[Bugfix] Fix score api for missing max_model_len validation

tests/entrypoints/openai/test_score.py

@@ -20,8 +20,7 @@ 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 +44,7 @@ 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 +71,7 @@ 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 +88,41 @@ 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(model_name: str):
+
+    args = ["--enforce-eager", "--max-model-len", "5"]
+
+    with RemoteOpenAIServer(model_name, args) as remote_server:
+
+        text_1 = "What is the capital of France?"
+        text_2 = [
+            "The capital of Brazil is Brasilia.",
+            "The capital of France is Paris."
+        ]
+
+        score_response = requests.post(remote_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(remote_server.url_for("score"),
+                                       json={
+                                           "model": model_name,
+                                           "text_1": text_1,
+                                           "text_2": text_2,
+                                           "truncate_prompt_tokens": 10
+                                       })
+        assert score_response.status_code == 400
+        assert "Please, select a smaller truncation size." in \
+            score_response.text

vllm/entrypoints/openai/serving_score.py

@@ -101,6 +101,13 @@ 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.")
+
         except ValueError as e:
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))
@@ -123,8 +130,19 @@ async def create_score(
             prompt_inputs = await tokenize_async(text=q,
                                                  text_pair=t,
                                                  **tokenization_kwargs)
+
+            input_ids = prompt_inputs["input_ids"]
+            token_num = len(input_ids)
+            if len(input_ids) > self.max_model_len:
+                err_msg = (
+                    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 score. "
+                    f"Please reduce the length of the input.")
+                logger.error(err_msg)
+                return self.create_error_response(err_msg)
             engine_prompt = TokensPrompt(
-                prompt_token_ids=prompt_inputs["input_ids"],
+                prompt_token_ids=input_ids,
                 token_type_ids=prompt_inputs.get("token_type_ids"))
 
             request_prompts.append(request_prompt)