encoders.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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Transformer Encoders.

Includes configurations and factory methods.
"""
from typing import Optional

import dataclasses
import gin
import tensorflow as tf

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.bigbird import encoder as bigbird_encoder


@dataclasses.dataclass
class BertEncoderConfig(hyperparams.Config):
  """BERT encoder configuration."""
  vocab_size: int = 30522
  hidden_size: int = 768
  num_layers: int = 12
  num_attention_heads: int = 12
  hidden_activation: str = "gelu"
  intermediate_size: int = 3072
  dropout_rate: float = 0.1
  attention_dropout_rate: float = 0.1
  max_position_embeddings: int = 512
  type_vocab_size: int = 2
  initializer_range: float = 0.02
  embedding_size: Optional[int] = None
  output_range: Optional[int] = None
  return_all_encoder_outputs: bool = False
  # Pre/Post-LN Transformer
  norm_first: bool = False


@dataclasses.dataclass
class MobileBertEncoderConfig(hyperparams.Config):
  """MobileBERT encoder configuration.

  Attributes:
    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: the 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.
    hidden_activation: 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: the size of bottleneck.
    initializer_range: The stddev of the truncated_normal_initializer for
      initializing all weight matrices.
    use_bottleneck_attention: Use attention inputs from the bottleneck
      transformation. If true, the following `key_query_shared_bottleneck`
      will be ignored.
    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.
  """
  word_vocab_size: int = 30522
  word_embed_size: int = 128
  type_vocab_size: int = 2
  max_sequence_length: int = 512
  num_blocks: int = 24
  hidden_size: int = 512
  num_attention_heads: int = 4
  intermediate_size: int = 4096
  hidden_activation: str = "gelu"
  hidden_dropout_prob: float = 0.1
  attention_probs_dropout_prob: float = 0.1
  intra_bottleneck_size: int = 1024
  initializer_range: float = 0.02
  use_bottleneck_attention: bool = False
  key_query_shared_bottleneck: bool = False
  num_feedforward_networks: int = 1
  normalization_type: str = "layer_norm"
  classifier_activation: bool = True
  input_mask_dtype: str = "int32"


@dataclasses.dataclass
class AlbertEncoderConfig(hyperparams.Config):
  """ALBERT encoder configuration."""
  vocab_size: int = 30000
  embedding_width: int = 128
  hidden_size: int = 768
  num_layers: int = 12
  num_attention_heads: int = 12
  hidden_activation: str = "gelu"
  intermediate_size: int = 3072
  dropout_rate: float = 0.0
  attention_dropout_rate: float = 0.0
  max_position_embeddings: int = 512
  type_vocab_size: int = 2
  initializer_range: float = 0.02


@dataclasses.dataclass
class BigBirdEncoderConfig(hyperparams.Config):
  """BigBird encoder configuration."""
  vocab_size: int = 50358
  hidden_size: int = 768
  num_layers: int = 12
  num_attention_heads: int = 12
  hidden_activation: str = "gelu"
  intermediate_size: int = 3072
  dropout_rate: float = 0.1
  attention_dropout_rate: float = 0.1
  # Pre/Post-LN Transformer
  norm_first: bool = False
  max_position_embeddings: int = 4096
  num_rand_blocks: int = 3
  block_size: int = 64
  type_vocab_size: int = 16
  initializer_range: float = 0.02
  embedding_width: Optional[int] = None
  use_gradient_checkpointing: bool = False


@dataclasses.dataclass
class KernelEncoderConfig(hyperparams.Config):
  """Linear encoder configuration."""
  vocab_size: int = 30522
  hidden_size: int = 768
  num_layers: int = 12
  num_attention_heads: int = 12
  hidden_activation: str = "gelu"
  intermediate_size: int = 3072
  dropout_rate: float = 0.1
  attention_dropout_rate: float = 0.1
  # Pre/Post-LN Transformer
  norm_first: bool = False
  max_position_embeddings: int = 512
  type_vocab_size: int = 2
  initializer_range: float = 0.02
  embedding_size: Optional[int] = None
  feature_transform: str = "exp"
  num_random_features: int = 256
  redraw: bool = False
  is_short_seq: bool = False
  begin_kernel: int = 0
  scale: Optional[float] = None


