(input_signature, buffer_length,
                               predict_mask=None)
Returns an initial state for causal attention layer fast inference.
Returns an initial state for causal attention layer fast inference.
Returns an initial state for causal attention layer fast inference .
def _fast_inference_init_state(input_signature, buffer_length,
                               predict_mask=None):
  """Returns an initial state for causal attention layer fast inference."""
  def zeros_for(batch_size, shape_dtype):
    shape, dtype = shape_dtype.as_tuple()
    d_feature = shape[-1]
    return jnp.zeros((batch_size, buffer_length, d_feature), dtype=dtype)

  batch_size = input_signature[0].shape[0]
  k = zeros_for(batch_size, input_signature[1])
  v = zeros_for(batch_size, input_signature[2])
  if predict_mask is not None:
    mask_for_predict = jnp.zeros((buffer_length,)) != 0
    return (mask_for_predict, k, v, jnp.array(0))
  else:
    return (k, v, jnp.array(0))
def _fast_inference_init_state ( input_signature , buffer_length , predict_mask = None ) : def zeros_for ( batch_size , shape_dtype ) : shape , dtype = shape_dtype . as_tuple ( ) d_feature = shape [ - 1 ] return jnp . zeros ( ( batch_size , buffer_length , d_feature ) , dtype = dtype ) batch_size = input_signature [ 0 ] . shape [ 0 ] k = zeros_for ( batch_size , input_signature [ 1 ] ) v = zeros_for ( batch_size , input_signature [ 2 ] ) if predict_mask is not None : mask_for_predict = jnp . zeros ( ( buffer_length , ) ) != 0 return ( mask_for_predict , k , v , jnp . array ( 0 ) ) else : return ( k , v , jnp . array ( 0 ) )