vllm.model_executor.models.aya_vision

AyaVisionDummyInputsBuilder

Bases: BaseDummyInputsBuilder[AyaVisionProcessingInfo]

Source code in vllm/model_executor/models/aya_vision.py

class AyaVisionDummyInputsBuilder(
        BaseDummyInputsBuilder[AyaVisionProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        processor = self.info.get_hf_processor()
        image_token = processor.image_token

        return image_token * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        image_size = \
            self.info.get_image_size_with_most_features()

        return {
            "image":
            self._get_dummy_images(width=image_size.width,
                                   height=image_size.height,
                                   num_images=num_images)
        }

get_dummy_mm_data

get_dummy_mm_data(
    seq_len: int, mm_counts: Mapping[str, int]
) -> MultiModalDataDict

Source code in vllm/model_executor/models/aya_vision.py

def get_dummy_mm_data(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> MultiModalDataDict:
    num_images = mm_counts.get("image", 0)
    image_size = \
        self.info.get_image_size_with_most_features()

    return {
        "image":
        self._get_dummy_images(width=image_size.width,
                               height=image_size.height,
                               num_images=num_images)
    }

get_dummy_text

get_dummy_text(mm_counts: Mapping[str, int]) -> str

Source code in vllm/model_executor/models/aya_vision.py

def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
    num_images = mm_counts.get("image", 0)

    processor = self.info.get_hf_processor()
    image_token = processor.image_token

    return image_token * num_images

AyaVisionForConditionalGeneration

Bases: Module, SupportsMultiModal, SupportsPP

Source code in vllm/model_executor/models/aya_vision.py

@MULTIMODAL_REGISTRY.register_processor(
    AyaVisionMultiModalProcessor,
    info=AyaVisionProcessingInfo,
    dummy_inputs=AyaVisionDummyInputsBuilder)
class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                                        SupportsPP):

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            # mapping for new names in checkpoint saved after transformers v4.52
            "model.language_model.": "language_model.model.",
            "model.vision_tower.": "vision_tower.",
            "model.multi_modal_projector.": "multi_modal_projector.",
            "lm_head.": "language_model.lm_head.",
        })

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<image>"

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: AyaVisionConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        num_hidden_layers = _get_num_hidden_layers(config)
        self.config = config
        self.quant_config = quant_config
        self.multimodal_config = multimodal_config

        self.vision_tower = SiglipVisionModel(
            config.vision_config,
            quant_config,
            num_hidden_layers_override=num_hidden_layers,
            prefix=maybe_prefix(prefix, "vision_model"))
        self.vocab_size = config.text_config.vocab_size
        self.multi_modal_projector = AyaVisionMultiModalProjector(config)
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "model"),
            # Cohere2ForCausalLM and CohereForCausalLM are the same on vllm
            architectures=["Cohere2ForCausalLM"])

    @property
    def dtype(self):
        return next(self.parameters()).dtype

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
                                  pixel_values: torch.Tensor,
                                  **kwargs) -> torch.Tensor:
        target_dtype = vision_tower.get_input_embeddings().weight.dtype
        image_features = vision_tower(pixel_values.to(dtype=target_dtype),
                                      **kwargs)

        def select_features(leaf: torch.Tensor):
            return self._select_image_features(
                leaf,
                strategy=self.config.vision_feature_select_strategy,
            )

        return cast(
            Union[torch.Tensor, tuple[torch.Tensor, ...]],
            json_map_leaves(select_features, image_features),
        )

    def _select_image_features(self, image_features: torch.Tensor, *,
                               strategy: str) -> torch.Tensor:
        if strategy == "default":
            return image_features[:, 1:]
        elif strategy == "full":
            return image_features

        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    def _process_image_input(self, image_input: AyaVisionImagePixelInputs,
                             **kwargs) -> list[torch.Tensor]:
        assert self.vision_tower is not None
        pixel_values = image_input["pixel_values"]
        num_patches = image_input["num_patches"]
        image_features = self._image_pixels_to_features(
            self.vision_tower, pixel_values=pixel_values)
        image_embeds = self.multi_modal_projector(image_features)
        return [
            e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
        ]

