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[Bugfix](gemma3_mm): handle flatten_batch constraint for multiple images (#6562)

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Chang Su
2025-05-23 18:11:54 -07:00
committed by GitHub
parent fefa19fec0
commit 7b02c32679
1 changed files with 15 additions and 6 deletions
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21
python/sglang/srt/models/gemma3_mm.py
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@@ -288,13 +288,22 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
"MM inputs where only some items are precomputed."
)
return torch.concat([item.precomputed_features for item in items])
pixel_values = torch.stack(
flatten_nested_list([item.pixel_values for item in items]), dim=0
)
pixel_values = pixel_values.to(device=self.vision_tower.device)
pixel_values = pixel_values.to(dtype=self.language_model.dtype())
vision_outputs = self.vision_tower(pixel_values=pixel_values)
# Process images one by one to handle flatten_batch=True constraint in vision_tower
all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
vision_outputs_list = []
for pixel_value in all_pixel_values:
# Add batch dimension for single image processing
pixel_value_batch = pixel_value.unsqueeze(0)
pixel_value_batch = pixel_value_batch.to(device=self.vision_tower.device)
pixel_value_batch = pixel_value_batch.to(dtype=self.language_model.dtype())
vision_output = self.vision_tower(pixel_values=pixel_value_batch)
vision_outputs_list.append(vision_output)
# Concatenate all vision outputs
vision_outputs = torch.cat(vision_outputs_list, dim=0)
image_features = self.multi_modal_projector(vision_outputs)
return image_features