Internal change

PiperOrigin-RevId: 408473818

official/nlp/modeling/networks/bert_dense_encoder.py

+# Copyright 2021 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.
+
+"""Transformer-based BERT encoder network."""
+# pylint: disable=g-classes-have-attributes
+
+from typing import Any, Callable, Optional, Union
+from absl import logging
+import tensorflow as tf
+
+from official.nlp.modeling import layers
+
+
+_Initializer = Union[str, tf.keras.initializers.Initializer]
+_approx_gelu = lambda x: tf.keras.activations.gelu(x, approximate=True)
+
+
+class BertDenseEncoder(tf.keras.layers.Layer):
+  """Bi-directional Transformer-based encoder network with dense features.
+
+  This network is the same as the BertEncoder except it also concats dense
+  features with the embeddings.
+
+  Args:
+    vocab_size: The size of the token vocabulary.
+    hidden_size: The size of the transformer hidden layers.
+    num_layers: The number of transformer layers.
+    num_attention_heads: The number of attention heads for each transformer. The
+      hidden size must be divisible by the number of attention heads.
+    max_sequence_length: The maximum sequence length that this encoder can
+      consume. If None, max_sequence_length uses the value from sequence length.
+      This determines the variable shape for positional embeddings.
+    type_vocab_size: The number of types that the 'type_ids' input can take.
+    inner_dim: The output dimension of the first Dense layer in a two-layer
+      feedforward network for each transformer.
+    inner_activation: The activation for the first Dense layer in a two-layer
+      feedforward network for each transformer.
+    output_dropout: Dropout probability for the post-attention and output
+      dropout.
+    attention_dropout: The dropout rate to use for the attention layers within
+      the transformer layers.
+    initializer: The initialzer to use for all weights in this encoder.
+    output_range: The sequence output range, [0, output_range), by slicing the
+      target sequence of the last transformer layer. `None` means the entire
+      target sequence will attend to the source sequence, which yields the full
+      output.
+    embedding_width: The width of the word embeddings. If the embedding width is
+      not equal to hidden size, embedding parameters will be factorized into two
+      matrices in the shape of ['vocab_size', 'embedding_width'] and
+      ['embedding_width', 'hidden_size'] ('embedding_width' is usually much
+      smaller than 'hidden_size').
+    embedding_layer: An optional Layer instance which will be called to generate
+      embeddings for the input word IDs.
+    norm_first: Whether to normalize inputs to attention and intermediate dense
+      layers. If set False, output of attention and intermediate dense layers is
+      normalized.
+  """
+
+  def __init__(
+      self,
+      vocab_size: int,
+      hidden_size: int = 768,
+      num_layers: int = 12,
+      num_attention_heads: int = 12,
+      max_sequence_length: int = 512,
+      type_vocab_size: int = 16,
+      inner_dim: int = 3072,
+      inner_activation: Callable[..., Any] = _approx_gelu,
+      output_dropout: float = 0.1,
+      attention_dropout: float = 0.1,
+      initializer: _Initializer = tf.keras.initializers.TruncatedNormal(
+          stddev=0.02),
+      output_range: Optional[int] = None,
+      embedding_width: Optional[int] = None,
+      embedding_layer: Optional[tf.keras.layers.Layer] = None,
+      norm_first: bool = False,
+      **kwargs):
+    # Pops kwargs that are used in V1 implementation.
