torch.randn(3, 224, 224, dtype=torch.bfloat16)
grid_2 = torch.tensor([[1, 2, 2]], dtype=torch.int64)
mm_item_2 = MultiModalKwargsItem.from_elems([
MultiModalFieldElem("image", "pixel_values", px_2,
MultiModalBatchedField()),
MultiModalFieldElem("image", "image_grid_thw", grid_2,
MultiModalBatchedField())
])
mm_item_2 = MultiModalKwargsItem({
"pixel_values":
MultiModalFieldElem(px_2, MultiModalBatchedField()),
"image_grid_thw":
MultiModalFieldElem(grid_2, MultiModalBatchedField())
})

req_state_2 = CachedRequestState(
req_id="req-2",
Expand Down
6 changes: 2 additions & 4 deletions tpu_inference/runner/multimodal_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,15 +92,15 @@ def execute_mm_encoder(self, scheduler_output: "VllmSchedulerOutput"):
return

# Batch the multi-modal inputs.
mm_kwargs = list[MultiModalKwargsItem]()
mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
# List of tuple (mm_hash, pos_info)
mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
req_state = self.runner.requests[req_id]
for mm_input_id in encoder_input_ids:
mm_feature = req_state.mm_features[mm_input_id]
mm_hash = mm_feature.identifier
mm_kwargs.append(mm_feature.data)
mm_kwargs.append((mm_feature.modality, mm_feature.data))
mm_hashes_pos.append((mm_hash, mm_feature.mm_position))

# Batch mm inputs as much as we can: if a request in the batch has
Expand Down Expand Up @@ -164,8 +164,6 @@ def execute_mm_encoder(self, scheduler_output: "VllmSchedulerOutput"):
mm_hashes_pos,
encoder_outputs,
):
if req_id not in self.runner.encoder_cache:
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mrjunwan-lang marked this conversation as resolved.
self.runner.encoder_cache[req_id] = {}

self.runner.encoder_cache[mm_hash] = scatter_mm_placeholders(
output,
Expand Down
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