(vocab_size,
                        d_model=512,
                        d_ff=2048,
                        n_layers=6,
                        n_heads=8,
                        dropout=0.1,
                        dropout_shared_axes=None,
                        max_len=2048,
                        mode='train',
                        ff_activation=tl.Relu)
return tl.Serial(              # tokens (or chunked tuple of tokens)
      tl.ShiftRight(mode=mode),  # toks
      positional_encoder,        # vecs
      decoder_blocks,            # vecs
      tl.LayerNorm(),            # vecs
      tl.Dense(vocab_size),      # vecs
  )
Returns a ReZero transformer language model.

  The input to the model is a tensor of tokens. (This model uses only the
  decoder part of the overall ReZero transformer.)

  Args:
    vocab_size: int: vocab size
    d_model: int:  depth of embedding
    d_ff: int: depth of feed-forward layer
    n_layers: int: number of encoder/decoder layers
    n_heads: int: number of attention heads
    dropout: float: dropout rate (how much to drop out)
    dropout_shared_axes: axes on which to share dropout mask
    max_len: int: maximum symbol length for positional encoding
    mode: str: 'train', 'eval' or 'predict', predict mode is for fast inference
    ff_activation: the non-linearity in feed-forward layer

  Returns:
    A ReZero transformer language model as a layer that maps from a tensor of
    tokens to activations over a vocab set.
Returns a ReZero transformer language model.
Returns a ReZero transformer language model .
def ReZeroTransformerLM(vocab_size,
                        d_model=512,
                        d_ff=2048,
                        n_layers=6,
                        n_heads=8,
                        dropout=0.1,
                        dropout_shared_axes=None,
                        max_len=2048,
                        mode='train',
                        ff_activation=tl.Relu):
  """Returns a ReZero transformer language model.

  The input to the model is a tensor of tokens. (This model uses only the
  decoder part of the overall ReZero transformer.)

  Args:
    vocab_size: int: vocab size
    d_model: int:  depth of embedding
    d_ff: int: depth of feed-forward layer
    n_layers: int: number of encoder/decoder layers
    n_heads: int: number of attention heads
    dropout: float: dropout rate (how much to drop out)
    dropout_shared_axes: axes on which to share dropout mask
    max_len: int: maximum symbol length for positional encoding
    mode: str: 'train', 'eval' or 'predict', predict mode is for fast inference
    ff_activation: the non-linearity in feed-forward layer

  Returns:
    A ReZero transformer language model as a layer that maps from a tensor of
    tokens to activations over a vocab set.
  """
  positional_encoder = [
      tl.Embedding(vocab_size, d_model),
      tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
      tl.PositionalEncoding(max_len=max_len, mode=mode)]

  decoder_blocks = [
      # pylint: disable=g-complex-comprehension
      _DecoderBlock(d_model, d_ff, n_heads,
                    dropout, dropout_shared_axes, mode, ff_activation)
      for i in range(n_layers)]

  # Assemble and return the model.
  return tl.Serial(              # tokens (or chunked tuple of tokens)
      tl.ShiftRight(mode=mode),  # toks
      positional_encoder,        # vecs
      decoder_blocks,            # vecs
      tl.LayerNorm(),            # vecs
      tl.Dense(vocab_size),      # vecs
  )
def ReZeroTransformerLM ( vocab_size , d_model = 512 , d_ff = 2048 , n_layers = 6 , n_heads = 8 , dropout = 0.1 , dropout_shared_axes = None , max_len = 2048 , mode = 'train' , ff_activation = tl . Relu ) : positional_encoder = [ tl . Embedding ( vocab_size , d_model ) , tl . Dropout ( rate = dropout , shared_axes = dropout_shared_axes , mode = mode ) , tl . PositionalEncoding ( max_len = max_len , mode = mode ) ] decoder_blocks = [ # pylint: disable=g-complex-comprehension _DecoderBlock ( d_model , d_ff , n_heads , dropout , dropout_shared_axes , mode , ff_activation ) for i in range ( n_layers ) ] # Assemble and return the model. return tl . Serial ( # tokens (or chunked tuple of tokens) tl . ShiftRight ( mode = mode ) , # toks positional_encoder , # vecs decoder_blocks , # vecs tl . LayerNorm ( ) , # vecs tl . Dense ( vocab_size ) , # vecs )