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# encoding: utf-8
"""Class Declaration of Transformer's Encoder Block."""
import chainer
import chainer.functions as F
from espnet.nets.chainer_backend.transformer.attention import MultiHeadAttention
from espnet.nets.chainer_backend.transformer.layer_norm import LayerNorm
from espnet.nets.chainer_backend.transformer.positionwise_feed_forward import (
PositionwiseFeedForward, # noqa: H301
)
class EncoderLayer(chainer.Chain):
"""Single encoder layer module.
Args:
n_units (int): Number of input/output dimension of a FeedForward layer.
d_units (int): Number of units of hidden layer in a FeedForward layer.
h (int): Number of attention heads.
dropout (float): Dropout rate
"""
def __init__(
self, n_units, d_units=0, h=8, dropout=0.1, initialW=None, initial_bias=None
):
"""Initialize EncoderLayer."""
super(EncoderLayer, self).__init__()
with self.init_scope():
self.self_attn = MultiHeadAttention(
n_units,
h,
dropout=dropout,
initialW=initialW,
initial_bias=initial_bias,
)
self.feed_forward = PositionwiseFeedForward(
n_units,
d_units=d_units,
dropout=dropout,
initialW=initialW,
initial_bias=initial_bias,
)
self.norm1 = LayerNorm(n_units)
self.norm2 = LayerNorm(n_units)
self.dropout = dropout
self.n_units = n_units
def forward(self, e, xx_mask, batch):
"""Forward Positional Encoding."""
n_e = self.norm1(e)
n_e = self.self_attn(n_e, mask=xx_mask, batch=batch)
e = e + F.dropout(n_e, self.dropout)
n_e = self.norm2(e)
n_e = self.feed_forward(n_e)
e = e + F.dropout(n_e, self.dropout)
return e
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