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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 FNet encoder network."""
from typing import Sequence
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.nlp.modeling import layers
from official.nlp.modeling.networks import fnet
class FNetTest(parameterized.TestCase, tf.test.TestCase):
def tearDown(self):
super(FNetTest, self).tearDown()
tf_keras.mixed_precision.set_global_policy("float32")
@parameterized.named_parameters(
("fnet", layers.MixingMechanism.FOURIER, ()),
("fnet_hybrid", layers.MixingMechanism.FOURIER, (1, 2)),
("hnet", layers.MixingMechanism.HARTLEY, ()),
("hnet_hybrid", layers.MixingMechanism.HARTLEY, (1, 2)),
("linear", layers.MixingMechanism.LINEAR, ()),
("linear_hybrid", layers.MixingMechanism.LINEAR, (0,)),
("bert", layers.MixingMechanism.FOURIER, (0, 1, 2)),
)
def test_network(self, mixing_mechanism: layers.MixingMechanism,
attention_layers: Sequence[int]):
num_layers = 3
hidden_size = 32
sequence_length = 21
test_network = fnet.FNet(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
max_sequence_length=sequence_length,
num_layers=num_layers,
mixing_mechanism=mixing_mechanism,
attention_layers=attention_layers)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
self.assertIsInstance(test_network.transformer_layers, list)
self.assertLen(test_network.transformer_layers, 3)
self.assertIsInstance(test_network.pooler_layer, tf_keras.layers.Dense)
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
def test_embeddings_as_inputs(self):
hidden_size = 32
sequence_length = 21
test_network = fnet.FNet(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
max_sequence_length=sequence_length,
num_layers=3)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf_keras.Input(shape=(sequence_length), dtype=tf.int32)
mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
test_network.build(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
embeddings = test_network.get_embedding_layer()(word_ids)
# Calls with the embeddings.
dict_outputs = test_network(
dict(
input_word_embeddings=embeddings,
input_mask=mask,
input_type_ids=type_ids))
all_encoder_outputs = dict_outputs["encoder_outputs"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertLen(all_encoder_outputs, 3)
for data in all_encoder_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
if __name__ == "__main__":
tf.test.main()
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