siglip.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from collections.abc import Callable, Iterable, Mapping
from functools import cached_property, partial
from typing import Annotated, Literal

import torch
from torch import nn
from transformers import (
    BatchFeature,
    SiglipConfig,
    SiglipProcessor,
    SiglipTextConfig,
    SiglipVisionConfig,
)

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import (
    EncoderOnlyAttention,
    MMEncoderAttention,
)
from vllm.model_executor.layers.conv import Conv2dLayer
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.pooler import DispatchPooler
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalInputs,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
    MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
    BaseDummyInputsBuilder,
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptIndexTargets,
    PromptReplacement,
    PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant
from .interfaces_base import default_pooling_type
from .utils import AutoWeightsLoader, maybe_prefix
from .vision import (
    VisionEncoderInfo,
    VisionFeatureSelectStrategy,
    VisionFeatureSelectStrategyStr,
    get_num_selected_vision_tokens,
    is_vit_use_data_parallel,
    resolve_visual_encoder_outputs,
)


class SiglipImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
    """

    type: Literal["pixel_values"]
    data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]


_POOLING_TYPE_TO_STRATEGY: dict[str, VisionFeatureSelectStrategyStr] = {
    "MEAN": "full",
    "ALL": "full",
    "CLS": "class",
}


def _get_vision_feature_select_strategy(
    pooling_type: str,
) -> VisionFeatureSelectStrategyStr:
    try:
        return _POOLING_TYPE_TO_STRATEGY[pooling_type]
    except KeyError:
        raise ValueError(
            f"No feature selection strategy is defined for "
            f"pooling_type: {pooling_type!r}"
        ) from None


class SiglipProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(SiglipConfig)

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

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

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"image": 1}

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

        pooler_config = self.ctx.model_config.pooler_config
        assert pooler_config is not None

        return get_num_selected_vision_tokens(
            vision_encoder_info.get_num_image_tokens(
                image_width=image_width,
                image_height=image_height,
            ),
            _get_vision_feature_select_strategy(pooler_config.seq_pooling_type),
        )

    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)

    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_image_tokens(
            image_width=target_width, image_height=target_height
        )


class SiglipDummyInputsBuilder(BaseDummyInputsBuilder[SiglipProcessingInfo]):
    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],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)

        target_width, target_height = self.info.get_image_size_with_most_features()

        image_overrides = mm_options.get("image")

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


class SiglipMultiModalProcessor(BaseMultiModalProcessor[SiglipProcessingInfo]):
    @cached_property
    def image_token_id(self) -> int:
        tokenizer = self.info.get_tokenizer()
        dummy_token_id = next(
            token_id
            for token_id in range(tokenizer.vocab_size)
            if token_id not in tokenizer.all_special_ids
        )

        return dummy_token_id

    def apply(
        self,
        prompt: str | list[int],
        mm_items: MultiModalDataItems,
        mm_uuid_items: MultiModalUUIDItems | None = None,
        hf_processor_mm_kwargs: Mapping[str, object] | None = None,
        tokenization_kwargs: Mapping[str, object] | None = None,
    ) -> MultiModalInputs:
        if mm_items:
            if isinstance(prompt, str):
                if len(prompt) > 0:
                    raise ValueError(
                        "SigLIP accepts text-only or image-only inputs, not both! "
                        "You must pass an image with an empty text prompt."
                    )
            else:
                special_tokens = self.info.get_tokenizer().all_special_ids
                if all(tok in special_tokens for tok in prompt):
                    prompt = []
                else:
                    raise ValueError(
                        "SigLIP accepts text-only or image-only inputs, not both! "
                        "You must pass an image with an empty token prompt."
                    )

            # For multi-modal data, the prompt after processing should
            # only contain the image token
            tokenization_kwargs = {
                **(tokenization_kwargs or {}),
                "add_special_tokens": False,
            }

        return super().apply(
            prompt=prompt,
            mm_items=mm_items,
            mm_uuid_items=mm_uuid_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
        )

    def _hf_processor_applies_updates(
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
    ) -> bool:
        return False

    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"))

