def weighted_cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
  """Compute weighted cross entropy and entropy for log probs and targets.

  Args:
   logits: [batch, length, num_classes] float array.
   targets: categorical targets [batch, length] int array.
   weights: None or array of shape [batch, length].
   label_smoothing: label smoothing constant, used to determine the on and off
     values.

  Returns:
    Tuple of scalar loss and batch normalizing factor.
  """
  if logits.ndim != targets.ndim + 1:
    raise ValueError('Incorrect shapes. Got shape %s logits and %s targets' %
                     (str(logits.shape), str(targets.shape)))
  vocab_size = logits.shape[-1]
  confidence = 1.0 - label_smoothing
  low_confidence = (1.0 - confidence) / (vocab_size - 1)
  normalizing_constant = -(
      confidence * jnp.log(confidence) +
      (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20))
  soft_targets = common_utils.onehot(
      targets, vocab_size, on_value=confidence, off_value=low_confidence)

  loss = -jnp.sum(soft_targets * nn.log_softmax(logits), axis=-1)
  loss = loss - normalizing_constant

  normalizing_factor = np.prod(targets.shape)
  if weights is not None:
    loss = loss * weights
    normalizing_factor = weights.sum()

  return loss.sum(), normalizing_factor
Compute weighted cross entropy and entropy for log probs and targets.

  Args:
   logits: [batch, length, num_classes] float array.
   targets: categorical targets [batch, length] int array.
   weights: None or array of shape [batch, length].
   label_smoothing: label smoothing constant, used to determine the on and off
     values.

  Returns:
    Tuple of scalar loss and batch normalizing factor.
  
language/gscan/xattn_model/train_utils.py