Transformer · Decoder: 6 layers

in: $y_n$ [B batch, T target positions, 512 d] $z$ [B batch, S source positions, 512 d] $M$ [B batch, 1, 1, S keys]
out: $y_{n+1}$ [B batch, T target positions, 512 d]
parameters: 4,204,032 · × 6 layers = 25,224,192

Parameters

4,204,032 parameters per layer, across its learned components

Exact per-instance accounting
Component	Calculation	Parameters
Masked multi-head self-attention	1,050,624 across its components	1,050,624
LayerNorm	$512 + 512$	1,024
Encoder–decoder attention	1,050,624 across its components	1,050,624
LayerNorm	$512 + 512$	1,024
Position-wise feed-forward network	2,099,712 across its components	2,099,712
LayerNorm	$512 + 512$	1,024
Per-instance total		4,204,032

Across 6 independent layers: $4{,}204{,}032 \times 6 = 25{,}224{,}192$ parameters.

Derived from the modules the pinned tensor2tensor v1.0.14 code creates, with a vocabulary of about 37,000 (the paper’s words); no trained weights were published to count.

Explanation

In addition to the two sublayers of an encoder layer, the decoder inserts a third, which performs multi-head attention over the output of the encoder stack (§3.1).

Codetransformer.py 59–61, 170–226

transformer.py 170–226

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

transformer.py 59–61

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

Sources2

Direct evidence

Each decoder layer: masked self-attention, attention over the encoder output with the encoder padding bias, then the feed-forward network, each through residual_fn.

decoder · tensorflow/tensor2tensor / tensor2tensor/models/transformer.py
Pinned revision 0c66117d4297 ↗ · lines 170–226

Read code excerpt

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

Direct evidence

Post-norm: each sublayer output is dropped out, added to its input and layer-normalized.

residual-fn · tensorflow/tensor2tensor / tensor2tensor/models/transformer.py
Pinned revision 0c66117d4297 ↗ · lines 59–61

Read code excerpt

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