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> list[PromptUpdate]:
        image_token_id = self.image_token_id

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

            num_image_tokens = self.info.get_num_image_tokens(
                image_width=image_size.width, image_height=image_size.height
            )
            return [image_token_id] * num_image_tokens

        return [
            PromptReplacement(
                modality="image",
                target=PromptIndexTargets.start(),
                replacement=get_replacement,
            ),
        ]


class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        return self.get_patch_grid_length() ** 2

    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
        return self.vision_config.patch_size

    def get_patch_grid_length(self) -> int:
        image_size, patch_size = self.get_image_size(), self.get_patch_size()
        return image_size // patch_size


# Adapted from https://github.com/huggingface/transformers/blob/v4.57.3/src/transformers/models/siglip/modeling_siglip.py#L216
class SiglipVisionEmbeddings(nn.Module):
    def __init__(self, config: SiglipVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.patch_embedding = Conv2dLayer(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions, dtype=torch.int64).expand((1, -1)),
            persistent=False,
        )

    def interpolate_pos_encoding(
        self, embeddings: torch.Tensor, height: int, width: int
    ) -> torch.Tensor:
        num_patches = embeddings.shape[1]
        num_positions = self.position_embedding.weight.shape[1]
        if num_patches == num_positions and height == width:
            return self.position_embedding(self.position_ids)

        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size
        new_width = width // self.patch_size

        sqrt_num_positions = int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(
            1, sqrt_num_positions, sqrt_num_positions, dim
        )
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return patch_pos_embed

    def forward(
        self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False
    ) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(
            pixel_values.to(dtype=target_dtype)
        )  # shape = [*, width, grid, grid]
        embeddings = patch_embeds.flatten(2).transpose(1, 2)

        if interpolate_pos_encoding:
            embeddings += self.interpolate_pos_encoding(embeddings, height, width)
        else:
            embeddings += self.position_embedding(self.position_ids)
        return embeddings


class SiglipAttention(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig | SiglipTextConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        prefix: str = "",
        attn_cls: type[EncoderOnlyAttention] | type[MMEncoderAttention],
    ) -> None:
        super().__init__()

        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads "
                f"(got `embed_dim`: {self.embed_dim} and "
                f"`num_heads`: {self.num_heads})."
            )

        self.scale = self.head_dim**-0.5

        use_data_parallel = is_vit_use_data_parallel()
        self.qkv_proj = QKVParallelLinear(
            hidden_size=self.embed_dim,
            head_size=self.head_dim,
            total_num_heads=self.num_heads,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
            disable_tp=use_data_parallel,
        )

        self.out_proj = RowParallelLinear(
            input_size=self.embed_dim,
            output_size=self.embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
            disable_tp=use_data_parallel,
        )

        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
        )
        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)

        if attn_cls == MMEncoderAttention:
            self.attn = attn_cls(
                self.num_heads_per_partition,
                self.head_dim,
                self.scale,
                prefix=f"{prefix}.attn",
            )
        else:
            self.attn = attn_cls(
                self.num_heads_per_partition,
                self.head_dim,
                self.scale,
                prefix=f"{prefix}.attn",
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, None]:
        """Input shape: Batch x Time x Channel"""
        qkv_states, _ = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
        out = self.attn(query_states, key_states, value_states)
        attn_output, _ = self.out_proj(out)

        return attn_output, None


class SiglipMLP(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig | SiglipTextConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        use_data_parallel = is_vit_use_data_parallel()
        self.activation_fn = get_act_fn(config.hidden_act)

        # Special handling for BNB and torchao quantization
        if quant_config and quant_config.get_name() in ["bitsandbytes", "torchao"]:
            quantizable = True
        else:
            # For other quantization, we require the hidden size to be a
            # multiple of 64
            quantizable = (
                config.hidden_size % 64 == 0 and config.intermediate_size % 64 == 0
            )

