def transformer_decoder_ffn_unit(x,
                                 hparams,
                                 nonpadding_mask=None):
  """Applies a feed-forward function which is parametrised for decoding.

  Args:
    x: input
    hparams: model hyper-parameters
    nonpadding_mask: optional Tensor with shape [batch_size, encoder_length]
    indicating what positions are not padding.  This is used
    to mask out padding in convoltutional layers.  We generally only
    need this mask for "packed" datasets, because for ordinary datasets,
    no padding is ever followed by nonpadding.

  Returns:
    the output tensor

  """

  with tf.variable_scope("ffn"):
    if hparams.transformer_ffn_type == "fc":
      y = transformer.transformer_ffn_layer(
          common_layers.layer_preprocess(x, hparams),
          hparams,
          conv_padding="LEFT",
          nonpadding_mask=nonpadding_mask)

    if hparams.transformer_ffn_type == "sepconv":
      y = common_layers.sepconv_relu_sepconv(
          common_layers.layer_preprocess(x, hparams),
          filter_size=hparams.filter_size,
          output_size=hparams.hidden_size,
          first_kernel_size=(3, 1),
          second_kernel_size=(5, 1),
          padding="LEFT",
          nonpadding_mask=nonpadding_mask,
          dropout=hparams.relu_dropout)

    x = common_layers.layer_postprocess(x, y, hparams)

  return x
Applies a feed-forward function which is parametrised for decoding.

  Args:
    x: input
    hparams: model hyper-parameters
    nonpadding_mask: optional Tensor with shape [batch_size, encoder_length]
    indicating what positions are not padding.  This is used
    to mask out padding in convoltutional layers.  We generally only
    need this mask for "packed" datasets, because for ordinary datasets,
    no padding is ever followed by nonpadding.

  Returns:
    the output tensor

  
tensor2tensor/models/research/universal_transformer_util.py