[yaml] - Add huggingface model handler

sdks/python/apache_beam/yaml/tests/runinference_huggingface.yaml

@@ -0,0 +1,62 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+fixtures:
+  - name: mock_pipeline
+    type: unittest.mock.patch
+    config:
+      target: transformers.pipeline
+
+pipelines:
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - text: "Hello world"
+              - text: "Bye world"
+        - type: RunInference
+          config:
+            model_handler:
+              type: "HuggingFacePipeline"
+              config:
+                task: "text-classification"
+                model: "unused"
+                inference_fn:
+                  callable: |
+                    def mock_inference(batch, pipeline, inference_args):
+                      return [[dict(label='POSITIVE', score=0.9)] for _ in batch]
+                preprocess:
+                  callable: 'lambda x: x.text'
+        - type: MapToFields
+          config:
+            language: python
+            fields:
+              text: text
+              inference:
+                callable: |
+                  def get_json(x):
+                    import json
+                    return json.dumps(x.inference.inference, indent=0).strip()
+        - type: AssertEqual
+          config:
+            elements:
+              - text: "Hello world"
+                inference: "[\n{\n\"label\": \"POSITIVE\",\n\"score\": 0.9\n}\n]"
+              - text: "Bye world"
+                inference: "[\n{\n\"label\": \"POSITIVE\",\n\"score\": 0.9\n}\n]"
+    options:
+      yaml_experimental_features: ['ML']

sdks/python/apache_beam/yaml/yaml_ml.py

@@ -282,6 +282,52 @@ def inference_output_type(self):
                                           ('model_id', Optional[str])])
 
 
+@ModelHandlerProvider.register_handler_type('HuggingFacePipeline')
+class HuggingFacePipelineProvider(ModelHandlerProvider):
+  def __init__(
+      self,
+      task: str = "",
+      model: str = "",
+      preprocess: Optional[dict[str, str]] = None,
+      postprocess: Optional[dict[str, str]] = None,
+      device: Optional[str] = None,
+      inference_fn: Optional[dict[str, str]] = None,
+      load_pipeline_args: Optional[dict[str, Any]] = None,
+      **kwargs):
+    try:
+      from apache_beam.ml.inference.huggingface_inference import HuggingFacePipelineModelHandler
+    except ImportError:
+      raise ValueError(
+          'Unable to import HuggingFacePipelineModelHandler. Please '
+          'install transformers dependencies.')
+
+    kwargs = {k: v for k, v in kwargs.items() if not k.startswith('_')}
+
+    inference_fn_obj = self.parse_processing_transform(
+        inference_fn, 'inference_fn') if inference_fn else None
+
+    handler_kwargs = {}
+    if inference_fn_obj:
+      handler_kwargs['inference_fn'] = inference_fn_obj
+
+    _handler = HuggingFacePipelineModelHandler(
+        task=task,
+        model=model,
+        device=device,
+        load_pipeline_args=load_pipeline_args,
+        **handler_kwargs,
+        **kwargs)
+
+    super().__init__(_handler, preprocess, postprocess)
+
+  @staticmethod
+  def validate(model_handler_spec):
+    pass
+
+  def inference_output_type(self):
+    return Any
+
+
 @beam.ptransform.ptransform_fn
 def run_inference(
     pcoll,