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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 pack_optimization."""
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import pack_optimization
class PackOptimizationTest(tf.test.TestCase):
def test_bert_embedding_packing(self):
batch_size, seq_len, embed_dim = 2, 4, 8
pack_sequences = 2
token_and_position_embed = tf.ones((batch_size, seq_len, embed_dim),
dtype=tf.float32)
input_mask = tf.ones((batch_size, seq_len), dtype=tf.int32)
layer = pack_optimization.PackBertEmbeddings(pack_sequences=pack_sequences)
outputs = layer(token_and_position_embed, input_mask)
self.assertEqual(outputs["packed_embeddings"].shape, (1, 8, embed_dim))
self.assertEqual(outputs["combined_attention_mask"].shape, (1, 8, 8))
def test_strided_transformer_encoder_block(self):
inputs = tf.zeros((2, 4, 8), dtype=tf.float32)
attention_mask = tf.ones((2, 4, 4), dtype=tf.float32)
transformer = pack_optimization.StridedTransformerEncoderBlock(
num_attention_heads=2, inner_dim=4, inner_activation="relu")
outputs = transformer([inputs, attention_mask],
stride=tf.constant(2, dtype=tf.int32))
self.assertEqual(outputs.shape, (2, 2, 8))
def test_strided_rezero_transformer(self):
inputs = tf.zeros((2, 4, 8), dtype=tf.float32)
attention_mask = tf.ones((2, 4, 4), dtype=tf.float32)
transformer = pack_optimization.StridedReZeroTransformer(
num_attention_heads=2, inner_dim=4, inner_activation="relu")
outputs = transformer([inputs, attention_mask],
stride=tf.constant(2, dtype=tf.int32))
self.assertEqual(outputs.shape, (2, 2, 8))
def test_strided_scaffold(self):
inputs = tf.zeros((2, 4, 8), dtype=tf.float32)
attention_mask = tf.ones((2, 4, 4), dtype=tf.float32)
test_layer = pack_optimization.StridedTransformerScaffold(
num_attention_heads=2,
inner_dim=128,
inner_activation="relu")
outputs = test_layer([inputs, attention_mask],
stride=tf.constant(2, dtype=tf.int32))
self.assertEqual(outputs.shape, (2, 2, 8))
if __name__ == "__main__":
tf.test.main()
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