Transformer解码器

Transformer解码器是Transformer模型的一部分，用于将编码器生成的特征向量转换为目标语言的句子。与编码器类似，解码器也由多个层组成，每个层都有多头自注意力机制和前馈神经网络。解码器的输入是编码器的输出和目标语言的嵌入向量，输出是目标语言的单词序列。解码器的每个层都会生成一个输出，这些输出将被馈送到下一层，直到生成完整的目标语言句子。

以下是一个简单的Transformer解码器的Python实现：

import torch
import torch.nn as nn

class TransformerDecoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward, dropout=0.1):
        super(TransformerDecoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None):
        tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)
        tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)
        tgt2 = self.linear2(self.dropout(torch.relu(self.linear1(tgt))))
        tgt = tgt + self.dropout3(tgt2)
        tgt = self.norm3(tgt)
        return tgt