layers.py

# -*- coding: utf-8 -*-

import torch.nn as nn
from sub_layers import MultiHeadAttention, PositionwiseFeedForward


class EncoderLayer(nn.Module):
    def __init__(self, d_model, d_inner, n_head, dropout=0.1):
        super(EncoderLayer, self).__init__()
        d_k = int(d_model / n_head)
        d_v = int(d_model / n_head)
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(self, enc_input, non_pad_mask=None, slf_attn_mask=None, encoding_mask=None):
        enc_output, enc_slf_attn = self.slf_attn(
            enc_input, enc_input, enc_input, mask=slf_attn_mask, encoding_mask=encoding_mask)
        enc_output *= non_pad_mask

        enc_output = self.pos_ffn(enc_output)
        enc_output *= non_pad_mask

        return enc_output, enc_slf_attn


class DecoderLayer(nn.Module):
    def __init__(self, d_model, d_inner, n_head, dropout=0.1):
        super(DecoderLayer, self).__init__()
        d_k = int(d_model / n_head)
        d_v = int(d_model / n_head)
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.enc_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(self, dec_input, enc_output, non_pad_mask=None, slf_attn_mask=None, dec_enc_attn_mask=None):
        dec_output, dec_slf_attn = self.slf_attn(
            dec_input, dec_input, dec_input, mask=slf_attn_mask)
        dec_output *= non_pad_mask

        dec_output, dec_enc_attn = self.enc_attn(
            dec_output, enc_output, enc_output, mask=dec_enc_attn_mask)
        dec_output *= non_pad_mask

        dec_output = self.pos_ffn(dec_output)
        dec_output *= non_pad_mask

        return dec_output, dec_slf_attn, dec_enc_attn