    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
        h = w = self.config.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            if d.shape != expected_dims:
                raise ValueError(
                    "The expected shape of pixel values per image per batch "
                    f"is {expected_dims}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        num_patches = kwargs.pop("num_patches", None)
        image_embeds = kwargs.pop("image_embeds", None)
        assert image_embeds is None, "Aya Vision does not support image_embeds."

        if not isinstance(pixel_values, (torch.Tensor, list)):
            raise ValueError("Incorrect type of pixel values. "
                             f"Got type: {type(pixel_values)}")
        if num_patches is not None and not isinstance(num_patches,
                                                      (torch.Tensor, list)):
            raise ValueError("Incorrect type of num_patches. "
                             f"Got type: {type(num_patches)}")

        pixel_values = flatten_bn(pixel_values, concat=True)
        num_patches = flatten_bn(num_patches, concat=True)

        return AyaVisionImagePixelInputs(
            type="pixel_values",
            pixel_values=self._validate_pixel_values(pixel_values),
            num_patches=num_patches,
        )

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input, **kwargs)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids=input_ids,
                inputs_embeds=inputs_embeds,
                multimodal_embeddings=multimodal_embeddings,
                placeholder_token_id=self.config.image_token_index,
            )

        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

config instance-attribute

config = config

dtype property

dtype

hf_to_vllm_mapper class-attribute instance-attribute

hf_to_vllm_mapper = WeightsMapper(
    orig_to_new_prefix={
        "model.language_model.": "language_model.model.",
        "model.vision_tower.": "vision_tower.",
        "model.multi_modal_projector.": "multi_modal_projector.",
        "lm_head.": "language_model.lm_head.",
    }
)

language_model instance-attribute

language_model = init_vllm_registered_model(
    vllm_config=vllm_config,
    hf_config=text_config,
    prefix=maybe_prefix(prefix, "model"),
    architectures=["Cohere2ForCausalLM"],
)

multi_modal_projector instance-attribute

multi_modal_projector = AyaVisionMultiModalProjector(config)

multimodal_config instance-attribute

multimodal_config = multimodal_config

quant_config instance-attribute

quant_config = quant_config

vision_tower instance-attribute

vision_tower = SiglipVisionModel(
    vision_config,
    quant_config,
    num_hidden_layers_override=num_hidden_layers,
    prefix=maybe_prefix(prefix, "vision_model"),
)

vocab_size instance-attribute

vocab_size = vocab_size

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')

Source code in vllm/model_executor/models/aya_vision.py

def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
    super().__init__()
    config: AyaVisionConfig = vllm_config.model_config.hf_config
    quant_config = vllm_config.quant_config
    multimodal_config = vllm_config.model_config.multimodal_config
    num_hidden_layers = _get_num_hidden_layers(config)
    self.config = config
    self.quant_config = quant_config
    self.multimodal_config = multimodal_config

    self.vision_tower = SiglipVisionModel(
        config.vision_config,
        quant_config,
        num_hidden_layers_override=num_hidden_layers,
        prefix=maybe_prefix(prefix, "vision_model"))
    self.vocab_size = config.text_config.vocab_size
    self.multi_modal_projector = AyaVisionMultiModalProjector(config)
    self.language_model = init_vllm_registered_model(
        vllm_config=vllm_config,
        hf_config=config.text_config,
        prefix=maybe_prefix(prefix, "model"),
        # Cohere2ForCausalLM and CohereForCausalLM are the same on vllm
        architectures=["Cohere2ForCausalLM"])

_image_pixels_to_features

_image_pixels_to_features(
    vision_tower: SiglipVisionModel,
    pixel_values: Tensor,
    **kwargs,
) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
                              pixel_values: torch.Tensor,
                              **kwargs) -> torch.Tensor:
    target_dtype = vision_tower.get_input_embeddings().weight.dtype
    image_features = vision_tower(pixel_values.to(dtype=target_dtype),
                                  **kwargs)

    def select_features(leaf: torch.Tensor):
        return self._select_image_features(
            leaf,
            strategy=self.config.vision_feature_select_strategy,
        )

    return cast(
        Union[torch.Tensor, tuple[torch.Tensor, ...]],
        json_map_leaves(select_features, image_features),
    )

_parse_and_validate_image_input

_parse_and_validate_image_input(
    **kwargs: object,
) -> Optional[AyaVisionImagePixelInputs]

