damccorm commented on code in PR #24911: URL: https://github.com/apache/beam/pull/24911#discussion_r1101952849
########## sdks/python/apache_beam/examples/inference/onnx_sentiment_classification.py: ########## @@ -0,0 +1,147 @@ +# +# 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 sentiment classification +using RoBERTa. + +This pipeline takes sentences from a custom text file, and then uses RoBERTa +from Hugging Face to predict the sentiment of a given review. The pipeline +then writes the prediction to an output file in which users can then compare against true labels. + +Model is fine-tuned RoBERTa from +https://github.com/SeldonIO/seldon-models/blob/master/pytorch/moviesentiment_roberta/pytorch-roberta-onnx.ipynb # pylint: disable=line-too-long +""" + +import argparse +import logging +from typing import Iterable +from typing import Iterator +from typing import Tuple + +import numpy as np + +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.onnx_inference import OnnxModelHandlerNumpy +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 RobertaTokenizer + + +def tokenize_sentence(text: str, + tokenizer: RobertaTokenizer) -> Tuple[str, torch.Tensor]: + tokenized_sentence = tokenizer.encode(text, add_special_tokens=True) + + # 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, torch.tensor(tokenized_sentence).numpy() + + +def filter_empty_lines(text: str) -> Iterator[str]: + if len(text.strip()) > 0: + yield text + + +class PostProcessor(beam.DoFn): + def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]: + filename, prediction_result = element + prediction = np.argmax(prediction_result.inference, axis=0) + yield filename + ';' + str(prediction) + + +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_uri', + dest='model_uri', + required=True, + help="Path to the model's uri.") + 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. + 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 + + # TODO: Remove once nested tensors https://github.com/pytorch/nestedtensor + # is officially released. + class OnnxNoBatchModelHandler(OnnxModelHandlerNumpy): + """Wrapper to OnnxModelHandlerNumpy to limit batch size to 1. + + The tokenized strings generated from RobertaTokenizer may have different + lengths, which doesn't work with torch.stack() in current RunInference + implementation since stack() requires tensors to be the same size. + + Restricting max_batch_size to 1 means there is only 1 example per `batch` + in the run_inference() call. + """ + def batch_elements_kwargs(self): + return {'max_batch_size': 1} Review Comment: I added this as an option to our other Model Handlers in #25370 (and #25398 as a quick follow up) - if you'd like to pick that up as a follow up PR following the same pattern, let me know, if not I will probably put up a quick PR to add it once this is merged. I don't think that should block merging this PR -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
