[Python] Implemented Hugging Face Model Handler

sdks/python/apache_beam/examples/inference/README.md

@@ -42,6 +42,7 @@ The RunInference API supports the Tensorflow framework. To use Tensorflow locall
 pip install tensorflow==2.12.0
 ```
 
+
 ### PyTorch dependencies
 
 The following installation requirements are for the files used in these examples.
@@ -65,6 +66,21 @@ For installation of the `torch` dependency on a distributed runner such as Dataf
 [PyPI dependency instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies).
 
 
+### Transformers dependencies
+
+The following installation requirement is for the Hugging Face model handler examples.
+
+The RunInference API supports loading models from the Hugging Face Hub. To use it, first install `transformers`.
+```
+pip install transformers==4.30.0
+```
+Additional dependicies for PyTorch and TensorFlow may need to be installed separately:
+```
+pip install tensorflow==2.12.0
+pip install torch==1.10.0
+```
+
+
 ### TensorRT dependencies
 
 The RunInference API supports TensorRT SDK for high-performance deep learning inference with NVIDIA GPUs.
@@ -687,3 +703,60 @@ MilkQualityAggregation(bad_quality_measurements=6, medium_quality_measurements=4
 MilkQualityAggregation(bad_quality_measurements=3, medium_quality_measurements=3, high_quality_measurements=3)
 MilkQualityAggregation(bad_quality_measurements=1, medium_quality_measurements=2, high_quality_measurements=1)
 ```
+
+---
+## Language modeling with Hugging Face Hub
+
+[`huggingface_language_modeling.py`](./huggingface_language_modeling.py) contains an implementation for a RunInference pipeline that performs masked language modeling (that is, decoding a masked token in a sentence) using the `AutoModelForMaskedLM` architecture from Hugging Face.
+
+The pipeline reads sentences, performs basic preprocessing to convert the last word into a `<mask>` token, passes the masked sentence to the Hugging Face implementation of RunInference, and then writes the predictions to a text file.
+
+### Dataset and model for language modeling
+
+To use this transform, you need a dataset and model for language modeling.
+
+1. Choose a checkpoint to load from Hugging Face Hub, eg:[MaskedLanguageModel](https://huggingface.co/stevhliu/my_awesome_eli5_mlm_model).
+2. (Optional) Create a file named `SENTENCES.txt` that contains sentences to feed into the model. The content of the file should be similar to the following example:
+```
+The capital of France is Paris .
+He looked up and saw the sun and stars .
+...
+```
+
+### Running `huggingface_language_modeling.py`
+
+To run the language modeling pipeline locally, use the following command:
+```sh
+python -m apache_beam.examples.inference.huggingface_language_modeling \
+  --input SENTENCES \
+  --output OUTPUT \
+  --model_name REPOSITORY_ID
+```
+The `input` argument is optional. If none is provided, it will run the pipeline with some
+example sentences.
+
+For example, if you've followed the naming conventions recommended above:
+```sh
+python -m apache_beam.examples.inference.huggingface_language_modeling \
+  --input SENTENCES.txt \
+  --output predictions.csv \
+  --model_name "stevhliu/my_awesome_eli5_mlm_model"
+```
+Or, using the default example sentences:
+```sh
+python -m apache_beam.examples.inference.huggingface_language_modeling \
+  --output predictions.csv \
+  --model_name "stevhliu/my_awesome_eli5_mlm_model"
+```
+
+This writes the output to the `predictions.csv` with contents like:
+```
+The capital of France is Paris .;paris
+He looked up and saw the sun and stars .;moon
+...
+```
+Each line has data separated by a semicolon ";".
+The first item is the input sentence. The model masks the last word and tries to predict it;
+the second item is the word that the model predicts for the mask.
+
+---

