Skip to content

[Bugfix][Model] fix mllama multi-image #14883

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Apr 1, 2025
Merged

[Bugfix][Model] fix mllama multi-image #14883

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
0c7b943
fix mllama multi-image
yma11 Mar 16, 2025
ac06066
refine unpack
yma11 Mar 21, 2025
8f9a1ce
fix UT
yma11 Mar 29, 2025
fd87971
Revert "fix UT"
yma11 Mar 31, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion tests/models/encoder_decoder/vision_language/test_mllama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -212,7 +212,7 @@ def _run_test(
with vllm_runner(model,
dtype=dtype,
max_model_len=4096,
max_num_seqs=2,
max_num_seqs=3,
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
Expand Down
34 changes: 28 additions & 6 deletions vllm/model_executor/models/mllama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1235,11 +1235,34 @@ def sample(
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens

def unpack_data(self,
image_data: Union[List[torch.Tensor], torch.Tensor],
padding_value=0) -> torch.Tensor:
if isinstance(image_data, torch.Tensor):
# torch.Tensor
return image_data
else:
assert isinstance(
image_data[0],
torch.Tensor), "Image data is not properly batched."
# List[torch.Tensor]
bsz = len(image_data)
max_length = max(t.size(0) for t in image_data)
trailing_dims = image_data[0].shape[1:]
for data in image_data:
cur_trailing_dims = data.shape[1:]
assert cur_trailing_dims == trailing_dims
output_tensor = torch.full((bsz, max_length, *trailing_dims),
padding_value,
dtype=image_data[0].dtype,
device=image_data[0].device)
for i, t in enumerate(image_data):
output_tensor[i, :t.size(0)] = t
return output_tensor

def _parse_and_validate_image_input(self, **kwargs: object):
# tensor with the same shape will be batched together by
# MultiModalKwargs.batch, so pixel_values here can be:
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@DarkLight1337 @ywang96 I think it is a common problem that images have different sizes and we need to pad them from list of tensors with different shape to one tensor. (See these code comments for details). Is there any utility functions for this in vLLM?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is usually done as part of the HF processor. If the HF processor doesn't do this, you can apply it manually like in Pixtral-HF.

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Thanks! Given that, I think it is OK to implement the unpacking in mllama.py.

# - List[List[torch.Tensor]]:
# with shape (num_tiles, 3, image_res, image_res)
# - List[torch.Tensor]:
# with shape (num_image, num_tiles, 3, image_res, image_res)
# - torch.Tensor:
Expand Down Expand Up @@ -1274,10 +1297,9 @@ def _parse_and_validate_image_input(self, **kwargs: object):

return MllamaImagePixelInputs(
type="pixel_values",
data=pixel_values,
aspect_ratio_ids=aspect_ratio_ids,
aspect_ratio_mask=aspect_ratio_mask,
)
data=self.unpack_data(pixel_values),
aspect_ratio_ids=self.unpack_data(aspect_ratio_ids),
aspect_ratio_mask=self.unpack_data(aspect_ratio_mask))

if image_embeds is not None:
raise NotImplementedError
Expand Down