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# 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.attention."""
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import bigbird_attention as attention
class BigbirdAttentionTest(tf.test.TestCase):
def test_attention(self):
num_heads = 12
key_dim = 64
seq_length = 1024
batch_size = 2
block_size = 64
mask_layer = attention.BigBirdMasks(block_size=block_size)
encoder_inputs_mask = tf.zeros((batch_size, seq_length), dtype=tf.int32)
test_layer = attention.BigBirdAttention(
num_heads=num_heads,
key_dim=key_dim,
from_block_size=block_size,
to_block_size=block_size,
seed=0)
query = tf.random.normal(
shape=(batch_size, seq_length, key_dim))
masks = mask_layer(query, tf.cast(encoder_inputs_mask, dtype=tf.float64))
value = query
output = test_layer(
query=query,
value=value,
attention_mask=masks)
self.assertEqual(output.shape, [batch_size, seq_length, key_dim])
def test_config(self):
num_heads = 12
key_dim = 64
block_size = 64
test_layer = attention.BigBirdAttention(
num_heads=num_heads,
key_dim=key_dim,
from_block_size=block_size,
to_block_size=block_size,
seed=0)
print(test_layer.get_config())
new_layer = attention.BigBirdAttention.from_config(
test_layer.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(test_layer.get_config(), new_layer.get_config())
if __name__ == '__main__':
tf.test.main()
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