[Model] Siglip Embedding Support (#27324)

Signed-off-by: piood <2477084691@qq.com>

docs/models/supported_models.md

@@ -800,12 +800,13 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
 
 The following table lists those that are tested in vLLM.
 
-| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
-|--------------|--------|--------|-------------------|----------------------|---------------------------|
-| `CLIPModel` | CLIP | T / I | `openai/clip-vit-base-patch32`, `openai/clip-vit-large-patch14`, etc. | | |
-| `LlavaNextForConditionalGeneration`<sup>C</sup> | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | ✅︎ |
-| `Phi3VForCausalLM`<sup>C</sup> | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | | ✅︎ |
-| `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* |
+| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) |
+|--------------|--------|--------|-------------------|----------------------|---------------------------|---------------------|
+| `CLIPModel` | CLIP | T / I | `openai/clip-vit-base-patch32`, `openai/clip-vit-large-patch14`, etc. | | | ✅︎ |
+| `LlavaNextForConditionalGeneration`<sup>C</sup> | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | ✅︎ | ✅︎ |
+| `Phi3VForCausalLM`<sup>C</sup> | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | | ✅︎ | ✅︎ |
+| `SiglipModel` | SigLIP | T / I | `google/siglip-base-patch16-224` | | | ✅︎ |
+| `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* | \* |
 
 <sup>C</sup> Automatically converted into an embedding model via `--convert embed`. ([details](./pooling_models.md#model-conversion))  
 \* Feature support is the same as that of the original model.

examples/offline_inference/vision_language_pooling.py

@@ -110,6 +110,53 @@ def run_e5_v(query: Query) -> ModelRequestData:
     )
 
 
+def run_jinavl_reranker(query: Query) -> ModelRequestData:
+    if query["modality"] != "text+images":
+        raise ValueError(f"Unsupported query modality: '{query['modality']}'")
+
+    engine_args = EngineArgs(
+        model="jinaai/jina-reranker-m0",
+        runner="pooling",
+        max_model_len=32768,
+        trust_remote_code=True,
+        mm_processor_kwargs={
+            "min_pixels": 3136,
+            "max_pixels": 602112,
+        },
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        query=query["text"],
+        documents=query["image"],
+    )
+
+
+def run_siglip(query: Query) -> ModelRequestData:
+    if query["modality"] == "text":
+        prompt = query["text"]
+        image = None
+    elif query["modality"] == "image":
+        prompt = ""  # For image input, make sure that the prompt text is empty
+        image = query["image"]
+    else:
+        modality = query["modality"]
+        raise ValueError(f"Unsupported query modality: '{modality}'")
+
+    engine_args = EngineArgs(
+        model="google/siglip-base-patch16-224",
+        runner="pooling",
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image=image,
+    )
+
+
 def _get_vlm2vec_prompt_image(query: Query, image_token: str):
     if query["modality"] == "text":
         text = query["text"]
@@ -211,29 +258,6 @@ def run_vlm2vec_qwen2vl(query: Query) -> ModelRequestData:
     )
 
 
-def run_jinavl_reranker(query: Query) -> ModelRequestData:
-    if query["modality"] != "text+images":
-        raise ValueError(f"Unsupported query modality: '{query['modality']}'")
-
-    engine_args = EngineArgs(
-        model="jinaai/jina-reranker-m0",
-        runner="pooling",
-        max_model_len=32768,
-        trust_remote_code=True,
-        mm_processor_kwargs={
-            "min_pixels": 3136,
-            "max_pixels": 602112,
-        },
-        limit_mm_per_prompt={"image": 1},
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        query=query["text"],
-        documents=query["image"],
-    )
-
-
 def get_query(modality: QueryModality):
     if modality == "text":
         return TextQuery(modality="text", text="A dog sitting in the grass")
@@ -328,9 +352,10 @@ def run_score(model: str, modality: QueryModality, seed: int | None):
 model_example_map = {
     "clip": run_clip,
     "e5_v": run_e5_v,
+    "jinavl_reranker": run_jinavl_reranker,
+    "siglip": run_siglip,
     "vlm2vec_phi3v": run_vlm2vec_phi3v,
     "vlm2vec_qwen2vl": run_vlm2vec_qwen2vl,
-    "jinavl_reranker": run_jinavl_reranker,
 }
 
