[V1] Support Pixtral-HF on V1

[ywang96]

Support Transformers compatible Pixtral checkpoints on V1

[github-actions]

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[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