[Frontend][4/n] Make pooling entrypoints request schema consensus | ScoreRequest (#33060)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>

examples/pooling/score/vision_rerank_api_online.py

@@ -89,6 +89,29 @@ def main(args):
     response = requests.post(rerank_url, json=prompt)
     pprint.pprint(response.json())
 
+    print("Query: string & Document: text + image url")
+    prompt = {
+        "model": model,
+        "query": query,
+        "documents": {"content": [documents[0], documents[1]]},
+    }
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: list")
+    prompt = {
+        "model": model,
+        "query": query,
+        "documents": [
+            document,
+            {"content": [documents[0]]},
+            {"content": [documents[1]]},
+            {"content": [documents[0], documents[1]]},
+        ],
+    }
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
+
 
 if __name__ == "__main__":
     args = parse_args()

examples/pooling/score/vision_score_api_online.py

@@ -92,6 +92,44 @@ def main(args):
     response = requests.post(score_url, json=prompt)
     pprint.pprint(response.json())
 
+    print("Query: string & Document: text + image url")
+    prompt = {
+        "model": model,
+        "queries": query,
+        "documents": {"content": [documents[0], documents[1]]},
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: list")
+    prompt = {
+        "model": model,
+        "queries": query,
+        "documents": [
+            document,
+            {"content": [documents[0]]},
+            {"content": [documents[1]]},
+            {"content": [documents[0], documents[1]]},
+        ],
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: list & Document: list")
+    data = [
+        document,
+        {"content": [documents[0]]},
+        {"content": [documents[1]]},
+        {"content": [documents[0], documents[1]]},
+    ]
+    prompt = {
+        "model": model,
+        "queries": data,
+        "documents": data,
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
+
 
 if __name__ == "__main__":
     args = parse_args()

tests/entrypoints/pooling/score/test_online_score.py

@@ -90,6 +90,35 @@ class TestModel:
         for i in range(len(vllm_outputs)):
             assert hf_outputs[i] == pytest.approx(vllm_outputs[i], rel=0.01)
 
+    def test_queries_str_items_str(
+        self, server: RemoteOpenAIServer, model: dict[str, Any], runner
+    ):
+        queries = "What is the capital of France?"
+        items = "The capital of France is Paris."
+
+        score_response = requests.post(
+            server.url_for("score"),
+            json={
+                "model": model["name"],
+                "queries": queries,
+                "items": items,
+            },
+        )
+        score_response.raise_for_status()
+        score = ScoreResponse.model_validate(score_response.json())
+
+        assert score.id is not None
+        assert score.data is not None
+        assert len(score.data) == 1
+
+        vllm_outputs = [d.score for d in score.data]
+
+        text_pairs = [[queries, items]]
+        hf_outputs = run_transformers(runner, model, text_pairs)
+
+        for i in range(len(vllm_outputs)):
+            assert hf_outputs[i] == pytest.approx(vllm_outputs[i], rel=0.01)
+
     def test_text_1_str_text_2_str(
         self, server: RemoteOpenAIServer, model: dict[str, Any], runner
     ):

tests/entrypoints/pooling/score/test_online_score_vision.py

@@ -5,7 +5,7 @@ import pytest
 import requests
 
 from tests.utils import VLLM_PATH, RemoteOpenAIServer
-from vllm.entrypoints.pooling.score.protocol import ScoreResponse
+from vllm.entrypoints.pooling.score.protocol import RerankResponse, ScoreResponse
 from vllm.multimodal.utils import encode_image_url, fetch_image
 
 MODEL_NAME = "Qwen/Qwen3-VL-Reranker-2B"
@@ -16,11 +16,12 @@ HF_OVERRIDES = {
 }
 
 query = "A cat standing in the snow."
+document = "This product was excellent and exceeded my expectations."
 image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/cat_snow.jpg"
 documents = [
     {
         "type": "text",
-        "text": query,
+        "text": document,
     },
     {
         "type": "image_url",
@@ -32,6 +33,11 @@ documents = [
     },
 ]
 
+TEXT_VS_TEXT = 0.10040374100208282
+TEXT_VS_IMAGE = 0.7423753142356873
+TEXT_VS_TEXT_PLUS_IMAGE = 0.5298863053321838
+TOL = 0.05
+
 
