[Model] CLIP Embedding Support (#26010)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/models/supported_models.md

@@ -829,6 +829,7 @@ 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) | [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` | | ✅︎ | ✅︎ |
 | `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* | \* |

examples/offline_inference/vision_language_pooling.py

@@ -58,6 +58,30 @@ class ModelRequestData(NamedTuple):
     documents: Optional[ScoreMultiModalParam] = None
 
 
+def run_clip(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="openai/clip-vit-base-patch32",
+        runner="pooling",
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image=image,
+    )
+
+
 def run_e5_v(query: Query) -> ModelRequestData:
     llama3_template = "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n"  # noqa: E501
 
@@ -146,7 +170,8 @@ def run_vlm2vec_qwen2vl(query: Query) -> ModelRequestData:
 
     processor = AutoProcessor.from_pretrained(
         model_id,
-        # `min_pixels` and `max_pixels` are deprecated
+        # `min_pixels` and `max_pixels` are deprecated for
+        # transformers `preprocessor_config.json`
         size={"shortest_edge": 3136, "longest_edge": 12845056},
     )
     processor.chat_template = load_chat_template(
@@ -172,8 +197,10 @@ def run_vlm2vec_qwen2vl(query: Query) -> ModelRequestData:
         model=merged_path,
         runner="pooling",
         max_model_len=4096,
-        trust_remote_code=True,
-        mm_processor_kwargs={"num_crops": 4},
+        mm_processor_kwargs={
+            "min_pixels": 3136,
+            "max_pixels": 12845056,
+        },
         limit_mm_per_prompt={"image": 1},
     )
 
@@ -299,6 +326,7 @@ def run_score(model: str, modality: QueryModality, seed: Optional[int]):
 
 
 model_example_map = {
+    "clip": run_clip,
     "e5_v": run_e5_v,
     "vlm2vec_phi3v": run_vlm2vec_phi3v,
     "vlm2vec_qwen2vl": run_vlm2vec_qwen2vl,

examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # ruff: noqa: E501
-"""Example Python client for multimodal embedding API using vLLM API server
-NOTE:
-    start a supported multimodal embeddings model server with `vllm serve`, e.g.
-    vllm serve TIGER-Lab/VLM2Vec-Full \
-        --runner pooling \
-        --trust-remote-code \
-        --max-model-len 4096 \
-        --chat-template examples/template_vlm2vec_phi3v.jinja
+"""Example Python client for multimodal embedding API using vLLM API server.
+
+Refer to each `run_*` function for the command to run the server for that model.
 """
 
 import argparse
@@ -47,7 +42,58 @@ def create_chat_embeddings(
     )
 
 
+def run_clip(client: OpenAI, model: str):
+    """
+    Start the server using:
+
+    vllm serve openai/clip-vit-base-patch32 \
+        --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:
+
+    vllm serve TIGER-Lab/VLM2Vec-Full \
+        --runner pooling \
+        --trust-remote-code \
+        --max-model-len 4096 \
+        --chat-template examples/template_vlm2vec_phi3v.jinja
+    """
+
     response = create_chat_embeddings(
         client,
         messages=[
@@ -103,6 +149,15 @@ def run_vlm2vec(client: OpenAI, model: str):
 
 
 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=[
@@ -156,6 +211,7 @@ def run_dse_qwen2_vl(client: OpenAI, model: str):
 
 
 model_example_map = {
+    "clip": run_clip,
     "vlm2vec": run_vlm2vec,
     "dse_qwen2_vl": run_dse_qwen2_vl,
 }

