[HWORKS-1885] Add vllm-openai deployment and support for config files

python/hsml/core/serving_api.py

@@ -29,7 +29,6 @@
     InferOutput,
     InferRequest,
 )
-from hsml.constants import ARTIFACT_VERSION
 from hsml.constants import INFERENCE_ENDPOINTS as IE
 
 
@@ -419,7 +418,4 @@ def _get_hopsworks_inference_path(self, project_id: int, deployment_instance):
         ]
 
     def _get_istio_inference_path(self, deployment_instance):
-        if deployment_instance.model_server == "VLLM":
-            return ["openai", "v1", "completions"]
-
         return ["v1", "models", deployment_instance.name + ":predict"]

python/hsml/deployment.py

@@ -415,6 +415,15 @@ def script_file(self):
     def script_file(self, script_file: str):
         self._predictor.script_file = script_file
 
+    @property
+    def config_file(self):
+        """Config file passed to the predictor."""
+        return self._predictor.config_file
+
+    @config_file.setter
+    def config_file(self, config_file: str):
+        self._predictor.config_file = config_file
+
     @property
     def resources(self):
         """Resource configuration for the predictor."""

python/hsml/engine/serving_engine.py

@@ -563,11 +563,13 @@ def predict(
         inputs: Union[Dict, List[Dict]],
     ):
         # validate user-provided payload
-        if deployment_instance.model_server != "VLLM":
-            self._validate_inference_payload(
-                deployment_instance.api_protocol, data, inputs
+        if deployment_instance.model_server == PREDICTOR.MODEL_SERVER_VLLM:
+            raise ModelServingException(
+                "Inference requests to LLM deployments are not supported by the `predict` method. Please, use any OpenAI API-compatible client instead."
             )
 
+        self._validate_inference_payload(deployment_instance.api_protocol, data, inputs)
+
         # build inference payload based on API protocol
         payload = self._build_inference_payload(
             deployment_instance.api_protocol, data, inputs

python/hsml/model.py

@@ -171,6 +171,7 @@ def deploy(
         artifact_version: Optional[str] = ARTIFACT_VERSION.CREATE,
         serving_tool: Optional[str] = None,
         script_file: Optional[str] = None,
+        config_file: Optional[str] = None,
         resources: Optional[Union[PredictorResources, dict]] = None,
         inference_logger: Optional[Union[InferenceLogger, dict]] = None,
         inference_batcher: Optional[Union[InferenceBatcher, dict]] = None,
@@ -202,6 +203,7 @@ def deploy(
             or `MODEL-ONLY` to reuse the shared artifact containing only the model files.
             serving_tool: Serving tool used to deploy the model server.
             script_file: Path to a custom predictor script implementing the Predict class.
+            config_file: Server configuration file to be passed to the model deployment.
             resources: Resources to be allocated for the predictor.
             inference_logger: Inference logger configuration.
             inference_batcher: Inference batcher configuration.
@@ -223,6 +225,7 @@ def deploy(
             artifact_version=artifact_version,
             serving_tool=serving_tool,
             script_file=script_file,
+            config_file=config_file,
             resources=resources,
             inference_logger=inference_logger,
             inference_batcher=inference_batcher,

python/hsml/model_serving.py

@@ -160,6 +160,7 @@ def create_predictor(
         artifact_version: Optional[str] = ARTIFACT_VERSION.CREATE,
         serving_tool: Optional[str] = None,
         script_file: Optional[str] = None,
+        config_file: Optional[str] = None,
         resources: Optional[Union[PredictorResources, dict]] = None,
         inference_logger: Optional[Union[InferenceLogger, dict, str]] = None,
         inference_batcher: Optional[Union[InferenceBatcher, dict]] = None,
@@ -197,6 +198,7 @@ def create_predictor(
             or `MODEL-ONLY` to reuse the shared artifact containing only the model files.
             serving_tool: Serving tool used to deploy the model server.
             script_file: Path to a custom predictor script implementing the Predict class.
+            config_file: Server configuration file to be passed to the model deployment.
             resources: Resources to be allocated for the predictor.
             inference_logger: Inference logger configuration.
             inference_batcher: Inference batcher configuration.
@@ -216,6 +218,7 @@ def create_predictor(
             artifact_version=artifact_version,
             serving_tool=serving_tool,
             script_file=script_file,
+            config_file=config_file,
             resources=resources,
             inference_logger=inference_logger,
             inference_batcher=inference_batcher,

