[Frontend] Use a proper chat template for VLM2Vec (#9912)

docs/source/models/vlm.rst

@@ -240,8 +240,7 @@ To consume the server, you can use the OpenAI client like in the example below:
     )
     print("Chat completion output:", chat_response.choices[0].message.content)
 
-
+A full code example can be found in `examples/openai_chat_completion_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client_for_multimodal.py>`_.
-A full code example can be found in `examples/openai_api_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_api_client_for_multimodal.py>`_.
 
 .. tip::
     There is no need to place image placeholders in the text content of the API request - they are already represented by the image content.
@@ -269,14 +268,19 @@ In this example, we will serve the ``TIGER-Lab/VLM2Vec-Full`` model.
 .. code-block:: bash
 
     vllm serve TIGER-Lab/VLM2Vec-Full --task embedding \
-      --trust-remote-code --max-model-len 4096
+      --trust-remote-code --max-model-len 4096 --chat-template examples/template_vlm2vec.jinja
 
 .. important::
 
     Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass ``--task embedding``
     to run this model in embedding mode instead of text generation mode.
 
-Since this schema is not defined by OpenAI client, we post a request to the server using the lower-level ``requests`` library:
+.. important::
+
+    VLM2Vec does not expect chat-based input. We use a `custom chat template <https://github.com/vllm-project/vllm/blob/main/examples/template_vlm2vec.jinja>`_
+    to combine the text and images together.
+
+Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level ``requests`` library:
 
 .. code-block:: python
 
@@ -301,3 +305,5 @@ Since this schema is not defined by OpenAI client, we post a request to the serv
     response.raise_for_status()
     response_json = response.json()
     print("Embedding output:", response_json["data"][0]["embedding"])
+
+A full code example can be found in `examples/openai_chat_embedding_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_embedding_client_for_multimodal.py>`_.

examples/openai_chat_embedding_client_for_multimodal.py

@@ -0,0 +1,33 @@
+import requests
+
+image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+
+response = requests.post(
+    "http://localhost:8000/v1/embeddings",
+    json={
+        "model":
+        "TIGER-Lab/VLM2Vec-Full",
+        "messages": [{
+            "role":
+            "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": image_url
+                    }
+                },
+                {
+                    "type": "text",
+                    "text": "Represent the given image."
+                },
+            ],
+        }],
+        "encoding_format":
+        "float",
+    },
+)
+response.raise_for_status()
+response_json = response.json()
+
+print("Embedding output:", response_json["data"][0]["embedding"])

examples/template_vlm2vec.jinja

@@ -0,0 +1,16 @@
+{%- if messages | length > 1 -%}
+    {{ raise_exception('Embedding models should only embed one message at a time') }}
+{%- endif -%}
+
+{% set vars = namespace(parts=[], next_image_id=1) %}
+{%- for message in messages -%}
+    {%- for content in message['content'] -%}
+        {%- if content['type'] == 'text' -%}
+            {%- set vars.parts = vars.parts + [content['text']] %}
+        {%- elif content['type'] == 'image' -%}
+            {%- set vars.parts = vars.parts + ['<|image_{i:d}|>'.format(i=vars.next_image_id)] %}
+            {%- set vars.next_image_id = vars.next_image_id + 1 %}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endfor -%}
+{{ vars.parts | join(' ') }}

tests/entrypoints/openai/test_vision_embedding.py

@@ -6,11 +6,14 @@ import requests
 
 from vllm.multimodal.utils import encode_image_base64, fetch_image
 
-from ...utils import RemoteOpenAIServer
+from ...utils import VLLM_PATH, RemoteOpenAIServer
 
 MODEL_NAME = "TIGER-Lab/VLM2Vec-Full"
 MAXIMUM_IMAGES = 2
 
+vlm2vec_jinja_path = VLLM_PATH / "examples/template_vlm2vec.jinja"
+assert vlm2vec_jinja_path.exists()
+
 # Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
 TEST_IMAGE_URLS = [
     "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
@@ -35,6 +38,8 @@ def server():
         "--trust-remote-code",
         "--limit-mm-per-prompt",
         f"image={MAXIMUM_IMAGES}",
+        "--chat-template",
+        str(vlm2vec_jinja_path),
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
@@ -90,5 +95,5 @@ async def test_image_embedding(server: RemoteOpenAIServer, model_name: str,
     assert len(embeddings["data"]) == 1
     assert len(embeddings["data"][0]["embedding"]) == 3072
     assert embeddings["usage"]["completion_tokens"] == 0
-    assert embeddings["usage"]["prompt_tokens"] == 771
+    assert embeddings["usage"]["prompt_tokens"] == 762
-    assert embeddings["usage"]["total_tokens"] == 771
+    assert embeddings["usage"]["total_tokens"] == 762

vllm/entrypoints/chat_utils.py

@@ -156,6 +156,10 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
 
         self._items: List[_T] = []
 
+    @property
+    def model_config(self) -> ModelConfig:
+        return self._model_config
+
     @staticmethod
     @lru_cache(maxsize=None)
     def _cached_token_str(tokenizer: AnyTokenizer, token_index: int) -> str:
@@ -491,10 +495,13 @@ def _parse_chat_message_content_parts(
     content: List[Union[str, Dict[str, str]]] = []
 
     mm_parser = mm_tracker.create_parser()
-    wrap_dicts = \
+    model_config = mm_tracker.model_config
-        mm_tracker._model_config.hf_config.model_type in \
+
-            MODEL_KEEP_MULTI_MODAL_CONTENT or \
+    wrap_dicts = (chat_template_text_format == "openai"
-        (chat_template_text_format == "openai")
+                  or (model_config.task == "embedding"
+                      and model_config.is_multimodal_model)
+                  or (model_config.hf_config.model_type
+                      in MODEL_KEEP_MULTI_MODAL_CONTENT))
 
     for part in parts:
         parse_res = _parse_chat_message_content_part(