 @pytest.fixture(scope="module")
 def server():
@@ -50,15 +56,12 @@ def server():
 
 
 def test_score_api_queries_str_documents_str(server: RemoteOpenAIServer):
-    queries = "What is the capital of France?"
-    documents = "The capital of France is Paris."
-
     score_response = requests.post(
         server.url_for("score"),
         json={
             "model": MODEL_NAME,
-            "queries": queries,
-            "documents": documents,
+            "queries": query,
+            "documents": document,
         },
     )
     score_response.raise_for_status()
@@ -67,6 +70,8 @@ def test_score_api_queries_str_documents_str(server: RemoteOpenAIServer):
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 1
+    assert score.usage.prompt_tokens == 81
+    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
 
 
 def test_score_api_queries_str_documents_text_content(server: RemoteOpenAIServer):
@@ -84,6 +89,8 @@ def test_score_api_queries_str_documents_text_content(server: RemoteOpenAIServer
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 1
+    assert score.usage.prompt_tokens == 81
+    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
 
 
 def test_score_api_queries_str_documents_image_url_content(server: RemoteOpenAIServer):
@@ -101,6 +108,8 @@ def test_score_api_queries_str_documents_image_url_content(server: RemoteOpenAIS
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 1
+    assert score.usage.prompt_tokens == 98
+    assert score.data[0].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
 
 
 def test_score_api_queries_str_documents_image_base64_content(
@@ -120,3 +129,111 @@ def test_score_api_queries_str_documents_image_base64_content(
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 1
+    assert score.usage.prompt_tokens == 98
+    assert score.data[0].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+
+
+def test_score_api_queries_str_documents_image_url_plus_text_content(
+    server: RemoteOpenAIServer,
+):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": query,
+            "documents": {"content": [documents[0], documents[1]]},
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 1
+    assert score.usage.prompt_tokens == 108
+    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)
+
+
+def test_score_api_queries_str_documents_list(server: RemoteOpenAIServer):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": query,
+            "documents": [
+                document,
+                {"content": [documents[0]]},
+                {"content": [documents[1]]},
+                {"content": [documents[0], documents[1]]},
+            ],
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 4
+    assert score.usage.prompt_tokens == 368
+    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert score.data[1].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert score.data[2].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+    assert score.data[3].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)
+
+
+def test_rerank_api_queries_str_documents_list(server: RemoteOpenAIServer):
+    rerank_response = requests.post(
+        server.url_for("rerank"),
+        json={
+            "model": MODEL_NAME,
+            "query": query,
+            "documents": [
+                document,
+                {"content": [documents[0]]},
+                {"content": [documents[1]]},
+                {"content": [documents[0], documents[1]]},
+            ],
+        },
+    )
+    rerank_response.raise_for_status()
+    rerank = RerankResponse.model_validate(rerank_response.json())
+
+    assert rerank.id is not None
+    assert rerank.model is not None
+    assert rerank.usage is not None
+    assert len(rerank.results) == 4
+
+    rerank.results.sort(key=lambda x: x.index)
+    assert rerank.results[0].relevance_score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert rerank.results[1].relevance_score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert rerank.results[2].relevance_score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+    assert rerank.results[3].relevance_score == pytest.approx(
+        TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL
+    )
+
+
+def test_score_api_queries_list_documents_list(server: RemoteOpenAIServer):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": [query] * 4,
+            "documents": [
+                document,
+                {"content": [documents[0]]},
+                {"content": [documents[1]]},
+                {"content": [documents[0], documents[1]]},
+            ],
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 4
+    assert score.usage.prompt_tokens == 368
+    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert score.data[1].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert score.data[2].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+    assert score.data[3].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)

vllm/entrypoints/llm.py

@@ -40,12 +40,12 @@ from vllm.entrypoints.chat_utils import (
     ChatTemplateContentFormatOption,
 )
 from vllm.entrypoints.pooling.score.utils import (
-    ScoreContentPartParam,
+    ScoreData,
     ScoreMultiModalParam,
     _cosine_similarity,
-    _validate_score_input_lens,
     compress_token_type_ids,
     get_score_prompt,
+    validate_score_input,
 )
 from vllm.entrypoints.utils import log_non_default_args
 from vllm.inputs import (
@@ -1326,8 +1326,8 @@ class LLM:
 
