vllm.model_executor.models.fuyu

PyTorch Fuyu model.

_IMAGE_TOKEN_ID module-attribute

_IMAGE_TOKEN_ID = 71011

_NEWLINE_TOKEN_ID module-attribute

_NEWLINE_TOKEN_ID = 71019

FuyuDummyInputsBuilder

Bases: BaseDummyInputsBuilder[FuyuProcessingInfo]

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

class FuyuDummyInputsBuilder(BaseDummyInputsBuilder[FuyuProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

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

        return {
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_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/fuyu.py

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

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

get_dummy_text

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

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

def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
    return ""

FuyuForCausalLM

Bases: Module, SupportsMultiModal, SupportsPP

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

@MULTIMODAL_REGISTRY.register_processor(FuyuMultiModalProcessor,
                                        info=FuyuProcessingInfo,
                                        dummy_inputs=FuyuDummyInputsBuilder)
class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):

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

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

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

        self.vocab_size = config.text_config.vocab_size
        self.image_token_id = _IMAGE_TOKEN_ID
        self.image_feature_size = config.patch_size**2 * config.num_channels

        self.vision_embed_tokens = ColumnParallelLinear(
            self.image_feature_size,
            config.hidden_size,
            quant_config=quant_config,
            gather_output=True,
        )
        self.language_model = PersimmonForCausalLM(
            vllm_config=vllm_config.with_hf_config(config.text_config),
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:

        h = w = self.config.patch_size
        num_channels = self.config.num_channels
        expected_dims = num_channels * h * w

        def _validate_shape(d: torch.Tensor):
            actual_dims = d.size(-1)

            if actual_dims != expected_dims:
                expected_expr = str(expected_dims)
                raise ValueError(
                    "The expected shape of pixel values per image per batch "
                    f"per patch is {expected_expr}. "
                    f"You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data.to(self.vision_embed_tokens.weight.dtype)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[FuyuImagePatchInputs]:
        image_patches = kwargs.pop("image_patches", None)
        if image_patches is not None:
            if not isinstance(image_patches, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image patches. "
                                 f"Got type: {type(image_patches)}")

            image_patches_flat = flatten_bn(image_patches)

            return FuyuImagePatchInputs(
                type="image_patches",
                flat_data=self._validate_pixel_values(
                    flatten_bn(image_patches_flat, concat=True)),
                patches_per_image=[x.size(0) for x in image_patches_flat],
            )

        return None

    def _process_image_input(
            self, image_input: FuyuImagePatchInputs) -> MultiModalEmbeddings:
        image_patches_flat = image_input["flat_data"]
        patches_per_image = image_input["patches_per_image"]

        assert self.vision_embed_tokens is not None
        vision_embeddings_flat, _ = self.vision_embed_tokens(
            image_patches_flat)

        return vision_embeddings_flat.split(patches_per_image, dim=0)

    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)

    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,
                inputs_embeds,
                multimodal_embeddings,
                _IMAGE_TOKEN_ID,
            )
        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,
    ):
        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(
            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]:
        logits = self.language_model.logits_processor(
            self.language_model.lm_head, hidden_states, sampling_metadata)
        return logits

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

config instance-attribute

config = config

hf_to_vllm_mapper class-attribute instance-attribute

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

image_feature_size instance-attribute

image_feature_size = patch_size ** 2 * num_channels

image_token_id instance-attribute

image_token_id = _IMAGE_TOKEN_ID

language_model instance-attribute

language_model = PersimmonForCausalLM(
    vllm_config=with_hf_config(text_config),
    prefix=maybe_prefix(prefix, "language_model"),
)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

multimodal_config instance-attribute

multimodal_config = multimodal_config

vision_embed_tokens instance-attribute

vision_embed_tokens = ColumnParallelLinear(
    image_feature_size,
    hidden_size,
    quant_config=quant_config,
    gather_output=True,
)

vocab_size instance-attribute

vocab_size = vocab_size

__init__

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

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

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

    self.vocab_size = config.text_config.vocab_size
    self.image_token_id = _IMAGE_TOKEN_ID
    self.image_feature_size = config.patch_size**2 * config.num_channels

    self.vision_embed_tokens = ColumnParallelLinear(
        self.image_feature_size,
        config.hidden_size,
        quant_config=quant_config,
        gather_output=True,
    )
    self.language_model = PersimmonForCausalLM(
        vllm_config=vllm_config.with_hf_config(config.text_config),
        prefix=maybe_prefix(prefix, "language_model"),
    )
    self.make_empty_intermediate_tensors = (
        self.language_model.make_empty_intermediate_tensors)

_parse_and_validate_image_input

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

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

def _parse_and_validate_image_input(
        self, **kwargs: object) -> Optional[FuyuImagePatchInputs]:
    image_patches = kwargs.pop("image_patches", None)
    if image_patches is not None:
        if not isinstance(image_patches, (torch.Tensor, list)):
            raise ValueError("Incorrect type of image patches. "
                             f"Got type: {type(image_patches)}")

        image_patches_flat = flatten_bn(image_patches)

        return FuyuImagePatchInputs(
            type="image_patches",
            flat_data=self._validate_pixel_values(
                flatten_bn(image_patches_flat, concat=True)),
            patches_per_image=[x.size(0) for x in image_patches_flat],
        )

    return None

_process_image_input

_process_image_input(
    image_input: FuyuImagePatchInputs,
) -> MultiModalEmbeddings