@dataclasses.dataclass
class XLNetEncoderConfig(hyperparams.Config):
  """XLNet encoder configuration."""
  vocab_size: int = 32000
  num_layers: int = 24
  hidden_size: int = 1024
  num_attention_heads: int = 16
  head_size: int = 64
  inner_size: int = 4096
  inner_activation: str = "gelu"
  dropout_rate: float = 0.1
  attention_dropout_rate: float = 0.1
  attention_type: str = "bi"
  bi_data: bool = False
  tie_attention_biases: bool = False
  memory_length: int = 0
  same_length: bool = False
  clamp_length: int = -1
  reuse_length: int = 0
  use_cls_mask: bool = False
  embedding_width: int = 1024
  initializer_range: float = 0.02
  two_stream: bool = False


@dataclasses.dataclass
class EncoderConfig(hyperparams.OneOfConfig):
  """Encoder configuration."""
  type: Optional[str] = "bert"
  albert: AlbertEncoderConfig = AlbertEncoderConfig()
  bert: BertEncoderConfig = BertEncoderConfig()
  bigbird: BigBirdEncoderConfig = BigBirdEncoderConfig()
  kernel: KernelEncoderConfig = KernelEncoderConfig()
  mobilebert: MobileBertEncoderConfig = MobileBertEncoderConfig()
  xlnet: XLNetEncoderConfig = XLNetEncoderConfig()


@gin.configurable
def build_encoder(config: EncoderConfig,
                  embedding_layer: Optional[tf.keras.layers.Layer] = None,
                  encoder_cls=None,
                  bypass_config: bool = False):
  """Instantiate a Transformer encoder network from EncoderConfig.

  Args:
    config: the one-of encoder config, which provides encoder parameters of a
      chosen encoder.
    embedding_layer: an external embedding layer passed to the encoder.
    encoder_cls: an external encoder cls not included in the supported encoders,
      usually used by gin.configurable.
    bypass_config: whether to ignore config instance to create the object with
      `encoder_cls`.

  Returns:
    An encoder instance.
  """
  if bypass_config:
    return encoder_cls()
  encoder_type = config.type
  encoder_cfg = config.get()
  if encoder_cls and encoder_cls.__name__ == "EncoderScaffold":
    embedding_cfg = dict(
        vocab_size=encoder_cfg.vocab_size,
        type_vocab_size=encoder_cfg.type_vocab_size,
        hidden_size=encoder_cfg.hidden_size,
        max_seq_length=encoder_cfg.max_position_embeddings,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        dropout_rate=encoder_cfg.dropout_rate,
    )
    hidden_cfg = dict(
        num_attention_heads=encoder_cfg.num_attention_heads,
        intermediate_size=encoder_cfg.intermediate_size,
        intermediate_activation=tf_utils.get_activation(
            encoder_cfg.hidden_activation),
        dropout_rate=encoder_cfg.dropout_rate,
        attention_dropout_rate=encoder_cfg.attention_dropout_rate,
        kernel_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
    )
    kwargs = dict(
        embedding_cfg=embedding_cfg,
        hidden_cfg=hidden_cfg,
        num_hidden_instances=encoder_cfg.num_layers,
        pooled_output_dim=encoder_cfg.hidden_size,
        pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        return_all_layer_outputs=encoder_cfg.return_all_encoder_outputs,
        dict_outputs=True)
    return encoder_cls(**kwargs)

  if encoder_type == "mobilebert":
    return networks.MobileBERTEncoder(
        word_vocab_size=encoder_cfg.word_vocab_size,
        word_embed_size=encoder_cfg.word_embed_size,
        type_vocab_size=encoder_cfg.type_vocab_size,
        max_sequence_length=encoder_cfg.max_sequence_length,
        num_blocks=encoder_cfg.num_blocks,
        hidden_size=encoder_cfg.hidden_size,
        num_attention_heads=encoder_cfg.num_attention_heads,
        intermediate_size=encoder_cfg.intermediate_size,
        intermediate_act_fn=encoder_cfg.hidden_activation,
        hidden_dropout_prob=encoder_cfg.hidden_dropout_prob,
        attention_probs_dropout_prob=encoder_cfg.attention_probs_dropout_prob,
        intra_bottleneck_size=encoder_cfg.intra_bottleneck_size,
        initializer_range=encoder_cfg.initializer_range,
        use_bottleneck_attention=encoder_cfg.use_bottleneck_attention,
        key_query_shared_bottleneck=encoder_cfg.key_query_shared_bottleneck,
        num_feedforward_networks=encoder_cfg.num_feedforward_networks,
        normalization_type=encoder_cfg.normalization_type,
        classifier_activation=encoder_cfg.classifier_activation,
        input_mask_dtype=encoder_cfg.input_mask_dtype)