     def _embedding_score(
         self,
-        text_1: list[SingletonPrompt],
-        text_2: list[SingletonPrompt],
+        data_1: list[ScoreData],
+        data_2: list[ScoreData],
         *,
         use_tqdm: bool | Callable[..., tqdm],
         pooling_params: PoolingParams | None,
@@ -1336,8 +1336,16 @@ class LLM:
     ) -> list[ScoringRequestOutput]:
         tokenizer = self.get_tokenizer()
 
+        input_texts: list[str] = []
+        for text in data_1 + data_2:
+            if not isinstance(text, str):
+                raise NotImplementedError(
+                    "Embedding scores currently do not support multimodal input."
+                )
+            input_texts.append(text)
+
         encoded_output = self.encode(
-            text_1 + text_2,
+            input_texts,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
             pooling_params=pooling_params,
@@ -1345,8 +1353,8 @@ class LLM:
             tokenization_kwargs=tokenization_kwargs,
         )
 
-        encoded_output_1 = encoded_output[0 : len(text_1)]
-        encoded_output_2 = encoded_output[len(text_1) :]
+        encoded_output_1 = encoded_output[0 : len(data_1)]
+        encoded_output_2 = encoded_output[len(data_1) :]
 
         if len(encoded_output_1) == 1:
             encoded_output_1 = encoded_output_1 * len(encoded_output_2)
@@ -1362,8 +1370,8 @@ class LLM:
 
     def _cross_encoding_score(
         self,
-        data_1: list[str] | list[ScoreContentPartParam],
-        data_2: list[str] | list[ScoreContentPartParam],
+        data_1: list[ScoreData],
+        data_2: list[ScoreData],
         *,
         use_tqdm: bool | Callable[..., tqdm],
         pooling_params: PoolingParams | None,
@@ -1424,8 +1432,14 @@ class LLM:
 
     def score(
         self,
-        data_1: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam,
-        data_2: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam,
+        data_1: SingletonPrompt
+        | Sequence[SingletonPrompt]
+        | ScoreMultiModalParam
+        | list[ScoreMultiModalParam],
+        data_2: SingletonPrompt
+        | Sequence[SingletonPrompt]
+        | ScoreMultiModalParam
+        | list[ScoreMultiModalParam],
         /,
         *,
         use_tqdm: bool | Callable[..., tqdm] = True,
@@ -1501,73 +1515,23 @@ class LLM:
                 "chat_template is only supported for cross-encoder models."
             )
 
-        # the tokenizer for models such as
-        # "cross-encoder/ms-marco-MiniLM-L-6-v2" doesn't support passing
-        # lists of tokens to the `text` and `text_pair` kwargs
-        tokenizer = self.get_tokenizer()
-
-        if not model_config.is_multimodal_model:
-
-            def check_data_type(
-                data: SingletonPrompt
-                | Sequence[SingletonPrompt]
-                | ScoreMultiModalParam,
-            ):
-                if isinstance(data, dict) and "content" in data:
-                    raise ValueError(
-                        "ScoreMultiModalParam is not supported "
-                        f"for {model_config.architecture}"
-                    )
-
-            check_data_type(data_1)
-            check_data_type(data_2)
+        is_multimodal_model = model_config.is_multimodal_model
+        architecture = model_config.architecture
 
