Internal change

PiperOrigin-RevId: 327919609

official/nlp/configs/encoders.py

@@ -28,7 +28,6 @@ from official.modeling import hyperparams
 from official.modeling import tf_utils
 from official.nlp.modeling import layers
 from official.nlp.modeling import networks
-from official.nlp.projects.mobilebert import modeling
 
 
 @dataclasses.dataclass
@@ -113,7 +112,7 @@ class EncoderConfig(hyperparams.OneOfConfig):
 
 ENCODER_CLS = {
     "bert": networks.TransformerEncoder,
-    "mobilebert": modeling.MobileBERTEncoder,
+    "mobilebert": networks.MobileBERTEncoder,
 }
 
 

official/nlp/modeling/networks/README.md

@@ -12,10 +12,14 @@ transformer layers and pooling layer.
 
 * [`AlbertTransformerEncoder`](albert_transformer_encoder.py) implements a
 Transformer-encoder described in the paper ["ALBERT: A Lite BERT for
-Self-supervised Learning of Language Representations]
+Self-supervised Learning of Language Representations"]
 (https://arxiv.org/abs/1909.11942). Compared with [BERT](https://arxiv.org/abs/1810.04805), ALBERT refactorizes embedding parameters
 into two smaller matrices and shares parameters across layers.
 
+* [`MobileBERTEncoder`](mobile_bert_encoder.py) implements the
+MobileBERT network described in the paper ["MobileBERT: a Compact Task-Agnostic
+BERT for Resource-Limited Devices"](https://arxiv.org/abs/2004.02984).
+
 * [`Classification`](classification.py) contains a single hidden layer, and is
 intended for use as a classification or regression (if number of classes is set
 to 1) head.

official/nlp/modeling/networks/__init__.py

@@ -16,5 +16,6 @@
 from official.nlp.modeling.networks.albert_transformer_encoder import AlbertTransformerEncoder
 from official.nlp.modeling.networks.classification import Classification
 from official.nlp.modeling.networks.encoder_scaffold import EncoderScaffold
+from official.nlp.modeling.networks.mobile_bert_encoder import MobileBERTEncoder
 from official.nlp.modeling.networks.span_labeling import SpanLabeling
 from official.nlp.modeling.networks.transformer_encoder import TransformerEncoder

