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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 Keras-based positional embedding layer."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import position_embedding
class PositionEmbeddingLayerTest(tf.test.TestCase):
def test_static_layer_output_shape(self):
# Create a 3-dimensional input (the first dimension is implicit).
sequence_length = 21
test_layer = position_embedding.PositionEmbedding(
max_length=sequence_length)
width = 30
input_tensor = tf_keras.Input(shape=(sequence_length, width))
output_tensor = test_layer(input_tensor)
# When using static positional embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [None, sequence_length, width]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
# The default output dtype for this layer should be tf.float32.
self.assertEqual(tf.float32, output_tensor.dtype)
def test_non_default_axis_static(self):
# Create a 3-dimensional input (the first dimension is implicit).
sequence_length = 21
test_layer = position_embedding.PositionEmbedding(
max_length=sequence_length, seq_axis=2)
width = 30
input_tensor = tf_keras.Input(shape=(width, sequence_length, width))
output_tensor = test_layer(input_tensor)
# When using static positional embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [None, width, sequence_length, width]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
# The default output dtype for this layer should be tf.float32.
self.assertEqual(tf.float32, output_tensor.dtype)
def test_float16_dtype(self):
# Create a 3-dimensional input (the first dimension is implicit).
sequence_length = 21
test_layer = position_embedding.PositionEmbedding(
max_length=sequence_length, dtype="float16")
width = 30
input_tensor = tf_keras.Input(shape=(sequence_length, width))
output_tensor = test_layer(input_tensor)
# When using static positional embedding shapes, the output is expected
# to be the same as the input shape in all dimensions save batch.
expected_output_shape = [None, sequence_length, width]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
# The default output dtype for this layer should be tf.float32.
self.assertEqual(tf.float16, output_tensor.dtype)
def test_dynamic_layer_output_shape(self):
max_sequence_length = 40
test_layer = position_embedding.PositionEmbedding(
max_length=max_sequence_length)
# Create a 3-dimensional input (the first dimension is implicit).
width = 30
input_tensor = tf_keras.Input(shape=(None, width))
output_tensor = test_layer(input_tensor)
# When using dynamic positional embedding shapes, the output is expected
# to be the same as the input shape in all dimensions - but may be None if
# the input shape is None there.
expected_output_shape = [None, None, width]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
def test_non_default_axis_dynamic(self):
max_sequence_length = 60
test_layer = position_embedding.PositionEmbedding(
max_length=max_sequence_length, seq_axis=2)
# Create a 3-dimensional input (the first dimension is implicit).
width = 30
input_tensor = tf_keras.Input(shape=(None, None, width))
output_tensor = test_layer(input_tensor)
# When using dynamic positional embedding shapes, the output is expected
# to be the same as the input shape in all dimensions - but may be None if
# the input shape is None there.
expected_output_shape = [None, None, None, width]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
def test_dynamic_layer_slicing(self):
max_sequence_length = 40
test_layer = position_embedding.PositionEmbedding(
max_length=max_sequence_length)
# Create a 3-dimensional input (the first dimension is implicit).
width = 30
input_tensor = tf_keras.Input(shape=(None, width))
output_tensor = test_layer(input_tensor)
model = tf_keras.Model(input_tensor, output_tensor)
# Create input data that is shorter than max_sequence_length, which should
# trigger a down-slice.
input_length = 17
# Note: This test explicitly uses a batch size of 1. This is to get around
# Keras' restriction on Model invocations: inputs are expected to have the
# same batch cardinality as outputs. In practice, this layer should be used
# inside a model, where it can be projected when added to another tensor.
input_data = np.ones((1, input_length, width))
output_data = model.predict(input_data)
self.assertAllEqual([1, input_length, width], output_data.shape)
class RelativePositionEmbeddingLayerTest(tf.test.TestCase):
def test_relative_tensor_input(self):
hidden_size = 8
test_layer = position_embedding.RelativePositionEmbedding(
hidden_size=hidden_size)
# create a 3-dimensional input for test_layer to infer length as 1.
input_tensor = tf.constant([[[0] * hidden_size]])
output_tensor = test_layer(input_tensor)
# expected output is the theoretical result of the input based on
# sine cosine relative position embedding formula.
expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]])
self.assertAllEqual(output_tensor, expected_output_tensor)
def test_relative_length_input(self):
hidden_size = 8
# When we do not have tensor as input, we explicitly specify length
# value when initializing test_layer.
test_layer = position_embedding.RelativePositionEmbedding(
hidden_size=hidden_size)
input_tensor = None
output_tensor = test_layer(input_tensor, length=1)
# expected output is the theoretical result of the input based on
# sine cosine relative position embedding formula.
expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]])
self.assertAllEqual(output_tensor, expected_output_tensor)
class RelativePositionBiasTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(("bidirectional", True),
("unidirectional", False))
def test_relative_position_bias(self, bidirectional):
query = tf.zeros((4, 4, 2))
key = tf.zeros((4, 2, 2))
l = position_embedding.RelativePositionBias(
num_heads=3,
bidirectional=bidirectional,
name="foo")
self.assertEqual(l(query, key).shape, (4, 3, 4, 2))
self.assertLen(l.trainable_variables, 1)
self.assertEqual(l.trainable_variables[0].name, "foo/rel_embedding:0")
def test_relative_position_bucket(self):
context_position = tf.range(3)[:, None]
memory_position = tf.range(2)[None, :]
relative_position = memory_position - context_position
outputs = position_embedding._relative_position_bucket(relative_position)
self.assertAllEqual(outputs.numpy(), np.array([[0, 17], [1, 0], [2, 1]]))
outputs = position_embedding._relative_position_bucket(
relative_position, bidirectional=False)
self.assertAllEqual(outputs.numpy(), np.array([[0, 0], [1, 0], [2, 1]]))
if __name__ == "__main__":
tf.test.main()
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