-            def ensure_str(prompt: SingletonPrompt):
-                if isinstance(prompt, dict):
-                    if "multi_modal_data" in prompt:
-                        raise ValueError(
-                            "Multi-modal prompt is not supported for scoring"
-                        )
-                    elif "prompt_token_ids" in prompt:
-                        prompt = tokenizer.decode(
-                            cast(TokensPrompt, prompt)["prompt_token_ids"]
+        score_data_1, score_data_2 = validate_score_input(
+            data_1,  # type: ignore[arg-type]
+            data_2,  # type: ignore[arg-type]
+            is_multimodal_model=is_multimodal_model,
+            architecture=architecture,
         )
-                    elif "prompt" in prompt:
-                        prompt = cast(TextPrompt, prompt)["prompt"]
-                assert type(prompt) is str
-                return prompt
-
-            if isinstance(data_1, (str, dict)):
-                # Convert a single prompt to a list.
-                data_1 = [data_1]  # type: ignore[list-item]
-
-            data_1 = [ensure_str(t) for t in data_1]
-
-            if isinstance(data_2, (str, dict)):
-                # Convert a single prompt to a list.
-                data_2 = [data_2]  # type: ignore[list-item]
-
-            data_2 = [ensure_str(t) for t in data_2]
-
-        if isinstance(data_1, dict) and "content" in data_1:
-            data_1 = data_1.get("content")  # type: ignore[assignment]
-        elif isinstance(data_1, str):
-            data_1 = [data_1]
-
-        if isinstance(data_2, dict) and "content" in data_2:
-            data_2 = data_2.get("content")  # type: ignore[assignment]
-        elif isinstance(data_2, str):
-            data_2 = [data_2]
-
-        _validate_score_input_lens(data_1, data_2)  # type: ignore[arg-type]
 
         tok_params = self._get_cmpl_tok_params(tokenization_kwargs)
         encode_kwargs = tok_params.get_encode_kwargs()
 
         if model_config.is_cross_encoder:
             return self._cross_encoding_score(
-                data_1,  # type: ignore[arg-type]
-                data_2,  # type: ignore[arg-type]
+                score_data_1,
+                score_data_2,
                 use_tqdm=use_tqdm,
                 pooling_params=pooling_params,
                 lora_request=lora_request,
@@ -1576,8 +1540,8 @@ class LLM:
             )
         else:
             return self._embedding_score(
-                data_1,  # type: ignore[arg-type]
-                data_2,  # type: ignore[arg-type]
+                score_data_1,
+                score_data_2,
                 use_tqdm=use_tqdm,
                 pooling_params=pooling_params,
                 lora_request=lora_request,

vllm/entrypoints/pooling/score/protocol.py

@@ -14,7 +14,8 @@ from vllm.entrypoints.pooling.base.protocol import (
 )
 from vllm.entrypoints.pooling.score.utils import (
     ScoreContentPartParam,
-    ScoreMultiModalParam,
+    ScoreInput,
+    ScoreInputs,
 )
 from vllm.renderers import TokenizeParams
 from vllm.utils import random_uuid
@@ -47,13 +48,13 @@ class ScoreRequestMixin(PoolingBasicRequestMixin, ClassifyRequestMixin):
 
 
 class ScoreDataRequest(ScoreRequestMixin):
-    data_1: list[str] | str | ScoreMultiModalParam
-    data_2: list[str] | str | ScoreMultiModalParam
+    data_1: ScoreInputs
+    data_2: ScoreInputs
 
 
 class ScoreQueriesDocumentsRequest(ScoreRequestMixin):
-    queries: list[str] | str | ScoreMultiModalParam
-    documents: list[str] | str | ScoreMultiModalParam
+    queries: ScoreInputs
+    documents: ScoreInputs
 
     @property
     def data_1(self):
@@ -64,9 +65,22 @@ class ScoreQueriesDocumentsRequest(ScoreRequestMixin):
         return self.documents
 
 
+class ScoreQueriesItemsRequest(ScoreRequestMixin):
+    queries: ScoreInputs
+    items: ScoreInputs
+
+    @property
+    def data_1(self):
+        return self.queries
+
+    @property
+    def data_2(self):
+        return self.items
+
+
 class ScoreTextRequest(ScoreRequestMixin):
-    text_1: list[str] | str | ScoreMultiModalParam
-    text_2: list[str] | str | ScoreMultiModalParam
+    text_1: ScoreInputs
+    text_2: ScoreInputs
 
     @property
     def data_1(self):
@@ -78,13 +92,16 @@ class ScoreTextRequest(ScoreRequestMixin):
 