+    if 'dict_outputs' in kwargs:
+      kwargs.pop('dict_outputs')
+    if 'return_all_encoder_outputs' in kwargs:
+      kwargs.pop('return_all_encoder_outputs')
+    if 'intermediate_size' in kwargs:
+      inner_dim = kwargs.pop('intermediate_size')
+    if 'activation' in kwargs:
+      inner_activation = kwargs.pop('activation')
+    if 'dropout_rate' in kwargs:
+      output_dropout = kwargs.pop('dropout_rate')
+    if 'attention_dropout_rate' in kwargs:
+      attention_dropout = kwargs.pop('attention_dropout_rate')
+    super().__init__(**kwargs)
+
+    activation = tf.keras.activations.get(inner_activation)
+    initializer = tf.keras.initializers.get(initializer)
+
+    if embedding_width is None:
+      embedding_width = hidden_size
+
+    if embedding_layer is None:
+      self._embedding_layer = layers.OnDeviceEmbedding(
+          vocab_size=vocab_size,
+          embedding_width=embedding_width,
+          initializer=initializer,
+          name='word_embeddings')
+    else:
+      self._embedding_layer = embedding_layer
+
+    self._position_embedding_layer = layers.PositionEmbedding(
+        initializer=initializer,
+        max_length=max_sequence_length,
+        name='position_embedding')
+
+    self._type_embedding_layer = layers.OnDeviceEmbedding(
+        vocab_size=type_vocab_size,
+        embedding_width=embedding_width,
+        initializer=initializer,
+        use_one_hot=True,
+        name='type_embeddings')
+
+    self._embedding_norm_layer = tf.keras.layers.LayerNormalization(
+        name='embeddings/layer_norm', axis=-1, epsilon=1e-12, dtype=tf.float32)
+
+    self._embedding_dropout = tf.keras.layers.Dropout(
+        rate=output_dropout, name='embedding_dropout')
+
+    # We project the 'embedding' output to 'hidden_size' if it is not already
+    # 'hidden_size'.
+    self._embedding_projection = None
+    if embedding_width != hidden_size:
+      self._embedding_projection = tf.keras.layers.experimental.EinsumDense(
+          '...x,xy->...y',
+          output_shape=hidden_size,
+          bias_axes='y',
+          kernel_initializer=initializer,
+          name='embedding_projection')
+
+    self._transformer_layers = []
+    self._attention_mask_layer = layers.SelfAttentionMask(
+        name='self_attention_mask')
+    for i in range(num_layers):
+      layer = layers.TransformerEncoderBlock(
+          num_attention_heads=num_attention_heads,
+          inner_dim=inner_dim,
+          inner_activation=inner_activation,
+          output_dropout=output_dropout,
+          attention_dropout=attention_dropout,
+          norm_first=norm_first,
+          output_range=output_range if i == num_layers - 1 else None,
+          kernel_initializer=initializer,
+          name='transformer/layer_%d' % i)
+      self._transformer_layers.append(layer)
+
+    self._pooler_layer = tf.keras.layers.Dense(
+        units=hidden_size,
+        activation='tanh',
+        kernel_initializer=initializer,
+        name='pooler_transform')
+
+    self._config = {
+        'vocab_size': vocab_size,
+        'hidden_size': hidden_size,
+        'num_layers': num_layers,
+        'num_attention_heads': num_attention_heads,
+        'max_sequence_length': max_sequence_length,
+        'type_vocab_size': type_vocab_size,
+        'inner_dim': inner_dim,
+        'inner_activation': tf.keras.activations.serialize(activation),
+        'output_dropout': output_dropout,
+        'attention_dropout': attention_dropout,
+        'initializer': tf.keras.initializers.serialize(initializer),
+        'output_range': output_range,
+        'embedding_width': embedding_width,
+        'embedding_layer': embedding_layer,
+        'norm_first': norm_first,
+    }
+    self.inputs = dict(
+        input_word_ids=tf.keras.Input(shape=(None,), dtype=tf.int32),
+        input_mask=tf.keras.Input(shape=(None,), dtype=tf.int32),
+        input_type_ids=tf.keras.Input(shape=(None,), dtype=tf.int32),
+        dense_inputs=tf.keras.Input(
+            shape=(None, embedding_width), dtype=tf.float32),
+        dense_mask=tf.keras.Input(shape=(None,), dtype=tf.int32),
+        dense_type_ids=tf.keras.Input(shape=(None,), dtype=tf.int32),
+    )
+
+  def call(self, inputs):
+    word_embeddings = None
+    if isinstance(inputs, dict):
+      word_ids = inputs.get('input_word_ids')
+      mask = inputs.get('input_mask')
+      type_ids = inputs.get('input_type_ids')
+      word_embeddings = inputs.get('input_word_embeddings', None)
+      dense_inputs = inputs.get('dense_inputs')
+      dense_mask = inputs.get('dense_mask')
+      dense_type_ids = inputs.get('dense_type_ids')
+    else:
+      raise ValueError('Unexpected inputs type to %s.' % self.__class__)
+
+    if word_embeddings is None:
+      word_embeddings = self._embedding_layer(word_ids)
+
+    # Concat the dense embeddings at sequence end.
