encoder_test.py

# Copyright 2023 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.

"""Tests for official.nlp.projects.bigbird.encoder."""

import numpy as np
import tensorflow as tf

from official.projects.bigbird import encoder


class BigBirdEncoderTest(tf.test.TestCase):

  def test_encoder(self):
    sequence_length = 1024
    batch_size = 2
    vocab_size = 1024
    network = encoder.BigBirdEncoder(
        num_layers=1, vocab_size=1024, max_position_embeddings=4096)
    word_id_data = np.random.randint(
        vocab_size, size=(batch_size, sequence_length))
    mask_data = np.random.randint(2, size=(batch_size, sequence_length))
    type_id_data = np.random.randint(2, size=(batch_size, sequence_length))
    outputs = network([word_id_data, mask_data, type_id_data])
    self.assertEqual(outputs["sequence_output"].shape,
                     (batch_size, sequence_length, 768))

  def test_save_restore(self):
    sequence_length = 1024
    batch_size = 2
    vocab_size = 1024
    network = encoder.BigBirdEncoder(
        num_layers=1, vocab_size=1024, max_position_embeddings=4096)
    word_id_data = np.random.randint(
        vocab_size, size=(batch_size, sequence_length))
    mask_data = np.random.randint(2, size=(batch_size, sequence_length))
    type_id_data = np.random.randint(2, size=(batch_size, sequence_length))
    inputs = dict(
        input_word_ids=word_id_data,
        input_mask=mask_data,
        input_type_ids=type_id_data)
    ref_outputs = network(inputs)
    model_path = self.get_temp_dir() + "/model"
    network.save(model_path)
    loaded = tf.keras.models.load_model(model_path)
    outputs = loaded(inputs)
    self.assertAllClose(outputs["sequence_output"],
                        ref_outputs["sequence_output"])


if __name__ == "__main__":
  tf.test.main()