 
 ScoreRequest: TypeAlias = (
-    ScoreQueriesDocumentsRequest | ScoreDataRequest | ScoreTextRequest
+    ScoreQueriesDocumentsRequest
+    | ScoreQueriesItemsRequest
+    | ScoreDataRequest
+    | ScoreTextRequest
 )
 
 
 class RerankRequest(PoolingBasicRequestMixin, ClassifyRequestMixin):
-    query: str | ScoreMultiModalParam
-    documents: list[str] | ScoreMultiModalParam
+    query: ScoreInput
+    documents: ScoreInputs
     top_n: int = Field(default_factory=lambda: 0)
 
     # --8<-- [start:rerank-extra-params]
@@ -108,7 +125,7 @@ class RerankRequest(PoolingBasicRequestMixin, ClassifyRequestMixin):
 
 class RerankDocument(BaseModel):
     text: str | None = None
-    multi_modal: ScoreContentPartParam | None = None
+    multi_modal: list[ScoreContentPartParam] | None = None
 
 
 class RerankResult(BaseModel):

vllm/entrypoints/pooling/score/serving.py

@@ -27,12 +27,12 @@ from vllm.entrypoints.pooling.score.protocol import (
     ScoreResponseData,
 )
 from vllm.entrypoints.pooling.score.utils import (
-    ScoreContentPartParam,
-    ScoreMultiModalParam,
+    ScoreData,
+    ScoreInputs,
     _cosine_similarity,
-    _validate_score_input_lens,
     compress_token_type_ids,
     get_score_prompt,
+    validate_score_input,
 )
 from vllm.inputs.data import TokensPrompt
 from vllm.logger import init_logger
@@ -65,15 +65,32 @@ class ServingScores(OpenAIServing):
 
         self._tokenizer_executor = ThreadPoolExecutor(max_workers=1)
 
+        self.is_cross_encoder = self.model_config.is_cross_encoder
+        self.is_multimodal_model = self.model_config.is_multimodal_model
+        self.architecture = self.model_config.architecture
+
+        if self.is_cross_encoder:
+            self._score_func = self._cross_encoding_score
+        else:
+            self._score_func = self._embedding_score
+
     async def _embedding_score(
         self,
-        data_1: list[str],
-        data_2: list[str],
+        data_1: list[ScoreData],
+        data_2: list[ScoreData],
         request: RerankRequest | ScoreRequest,
         request_id: str,
         lora_request: LoRARequest | None | None = None,
         trace_headers: Mapping[str, str] | None = None,
     ) -> list[PoolingRequestOutput] | ErrorResponse:
+        input_texts: list[str] = []
+        for text in data_1 + data_2:
+            if not isinstance(text, str):
+                raise NotImplementedError(
+                    "Embedding scores currently do not support multimodal input."
+                )
+            input_texts.append(text)
+
         model_config = self.model_config
         tokenizer = self.renderer.get_tokenizer()
 
@@ -82,8 +99,6 @@ class ServingScores(OpenAIServing):
             executor=self._tokenizer_executor,
         )
 
-        input_texts = data_1 + data_2
-
         tokenization_kwargs = request.build_tok_params(model_config).get_encode_kwargs()
         tokenized_prompts = await asyncio.gather(
             *(encode_async(t, **tokenization_kwargs) for t in input_texts)
@@ -157,60 +172,30 @@ class ServingScores(OpenAIServing):
 
         return final_res_batch
 
-    def _preprocess_score(
-        self,
-        request: RerankRequest | ScoreRequest,
-        tokenizer: TokenizerLike,
-        tokenization_kwargs: dict[str, Any],
-        data_1: str | ScoreContentPartParam,
-        data_2: str | ScoreContentPartParam,
-    ) -> tuple[str, TokensPrompt]:
-        model_config = self.model_config
-
-        full_prompt, engine_prompt = get_score_prompt(
-            model_config=model_config,
-            data_1=data_1,
-            data_2=data_2,
-            tokenizer=tokenizer,
-            tokenization_kwargs=tokenization_kwargs,
-            score_template=self.score_template,
-        )
-        self._validate_input(request, engine_prompt["prompt_token_ids"], full_prompt)
-        if request.mm_processor_kwargs is not None:
-            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
-
-        return full_prompt, engine_prompt
-
     async def _cross_encoding_score(
         self,
-        data_1: list[str] | list[ScoreContentPartParam],
-        data_2: list[str] | list[ScoreContentPartParam],
+        data_1: list[ScoreData],
+        data_2: list[ScoreData],
         request: RerankRequest | ScoreRequest,
         request_id: str,
         lora_request: LoRARequest | None | None = None,
         trace_headers: Mapping[str, str] | None = None,
     ) -> list[PoolingRequestOutput] | ErrorResponse:
-        model_config = self.model_config
         tokenizer = self.renderer.get_tokenizer()
+        if isinstance(tokenizer, MistralTokenizer):
+            raise ValueError("MistralTokenizer not supported for cross-encoding")
 