 

examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py

@@ -83,6 +83,109 @@ def run_clip(client: OpenAI, model: str):
     print("Text embedding output:", response.data[0].embedding)
 
 
+def run_dse_qwen2_vl(client: OpenAI, model: str):
+    """
+    Start the server using:
+
+    vllm serve MrLight/dse-qwen2-2b-mrl-v1 \
+        --runner pooling \
+        --trust-remote-code \
+        --max-model-len 8192 \
+        --chat-template examples/template_dse_qwen2_vl.jinja
+    """
+    response = create_chat_embeddings(
+        client,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": image_url,
+                        },
+                    },
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            }
+        ],
+        model=model,
+        encoding_format="float",
+    )
+
+    print("Image embedding output:", response.data[0].embedding)
+
+    # MrLight/dse-qwen2-2b-mrl-v1 requires a placeholder image
+    # of the minimum input size
+    buffer = io.BytesIO()
+    image_placeholder = Image.new("RGB", (56, 56))
+    image_placeholder.save(buffer, "png")
+    buffer.seek(0)
+    image_placeholder = base64.b64encode(buffer.read()).decode("utf-8")
+    response = create_chat_embeddings(
+        client,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{image_placeholder}",
+                        },
+                    },
+                    {"type": "text", "text": "Query: What is the weather like today?"},
+                ],
+            }
+        ],
+        model=model,
+        encoding_format="float",
+    )
+
+    print("Text embedding output:", response.data[0].embedding)
+
+
+def run_siglip(client: OpenAI, model: str):
+    """
+    Start the server using:
+
+    vllm serve google/siglip-base-patch16-224 \
+        --runner pooling
+    """
+
+    response = create_chat_embeddings(
+        client,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": image_url}},
+                ],
+            }
+        ],
+        model=model,
+        encoding_format="float",
+    )
+
+    print("Image embedding output:", response.data[0].embedding)
+
+    response = create_chat_embeddings(
+        client,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "a photo of a cat"},
+                ],
+            }
+        ],
+        model=model,
+        encoding_format="float",
+    )
+
+    print("Text embedding output:", response.data[0].embedding)
+
+
 def run_vlm2vec(client: OpenAI, model: str):
     """
     Start the server using:
@@ -148,72 +251,11 @@ def run_vlm2vec(client: OpenAI, model: str):
     print("Text embedding output:", response.data[0].embedding)
 
 
-def run_dse_qwen2_vl(client: OpenAI, model: str):
-    """
-    Start the server using:
-
-    vllm serve MrLight/dse-qwen2-2b-mrl-v1 \
-        --runner pooling \
-        --trust-remote-code \
-        --max-model-len 8192 \
-        --chat-template examples/template_dse_qwen2_vl.jinja
-    """
-    response = create_chat_embeddings(
-        client,
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": image_url,
-                        },
-                    },
-                    {"type": "text", "text": "What is shown in this image?"},
-                ],
-            }
-        ],
-        model=model,
-        encoding_format="float",
-    )
-
-    print("Image embedding output:", response.data[0].embedding)
-
-    # MrLight/dse-qwen2-2b-mrl-v1 requires a placeholder image
-    # of the minimum input size
-    buffer = io.BytesIO()
-    image_placeholder = Image.new("RGB", (56, 56))
-    image_placeholder.save(buffer, "png")
-    buffer.seek(0)
-    image_placeholder = base64.b64encode(buffer.read()).decode("utf-8")
-    response = create_chat_embeddings(
-        client,
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{image_placeholder}",
-                        },
-                    },
-                    {"type": "text", "text": "Query: What is the weather like today?"},
-                ],
-            }
-        ],
-        model=model,
-        encoding_format="float",
-    )
-
-    print("Text embedding output:", response.data[0].embedding)
-
-
 model_example_map = {
     "clip": run_clip,
-    "vlm2vec": run_vlm2vec,
     "dse_qwen2_vl": run_dse_qwen2_vl,
+    "siglip": run_siglip,
+    "vlm2vec": run_vlm2vec,
 }
 