+    combined_embeddings = tf.concat([word_embeddings, dense_inputs], axis=1)
+    combined_type_ids = tf.concat([type_ids, dense_type_ids], axis=1)
+    combined_mask = tf.concat([mask, dense_mask], axis=1)
+
+    # absolute position embeddings.
+    position_embeddings = self._position_embedding_layer(combined_embeddings)
+    type_embeddings = self._type_embedding_layer(combined_type_ids)
+
+    embeddings = combined_embeddings + position_embeddings + type_embeddings
+    embeddings = self._embedding_norm_layer(embeddings)
+    embeddings = self._embedding_dropout(embeddings)
+
+    if self._embedding_projection is not None:
+      embeddings = self._embedding_projection(embeddings)
+
+    attention_mask = self._attention_mask_layer(embeddings, combined_mask)
+
+    encoder_outputs = []
+    x = embeddings
+    for layer in self._transformer_layers:
+      x = layer([x, attention_mask])
+      encoder_outputs.append(x)
+
+    last_encoder_output = encoder_outputs[-1]
+    first_token_tensor = last_encoder_output[:, 0, :]
+    pooled_output = self._pooler_layer(first_token_tensor)
+
+    return dict(
+        sequence_output=encoder_outputs[-1],
+        pooled_output=pooled_output,
+        encoder_outputs=encoder_outputs)
+
+  def get_embedding_table(self):
+    return self._embedding_layer.embeddings
+
+  def get_embedding_layer(self):
+    return self._embedding_layer
+
+  def get_config(self):
+    return dict(self._config)
+
+  @property
+  def transformer_layers(self):
+    """List of Transformer layers in the encoder."""
+    return self._transformer_layers
+
+  @property
+  def pooler_layer(self):
+    """The pooler dense layer after the transformer layers."""
+    return self._pooler_layer
+
+  @classmethod
+  def from_config(cls, config, custom_objects=None):
+    if 'embedding_layer' in config and config['embedding_layer'] is not None:
+      warn_string = (
+          'You are reloading a model that was saved with a '
+          'potentially-shared embedding layer object. If you contine to '
+          'train this model, the embedding layer will no longer be shared. '
+          'To work around this, load the model outside of the Keras API.')
+      print('WARNING: ' + warn_string)
+      logging.warn(warn_string)
+
+    return cls(**config)

official/nlp/modeling/networks/bert_dense_encoder_test.py

+# Copyright 2021 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 transformer-based bert encoder network."""
+
+# Import libraries
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import
+from official.nlp.modeling.networks import bert_dense_encoder
+
+
+# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
+# guarantees forward compatibility of this code for the V2 switchover.
+@keras_parameterized.run_all_keras_modes
+class BertDenseEncoderTest(keras_parameterized.TestCase):
+
+  def tearDown(self):
+    super(BertDenseEncoderTest, self).tearDown()
+    tf.keras.mixed_precision.set_global_policy("float32")
+
+  def test_dict_outputs_network_creation(self):
+    hidden_size = 32
+    sequence_length = 21
+    dense_sequence_length = 20
+    # Create a small dense BertDenseEncoder for testing.
