vllm.model_executor.models.mistral3

_I module-attribute

_I = TypeVar('_I', bound=BaseLlavaProcessingInfo)

BaseLlavaProcessingInfo

Bases: BaseProcessingInfo

Source code in vllm/model_executor/models/mistral3.py

class BaseLlavaProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self) -> LlavaLikeConfig:
        return self.ctx.get_hf_config(Mistral3Config)

    def get_vision_encoder_info(self):
        return get_vision_encoder_info(self.get_hf_config())

    @abstractmethod
    def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor:
        raise NotImplementedError

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

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        vision_encoder_info = self.get_vision_encoder_info()
        return vision_encoder_info.get_num_image_tokens(
            image_width=image_width,
            image_height=image_height,
        )

    def get_image_size_with_most_features(self) -> ImageSize:
        vision_encoder_info = self.get_vision_encoder_info()
        width = height = vision_encoder_info.get_image_size()
        return ImageSize(width=width, height=height)

get_hf_config

get_hf_config() -> LlavaLikeConfig

Source code in vllm/model_executor/models/mistral3.py

def get_hf_config(self) -> LlavaLikeConfig:
    return self.ctx.get_hf_config(Mistral3Config)

get_hf_processor abstractmethod

get_hf_processor(**kwargs: object) -> LlavaLikeProcessor

Source code in vllm/model_executor/models/mistral3.py

@abstractmethod
def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor:
    raise NotImplementedError

get_image_size_with_most_features

get_image_size_with_most_features() -> ImageSize

Source code in vllm/model_executor/models/mistral3.py

def get_image_size_with_most_features(self) -> ImageSize:
    vision_encoder_info = self.get_vision_encoder_info()
    width = height = vision_encoder_info.get_image_size()
    return ImageSize(width=width, height=height)

get_num_image_tokens

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

Source code in vllm/model_executor/models/mistral3.py

def get_num_image_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    vision_encoder_info = self.get_vision_encoder_info()
    return vision_encoder_info.get_num_image_tokens(
        image_width=image_width,
        image_height=image_height,
    )

get_supported_mm_limits

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

Source code in vllm/model_executor/models/mistral3.py

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

get_vision_encoder_info

get_vision_encoder_info()

Source code in vllm/model_executor/models/mistral3.py

def get_vision_encoder_info(self):
    return get_vision_encoder_info(self.get_hf_config())

LlavaLikeConfig

Bases: Protocol

Source code in vllm/model_executor/models/mistral3.py

class LlavaLikeConfig(Protocol):
    vision_config: Final[PretrainedConfig]
    image_token_index: Final[int]
    vision_feature_select_strategy: Final[str]
    vision_feature_layer: Final[Union[int, list[int]]]

image_token_index instance-attribute

image_token_index: Final[int]

vision_config instance-attribute

vision_config: Final[PretrainedConfig]

vision_feature_layer instance-attribute

vision_feature_layer: Final[Union[int, list[int]]]

vision_feature_select_strategy instance-attribute

vision_feature_select_strategy: Final[str]

LlavaLikeProcessor

Bases: Protocol

Source code in vllm/model_executor/models/mistral3.py

class LlavaLikeProcessor(Protocol):
    image_token: Final[str]

image_token instance-attribute

image_token: Final[str]

Mistral3DummyInputsBuilder

Bases: BaseDummyInputsBuilder[_I]

Source code in vllm/model_executor/models/mistral3.py

class Mistral3DummyInputsBuilder(BaseDummyInputsBuilder[_I]):

    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)

        target_width, target_height = \
            self.info.get_image_size_with_most_features()

        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/mistral3.py

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

    target_width, target_height = \
        self.info.get_image_size_with_most_features()

    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/mistral3.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

Mistral3ForConditionalGeneration

Bases: Module, SupportsLoRA, SupportsMultiModal, SupportsPP

Source code in vllm/model_executor/models/mistral3.py

@MULTIMODAL_REGISTRY.register_processor(
    _build_mistral3_processor,
    info=_build_mistral3_info,
    dummy_inputs=Mistral3DummyInputsBuilder)
class Mistral3ForConditionalGeneration(nn.Module, SupportsLoRA,
                                       SupportsMultiModal, SupportsPP):

    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"]
    }

    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 None

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        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