        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc1",
            disable_tp=use_data_parallel,
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc2",
            disable_tp=use_data_parallel,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states, _ = self.fc2(hidden_states)
        return hidden_states


class SiglipEncoderLayer(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig | SiglipTextConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        prefix: str = "",
        attn_cls: type[EncoderOnlyAttention] | type[MMEncoderAttention],
    ) -> None:
        super().__init__()

        self.embed_dim = config.hidden_size

        self.self_attn = SiglipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
            attn_cls=attn_cls,
        )
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, None]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
        hidden_states += residual

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states += residual

        return hidden_states, None


class SiglipEncoder(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig | SiglipTextConfig,
        quant_config: QuantizationConfig | None = None,
        num_hidden_layers_override: int | None = None,
        *,
        prefix: str = "",
        attn_cls: type[EncoderOnlyAttention] | type[MMEncoderAttention],
    ) -> None:
        super().__init__()

        self.config = config

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

        self.layers = nn.ModuleList(
            [
                SiglipEncoderLayer(
                    config,
                    quant_config=quant_config,
                    prefix=f"{prefix}.layers.{layer_idx}",
                    attn_cls=attn_cls,
                )
                for layer_idx in range(num_hidden_layers)
            ]
        )

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        return_all_hidden_states: bool,
    ) -> torch.Tensor | list[torch.Tensor]:
        hidden_states_pool = [inputs_embeds]
        hidden_states = inputs_embeds

        for encoder_layer in self.layers:
            hidden_states, _ = encoder_layer(hidden_states)
            if return_all_hidden_states:
                hidden_states_pool.append(hidden_states)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
        return hidden_states


class SiglipTextTransformer(nn.Module):
    def __init__(
        self,
        config: SiglipTextConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = SiglipTextEmbeddings(config)

        self.encoder = SiglipEncoder(
            config=config,
            quant_config=quant_config,
            prefix=f"{prefix}.encoder",
            attn_cls=EncoderOnlyAttention,
        )

        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        self.head = nn.Linear(embed_dim, config.projection_size)

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embeddings.token_embedding(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        position_ids: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        hidden_states = self.embeddings(input_ids, position_ids, inputs_embeds)

        last_hidden_state = self.encoder(
            inputs_embeds=hidden_states, return_all_hidden_states=False
        )

        last_hidden_state = self.final_layer_norm(last_hidden_state)

        return last_hidden_state

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        for name, loaded_weight in weights:
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class SiglipMultiheadAttentionPoolingHead(nn.Module):
    """Multihead Attention Pooling."""

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        # TODO(ChristopherCho): Implement vLLM version of MultiheadAttention
        self.attention = torch.nn.MultiheadAttention(
            config.hidden_size, config.num_attention_heads, batch_first=True
        )
        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config=config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        batch_size = hidden_state.size(0)

        probe = self.probe.expand(batch_size, -1, -1)

        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]

        residual = hidden_state
        hidden_state = self.layernorm(hidden_state)
        hidden_state = self.mlp(hidden_state)
        hidden_state += residual

        # Handled by resolve_visual_encoder_outputs
        # return hidden_state[:, 0]
        return hidden_state


class SiglipVisionTransformer(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
        prefix: str = "",
        use_head: bool | None = False,
    ) -> None:
        super().__init__()

        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = SiglipVisionEmbeddings(config)

        self.encoder = SiglipEncoder(
            config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.encoder",
            attn_cls=MMEncoderAttention,
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.encoder.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.encoder.layers)} layers."
            )

        # If possible, skip post_layernorm to conserve memory
        if require_post_norm is None:
            require_post_norm = len(self.encoder.layers) == num_hidden_layers

        if require_post_norm:
            self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        else:
            self.post_layernorm = None

        # Fall back to the config if a bool is not provided explicitly;
        # note that many config types, including SiglipVisionConfig,
        # do not have vision_use_head as a defined attribute.
        if isinstance(use_head, bool):
            self.use_head = use_head
        else:
            self.use_head = (
                True
                if not hasattr(config, "vision_use_head")
                else config.vision_use_head
            )

        # Only create and load the head weights if we actually need them
        self.head = (
            SiglipMultiheadAttentionPoolingHead(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.head",
            )
            if self.use_head
            else None
        )
        self.last_hs_proc = partial(self.maybe_layer_norm_and_apply_head)

