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