python/hsml/predictor.py

@@ -48,6 +48,7 @@ def __init__(
         model_server: str,
         serving_tool: Optional[str] = None,
         script_file: Optional[str] = None,
+        config_file: Optional[str] = None,
         resources: Optional[Union[PredictorResources, dict, Default]] = None,  # base
         inference_logger: Optional[
             Union[InferenceLogger, dict, Default]
@@ -87,6 +88,7 @@ def __init__(
         self._artifact_version = artifact_version
         self._serving_tool = serving_tool
         self._model_server = model_server
+        self._config_file = config_file
         self._id = id
         self._description = description
         self._created_at = created_at
@@ -167,12 +169,9 @@ def _validate_serving_tool(cls, serving_tool):
 
     @classmethod
     def _validate_script_file(cls, model_framework, script_file):
-        if script_file is None and (
-            model_framework == MODEL.FRAMEWORK_PYTHON
-            or model_framework == MODEL.FRAMEWORK_LLM
-        ):
+        if script_file is None and (model_framework == MODEL.FRAMEWORK_PYTHON):
             raise ValueError(
-                "Predictor scripts are required in deployments for custom Python models and LLMs."
+                "Predictor scripts are required in deployments for custom Python models."
             )
 
     @classmethod
@@ -273,6 +272,9 @@ def extract_fields_from_json(cls, json_decamelized):
         kwargs["script_file"] = util.extract_field_from_json(
             json_decamelized, "predictor"
         )
+        kwargs["config_file"] = util.extract_field_from_json(
+            json_decamelized, "config_file"
+        )
         kwargs["resources"] = PredictorResources.from_json(json_decamelized)
         kwargs["inference_logger"] = InferenceLogger.from_json(json_decamelized)
         kwargs["inference_batcher"] = InferenceBatcher.from_json(json_decamelized)
@@ -311,6 +313,7 @@ def to_dict(self):
             "modelServer": self._model_server,
             "servingTool": self._serving_tool,
             "predictor": self._script_file,
+            "configFile": self._config_file,
             "apiProtocol": self._api_protocol,
             "projectNamespace": self._project_namespace,
         }
@@ -442,6 +445,16 @@ def script_file(self, script_file: str):
         self._script_file = script_file
         self._artifact_version = ARTIFACT_VERSION.CREATE
 
+    @property
+    def config_file(self):
+        """Server config file to be passed to the model deployment."""
+        return self._config_file
+
+    @config_file.setter
+    def config_file(self, config_file: str):
+        self._config_file = config_file 
+        self._artifact_version = ARTIFACT_VERSION.CREATE
+
     @property
     def inference_logger(self):
         """Configuration of the inference logger attached to this predictor."""

python/tests/fixtures/predictor_fixtures.json

@@ -19,6 +19,7 @@
           "artifact_version": 2,
           "predictor": "predictor_file",
           "transformer": "transformer_file",
+          "config_file": "config_file",
           "requested_instances": 1,
           "requested_transformer_instances": 1,
           "predictor_resources": {
@@ -74,6 +75,7 @@
           "api_protocol": "REST",
           "artifact_version": 2,
           "predictor": "predictor_file",
+          "config_file": "config_file",
           "transformer": "transformer_file",
           "requested_instances": 1,
           "requested_transformer_instances": 1,
@@ -117,6 +119,7 @@
           "api_protocol": "REST",
           "artifact_version": 3,
           "predictor": "predictor_file",
+          "config_file": "config_file",
           "transformer": "transformer_file",
           "requested_instances": 1,
           "requested_transformer_instances": 1,

