(vocab_size,
                          d_model=512,
                          d_ff=2048,
                          n_layers=6,
                          n_heads=8,
                          dropout=0.1,
                          max_len=2048,
                          mode='train',
                          ff_activation=tl.Relu,
                          skip_fraction=0.4)
return tl.Serial(
      tl.ShiftRight(mode=mode),
      embedder,
      # stack: embedding
      tl.RandomUniform(minimum_layers, maximum_layers, sync=True),
      # stack: n_layers_to_keep, embedding
      tl.Swap(),
      # stack: embedding, n_layers_to_keep
      [ConditionedBlock(i) for i in range(n_layers)],
      # stack: embedding, n_layers_to_keep
      tl.AssertShape('...sd,'),
      tl.Select([0], n_in=2),  # stack: embedding
      tl.AssertShape('...sd'),
      tl.LayerNorm(),
      tl.Dense(vocab_size),
  )
Returns a Skipping Transformer language model.

  The input to the model is a tensor of tokens. (This model uses only the
  decoder part of the overall 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)
    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
    skip_fraction: fraction of times to skip some layers

  Returns:
    A Transformer language model as a layer that maps from a tensor of tokens
    to activations over a vocab set.
Returns a Skipping Transformer language model.
Returns a Skipping Transformer language model .
def SkippingTransformerLM(vocab_size,
                          d_model=512,
                          d_ff=2048,
                          n_layers=6,
                          n_heads=8,
                          dropout=0.1,
                          max_len=2048,
                          mode='train',
                          ff_activation=tl.Relu,
                          skip_fraction=0.4):
  """Returns a Skipping Transformer language model.

  The input to the model is a tensor of tokens. (This model uses only the
  decoder part of the overall 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)
    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
    skip_fraction: fraction of times to skip some layers

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

  @assert_shape('...sd,->...sd,')
  def ConditionedBlock(current_layer_num):
    return tl.Serial(
        # stack: embedding, n_layers_to_keep
        tl.Select([1, 0, 1]),  # n_layers_to_keep, embedding, n_layers_to_keep
        tl.Cond(
            # if n_layers_to_keep > current_layer_num
            LargerThan(float(current_layer_num)),
            # then: run block
            tl.Serial(transformer._DecoderBlock(  # pylint: disable=g-complex-comprehension,protected-access
                d_model, d_ff, n_heads, dropout, [], mode, ff_activation)),
            # else: run noop
            tl.Serial()
            )
        # stack: embedding, n_layers_to_keep
        )

  if mode == 'train':
    if skip_fraction == 0.0:
      minimum_layers = float(n_layers)
      maximum_layers = float(n_layers)
    else:
      minimum_layers = 0.0
      maximum_layers = float(n_layers) / skip_fraction
  else:
    minimum_layers = maximum_layers = float(n_layers)

  return tl.Serial(
      tl.ShiftRight(mode=mode),
      embedder,
      # stack: embedding
      tl.RandomUniform(minimum_layers, maximum_layers, sync=True),
      # stack: n_layers_to_keep, embedding
      tl.Swap(),
      # stack: embedding, n_layers_to_keep
      [ConditionedBlock(i) for i in range(n_layers)],
      # stack: embedding, n_layers_to_keep
      tl.AssertShape('...sd,'),
      tl.Select([0], n_in=2),  # stack: embedding
      tl.AssertShape('...sd'),
      tl.LayerNorm(),
      tl.Dense(vocab_size),
  )
def SkippingTransformerLM ( vocab_size , d_model = 512 , d_ff = 2048 , n_layers = 6 , n_heads = 8 , dropout = 0.1 , max_len = 2048 , mode = 'train' , ff_activation = tl . Relu , skip_fraction = 0.4 ) : embedder = [ tl . Embedding ( vocab_size , d_model ) , tl . Dropout ( rate = dropout , mode = mode ) , tl . PositionalEncoding ( max_len = max_len , mode = mode ) , ] @ assert_shape ( '...sd,->...sd,' ) def ConditionedBlock ( current_layer_num ) : return tl . Serial ( # stack: embedding, n_layers_to_keep tl . Select ( [ 1 , 0 , 1 ] ) , # n_layers_to_keep, embedding, n_layers_to_keep tl . Cond ( # if n_layers_to_keep > current_layer_num LargerThan ( float ( current_layer_num ) ) , # then: run block tl . Serial ( transformer . _DecoderBlock ( # pylint: disable=g-complex-comprehension,protected-access d_model , d_ff , n_heads , dropout , [ ] , mode , ff_activation ) ) , # else: run noop tl . Serial ( ) ) # stack: embedding, n_layers_to_keep ) if mode == 'train' : if skip_fraction == 0.0 : minimum_layers = float ( n_layers ) maximum_layers = float ( n_layers ) else : minimum_layers = 0.0 maximum_layers = float ( n_layers ) / skip_fraction else : minimum_layers = maximum_layers = float ( n_layers ) return tl . Serial ( tl . ShiftRight ( mode = mode ) , embedder , # stack: embedding tl . RandomUniform ( minimum_layers , maximum_layers , sync = True ) , # stack: n_layers_to_keep, embedding tl . Swap ( ) , # stack: embedding, n_layers_to_keep [ ConditionedBlock ( i ) for i in range ( n_layers ) ] , # stack: embedding, n_layers_to_keep tl . AssertShape ( '...sd,' ) , tl . Select ( [ 0 ] , n_in = 2 ) , # stack: embedding tl . AssertShape ( '...sd' ) , tl . LayerNorm ( ) , tl . Dense ( vocab_size ) , )