huggingface_language_modeling.py

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"""A pipeline that uses RunInference to perform Language Modeling with
masked language 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.
"""

importargparse
importlogging
fromcollections.abcimportIterable
fromcollections.abcimportIterator

importapache_beamasbeam
importtorch
fromapache_beam.ml.inference.baseimportKeyedModelHandler
fromapache_beam.ml.inference.baseimportPredictionResult
fromapache_beam.ml.inference.baseimportRunInference
fromapache_beam.ml.inference.huggingface_inferenceimportHuggingFaceModelHandlerKeyedTensor
fromapache_beam.options.pipeline_optionsimportPipelineOptions
fromapache_beam.options.pipeline_optionsimportSetupOptions
fromapache_beam.runners.runnerimportPipelineResult
fromtransformersimportAutoModelForMaskedLM
fromtransformersimportAutoTokenizer


defadd_mask_to_last_word(text: str) ->tuple[str, str]:
text_list=text.split()
returntext, ' '.join(text_list[:-2] + ['<mask>', text_list[-1]])


deftokenize_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
returntext, {
k: torch.squeeze(v)
fork, vindict(tokenized_sentence).items()
 }


deffilter_empty_lines(text: str) ->Iterator[str]:
iflen(text.strip()) >0:
yieldtext


classPostProcessor(beam.DoFn):
"""Processes the PredictionResult to get the predicted word.

 The logits are the output of the Model. 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

defprocess(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)
yieldtext+';'+decoded_word


defparse_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')
parser.add_argument(
'--large_model',
action='store_true',
dest='large_model',
default=False,
help='Set to true if your model is large enough to run into memory '
'pressure if you load multiple copies.')
returnparser.parse_known_args(argv)


defrun(
argv=None, save_main_session=True, test_pipeline=None) ->PipelineResult:
"""
 Args:
 argv: Command line arguments defined for this example.
 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
ifnottest_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,
framework='pt',
max_batch_size=1,
large_model=known_args.large_model)
ifnotknown_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(lambdax: 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(
known_args.output, shard_name_template='', append_trailing_newlines=True)

result=pipeline.run()
result.wait_until_finish()
returnresult


if__name__=='__main__':
logging.getLogger().setLevel(logging.INFO)
run()