[Bugfix] Fix score api for missing max_model_len validation

tests/entrypoints/openai/test_score.py

@@ -12,6 +12,9 @@
 def server():
     args = [
         "--enforce-eager",
+        # Will be used on tests to compare prompt input length
+        "--max-model-len",
+        "100"
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
@@ -20,8 +23,7 @@ def server():
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_text_1_str_text_2_list(server: RemoteOpenAIServer,
-                                      model_name: str):
+def test_text_1_str_text_2_list(server: RemoteOpenAIServer, model_name: str):
     text_1 = "What is the capital of France?"
     text_2 = [
         "The capital of Brazil is Brasilia.", "The capital of France is Paris."
@@ -45,8 +47,7 @@ async def test_text_1_str_text_2_list(server: RemoteOpenAIServer,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_text_1_list_text_2_list(server: RemoteOpenAIServer,
-                                       model_name: str):
+def test_text_1_list_text_2_list(server: RemoteOpenAIServer, model_name: str):
     text_1 = [
         "What is the capital of the United States?",
         "What is the capital of France?"
@@ -73,8 +74,7 @@ async def test_text_1_list_text_2_list(server: RemoteOpenAIServer,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_text_1_str_text_2_str(server: RemoteOpenAIServer,
-                                     model_name: str):
+def test_text_1_str_text_2_str(server: RemoteOpenAIServer, model_name: str):
     text_1 = "What is the capital of France?"
     text_2 = "The capital of France is Paris."
 
@@ -91,3 +91,36 @@ async def test_text_1_str_text_2_str(server: RemoteOpenAIServer,
     assert score.data is not None
     assert len(score.data) == 1
     assert score.data[0].score >= 0.9
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_score_max_model_len(server: RemoteOpenAIServer, model_name: str):
+
+    text_1 = "What is the capital of France?" * 20
+    text_2 = [
+        "The capital of Brazil is Brasilia.", "The capital of France is Paris."
+    ]
+
+    score_response = requests.post(server.url_for("score"),
+                                   json={
+                                       "model": model_name,
+                                       "text_1": text_1,
+                                       "text_2": text_2,
+                                   })
+    assert score_response.status_code == 400
+    # Assert just a small fragments of the response
+    assert "Please reduce the length of the input." in \
+        score_response.text
+
+    # Test truncation
+    score_response = requests.post(server.url_for("score"),
+                                   json={
+                                       "model": model_name,
+                                       "text_1": text_1,
+                                       "text_2": text_2,
+                                       "truncate_prompt_tokens": 101
+                                   })
+    assert score_response.status_code == 400
+    assert "Please, select a smaller truncation size." in \
+        score_response.text

vllm/entrypoints/openai/serving_engine.py

@@ -203,15 +203,19 @@ def _validate_input(
     ) -> TextTokensPrompt:
         token_num = len(input_ids)
 
-        # Note: EmbeddingRequest doesn't have max_tokens
-        if isinstance(request,
-                      (EmbeddingChatRequest, EmbeddingCompletionRequest)):
+        # Note: EmbeddingRequest and ScoreRequest doesn't have max_tokens
+        if isinstance(
+                request,
+            (EmbeddingChatRequest, EmbeddingCompletionRequest, ScoreRequest)):
+
+            operation = "score" if isinstance(request, ScoreRequest) \
+                else "embedding generation"
             if token_num > self.max_model_len:
                 raise ValueError(
                     f"This model's maximum context length is "
                     f"{self.max_model_len} tokens. However, you requested "
-                    f"{token_num} tokens in the input for embedding "
-                    f"generation. Please reduce the length of the input.")
+                    f"{token_num} tokens in the input for {operation}. "
+                    f"Please reduce the length of the input.")
             return TextTokensPrompt(prompt=input_text,
                                     prompt_token_ids=input_ids)
 

vllm/entrypoints/openai/serving_score.py

@@ -101,35 +101,45 @@ async def create_score(
             if not self.model_config.is_cross_encoder:
                 raise ValueError("Model is not cross encoder.")
 
+            if truncate_prompt_tokens is not None and \
+                truncate_prompt_tokens > self.max_model_len:
+                raise ValueError(
+                    f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
+                    f"is greater than max_model_len ({self.max_model_len})."
+                    f" Please, select a smaller truncation size.")
+
+            input_pairs = make_pairs(request.text_1, request.text_2)
+            for q, t in input_pairs:
+                request_prompt = f"{q}{tokenizer.sep_token}{t}"
+
+                tokenization_kwargs: Dict[str, Any] = {}
+                if truncate_prompt_tokens is not None:
+                    tokenization_kwargs["truncation"] = True
+                    tokenization_kwargs["max_length"] = truncate_prompt_tokens
+
+                tokenize_async = make_async(tokenizer.__call__,
+                                            executor=self._tokenizer_executor)
+                prompt_inputs = await tokenize_async(text=q,
+                                                     text_pair=t,
+                                                     **tokenization_kwargs)
+
+                input_ids = prompt_inputs["input_ids"]
+                text_token_prompt = \
+                    self._validate_input(request, input_ids, request_prompt)
+                engine_prompt = TokensPrompt(
+                    prompt_token_ids=text_token_prompt["prompt_token_ids"],
+                    token_type_ids=prompt_inputs.get("token_type_ids"))
+
+                request_prompts.append(request_prompt)
+                engine_prompts.append(engine_prompt)
+
         except ValueError as e:
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))
 
         # Schedule the request and get the result generator.
         generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
 
-        input_pairs = make_pairs(request.text_1, request.text_2)
-
-        for q, t in input_pairs:
-            request_prompt = f"{q}{tokenizer.sep_token}{t}"
-
-            tokenization_kwargs: Dict[str, Any] = {}
-            if truncate_prompt_tokens is not None:
-                tokenization_kwargs["truncation"] = True
-                tokenization_kwargs["max_length"] = truncate_prompt_tokens
-
-            tokenize_async = make_async(tokenizer.__call__,
-                                        executor=self._tokenizer_executor)
-            prompt_inputs = await tokenize_async(text=q,
-                                                 text_pair=t,
-                                                 **tokenization_kwargs)
-            engine_prompt = TokensPrompt(
-                prompt_token_ids=prompt_inputs["input_ids"],
-                token_type_ids=prompt_inputs.get("token_type_ids"))
-
-            request_prompts.append(request_prompt)
-            engine_prompts.append(engine_prompt)
-
         try:
             pooling_params = request.to_pooling_params()