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

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

    def forward(
        self,
        pixel_values: torch.Tensor,
        *,
        interpolate_pos_encoding: bool = False,
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
    ) -> torch.Tensor:
        hidden_states = self.embeddings(
            pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
        )
        # Produces either the last layer output or all of the hidden states,
        # depending on if we have select_layers or not
        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            return_all_hidden_states=select_layers is not None,
        )

        # In the case that we have multiple feature layers,
        # we stack and concatenate them into a tensor.
        # NOTE: post layer norm and the attention pooling head
        # are handled by last_hs_proc, which runs before applying
        # the vision feature selection strategy.
        encoder_outputs = resolve_visual_encoder_outputs(
            encoder_outputs,
            None,
            select_layers=select_layers,
            max_possible_layers=self.config.num_hidden_layers,
            last_hs_proc=self.last_hs_proc,
            feature_select_strategy=feature_select_strategy,
        )

        return encoder_outputs

    def maybe_layer_norm_and_apply_head(
        self, encoder_outputs: torch.Tensor
    ) -> torch.Tensor:
        """Apply the post layer norm and head if they are enabled,
        given the last hidden states tensor.

        args:
            encoder_outputs: The last hidden states from the visual encoder.
        """
        if self.post_layernorm is not None:
            encoder_outputs = self.post_layernorm(encoder_outputs)
        if self.head is not None:
            encoder_outputs = self.head(encoder_outputs)
        return encoder_outputs

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        layer_count = len(self.encoder.layers)

        for name, loaded_weight in weights:
            # post_layernorm is not needed in SiglipVisionTransformer
            if name.startswith("post_layernorm") and self.post_layernorm is None:
                continue

            # if the model configuration is not going to use
            # the pooling head for inference, don't load its weights
            if self.head is None and name.startswith("head"):
                continue

            # omit layers when num_hidden_layers_override is set
            if name.startswith("encoder.layers"):
                layer_idx = int(name.split(".")[2])
                if layer_idx >= layer_count:
                    continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class SiglipVisionModel(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: QuantizationConfig | None = None,
        *,
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
        prefix: str = "",
        use_head: bool | None = False,
    ) -> None:
        super().__init__()

        self.quant_config = quant_config
        self.vision_model = SiglipVisionTransformer(
            config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            require_post_norm=require_post_norm,
            prefix=f"{prefix}.vision_model",
            use_head=use_head,
        )

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    @property
    def dtype(self):
        return self.vision_model.dtype

    @property
    def device(self):
        return self.vision_model.device

    def forward(
        self,
        pixel_values: torch.Tensor,
        interpolate_pos_encoding: bool = False,
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
    ) -> torch.Tensor:
        return self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            select_layers=select_layers,
            feature_select_strategy=feature_select_strategy,
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        layer_count = len(self.vision_model.encoder.layers)

        for name, loaded_weight in weights:
            # post_layernorm is optional in SiglipVisionModel
            if (
                name.startswith("vision_model.post_layernorm")
                and self.vision_model.post_layernorm is None
            ):
                continue

            # if the model configuration is not going to use
            # the pooling head for inference, don't load its weights
            if self.vision_model.head is None and name.startswith("vision_model.head"):
                continue

            # omit layers when num_hidden_layers_override is set
            if name.startswith("vision_model.encoder.layers"):
                layer_idx = int(name.split(".")[3])
                if layer_idx >= layer_count:
                    continue

            # Check if this is a scale parameter that needs remapping first
            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
                # Try to remap the scale name first
                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
                if remapped_name is not None and remapped_name in params_dict:
                    # Successfully remapped, use the remapped name
                    param = params_dict[remapped_name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
                    loaded_params.add(remapped_name)
                    continue
                # If remapping failed, continue with normal processing

