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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() | |