# 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. from absl.testing import parameterized import numpy as np import tensorflow as tf, tf_keras from official.nlp.modeling.layers import mobile_bert_layers from official.nlp.modeling.networks import mobile_bert_encoder def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0): """Generate consistent fake integer input sequences.""" np.random.seed(seed) fake_input = [] for _ in range(batch_size): fake_input.append([]) for _ in range(seq_len): fake_input[-1].append(np.random.randint(0, vocab_size)) fake_input = np.asarray(fake_input) return fake_input class MobileBertEncoderTest(parameterized.TestCase, tf.test.TestCase): def test_embedding_layer_with_token_type(self): layer = mobile_bert_layers.MobileBertEmbedding(10, 8, 2, 16) input_seq = tf.Variable([[2, 3, 4, 5]]) token_type = tf.Variable([[0, 1, 1, 1]]) output = layer(input_seq, token_type) output_shape = output.shape.as_list() expected_shape = [1, 4, 16] self.assertListEqual(output_shape, expected_shape, msg=None) def test_embedding_layer_without_token_type(self): layer = mobile_bert_layers.MobileBertEmbedding(10, 8, 2, 16) input_seq = tf.Variable([[2, 3, 4, 5]]) output = layer(input_seq) output_shape = output.shape.as_list() expected_shape = [1, 4, 16] self.assertListEqual(output_shape, expected_shape, msg=None) def test_embedding_layer_get_config(self): layer = mobile_bert_layers.MobileBertEmbedding( word_vocab_size=16, word_embed_size=32, type_vocab_size=4, output_embed_size=32, max_sequence_length=32, normalization_type='layer_norm', initializer=tf_keras.initializers.TruncatedNormal(stddev=0.01), dropout_rate=0.5) layer_config = layer.get_config() new_layer = mobile_bert_layers.MobileBertEmbedding.from_config(layer_config) self.assertEqual(layer_config, new_layer.get_config()) def test_no_norm(self): layer = mobile_bert_layers.NoNorm() feature = tf.random.normal([2, 3, 4]) output = layer(feature) output_shape = output.shape.as_list() expected_shape = [2, 3, 4] self.assertListEqual(output_shape, expected_shape, msg=None) @parameterized.named_parameters(('with_kq_shared_bottleneck', False), ('without_kq_shared_bottleneck', True)) def test_transfomer_kq_shared_bottleneck(self, is_kq_shared): feature = tf.random.uniform([2, 3, 512]) layer = mobile_bert_layers.MobileBertTransformer( key_query_shared_bottleneck=is_kq_shared) output = layer(feature) output_shape = output.shape.as_list() expected_shape = [2, 3, 512] self.assertListEqual(output_shape, expected_shape, msg=None) def test_transfomer_with_mask(self): feature = tf.random.uniform([2, 3, 512]) input_mask = [[[0., 0., 1.], [0., 0., 1.], [0., 0., 1.]], [[0., 1., 1.], [0., 1., 1.], [0., 1., 1.]]] input_mask = np.asarray(input_mask) layer = mobile_bert_layers.MobileBertTransformer() output = layer(feature, input_mask) output_shape = output.shape.as_list() expected_shape = [2, 3, 512] self.assertListEqual(output_shape, expected_shape, msg=None) def test_transfomer_return_attention_score(self): sequence_length = 5 num_attention_heads = 8 feature = tf.random.uniform([2, sequence_length, 512]) layer = mobile_bert_layers.MobileBertTransformer( num_attention_heads=num_attention_heads) _, attention_score = layer(feature, return_attention_scores=True) expected_shape = [2, num_attention_heads, sequence_length, sequence_length] self.assertListEqual( attention_score.shape.as_list(), expected_shape, msg=None) def test_transformer_get_config(self): layer = mobile_bert_layers.MobileBertTransformer( hidden_size=32, num_attention_heads=2, intermediate_size=48, intermediate_act_fn='gelu', hidden_dropout_prob=0.5, attention_probs_dropout_prob=0.4, intra_bottleneck_size=64, use_bottleneck_attention=True, key_query_shared_bottleneck=False, num_feedforward_networks=2, normalization_type='layer_norm', initializer=tf_keras.initializers.TruncatedNormal(stddev=0.01), name='block') layer_config = layer.get_config() new_layer = mobile_bert_layers.MobileBertTransformer.from_config( layer_config) self.assertEqual(layer_config, new_layer.get_config()) class MobileBertMaskedLMTest(tf.test.TestCase): def create_layer(self, vocab_size, hidden_size, embedding_width, output='predictions', xformer_stack=None): # First, create a transformer stack that we can use to get the LM's # vocabulary weight. if xformer_stack is None: xformer_stack = mobile_bert_encoder.MobileBERTEncoder( word_vocab_size=vocab_size, num_blocks=1, hidden_size=hidden_size, num_attention_heads=4, word_embed_size=embedding_width) # Create