 

tests/models/multimodal/pooling/test_siglip.py

@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import SiglipModel
+
+from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
+from ...utils import check_embeddings_close
+
+HF_TEXT_PROMPTS = [
+    "a photo of a stop sign",
+    "a photo of a cherry blossom",
+]
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
+    {
+        "stop_sign": "",
+        "cherry_blossom": "",
+    }
+)
+
+MODELS = ["google/siglip-base-patch16-224"]
+
+
+def _run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    input_texts: list[str],
+    input_images: PromptImageInput,
+    model: str,
+    *,
+    dtype: str,
+) -> None:
+    with vllm_runner(
+        model, runner="pooling", dtype=dtype, enforce_eager=True, max_model_len=64
+    ) as vllm_model:
+        vllm_outputs = vllm_model.embed(input_texts, images=input_images)
+
+    with hf_runner(model, dtype=dtype, auto_cls=SiglipModel) as hf_model:
+        all_inputs = hf_model.get_inputs(input_texts, images=input_images)
+
+        all_outputs = []
+        for inputs in all_inputs:
+            inputs = hf_model.wrap_device(inputs)
+
+            if "pixel_values" in inputs:
+                pooled_output = hf_model.model.get_image_features(
+                    pixel_values=inputs.pixel_values,
+                ).squeeze(0)
+            else:
+                pooled_output = hf_model.model.get_text_features(
+                    input_ids=inputs.input_ids,
+                ).squeeze(0)
+
+            all_outputs.append(pooled_output.tolist())
+
+        hf_outputs = all_outputs
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_models_text(
+    hf_runner,
+    vllm_runner,
+    image_assets,
+    model: str,
+    dtype: str,
+) -> None:
+    input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS]
+    input_texts = [text for text, _ in input_texts_images]
+    input_images = [image for _, image in input_texts_images]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        input_texts,
+        input_images,  # type: ignore
+        model,
+        dtype=dtype,
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_models_image(
+    hf_runner,
+    vllm_runner,
+    image_assets,
+    model: str,
+    dtype: str,
+) -> None:
+    input_texts_images = [
+        (text, asset.pil_image) for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
+    ]
+    input_texts = [text for text, _ in input_texts_images]
+    input_images = [image for _, image in input_texts_images]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        input_texts,
+        input_images,
+        model,
+        dtype=dtype,
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_models_text_image_no_crash(
+    vllm_runner,
+    image_assets,
+    model: str,
+    dtype: str,
+) -> None:
+    texts = [HF_TEXT_PROMPTS[0]]
+    images = [image_assets[0].pil_image]
+
+    with vllm_runner(
+        model,
+        runner="pooling",
+        dtype=dtype,
+        enforce_eager=True,
+        max_model_len=64,
+    ) as vllm_model:
+        with pytest.raises(ValueError, match="not both"):
+            vllm_model.embed(texts, images=images)
+
+        vllm_model.embed(texts)
+        vllm_model.embed([""], images=images)

tests/models/registry.py

@@ -471,6 +471,7 @@ _EMBEDDING_EXAMPLE_MODELS = {
         "TIGER-Lab/VLM2Vec-Full", trust_remote_code=True
     ),
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("MrLight/dse-qwen2-2b-mrl-v1"),
+    "SiglipModel": _HfExamplesInfo("google/siglip-base-patch16-224"),
     "PrithviGeoSpatialMAE": _HfExamplesInfo(
         "ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",
         dtype="float16",

vllm/model_executor/models/registry.py

@@ -209,6 +209,7 @@ _EMBEDDING_MODELS = {
     ),
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
+    "SiglipModel": ("siglip", "SiglipEmbeddingModel"),
     # Technically Terratorch models work on images, both in
     # input and output. I am adding it here because it piggy-backs on embedding
     # models for the time being.

