feature: Inferentia Neuron support for HuggingFace

src/sagemaker/huggingface/model.py

@@ -25,6 +25,7 @@
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.predictor import Predictor
 from sagemaker.serializers import JSONSerializer
+from sagemaker.session import Session
 
 logger = logging.getLogger("sagemaker")
 
@@ -169,9 +170,121 @@ def __init__(
         super(HuggingFaceModel, self).__init__(
             model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs
         )
+        self.sagemaker_session = self.sagemaker_session or Session()
 
         self.model_server_workers = model_server_workers
 
+    def deploy(
+        self,
+        initial_instance_count=None,
+        instance_type=None,
+        serializer=None,
+        deserializer=None,
+        accelerator_type=None,
+        endpoint_name=None,
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config=None,
+        async_inference_config=None,
+        serverless_inference_config=None,
+        **kwargs,
+    ):
+        """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
+
+        Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
+        ``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None,
+        this method returns a the result of invoking ``self.predictor_cls`` on
+        the created endpoint name.
+
+        The name of the created model is accessible in the ``name`` field of
+        this ``Model`` after deploy returns
+
+        The name of the created endpoint is accessible in the
+        ``endpoint_name`` field of this ``Model`` after deploy returns.
+
+        Args:
+            initial_instance_count (int): The initial number of instances to run
+                in the ``Endpoint`` created from this ``Model``. If not using
+                serverless inference, then it need to be a number larger or equals
+                to 1 (default: None)
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge', or 'local' for local mode. If not using
+                serverless inference, then it is required to deploy a model.
+                (default: None)
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: None). If ``serializer`` is not None, then
+                ``serializer`` will override the default serializer. The
+                default serializer is set by the ``predictor_cls``.
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: None). If ``deserializer`` is not None, then
+                ``deserializer`` will override the default deserializer. The
+                default deserializer is set by the ``predictor_cls``.
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                deploy this model for model loading and inference, for example,
+                'ml.eia1.medium'. If not specified, no Elastic Inference
+                accelerator will be attached to the endpoint. For more
+                information:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
+            endpoint_name (str): The name of the endpoint to create (default:
+                None). If not specified, a unique endpoint name will be created.
+            tags (List[dict[str, str]]): The list of tags to attach to this
+                specific endpoint.
+            kms_key (str): The ARN of the KMS key that is used to encrypt the
+                data on the storage volume attached to the instance hosting the
+                endpoint.
+            wait (bool): Whether the call should wait until the deployment of
+                this model completes (default: True).
+            data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies
+                configuration related to Endpoint data capture for use with
+                Amazon SageMaker Model Monitoring. Default: None.
+            async_inference_config (sagemaker.model_monitor.AsyncInferenceConfig): Specifies
+                configuration related to async endpoint. Use this configuration when trying
+                to create async endpoint and make async inference. If empty config object
+                passed through, will use default config to deploy async endpoint. Deploy a
+                real-time endpoint if it's None. (default: None)
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Use this configuration
+                when trying to create serverless endpoint and make serverless inference. If
+                empty object passed through, will use pre-defined values in
+                ``ServerlessInferenceConfig`` class to deploy serverless endpoint. Deploy an
+                instance based endpoint if it's None. (default: None)
+        Raises:
+             ValueError: If arguments combination check failed in these circumstances:
+                - If no role is specified or
+                - If serverless inference config is not specified and instance type and instance
+                    count are also not specified or
+                - If a wrong type of object is provided as serverless inference config or async
+                    inference config
+        Returns:
+            callable[string, sagemaker.session.Session] or None: Invocation of
+                ``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls``
+                is not None. Otherwise, return None.
+        """
+
+        if not self.image_uri and instance_type.startswith("ml.inf"):
+            self.image_uri = self.serving_image_uri(
+                region_name=self.sagemaker_session.boto_session.region_name,
+                instance_type=instance_type,
+            )
+
+        return super(HuggingFaceModel, self).deploy(
+            initial_instance_count,
+            instance_type,
+            serializer,
+            deserializer,
+            accelerator_type,
+            endpoint_name,
+            tags,
+            kms_key,
+            wait,
+            data_capture_config,
+            async_inference_config,
+            serverless_inference_config,
+        )
+
     def register(
         self,
         content_types,