-        request_prompts: list[str] = []
-        engine_prompts: list[TokensPrompt] = []
+        model_config = self.model_config
 
         if len(data_1) == 1:
             data_1 = data_1 * len(data_2)
 
-        if isinstance(tokenizer, MistralTokenizer):
-            raise ValueError("MistralTokenizer not supported for cross-encoding")
-
         tok_kwargs = request.build_tok_params(model_config).get_encode_kwargs()
-
         input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)]
-
         preprocess_async = make_async(
             self._preprocess_score,
             executor=self._tokenizer_executor,
         )
-
         preprocessed_prompts = await asyncio.gather(
             *(
                 preprocess_async(
@@ -224,6 +209,8 @@ class ServingScores(OpenAIServing):
             )
         )
 
+        request_prompts: list[str] = []
+        engine_prompts: list[TokensPrompt] = []
         for full_prompt, engine_prompt in preprocessed_prompts:
             request_prompts.append(full_prompt)
             engine_prompts.append(engine_prompt)
@@ -278,10 +265,33 @@ class ServingScores(OpenAIServing):
 
         return [out for out in final_res_batch if out is not None]
 
+    def _preprocess_score(
+        self,
+        request: RerankRequest | ScoreRequest,
+        tokenizer: TokenizerLike,
+        tokenization_kwargs: dict[str, Any],
+        data_1: ScoreData,
+        data_2: ScoreData,
+    ) -> tuple[str, TokensPrompt]:
+        model_config = self.model_config
+        full_prompt, engine_prompt = get_score_prompt(
+            model_config=model_config,
+            data_1=data_1,
+            data_2=data_2,
+            tokenizer=tokenizer,
+            tokenization_kwargs=tokenization_kwargs,
+            score_template=self.score_template,
+        )
+        self._validate_input(request, engine_prompt["prompt_token_ids"], full_prompt)
+        if request.mm_processor_kwargs is not None:
+            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
+
+        return full_prompt, engine_prompt
+
     async def _run_scoring(
         self,
-        data_1: list[str] | str | ScoreMultiModalParam,
-        data_2: list[str] | str | ScoreMultiModalParam,
+        data_1: ScoreInputs,
+        data_2: ScoreInputs,
         request: ScoreRequest | RerankRequest,
         request_id: str,
         raw_request: Request | None = None,
@@ -294,39 +304,16 @@ class ServingScores(OpenAIServing):
             else await self._get_trace_headers(raw_request.headers)
         )
 
-        if not self.model_config.is_multimodal_model and (
-            isinstance(data_1, dict) or isinstance(data_2, dict)
-        ):
-            raise ValueError(
-                f"MultiModalParam is not supported for {self.model_config.architecture}"  # noqa: E501
-            )
-
-        if isinstance(data_1, str):
-            data_1 = [data_1]
-        elif isinstance(data_1, dict):
-            data_1 = data_1.get("content")  # type: ignore[assignment]
-
-        if isinstance(data_2, str):
-            data_2 = [data_2]
-        elif isinstance(data_2, dict):
-            data_2 = data_2.get("content")  # type: ignore[assignment]
-
-        _validate_score_input_lens(data_1, data_2)  # type: ignore[arg-type]
-
-        if self.model_config.is_cross_encoder:
-            return await self._cross_encoding_score(
-                data_1=data_1,  # type: ignore[arg-type]
-                data_2=data_2,  # type: ignore[arg-type]
-                request=request,
-                request_id=request_id,
-                lora_request=lora_request,
-                trace_headers=trace_headers,
+        score_data_1, score_data_2 = validate_score_input(
+            data_1,
+            data_2,
+            is_multimodal_model=self.is_multimodal_model,
+            architecture=self.architecture,
         )
 
