(
    d_feature, n_heads=1, sparsity=None, dropout=0.0, max_inference_length=2048,
    mode='train')
return tl.ConfigurableAttention(
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature), n_heads=n_heads,
      qkv_attention_layer=tl.DotProductCausalAttention(
          dropout=dropout, max_inference_length=max_inference_length,
          mode=mode))
Returns a layer that maps activations to activations, with causal masking.

  Like `CausalAttention`, this layer type represents one pass of multi-head
  self-attention with causal masking rather than padding-based masking. However,
  for computing Q/K/V instead of a Dense layer it combines
  FactoredDense layer with LocallyConnectedLayer.

  Args:
    d_feature: Depth/dimensionality of feature embedding.
    n_heads: Number of attention heads.
    sparsity: The sparsity of the layer; usually it should be equal to n_heads.
    dropout: Probababilistic rate for internal dropout applied to attention
        activations (based on query-key pairs) before dotting them with values.
    max_inference_length: maximum length for inference.
    mode: One of `'train'`, `'eval'`, or `'predict'`.
Returns a layer that maps activations to activations, with causal masking.
Returns a layer that maps activations to activations with causal masking .
def MultiplicativeModularCausalAttention(
    d_feature, n_heads=1, sparsity=None, dropout=0.0, max_inference_length=2048,
    mode='train'):
  """Returns a layer that maps activations to activations, with causal masking.

  Like `CausalAttention`, this layer type represents one pass of multi-head
  self-attention with causal masking rather than padding-based masking. However,
  for computing Q/K/V instead of a Dense layer it combines
  FactoredDense layer with LocallyConnectedLayer.

  Args:
    d_feature: Depth/dimensionality of feature embedding.
    n_heads: Number of attention heads.
    sparsity: The sparsity of the layer; usually it should be equal to n_heads.
    dropout: Probababilistic rate for internal dropout applied to attention
        activations (based on query-key pairs) before dotting them with values.
    max_inference_length: maximum length for inference.
    mode: One of `'train'`, `'eval'`, or `'predict'`.
  """
  sparsity = n_heads if sparsity is None else sparsity
  return tl.ConfigurableAttention(
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature),
      MultiplicativeModularSparseDense(sparsity, d_feature), n_heads=n_heads,
      qkv_attention_layer=tl.DotProductCausalAttention(
          dropout=dropout, max_inference_length=max_inference_length,
          mode=mode))
def MultiplicativeModularCausalAttention ( d_feature , n_heads = 1 , sparsity = None , dropout = 0.0 , max_inference_length = 2048 , mode = 'train' ) : sparsity = n_heads if sparsity is None else sparsity return tl . ConfigurableAttention ( MultiplicativeModularSparseDense ( sparsity , d_feature ) , MultiplicativeModularSparseDense ( sparsity , d_feature ) , MultiplicativeModularSparseDense ( sparsity , d_feature ) , MultiplicativeModularSparseDense ( sparsity , d_feature ) , n_heads = n_heads , qkv_attention_layer = tl . DotProductCausalAttention ( dropout = dropout , max_inference_length = max_inference_length , mode = mode ) )