        # NOTE: These are special cases for Pixtral-12B in the HF-format
        # https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json  # noqa
        if (config.text_config.architectures is None
                and config.text_config.model_type == "mistral"):
            config.text_config.architectures = ["MistralForCausalLM"]
        if (config.projector_hidden_act is None
                and config.vision_config.hidden_act == "gelu"):
            config.projector_hidden_act = "gelu"

        # TODO: Optionally initializes this for supporting embeddings.
        self.vision_tower = init_vision_tower_for_llava(
            config,
            quant_config,
            require_post_norm=False,
            prefix=maybe_prefix(prefix, "vision_tower"))
        self.multi_modal_projector = Mistral3MultiModalProjector(
            vision_hidden_size=config.vision_config.hidden_size,
            text_hidden_size=config.text_config.hidden_size,
            projector_hidden_act=config.projector_hidden_act,
            spatial_merge_size=config.spatial_merge_size,
            patch_size=config.vision_config.patch_size,
            multimodal_projector_bias=config.multimodal_projector_bias,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "multi_modal_projector"))

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            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.vision_config.image_size
        expected_dims = (3, h, w)
        actual_dims = tuple(data.shape[1:])

        if actual_dims != expected_dims:
            expected_expr = ("batch_size", *map(str, expected_dims))
            raise ValueError(
                f"The expected shape of pixel values is {expected_expr}. "
                f"You supplied {tuple(data.shape)}.")

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[Mistral3ImagePixelInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

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

        return Mistral3ImagePixelInputs(
            type="pixel_values_pixtral",
            pixel_values=flatten_bn(pixel_values),
        )

    def _process_image_input(
        self,
        image_input: Mistral3ImagePixelInputs,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
        if image_input["type"] == "image_embeds":
            return image_input["data"]

        image_sizes = [(img.shape[-2], img.shape[-1])
                       for img in image_input["pixel_values"]]

        image_features = self.vision_tower(image_input["pixel_values"])

        if isinstance(image_features, torch.Tensor):
            return self.multi_modal_projector(image_features, image_sizes)

        feature_sizes = [
            image_feature.shape[0] // self.config.spatial_merge_size**2
            for image_feature in image_features
        ]

        image_embeds = self.multi_modal_projector(torch.cat(image_features),
                                                  image_sizes)
        if len(feature_sizes) > 1:
            image_embeds = torch.split(image_embeds, feature_sizes)
        else:
            image_embeds = (image_embeds, )
        return image_embeds

    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 []

        vision_embeddings = self._process_image_input(image_input)

        return vision_embeddings

    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,
                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]:
        """Run forward pass for Mistral3.

        One key thing to understand is the `input_ids` already accounts for the
        positions of the to-be-inserted image embeddings.

        Concretely, consider a text prompt:
        `"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.

        Tokenizer outputs:
        `[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
        278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.

        To reserve space in KV cache, we have to insert placeholder tokens
        before they are inputted to the model, so the input processor prepends
        additional image tokens (denoted as `32000`), resulting in:
        `[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
        29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
        29901]`.

        We insert 575 tokens so that including the original image token in the
        input, there are a total of 576 (24 * 24) image tokens, which
        corresponds to the number of image tokens inputted to the language
        model, i.e. the number of image tokens outputted by the visual encoder.

        This way, the `positions` and `attn_metadata` are consistent
        with the `input_ids`.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            pixel_values: The pixels in each input image.

        Info:
            [Mistral3ImagePixelInputs][]
        """
        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,
                                                  positions,
                                                  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)

    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 get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="multi_modal_projector",
            tower_model="vision_tower")

config instance-attribute

config = config

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, "language_model"),
)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

multi_modal_projector instance-attribute

multi_modal_projector = Mistral3MultiModalProjector(
    vision_hidden_size=hidden_size,
    text_hidden_size=hidden_size,
    projector_hidden_act=projector_hidden_act,
    spatial_merge_size=spatial_merge_size,
    patch_size=patch_size,
    multimodal_projector_bias=multimodal_projector_bias,
    quant_config=quant_config,
    prefix=maybe_prefix(prefix, "multi_modal_projector"),
)

multimodal_config instance-attribute

multimodal_config = multimodal_config

packed_modules_mapping class-attribute instance-attribute

packed_modules_mapping = {
    "qkv_proj": ["q_proj", "k_proj", "v_proj"],
    "gate_up_proj": ["gate_proj", "up_proj"],
}

vision_tower instance-attribute

vision_tower = init_vision_tower_for_llava(
    config,
    quant_config,
    require_post_norm=False,
    prefix=maybe_prefix(prefix, "vision_tower"),
)