src/sagemaker/image_uri_config/huggingface-neuron.json

@@ -0,0 +1,44 @@
+{
+    "inference": {
+        "processors": ["inf"],
+        "version_aliases": {"4.12": "4.12.3"},
+        "versions": {
+            "4.12.3": {
+                "version_aliases": {"pytorch1.9": "pytorch1.9.1"},
+                "pytorch1.9.1": {
+                    "py_versions": ["py37"],
+                    "repository": "huggingface-pytorch-inference-neuron",
+                    "registries": {
+                        "af-south-1": "626614931356",
+                        "ap-east-1": "871362719292",
+                        "ap-northeast-1": "763104351884",
+                        "ap-northeast-2": "763104351884",
+                        "ap-northeast-3": "364406365360",
+                        "ap-south-1": "763104351884",
+                        "ap-southeast-1": "763104351884",
+                        "ap-southeast-2": "763104351884",
+                        "ca-central-1": "763104351884",
+                        "cn-north-1": "727897471807",
+                        "cn-northwest-1": "727897471807",
+                        "eu-central-1": "763104351884",
+                        "eu-north-1": "763104351884",
+                        "eu-west-1": "763104351884",
+                        "eu-west-2": "763104351884",
+                        "eu-west-3": "763104351884",
+                        "eu-south-1": "692866216735",
+                        "me-south-1": "217643126080",
+                        "sa-east-1": "763104351884",
+                        "us-east-1": "763104351884",
+                        "us-east-2": "763104351884",
+                        "us-gov-west-1": "442386744353",
+                        "us-iso-east-1": "886529160074",
+                        "us-west-1": "763104351884",
+                        "us-west-2": "763104351884"
+                    },
+                    "container_version": {"inf": "ubuntu18.04"},
+                    "sdk_versions": ["sdk1.17.1"]
+                }
+            }
+        }
+    }
+}

src/sagemaker/image_uris.py

@@ -24,7 +24,6 @@
 from sagemaker.spark import defaults
 from sagemaker.jumpstart import artifacts
 
-
 logger = logging.getLogger(__name__)
 
 ECR_URI_TEMPLATE = "{registry}.dkr.{hostname}/{repository}"
@@ -47,6 +46,8 @@ def retrieve(
     model_version=None,
     tolerate_vulnerable_model=False,
     tolerate_deprecated_model=False,
+    sdk_version=None,
+    inference_tool=None,
 ) -> str:
     """Retrieves the ECR URI for the Docker image matching the given arguments.
 
@@ -88,6 +89,11 @@ def retrieve(
         tolerate_deprecated_model (bool): True if deprecated versions of model specifications
             should be tolerated without an exception raised. If False, raises an exception
             if the version of the model is deprecated. (Default: False).
+        sdk_version (str): the version of python-sdk that will be used in the image retrieval.
+            (default: None).
+        inference_tool (str): the tool that will be used to aid in the inference.
+            Valid values: "neuron, None"
+            (default: None).
 
     Returns:
         str: The ECR URI for the corresponding SageMaker Docker image.
@@ -100,7 +106,6 @@ def retrieve(
         DeprecatedJumpStartModelError: If the version of the model is deprecated.
     """
     if is_jumpstart_model_input(model_id, model_version):
-
         return artifacts._retrieve_image_uri(
             model_id,
             model_version,
@@ -118,9 +123,13 @@ def retrieve(
             tolerate_vulnerable_model,
             tolerate_deprecated_model,
         )
-
     if training_compiler_config is None:
-        config = _config_for_framework_and_scope(framework, image_scope, accelerator_type)
+        _framework = framework
+        if framework == HUGGING_FACE_FRAMEWORK:
+            inference_tool = _get_inference_tool(inference_tool, instance_type)
+            if inference_tool == "neuron":
+                _framework = "{}-{}".format(framework, inference_tool)
+        config = _config_for_framework_and_scope(_framework, image_scope, accelerator_type)
     elif framework == HUGGING_FACE_FRAMEWORK:
         config = _config_for_framework_and_scope(
             framework + "-training-compiler", image_scope, accelerator_type
@@ -129,6 +138,7 @@ def retrieve(
         raise ValueError(
             "Unsupported Configuration: Training Compiler is only supported with HuggingFace"
         )
+
     original_version = version
     version = _validate_version_and_set_if_needed(version, config, framework)
     version_config = config["versions"][_version_for_config(version, config)]
@@ -138,7 +148,6 @@ def retrieve(
             full_base_framework_version = version_config["version_aliases"].get(
                 base_framework_version, base_framework_version
             )
-
         _validate_arg(full_base_framework_version, list(version_config.keys()), "base framework")
         version_config = version_config.get(full_base_framework_version)
 