-        else:
-            return await self._embedding_score(
-                data_1=data_1,  # type: ignore[arg-type]
-                data_2=data_2,  # type: ignore[arg-type]
+        return await self._score_func(
+            data_1=score_data_1,
+            data_2=score_data_2,
             request=request,
             request_id=request_id,
             lora_request=lora_request,
@@ -391,15 +378,6 @@ class ServingScores(OpenAIServing):
 
         request_id = f"rerank-{self._base_request_id(raw_request)}"
         documents = request.documents
-        top_n = (
-            request.top_n
-            if request.top_n > 0
-            else (
-                len(documents)
-                if isinstance(documents, list)
-                else len(documents["content"])
-            )
-        )
 
         try:
             final_res_batch = await self._run_scoring(
@@ -412,6 +390,8 @@ class ServingScores(OpenAIServing):
             if isinstance(final_res_batch, ErrorResponse):
                 return final_res_batch
 
+            top_n = request.top_n if request.top_n > 0 else len(final_res_batch)
+
             return self.request_output_to_rerank_response(
                 final_res_batch,
                 request_id,
@@ -465,22 +445,32 @@ class ServingScores(OpenAIServing):
         final_res_batch: list[PoolingRequestOutput],
         request_id: str,
         model_name: str,
-        documents: list[str] | ScoreMultiModalParam,
+        documents: ScoreInputs,
         top_n: int,
     ) -> RerankResponse:
         """
         Convert the output of do_rank to a RerankResponse
         """
+
+        if not isinstance(documents, list):
+            documents = [documents]
+
         results: list[RerankResult] = []
         num_prompt_tokens = 0
         for idx, final_res in enumerate(final_res_batch):
             classify_res = ScoringRequestOutput.from_base(final_res)
 
+            document = documents[idx]
+            if isinstance(document, str):
+                rerank_document = RerankDocument(text=document)
+            else:
+                rerank_document = RerankDocument(
+                    multi_modal=document.get("content", [])
+                )
+
             result = RerankResult(
                 index=idx,
-                document=RerankDocument(text=documents[idx])
-                if isinstance(documents, list)
-                else RerankDocument(multi_modal=documents["content"][idx]),
+                document=rerank_document,
                 relevance_score=classify_res.outputs.score,
             )
             results.append(result)

vllm/entrypoints/pooling/score/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
 from typing import Any, TypeAlias, cast
 
 from torch.nn import CosineSimilarity
@@ -10,12 +11,13 @@ from vllm.entrypoints.chat_utils import (
     BaseMultiModalItemTracker,
     ChatCompletionContentPartImageEmbedsParam,
     ChatCompletionContentPartImageParam,
+    ChatCompletionContentPartParam,
     ChatCompletionContentPartTextParam,
     ChatCompletionContentPartVideoParam,
     ChatTemplateResolutionError,
+    ConversationMessage,
     MultiModalItemTracker,
-    _ContentPart,
-    _parse_chat_message_content_part,
+    _parse_chat_message_content_parts,
 )
 from vllm.inputs import TokensPrompt
 from vllm.model_executor.models.interfaces import supports_score_template
@@ -46,6 +48,13 @@ class ScoreMultiModalParam(TypedDict, total=False):
     """The multimodal contents"""
 
 
+# Raw input data with content key in ScoreMultiModalParam.
+ScoreInput = str | ScoreMultiModalParam
+ScoreInputs = ScoreInput | list[ScoreInput]
+# Score data without content key.
+ScoreData = str | list[ScoreContentPartParam]
+
+
 def _cosine_similarity(
     tokenizer: TokenizerLike,
     embed_1: list[PoolingRequestOutput],
@@ -77,8 +86,8 @@ def _cosine_similarity(
 
 
 def _validate_score_input_lens(
-    data_1: list[str] | list[ScoreContentPartParam],
-    data_2: list[str] | list[ScoreContentPartParam],
+    data_1: list[ScoreData],
+    data_2: list[ScoreData],
 ):
     len_1 = len(data_1)
     len_2 = len(data_2)
@@ -91,19 +100,56 @@ def _validate_score_input_lens(
         raise ValueError("At least one text_pair element must be given")
 