__init__

__init__(
    *, vllm_config: VllmConfig, prefix: str = ""
) -> None

Source code in vllm/model_executor/models/mistral3.py

def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
    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

    # NOTE: These are special cases for Pixtral-12B in the HF-format
    # https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json  # noqa
    if (config.text_config.architectures is None
            and config.text_config.model_type == "mistral"):
        config.text_config.architectures = ["MistralForCausalLM"]
    if (config.projector_hidden_act is None
            and config.vision_config.hidden_act == "gelu"):
        config.projector_hidden_act = "gelu"

    # TODO: Optionally initializes this for supporting embeddings.
    self.vision_tower = init_vision_tower_for_llava(
        config,
        quant_config,
        require_post_norm=False,
        prefix=maybe_prefix(prefix, "vision_tower"))
    self.multi_modal_projector = Mistral3MultiModalProjector(
        vision_hidden_size=config.vision_config.hidden_size,
        text_hidden_size=config.text_config.hidden_size,
        projector_hidden_act=config.projector_hidden_act,
        spatial_merge_size=config.spatial_merge_size,
        patch_size=config.vision_config.patch_size,
        multimodal_projector_bias=config.multimodal_projector_bias,
        quant_config=quant_config,
        prefix=maybe_prefix(prefix, "multi_modal_projector"))

    self.language_model = init_vllm_registered_model(
        vllm_config=vllm_config,
        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[Mistral3ImagePixelInputs]

Source code in vllm/model_executor/models/mistral3.py

def _parse_and_validate_image_input(
        self, **kwargs: object) -> Optional[Mistral3ImagePixelInputs]:
    pixel_values = kwargs.pop("pixel_values", None)
    image_embeds = kwargs.pop("image_embeds", None)

    if pixel_values is None and image_embeds is None:
        return None

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

    return Mistral3ImagePixelInputs(
        type="pixel_values_pixtral",
        pixel_values=flatten_bn(pixel_values),
    )

_process_image_input

_process_image_input(
    image_input: Mistral3ImagePixelInputs,
) -> Union[Tensor, tuple[Tensor, ...]]

Source code in vllm/model_executor/models/mistral3.py

def _process_image_input(
    self,
    image_input: Mistral3ImagePixelInputs,
) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
    if image_input["type"] == "image_embeds":
        return image_input["data"]

    image_sizes = [(img.shape[-2], img.shape[-1])
                   for img in image_input["pixel_values"]]

    image_features = self.vision_tower(image_input["pixel_values"])

    if isinstance(image_features, torch.Tensor):
        return self.multi_modal_projector(image_features, image_sizes)

    feature_sizes = [
        image_feature.shape[0] // self.config.spatial_merge_size**2
        for image_feature in image_features
    ]

    image_embeds = self.multi_modal_projector(torch.cat(image_features),
                                              image_sizes)
    if len(feature_sizes) > 1:
        image_embeds = torch.split(image_embeds, feature_sizes)
    else:
        image_embeds = (image_embeds, )
    return image_embeds

_validate_pixel_values

_validate_pixel_values(data: Tensor) -> Tensor

Source code in vllm/model_executor/models/mistral3.py

def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
    h = w = self.config.vision_config.image_size
    expected_dims = (3, h, w)
    actual_dims = tuple(data.shape[1:])

    if actual_dims != expected_dims:
        expected_expr = ("batch_size", *map(str, expected_dims))
        raise ValueError(
            f"The expected shape of pixel values is {expected_expr}. "
            f"You supplied {tuple(data.shape)}.")

    return data

compute_logits

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

Source code in vllm/model_executor/models/mistral3.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]

Run forward pass for Mistral3.

One key thing to understand is the input_ids already accounts for the positions of the to-be-inserted image embeddings.

Concretely, consider a text prompt: "USER: <image>\nWhat's the content of the image?\nASSISTANT:".

Tokenizer outputs: [1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901].

To reserve space in KV cache, we have to insert placeholder tokens before they are inputted to the model, so the input processor prepends additional image tokens (denoted as 32000), resulting in: [1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618, 29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901].

We insert 575 tokens so that including the original image token in the input, there are a total of 576 (24 * 24) image tokens, which corresponds to the number of image tokens inputted to the language model, i.e. the number of image tokens outputted by the visual encoder.