python/tests/test_model.py

@@ -211,6 +211,7 @@ def test_deploy(self, mocker, backend_fixtures):
             artifact_version=p_json["artifact_version"],
             serving_tool=p_json["serving_tool"],
             script_file=p_json["predictor"],
+            config_file=p_json["config_file"],
             resources=resources,
             inference_logger=inference_logger,
             inference_batcher=inference_batcher,
@@ -227,6 +228,7 @@ def test_deploy(self, mocker, backend_fixtures):
             artifact_version=p_json["artifact_version"],
             serving_tool=p_json["serving_tool"],
             script_file=p_json["predictor"],
+            config_file=p_json["config_file"],
             resources=resources,
             inference_logger=inference_logger,
             inference_batcher=inference_batcher,

python/tests/test_predictor.py

@@ -80,6 +80,7 @@ def test_from_response_json_singleton(self, mocker, backend_fixtures):
         assert p.artifact_version == p_json["artifact_version"]
         assert p.environment == p_json["environment_dto"]["name"]
         assert p.script_file == p_json["predictor"]
+        assert p.config_file == p_json["config_file"]
         assert isinstance(p.resources, resources.PredictorResources)
         assert isinstance(p.transformer, transformer.Transformer)
         assert p.transformer.script_file == p_json["transformer"]
@@ -123,6 +124,7 @@ def test_from_response_json_list(self, mocker, backend_fixtures):
             assert p.environment == p_json["environment_dto"]["name"]
             assert p.artifact_version == p_json["artifact_version"]
             assert p.script_file == p_json["predictor"]
+            assert p.config_file == p_json["config_file"]
             assert isinstance(p.resources, resources.PredictorResources)
             assert isinstance(p.transformer, transformer.Transformer)
             assert p.transformer.script_file == p_json["transformer"]
@@ -161,6 +163,7 @@ def test_from_response_json_single(self, mocker, backend_fixtures):
         assert p.environment == p_json["environment_dto"]["name"]
         assert p.artifact_version == p_json["artifact_version"]
         assert p.script_file == p_json["predictor"]
+        assert p.config_file == p_json["config_file"]
         assert isinstance(p.resources, resources.PredictorResources)
         assert isinstance(p.transformer, transformer.Transformer)
         assert p.transformer.script_file == p_json["transformer"]
@@ -213,6 +216,7 @@ def test_constructor(self, mocker, backend_fixtures):
             environment=p_json["environment_dto"]["name"],
             artifact_version=p_json["artifact_version"],
             script_file=p_json["predictor"],
+            config_file=p_json["config_file"],
             resources=p_json["predictor_resources"],
             transformer={
                 "script_file": p_json["transformer"],
@@ -241,6 +245,7 @@ def test_constructor(self, mocker, backend_fixtures):
         assert p.environment == p_json["environment_dto"]["name"]
         assert p.artifact_version == p_json["artifact_version"]
         assert p.script_file == p_json["predictor"]
+        assert p.config_file == p_json["config_file"]
         assert isinstance(p.resources, resources.PredictorResources)
         assert isinstance(p.transformer, transformer.Transformer)
         assert p.transformer.script_file == p_json["transformer"]
@@ -340,14 +345,6 @@ def test_validate_script_file_py_none(self):
         # Assert
         assert "Predictor scripts are required" in str(e_info.value)
 
-    def test_validate_script_file_llm_none(self):
-        # Act
-        with pytest.raises(ValueError) as e_info:
-            _ = predictor.Predictor._validate_script_file(MODEL.FRAMEWORK_LLM, None)
-
-        # Assert
-        assert "Predictor scripts are required" in str(e_info.value)
-
     def test_validate_script_file_tf_script_file(self):
         # Act
         predictor.Predictor._validate_script_file(
@@ -659,6 +656,7 @@ def extract_fields_from_json(self, mocker, backend_fixtures):
         assert kwargs["model_server"] == p_json["model_server"]
         assert kwargs["serving_tool"] == p_json["serving_tool"]
         assert kwargs["script_file"] == p_json["predictor"]
+        assert kwargs["config_file"] == p_json["config_file"]
         assert isinstance(kwargs["resources"], resources.PredictorResources)
         assert isinstance(kwargs["inference_logger"], inference_logger.InferenceLogger)
         assert kwargs["inference_logger"].mode == p_json["inference_logging"]