tests/models/multimodal/pooling/test_clip.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import CLIPModel
+
+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 = ["openai/clip-vit-base-patch32"]
+
+
+def _run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    input_texts: list[str],
+    input_images: PromptImageInput,
+    model: str,
+    *,
+    dtype: str,
+) -> None:
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+    with vllm_runner(model,
+                     runner="pooling",
+                     dtype=dtype,
+                     enforce_eager=True,
+                     max_model_len=77) as vllm_model:
+        vllm_outputs = vllm_model.embed(input_texts, images=input_images)
+
+    with hf_runner(model, dtype=dtype, auto_cls=CLIPModel) as hf_model:
+        all_inputs = hf_model.get_inputs(input_texts, images=input_images)
+
+        all_outputs = []
+        for inputs in all_inputs:
+            if "pixel_values" in inputs:
+                inputs.pop("input_ids")
+                pooled_output = hf_model.model.get_image_features(
+                    **hf_model.wrap_device(inputs)).squeeze(0)
+            else:
+                pooled_output = hf_model.model.get_text_features(
+                    **hf_model.wrap_device(inputs)).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=77) as vllm_model:
+        with pytest.raises(ValueError, match="not both"):
+            vllm_model.embed(texts, images=images)
+
+        # Should still be able to run subsequent requests
+        vllm_model.embed(texts)
+        vllm_model.embed([""], images=images)

tests/models/registry.py

@@ -389,6 +389,7 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "RobertaForMaskedLM": _HfExamplesInfo("sentence-transformers/all-roberta-large-v1"),  # noqa: E501
     "XLMRobertaModel": _HfExamplesInfo("intfloat/multilingual-e5-small"),  # noqa: E501
     # [Multimodal]
+    "CLIPModel": _HfExamplesInfo("openai/clip-vit-base-patch32"),
     "LlavaNextForConditionalGeneration": _HfExamplesInfo("royokong/e5-v"),
     "Phi3VForCausalLM": _HfExamplesInfo("TIGER-Lab/VLM2Vec-Full",
                                          trust_remote_code=True),
@@ -687,7 +688,11 @@ class HfExampleModels:
         return self.hf_models.keys()
 
     def get_hf_info(self, model_arch: str) -> _HfExamplesInfo:
+        try:
             return self.hf_models[model_arch]
+        except KeyError:
+            raise ValueError(f"No example model defined for {model_arch}; "
+                             f"please update this file.") from None
 
     def find_hf_info(self, model_id: str) -> _HfExamplesInfo:
         for info in self.hf_models.values():
@@ -699,7 +704,8 @@ class HfExampleModels:
             if any(extra == model_id for extra in info.extras.values()):
                 return info
 
-        raise ValueError(f"No example model defined for {model_id}")
+        raise ValueError(f"No example model defined for {model_id}; "
+                         f"please update this file.")
 
 
 HF_EXAMPLE_MODELS = HfExampleModels(_EXAMPLE_MODELS)

vllm/attention/layer.py

@@ -417,12 +417,16 @@ class MultiHeadAttention(nn.Module):
         head_size: int,
         scale: float,
         num_kv_heads: Optional[int] = None,
-    ):
+        # This has no effect, it is only here to make it easier to swap
+        # between Attention and MultiHeadAttention
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.num_heads = num_heads
         self.head_size = head_size
         self.scale = scale
         self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
+        self.layer_name = prefix
 
         assert self.num_heads % self.num_kv_heads == 0, \
             f"num_heads ({self.num_heads}) is not " \

vllm/model_executor/models/bert.py

@@ -351,7 +351,7 @@ class BertModel(nn.Module, SupportsQuant):
                                    prefix=f"{prefix}.encoder")
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.embeddings(input_ids)
+        return self.embeddings.word_embeddings(input_ids)
 
     def forward(
         self,

vllm/model_executor/models/clip.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Minimal implementation of CLIPVisionModel intended to be only used
-within a vision language model."""
-from collections.abc import Iterable
-from typing import Optional, Union
+from collections.abc import Iterable, Mapping, Sequence
+from functools import cached_property
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 import torch.nn as nn
-from transformers import CLIPVisionConfig
+from transformers import (BatchFeature, CLIPConfig, CLIPProcessor,
+                          CLIPTextConfig, CLIPVisionConfig)
 
+from vllm.attention import Attention
 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 (ColumnParallelLinear,
                                                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
 from vllm.model_executor.models.interfaces import SupportsQuant
-
+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
+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 CLIPImagePixelInputs(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")]
+
+
 class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
 
     def get_num_image_tokens(
@@ -45,7 +80,214 @@ class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
         return image_size // patch_size
 