This way, the positions and attn_metadata are consistent with the input_ids.

Parameters:

Name	Type	Description	Default
input_ids	Tensor	Flattened (concatenated) input_ids corresponding to a batch.	required
pixel_values		The pixels in each input image.	required

Info

[Mistral3ImagePixelInputs][]

Source code in vllm/model_executor/models/mistral3.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]:
    """Run forward pass for Mistral3.

    One key thing to understand is the `input_ids` already accounts for the
    positions of the to-be-inserted image embeddings.

    Concretely, consider a text prompt:
    `"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.

    Tokenizer outputs:
    `[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
    278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.

    To reserve space in KV cache, we have to insert placeholder tokens
    before they are inputted to the model, so the input processor prepends
    additional image tokens (denoted as `32000`), resulting in:
    `[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
    29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
    29901]`.

    We insert 575 tokens so that including the original image token in the
    input, there are a total of 576 (24 * 24) image tokens, which
    corresponds to the number of image tokens inputted to the language
    model, i.e. the number of image tokens outputted by the visual encoder.

    This way, the `positions` and `attn_metadata` are consistent
    with the `input_ids`.

    Args:
        input_ids: Flattened (concatenated) input_ids corresponding to a
            batch.
        pixel_values: The pixels in each input image.

    Info:
        [Mistral3ImagePixelInputs][]
    """
    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,
                                              positions,
                                              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/mistral3.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,
            self.config.image_token_index,
        )
    return inputs_embeds

get_language_model

get_language_model() -> Module

Source code in vllm/model_executor/models/mistral3.py

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

get_mm_mapping

get_mm_mapping() -> MultiModelKeys

Get the module prefix in multimodal models

Source code in vllm/model_executor/models/mistral3.py

def get_mm_mapping(self) -> MultiModelKeys:
    """
    Get the module prefix in multimodal models
    """
    return MultiModelKeys.from_string_field(
        language_model="language_model",
        connector="multi_modal_projector",
        tower_model="vision_tower")

get_multimodal_embeddings

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

Source code in vllm/model_executor/models/mistral3.py

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

    vision_embeddings = self._process_image_input(image_input)

    return vision_embeddings

get_placeholder_str classmethod

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

Source code in vllm/model_executor/models/mistral3.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/mistral3.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)

Mistral3ImagePixelInputs

Bases: TypedDict

Source code in vllm/model_executor/models/mistral3.py

class Mistral3ImagePixelInputs(TypedDict):
    type: Literal["pixel_values_pixtral"]
    pixel_values: Union[torch.Tensor, list[torch.Tensor]]
    """
    Shape: `(batch_size * num_images, num_channels, height, width)`

    Note that `height` or `width` may be different per batch and image,
    in which case the data is passed as a list instead of a batched tensor.
    """

pixel_values instance-attribute

pixel_values: Union[Tensor, list[Tensor]]

Shape: (batch_size * num_images, num_channels, height, width)

Note that height or width may be different per batch and image, in which case the data is passed as a list instead of a batched tensor.

type instance-attribute

type: Literal['pixel_values_pixtral']

Mistral3MultiModalProcessor

Bases: BaseMultiModalProcessor[Mistral3ProcessingInfo]

Source code in vllm/model_executor/models/mistral3.py

class Mistral3MultiModalProcessor(
        BaseMultiModalProcessor[Mistral3ProcessingInfo]):

    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=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        pixel_values = processed_outputs.get("pixel_values")
        if pixel_values is not None:

            # Avoid padding since we need the output for each image to be
            # independent of other images for the cache to work correctly
            image_sizes = processed_outputs["image_sizes"]
            assert len(pixel_values) == len(image_sizes)

            processed_outputs["pixel_values"] = [
                p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
            ]

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(
            pixel_values=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]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        hf_config = self.info.get_hf_config()
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        image_break_id = vocab[processor.image_break_token]
        image_token_id = hf_config.image_token_index
        image_end_id = vocab[processor.image_end_token]

        assert isinstance(hf_config.vision_config, PixtralVisionConfig)
        encoder_info = PixtralHFEncoderInfo(hf_config)

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

            ncols, nrows = encoder_info.get_patch_grid_size(
                image_width=image_size.width,
                image_height=image_size.height,
            )

