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# Lint as: python3
# Copyright 2020 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 BERT configurations and models instantiation."""
import tensorflow as tf
from official.nlp.configs import bert
from official.nlp.configs import encoders
class BertModelsTest(tf.test.TestCase):
def test_network_invocation(self):
config = bert.BertPretrainerConfig(
encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1))
_ = bert.instantiate_bertpretrainer_from_cfg(config)
# Invokes with classification heads.
config = bert.BertPretrainerConfig(
encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1),
cls_heads=[
bert.ClsHeadConfig(
inner_dim=10, num_classes=2, name="next_sentence")
])
_ = bert.instantiate_bertpretrainer_from_cfg(config)
with self.assertRaises(ValueError):
config = bert.BertPretrainerConfig(
encoder=encoders.TransformerEncoderConfig(
vocab_size=10, num_layers=1),
cls_heads=[
bert.ClsHeadConfig(
inner_dim=10, num_classes=2, name="next_sentence"),
bert.ClsHeadConfig(
inner_dim=10, num_classes=2, name="next_sentence")
])
_ = bert.instantiate_bertpretrainer_from_cfg(config)
def test_checkpoint_items(self):
config = bert.BertPretrainerConfig(
encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1),
cls_heads=[
bert.ClsHeadConfig(
inner_dim=10, num_classes=2, name="next_sentence")
])
encoder = bert.instantiate_bertpretrainer_from_cfg(config)
self.assertSameElements(encoder.checkpoint_items.keys(),
["encoder", "next_sentence.pooler_dense"])
if __name__ == "__main__":
tf.test.main()
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