@@ -161,25 +170,37 @@ def retrieve(
         pt_or_tf_version = (
             re.compile("^(pytorch|tensorflow)(.*)$").match(base_framework_version).group(2)
         )
-
         _version = original_version
+
         if repo in [
             "huggingface-pytorch-trcomp-training",
             "huggingface-tensorflow-trcomp-training",
         ]:
             _version = version
+        if repo in ["huggingface-pytorch-inference-neuron"]:
+            if not sdk_version:
+                sdk_version = _get_latest_versions(version_config["sdk_versions"])
+            container_version = sdk_version + "-" + container_version
+            if config.get("version_aliases").get(original_version):
+                _version = config.get("version_aliases")[original_version]
+            if (
+                config.get("versions", {})
+                .get(_version, {})
+                .get("version_aliases", {})
+                .get(base_framework_version, {})
+            ):
+                _base_framework_version = config.get("versions")[_version]["version_aliases"][
+                    base_framework_version
+                ]
+                pt_or_tf_version = (
+                    re.compile("^(pytorch|tensorflow)(.*)$").match(_base_framework_version).group(2)
+                )
 
         tag_prefix = f"{pt_or_tf_version}-transformers{_version}"
-
     else:
         tag_prefix = version_config.get("tag_prefix", version)
 
-    tag = _format_tag(
-        tag_prefix,
-        processor,
-        py_version,
-        container_version,
-    )
+    tag = _format_tag(tag_prefix, processor, py_version, container_version, inference_tool)
 
     if _should_auto_select_container_version(instance_type, distribution):
         container_versions = {
@@ -248,6 +269,20 @@ def config_for_framework(framework):
         return json.load(f)
 
 
+def _get_inference_tool(inference_tool, instance_type):
+    """Extract the inference tool name from instance type."""
+    if not inference_tool and instance_type:
+        match = re.match(r"^ml[\._]([a-z\d]+)\.?\w*$", instance_type)
+        if match and match[1].startswith("inf"):
+            return "neuron"
+    return inference_tool
+
+
+def _get_latest_versions(list_of_versions):
+    """Extract the latest version from the input list of available versions."""
+    return sorted(list_of_versions, reverse=True)[0]
+
+
 def _validate_accelerator_type(accelerator_type):
     """Raises a ``ValueError`` if ``accelerator_type`` is invalid."""
     if not accelerator_type.startswith("ml.eia") and accelerator_type != "local_sagemaker_notebook":
@@ -310,6 +345,8 @@ def _processor(instance_type, available_processors):
 
     if instance_type.startswith("local"):
         processor = "cpu" if instance_type == "local" else "gpu"
+    elif instance_type.startswith("neuron"):
+        processor = "neuron"
     else:
         # looks for either "ml.<family>.<size>" or "ml_<family>"
         match = re.match(r"^ml[\._]([a-z\d]+)\.?\w*$", instance_type)
@@ -387,8 +424,10 @@ def _validate_arg(arg, available_options, arg_name):
         )
 
 
-def _format_tag(tag_prefix, processor, py_version, container_version):
+def _format_tag(tag_prefix, processor, py_version, container_version, inference_tool=None):
     """Creates a tag for the image URI."""
+    if inference_tool:
+        return "-".join(x for x in (tag_prefix, inference_tool, py_version, container_version) if x)
     return "-".join(x for x in (tag_prefix, processor, py_version, container_version) if x)
 
 

tests/conftest.py

@@ -269,6 +269,21 @@ def huggingface_tensorflow_latest_training_py_version():
     return "py37"
 
 
+@pytest.fixture(scope="module")
+def huggingface_neuron_latest_inference_pytorch_version():
+    return "1.9"
+
+
+@pytest.fixture(scope="module")
+def huggingface_neuron_latest_inference_transformer_version():
+    return "4.12"
+
+
+@pytest.fixture(scope="module")
+def huggingface_neuron_latest_inference_py_version():
+    return "py37"
+
+
 @pytest.fixture(scope="module")
 def pytorch_eia_py_version():
     return "py3"

tests/unit/sagemaker/huggingface/test_estimator.py

@@ -19,7 +19,7 @@
 import pytest
 from mock import MagicMock, Mock, patch
 
-from sagemaker.huggingface import HuggingFace
+from sagemaker.huggingface import HuggingFace, HuggingFaceModel
 
 from .huggingface_utils import get_full_gpu_image_uri, GPU_INSTANCE_TYPE, REGION
 
@@ -252,6 +252,26 @@ def test_huggingface(
     assert actual_train_args == expected_train_args
 
 
+def test_huggingface_neuron(
+    sagemaker_session,
+    huggingface_neuron_latest_inference_pytorch_version,
+    huggingface_neuron_latest_inference_transformer_version,
+    huggingface_neuron_latest_inference_py_version,
+):
+
+    inputs = "s3://mybucket/train"
+    huggingface_model = HuggingFaceModel(
+        model_data=inputs,
+        transformers_version=huggingface_neuron_latest_inference_transformer_version,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        pytorch_version=huggingface_neuron_latest_inference_pytorch_version,
+        py_version=huggingface_neuron_latest_inference_py_version,
+    )
+    container = huggingface_model.prepare_container_def("ml.inf.xlarge")
+    assert container["Image"]
+
+
 def test_attach(
     sagemaker_session,
     huggingface_training_version,