official/nlp/modeling/networks/mobile_bert_encoder.py

+# Copyright 2020 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.
+# ==============================================================================
+"""MobileBERT text encoder network."""
+import gin
+import tensorflow as tf
+
+from official.nlp.modeling import layers
+
+
+class NoNorm(tf.keras.layers.Layer):
+  """Apply element-wise linear transformation to the last dimension."""
+
+  def __init__(self, name=None):
+    super(NoNorm, self).__init__(name=name)
+
+  def build(self, shape):
+    kernal_size = shape[-1]
+    self.bias = self.add_weight('beta',
+                                shape=[kernal_size],
+                                initializer='zeros')
+    self.scale = self.add_weight('gamma',
+                                 shape=[kernal_size],
+                                 initializer='ones')
+
+  def call(self, feature):
+    output = feature * self.scale + self.bias
+    return output
+
+
+def _get_norm_layer(normalization_type='no_norm', name=None):
+  """Get normlization layer.
+
+  Arguments:
+      normalization_type: String. The type of normalization_type, only
+        'no_norm' and 'layer_norm' are supported.
+      name: Name for the norm layer.
+
+  Returns:
+    layer norm class.
+  """
+  if normalization_type == 'no_norm':
+    layer = NoNorm(name=name)
+  elif normalization_type == 'layer_norm':
+    layer = tf.keras.layers.LayerNormalization(
+        name=name,
+        axis=-1,
+        epsilon=1e-12,
+        dtype=tf.float32)
+  else:
+    raise NotImplementedError('Only "no_norm" and "layer_norm" and supported.')
+  return layer
+
+
+class MobileBertEmbedding(tf.keras.layers.Layer):
+  """Performs an embedding lookup for MobileBERT.
+
+  This layer includes word embedding, token type embedding, position embedding.
+  """
+
+  def __init__(self,
+               word_vocab_size,
+               word_embed_size,
+               type_vocab_size,
+               output_embed_size,
+               max_sequence_length=512,
+               normalization_type='no_norm',
+               initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
+               dropout_rate=0.1):
+    """Class initialization.
+
+    Arguments:
+      word_vocab_size: Number of words in the vocabulary.
+      word_embed_size: Word embedding size.
+      type_vocab_size: Number of word types.
+      output_embed_size: Embedding size for the final embedding output.
+      max_sequence_length: Maximum length of input sequence.
+      normalization_type: String. The type of normalization_type, only
+        'no_norm' and 'layer_norm' are supported.
+      initializer: The initializer to use for the embedding weights and
+        linear projection weights.
+      dropout_rate: Dropout rate.
+    """
+    super(MobileBertEmbedding, self).__init__()
+    self.word_vocab_size = word_vocab_size
+    self.word_embed_size = word_embed_size
+    self.type_vocab_size = type_vocab_size
+    self.output_embed_size = output_embed_size
+    self.max_sequence_length = max_sequence_length
+    self.dropout_rate = dropout_rate
+
+    self.word_embedding = layers.OnDeviceEmbedding(
+        self.word_vocab_size,
+        self.word_embed_size,
+        initializer=initializer,
+        name='word_embedding')
+    self.type_embedding = layers.OnDeviceEmbedding(
+        self.type_vocab_size,
+        self.output_embed_size,
+        use_one_hot=True,
+        initializer=initializer,
+        name='type_embedding')
+    self.pos_embedding = layers.PositionEmbedding(
+        use_dynamic_slicing=True,
+        max_sequence_length=max_sequence_length,
+        initializer=initializer,
+        name='position_embedding')
+    self.word_embedding_proj = tf.keras.layers.experimental.EinsumDense(
+        'abc,cd->abd',
+        output_shape=[None, self.output_embed_size],
+        kernel_initializer=initializer,
+        bias_axes='d',
+        name='embedding_projection')
+    self.layer_norm = _get_norm_layer(normalization_type, 'embedding_norm')
+    self.dropout_layer = tf.keras.layers.Dropout(
+        self.dropout_rate,
+        name='embedding_dropout')
+
+  def call(self, input_ids, token_type_ids=None, training=False):
