Move mobilebert embedding and transformer layers to modeling/layers

PiperOrigin-RevId: 341913299

official/nlp/modeling/layers/README.md

@@ -84,3 +84,8 @@ assemble new layers, networks, or models.
     block containing either one or two stream relative self-attention as well as
     subsequent feedforward networks. It also contains `TransformerXL`, which
     contains attention biases as well as multiple `TransformerXLBlocks`.
+
+*   [MobileBertEmbedding](mobile_bert_layers.py) and
+    [MobileBertTransformer](mobile_bert_layers.py) implement the embedding layer
+    and also transformer layer proposed in the
+    [MobileBERT paper](https://arxiv.org/pdf/2004.02984.pdf).

official/nlp/modeling/layers/__init__.py

@@ -21,6 +21,8 @@ from official.nlp.modeling.layers.gated_feedforward import GatedFeedforward
 from official.nlp.modeling.layers.masked_lm import MaskedLM
 from official.nlp.modeling.layers.masked_softmax import MaskedSoftmax
 from official.nlp.modeling.layers.mat_mul_with_margin import MatMulWithMargin
+from official.nlp.modeling.layers.mobile_bert_layers import MobileBertEmbedding
+from official.nlp.modeling.layers.mobile_bert_layers import MobileBertTransformer
 from official.nlp.modeling.layers.multi_channel_attention import *
 from official.nlp.modeling.layers.on_device_embedding import OnDeviceEmbedding
 from official.nlp.modeling.layers.position_embedding import RelativePositionEmbedding

official/nlp/modeling/layers/mobile_bert_layers.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 embedding and transformer layers."""
+import tensorflow as tf
+
+from official.nlp import keras_nlp
+
+
+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 = keras_nlp.layers.OnDeviceEmbedding(
+        self.word_vocab_size,
+        self.word_embed_size,
+        initializer=initializer,
+        name='word_embedding')
+    self.type_embedding = keras_nlp.layers.OnDeviceEmbedding(
+        self.type_vocab_size,
+        self.output_embed_size,
+        use_one_hot=True,
+        initializer=initializer,
+        name='type_embedding')
+    self.pos_embedding = keras_nlp.layers.PositionEmbedding(
+        max_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):
+    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)
+
+    return embedding_out
+
+
+class MobileBertTransformer(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(MobileBertTransformer, 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,
+           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.
+      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,
+    )
+    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)
+    layer_output = layer_norm(layer_output + prev_output)
+
+    if return_attention_scores:
+      return layer_output, attention_scores
+    else:
+      return layer_output

official/nlp/modeling/layers/mobile_bert_layers_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.layers import mobile_bert_layers
+
+
+def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
+  """Generate consisitant 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_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)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/nlp/modeling/networks/mobile_bert_encoder.py

@@ -16,370 +16,9 @@
 import gin
 import tensorflow as tf
 
-from official.nlp import keras_nlp
 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 = keras_nlp.layers.OnDeviceEmbedding(
-        self.word_vocab_size,
-        self.word_embed_size,
-        initializer=initializer,
-        name='word_embedding')
-    self.type_embedding = keras_nlp.layers.OnDeviceEmbedding(
-        self.type_vocab_size,
-        self.output_embed_size,
-        use_one_hot=True,
-        initializer=initializer,
-        name='type_embedding')
-    self.pos_embedding = keras_nlp.layers.PositionEmbedding(
-        max_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):
-    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)
-
-    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,
-           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.
-      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,
-    )
-    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)
-    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."""
@@ -439,7 +78,7 @@ class MobileBERTEncoder(tf.keras.Model):
         stddev=initializer_range)
 
     # layer instantiation
-    self.embedding_layer = MobileBertEmbedding(
+    self.embedding_layer = layers.MobileBertEmbedding(
         word_vocab_size=word_vocab_size,
         word_embed_size=word_embed_size,
         type_vocab_size=type_vocab_size,
@@ -451,7 +90,7 @@ class MobileBERTEncoder(tf.keras.Model):
 
     self._transformer_layers = []
     for layer_idx in range(num_blocks):
-      transformer = TransformerLayer(
+      transformer = layers.MobileBertTransformer(
           hidden_size=hidden_size,
           num_attention_heads=num_attention_heads,
           intermediate_size=intermediate_size,

official/nlp/modeling/networks/mobile_bert_encoder_test.py

@@ -34,64 +34,6 @@ def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
 
 class MobileBertEncoderTest(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),