  if encoder_type == "albert":
    return networks.AlbertEncoder(
        vocab_size=encoder_cfg.vocab_size,
        embedding_width=encoder_cfg.embedding_width,
        hidden_size=encoder_cfg.hidden_size,
        num_layers=encoder_cfg.num_layers,
        num_attention_heads=encoder_cfg.num_attention_heads,
        max_sequence_length=encoder_cfg.max_position_embeddings,
        type_vocab_size=encoder_cfg.type_vocab_size,
        intermediate_size=encoder_cfg.intermediate_size,
        activation=tf_utils.get_activation(encoder_cfg.hidden_activation),
        dropout_rate=encoder_cfg.dropout_rate,
        attention_dropout_rate=encoder_cfg.attention_dropout_rate,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        dict_outputs=True)

  if encoder_type == "bigbird":
    # TODO(frederickliu): Support use_gradient_checkpointing and update
    # experiments to use the EncoderScaffold only.
    if encoder_cfg.use_gradient_checkpointing:
      return bigbird_encoder.BigBirdEncoder(
          vocab_size=encoder_cfg.vocab_size,
          hidden_size=encoder_cfg.hidden_size,
          num_layers=encoder_cfg.num_layers,
          num_attention_heads=encoder_cfg.num_attention_heads,
          intermediate_size=encoder_cfg.intermediate_size,
          activation=tf_utils.get_activation(encoder_cfg.hidden_activation),
          dropout_rate=encoder_cfg.dropout_rate,
          attention_dropout_rate=encoder_cfg.attention_dropout_rate,
          num_rand_blocks=encoder_cfg.num_rand_blocks,
          block_size=encoder_cfg.block_size,
          max_position_embeddings=encoder_cfg.max_position_embeddings,
          type_vocab_size=encoder_cfg.type_vocab_size,
          initializer=tf.keras.initializers.TruncatedNormal(
              stddev=encoder_cfg.initializer_range),
          embedding_width=encoder_cfg.embedding_width,
          use_gradient_checkpointing=encoder_cfg.use_gradient_checkpointing)
    embedding_cfg = dict(
        vocab_size=encoder_cfg.vocab_size,
        type_vocab_size=encoder_cfg.type_vocab_size,
        hidden_size=encoder_cfg.hidden_size,
        max_seq_length=encoder_cfg.max_position_embeddings,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        dropout_rate=encoder_cfg.dropout_rate)
    attention_cfg = dict(
        num_heads=encoder_cfg.num_attention_heads,
        key_dim=int(encoder_cfg.hidden_size // encoder_cfg.num_attention_heads),
        kernel_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        max_rand_mask_length=encoder_cfg.max_position_embeddings,
        num_rand_blocks=encoder_cfg.num_rand_blocks,
        from_block_size=encoder_cfg.block_size,
        to_block_size=encoder_cfg.block_size,
        )
    hidden_cfg = dict(
        num_attention_heads=encoder_cfg.num_attention_heads,
        intermediate_size=encoder_cfg.intermediate_size,
        intermediate_activation=tf_utils.get_activation(
            encoder_cfg.hidden_activation),
        dropout_rate=encoder_cfg.dropout_rate,
        attention_dropout_rate=encoder_cfg.attention_dropout_rate,
        norm_first=encoder_cfg.norm_first,
        kernel_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        attention_cls=layers.BigBirdAttention,
        attention_cfg=attention_cfg)
    kwargs = dict(
        embedding_cfg=embedding_cfg,
        hidden_cls=layers.TransformerScaffold,
        hidden_cfg=hidden_cfg,
        num_hidden_instances=encoder_cfg.num_layers,
        mask_cls=layers.BigBirdMasks,
        mask_cfg=dict(block_size=encoder_cfg.block_size),
        pooled_output_dim=encoder_cfg.hidden_size,
        pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        return_all_layer_outputs=False,
        dict_outputs=True,
        layer_idx_as_attention_seed=True)
    return networks.EncoderScaffold(**kwargs)