            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
            tokens[-1] = image_end_id

            return PromptUpdateDetails.select_token_id(tokens, image_token_id)

        return [
            PromptReplacement(
                modality="image",
                target=[image_token_id],
                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/mistral3.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=prompt,
        mm_data=mm_data,
        mm_kwargs=mm_kwargs,
        tok_kwargs=tok_kwargs,
    )

    pixel_values = processed_outputs.get("pixel_values")
    if pixel_values is not None:

        # Avoid padding since we need the output for each image to be
        # independent of other images for the cache to work correctly
        image_sizes = processed_outputs["image_sizes"]
        assert len(pixel_values) == len(image_sizes)

        processed_outputs["pixel_values"] = [
            p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
        ]

    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/mistral3.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(
        pixel_values=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/mistral3.py

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
    hf_config = self.info.get_hf_config()
    tokenizer = self.info.get_tokenizer()
    vocab = tokenizer.get_vocab()

    image_break_id = vocab[processor.image_break_token]
    image_token_id = hf_config.image_token_index
    image_end_id = vocab[processor.image_end_token]

    assert isinstance(hf_config.vision_config, PixtralVisionConfig)
    encoder_info = PixtralHFEncoderInfo(hf_config)

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

        ncols, nrows = encoder_info.get_patch_grid_size(
            image_width=image_size.width,
            image_height=image_size.height,
        )

        tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
        tokens[-1] = image_end_id

        return PromptUpdateDetails.select_token_id(tokens, image_token_id)

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

Mistral3MultiModalProjector

Bases: Module

Source code in vllm/model_executor/models/mistral3.py

class Mistral3MultiModalProjector(nn.Module):

    def __init__(self,
                 vision_hidden_size: int,
                 text_hidden_size: int,
                 spatial_merge_size: int,
                 patch_size: int,
                 projector_hidden_act: str,
                 multimodal_projector_bias: bool,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        super().__init__()

        self.norm = RMSNorm(vision_hidden_size, eps=1e-5)
        self.patch_merger = Mistral3PatchMerger(
            vision_hidden_size=vision_hidden_size,
            spatial_merge_size=spatial_merge_size,
            patch_size=patch_size)

        self.linear_1 = ColumnParallelLinear(vision_hidden_size,
                                             text_hidden_size,
                                             bias=multimodal_projector_bias,
                                             quant_config=quant_config,
                                             prefix=f"{prefix}.linear_1")
        self.act = get_act_fn(projector_hidden_act)
        self.linear_2 = RowParallelLinear(text_hidden_size,
                                          text_hidden_size,
                                          bias=multimodal_projector_bias,
                                          quant_config=quant_config,
                                          prefix=f"{prefix}.linear_2")

    def forward(self, image_features: torch.Tensor,
                image_sizes: torch.Tensor) -> torch.Tensor:
        image_features = self.norm(image_features)
        image_features = self.patch_merger(image_features, image_sizes)
        hidden_states, _ = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.linear_2(hidden_states)
        return hidden_states

act instance-attribute

act = get_act_fn(projector_hidden_act)

linear_1 instance-attribute

linear_1 = ColumnParallelLinear(
    vision_hidden_size,
    text_hidden_size,
    bias=multimodal_projector_bias,
    quant_config=quant_config,
    prefix=f"{prefix}.linear_1",
)

linear_2 instance-attribute

linear_2 = RowParallelLinear(
    text_hidden_size,
    text_hidden_size,
    bias=multimodal_projector_bias,
    quant_config=quant_config,
    prefix=f"{prefix}.linear_2",
)

norm instance-attribute

norm = RMSNorm(vision_hidden_size, eps=1e-05)

patch_merger instance-attribute

patch_merger = Mistral3PatchMerger(
    vision_hidden_size=vision_hidden_size,
    spatial_merge_size=spatial_merge_size,
    patch_size=patch_size,
)

__init__

__init__(
    vision_hidden_size: int,
    text_hidden_size: int,
    spatial_merge_size: int,
    patch_size: int,
    projector_hidden_act: str,
    multimodal_projector_bias: bool,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
)