vllm/model_executor/models/siglip.py

@@ -4,13 +4,23 @@
 within a vision language model."""
 
 import math
-from collections.abc import Iterable
+from collections.abc import Iterable, Mapping
+from functools import cached_property
+from typing import Annotated, Literal
 
 import torch
 from torch import nn
-from transformers import SiglipVisionConfig
+from transformers import (
+    BatchFeature,
+    SiglipConfig,
+    SiglipProcessor,
+    SiglipTextConfig,
+    SiglipVisionConfig,
+)
 
 from vllm.attention.layer import MultiHeadAttention
+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.linear import (
@@ -18,20 +28,232 @@ from vllm.model_executor.layers.linear import (
     QKVParallelLinear,
     RowParallelLinear,
 )
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
 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,
+    MultiModalUUIDDict,
+)
+from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptIndexTargets,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+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,
     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.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] | None = None,
+    ) -> 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") if mm_options else None
+
+        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 = 0
+
+        assert dummy_token_id not in tokenizer.all_special_ids
+
+        return dummy_token_id
+
+    def apply(
+        self,
+        prompt: str | list[int],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object] | None = None,
+        *,
+        mm_uuids: MultiModalUUIDDict | None = None,
+    ) -> MultiModalInputs:
+        if prompt and mm_data:
+            raise ValueError(
+                "Siglip accepts text-only or image-only inputs, not both! "
+                "Image-only inputs means passing an image with an empty text "
+                "prompt."
+            )
+
+        if mm_data:
+            # 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_data=mm_data,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
+            mm_uuids=mm_uuids,
+        )
+
+    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,
@@ -151,8 +373,9 @@ class SiglipVisionEmbeddings(nn.Module):
 class SiglipAttention(nn.Module):
     def __init__(
         self,
-        config: SiglipVisionConfig,
+        config: SiglipVisionConfig | SiglipTextConfig,
         quant_config: QuantizationConfig | None = None,
+        *,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -195,12 +418,29 @@ class SiglipAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-    ) -> 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)
 
+        needs_unsqueeze = query_states.ndim == 2
+        if needs_unsqueeze:
+            query_states, key_states, value_states = (
+                query_states.unsqueeze(0),
+                key_states.unsqueeze(0),
+                value_states.unsqueeze(0),
+            )
+
         out = self.attn(query_states, key_states, value_states)
+
+        if needs_unsqueeze:
+            out, query_states, key_states, value_states = (
+                out.squeeze(0),
+                query_states.squeeze(0),
+                key_states.squeeze(0),
+                value_states.squeeze(0),
+            )
+
         attn_output, _ = self.out_proj(out)
 
         return attn_output, None
@@ -209,7 +449,7 @@ class SiglipAttention(nn.Module):
 class SiglipMLP(nn.Module):
     def __init__(
         self,
-        config: SiglipVisionConfig,
+        config: SiglipVisionConfig | SiglipTextConfig,
         quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ) -> None:
@@ -249,8 +489,9 @@ class SiglipMLP(nn.Module):
 class SiglipEncoderLayer(nn.Module):
     def __init__(
         self,
-        config: SiglipVisionConfig,
+        config: SiglipVisionConfig | SiglipTextConfig,
         quant_config: QuantizationConfig | None = None,
+        *,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -291,9 +532,10 @@ class SiglipEncoderLayer(nn.Module):
 class SiglipEncoder(nn.Module):
     def __init__(
         self,
-        config: SiglipVisionConfig,
+        config: SiglipVisionConfig | SiglipTextConfig,
         quant_config: QuantizationConfig | None = None,
         num_hidden_layers_override: int | None = None,
+        *,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -335,6 +577,76 @@ class SiglipEncoder(nn.Module):
         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",
+        )
+
+        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.head = nn.Linear(embed_dim, config.projection_size)
+
+    def get_input_embeddings(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."""
 
