Transformer

def multihead_attention(query_antecedent,
                        memory_antecedent,
                        bias,
                        total_key_depth,
                        total_value_depth,
                        output_depth,
                        num_heads,
                        dropout_rate,
                        summaries=False,
                        image_shapes=None,
                        name=None):
  """Multihead scaled-dot-product attention with input/output transformations.

  Args:
    query_antecedent: a Tensor with shape [batch, length_q, channels]
    memory_antecedent: a Tensor with shape [batch, length_m, channels]
    bias: bias Tensor (see attention_bias())
    total_key_depth: an integer
    total_value_depth: an integer
    output_depth: an integer
    num_heads: an integer dividing total_key_depth and total_value_depth
    dropout_rate: a floating point number
    summaries: a boolean
    image_shapes: optional tuple of integer scalars.
      see comments for attention_image_summary()
    name: an optional string

  Returns:
    A Tensor.

  Raises:
    ValueError: if the key depth or value depth are not divisible by the
      number of attention heads.
  """
  if total_key_depth % num_heads != 0:
    raise ValueError("Key depth (%d) must be divisible by the number of "
                     "attention heads (%d)." % (total_key_depth, num_heads))
  if total_value_depth % num_heads != 0:
    raise ValueError("Value depth (%d) must be divisible by the number of "
                     "attention heads (%d)." % (total_value_depth, num_heads))

  with tf.variable_scope(
      name,
      default_name="multihead_attention",
      values=[query_antecedent, memory_antecedent]):
    if memory_antecedent is None:
      # self attention
      combined = common_layers.conv1d(
          query_antecedent,
          total_key_depth * 2 + total_value_depth,
          1,
          name="qkv_transform")
      q, k, v = tf.split(
          combined, [total_key_depth, total_key_depth, total_value_depth],
          axis=2)
    else:
      q = common_layers.conv1d(
          query_antecedent, total_key_depth, 1, name="q_transform")
      combined = common_layers.conv1d(
          memory_antecedent,
          total_key_depth + total_value_depth,
          1,
          name="kv_transform")
      k, v = tf.split(combined, [total_key_depth, total_value_depth], axis=2)
    q = split_heads(q, num_heads)
    k = split_heads(k, num_heads)
    v = split_heads(v, num_heads)
    key_depth_per_head = total_key_depth // num_heads
    q *= key_depth_per_head**-0.5
    x = dot_product_attention(
        q, k, v, bias, dropout_rate, summaries, image_shapes)
    x = combine_heads(x)
    x = common_layers.conv1d(x, output_depth, 1, name="output_transform")
    return x

def transformer_decoder(decoder_input,
                        encoder_output,
                        residual_fn,
                        decoder_self_attention_bias,
                        encoder_decoder_attention_bias,
                        hparams,
                        name="decoder"):
  """A stack of transformer layers.

  Args:
    decoder_input: a Tensor
    encoder_output: a Tensor
    residual_fn: a function from (layer_input, layer_output) -> combined_output
    decoder_self_attention_bias: bias Tensor for self-attention
      (see common_attention.attention_bias())
    encoder_decoder_attention_bias: bias Tensor for encoder-decoder attention
      (see common_attention.attention_bias())
    hparams: hyperparameters for model
    name: a string

  Returns:
    y: a Tensors
  """
  x = decoder_input
  # Summaries don't work in multi-problem setting yet.
  summaries = "problems" not in hparams.values() or len(hparams.problems) == 1
  with tf.variable_scope(name):
    for layer in xrange(hparams.num_hidden_layers):
      with tf.variable_scope("layer_%d" % layer):
        x = residual_fn(
            x,
            common_attention.multihead_attention(
                x,
                None,
                decoder_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,
                summaries=summaries,
                name="decoder_self_attention"))
        x = residual_fn(
            x,
            common_attention.multihead_attention(
                x,
                encoder_output,
                encoder_decoder_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,
                summaries=summaries,
                name="encdec_attention"))
        x = residual_fn(x, transformer_ffn_layer(x, hparams))
  return x

    def residual_fn(x, y):
      return common_layers.layer_norm(x + tf.nn.dropout(
          y, 1.0 - hparams.residual_dropout))