+    word_embedding_out = self.word_embedding(input_ids)
+    word_embedding_out = tf.concat(
+        [tf.pad(word_embedding_out[:, 1:], ((0, 0), (0, 1), (0, 0))),
+         word_embedding_out,
+         tf.pad(word_embedding_out[:, :-1], ((0, 0), (1, 0), (0, 0)))],
+        axis=2)
+    word_embedding_out = self.word_embedding_proj(word_embedding_out)
+
+    pos_embedding_out = self.pos_embedding(word_embedding_out)
+    embedding_out = word_embedding_out + pos_embedding_out
+    if token_type_ids is not None:
+      type_embedding_out = self.type_embedding(token_type_ids)
+      embedding_out += type_embedding_out
+    embedding_out = self.layer_norm(embedding_out)
+    embedding_out = self.dropout_layer(embedding_out, training=training)
+
+    return embedding_out
+
+
+class TransformerLayer(tf.keras.layers.Layer):
+  """Transformer block for MobileBERT.
+
+  An implementation of one layer (block) of Transformer with bottleneck and
+  inverted-bottleneck for MobilerBERT.
+
+  Original paper for MobileBERT:
+  https://arxiv.org/pdf/2004.02984.pdf
+  """
+
+  def __init__(self,
+               hidden_size=512,
+               num_attention_heads=4,
+               intermediate_size=512,
+               intermediate_act_fn='relu',
+               hidden_dropout_prob=0.1,
+               attention_probs_dropout_prob=0.1,
+               intra_bottleneck_size=128,
+               key_query_shared_bottleneck=True,
+               num_feedforward_networks=4,
+               normalization_type='no_norm',
+               initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
+               name=None):
+    """Class initialization.
+
+    Arguments:
+      hidden_size: Hidden size for the Transformer input and output tensor.
+      num_attention_heads: Number of attention heads in the Transformer.
+      intermediate_size: The size of the "intermediate" (a.k.a., feed
+        forward) layer.
+      intermediate_act_fn: The non-linear activation function to apply
+        to the output of the intermediate/feed-forward layer.
+      hidden_dropout_prob: Dropout probability for the hidden layers.
+      attention_probs_dropout_prob: Dropout probability of the attention
+        probabilities.
+      intra_bottleneck_size: Size of bottleneck.
+      key_query_shared_bottleneck: Whether to share linear transformation for
+        keys and queries.
+      num_feedforward_networks: Number of stacked feed-forward networks.
+      normalization_type: The type of normalization_type, only 'no_norm' and
+        'layer_norm' are supported. 'no_norm' represents the element-wise
+        linear transformation for the student model, as suggested by the
+        original MobileBERT paper. 'layer_norm' is used for the teacher model.
+      initializer: The initializer to use for the embedding weights and
+        linear projection weights.
+      name: A string represents the layer name.
+
+    Raises:
+      ValueError: A Tensor shape or parameter is invalid.
+    """
+    super(TransformerLayer, self).__init__(name=name)
+    self.hidden_size = hidden_size
+    self.num_attention_heads = num_attention_heads
+    self.intermediate_size = intermediate_size
+    self.intermediate_act_fn = intermediate_act_fn
+    self.hidden_dropout_prob = hidden_dropout_prob
+    self.attention_probs_dropout_prob = attention_probs_dropout_prob
+    self.intra_bottleneck_size = intra_bottleneck_size
+    self.key_query_shared_bottleneck = key_query_shared_bottleneck
+    self.num_feedforward_networks = num_feedforward_networks
+    self.normalization_type = normalization_type
+
+    if intra_bottleneck_size % num_attention_heads != 0:
+      raise ValueError(
+          (f'The bottleneck size {intra_bottleneck_size} is not a multiple '
+           f'of the number of attention heads {num_attention_heads}.'))
+    attention_head_size = int(intra_bottleneck_size / num_attention_heads)
+
+    self.block_layers = {}
+    # add input bottleneck
+    dense_layer_2d = tf.keras.layers.experimental.EinsumDense(
+        'abc,cd->abd',
+        output_shape=[None, self.intra_bottleneck_size],
+        bias_axes='d',
+        kernel_initializer=initializer,
+        name='bottleneck_input/dense')
+    layer_norm = _get_norm_layer(self.normalization_type,