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                param = maybe_swap_ffn_param(
                    name, param, loaded_weight, params_dict, self.quant_config
                )
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


def maybe_swap_ffn_param(
    name: str,
    param: torch.Tensor,
    loaded_weight: torch.Tensor,
    params_dict: dict[str, torch.Tensor],
    quant_config: QuantizationConfig,
) -> torch.Tensor:
    if not (quant_config and quant_config.get_name() == "gguf") or ".fc" not in name:
        return param
    # Some GGUF models have fc1 and fc2 weights swapped
    tp_size = get_tensor_model_parallel_world_size()
    output_dim = getattr(param, "output_dim", 0)
    output_size = param.size(output_dim) * tp_size
    weight_out_size = loaded_weight.size(output_dim)
    if ".fc1." in name and output_size != weight_out_size:
        new_name = name.replace(".fc1.", ".fc2.")
        param = params_dict[new_name]
    elif ".fc2." in name and output_size != weight_out_size:
        new_name = name.replace(".fc2.", ".fc1.")
        param = params_dict[new_name]
    return param


# Adapted from: https://github.com/huggingface/transformers/blob/v4.54.1/src/transformers/models/siglip/modeling_siglip.py#L200
class SiglipTextEmbeddings(nn.Module):
    def __init__(self, config: SiglipTextConfig):
        super().__init__()
        self.config = config

        self.token_embedding = VocabParallelEmbedding(
            config.vocab_size, config.hidden_size
        )

        self.position_embedding = VocabParallelEmbedding(
            config.max_position_embeddings, config.hidden_size
        )

        self.register_buffer(
            "position_ids",
            torch.arange(config.max_position_embeddings).expand((1, -1)),
            persistent=False,
        )

    def forward(
        self,
        input_ids: torch.Tensor | None,
        position_ids: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if inputs_embeds is None:
            inputs_embeds = self.token_embedding(input_ids)

        position_embeddings = self.position_embedding(position_ids)
        embeddings = inputs_embeds + position_embeddings

        return embeddings


# Assume EOS token corresponds to CLS token in text model
@default_pooling_type(seq_pooling_type="CLS")
@MULTIMODAL_REGISTRY.register_processor(
    SiglipMultiModalProcessor,
    info=SiglipProcessingInfo,
    dummy_inputs=SiglipDummyInputsBuilder,
)
class SiglipEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
    is_pooling_model = True

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

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

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config: SiglipConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config

        if hasattr(config, "num_labels"):
            config.num_labels = 0

        text_config = config.text_config
        vision_config = config.vision_config

        self.text_embed_dim = text_config.hidden_size
        self.vision_embed_dim = vision_config.hidden_size
        self.text_projection_size = text_config.projection_size

        with self._mark_language_model(vllm_config):
            self.text_model = SiglipTextTransformer(
                text_config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "text_model"),
            )

        with self._mark_tower_model(vllm_config, "image"):
            self.vision_model = SiglipVisionTransformer(
                vision_config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "vision_model"),
                use_head=None,  # Allows potential pooling head
            )

        pooler_config = vllm_config.model_config.pooler_config
        assert pooler_config is not None
        self.pooler_config = pooler_config

        self.pooler = DispatchPooler.for_embedding(pooler_config)

        self._is_text_input = True

    def get_text_features(
        self,
        input_ids: torch.Tensor | None,
        position_ids: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        last_hidden_state = self.text_model(
            input_ids=input_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
        )
        text_features = self.text_model.head(last_hidden_state)

        # SigLIP uses reversed position_ids;
        # flip sequences to move EOS token to first position
        text_features = self._flip_sequences_by_position_ids(
            text_features, position_ids
        )

        return text_features

    def _flip_sequences_by_position_ids(
        self,
        features: torch.Tensor,
        position_ids: torch.Tensor,
    ) -> torch.Tensor:
        """Flip sequences so EOS token moves to first position for CLS pooling.