+    kwargs = {}
+    test_network = bert_dense_encoder.BertDenseEncoder(
+        vocab_size=100,
+        hidden_size=hidden_size,
+        num_attention_heads=2,
+        num_layers=3,
+        **kwargs)
+    # 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)
+
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, hidden_size), dtype=tf.float32)
+    dense_mask = tf.keras.Input(shape=(dense_sequence_length,), dtype=tf.int32)
+    dense_type_ids = tf.keras.Input(
+        shape=(dense_sequence_length,), dtype=tf.int32)
+
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_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 + dense_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_dict_outputs_all_encoder_outputs_network_creation(self):
+    hidden_size = 32
+    sequence_length = 21
+    dense_sequence_length = 20
+    # Create a small BertEncoder for testing.
+    test_network = bert_dense_encoder.BertDenseEncoder(
+        vocab_size=100,
+        hidden_size=hidden_size,
+        num_attention_heads=2,
+        num_layers=3,
+        dict_outputs=True)
+    # 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)
+
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, hidden_size), dtype=tf.float32)
+    dense_mask = tf.keras.Input(shape=(dense_sequence_length,), dtype=tf.int32)
+    dense_type_ids = tf.keras.Input(
+        shape=(dense_sequence_length,), dtype=tf.int32)
+
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+
+    all_encoder_outputs = dict_outputs["encoder_outputs"]
+    pooled = dict_outputs["pooled_output"]
+
+    expected_data_shape = [
+        None, sequence_length + dense_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)
+
+  def test_dict_outputs_network_creation_with_float16_dtype(self):
+    hidden_size = 32
+    sequence_length = 21
+    dense_sequence_length = 20
+    tf.keras.mixed_precision.set_global_policy("mixed_float16")
+    # Create a small BertEncoder for testing.
+    test_network = bert_dense_encoder.BertDenseEncoder(
+        vocab_size=100,
+        hidden_size=hidden_size,
+        num_attention_heads=2,
+        num_layers=3,
+        dict_outputs=True)
+    # 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)
+
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, hidden_size), dtype=tf.float32)
+    dense_mask = tf.keras.Input(shape=(dense_sequence_length,), dtype=tf.int32)
+    dense_type_ids = tf.keras.Input(
+        shape=(dense_sequence_length,), dtype=tf.int32)
+
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+
+    data = dict_outputs["sequence_output"]
+    pooled = dict_outputs["pooled_output"]
+
+    expected_data_shape = [
+        None, sequence_length + dense_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())
+
+    # If float_dtype is set to float16, the data output is float32 (from a layer
+    # norm) and pool output should be float16.
+    self.assertAllEqual(tf.float32, data.dtype)
+    self.assertAllEqual(tf.float16, pooled.dtype)
+
+  @parameterized.named_parameters(
+      ("all_sequence_encoder_v2", bert_dense_encoder.BertDenseEncoder, None,
+       41),
+      ("output_range_encoder_v2", bert_dense_encoder.BertDenseEncoder, 1, 1),
+  )
+  def test_dict_outputs_network_invocation(
+      self, encoder_cls, output_range, out_seq_len):
+    hidden_size = 32
+    sequence_length = 21
+    dense_sequence_length = 20
+    vocab_size = 57
+    num_types = 7
+    # Create a small BertEncoder for testing.
+    test_network = encoder_cls(
+        vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        num_attention_heads=2,
+        num_layers=3,
+        type_vocab_size=num_types,
+        output_range=output_range,
+        dict_outputs=True)
+    # 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)
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, hidden_size), dtype=tf.float32)
+    dense_mask = tf.keras.Input(shape=(dense_sequence_length,), dtype=tf.int32)
+    dense_type_ids = tf.keras.Input(
+        shape=(dense_sequence_length,), dtype=tf.int32)
+
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+    data = dict_outputs["sequence_output"]
+    pooled = dict_outputs["pooled_output"]
+
+    # Create a model based off of this network:
+    model = tf.keras.Model(
+        [word_ids, mask, type_ids, dense_inputs, dense_mask, dense_type_ids],
+        [data, pooled])
+
+    # Invoke the model. We can't validate the output data here (the model is too
+    # complex) but this will catch structural runtime errors.
