def local_within_block_attention(x,
                                 self_attention_bias,
                                 hparams,
                                 attention_type="local_within_block_mask_right",
                                 q_padding="VALID",
                                 kv_padding="VALID"):
  """Local within block self attention."""
  x_new, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
  with tf.variable_scope("local_within_block"):
    y = common_attention.multihead_attention(
        common_layers.layer_preprocess(x_new, hparams),
        None,
        self_attention_bias,
        hparams.attention_key_channels or hparams.hidden_size,
        hparams.attention_value_channels or hparams.hidden_size,
        hparams.hidden_size,
        hparams.num_heads,
        hparams.attention_dropout,
        attention_type=attention_type,
        block_width=hparams.block_width,
        block_length=hparams.block_length,
        q_padding=q_padding,
        kv_padding=kv_padding,
        q_filter_width=hparams.q_filter_width,
        kv_filter_width=hparams.kv_filter_width,
        name="local_within_block")
    if is_4d:
      y = tf.reshape(y, x_shape)
    return y
Local within block self attention.