Source code in vllm/model_executor/models/aya_vision.py

def _parse_and_validate_image_input(
        self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
    pixel_values = kwargs.pop("pixel_values", None)
    num_patches = kwargs.pop("num_patches", None)
    image_embeds = kwargs.pop("image_embeds", None)
    assert image_embeds is None, "Aya Vision does not support image_embeds."

    if not isinstance(pixel_values, (torch.Tensor, list)):
        raise ValueError("Incorrect type of pixel values. "
                         f"Got type: {type(pixel_values)}")
    if num_patches is not None and not isinstance(num_patches,
                                                  (torch.Tensor, list)):
        raise ValueError("Incorrect type of num_patches. "
                         f"Got type: {type(num_patches)}")

    pixel_values = flatten_bn(pixel_values, concat=True)
    num_patches = flatten_bn(num_patches, concat=True)

    return AyaVisionImagePixelInputs(
        type="pixel_values",
        pixel_values=self._validate_pixel_values(pixel_values),
        num_patches=num_patches,
    )

_process_image_input

_process_image_input(
    image_input: AyaVisionImagePixelInputs, **kwargs
) -> list[Tensor]

Source code in vllm/model_executor/models/aya_vision.py

def _process_image_input(self, image_input: AyaVisionImagePixelInputs,
                         **kwargs) -> list[torch.Tensor]:
    assert self.vision_tower is not None
    pixel_values = image_input["pixel_values"]
    num_patches = image_input["num_patches"]
    image_features = self._image_pixels_to_features(
        self.vision_tower, pixel_values=pixel_values)
    image_embeds = self.multi_modal_projector(image_features)
    return [
        e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
    ]

_select_image_features

_select_image_features(
    image_features: Tensor, *, strategy: str
) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def _select_image_features(self, image_features: torch.Tensor, *,
                           strategy: str) -> torch.Tensor:
    if strategy == "default":
        return image_features[:, 1:]
    elif strategy == "full":
        return image_features

    raise ValueError(f"Unexpected select feature strategy: {strategy}")

_validate_pixel_values

_validate_pixel_values(data: Tensor) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
    h = w = self.config.vision_config.image_size
    expected_dims = (3, h, w)

    def _validate_shape(d: torch.Tensor):
        if d.shape != expected_dims:
            raise ValueError(
                "The expected shape of pixel values per image per batch "
                f"is {expected_dims}. You supplied {tuple(d.shape)}.")

    for d in data:
        _validate_shape(d)

    return data

compute_logits

compute_logits(
    hidden_states: Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[Tensor]

Source code in vllm/model_executor/models/aya_vision.py

def compute_logits(
    self,
    hidden_states: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
    return self.language_model.compute_logits(hidden_states,
                                              sampling_metadata)

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
    **kwargs: object,
) -> Union[Tensor, IntermediateTensors]

Source code in vllm/model_executor/models/aya_vision.py

def forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
    **kwargs: object,
) -> Union[torch.Tensor, IntermediateTensors]:
    if intermediate_tensors is not None:
        inputs_embeds = None

    # NOTE: In v1, inputs_embeds is always generated at model runner, this
    # condition is for v0 compatibility.
    elif inputs_embeds is None:
        vision_embeddings = self.get_multimodal_embeddings(**kwargs)
        inputs_embeds = self.get_input_embeddings(input_ids,
                                                  vision_embeddings)
        input_ids = None

    hidden_states = self.language_model.model(
        input_ids=input_ids,
        positions=positions,
        intermediate_tensors=intermediate_tensors,
        inputs_embeds=inputs_embeds,
    )
    return hidden_states

get_input_embeddings

get_input_embeddings(
    input_ids: Tensor,
    multimodal_embeddings: Optional[
        MultiModalEmbeddings
    ] = None,
) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def get_input_embeddings(
    self,
    input_ids: torch.Tensor,
    multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
) -> torch.Tensor:
    inputs_embeds = self.language_model.get_input_embeddings(input_ids)
    if multimodal_embeddings is not None \
        and len(multimodal_embeddings) != 0:
        inputs_embeds = merge_multimodal_embeddings(
            input_ids=input_ids,
            inputs_embeds=inputs_embeds,
            multimodal_embeddings=multimodal_embeddings,
            placeholder_token_id=self.config.image_token_index,
        )

