Source code for espnet.nets.chainer_backend.transformer.encoder_layer

# 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 (

[docs]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
[docs] 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