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Keep grids tensor on CPU in multimodal kwargs #2019

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Keep grids tensor on CPU in multimodal kwargs #2019

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2 changes: 1 addition & 1 deletion vllm/transformers_utils/processors/ovis2_5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -669,7 +669,7 @@ def preprocess_multidata(

# grids/placeholder
grid_t = 1
grids = torch.tensor([[grid_t, Ty, Tx]] * B, device=flatten_patches.device)
grids = torch.tensor([[grid_t, Ty, Tx]] * B, device='cpu')
visual_placeholders = [
self.construct_visual_placeholders([grid_t, Ty, Tx], is_video=False)
for _ in range(B)
Expand Down
14 changes: 8 additions & 6 deletions vllm/worker/hpu_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1901,14 +1901,16 @@ def _prepare_prompt(
"false").lower() in ("1", "true", "yes")
if use_mediapipe:
# With mediapipe path some tensors will already be on HPU, we only move to HPU if needed
for key in multi_modal_kwargs.keys():
if hasattr(multi_modal_kwargs[key], "device"
) and multi_modal_kwargs[key].device != self.device:
multi_modal_kwargs[key] = self.move_to_device(
multi_modal_kwargs[key])
for key, value in multi_modal_kwargs.items():
if key == "grids":
continue # keeping grids on CPU
if hasattr(value, "device") and value.device != self.device:
multi_modal_kwargs[key] = self.move_to_device(value)
else:
multi_modal_kwargs = MultiModalKwargs.as_kwargs(multi_modal_kwargs,
device=self.device)
if "grids" in multi_modal_kwargs: # keeping grids on CPU
multi_modal_kwargs["grids"] = multi_modal_kwargs["grids"].cpu()

return PreparePromptMetadata(input_tokens=input_tokens_tensor,
input_positions=input_positions,
Expand Down Expand Up @@ -2775,7 +2777,7 @@ def create_dummy_multi_modal_seq_group_metadata(self, group_id, img_args,
num_image_tokens = int(image_h * image_w //
(vit_cfg.hidden_stride**2))
image_grid_thw = torch.tensor([[1, image_h, image_w]],
dtype=torch.int64)
dtype=torch.int64, device='cpu')

pixel_values = torch.randn(1,
image_grid_thw[0].prod(),
Expand Down
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