  if encoder_type == "kernel":
    embedding_cfg = dict(
        vocab_size=encoder_cfg.vocab_size,
        type_vocab_size=encoder_cfg.type_vocab_size,
        hidden_size=encoder_cfg.hidden_size,
        max_seq_length=encoder_cfg.max_position_embeddings,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        dropout_rate=encoder_cfg.dropout_rate)
    attention_cfg = dict(
        num_heads=encoder_cfg.num_attention_heads,
        key_dim=int(encoder_cfg.hidden_size // encoder_cfg.num_attention_heads),
        kernel_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        feature_transform=encoder_cfg.feature_transform,
        num_random_features=encoder_cfg.num_random_features,
        redraw=encoder_cfg.redraw,
        is_short_seq=encoder_cfg.is_short_seq,
        begin_kernel=encoder_cfg.begin_kernel,
        scale=encoder_cfg.scale,
        )
    hidden_cfg = dict(
        num_attention_heads=encoder_cfg.num_attention_heads,
        intermediate_size=encoder_cfg.intermediate_size,
        intermediate_activation=tf_utils.get_activation(
            encoder_cfg.hidden_activation),
        dropout_rate=encoder_cfg.dropout_rate,
        attention_dropout_rate=encoder_cfg.attention_dropout_rate,
        norm_first=encoder_cfg.norm_first,
        kernel_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        attention_cls=layers.KernelAttention,
        attention_cfg=attention_cfg)
    kwargs = dict(
        embedding_cfg=embedding_cfg,
        hidden_cls=layers.TransformerScaffold,
        hidden_cfg=hidden_cfg,
        num_hidden_instances=encoder_cfg.num_layers,
        mask_cls=layers.KernelMask,
        pooled_output_dim=encoder_cfg.hidden_size,
        pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=encoder_cfg.initializer_range),
        return_all_layer_outputs=False,
        dict_outputs=True,
        layer_idx_as_attention_seed=True)
    return networks.EncoderScaffold(**kwargs)

  if encoder_type == "xlnet":
    return networks.XLNetBase(
        vocab_size=encoder_cfg.vocab_size,
        num_layers=encoder_cfg.num_layers,
        hidden_size=encoder_cfg.hidden_size,
        num_attention_heads=encoder_cfg.num_attention_heads,
        head_size=encoder_cfg.head_size,
        inner_size=encoder_cfg.inner_size,
        dropout_rate=encoder_cfg.dropout_rate,
        attention_dropout_rate=encoder_cfg.attention_dropout_rate,
        attention_type=encoder_cfg.attention_type,
        bi_data=encoder_cfg.bi_data,
        two_stream=encoder_cfg.two_stream,
        tie_attention_biases=encoder_cfg.tie_attention_biases,
        memory_length=encoder_cfg.memory_length,
        clamp_length=encoder_cfg.clamp_length,
        reuse_length=encoder_cfg.reuse_length,
        inner_activation=encoder_cfg.inner_activation,
        use_cls_mask=encoder_cfg.use_cls_mask,
        embedding_width=encoder_cfg.embedding_width,
        initializer=tf.keras.initializers.RandomNormal(
            stddev=encoder_cfg.initializer_range))

  # Uses the default BERTEncoder configuration schema to create the encoder.
  # If it does not match, please add a switch branch by the encoder type.
  return networks.BertEncoder(
      vocab_size=encoder_cfg.vocab_size,
      hidden_size=encoder_cfg.hidden_size,
      num_layers=encoder_cfg.num_layers,
      num_attention_heads=encoder_cfg.num_attention_heads,
      intermediate_size=encoder_cfg.intermediate_size,
      activation=tf_utils.get_activation(encoder_cfg.hidden_activation),
      dropout_rate=encoder_cfg.dropout_rate,
      attention_dropout_rate=encoder_cfg.attention_dropout_rate,
      max_sequence_length=encoder_cfg.max_position_embeddings,
      type_vocab_size=encoder_cfg.type_vocab_size,
      initializer=tf.keras.initializers.TruncatedNormal(
          stddev=encoder_cfg.initializer_range),
      output_range=encoder_cfg.output_range,
      embedding_width=encoder_cfg.embedding_size,
      embedding_layer=embedding_layer,
      return_all_encoder_outputs=encoder_cfg.return_all_encoder_outputs,
      dict_outputs=True,
      norm_first=encoder_cfg.norm_first)