Merge branch 'master' into enable-github-actions-ci

src/sagemaker/huggingface/llm_utils.py

@@ -81,7 +81,8 @@ def get_huggingface_model_metadata(model_id: str, hf_hub_token: Optional[str] =
     Returns:
         dict: The model metadata retrieved with the HuggingFace API
     """
-
+    if not model_id:
+        raise ValueError("Model ID is empty. Please provide a valid Model ID.")
     hf_model_metadata_url = f"https://huggingface.co/api/models/{model_id}"
     hf_model_metadata_json = None
     try:

src/sagemaker/model.py

@@ -766,8 +766,8 @@ def _upload_code(self, key_prefix: str, repack: bool = False) -> None:
 
     def _script_mode_env_vars(self):
         """Returns a mapping of environment variables for script mode execution"""
-        script_name = None
-        dir_name = None
+        script_name = self.env.get(SCRIPT_PARAM_NAME.upper(), "")
+        dir_name = self.env.get(DIR_PARAM_NAME.upper(), "")
         if self.uploaded_code:
             script_name = self.uploaded_code.script_name
             if self.repacked_model_data or self.enable_network_isolation():
@@ -783,8 +783,8 @@ def _script_mode_env_vars(self):
                     else "file://" + self.source_dir
                 )
         return {
-            SCRIPT_PARAM_NAME.upper(): script_name or str(),
-            DIR_PARAM_NAME.upper(): dir_name or str(),
+            SCRIPT_PARAM_NAME.upper(): script_name,
+            DIR_PARAM_NAME.upper(): dir_name,
             CONTAINER_LOG_LEVEL_PARAM_NAME.upper(): to_string(self.container_log_level),
             SAGEMAKER_REGION_PARAM_NAME.upper(): self.sagemaker_session.boto_region_name,
         }

src/sagemaker/serve/builder/model_builder.py

@@ -124,8 +124,8 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers):
             into a stream. All translations between the server and the client are handled
             automatically with the specified input and output.
         model (Optional[Union[object, str]): Model object (with ``predict`` method to perform
-            inference) or a HuggingFace/JumpStart Model ID. Either ``model`` or
-            ``inference_spec`` is required for the model builder to build the artifact.
+            inference) or a HuggingFace/JumpStart Model ID. Either ``model`` or ``inference_spec``
+            is required for the model builder to build the artifact.
         inference_spec (InferenceSpec): The inference spec file with your customized
             ``invoke`` and ``load`` functions.
         image_uri (Optional[str]): The container image uri (which is derived from a
@@ -145,6 +145,8 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers):
             to the model server). Possible values for this argument are
             ``TORCHSERVE``, ``MMS``, ``TENSORFLOW_SERVING``, ``DJL_SERVING``,
             ``TRITON``, and``TGI``.
+        model_metadata (Optional[Dict[str, Any]): Dictionary used to override the HuggingFace
+            model metadata. Currently ``HF_TASK`` is overridable.
     """
 
     model_path: Optional[str] = field(
@@ -241,6 +243,10 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers):
     model_server: Optional[ModelServer] = field(
         default=None, metadata={"help": "Define the model server to deploy to."}
     )
+    model_metadata: Optional[Dict[str, Any]] = field(
+        default=None,
+        metadata={"help": "Define the model metadata to override, currently supports `HF_TASK`"},
+    )
 
     def _build_validations(self):
         """Placeholder docstring"""
@@ -616,6 +622,9 @@ def build(  # pylint: disable=R0911
         self._is_custom_image_uri = self.image_uri is not None
 
         if isinstance(self.model, str):
+            model_task = None
+            if self.model_metadata:
+                model_task = self.model_metadata.get("HF_TASK")
             if self._is_jumpstart_model_id():
                 return self._build_for_jumpstart()
             if self._is_djl():  # pylint: disable=R1705
@@ -625,10 +634,10 @@ def build(  # pylint: disable=R0911
                     self.model, self.env_vars.get("HUGGING_FACE_HUB_TOKEN")
                 )
 
-                model_task = hf_model_md.get("pipeline_tag")
-                if self.schema_builder is None and model_task:
+                if model_task is None:
+                    model_task = hf_model_md.get("pipeline_tag")
+                if self.schema_builder is None and model_task is not None:
                     self._schema_builder_init(model_task)
-
                 if model_task == "text-generation":  # pylint: disable=R1705
                     return self._build_for_tgi()
                 elif self._can_fit_on_single_gpu():

