Transformer language model (only uses the decoder part of Transformer).

  Args:
    vocab_size: int: vocab size
    feature_depth: int:  depth of embedding
    feedforward_depth: int: depth of feed-forward layer
    num_layers: int: number of encoder/decoder layers
    num_heads: int: number of attention heads
    dropout: float: dropout rate (how much to drop out)
    max_len: int: maximum symbol length for positional encoding
    mode: str: 'train' or 'eval'

  Returns:
    the layer.

def TransformerLM(vocab_size,
                  feature_depth=512,
                  feedforward_depth=2048,
                  num_layers=6,
                  num_heads=8,
                  dropout=0.1,
                  max_len=2048,
                  mode='train'):
  """Transformer language model (only uses the decoder part of Transformer).

  Args:
    vocab_size: int: vocab size
    feature_depth: int:  depth of embedding
    feedforward_depth: int: depth of feed-forward layer
    num_layers: int: number of encoder/decoder layers
    num_heads: int: number of attention heads
    dropout: float: dropout rate (how much to drop out)
    max_len: int: maximum symbol length for positional encoding
    mode: str: 'train' or 'eval'

  Returns:
    the layer.
  """
  return layers.Serial(
      layers.ShiftRight(),
      layers.Embedding(feature_depth, vocab_size),
      layers.Dropout(rate=dropout, mode=mode),
      layers.PositionalEncoding(max_len=max_len),
      layers.Serial(*[DecoderLayer(feature_depth, feedforward_depth, num_heads,
                                   dropout, mode)
                      for _ in range(num_layers)]),
      layers.LayerNorm(),
      layers.Dense(vocab_size),
      layers.LogSoftmax()
  )