+                                 name='bottleneck_input/norm')
+    self.block_layers['bottleneck_input'] = [dense_layer_2d,
+                                             layer_norm]
+
+    if self.key_query_shared_bottleneck:
+      dense_layer_2d = tf.keras.layers.experimental.EinsumDense(
+          'abc,cd->abd',
+          output_shape=[None, self.intra_bottleneck_size],
+          bias_axes='d',
+          kernel_initializer=initializer,
+          name='kq_shared_bottleneck/dense')
+      layer_norm = _get_norm_layer(self.normalization_type,
+                                   name='kq_shared_bottleneck/norm')
+      self.block_layers['kq_shared_bottleneck'] = [dense_layer_2d,
+                                                   layer_norm]
+
+    # add attention layer
+    attention_layer = tf.keras.layers.MultiHeadAttention(
+        num_heads=self.num_attention_heads,
+        key_dim=attention_head_size,
+        value_dim=attention_head_size,
+        dropout=self.attention_probs_dropout_prob,
+        output_shape=self.intra_bottleneck_size,
+        kernel_initializer=initializer,
+        name='attention')
+    layer_norm = _get_norm_layer(self.normalization_type,
+                                 name='attention/norm')
+    self.block_layers['attention'] = [attention_layer,
+                                      layer_norm]
+
+    # add stacked feed-forward networks
+    self.block_layers['ffn'] = []
+    for ffn_layer_idx in range(self.num_feedforward_networks):
+      layer_prefix = f'ffn_layer_{ffn_layer_idx}'
+      layer_name = layer_prefix + '/intermediate_dense'
+      intermediate_layer = tf.keras.layers.experimental.EinsumDense(
+          'abc,cd->abd',
+          activation=self.intermediate_act_fn,
+          output_shape=[None, self.intermediate_size],
+          bias_axes='d',
+          kernel_initializer=initializer,
+          name=layer_name)
+      layer_name = layer_prefix + '/output_dense'
+      output_layer = tf.keras.layers.experimental.EinsumDense(
+          'abc,cd->abd',
+          output_shape=[None, self.intra_bottleneck_size],
+          bias_axes='d',
+          kernel_initializer=initializer,
+          name=layer_name)
+      layer_name = layer_prefix + '/norm'
+      layer_norm = _get_norm_layer(self.normalization_type,
+                                   name=layer_name)
+      self.block_layers['ffn'].append([intermediate_layer,
+                                       output_layer,
+                                       layer_norm])
+
+    # add output bottleneck
+    bottleneck = tf.keras.layers.experimental.EinsumDense(
+        'abc,cd->abd',
+        output_shape=[None, self.hidden_size],
+        activation=None,
+        bias_axes='d',
+        kernel_initializer=initializer,
+        name='bottleneck_output/dense')
+    dropout_layer = tf.keras.layers.Dropout(
+        self.hidden_dropout_prob,
+        name='bottleneck_output/dropout')
+    layer_norm = _get_norm_layer(self.normalization_type,
+                                 name='bottleneck_output/norm')
+    self.block_layers['bottleneck_output'] = [bottleneck,
+                                              dropout_layer,
+                                              layer_norm]
+
+  def call(self,
+           input_tensor,
+           attention_mask=None,
+           training=False,
+           return_attention_scores=False):
+    """Implementes the forward pass.
+
+    Arguments:
+      input_tensor: Float tensor of shape [batch_size, seq_length, hidden_size].
+      attention_mask: (optional) int32 tensor of shape [batch_size, seq_length,
+        seq_length], with 1 for positions that can be attended to and 0 in
+        positions that should not be.
+      training: If the model is in training mode.
+      return_attention_scores: If return attention score.
+
+    Returns:
+      layer_output: Float tensor of shape [batch_size, seq_length, hidden_size].
+      attention_scores (Optional): Only when return_attention_scores is True.
+
+    Raises:
+      ValueError: A Tensor shape or parameter is invalid.
+    """
+    input_width = input_tensor.shape.as_list()[-1]
+    if input_width != self.hidden_size:
+      raise ValueError(
+          (f'The width of the input tensor {input_width} != '
