[Frontend] generation_config.json for maximum tokens(vllm-project#12242)

Signed-off-by: Matthew Hendrey <[email protected]>
Signed-off-by: Shangming Cai <[email protected]>
Signed-off-by: youkaichao <[email protected]>
Signed-off-by: Harry Mellor <[email protected]>
Signed-off-by: Yuan Tang <[email protected]>
Signed-off-by: Isotr0py <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: Chen Zhang <[email protected]>
Signed-off-by: wangxiyuan <[email protected]>
Co-authored-by: shangmingc <[email protected]>
Co-authored-by: youkaichao <[email protected]>
Co-authored-by: Harry Mellor <[email protected]>
Co-authored-by: Yuan Tang <[email protected]>
Co-authored-by: Isotr0py <[email protected]>
Co-authored-by: Cyrus Leung <[email protected]>
Co-authored-by: Chen Zhang <[email protected]>
Co-authored-by: wangxiyuan <[email protected]>

Author: 9 people

tests/entrypoints/openai/test_serving_chat.py

@@ -103,6 +103,116 @@ def test_serving_chat_should_set_correct_max_tokens():
 
     assert mock_engine.generate.call_args.args[1].max_tokens == 10
 
+    # Setting server's max_tokens in the generation_config.json
+    # lower than context_window - prompt_tokens
+    mock_model_config = MockModelConfig()
+    mock_model_config.diff_sampling_param = {
+        "max_tokens": 10  # Setting server-side max_tokens limit
+    }
+
+    # Reinitialize the engine with new settings
+    mock_engine = MagicMock(spec=MQLLMEngineClient)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+
+    # Initialize the serving chat
+    models = OpenAIServingModels(engine_client=mock_engine,
+                                 base_model_paths=BASE_MODEL_PATHS,
+                                 model_config=mock_model_config)
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     mock_model_config,
+                                     models,
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     chat_template_content_format="auto",
+                                     request_logger=None)
+
+    # Test Case 1: No max_tokens specified in request
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        guided_decoding_backend="outlines",
+    )
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 10
+
+    # Test Case 2: Request's max_tokens set higher than server accepts
+    req.max_tokens = 15
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 10
+
+    # Test Case 3: Request's max_tokens set lower than server accepts
+    req.max_tokens = 5
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 5
+
+    # Setting server's max_tokens in the generation_config.json
+    # higher than context_window - prompt_tokens
+    mock_model_config = MockModelConfig()
+    mock_model_config.diff_sampling_param = {
+        "max_tokens": 200  # Setting server-side max_tokens limit
+    }
+
+    # Reinitialize the engine with new settings
+    mock_engine = MagicMock(spec=MQLLMEngineClient)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+
+    # Initialize the serving chat
+    models = OpenAIServingModels(engine_client=mock_engine,
+                                 base_model_paths=BASE_MODEL_PATHS,
+                                 model_config=mock_model_config)
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     mock_model_config,
+                                     models,
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     chat_template_content_format="auto",
+                                     request_logger=None)
+
+    # Test case 1: No max_tokens specified, defaults to context_window
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        guided_decoding_backend="outlines",
+    )
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    # Test Case 2: Request's max_tokens set higher than server accepts
+    req.max_tokens = 100
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    # Test Case 3: Request's max_tokens set lower than server accepts
+    req.max_tokens = 5
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 5
+
 
 def test_serving_chat_could_load_correct_generation_config():
 

vllm/config.py

@@ -910,12 +910,18 @@ def get_diff_sampling_param(self) -> Dict[str, Any]:
             "top_k",
             "top_p",
             "min_p",
+            "max_new_tokens",
         ]
         if any(p in config for p in available_params):
             diff_sampling_param = {
                 p: config.get(p)
                 for p in available_params if config.get(p) is not None
             }
+            # Huggingface definition of max_new_tokens is equivalent
+            # to vLLM's max_tokens
+            if "max_new_tokens" in diff_sampling_param:
+                diff_sampling_param["max_tokens"] = diff_sampling_param.pop(
+                    "max_new_tokens")
         else:
             diff_sampling_param = {}
         return diff_sampling_param

vllm/engine/arg_utils.py

@@ -939,7 +939,9 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "Defaults to None, will use the default generation config in vLLM. "
             "If set to 'auto', the generation config will be automatically "
             "loaded from model. If set to a folder path, the generation config "
-            "will be loaded from the specified folder path.")
+            "will be loaded from the specified folder path. If "
+            "`max_new_tokens` is specified, then it sets a server-wide limit "
+            "on the number of output tokens for all requests.")
 
         parser.add_argument("--enable-sleep-mode",
                             action="store_true",

vllm/entrypoints/openai/protocol.py

@@ -380,13 +380,17 @@ def to_beam_search_params(
     ) -> BeamSearchParams:
         # TODO(#9845): remove max_tokens when field is removed from OpenAI API
         max_tokens = self.max_completion_tokens or self.max_tokens
-        if max_tokens is None:
-            max_tokens = default_max_tokens
 
         if default_sampling_params is None:
             default_sampling_params = {}
         n = self.n if self.n is not None else 1
 
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
         if (temperature := self.temperature) is None:
             temperature = default_sampling_params.get(
                 "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
@@ -406,11 +410,16 @@ def to_sampling_params(
             default_sampling_params: Optional[dict] = None) -> SamplingParams:
         # TODO(#9845): remove max_tokens when field is removed from OpenAI API
         max_tokens = self.max_completion_tokens or self.max_tokens
-        if max_tokens is None:
-            max_tokens = default_max_tokens
 
         if default_sampling_params is None:
             default_sampling_params = {}
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
         # Default parameters
         if (repetition_penalty := self.repetition_penalty) is None:
             repetition_penalty = default_sampling_params.get(
@@ -740,13 +749,17 @@ def to_beam_search_params(
             default_sampling_params: Optional[dict] = None
     ) -> BeamSearchParams:
         max_tokens = self.max_tokens
-        if max_tokens is None:
-            max_tokens = default_max_tokens
 
         if default_sampling_params is None:
             default_sampling_params = {}
         n = self.n if self.n is not None else 1
 
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
         if (temperature := self.temperature) is None:
             temperature = default_sampling_params.get("temperature", 1.0)
 
@@ -764,11 +777,16 @@ def to_sampling_params(
             logits_processor_pattern: Optional[str],
             default_sampling_params: Optional[dict] = None) -> SamplingParams:
         max_tokens = self.max_tokens
-        if max_tokens is None:
-            max_tokens = default_max_tokens
 
         if default_sampling_params is None:
             default_sampling_params = {}
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
         # Default parameters
         if (repetition_penalty := self.repetition_penalty) is None:
             repetition_penalty = default_sampling_params.get(