    return inputs_embeds

get_language_model

get_language_model() -> Module

Source code in vllm/model_executor/models/aya_vision.py

def get_language_model(self) -> torch.nn.Module:
    return self.language_model

get_multimodal_embeddings

get_multimodal_embeddings(
    **kwargs: object,
) -> MultiModalEmbeddings

Source code in vllm/model_executor/models/aya_vision.py

def get_multimodal_embeddings(self,
                              **kwargs: object) -> MultiModalEmbeddings:
    image_input = self._parse_and_validate_image_input(**kwargs)
    if image_input is None:
        return []

    return self._process_image_input(image_input, **kwargs)

get_placeholder_str classmethod

get_placeholder_str(modality: str, i: int) -> Optional[str]

Source code in vllm/model_executor/models/aya_vision.py

@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
    if modality.startswith("image"):
        return "<image>"

    raise ValueError("Only image modality is supported")

load_weights

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]

Source code in vllm/model_executor/models/aya_vision.py

def load_weights(self, weights: Iterable[tuple[str,
                                               torch.Tensor]]) -> set[str]:
    loader = AutoWeightsLoader(self)
    return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

AyaVisionImagePixelInputs

Bases: TypedDict

Source code in vllm/model_executor/models/aya_vision.py

class AyaVisionImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    pixel_values: torch.Tensor
    """
    Shape: `(num_patches_total, num_channels, height, width)`

    `num_patches_total` is the total number of patches over each image over each
    prompt in the batch.
    """

    num_patches: torch.Tensor
    """Shape: `(batch_size * num_images)`"""

num_patches instance-attribute

num_patches: Tensor

Shape: (batch_size * num_images)

pixel_values instance-attribute

pixel_values: Tensor

Shape: (num_patches_total, num_channels, height, width)

num_patches_total is the total number of patches over each image over each prompt in the batch.

type instance-attribute

type: Literal['pixel_values']

AyaVisionMultiModalProcessor

Bases: BaseMultiModalProcessor[AyaVisionProcessingInfo]

Source code in vllm/model_executor/models/aya_vision.py

class AyaVisionMultiModalProcessor(
        BaseMultiModalProcessor[AyaVisionProcessingInfo]):

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt,
            mm_data,
            mm_kwargs,
            tok_kwargs,
        )
        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        image_processor = hf_processor.image_processor

        # HF processor pops the `num_patches` kwarg, which is needed by vLLM
        if (images := mm_data.get("images")) is not None:
            parsed_images = (self._get_data_parser().parse_mm_data({
                "image":
                images
            }).get_items("image", ImageProcessorItems))
            image_sizes = [
                parsed_images.get_image_size(i)
                for i in range(len(parsed_images))
            ]

            num_patches = [
                self.info.get_num_patches(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    size=image_processor.size,
                    min_patches=image_processor.min_patches,
                    max_patches=image_processor.max_patches)
                for image_size in image_sizes
            ]
            processed_outputs["num_patches"] = torch.tensor(num_patches)

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        num_patches = hf_inputs.get("num_patches", torch.empty(0))
        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes(
                "image", num_patches),
            num_patches=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_token = hf_processor.image_token
        img_patch_token = hf_processor.img_patch_token
        image_processor = hf_processor.image_processor

        def get_replacement(item_idx: int):
            images: ImageProcessorItems = mm_items.get("image",
                                                       ImageProcessorItems)
            image_size: ImageSize = images.get_image_size(item_idx)
            num_patches = self.info.get_num_patches(
                image_width=image_size.width,
                image_height=image_size.height,
                size=image_processor.size,
                min_patches=image_processor.min_patches,
                max_patches=image_processor.max_patches,
            )
            repl = hf_processor._prompt_split_image(num_patches=num_patches)

            return PromptUpdateDetails.select_text(repl, img_patch_token)

        return [
            PromptReplacement(
                modality="image",
                target=image_token,
                replacement=get_replacement,
            )
        ]

_call_hf_processor

_call_hf_processor(
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
    tok_kwargs: Mapping[str, object],
) -> BatchFeature

Source code in vllm/model_executor/models/aya_vision.py

def _call_hf_processor(
    self,
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
    tok_kwargs: Mapping[str, object],
) -> BatchFeature:
    processed_outputs = super()._call_hf_processor(
        prompt,
        mm_data,
        mm_kwargs,
        tok_kwargs,
    )
    hf_processor = self.info.get_hf_processor(**mm_kwargs)
    image_processor = hf_processor.image_processor