Source code in vllm/model_executor/models/mistral3.py

def __init__(self,
             vision_hidden_size: int,
             text_hidden_size: int,
             spatial_merge_size: int,
             patch_size: int,
             projector_hidden_act: str,
             multimodal_projector_bias: bool,
             quant_config: Optional[QuantizationConfig] = None,
             prefix: str = ""):
    super().__init__()

    self.norm = RMSNorm(vision_hidden_size, eps=1e-5)
    self.patch_merger = Mistral3PatchMerger(
        vision_hidden_size=vision_hidden_size,
        spatial_merge_size=spatial_merge_size,
        patch_size=patch_size)

    self.linear_1 = ColumnParallelLinear(vision_hidden_size,
                                         text_hidden_size,
                                         bias=multimodal_projector_bias,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.linear_1")
    self.act = get_act_fn(projector_hidden_act)
    self.linear_2 = RowParallelLinear(text_hidden_size,
                                      text_hidden_size,
                                      bias=multimodal_projector_bias,
                                      quant_config=quant_config,
                                      prefix=f"{prefix}.linear_2")

forward

forward(
    image_features: Tensor, image_sizes: Tensor
) -> Tensor

Source code in vllm/model_executor/models/mistral3.py

def forward(self, image_features: torch.Tensor,
            image_sizes: torch.Tensor) -> torch.Tensor:
    image_features = self.norm(image_features)
    image_features = self.patch_merger(image_features, image_sizes)
    hidden_states, _ = self.linear_1(image_features)
    hidden_states = self.act(hidden_states)
    hidden_states, _ = self.linear_2(hidden_states)
    return hidden_states

Mistral3PatchMerger

Bases: Module

Learned merging of spatial_merge_size ** 2 patches

Source code in vllm/model_executor/models/mistral3.py

class Mistral3PatchMerger(nn.Module):
    """
    Learned merging of spatial_merge_size ** 2 patches
    """

    def __init__(self, vision_hidden_size: int, spatial_merge_size: int,
                 patch_size: int):
        super().__init__()

        self.vision_hidden_size = vision_hidden_size
        self.spatial_merge_size = spatial_merge_size
        self.patch_size = patch_size
        self.merging_layer = nn.Linear(vision_hidden_size *
                                       self.spatial_merge_size**2,
                                       vision_hidden_size,
                                       bias=False)

    def forward(self, image_features: torch.Tensor,
                image_sizes: torch.Tensor) -> torch.Tensor:
        image_sizes = [(image_size[0] // self.patch_size,
                        image_size[1] // self.patch_size)
                       for image_size in image_sizes]

        tokens_per_image = [h * w for h, w in image_sizes]
        d = image_features.shape[-1]

        permuted_tensor = []
        for image_index, image_tokens in enumerate(
                image_features.split(tokens_per_image)):
            # Reshape image_tokens into a 2D grid
            h, w = image_sizes[image_index]
            image_grid = image_tokens.view(h, w, d).permute(2, 0,
                                                            1).unsqueeze(0)
            grid = torch.nn.functional.unfold(
                image_grid,
                kernel_size=self.spatial_merge_size,
                stride=self.spatial_merge_size)
            grid = grid.view(d * self.spatial_merge_size**2, -1).t()
            permuted_tensor.append(grid)

        image_features = torch.cat(permuted_tensor, dim=0)
        image_features = self.merging_layer(image_features)
        return image_features

merging_layer instance-attribute

merging_layer = Linear(
    vision_hidden_size * spatial_merge_size**2,
    vision_hidden_size,
    bias=False,
)

patch_size instance-attribute

patch_size = patch_size

spatial_merge_size instance-attribute

spatial_merge_size = spatial_merge_size

vision_hidden_size instance-attribute

vision_hidden_size = vision_hidden_size

__init__

__init__(
    vision_hidden_size: int,
    spatial_merge_size: int,
    patch_size: int,
)

Source code in vllm/model_executor/models/mistral3.py

def __init__(self, vision_hidden_size: int, spatial_merge_size: int,
             patch_size: int):
    super().__init__()

    self.vision_hidden_size = vision_hidden_size
    self.spatial_merge_size = spatial_merge_size
    self.patch_size = patch_size
    self.merging_layer = nn.Linear(vision_hidden_size *
                                   self.spatial_merge_size**2,
                                   vision_hidden_size,
                                   bias=False)

forward

forward(
    image_features: Tensor, image_sizes: Tensor
) -> Tensor

Source code in vllm/model_executor/models/mistral3.py

def forward(self, image_features: torch.Tensor,
            image_sizes: torch.Tensor) -> torch.Tensor:
    image_sizes = [(image_size[0] // self.patch_size,
                    image_size[1] // self.patch_size)
                   for image_size in image_sizes]

    tokens_per_image = [h * w for h, w in image_sizes]
    d = image_features.shape[-1]

    permuted_tensor = []
    for image_index, image_tokens in enumerate(
            image_features.split(tokens_per_image)):
        # Reshape image_tokens into a 2D grid
        h, w = image_sizes[image_index]
        image_grid = image_tokens.view(h, w, d).permute(2, 0,
                                                        1).unsqueeze(0)
        grid = torch.nn.functional.unfold(
            image_grid,
            kernel_size=self.spatial_merge_size,
            stride=self.spatial_merge_size)
        grid = grid.view(d * self.spatial_merge_size**2, -1).t()
        permuted_tensor.append(grid)

    image_features = torch.cat(permuted_tensor, dim=0)
    image_features = self.merging_layer(image_features)
    return image_features

