(d_feature,
                             context_bias_layer,
                             location_bias_layer,
                             total_kv_pooling,
                             separate_cls=False,
                             n_heads=1,
                             dropout=0.0,
                             mode='train')
return cb.Serial(
      CreateAttentionMaskLayer(),  # q, k, v, mask
      attention,  # vecs, mask
      cb.Select([0], n_in=2),  # vecs
  )
Returns a layer that maps (q, k, v) to (activations).

  Same as standard Relative attention layer but additionally based on sizes
  of queries and keys prepares a mask that masks out the future.
  Masking the future is the concept primarily used for Language Modelling.
  Args:
    d_feature: Depth/dimensionality of feature embedding.
    context_bias_layer: Global context bias from Transformer XL's attention.
      There should be one such layer shared for all relative attention layers
    location_bias_layer: Global location bias from Transformer XL's attention.
      There should be one such layer shared for all relative attention layers.
    total_kv_pooling: Accumulated pool size of keys/values used at this layer.
    separate_cls: True/False if we separate_cls in calculations.
    n_heads: Number of attention heads.
    dropout: Probabilistic rate for internal dropout applied to attention
      activations (based on query-key pairs) before dotting them with values.
    mode: One of `'train'`, `'eval'`, or `'predict'`.
Returns a layer that maps (q, k, v) to (activations).
Returns a layer that maps ( q k v ) to ( activations ) .
def RelativeAttentionLMLayer(d_feature,
                             context_bias_layer,
                             location_bias_layer,
                             total_kv_pooling,
                             separate_cls=False,
                             n_heads=1,
                             dropout=0.0,
                             mode='train'):
  """Returns a layer that maps (q, k, v) to (activations).

  Same as standard Relative attention layer but additionally based on sizes
  of queries and keys prepares a mask that masks out the future.
  Masking the future is the concept primarily used for Language Modelling.
  Args:
    d_feature: Depth/dimensionality of feature embedding.
    context_bias_layer: Global context bias from Transformer XL's attention.
      There should be one such layer shared for all relative attention layers
    location_bias_layer: Global location bias from Transformer XL's attention.
      There should be one such layer shared for all relative attention layers.
    total_kv_pooling: Accumulated pool size of keys/values used at this layer.
    separate_cls: True/False if we separate_cls in calculations.
    n_heads: Number of attention heads.
    dropout: Probabilistic rate for internal dropout applied to attention
      activations (based on query-key pairs) before dotting them with values.
    mode: One of `'train'`, `'eval'`, or `'predict'`.
  """

  attention = RelativeAttentionLayer(
      d_feature,
      context_bias_layer,
      location_bias_layer,
      total_kv_pooling,
      separate_cls,
      n_heads=n_heads,
      dropout=dropout,
      mode=mode)

  return cb.Serial(
      CreateAttentionMaskLayer(),  # q, k, v, mask
      attention,  # vecs, mask
      cb.Select([0], n_in=2),  # vecs
  )
def RelativeAttentionLMLayer ( d_feature , context_bias_layer , location_bias_layer , total_kv_pooling , separate_cls = False , n_heads = 1 , dropout = 0.0 , mode = 'train' ) : attention = RelativeAttentionLayer ( d_feature , context_bias_layer , location_bias_layer , total_kv_pooling , separate_cls , n_heads = n_heads , dropout = dropout , mode = mode ) return cb . Serial ( CreateAttentionMaskLayer ( ) , # q, k, v, mask attention , # vecs, mask cb . Select ( [ 0 ] , n_in = 2 ) , # vecs )