        SigLIP position_ids are reversed within each sequence. This method detects
        sequence boundaries and flips each sequence individually.
        """
        if len(features) == 1:
            return features

        # Detect sequence boundaries where position_ids decrease
        position_diffs = position_ids[1:] - position_ids[:-1]
        boundary_mask = position_diffs <= 0

        boundary_indices = torch.cat(
            [
                torch.tensor([0], device=features.device),
                torch.where(boundary_mask)[0] + 1,
                torch.tensor([len(features)], device=features.device),
            ]
        )

        # For each sequence [start, end), position i flips to: start + end - 1 - i
        lengths = boundary_indices[1:] - boundary_indices[:-1]
        starts = boundary_indices[:-1]
        ends = boundary_indices[1:]

        # Assign sequence ID to each element
        sequence_ids = torch.arange(
            len(lengths), device=features.device
        ).repeat_interleave(lengths)

        # Calculate flipped indices for all positions at once
        current_positions = torch.arange(len(features), device=features.device)
        flip_indices = starts[sequence_ids] + ends[sequence_ids] - 1 - current_positions

        return features[flip_indices]

    def get_image_features(
        self,
        pixel_values: torch.Tensor,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
    ) -> torch.Tensor:
        if feature_select_strategy is None:
            feature_select_strategy = _get_vision_feature_select_strategy(
                self.pooler_config.seq_pooling_type
            )

        pooled_output = self.vision_model(
            pixel_values=pixel_values,
            select_layers=None,
            feature_select_strategy=feature_select_strategy,
        )

        return pooled_output

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> SiglipImagePixelInputs | None:
        pixel_values = kwargs.pop("pixel_values", None)
        if pixel_values is None:
            return None

        expected_h = expected_w = self.config.vision_config.image_size
        return SiglipImagePixelInputs(
            type="pixel_values",
            data=pixel_values,
            resolve_bindings={"h": expected_h, "w": expected_w},
        )

    def _process_image_inputs(self, inputs: SiglipImagePixelInputs) -> torch.Tensor:
        pixel_values = inputs["data"]

        return self.get_image_features(pixel_values)

    def _embed_text_input_ids(
        self,
        input_ids: torch.Tensor,
        embed_input_ids: Callable[[torch.Tensor], torch.Tensor],
        *,
        is_multimodal: torch.Tensor | None,
        handle_oov_mm_token: bool,
    ) -> torch.Tensor:
        inputs_embeds = super()._embed_text_input_ids(
            input_ids,
            embed_input_ids,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

        # NOTE: inputs_embeds in model runner has size text_config.projection_size
        # (instead of text_config.hidden_size) to accommodate image embeddings
        inputs_embeds_size = self.text_projection_size
        if inputs_embeds.shape[1] < inputs_embeds_size:
            inputs_embeds = torch.cat(
                [
                    inputs_embeds,
                    inputs_embeds.new_empty(
                        inputs_embeds.shape[0],
                        inputs_embeds_size - inputs_embeds.shape[1],
                    ),
                ],
                dim=1,
            )
        elif inputs_embeds.shape[1] > inputs_embeds_size:
            # No need to handle this case for now
            raise NotImplementedError

        return inputs_embeds

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
        handle_oov_mm_token: bool = False,
    ) -> torch.Tensor:
        self._is_text_input = (
            multimodal_embeddings is None or len(multimodal_embeddings) == 0
        )

        if multimodal_embeddings is None or is_multimodal is None:
            return super().embed_input_ids(input_ids)

        return super().embed_input_ids(
            input_ids,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

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

        vision_embeddings = self._process_image_inputs(image_input)
        return vision_embeddings

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor:
        if intermediate_tensors is not None:
            raise RuntimeError("PP is not supported for this model")

        # Multimodal inputs (image embeddings)
        if not self._is_text_input:
            return inputs_embeds

        # NOTE: inputs_embeds in model runner has size text_config.projection_size
        # (instead of text_config.hidden_size) to accommodate image embeddings
        hidden_size = self.text_embed_dim
        if inputs_embeds.shape[1] > hidden_size:
            inputs_embeds = inputs_embeds[:, :hidden_size]
        elif inputs_embeds.shape[1] < hidden_size:
            # No need to handle this case for now
            raise NotImplementedError

        return self.get_text_features(input_ids, positions, inputs_embeds)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        loader = AutoWeightsLoader(
            self,
            skip_substrs=[".position_ids"],
            ignore_unexpected_prefixes=["logit_scale.", "logit_bias."],
        )

        return loader.load_weights(weights)