Transformer · Layer output

Encoder 6 layers

Layer output

tensorx · 166

The residual features after both mechanism updates.

shape: $x_{n+1}$ [B batch, S source positions, 512 d]

Sources

Contextual evidence from Encoder: 6 layers

Each of num_hidden_layers encoder layers: self-attention over the encoder input with the padding bias, then the feed-forward network, each through residual_fn.

encoder · tensorflow/tensor2tensor / tensor2tensor/models/transformer.py
Pinned revision 0c66117d4297 ↗ · lines 129–167

Read code excerpt

def transformer_encoder(encoder_input,
                        residual_fn,
                        encoder_self_attention_bias,
                        hparams,
                        name="encoder"):
  """A stack of transformer layers.

  Args:
    encoder_input: a Tensor
    residual_fn: a function from (layer_input, layer_output) -> combined_output
    encoder_self_attention_bias: bias Tensor for self-attention
       (see common_attention.attention_bias())
    hparams: hyperparameters for model
    name: a string

  Returns:
    y: a Tensors
  """
  x = encoder_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,
                encoder_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="encoder_self_attention"))
        x = residual_fn(x, transformer_ffn_layer(x, hparams))
  return x

Contextual evidence from Encoder: 6 layers

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))

Contextual evidence from LayerNorm

LayerNorm over the last axis with a learned scale and bias and ε = 1e-6.

layer-norm · tensorflow/tensor2tensor / tensor2tensor/models/common_layers.py
Pinned revision 0c66117d4297 ↗ · lines 406–443

Read code excerpt

def layer_norm_compute_python(x, epsilon, scale, bias):
  """Layer norm raw computation."""
  mean = tf.reduce_mean(x, axis=[-1], keep_dims=True)
  variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keep_dims=True)
  norm_x = (x - mean) * tf.rsqrt(variance + epsilon)
  return norm_x * scale + bias


@function.Defun(compiled=True)
def layer_norm_compute_grad(x, epsilon, scale, bias, dy):
  y = layer_norm_compute_python(x, epsilon, scale, bias)
  dx = tf.gradients(ys=[y], xs=[x, epsilon, scale, bias], grad_ys=[dy])
  return dx


@function.Defun(
    compiled=True,
    separate_compiled_gradients=True,
    grad_func=layer_norm_compute_grad)
def layer_norm_compute(x, epsilon, scale, bias):
  return layer_norm_compute_python(x, epsilon, scale, bias)


def layer_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):
  """Layer normalize the tensor x, averaging over the last dimension."""
  if filters is None:
    filters = x.get_shape()[-1]
  with tf.variable_scope(
      name, default_name="layer_norm", values=[x], reuse=reuse):
    scale = tf.get_variable(
        "layer_norm_scale", [filters], initializer=tf.ones_initializer())
    bias = tf.get_variable(
        "layer_norm_bias", [filters], initializer=tf.zeros_initializer())
    if allow_defun:
      result = layer_norm_compute(x, tf.constant(epsilon), scale, bias)
      result.set_shape(x.get_shape())
    else:
      result = layer_norm_compute_python(x, epsilon, scale, bias)