a maskedLM from the transformer stack. test_layer = mobile_bert_layers.MobileBertMaskedLM( embedding_table=xformer_stack.get_embedding_table(), output=output) return test_layer def test_layer_creation(self): vocab_size = 100 sequence_length = 32 hidden_size = 64 embedding_width = 32 num_predictions = 21 test_layer = self.create_layer( vocab_size=vocab_size, hidden_size=hidden_size, embedding_width=embedding_width) # Make sure that the output tensor of the masked LM is the right shape. lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size)) masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32) output = test_layer(lm_input_tensor, masked_positions=masked_positions) expected_output_shape = [None, num_predictions, vocab_size] self.assertEqual(expected_output_shape, output.shape.as_list()) def test_layer_invocation_with_external_logits(self): vocab_size = 100 sequence_length = 32 hidden_size = 64 embedding_width = 32 num_predictions = 21 xformer_stack = mobile_bert_encoder.MobileBERTEncoder( word_vocab_size=vocab_size, num_blocks=1, hidden_size=hidden_size, num_attention_heads=4, word_embed_size=embedding_width) test_layer = self.create_layer( vocab_size=vocab_size, hidden_size=hidden_size, embedding_width=embedding_width, xformer_stack=xformer_stack, output='predictions') logit_layer = self.create_layer( vocab_size=vocab_size, hidden_size=hidden_size, embedding_width=embedding_width, xformer_stack=xformer_stack, output='logits') # Create a model from the masked LM layer. lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size)) masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32) output = test_layer(lm_input_tensor, masked_positions) logit_output = logit_layer(lm_input_tensor, masked_positions) logit_output = tf_keras.layers.Activation(tf.nn.log_softmax)(logit_output) logit_layer.set_weights(test_layer.get_weights()) model = tf_keras.Model([lm_input_tensor, masked_positions], output) logits_model = tf_keras.Model(([lm_input_tensor, masked_positions]), logit_output) # Invoke the masked LM on some fake data to make sure there are no runtime # errors in the code. batch_size = 3 lm_input_data = 10 * np.random.random_sample( (batch_size, sequence_length, hidden_size)) masked_position_data = np.random.randint( sequence_length, size=(batch_size, num_predictions)) # ref_outputs = model.predict([lm_input_data, masked_position_data]) # outputs = logits_model.predict([lm_input_data, masked_position_data]) ref_outputs = model([lm_input_data, masked_position_data]) outputs = logits_model([lm_input_data, masked_position_data]) # Ensure that the tensor shapes are correct. expected_output_shape = (batch_size, num_predictions, vocab_size) self.assertEqual(expected_output_shape, ref_outputs.shape) self.assertEqual(expected_output_shape, outputs.shape) self.assertAllClose(ref_outputs, outputs) def test_layer_invocation(self): vocab_size = 100 sequence_length = 32 hidden_size = 64 embedding_width = 32 num_predictions = 21 test_layer = self.create_layer( vocab_size=vocab_size, hidden_size=hidden_size, embedding_width=embedding_width) # Create a model from the masked LM layer. lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size)) masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32) output = test_layer(lm_input_tensor, masked_positions) model = tf_keras.Model([lm_input_tensor, masked_positions], output) # Invoke the masked LM on some fake data to make sure there are no runtime # errors in the code. batch_size = 3 lm_input_data = 10 * np.random.random_sample( (batch_size, sequence_length, hidden_size)) masked_position_data = np.random.randint( 2, size=(batch_size, num_predictions)) _ = model.predict([lm_input_data, masked_position_data]) def test_unknown_output_type_fails(self): with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'): _ = self.create_layer( vocab_size=8, hidden_size=8, embedding_width=4, output='bad') def test_hidden_size_smaller_than_embedding_width(self): hidden_size = 8 sequence_length = 32 num_predictions = 20 with self.assertRaisesRegex( ValueError, 'hidden size 8 cannot be smaller than embedding width 16.'): test_layer = self.create_layer( vocab_size=8, hidden_size=8, embedding_width=16) lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size)) masked_positions = tf_keras.Input( shape=(num_predictions,), dtype=tf.int32) _ = test_layer(lm_input_tensor, masked_positions) if __name__ == '__main__': tf.test.main()