    # HF processor pops the `num_patches` kwarg, which is needed by vLLM
    if (images := mm_data.get("images")) is not None:
        parsed_images = (self._get_data_parser().parse_mm_data({
            "image":
            images
        }).get_items("image", ImageProcessorItems))
        image_sizes = [
            parsed_images.get_image_size(i)
            for i in range(len(parsed_images))
        ]

        num_patches = [
            self.info.get_num_patches(
                image_width=image_size.width,
                image_height=image_size.height,
                size=image_processor.size,
                min_patches=image_processor.min_patches,
                max_patches=image_processor.max_patches)
            for image_size in image_sizes
        ]
        processed_outputs["num_patches"] = torch.tensor(num_patches)

    return processed_outputs

_get_mm_fields_config

_get_mm_fields_config(
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]

Source code in vllm/model_executor/models/aya_vision.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    num_patches = hf_inputs.get("num_patches", torch.empty(0))
    return dict(
        pixel_values=MultiModalFieldConfig.flat_from_sizes(
            "image", num_patches),
        num_patches=MultiModalFieldConfig.batched("image"),
        image_embeds=MultiModalFieldConfig.batched("image"),
    )

_get_prompt_updates

_get_prompt_updates(
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]

Source code in vllm/model_executor/models/aya_vision.py

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
    image_token = hf_processor.image_token
    img_patch_token = hf_processor.img_patch_token
    image_processor = hf_processor.image_processor

    def get_replacement(item_idx: int):
        images: ImageProcessorItems = mm_items.get("image",
                                                   ImageProcessorItems)
        image_size: ImageSize = images.get_image_size(item_idx)
        num_patches = self.info.get_num_patches(
            image_width=image_size.width,
            image_height=image_size.height,
            size=image_processor.size,
            min_patches=image_processor.min_patches,
            max_patches=image_processor.max_patches,
        )
        repl = hf_processor._prompt_split_image(num_patches=num_patches)

        return PromptUpdateDetails.select_text(repl, img_patch_token)

    return [
        PromptReplacement(
            modality="image",
            target=image_token,
            replacement=get_replacement,
        )
    ]

AyaVisionMultiModalProjector

Bases: Module

Source code in vllm/model_executor/models/aya_vision.py

class AyaVisionMultiModalProjector(nn.Module):

    def __init__(self, config: AyaVisionConfig):
        super().__init__()
        self.config = config
        self.downsample_factor = config.downsample_factor
        self.alignment_intermediate_size = getattr(
            config, "alignment_intermediate_size",
            config.text_config.hidden_size)
        self.layernorm = nn.LayerNorm(config.vision_config.hidden_size *
                                      (config.downsample_factor**2),
                                      eps=config.adapter_layer_norm_eps)

        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * (config.downsample_factor**2),
            self.alignment_intermediate_size,
            bias=True,
        )

        self.act = ACT2FN["silu"]  # SwiGLU uses SiLU activation
        # For SwiGLU, project down to half size since we split intermediate dim
        self.linear_2 = nn.Linear(self.alignment_intermediate_size // 2,
                                  config.text_config.hidden_size,
                                  bias=True)

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        image_features = self.pixel_shuffle(image_features)
        image_features = self.layernorm(image_features)
        hidden_states = self.linear_1(image_features)

        # Split along last dimension and apply SwiGLU
        x, gate = hidden_states.chunk(2, dim=-1)
        hidden_states = self.act(gate) * x

        hidden_states = self.linear_2(hidden_states)
        return hidden_states

    def pixel_shuffle(self,
                      image_features: torch.Tensor) -> torch.Tensor:  # B, S, D
        batch_size, seq_length, _ = image_features.shape
        height = width = int(seq_length**0.5)
        image_features = image_features.reshape(image_features.shape[0], width,
                                                height, -1)
        channels = image_features.shape[-1]
        image_features = image_features.reshape(
            batch_size, width, int(height / self.downsample_factor),
            int(channels * self.downsample_factor))
        image_features = image_features.permute(0, 2, 1, 3)
        image_features = image_features.reshape(
            batch_size, int(height / self.downsample_factor),
            int(width / self.downsample_factor), -1)
        image_features = image_features.permute(0, 2, 1, 3)
        return image_features

