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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 the attention layer."""
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import attention
def _create_cache(batch_size, init_decode_length, num_heads, head_size):
return {
"key":
tf.zeros([batch_size, init_decode_length, num_heads, head_size],
dtype=tf.float32),
"value":
tf.zeros([batch_size, init_decode_length, num_heads, head_size],
dtype=tf.float32)
}
class CachedAttentionTest(tf.test.TestCase):
def test_masked_attention(self):
"""Test with a mask tensor."""
num_heads, head_size = 2, 2
# Create a 3-dimensional input (the first dimension is implicit).
from_seq_length = 4
batch_size = 3
# GPU/CPU case.
init_decode_length = 0
# Directly tests the keras layer.
cache = _create_cache(batch_size, init_decode_length, num_heads, head_size)
layer = attention.CachedAttention(num_heads=num_heads, key_dim=head_size)
# Generate data for the input (non-mask) tensors.
from_data = tf.zeros((batch_size, from_seq_length, 8), dtype=np.float32)
# Invoke the data with a random set of mask data. This should mask at least
# one element.
mask_data = np.random.randint(
2, size=(batch_size, from_seq_length, from_seq_length))
masked_output_data, cache = layer(
query=from_data, value=from_data, attention_mask=mask_data, cache=cache)
self.assertEqual(masked_output_data.shape, (3, 4, 8))
self.assertEqual(cache["value"].shape, (3, 4, 2, 2))
# Tests inputs without cache.
masked_output_data, cache = layer(
query=from_data, value=from_data, attention_mask=mask_data)
self.assertEqual(masked_output_data.shape, (3, 4, 8))
self.assertIsNone(cache)
def test_padded_decode(self):
"""Test with a mask tensor."""
num_heads, head_size = 2, 2
from_seq_length = 4
# TPU decoding should pre-allocate the entire sequence.
batch_size = 3
init_decode_length = from_seq_length
# Directly tests the keras layer.
cache = _create_cache(batch_size, init_decode_length, num_heads, head_size)
layer = attention.CachedAttention(num_heads=num_heads, key_dim=head_size)
# Generate data for the input (non-mask) tensors.
from_data = tf.zeros((batch_size, from_seq_length, 8), dtype=np.float32)
decode_loop_step = 2
mask_data = np.random.randint(
2, size=(batch_size, from_seq_length, from_seq_length), dtype=np.int32)
# Testing the invocation directly as Keras cannot consume inputs correctly.
masked_output_data, cache = layer(
query=from_data,
value=from_data,
attention_mask=mask_data,
cache=cache,
decode_loop_step=decode_loop_step)
self.assertEqual(masked_output_data.shape, (3, 4, 8))
self.assertEqual(cache["value"].shape, (3, 4, 2, 2))
if __name__ == "__main__":
tf.test.main()
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