# Copyright 2021 The TensorFlow Authors. All Rights Reserved. # # Licensed 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. """TriviaQA script for inference.""" import collections import contextlib import functools import json import operator from absl import app from absl import flags from absl import logging import tensorflow as tf import tensorflow_datasets as tfds import sentencepiece as spm from official.nlp.configs import encoders # pylint: disable=unused-import from official.nlp.projects.triviaqa import evaluation from official.nlp.projects.triviaqa import inputs from official.nlp.projects.triviaqa import prediction flags.DEFINE_string('data_dir', None, 'TensorFlow Datasets directory.') flags.DEFINE_enum('split', None, [tfds.Split.TRAIN, tfds.Split.VALIDATION, tfds.Split.TEST], 'For which split to generate predictions.') flags.DEFINE_string('predictions_path', None, 'Output for predictions.') flags.DEFINE_string('sentencepiece_model_path', None, 'Path to sentence piece model.') flags.DEFINE_integer('bigbird_block_size', 64, 'Size of blocks for sparse block attention.') flags.DEFINE_string('saved_model_dir', None, 'Path from which to initialize model and weights.') flags.DEFINE_integer('sequence_length', 4096, 'Maximum number of tokens.') flags.DEFINE_integer('global_sequence_length', 320, 'Maximum number of global tokens.') flags.DEFINE_integer('batch_size', 32, 'Size of batch.') flags.DEFINE_string('master', '', 'Address of the TPU master.') flags.DEFINE_integer('decode_top_k', 8, 'Maximum number of tokens to consider for begin/end.') flags.DEFINE_integer('decode_max_size', 16, 'Maximum number of sentence pieces in an answer.') FLAGS = flags.FLAGS @contextlib.contextmanager def worker_context(): if FLAGS.master: with tf.device('/job:worker') as d: yield d else: yield def read_sentencepiece_model(path): with tf.io.gfile.GFile(path, 'rb') as file: processor = spm.SentencePieceProcessor() processor.LoadFromSerializedProto(file.read()) return processor def predict(sp_processor, features_map_fn, logits_fn, decode_logits_fn, split_and_pad_fn, distribute_strategy, dataset): """Make predictions.""" predictions = collections.defaultdict(list) for _, features in dataset.enumerate(): token_ids = features['token_ids'] x = split_and_pad_fn(features_map_fn(features)) logits = tf.concat( distribute_strategy.experimental_local_results(logits_fn(x)), 0) logits = logits[:features['token_ids'].shape[0]] end_limit = token_ids.row_lengths() - 1 # inclusive begin, end, scores = decode_logits_fn(logits, end_limit) answers = prediction.decode_answer(features['context'], begin, end, features['token_offsets'], end_limit).numpy() for j, (qid, token_id, offset, score, answer) in enumerate( zip(features['qid'].numpy(), tf.gather(features['token_ids'], begin, batch_dims=1).numpy(), tf.gather(features['token_offsets'], begin, batch_dims=1).numpy(), scores, answers)): if not answer: logging.info('%s: %s | NO_ANSWER, %f', features['id'][j].numpy().decode('utf-8'), features['question'][j].numpy().decode('utf-8'), score) continue if sp_processor.IdToPiece(int(token_id)).startswith('▁') and offset > 0: answer = answer[1:] logging.info('%s: %s | %s, %f', features['id'][j].numpy().decode('utf-8'), features['question'][j].numpy().decode('utf-8'), answer.decode('utf-8'), score) predictions[qid.decode('utf-8')].append((score, answer.decode('utf-8'))) predictions = { qid: evaluation.normalize_answer( sorted(answers, key=operator.itemgetter(0), reverse=True)[0][1]) for qid, answers in predictions.items() } return predictions def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') # Configure input processing. sp_processor = read_sentencepiece_model(FLAGS.sentencepiece_model_path) features_map_fn = tf.function( functools.partial( inputs.features_map_fn, local_radius=FLAGS.bigbird_block_size, relative_pos_max_distance=24, use_hard_g2l_mask=True, sequence_length=FLAGS.sequence_length, global_sequence_length=FLAGS.global_sequence_length, padding_id=sp_processor.PieceToId(''), eos_id=sp_processor.PieceToId(''), null_id=sp_processor.PieceToId(''), cls_id=sp_processor.PieceToId(''), sep_id=sp_processor.PieceToId('')), autograph=False) # Connect to TPU cluster. if FLAGS.master: resolver = tf.distribute.cluster_resolver.TPUClusterResolver(FLAGS.master) tf.config.experimental_connect_to_cluster(resolver) tf.tpu.experimental.initialize_tpu_system(resolver) strategy = tf.distribute.TPUStrategy(resolver) else: strategy = tf.distribute.MirroredStrategy() # Initialize datasets. with worker_context(): _ = tf.random.get_global_generator() dataset = inputs.read_batches( FLAGS.data_dir, FLAGS.split, FLAGS.batch_size, include_answers=False) # Initialize model and compile. with strategy.scope(): model = tf.keras.models.load_model(FLAGS.saved_model_dir, compile=False) logging.info('Model initialized. Beginning prediction loop.') logits_fn = tf.function( functools.partial(prediction.distributed_logits_fn, model)) decode_logits_fn = tf.function( functools.partial(prediction.decode_logits, FLAGS.decode_top_k, FLAGS.decode_max_size)) split_and_pad_fn = tf.function( functools.partial(prediction.split_and_pad, strategy, FLAGS.batch_size)) # Prediction strategy. predict_fn = functools.partial( predict, sp_processor=sp_processor, features_map_fn=features_map_fn, logits_fn=logits_fn, decode_logits_fn=decode_logits_fn, split_and_pad_fn=split_and_pad_fn, distribute_strategy=strategy, dataset=dataset) with worker_context(): predictions = predict_fn() with tf.io.gfile.GFile(FLAGS.predictions_path, 'w') as f: json.dump(predictions, f) if __name__ == '__main__': flags.mark_flags_as_required(['split', 'predictions_path', 'saved_model_dir']) app.run(main)