act instance-attribute

act = ACT2FN['silu']

alignment_intermediate_size instance-attribute

alignment_intermediate_size = getattr(
    config, "alignment_intermediate_size", hidden_size
)

config instance-attribute

config = config

downsample_factor instance-attribute

downsample_factor = downsample_factor

layernorm instance-attribute

layernorm = LayerNorm(
    hidden_size * downsample_factor**2,
    eps=adapter_layer_norm_eps,
)

linear_1 instance-attribute

linear_1 = Linear(
    hidden_size * downsample_factor**2,
    alignment_intermediate_size,
    bias=True,
)

linear_2 instance-attribute

linear_2 = Linear(
    alignment_intermediate_size // 2, hidden_size, bias=True
)

__init__

__init__(config: AyaVisionConfig)

Source code in vllm/model_executor/models/aya_vision.py

def __init__(self, config: AyaVisionConfig):
    super().__init__()
    self.config = config
    self.downsample_factor = config.downsample_factor
    self.alignment_intermediate_size = getattr(
        config, "alignment_intermediate_size",
        config.text_config.hidden_size)
    self.layernorm = nn.LayerNorm(config.vision_config.hidden_size *
                                  (config.downsample_factor**2),
                                  eps=config.adapter_layer_norm_eps)

    self.linear_1 = nn.Linear(
        config.vision_config.hidden_size * (config.downsample_factor**2),
        self.alignment_intermediate_size,
        bias=True,
    )

    self.act = ACT2FN["silu"]  # SwiGLU uses SiLU activation
    # For SwiGLU, project down to half size since we split intermediate dim
    self.linear_2 = nn.Linear(self.alignment_intermediate_size // 2,
                              config.text_config.hidden_size,
                              bias=True)

forward

forward(image_features: Tensor) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def forward(self, image_features: torch.Tensor) -> torch.Tensor:
    image_features = self.pixel_shuffle(image_features)
    image_features = self.layernorm(image_features)
    hidden_states = self.linear_1(image_features)

    # Split along last dimension and apply SwiGLU
    x, gate = hidden_states.chunk(2, dim=-1)
    hidden_states = self.act(gate) * x

    hidden_states = self.linear_2(hidden_states)
    return hidden_states

pixel_shuffle

pixel_shuffle(image_features: Tensor) -> Tensor

Source code in vllm/model_executor/models/aya_vision.py

def pixel_shuffle(self,
                  image_features: torch.Tensor) -> torch.Tensor:  # B, S, D
    batch_size, seq_length, _ = image_features.shape
    height = width = int(seq_length**0.5)
    image_features = image_features.reshape(image_features.shape[0], width,
                                            height, -1)
    channels = image_features.shape[-1]
    image_features = image_features.reshape(
        batch_size, width, int(height / self.downsample_factor),
        int(channels * self.downsample_factor))
    image_features = image_features.permute(0, 2, 1, 3)
    image_features = image_features.reshape(
        batch_size, int(height / self.downsample_factor),
        int(width / self.downsample_factor), -1)
    image_features = image_features.permute(0, 2, 1, 3)
    return image_features

AyaVisionProcessingInfo

Bases: BaseProcessingInfo

Source code in vllm/model_executor/models/aya_vision.py

class AyaVisionProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self) -> AyaVisionConfig:
        return self.ctx.get_hf_config(AyaVisionConfig)

    def get_hf_processor(self, **kwargs: object) -> AyaVisionProcessor:
        processor = self.ctx.get_hf_processor(AyaVisionProcessor, **kwargs)

        # Temporary workaround since this processor has multiple image tokens
        # See https://github.com/huggingface/transformers/issues/38350
        processor._check_special_mm_tokens = lambda *args, **kwargs: None

        return processor

    def get_image_processor(self) -> GotOcr2ImageProcessor:
        return self.get_hf_processor().image_processor