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

def _process_image_input(
        self, image_input: FuyuImagePatchInputs) -> MultiModalEmbeddings:
    image_patches_flat = image_input["flat_data"]
    patches_per_image = image_input["patches_per_image"]

    assert self.vision_embed_tokens is not None
    vision_embeddings_flat, _ = self.vision_embed_tokens(
        image_patches_flat)

    return vision_embeddings_flat.split(patches_per_image, dim=0)

_validate_pixel_values

_validate_pixel_values(data: Tensor) -> Tensor

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

def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:

    h = w = self.config.patch_size
    num_channels = self.config.num_channels
    expected_dims = num_channels * h * w

    def _validate_shape(d: torch.Tensor):
        actual_dims = d.size(-1)

        if actual_dims != expected_dims:
            expected_expr = str(expected_dims)
            raise ValueError(
                "The expected shape of pixel values per image per batch "
                f"per patch is {expected_expr}. "
                f"You supplied {tuple(d.shape)}.")

    for d in data:
        _validate_shape(d)

    return data.to(self.vision_embed_tokens.weight.dtype)

compute_logits

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

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

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

forward

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

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

def forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
    **kwargs: object,
):
    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(
        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/fuyu.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,
            inputs_embeds,
            multimodal_embeddings,
            _IMAGE_TOKEN_ID,
        )
    return inputs_embeds

get_language_model

get_language_model() -> Module

Source code in vllm/model_executor/models/fuyu.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/fuyu.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)

get_placeholder_str classmethod

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

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

@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
    if modality.startswith("image"):
        return None

    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/fuyu.py

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

FuyuImagePatchInputs

Bases: TypedDict

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

class FuyuImagePatchInputs(TypedDict):
    type: Literal["image_patches"]
    flat_data: torch.Tensor
    """
    Shape: 
    `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
    """

    patches_per_image: list[int]
    """
    The number of total patches for each image in the batch.

    This is used to split the embeddings which has the first two dimensions
    flattened just like `flat_data`.
    """

flat_data instance-attribute

flat_data: Tensor

Shape: (batch_size * num_patches, patch_size_x * patch_size_y * num_channels)

patches_per_image instance-attribute

patches_per_image: list[int]

The number of total patches for each image in the batch.

This is used to split the embeddings which has the first two dimensions flattened just like flat_data.

type instance-attribute

type: Literal['image_patches']

FuyuMultiModalProcessor

Bases: BaseMultiModalProcessor[FuyuProcessingInfo]

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

class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        if not mm_data:
            # Avoid warning from HF logger for text-only input
            prompt_ids = self.info.get_tokenizer().encode(prompt)
            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        image_patches = processed_outputs.get("image_patches")
        if image_patches is not None:
            images = mm_data["images"]
            assert isinstance(images, list)

            # Original output: (1, num_images, Pn, Px * Py * C)
            # New output: (num_images, Pn, Px * Py * C)
            assert (isinstance(image_patches, list)
                    and len(image_patches) == 1)
            assert (isinstance(image_patches[0], torch.Tensor)
                    and len(image_patches[0]) == len(images))

            processed_outputs["image_patches"] = image_patches[0]

        return processed_outputs

    def _apply_hf_processor_tokens_only(
        self,
        prompt_tokens: list[int],
    ) -> list[int]:
        # HF processor adds boa_token_id
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        boa_token_id = vocab["<0x04>"]

        return prompt_tokens + [boa_token_id]

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(image_patches=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_config = self.info.get_hf_config()
        bos_token_id = hf_config.bos_token_id
        assert isinstance(bos_token_id, int)

        tokenizer = self.info.get_tokenizer()
        eot_token_id = tokenizer.bos_token_id
        assert isinstance(eot_token_id, int)

        def get_replacement_fuyu(item_idx: int):
            images = mm_items.get_items("image", ImageProcessorItems)
            image_size = images.get_image_size(item_idx)

            ncols, nrows = self.info.get_image_feature_grid_size(
                image_width=image_size.width,
                image_height=image_size.height,
            )
            image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                            [_NEWLINE_TOKEN_ID]) * nrows

            return PromptUpdateDetails.select_token_id(
                image_tokens + [bos_token_id],
                embed_token_id=_IMAGE_TOKEN_ID,
            )

        return [
            PromptReplacement(
                modality="image",
                target=[eot_token_id],
                replacement=get_replacement_fuyu,
            )
        ]

_apply_hf_processor_tokens_only

_apply_hf_processor_tokens_only(
    prompt_tokens: list[int],
) -> list[int]

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

def _apply_hf_processor_tokens_only(
    self,
    prompt_tokens: list[int],
) -> list[int]:
    # HF processor adds boa_token_id
    tokenizer = self.info.get_tokenizer()
    vocab = tokenizer.get_vocab()

    boa_token_id = vocab["<0x04>"]

    return prompt_tokens + [boa_token_id]