 
-# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
+_POOLING_TYPE_TO_STRATEGY: dict[str, VisionFeatureSelectStrategyStr] = {
+    "MEAN": "full",
+    "ALL": "full",
+    "CLS": "class",
+    # This lets us use the same pooling type for both text and image
+    "LAST": "class",
+}
+
+
+def _get_vision_feature_select_strategy(pooling_type: str):
+    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 CLIPProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(CLIPConfig)
+
+    def get_vision_encoder_info(self):
+        return CLIPEncoderInfo(self.get_hf_config())
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(CLIPProcessor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        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 CLIPDummyInputsBuilder(BaseDummyInputsBuilder[CLIPProcessingInfo]):
+
+    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: Optional[Mapping[str, BaseDummyOptions]] = 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 CLIPMultiModalProcessor(BaseMultiModalProcessor[CLIPProcessingInfo]):
+
+    @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: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Optional[Mapping[str, object]] = None,
+        *,
+        mm_uuids: Optional[MultiModalUUIDDict] = None,
+    ) -> MultiModalInputs:
+        if prompt and mm_data:
+            raise ValueError(
+                "CLIP 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 dummy image tokens
+            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,
+    ) -> Sequence[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,
+            ),
+        ]
+
+
+# Adapted from: https://github.com/huggingface/transformers/blob/v4.56.2/src/transformers/models/clip/modeling_clip.py
+class CLIPTextEmbeddings(nn.Module):
+
+    def __init__(self, config: CLIPTextConfig):
+        super().__init__()
+
+        embed_dim = config.hidden_size
+
+        self.token_embedding = VocabParallelEmbedding(config.vocab_size,
+                                                      embed_dim)
+        self.position_embedding = VocabParallelEmbedding(
+            config.max_position_embeddings, embed_dim)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        position_ids: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            if input_ids is None:
+                raise ValueError(
+                    "Either `input_ids` or `input_embeds` must be provided")
+
+            inputs_embeds = self.token_embedding(input_ids)
+
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = inputs_embeds + position_embeddings
+
+        return embeddings
+
+
 class CLIPVisionEmbeddings(nn.Module):
 
     def __init__(self, config: CLIPVisionConfig):
@@ -89,15 +331,17 @@ class CLIPVisionEmbeddings(nn.Module):
 
 
 class CLIPAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
 
     def __init__(
         self,
-        config: CLIPVisionConfig,
+        config: Union[CLIPTextConfig, CLIPVisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
+        *,
         prefix: str = "",
-    ):
+        attn_cls: Union[type[Attention], type[MultiHeadAttention]],
+    ) -> None:
         super().__init__()
+
         self.config = config
         self.embed_dim = config.hidden_size
         self.num_heads = config.num_attention_heads
@@ -127,8 +371,12 @@ class CLIPAttention(nn.Module):
         self.tp_size = get_tensor_model_parallel_world_size()
         self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
 
-        self.attn = MultiHeadAttention(self.num_heads_per_partition,
-                                       self.head_dim, self.scale)
+        self.attn = attn_cls(
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            prefix=f"{prefix}.attn",
+        )
 
     def forward(
         self,
@@ -148,7 +396,7 @@ class CLIPMLP(nn.Module):
 
     def __init__(
         self,
-        config: CLIPVisionConfig,
+        config: Union[CLIPTextConfig, CLIPVisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ) -> None:
@@ -178,15 +426,18 @@ class CLIPEncoderLayer(nn.Module):
 
     def __init__(
         self,
-        config: CLIPVisionConfig,
+        config: Union[CLIPTextConfig, CLIPVisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
+        *,
         prefix: str = "",
+        attn_cls: Union[type[Attention], type[MultiHeadAttention]],
     ) -> None:
         super().__init__()
         self.self_attn = CLIPAttention(
             config,
             quant_config=quant_config,
             prefix=f"{prefix}.self_attn",
+            attn_cls=attn_cls,
         )
         self.layer_norm1 = nn.LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
@@ -223,10 +474,12 @@ class CLIPEncoder(nn.Module):
 
     def __init__(
         self,
-        config: CLIPVisionConfig,
+        config: Union[CLIPTextConfig, CLIPVisionConfig],
         quant_config: Optional[QuantizationConfig] = None,
         num_hidden_layers_override: Optional[int] = None,
+        *,
         prefix: str = "",
+        attn_cls: Union[type[Attention], type[MultiHeadAttention]],
     ) -> None:
         super().__init__()
 
