Transformer与LSTM相结合

Transformer与LSTM相结合可以用于跨话语信息表示，即在对话系统中，将多个话语的信息结合起来进行处理。具体实现方法是在每个Transformer block前分别建立一个LSTM，将前面所有话语的信息进行编码，然后将编码结果与当前话语的Transformer block输入进行拼接，再进行后续的处理。这样可以使得模型更好地捕捉到跨话语的信息，提高对话系统的效果。

下面是一个简单的示例代码，其中包含了一个LSTM和一个Transformer block：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

class TransformerLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
        super(TransformerLSTM, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.transformer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
        
    def forward(self, x, lengths):
        # 对输入进行pack，以便LSTM处理变长序列
        packed = pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)
        # LSTM编码
        lstm_out, _ = self.lstm(packed)
        # 对LSTM输出进行unpack
        lstm_out, _ = pad_packed_sequence(lstm_out, batch_first=True)
        # Transformer输入需要为(batch_size, seq_len, hidden_dim)
        transformer_in = lstm_out.unsqueeze(1)
        # Transformer编码
        transformer_out = self.transformer(transformer_in)
        # 将Transformer输出进行拼接
        out = torch.cat([lstm_out, transformer_out.squeeze(1)], dim=-1)
        return out