Mistral3ProcessingInfo

Bases: BaseLlavaProcessingInfo

Source code in vllm/model_executor/models/mistral3.py

class Mistral3ProcessingInfo(BaseLlavaProcessingInfo):

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

get_hf_processor

get_hf_processor(**kwargs: object)

Source code in vllm/model_executor/models/mistral3.py

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

_build_mistral3_info

_build_mistral3_info(
    ctx: InputProcessingContext,
) -> BaseLlavaProcessingInfo

Source code in vllm/model_executor/models/mistral3.py

def _build_mistral3_info(
    ctx: InputProcessingContext, ) -> BaseLlavaProcessingInfo:
    hf_config = ctx.get_hf_config(Mistral3Config)
    assert isinstance(hf_config.vision_config, PixtralVisionConfig)
    return Mistral3ProcessingInfo(ctx)

_build_mistral3_processor

_build_mistral3_processor(
    info: _I,
    dummy_inputs: BaseDummyInputsBuilder[_I],
    *,
    cache: Optional[ProcessingCache] = None,
) -> BaseMultiModalProcessor

Source code in vllm/model_executor/models/mistral3.py

def _build_mistral3_processor(
    info: _I,
    dummy_inputs: BaseDummyInputsBuilder[_I],
    *,
    cache: Optional[ProcessingCache] = None,
) -> BaseMultiModalProcessor:
    assert isinstance(info, Mistral3ProcessingInfo)
    return Mistral3MultiModalProcessor(
        info,
        dummy_inputs,  # type: ignore
        cache=cache,
    )

_get_layer_index

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

Given a signed vision feature layer, get the number of hidden layers needed to leverage it.

Parameters:

Name	Type	Description	Default
feature_layer_index	int	Index of a required layer in the visual encoder.	required
num_hidden_layers	int	The total number of hidden layers in the visual encoder.	required

Source code in vllm/model_executor/models/mistral3.py

def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
    """Given a signed vision feature layer, get the number of hidden layers
    needed to leverage it.

    Args:
        feature_layer_index: Index of a required layer in the visual encoder.
        num_hidden_layers: The total number of hidden layers in the visual
            encoder.
    """
    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: LlavaLikeConfig) -> int

Determine the number of hidden layers to initialize up to in the visual encoder.

Parameters:

Name	Type	Description	Default
hf_config	LlavaLikeConfig	Model config with vision feature layer(s).	required

Source code in vllm/model_executor/models/mistral3.py

def _get_num_hidden_layers(hf_config: LlavaLikeConfig) -> int:
    """Determine the number of hidden layers to initialize up to in the
    visual encoder.

    Args:
        hf_config: Model config with vision feature layer(s).
    """
    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 one
    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")

init_vision_tower_for_llava

init_vision_tower_for_llava(
    hf_config: LlavaLikeConfig,
    quant_config: Optional[QuantizationConfig],
    *,
    require_post_norm: Optional[bool] = None,
    prefix: str = "",
) -> PixtralHFVisionModel

Source code in vllm/model_executor/models/mistral3.py

def init_vision_tower_for_llava(
    hf_config: LlavaLikeConfig,
    quant_config: Optional[QuantizationConfig],
    *,
    require_post_norm: Optional[bool] = None,
    prefix: str = "",
) -> PixtralHFVisionModel:
    vision_config = hf_config.vision_config

    # Initialize the vision tower only up to the deepest required feature layer
    num_hidden_layers = _get_num_hidden_layers(hf_config)

    assert isinstance(vision_config, PixtralVisionConfig)

    return PixtralHFVisionModel(
        vision_config,
        quant_config=quant_config,
        num_hidden_layers_override=num_hidden_layers,
        require_post_norm=require_post_norm,
        prefix=prefix,
    )