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25e57c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # 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.lra.mega_encoder."""
import numpy as np
import tensorflow as tf, tf_keras
from official.projects.lra import mega_encoder
class MegaEncoderTest(tf.test.TestCase):
def test_encoder(self):
sequence_length = 1024
batch_size = 2
vocab_size = 1024
network = mega_encoder.MegaEncoder(
num_layers=1,
vocab_size=1024,
max_sequence_length=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({
"input_word_ids": word_id_data,
"input_mask": mask_data,
"input_type_ids": type_id_data,
})
self.assertEqual(
outputs["sequence_output"].shape,
(batch_size, sequence_length, 128),
)
if __name__ == "__main__":
tf.test.main()
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