+           f'hidden size {self.hidden_size}'))
+
+    prev_output = input_tensor
+
+    # input bottleneck
+    dense_layer = self.block_layers['bottleneck_input'][0]
+    layer_norm = self.block_layers['bottleneck_input'][1]
+    layer_input = dense_layer(prev_output)
+    layer_input = layer_norm(layer_input)
+
+    if self.key_query_shared_bottleneck:
+      dense_layer = self.block_layers['kq_shared_bottleneck'][0]
+      layer_norm = self.block_layers['kq_shared_bottleneck'][1]
+      shared_attention_input = dense_layer(prev_output)
+      shared_attention_input = layer_norm(shared_attention_input)
+      key_tensor = shared_attention_input
+      query_tensor = shared_attention_input
+      value_tensor = prev_output
+    else:
+      key_tensor = layer_input
+      query_tensor = layer_input
+      value_tensor = layer_input
+
+    # attention layer
+    attention_layer = self.block_layers['attention'][0]
+    layer_norm = self.block_layers['attention'][1]
+    attention_output, attention_scores = attention_layer(
+        query_tensor,
+        value_tensor,
+        key_tensor,
+        attention_mask,
+        return_attention_scores=True,
+        training=training
+    )
+    attention_output = layer_norm(attention_output + layer_input)
+
+    # stacked feed-forward networks
+    layer_input = attention_output
+    for ffn_idx in range(self.num_feedforward_networks):
+      intermediate_layer = self.block_layers['ffn'][ffn_idx][0]
+      output_layer = self.block_layers['ffn'][ffn_idx][1]
+      layer_norm = self.block_layers['ffn'][ffn_idx][2]
+      intermediate_output = intermediate_layer(layer_input)
+      layer_output = output_layer(intermediate_output)
+      layer_output = layer_norm(layer_output + layer_input)
+      layer_input = layer_output
+
+    # output bottleneck
+    bottleneck = self.block_layers['bottleneck_output'][0]
+    dropout_layer = self.block_layers['bottleneck_output'][1]
+    layer_norm = self.block_layers['bottleneck_output'][2]
+    layer_output = bottleneck(layer_output)
+    layer_output = dropout_layer(layer_output, training=training)
+    layer_output = layer_norm(layer_output + prev_output)
+
+    if return_attention_scores:
+      return layer_output, attention_scores
+    else:
+      return layer_output
+
+
+@gin.configurable
+class MobileBERTEncoder(tf.keras.Model):
+  """A Keras functional API implementation for MobileBERT encoder."""
+
+  def __init__(self,
+               word_vocab_size=30522,
+               word_embed_size=128,
+               type_vocab_size=2,
+               max_sequence_length=512,
+               num_blocks=24,
+               hidden_size=512,
+               num_attention_heads=4,
+               intermediate_size=512,
+               intermediate_act_fn='relu',
+               hidden_dropout_prob=0.1,
+               attention_probs_dropout_prob=0.1,
+               intra_bottleneck_size=128,
+               initializer_range=0.02,
+               key_query_shared_bottleneck=True,
+               num_feedforward_networks=4,
+               normalization_type='no_norm',
+               classifier_activation=False,
+               return_all_layers=False,
+               return_attention_score=False,
+               **kwargs):
+    """Class initialization.
+
+    Arguments:
+      word_vocab_size: Number of words in the vocabulary.
+      word_embed_size: Word embedding size.
+      type_vocab_size: Number of word types.
+      max_sequence_length: Maximum length of input sequence.
+      num_blocks: Number of transformer block in the encoder model.
+      hidden_size: Hidden size for the transformer block.
+      num_attention_heads: Number of attention heads in the transformer block.
+      intermediate_size: The size of the "intermediate" (a.k.a., feed
+        forward) layer.
+      intermediate_act_fn: The non-linear activation function to apply
+        to the output of the intermediate/feed-forward layer.
+      hidden_dropout_prob: Dropout probability for the hidden layers.
+      attention_probs_dropout_prob: Dropout probability of the attention
+        probabilities.
+      intra_bottleneck_size: Size of bottleneck.
+      initializer_range: The stddev of the truncated_normal_initializer for