 
+def _validate_mm_score_input(
+    data: list[ScoreInput],
+    is_multimodal_model: bool,
+    architecture: str,
+) -> list[ScoreData]:
+    out: list[ScoreData] = []
+    for d in data:
+        if isinstance(d, str):
+            out.append(d)
+        else:
+            if not is_multimodal_model:
+                raise ValueError(f"MultiModalParam is not supported for {architecture}")
+            content = cast(list[ScoreContentPartParam], d.get("content", []))
+            out.append(content)
+    return out
+
+
+def validate_score_input(
+    data_1: ScoreInputs,
+    data_2: ScoreInputs,
+    is_multimodal_model: bool,
+    architecture: str,
+) -> tuple[list[ScoreData], list[ScoreData]]:
+    if not isinstance(data_1, list):
+        data_1 = [data_1]
+
+    if not isinstance(data_2, list):
+        data_2 = [data_2]
+
+    score_input_1 = _validate_mm_score_input(data_1, is_multimodal_model, architecture)
+    score_input_2 = _validate_mm_score_input(data_2, is_multimodal_model, architecture)
+    _validate_score_input_lens(score_input_1, score_input_2)
+    return score_input_1, score_input_2
+
+
 def parse_score_data(
-    data_1: str | ScoreContentPartParam,
-    data_2: str | ScoreContentPartParam,
+    data_1: ScoreData,
+    data_2: ScoreData,
     model_config: ModelConfig,
 ) -> tuple[str, str, MultiModalDataDict | None, MultiModalUUIDDict | None]:
     mm_tracker = MultiModalItemTracker(model_config)
 
-    content_1 = _parse_score_content(data_1, mm_tracker)
-    content_2 = _parse_score_content(data_2, mm_tracker)
+    content_1 = _parse_score_content("query", data_1, mm_tracker)
+    content_2 = _parse_score_content("document", data_2, mm_tracker)
 
-    def ensure_str(content: _ContentPart | None) -> str:
-        if content is not None and isinstance(content, str):
-            return cast(str, content)
+    def ensure_str(content: list[ConversationMessage]) -> str:
+        assert len(content) == 1
+        prompt = content[0]["content"]
+        if prompt is not None and isinstance(prompt, str):
+            return cast(str, prompt)
         else:
             raise ValueError(f"Only string content is supported, but got {content}.")
 
@@ -115,19 +161,22 @@ def parse_score_data(
 
 
 def _parse_score_content(
-    data: str | ScoreContentPartParam,
+    role: str,
+    data: ScoreData,
     mm_tracker: BaseMultiModalItemTracker,
-) -> _ContentPart | None:
+) -> list[ConversationMessage]:
+    parts: Iterable[ChatCompletionContentPartParam]
     if isinstance(data, str):
-        part = ChatCompletionContentPartTextParam(type="text", text=data)
+        parts = [ChatCompletionContentPartTextParam(type="text", text=data)]
     else:
-        part = data
+        parts = cast(Iterable[ChatCompletionContentPartParam], data)
 
     mm_parser = mm_tracker.create_parser()
 
-    parse_res = _parse_chat_message_content_part(
-        part,
-        mm_parser,
+    parse_res = _parse_chat_message_content_parts(
+        role=role,
+        parts=parts,
+        mm_tracker=mm_tracker,
         wrap_dicts=False,
         interleave_strings=False,
     )
@@ -184,8 +233,8 @@ def get_score_prompt(
     model_config: ModelConfig,
     tokenizer: TokenizerLike,
     tokenization_kwargs: dict[str, Any],
-    data_1: str | ScoreContentPartParam,
-    data_2: str | ScoreContentPartParam,
+    data_1: ScoreData,
+    data_2: ScoreData,
     score_template: str | None = None,
 ) -> tuple[str, TokensPrompt]:
     prompt_1, prompt_2, mm_data, mm_uuids = parse_score_data(