@@ -239,12 +492,15 @@ class CLIPEncoder(nn.Module):
         self.layers = nn.ModuleList([
             CLIPEncoderLayer(config=config,
                              quant_config=quant_config,
-                             prefix=f"{prefix}.layers.{layer_idx}")
+                             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
+        self,
+        inputs_embeds: torch.Tensor,
+        return_all_hidden_states: bool,
     ) -> Union[torch.Tensor, list[torch.Tensor]]:
         hidden_states_pool = [inputs_embeds]
         hidden_states = inputs_embeds
@@ -260,6 +516,87 @@ class CLIPEncoder(nn.Module):
         return hidden_states
 
 
+class CLIPTextTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPTextConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = CLIPTextEmbeddings(config)
+
+        self.encoder = CLIPEncoder(
+            config=config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder",
+            attn_cls=Attention,
+        )
+
+        self.final_layer_norm = nn.LayerNorm(
+            embed_dim,
+            eps=config.layer_norm_eps,
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embeddings.token_embedding(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        position_ids: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=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 CLIPVisionTransformer(nn.Module):
 
     def __init__(
@@ -287,6 +624,7 @@ class CLIPVisionTransformer(nn.Module):
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
             prefix=f"{prefix}.encoder",
+            attn_cls=MultiHeadAttention,
         )
 
         num_hidden_layers = config.num_hidden_layers
@@ -306,6 +644,14 @@ class CLIPVisionTransformer(nn.Module):
         else:
             self.post_layernorm = None
 
+    @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,
@@ -335,11 +681,49 @@ class CLIPVisionTransformer(nn.Module):
 
         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 CLIPVisionModel
+            if (name.startswith("post_layernorm")
+                    and self.post_layernorm is None):
+                continue
 
-class CLIPVisionModel(nn.Module, SupportsQuant):
-    config_class = CLIPVisionConfig
-    main_input_name = "pixel_values"
-    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+            # 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 CLIPVisionModel(nn.Module):
 
     def __init__(
         self,
@@ -351,12 +735,14 @@ class CLIPVisionModel(nn.Module, SupportsQuant):
         prefix: str = "",
     ) -> None:
         super().__init__()
+
         self.vision_model = CLIPVisionTransformer(
             config=config,
             quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers_override,
             require_post_norm=require_post_norm,
-            prefix=f"{prefix}.vision_model")
+            prefix=f"{prefix}.vision_model",
+        )
 
     def forward(
         self,
@@ -370,49 +756,198 @@ class CLIPVisionModel(nn.Module, SupportsQuant):
             feature_select_strategy=feature_select_strategy,
         )
 
+    @property
+    def dtype(self):
+        return self.vision_model.dtype
+
     @property
     def device(self):
-        return next(self.parameters()).device
+        return self.vision_model.device
 
-    # (TODO) Add prefix argument for filtering out weights to be loaded
-    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
-    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 not needed in CLIPVisionModel
-            if (name.startswith("vision_model.post_layernorm")
-                    and self.vision_model.post_layernorm is None):
-                continue
+# Assume EOS token corresponds to LAST token in text model
+@default_pooling_type("LAST")
+@MULTIMODAL_REGISTRY.register_processor(CLIPMultiModalProcessor,
+                                        info=CLIPProcessingInfo,
+                                        dummy_inputs=CLIPDummyInputsBuilder)
+class CLIPEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
 
-            # 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
+    is_pooling_model = True
 
-            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)
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+    merge_by_field_config = True
 