+        initializing all weight matrices.
+      key_query_shared_bottleneck: Whether to share linear transformation for
+        keys and queries.
+      num_feedforward_networks: Number of stacked feed-forward networks.
+      normalization_type: The type of normalization_type, only 'no_norm' and
+        'layer_norm' are supported. 'no_norm' represents the element-wise linear
+        transformation for the student model, as suggested by the original
+        MobileBERT paper. 'layer_norm' is used for the teacher model.
+      classifier_activation: If using the tanh activation for the final
+        representation of the [CLS] token in fine-tuning.
+      return_all_layers: If return all layer outputs.
+      return_attention_score: If return attention scores for each layer.
+      **kwargs: Other keyworded and arguments.
+    """
+    self._self_setattr_tracking = False
+    initializer = tf.keras.initializers.TruncatedNormal(
+        stddev=initializer_range)
+
+    # layer instantiation
+    self.embedding_layer = MobileBertEmbedding(
+        word_vocab_size=word_vocab_size,
+        word_embed_size=word_embed_size,
+        type_vocab_size=type_vocab_size,
+        output_embed_size=hidden_size,
+        max_sequence_length=max_sequence_length,
+        normalization_type=normalization_type,
+        initializer=initializer,
+        dropout_rate=hidden_dropout_prob)
+
+    self._transformer_layers = []
+    for layer_idx in range(num_blocks):
+      transformer = TransformerLayer(
+          hidden_size=hidden_size,
+          num_attention_heads=num_attention_heads,
+          intermediate_size=intermediate_size,
+          intermediate_act_fn=intermediate_act_fn,
+          hidden_dropout_prob=hidden_dropout_prob,
+          attention_probs_dropout_prob=attention_probs_dropout_prob,
+          intra_bottleneck_size=intra_bottleneck_size,
+          key_query_shared_bottleneck=key_query_shared_bottleneck,
+          num_feedforward_networks=num_feedforward_networks,
+          normalization_type=normalization_type,
+          initializer=initializer,
+          name=f'transformer_layer_{layer_idx}')
+      self._transformer_layers.append(transformer)
+
+    # input tensor
+    input_ids = tf.keras.layers.Input(
+        shape=(None,), dtype=tf.int32, name='input_word_ids')
+    input_mask = tf.keras.layers.Input(
+        shape=(None,), dtype=tf.int32, name='input_mask')
+    type_ids = tf.keras.layers.Input(
+        shape=(None,), dtype=tf.int32, name='input_token_type_ids')
+    self.inputs = [input_ids, input_mask, type_ids]
+    attention_mask = layers.SelfAttentionMask()([input_ids, input_mask])
+
+    # build the computation graph
+    all_layer_outputs = []
+    all_attention_scores = []
+    embedding_output = self.embedding_layer(input_ids, type_ids)
+    all_layer_outputs.append(embedding_output)
+    prev_output = embedding_output
+
+    for layer_idx in range(num_blocks):
+      layer_output, attention_score = self._transformer_layers[layer_idx](
+          prev_output,
+          attention_mask,
+          return_attention_scores=True)
+      all_layer_outputs.append(layer_output)
+      all_attention_scores.append(attention_score)
+      prev_output = layer_output
+    first_token = tf.squeeze(prev_output[:, 0:1, :], axis=1)
+
+    if classifier_activation:
+      self._pooler_layer = tf.keras.layers.experimental.EinsumDense(
+          'ab,bc->ac',
+          output_shape=hidden_size,
+          activation=tf.tanh,
+          bias_axes='c',
+          kernel_initializer=initializer,
+          name='pooler')
+      first_token = self._pooler_layer(first_token)
+    else:
+      self._pooler_layer = None
+
+    if return_all_layers:
+      outputs = [all_layer_outputs, first_token]
+    else:
+      outputs = [prev_output, first_token]
+    if return_attention_score:
+      outputs.append(all_attention_scores)
+
+    super(MobileBERTEncoder, self).__init__(
+        inputs=self.inputs, outputs=outputs, **kwargs)
+
+  def get_embedding_table(self):
+    return self.embedding_layer.word_embedding.embeddings
+
+  def get_embedding_layer(self):
+    return self.embedding_layer.word_embedding
+
+  @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