+    batch_size = 3
+    word_id_data = np.random.randint(
+        vocab_size, size=(batch_size, sequence_length))
+    mask_data = np.random.randint(2, size=(batch_size, sequence_length))
+    type_id_data = np.random.randint(
+        num_types, size=(batch_size, sequence_length))
+
+    dense_input_data = np.random.rand(batch_size, dense_sequence_length,
+                                      hidden_size)
+    dense_mask_data = np.random.randint(
+        2, size=(batch_size, dense_sequence_length))
+    dense_type_ids_data = np.random.randint(
+        num_types, size=(batch_size, dense_sequence_length))
+
+    outputs = model.predict([
+        word_id_data, mask_data, type_id_data, dense_input_data,
+        dense_mask_data, dense_type_ids_data
+    ])
+    self.assertEqual(outputs[0].shape[1], out_seq_len)
+
+    # Creates a BertEncoder with max_sequence_length != sequence_length
+    max_sequence_length = 128
+    test_network = encoder_cls(
+        vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        max_sequence_length=max_sequence_length,
+        num_attention_heads=2,
+        num_layers=3,
+        type_vocab_size=num_types,
+        dict_outputs=True)
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+    data = dict_outputs["sequence_output"]
+    pooled = dict_outputs["pooled_output"]
+    model = tf.keras.Model(
+        [word_ids, mask, type_ids, dense_inputs, dense_mask, dense_type_ids],
+        [data, pooled])
+    outputs = model.predict([
+        word_id_data, mask_data, type_id_data, dense_input_data,
+        dense_mask_data, dense_type_ids_data
+    ])
+    self.assertEqual(outputs[0].shape[1],
+                     sequence_length + dense_sequence_length)
+
+    # Creates a BertEncoder with embedding_width != hidden_size
+    embedding_width = 16
+    test_network = bert_dense_encoder.BertDenseEncoder(
+        vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        max_sequence_length=max_sequence_length,
+        num_attention_heads=2,
+        num_layers=3,
+        type_vocab_size=num_types,
+        embedding_width=embedding_width,
+        dict_outputs=True)
+
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, embedding_width), dtype=tf.float32)
+    dense_input_data = np.zeros(
+        (batch_size, dense_sequence_length, embedding_width), dtype=float)
+
+    dict_outputs = test_network(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+    data = dict_outputs["sequence_output"]
+    pooled = dict_outputs["pooled_output"]
+    model = tf.keras.Model(
+        [word_ids, mask, type_ids, dense_inputs, dense_mask, dense_type_ids],
+        [data, pooled])
+    outputs = model.predict([
+        word_id_data, mask_data, type_id_data, dense_input_data,
+        dense_mask_data, dense_type_ids_data
+    ])
+    self.assertEqual(outputs[0].shape[-1], hidden_size)
+    self.assertTrue(hasattr(test_network, "_embedding_projection"))
+
+  def test_embeddings_as_inputs(self):
+    hidden_size = 32
+    sequence_length = 21
+    dense_sequence_length = 20
+    # Create a small BertEncoder for testing.
+    test_network = bert_dense_encoder.BertDenseEncoder(
+        vocab_size=100,
+        hidden_size=hidden_size,
+        num_attention_heads=2,
+        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)
+
+    dense_inputs = tf.keras.Input(
+        shape=(dense_sequence_length, hidden_size), dtype=tf.float32)
+    dense_mask = tf.keras.Input(shape=(dense_sequence_length,), dtype=tf.int32)
+    dense_type_ids = tf.keras.Input(
+        shape=(dense_sequence_length,), dtype=tf.int32)
+
+    test_network.build(
+        dict(
+            input_word_ids=word_ids,
+            input_mask=mask,
+            input_type_ids=type_ids,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_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,
+            dense_inputs=dense_inputs,
+            dense_mask=dense_mask,
+            dense_type_ids=dense_type_ids))
+
+    all_encoder_outputs = dict_outputs["encoder_outputs"]
+    pooled = dict_outputs["pooled_output"]
+
+    expected_data_shape = [
+        None, sequence_length + dense_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()