@@ -357,8 +669,9 @@ class SiglipMultiheadAttentionPoolingHead(nn.Module):
         )
 
     def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
-        batch_size = hidden_state.shape[0]
-        probe = self.probe.repeat(batch_size, 1, 1)
+        batch_size = hidden_state.size(0)
+
+        probe = self.probe.expand(batch_size, -1, -1)
 
         hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
 
@@ -367,7 +680,9 @@ class SiglipMultiheadAttentionPoolingHead(nn.Module):
         hidden_state = self.mlp(hidden_state)
         hidden_state += residual
 
-        return hidden_state[:, 0]
+        pooled = hidden_state[:, 0]
+
+        return pooled.unsqueeze(1)
 
 
 class SiglipVisionTransformer(nn.Module):
@@ -420,6 +735,14 @@ class SiglipVisionTransformer(nn.Module):
                 prefix=f"{prefix}.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,
@@ -432,7 +755,6 @@ class SiglipVisionTransformer(nn.Module):
             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(
@@ -440,21 +762,60 @@ class SiglipVisionTransformer(nn.Module):
             return_all_hidden_states=select_layers is not None,
         )
 
-        # Handle post-norm (if applicable) and stacks feature layers if needed
+        if self.post_layernorm is not None:
+            encoder_outputs = self.post_layernorm(encoder_outputs)
+
+        if self.use_head:
+            encoder_outputs = self.head(encoder_outputs)
+
+        # stacks feature layers if needed
         encoder_outputs = resolve_visual_encoder_outputs(
             encoder_outputs,
-            self.post_layernorm,
+            None,
             select_layers=select_layers,
             max_possible_layers=self.config.num_hidden_layers,
             feature_select_strategy=feature_select_strategy,
         )
 
-        # TODO: add this back when pooled_output is used in inference.
-        # if self.use_head:
-        # pooled_output = 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
+
+            # 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):
     config_class = SiglipVisionConfig
@@ -484,7 +845,11 @@ class SiglipVisionModel(nn.Module):
 
     @property
     def dtype(self):
-        return self.get_input_embeddings().weight.dtype
+        return self.vision_model.dtype
+
+    @property
+    def device(self):
+        return self.vision_model.device
 
     def forward(
         self,
@@ -555,3 +920,214 @@ class SiglipVisionModel(nn.Module):
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
+
+
+# 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("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"]}
+    merge_by_field_config = True
+
+    @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
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_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_model = SiglipTextTransformer(
+            text_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "text_model"),
+        )
+        self.vision_model = SiglipVisionTransformer(
+            vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.text_projection_size = text_config.projection_size
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+        self.pooler_config = pooler_config
+
+        self.pooler = DispatchPooler(
+            {
+                "token_embed": Pooler.for_token_embed(pooler_config),
+                "embed": Pooler.for_embed(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)
+        # Flip to extract CLS token (first token after reversal) for pooling
+        text_features = text_features.flip(0)
+        return text_features
+
+    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.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 get_language_model(self) -> torch.nn.Module:
+        return self.text_model
+
+    def get_input_embeddings(
+        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().get_input_embeddings(input_ids)
+
+        return super().get_input_embeddings(
+            input_ids,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
+        )
+
+    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_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
+
+        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)

vllm/transformers_utils/chat_templates/registry.py

@@ -31,14 +31,15 @@ def _get_minicpmv_chat_template_fallback(tokenizer_name_or_path: str) -> Path |
 
 _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK: dict[str, ChatTemplatePath] = {
     "blip-2": CHAT_TEMPLATES_DIR / "template_blip2.jinja",
-    "clip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "chameleon": CHAT_TEMPLATES_DIR / "template_basic.jinja",
-    "deepseek_vl_v2": CHAT_TEMPLATES_DIR / "template_deepseek_vl2.jinja",
+    "clip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "deepseek_ocr": CHAT_TEMPLATES_DIR / "template_deepseek_ocr.jinja",
+    "deepseek_vl_v2": CHAT_TEMPLATES_DIR / "template_deepseek_vl2.jinja",
     "fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",
     "minicpmv": _get_minicpmv_chat_template_fallback,
     "paligemma": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "qwen": _get_qwen_chat_template_fallback,
+    "siglip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
 }