code: # 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.
  
  """ALBERT (https://arxiv.org/abs/1810.04805) text encoder network."""
  # pylint: disable=g-classes-have-attributes
  import collections
  import tensorflow as tf
  
  from official.modeling import activations
  from official.nlp.modeling import layers
  
  
  @tf.keras.utils.register_keras_serializable(package='Text')
  class AlbertEncoder(tf.keras.Model):
    """ALBERT (https://arxiv.org/abs/1810.04805) text encoder network.
  
    This network implements the encoder described in the paper "ALBERT: A Lite
    BERT for 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.
  
    The default values for this object are taken from the ALBERT-Base
    implementation described in the paper.
  
    *Note* that the network is constructed by Keras Functional API.
  
    Args:
      vocab_size: The size of the token vocabulary.
      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)`, where `embedding_width` is usually much
        smaller than `hidden_size`.
      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.
      intermediate_size: The intermediate size for the transformer layers.
      activation: The activation to use for the transformer layers.
      dropout_rate: The dropout rate to use for the transformer layers.
      attention_dropout_rate: The dropout rate to use for the attention layers
        within the transformer layers.
      initializer: The initialzer to use for all weights in this encoder.
      dict_outputs: Whether to use a dictionary as the model outputs.
    """
  
    def __init__(self,
                 vocab_size,
                 embedding_width=128,
                 hidden_size=768,
                 num_layers=12,
                 num_attention_heads=12,
                 max_sequence_length=512,
                 type_vocab_size=16,
                 intermediate_size=3072,
                 activation=activations.gelu,
                 dropout_rate=0.1,
                 attention_dropout_rate=0.1,
                 initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
                 dict_outputs=False,
                 **kwargs):
      activation = tf.keras.activations.get(activation)
      initializer = tf.keras.initializers.get(initializer)
  
      word_ids = tf.keras.layers.Input(
          shape=(None,), dtype=tf.int32, name='input_word_ids')
      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_type_ids')
  
      if embedding_width is None:
        embedding_width = hidden_size
      embedding_layer = layers.OnDeviceEmbedding(
          vocab_size=vocab_size,
          embedding_width=embedding_width,
          initializer=initializer,
          name='word_embeddings')
      word_embeddings = embedding_layer(word_ids)
  
      # Always uses dynamic slicing for simplicity.
      position_embedding_layer = layers.PositionEmbedding(
          initializer=initializer,
          max_length=max_sequence_length,
          name='position_embedding')
      position_embeddings = position_embedding_layer(word_embeddings)
  
      type_embeddings = (
          layers.OnDeviceEmbedding(
              vocab_size=type_vocab_size,
              embedding_width=embedding_width,
              initializer=initializer,
              use_one_hot=True,
              name='type_embeddings')(type_ids))
  
      embeddings = tf.keras.layers.Add()(
          [word_embeddings, position_embeddings, type_embeddings])
      embeddings = (
          tf.keras.layers.LayerNormalization(
              name='embeddings/layer_norm',
              axis=-1,
              epsilon=1e-12,
              dtype=tf.float32)(embeddings))
      embeddings = (tf.keras.layers.Dropout(rate=dropout_rate)(embeddings))
      # We project the 'embedding' output to 'hidden_size' if it is not already
      # 'hidden_size'.
      if embedding_width != hidden_size:
        embeddings = tf.keras.layers.experimental.EinsumDense(
            '...x,xy->...y',
            output_shape=hidden_size,
            bias_axes='y',
            kernel_initializer=initializer,
            name='embedding_projection')(
                embeddings)
  
      data = embeddings
      attention_mask = layers.SelfAttentionMask()(data, mask)
      shared_layer = layers.TransformerEncoderBlock(
          num_attention_heads=num_attention_heads,
          inner_dim=intermediate_size,
          inner_activation=activation,
          output_dropout=dropout_rate,
          attention_dropout=attention_dropout_rate,
          kernel_initializer=initializer,
          name='transformer')
      encoder_outputs = []
      for _ in range(num_layers):
        data = shared_layer([data, attention_mask])
        encoder_outputs.append(data)
  
      # Applying a tf.slice op (through subscript notation) to a Keras tensor
      # like this will create a SliceOpLambda layer. This is better than a Lambda
      # layer with Python code, because that is fundamentally less portable.
      first_token_tensor = data[:, 0, :]
      cls_output = tf.keras.layers.Dense(
          units=hidden_size,
          activation='tanh',
          kernel_initializer=initializer,
          name='pooler_transform')(
              first_token_tensor)
      if dict_outputs:
        outputs = dict(
            sequence_output=data,
            encoder_outputs=encoder_outputs,
            pooled_output=cls_output,
        )
      else:
        outputs = [data, cls_output]
  
      # b/164516224
      # Once we've created the network using the Functional API, we call
      # super().__init__ as though we were invoking the Functional API Model
      # constructor, resulting in this object having all the properties of a model
      # created using the Functional API. Once super().__init__ is called, we
      # can assign attributes to `self` - note that all `self` assignments are
      # below this line.
      super(AlbertEncoder, self).__init__(
          inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs)
      config_dict = {
          'vocab_size': vocab_size,
          'embedding_width': embedding_width,
          '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,
          'intermediate_size': intermediate_size,
          'activation': tf.keras.activations.serialize(activation),
          'dropout_rate': dropout_rate,
          'attention_dropout_rate': attention_dropout_rate,
          'initializer': tf.keras.initializers.serialize(initializer),
      }
  
      # We are storing the config dict as a namedtuple here to ensure checkpoint
      # compatibility with an earlier version of this model which did not track
      # the config dict attribute. TF does not track immutable attrs which
      # do not contain Trackables, so by creating a config namedtuple instead of
      # a dict we avoid tracking it.
      config_cls = collections.namedtuple('Config', config_dict.keys())
      self._config = config_cls(**config_dict)
      self._embedding_layer = embedding_layer
      self._position_embedding_layer = position_embedding_layer
  
    def get_embedding_table(self):
      return self._embedding_layer.embeddings
  
    def get_config(self):
      return dict(self._config._asdict())
  
    @classmethod
    def from_config(cls, config):
      return cls(**config)
  
apis: tensorflow.keras.layers.LayerNormalization tensorflow.keras.activations.serialize tensorflow.keras.initializers.get tensorflow.keras.layers.Dropout tensorflow.keras.layers.experimental.EinsumDense tensorflow.keras.initializers.TruncatedNormal tensorflow.keras.utils.register_keras_serializable tensorflow.keras.layers.Add tensorflow.keras.layers.Dense tensorflow.keras.initializers.serialize tensorflow.keras.activations.get tensorflow.keras.layers.Input