Hugging face model handler #3

.github/trigger_files/beam_PostCommit_Yaml_Xlang_Direct.json

@@ -1,4 +1,4 @@
 {
  "comment": "Modify this file in a trivial way to cause this test suite to run",
- "revision": 2
+ "revision": 3
 }

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.
+
+pipelines:
+ - pipeline:
+ type: chain
+ transforms:
+ - type: Create
+ config:
+ elements:
+ - text: "I love Apache Beam!"
+ - text: "I hate this error."
+ - type: RunInference
+ config:
+ model_handler:
+ type: "HuggingFacePipeline"
+ config:
+ task: "text-classification"
+ inference_fn:
+ callable: |
+ def real_inference(batch, pipeline, inference_args):
+ predictions = pipeline(batch, **inference_args) 
+
+ # If it's a single dictionary (batch size of 1), wrap it in a list
+ if isinstance(predictions, dict):
+ predictions = [predictions]
+
+ return {
+ 'label': [p['label'] for p in predictions],
+ 'score': [p['score'] for p in predictions]
+ }
+ preprocess:
+ callable: 'lambda x: x.text'
+ - type: MapToFields
+ config:
+ language: python
+ fields:
+ text: text
+ sentiment:
+ callable: 'lambda x: x.inference.inference["label"]'
+ - type: AssertEqual
+ config:
+ elements:
+ - text: "I love Apache Beam!"
+ sentiment: "POSITIVE"
+ - text: "I hate this error."
+ sentiment: "NEGATIVE"
+
+ options:
+ yaml_experimental_features: ['ML']

sdks/python/apache_beam/yaml/yaml_ml.py

@@ -282,6 +282,55 @@ def inference_output_type(self):
  ('model_id', Optional[str])])
 
 
+@ModelHandlerProvider.register_handler_type('HuggingFacePipeline')
+class HuggingFacePipelineProvider(ModelHandlerProvider):
+ def __init__(
+ self,
+ task: Optional[str] = None,
+ model: Optional[str] = None,
+ preprocess: Optional[dict[str, str]] = None,
+ postprocess: Optional[dict[str, str]] = None,
+ device: Optional[Any] = 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(config):
+ if not config or (not config.get('task') and not config.get('model')):
+ raise ValueError(
+ "HuggingFacePipeline requires either 'task' or "
+ "'model' to be specified.")
+
+ def inference_output_type(self):
+ return Any
+
+
 @beam.ptransform.ptransform_fn
 def run_inference(
  pcoll,

sdks/python/setup.py

@@ -654,7 +654,8 @@ def get_portability_package_data():
  'transformers': [
  'transformers>=4.28.0,<4.56.0',
  'tensorflow>=2.12.0',
- 'torch>=1.9.0'
+ # Avoid torch 2.12.0+ which fails to run unit tests with segfault
+ 'torch>=1.9.0,<2.12.0'
  ],
  'ml_cpu': [
  'tensorflow>=2.12.0',