_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/fuyu.py

def _call_hf_processor(
    self,
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
    tok_kwargs: Mapping[str, object],
) -> BatchFeature:
    if not mm_data:
        # Avoid warning from HF logger for text-only input
        prompt_ids = self.info.get_tokenizer().encode(prompt)
        prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
        return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

    processed_outputs = super()._call_hf_processor(
        prompt=prompt,
        mm_data=mm_data,
        mm_kwargs=mm_kwargs,
        tok_kwargs=tok_kwargs,
    )

    image_patches = processed_outputs.get("image_patches")
    if image_patches is not None:
        images = mm_data["images"]
        assert isinstance(images, list)

        # Original output: (1, num_images, Pn, Px * Py * C)
        # New output: (num_images, Pn, Px * Py * C)
        assert (isinstance(image_patches, list)
                and len(image_patches) == 1)
        assert (isinstance(image_patches[0], torch.Tensor)
                and len(image_patches[0]) == len(images))

        processed_outputs["image_patches"] = image_patches[0]

    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/fuyu.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(image_patches=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/fuyu.py

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    hf_config = self.info.get_hf_config()
    bos_token_id = hf_config.bos_token_id
    assert isinstance(bos_token_id, int)

    tokenizer = self.info.get_tokenizer()
    eot_token_id = tokenizer.bos_token_id
    assert isinstance(eot_token_id, int)

    def get_replacement_fuyu(item_idx: int):
        images = mm_items.get_items("image", ImageProcessorItems)
        image_size = images.get_image_size(item_idx)

        ncols, nrows = self.info.get_image_feature_grid_size(
            image_width=image_size.width,
            image_height=image_size.height,
        )
        image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                        [_NEWLINE_TOKEN_ID]) * nrows

        return PromptUpdateDetails.select_token_id(
            image_tokens + [bos_token_id],
            embed_token_id=_IMAGE_TOKEN_ID,
        )

    return [
        PromptReplacement(
            modality="image",
            target=[eot_token_id],
            replacement=get_replacement_fuyu,
        )
    ]

FuyuProcessingInfo

Bases: BaseProcessingInfo

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

class FuyuProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(FuyuConfig)

    def get_hf_processor(self, **kwargs: object):
        return self.ctx.get_hf_processor(FuyuProcessor, **kwargs)

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

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": 1}

    def get_image_feature_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        image_processor = self.get_image_processor()
        target_width = image_processor.size["width"]
        target_height = image_processor.size["height"]
        patch_width = image_processor.patch_size["width"]
        patch_height = image_processor.patch_size["height"]

        if not (image_width <= target_width and image_height <= target_height):
            height_scale_factor = target_height / image_height
            width_scale_factor = target_width / image_width
            optimal_scale_factor = min(height_scale_factor, width_scale_factor)

            image_height = int(image_height * optimal_scale_factor)
            image_width = int(image_width * optimal_scale_factor)

        ncols = math.ceil(image_width / patch_width)
        nrows = math.ceil(image_height / patch_height)
        return ncols, nrows

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        ncols, nrows = self.get_image_feature_grid_size(
            image_width=image_width,
            image_height=image_height,
        )

        return ncols * nrows

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

get_hf_config

get_hf_config()

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

def get_hf_config(self):
    return self.ctx.get_hf_config(FuyuConfig)

get_hf_processor

get_hf_processor(**kwargs: object)

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

def get_hf_processor(self, **kwargs: object):
    return self.ctx.get_hf_processor(FuyuProcessor, **kwargs)

get_image_feature_grid_size

get_image_feature_grid_size(
    *, image_width: int, image_height: int
) -> tuple[int, int]

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

def get_image_feature_grid_size(
    self,
    *,
    image_width: int,
    image_height: int,
) -> tuple[int, int]:
    image_processor = self.get_image_processor()
    target_width = image_processor.size["width"]
    target_height = image_processor.size["height"]
    patch_width = image_processor.patch_size["width"]
    patch_height = image_processor.patch_size["height"]

    if not (image_width <= target_width and image_height <= target_height):
        height_scale_factor = target_height / image_height
        width_scale_factor = target_width / image_width
        optimal_scale_factor = min(height_scale_factor, width_scale_factor)

        image_height = int(image_height * optimal_scale_factor)
        image_width = int(image_width * optimal_scale_factor)

    ncols = math.ceil(image_width / patch_width)
    nrows = math.ceil(image_height / patch_height)
    return ncols, nrows

get_image_processor

get_image_processor() -> FuyuImageProcessor

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

def get_image_processor(self) -> FuyuImageProcessor:
    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/fuyu.py

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

get_num_image_tokens

get_num_image_tokens(
    *, image_width: int, image_height: int
) -> int

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

def get_num_image_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    ncols, nrows = self.get_image_feature_grid_size(
        image_width=image_width,
        image_height=image_height,
    )

    return ncols * nrows

get_supported_mm_limits

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

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

def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"image": 1}