[Model] CLIP Embedding Support

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

@@ -1,14 +1,9 @@
 # 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

@@ -0,0 +1,138 @@
+# 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 @@ def check_available_online(
     "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 @@ def get_supported_archs(self) -> Set[str]:
         return self.hf_models.keys()
 
     def get_hf_info(self, model_arch: str) -> _HfExamplesInfo:
-        return self.hf_models[model_arch]
+        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 @@ def find_hf_info(self, model_id: str) -> _HfExamplesInfo:
             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 @@ def __init__(
         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 @@ def __init__(
                                    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,