-                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
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config: CLIPConfig = 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
+
+        text_config = config.text_config
+        vision_config = config.vision_config
+
+        self.projection_dim = config.projection_dim
+        self.text_embed_dim = text_config.hidden_size
+        self.vision_embed_dim = vision_config.hidden_size
+
+        self.text_model = CLIPTextTransformer(
+            text_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "text_model"),
+        )
+        self.vision_model = CLIPVisionTransformer(
+            vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.visual_projection = nn.Linear(
+            self.vision_embed_dim,
+            self.projection_dim,
+            bias=False,
+        )
+        self.text_projection = nn.Linear(
+            self.text_embed_dim,
+            self.projection_dim,
+            bias=False,
+        )
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+        self.pooler_config = pooler_config
+
+        self.pooler = DispatchPooler({
+            "encode": Pooler.for_encode(pooler_config),
+            "embed": Pooler.for_embed(pooler_config),
+        })
+
+        # Assumes that self.forward is called after self.get_input_embeddings
+        self._is_text_input = True
+
+    def get_text_features(
+        self,
+        input_ids: Optional[torch.Tensor],
+        position_ids: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        pooled_output = self.text_model(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        text_features = self.text_projection(pooled_output)
+
+        return text_features
+
+    def get_image_features(
+        self,
+        pixel_values: torch.Tensor,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = 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,
+        )
+
+        image_features = self.visual_projection(pooled_output)
+
+        return image_features
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[CLIPImagePixelInputs]:
+        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 CLIPImagePixelInputs(type="pixel_values",
+                                    data=pixel_values,
+                                    resolve_bindings={
+                                        "h": expected_h,
+                                        "w": expected_w
+                                    })
+
+    def _process_image_inputs(self,
+                              inputs: CLIPImagePixelInputs) -> 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: Optional[MultiModalEmbeddings] = None,
+        *,
+        is_multimodal: Optional[torch.Tensor] = None,
+        handle_oov_mm_token: bool = False,
+    ) -> torch.Tensor:
+        self._is_text_input = (multimodal_embeddings is None
+                               or len(multimodal_embeddings) == 0)
+
+        # This is to satisfy the type checker for each overload
+        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: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> torch.Tensor:
+        if intermediate_tensors is not None:
+            raise RuntimeError("PP is not supported for this model")
+
+        # Multimodal inputs
+        if not self._is_text_input:
+            return inputs_embeds
+
+        # Text inputs
+        return self.get_text_features(input_ids=input_ids,
+                                      position_ids=positions,
+                                      inputs_embeds=inputs_embeds)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(
+            self,
+            skip_substrs=[".position_ids"],
+            ignore_unexpected_prefixes=["logit_scale."],
+        )
+
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -187,6 +187,7 @@ _EMBEDDING_MODELS = {
     "TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
     "XLMRobertaModel": ("roberta", "RobertaEmbeddingModel"),
     # [Multimodal]
+    "CLIPModel": ("clip", "CLIPEmbeddingModel"),
     "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501

vllm/model_executor/models/vision.py

@@ -92,8 +92,10 @@ def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend:
     return current_platform.get_vit_attn_backend(head_size, dtype)
 
 
+VisionFeatureSelectStrategyStr = Literal["class", "default", "full"]
+
 VisionFeatureSelectStrategy = Union[
-    Literal["class", "default", "full"],
+    VisionFeatureSelectStrategyStr,
     Callable[[torch.Tensor], torch.Tensor],
 ]
 
@@ -106,7 +108,7 @@ def _get_vision_feature_selector(
 
     # https://github.com/huggingface/transformers/blob/cd74917ffc3e8f84e4a886052c5ab32b7ac623cc/src/transformers/models/clip/modeling_clip.py#L762
     if strategy == "class":
-        return lambda feats: feats[:, 0, :]
+        return lambda feats: feats[:, :1, :]
 
     # https://github.com/huggingface/transformers/blob/4a02bc7004285bdb12cc033e87ad2578ce2fa900/src/transformers/models/llava/modeling_llava.py#L196
     if strategy == "default":

vllm/transformers_utils/chat_templates/registry.py

@@ -33,6 +33,7 @@ def _get_minicpmv_chat_template_fallback(
 # yapf: disable
 _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",
     "fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",