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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 layers in Transformer."""
import tensorflow as tf, tf_keras
from official.legacy.transformer import attention_layer
from official.legacy.transformer import embedding_layer
from official.legacy.transformer import ffn_layer
from official.legacy.transformer import metrics
class TransformerLayersTest(tf.test.TestCase):
def test_attention_layer(self):
hidden_size = 64
num_heads = 4
dropout = 0.5
dim_per_head = hidden_size // num_heads
layer = attention_layer.SelfAttention(hidden_size, num_heads, dropout)
self.assertDictEqual(
layer.get_config(), {
"hidden_size": hidden_size,
"num_heads": num_heads,
"attention_dropout": dropout,
})
length = 2
x = tf.ones([1, length, hidden_size])
bias = tf.ones([1])
cache = {
"k": tf.zeros([1, 0, num_heads, dim_per_head]),
"v": tf.zeros([1, 0, num_heads, dim_per_head]),
}
y = layer(x, bias, training=True, cache=cache)
self.assertEqual(y.shape, (
1,
length,
64,
))
self.assertEqual(cache["k"].shape, (
1,
length,
num_heads,
dim_per_head,
))
self.assertEqual(cache["v"].shape, (
1,
length,
num_heads,
dim_per_head,
))
def test_embedding_shared_weights(self):
vocab_size = 50
hidden_size = 64
length = 2
layer = embedding_layer.EmbeddingSharedWeights(vocab_size, hidden_size)
self.assertDictEqual(layer.get_config(), {
"vocab_size": 50,
"hidden_size": 64,
})
idx = tf.ones([1, length], dtype="int32")
y = layer(idx)
self.assertEqual(y.shape, (
1,
length,
hidden_size,
))
x = tf.ones([1, length, hidden_size])
output = layer(x, "linear")
self.assertEqual(output.shape, (
1,
length,
vocab_size,
))
def test_feed_forward_network(self):
hidden_size = 64
filter_size = 32
relu_dropout = 0.5
layer = ffn_layer.FeedForwardNetwork(hidden_size, filter_size, relu_dropout)
self.assertDictEqual(
layer.get_config(), {
"hidden_size": hidden_size,
"filter_size": filter_size,
"relu_dropout": relu_dropout,
})
length = 2
x = tf.ones([1, length, hidden_size])
y = layer(x, training=True)
self.assertEqual(y.shape, (
1,
length,
hidden_size,
))
def test_metric_layer(self):
vocab_size = 50
logits = tf_keras.layers.Input((None, vocab_size),
dtype="float32",
name="logits")
targets = tf_keras.layers.Input((None,), dtype="int64", name="targets")
output_logits = metrics.MetricLayer(vocab_size)([logits, targets])
self.assertEqual(output_logits.shape.as_list(), [
None,
None,
vocab_size,
])
if __name__ == "__main__":
tf.test.main()
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