[V1] Support Pixtral-HF on V1

[ywang96]

Support Transformers compatible Pixtral checkpoints on V1

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.
Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can do one of these:

• Add ready label to the PR
• Enable auto-merge.

🚀

[ywang96]

cc @mgoin

Currently I'm facing some difficulties on how to patch the result image embeddings tensors with the [IMG_BREAK] and [IMG_END] tokens so that the shape indeed matches the placeholder ranges for fine-grained scheduling. I've verified this is indeed the missing piece we need.

PlaceholderRange: [{'offset': 10, 'length': 2795}]
Image embedding shape: torch.Size([2752, 5120])
# of image tokens:  2752
# of image break tokens:  42
# of image end tokens:  1

and 2752 + 42 + 1 = 2795

For mistral-format Pixtral, this wasn't an issue because MultiModalKwargs contain the actual complete image token ids (with break and end included) so it's easy to patch. See

vllm/vllm/model_executor/models/pixtral.py

Lines 227 to 256 in 72d9c31

	def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
	image_input, image_tokens = self._parse_and_validate_image_input(
	**kwargs)
	if image_input is None:
	return None
	vision_embeddings = self._process_image_input(image_input)
	# NOTE: We patch the outputs of the vision encoder with embeddings
	# from `[IMG_BREAK]` and `[IMG_END]` tokens.
	image_embeds = self.language_model.get_input_embeddings(image_tokens)
	image_token_mask = image_tokens == self.vision_args.image_token_id
	image_embeds[image_token_mask] = vision_embeddings
	# NOTE: Image embeddings are split into separate tensors for each image
	# by the indices of `[IMG_END]` token.
	image_end_condition = (image_tokens == PIXTRAL_12B_IMAGE_END_ID) | (
	image_tokens == PIXTRAL_LARGE_IMAGE_END_ID)
	split_indices = torch.where(image_end_condition)[0] + 1
	if len(split_indices) <= 1:
	# Do not split, return as tensor of shape [1, fs, hs]
	return image_embeds.unsqueeze(0)
	# If the last split index is the last index in image_tokens, we
	# ignore it to avoid empty split tensor
	if split_indices[-1] == len(image_tokens):
	split_indices = split_indices[:-1]
	image_embeds = image_embeds.tensor_split(split_indices.cpu())
	return image_embeds

[DarkLight1337]

I think we can close this now? Actually I just tried both single and multi-image input, it still doesn't work as of now.

[lk-chen]

Per my test, there are several cases:

1. Inference one-image VLLM_USE_V1=1 python examples/offline_inference/vision_language.py --model pixtral_hf --num-prompts=1, works ✅
2. Inference multi-image VLLM_USE_V1=1 python examples/offline_inference/vision_language_multi_image.py --model-type pixtral_hf, works ✅
3. inference one-image VLLM_USE_V1=1 python examples/offline_inference/vision_language.py --model pixtral_hf --num-prompts=2, failed ❎ with error

ERROR 02-10 08:06:05 core.py:208] File "/home/ray/default/vllm/vllm/model_executor/models/utils.py", line 378, in _merge_multimodal_embeddings
ERROR 02-10 08:06:05 core.py:208] raise ValueError(
ERROR 02-10 08:06:05 core.py:208] ValueError: Attempted to assign 0 = 0 multimodal tokens to 4 placeholders

I think the bug is from GpuModelRunner._gather_encoder_outputs where the offset calculation didn't take image break and image end token into consideration.

This should be fixed with #13080