official/nlp/modeling/networks/mobile_bert_encoder_test.py

+# Copyright 2020 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
+from official.nlp.modeling import models
+from official.nlp.modeling.networks import mobile_bert_encoder
+from official.nlp.projects.mobilebert import utils
+
+
+class ModelingTest(parameterized.TestCase, tf.test.TestCase):
+
+  def test_embedding_layer_with_token_type(self):
+    layer = mobile_bert_encoder.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_encoder.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_no_norm(self):
+    layer = mobile_bert_encoder.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_encoder.TransformerLayer(
+        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_encoder.TransformerLayer()
+    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_encoder.TransformerLayer(
+        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)
+
+  @parameterized.named_parameters(
+      ('default_setting', 'relu', True, 'no_norm', False),
+      ('gelu', 'gelu', True, 'no_norm', False),
+      ('kq_not_shared', 'relu', False, 'no_norm', False),
+      ('layer_norm', 'relu', True, 'layer_norm', False),
+      ('use_pooler', 'relu', True, 'no_norm', True),
+      ('with_pooler_layer', 'relu', True, 'layer_norm', False))
+  def test_mobilebert_encoder(self, act_fn, kq_shared_bottleneck,
+                              normalization_type, use_pooler):
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        intermediate_act_fn=act_fn,
+        key_query_shared_bottleneck=kq_shared_bottleneck,
+        normalization_type=normalization_type,
+        classifier_activation=use_pooler)
+
+    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)
+    layer_output, pooler_output = test_network([word_ids, mask, type_ids])
+
+    self.assertIsInstance(test_network.transformer_layers, list)
+    self.assertLen(test_network.transformer_layers, num_blocks)
+
+    layer_output_shape = [None, sequence_length, hidden_size]
+    self.assertAllEqual(layer_output.shape.as_list(), layer_output_shape)
+    pooler_output_shape = [None, hidden_size]
+    self.assertAllEqual(pooler_output.shape.as_list(), pooler_output_shape)
+    self.assertAllEqual(tf.float32, layer_output.dtype)
+
+  def test_mobilebert_encoder_return_all_layer_output(self):
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        return_all_layers=True)
+
+    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)
+    all_layer_output, _ = test_network([word_ids, mask, type_ids])
+
+    self.assertIsInstance(all_layer_output, list)
+    self.assertLen(all_layer_output, num_blocks + 1)
+
+  def test_mobilebert_encoder_invocation(self):
+    vocab_size = 100
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        return_all_layers=False)
+
+    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)
+    layer_out_tensor, pooler_out_tensor = test_network([word_ids,
+                                                        mask, type_ids])
+    model = tf.keras.Model([word_ids, mask, type_ids],
+                           [layer_out_tensor, pooler_out_tensor])
+
+    input_seq = utils.generate_fake_input(batch_size=1,
+                                          seq_len=sequence_length,
+                                          vocab_size=vocab_size)
+    input_mask = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    token_type = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    layer_output, pooler_output = model.predict([input_seq, input_mask,
+                                                 token_type])
+
+    layer_output_shape = [1, sequence_length, hidden_size]
+    self.assertAllEqual(layer_output.shape, layer_output_shape)
+    pooler_output_shape = [1, hidden_size]
+    self.assertAllEqual(pooler_output.shape, pooler_output_shape)
+
+  def test_mobilebert_encoder_invocation_with_attention_score(self):
+    vocab_size = 100
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        return_all_layers=False,
+        return_attention_score=True)
+
+    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)
+    layer_out_tensor, pooler_out_tensor, attention_out_tensor = test_network(
+        [word_ids, mask, type_ids])
+    model = tf.keras.Model([word_ids, mask, type_ids],
+                           [layer_out_tensor, pooler_out_tensor,
+                            attention_out_tensor])
+
+    input_seq = utils.generate_fake_input(batch_size=1,
+                                          seq_len=sequence_length,
+                                          vocab_size=vocab_size)
+    input_mask = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    token_type = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    _, _, attention_score_output = model.predict([input_seq, input_mask,
+                                                  token_type])
+    self.assertLen(attention_score_output, num_blocks)
+
+  @parameterized.named_parameters(
+      ('sequence_classification', models.BertClassifier, [None, 5]),
+      ('token_classification', models.BertTokenClassifier, [None, 16, 5]))
+  def test_mobilebert_encoder_for_downstream_task(self, task, prediction_shape):
+    hidden_size = 32
+    sequence_length = 16
+    mobilebert_encoder = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100, hidden_size=hidden_size)
+    num_classes = 5
+    classifier = task(network=mobilebert_encoder,
+                      num_classes=num_classes)
+
+    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)
+    prediction = classifier([word_ids, mask, type_ids])
+    self.assertAllEqual(prediction.shape.as_list(), prediction_shape)
+
+if __name__ == '__main__':
+  tf.test.main()