src/sagemaker/serve/schema/task.json

@@ -1,28 +1,28 @@
 {
 	"fill-mask": {
-        "sample_inputs": {
+		"sample_inputs": {
 			"properties": {
 				"inputs": "Paris is the [MASK] of France.",
 				"parameters": {}
 			}
-        },
-        "sample_outputs": {
+		},
+		"sample_outputs": {
 			"properties": [
 				{
 					"sequence": "Paris is the capital of France.",
 					"score": 0.7
 				}
 			]
 		}
-    },
+	},
 	"question-answering": {
-        "sample_inputs": {
+		"sample_inputs": {
 			"properties": {
 				"context": "I have a German Shepherd dog, named Coco.",
 				"question": "What is my dog's breed?"
 			}
-        },
-        "sample_outputs": {
+		},
+		"sample_outputs": {
 			"properties": [
 				{
 					"answer": "German Shepherd",
@@ -32,36 +32,36 @@
 				}
 			]
 		}
-    },
+	},
 	"text-classification": {
-        "sample_inputs": {
+		"sample_inputs": {
 			"properties": {
 				"inputs": "Where is the capital of France?, Paris is the capital of France.",
 				"parameters": {}
 			}
-        },
-        "sample_outputs": {
+		},
+		"sample_outputs": {
 			"properties": [
 				{
 					"label": "entailment",
 					"score": 0.997
 				}
 			]
 		}
-    },
-    "text-generation": {
-        "sample_inputs": {
+	},
+	"text-generation": {
+		"sample_inputs": {
 			"properties": {
 				"inputs": "Hello, I'm a language model",
 				"parameters": {}
 			}
-        },
-        "sample_outputs": {
+		},
+		"sample_outputs": {
 			"properties": [
-				{
-					"generated_text": "Hello, I'm a language modeler. So while writing this, when I went out to meet my wife or come home she told me that my"
-				}
+			{
+				"generated_text": "Hello, I'm a language modeler. So while writing this, when I went out to meet my wife or come home she told me that my"
+			}
 			]
 		}
-    }
+	}
 }

tests/integ/sagemaker/serve/test_schema_builder.py

@@ -99,3 +99,69 @@ def test_model_builder_negative_path(sagemaker_session):
         match="Error Message: Schema builder for text-to-image could not be found.",
     ):
         model_builder.build(sagemaker_session=sagemaker_session)
+
+
+@pytest.mark.skipif(
+    PYTHON_VERSION_IS_NOT_310,
+    reason="Testing Schema Builder Simplification feature",
+)
+@pytest.mark.parametrize(
+    "model_id, task_provided",
+    [
+        ("bert-base-uncased", "fill-mask"),
+        ("bert-large-uncased-whole-word-masking-finetuned-squad", "question-answering"),
+    ],
+)
+def test_model_builder_happy_path_with_task_provided(
+    model_id, task_provided, sagemaker_session, gpu_instance_type
+):
+    model_builder = ModelBuilder(model=model_id, model_metadata={"HF_TASK": task_provided})
+
+    model = model_builder.build(sagemaker_session=sagemaker_session)
+
+    assert model is not None
+    assert model_builder.schema_builder is not None
+
+    inputs, outputs = task.retrieve_local_schemas(task_provided)
+    assert model_builder.schema_builder.sample_input == inputs
+    assert model_builder.schema_builder.sample_output == outputs
+
+    with timeout(minutes=SERVE_SAGEMAKER_ENDPOINT_TIMEOUT):
+        caught_ex = None
+        try:
+            iam_client = sagemaker_session.boto_session.client("iam")
+            role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]
+
+            logger.info("Deploying and predicting in SAGEMAKER_ENDPOINT mode...")
+            predictor = model.deploy(
+                role=role_arn, instance_count=1, instance_type=gpu_instance_type
+            )
+
+            predicted_outputs = predictor.predict(inputs)
+            assert predicted_outputs is not None
+
+        except Exception as e:
+            caught_ex = e
+        finally:
+            cleanup_model_resources(
+                sagemaker_session=model_builder.sagemaker_session,
+                model_name=model.name,
+                endpoint_name=model.endpoint_name,
+            )
+            if caught_ex:
+                logger.exception(caught_ex)
+                assert (
+                    False
+                ), f"{caught_ex} was thrown when running transformers sagemaker endpoint test"
+
+
+def test_model_builder_negative_path_with_invalid_task(sagemaker_session):
+    model_builder = ModelBuilder(
+        model="bert-base-uncased", model_metadata={"HF_TASK": "invalid-task"}
+    )
+
+    with pytest.raises(
+        TaskNotFoundException,
+        match="Error Message: Schema builder for invalid-task could not be found.",
+    ):
+        model_builder.build(sagemaker_session=sagemaker_session)

tests/unit/sagemaker/serve/builder/test_model_builder.py

@@ -1076,7 +1076,7 @@ def test_build_negative_path_when_schema_builder_not_present(
 
         model_builder = ModelBuilder(model="CompVis/stable-diffusion-v1-4")
 