sdks/python/apache_beam/examples/inference/huggingface_language_modeling.py

@@ -0,0 +1,180 @@
+#
+# 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.
+#
+
+""""A pipeline that uses RunInference to perform Language Modeling with
+model from Hugging Face.
+
+This pipeline takes sentences from a custom text file, converts the last word
+of the sentence into a <mask> token, and then uses the AutoModelForMaskedLM from
+Hugging Face to predict the best word for the masked token given all the words
+already in the sentence. The pipeline then writes the prediction to an output
+file in which users can then compare against the original sentence.
+"""
+
+import argparse
+import logging
+from typing import Dict
+from typing import Iterable
+from typing import Iterator
+from typing import Tuple
+
+import apache_beam as beam
+import torch
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.huggingface_inference import HuggingFaceModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import AutoModelForMaskedLM
+from transformers import AutoTokenizer
+
+
+def add_mask_to_last_word(text: str) -> Tuple[str, str]:
+  text_list = text.split()
+  return text, ' '.join(text_list[:-2] + ['<mask>', text_list[-1]])
+
+
+def tokenize_sentence(
+    text_and_mask: Tuple[str, str],
+    tokenizer: AutoTokenizer) -> Tuple[str, Dict[str, torch.Tensor]]:
+  text, masked_text = text_and_mask
+  tokenized_sentence = tokenizer.encode_plus(masked_text, return_tensors="pt")
+
+  # Workaround to manually remove batch dim until we have the feature to
+  # add optional batching flag.
+  # TODO(https://github.com/apache/beam/issues/21863): Remove once optional
+  # batching flag added
+  return text, {
+      k: torch.squeeze(v)
+      for k, v in dict(tokenized_sentence).items()
+  }
+
+
+def filter_empty_lines(text: str) -> Iterator[str]:
+  if len(text.strip()) > 0:
+    yield text
+
+
+class PostProcessor(beam.DoFn):
+  """Processes the PredictionResult to get the predicted word.
+
+  The logits are the output of the Model. After applying a softmax
+  activation function to the logits, we get probabilistic distributions for each
+  of the words in the model's vocabulary. We can get the word with the highest
+  probability of being a candidate replacement word by taking the argmax.
+  """
+  def __init__(self, tokenizer: AutoTokenizer):
+    super().__init__()
+    self.tokenizer = tokenizer
+
+  def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
+    text, prediction_result = element
+    inputs = prediction_result.example
+    logits = prediction_result.inference['logits']
+    mask_token_index = torch.where(
+        inputs["input_ids"] == self.tokenizer.mask_token_id)[0]
+    predicted_token_id = logits[mask_token_index].argmax(axis=-1)
+    decoded_word = self.tokenizer.decode(predicted_token_id)
+    yield text + ';' + decoded_word
+
+
+def parse_known_args(argv):
+  """Parses args for the workflow."""
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      '--input',
+      dest='input',
+      help='Path to the text file containing sentences.')
+  parser.add_argument(
+      '--output',
+      dest='output',
+      required=True,
+      help='Path of file in which to save the output predictions.')
+  parser.add_argument(
+      '--model_name',
+      dest='model_name',
+      required=True,
+      help='bert uncased model. This can be base model or large model')
+  parser.add_argument(
+      '--model_class',
+      dest='model_class',
+      default=AutoModelForMaskedLM,
+      help="Name of the model from Hugging Face")
+  return parser.parse_known_args(argv)
+
+
+def run(
+    argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+  """
+  Args:
+    argv: Command line arguments defined for this example.
+    model_class: Reference to the class definition of the model.
+    model_name: Name of the pretrained model to be loaded.
+    save_main_session: Used for internal testing.
+    test_pipeline: Used for internal testing.
+  """
+  known_args, pipeline_args = parse_known_args(argv)
+  pipeline_options = PipelineOptions(pipeline_args)
+  pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+
+  pipeline = test_pipeline
+  if not test_pipeline:
+    pipeline = beam.Pipeline(options=pipeline_options)
+
+  tokenizer = AutoTokenizer.from_pretrained(known_args.model_name)
+
+  model_handler = HuggingFaceModelHandlerKeyedTensor(
+      model_uri=known_args.model_name,
+      model_class=known_args.model_class,
+      max_batch_size=1)
+  if not known_args.input:
+    text = (
+        pipeline | 'CreateSentences' >> beam.Create([
+            'The capital of France is Paris .',
+            'It is raining cats and dogs .',
+            'Today is Monday and tomorrow is Tuesday .',
+            'There are 5 coconuts on this palm tree .',
+            'The strongest person in the world is not famous .',
+            'The secret ingredient to his wonderful life was gratitude .',
+            'The biggest animal in the world is the whale .',
+        ]))
+  else:
+    text = (
+        pipeline | 'ReadSentences' >> beam.io.ReadFromText(known_args.input))
+  text_and_tokenized_text_tuple = (
+      text
+      | 'FilterEmptyLines' >> beam.ParDo(filter_empty_lines)
+      | 'AddMask' >> beam.Map(add_mask_to_last_word)
+      |
+      'TokenizeSentence' >> beam.Map(lambda x: tokenize_sentence(x, tokenizer)))
+  output = (
+      text_and_tokenized_text_tuple
+      | 'RunInference' >> RunInference(KeyedModelHandler(model_handler))
+      | 'ProcessOutput' >> beam.ParDo(PostProcessor(tokenizer=tokenizer)))
+  output | "WriteOutput" >> beam.io.WriteToText( # pylint: disable=expression-not-assigned
+      known_args.output, shard_name_template='', append_trailing_newlines=True)
+
+  result = pipeline.run()
+  result.wait_until_finish()
+  return result
+
+
+if __name__ == '__main__':
+  logging.getLogger().setLevel(logging.INFO)
+  run()