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None}

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_image_processor()
        height = image_processor.size['height']
        width = image_processor.size['width']
        max_patches = image_processor.max_patches
        return ImageSize(height=height * max_patches,
                         width=width * max_patches)

    def get_num_patches(self, *, image_width: int, image_height: int,
                        size: dict, min_patches: int, max_patches: int) -> int:
        """
        Calculate the number of patches needed for a given image based on size
        constraints.  This method replicates and adjusts the logic from:
        transformers/models/got_ocr2/image_processing_got_ocr2
        """
        size = get_size_dict(size, default_to_square=False)
        num_columns, num_rows = get_optimal_tiled_canvas(
            (image_height, image_width), (size["height"], size["width"]),
            min_patches, max_patches)
        num_blocks = num_columns * num_rows
        return num_blocks if num_blocks == 1 else num_blocks + 1

get_hf_config

get_hf_config() -> AyaVisionConfig

Source code in vllm/model_executor/models/aya_vision.py

def get_hf_config(self) -> AyaVisionConfig:
    return self.ctx.get_hf_config(AyaVisionConfig)

get_hf_processor

get_hf_processor(**kwargs: object) -> AyaVisionProcessor

Source code in vllm/model_executor/models/aya_vision.py

def get_hf_processor(self, **kwargs: object) -> AyaVisionProcessor:
    processor = self.ctx.get_hf_processor(AyaVisionProcessor, **kwargs)

    # Temporary workaround since this processor has multiple image tokens
    # See https://github.com/huggingface/transformers/issues/38350
    processor._check_special_mm_tokens = lambda *args, **kwargs: None

    return processor

get_image_processor

get_image_processor() -> GotOcr2ImageProcessor

Source code in vllm/model_executor/models/aya_vision.py

def get_image_processor(self) -> GotOcr2ImageProcessor:
    return self.get_hf_processor().image_processor

get_image_size_with_most_features

get_image_size_with_most_features() -> ImageSize

Source code in vllm/model_executor/models/aya_vision.py

def get_image_size_with_most_features(self) -> ImageSize:
    image_processor = self.get_image_processor()
    height = image_processor.size['height']
    width = image_processor.size['width']
    max_patches = image_processor.max_patches
    return ImageSize(height=height * max_patches,
                     width=width * max_patches)

get_num_patches

get_num_patches(
    *,
    image_width: int,
    image_height: int,
    size: dict,
    min_patches: int,
    max_patches: int,
) -> int

Calculate the number of patches needed for a given image based on size constraints. This method replicates and adjusts the logic from: transformers/models/got_ocr2/image_processing_got_ocr2

Source code in vllm/model_executor/models/aya_vision.py

def get_num_patches(self, *, image_width: int, image_height: int,
                    size: dict, min_patches: int, max_patches: int) -> int:
    """
    Calculate the number of patches needed for a given image based on size
    constraints.  This method replicates and adjusts the logic from:
    transformers/models/got_ocr2/image_processing_got_ocr2
    """
    size = get_size_dict(size, default_to_square=False)
    num_columns, num_rows = get_optimal_tiled_canvas(
        (image_height, image_width), (size["height"], size["width"]),
        min_patches, max_patches)
    num_blocks = num_columns * num_rows
    return num_blocks if num_blocks == 1 else num_blocks + 1

get_supported_mm_limits

get_supported_mm_limits() -> Mapping[str, Optional[int]]

Source code in vllm/model_executor/models/aya_vision.py

def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"image": None}

_get_layer_index

_get_layer_index(
    feature_layer_index: int, num_hidden_layers: int
) -> int

Source code in vllm/model_executor/models/aya_vision.py

def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
    if feature_layer_index < 0:
        return num_hidden_layers + feature_layer_index + 1
    return feature_layer_index

_get_num_hidden_layers

_get_num_hidden_layers(hf_config: AyaVisionConfig) -> int

Source code in vllm/model_executor/models/aya_vision.py

def _get_num_hidden_layers(hf_config: AyaVisionConfig) -> int:
    feature_layers = hf_config.vision_feature_layer
    num_hidden_layers = hf_config.vision_config.num_hidden_layers
    # If we have one feature layer, initialize up to that layer
    if isinstance(feature_layers, int):
        return _get_layer_index(feature_layers, num_hidden_layers)
    # If we have multiple feature layers, initialize up to the deepest m
    elif isinstance(feature_layers, (list, tuple)):
        return max(
            _get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
    raise TypeError(f"vision_layer_feature type: {type(feature_layers)}"
                    " is not supported")