-        self.assertRaisesRegexp(
+        self.assertRaisesRegex(
             TaskNotFoundException,
             "Error Message: Schema builder for text-to-image could not be found.",
             lambda: model_builder.build(sagemaker_session=mock_session),
@@ -1593,3 +1593,126 @@ def test_total_inference_model_size_mib_throws(
         model_builder.build(sagemaker_session=mock_session)
 
         self.assertEqual(model_builder._can_fit_on_single_gpu(), False)
+
+    @patch("sagemaker.serve.builder.tgi_builder.HuggingFaceModel")
+    @patch("sagemaker.image_uris.retrieve")
+    @patch("sagemaker.djl_inference.model.urllib")
+    @patch("sagemaker.djl_inference.model.json")
+    @patch("sagemaker.huggingface.llm_utils.urllib")
+    @patch("sagemaker.huggingface.llm_utils.json")
+    @patch("sagemaker.model_uris.retrieve")
+    @patch("sagemaker.serve.builder.model_builder._ServeSettings")
+    def test_build_happy_path_override_with_task_provided(
+        self,
+        mock_serveSettings,
+        mock_model_uris_retrieve,
+        mock_llm_utils_json,
+        mock_llm_utils_urllib,
+        mock_model_json,
+        mock_model_urllib,
+        mock_image_uris_retrieve,
+        mock_hf_model,
+    ):
+        # Setup mocks
+
+        mock_setting_object = mock_serveSettings.return_value
+        mock_setting_object.role_arn = mock_role_arn
+        mock_setting_object.s3_model_data_url = mock_s3_model_data_url
+
+        # HF Pipeline Tag
+        mock_model_uris_retrieve.side_effect = KeyError
+        mock_llm_utils_json.load.return_value = {"pipeline_tag": "fill-mask"}
+        mock_llm_utils_urllib.request.Request.side_effect = Mock()
+
+        # HF Model config
+        mock_model_json.load.return_value = {"some": "config"}
+        mock_model_urllib.request.Request.side_effect = Mock()
+
+        mock_image_uris_retrieve.return_value = "https://some-image-uri"
+
+        model_builder = ModelBuilder(
+            model="bert-base-uncased", model_metadata={"HF_TASK": "text-generation"}
+        )
+        model_builder.build(sagemaker_session=mock_session)
+
+        self.assertIsNotNone(model_builder.schema_builder)
+        sample_inputs, sample_outputs = task.retrieve_local_schemas("text-generation")
+        self.assertEqual(
+            sample_inputs["inputs"], model_builder.schema_builder.sample_input["inputs"]
+        )
+        self.assertEqual(sample_outputs, model_builder.schema_builder.sample_output)
+
+    @patch("sagemaker.image_uris.retrieve")
+    @patch("sagemaker.djl_inference.model.urllib")
+    @patch("sagemaker.djl_inference.model.json")
+    @patch("sagemaker.huggingface.llm_utils.urllib")
+    @patch("sagemaker.huggingface.llm_utils.json")
+    @patch("sagemaker.model_uris.retrieve")
+    @patch("sagemaker.serve.builder.model_builder._ServeSettings")
+    def test_build_task_override_with_invalid_task_provided(
+        self,
+        mock_serveSettings,
+        mock_model_uris_retrieve,
+        mock_llm_utils_json,
+        mock_llm_utils_urllib,
+        mock_model_json,
+        mock_model_urllib,
+        mock_image_uris_retrieve,
+    ):
+        # Setup mocks
+
+        mock_setting_object = mock_serveSettings.return_value
+        mock_setting_object.role_arn = mock_role_arn
+        mock_setting_object.s3_model_data_url = mock_s3_model_data_url
+
+        # HF Pipeline Tag
+        mock_model_uris_retrieve.side_effect = KeyError
+        mock_llm_utils_json.load.return_value = {"pipeline_tag": "fill-mask"}
+        mock_llm_utils_urllib.request.Request.side_effect = Mock()
+
+        # HF Model config
+        mock_model_json.load.return_value = {"some": "config"}
+        mock_model_urllib.request.Request.side_effect = Mock()
+
+        mock_image_uris_retrieve.return_value = "https://some-image-uri"
+        model_ids_with_invalid_task = {
+            "bert-base-uncased": "invalid-task",
+            "bert-large-uncased-whole-word-masking-finetuned-squad": "",
+        }
+        for model_id in model_ids_with_invalid_task:
+            provided_task = model_ids_with_invalid_task[model_id]
+            model_builder = ModelBuilder(model=model_id, model_metadata={"HF_TASK": provided_task})
+
+            self.assertRaisesRegex(
+                TaskNotFoundException,
+                f"Error Message: Schema builder for {provided_task} could not be found.",
+                lambda: model_builder.build(sagemaker_session=mock_session),
+            )
+
+    @patch("sagemaker.image_uris.retrieve")
+    @patch("sagemaker.model_uris.retrieve")
+    @patch("sagemaker.serve.builder.model_builder._ServeSettings")
+    def test_build_task_override_with_invalid_model_provided(
+        self,
+        mock_serveSettings,
+        mock_model_uris_retrieve,
+        mock_image_uris_retrieve,
+    ):
+        # Setup mocks
+
+        mock_setting_object = mock_serveSettings.return_value
+        mock_setting_object.role_arn = mock_role_arn
+        mock_setting_object.s3_model_data_url = mock_s3_model_data_url
+
+        # HF Pipeline Tag
+        mock_model_uris_retrieve.side_effect = KeyError
+
+        mock_image_uris_retrieve.return_value = "https://some-image-uri"
+        invalid_model_id = ""
+        provided_task = "fill-mask"
+
+        model_builder = ModelBuilder(
+            model=invalid_model_id, model_metadata={"HF_TASK": provided_task}
+        )
+        with self.assertRaises(Exception):
+            model_builder.build(sagemaker_session=mock_session)