transformer implementation

您可以参考以下步骤来实现Transformer模型：
1. 定义输入和输出的嵌入层
2. 定义Transformer的编码器和解码器
3. 定义Transformer的注意力机制
4. 定义Transformer的前向传播函数

以下是一个简单的Transformer实现的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Transformer(nn.Module):
    def __init__(self, input_vocab_size, output_vocab_size, d_model, nhead, num_layers):
        super(Transformer, self).__init__()
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        
        self.encoder_embedding = nn.Embedding(input_vocab_size, d_model)
        self.decoder_embedding = nn.Embedding(output_vocab_size, d_model)
        
        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead), num_layers=num_layers)
        self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(d_model=d_model, nhead=nhead), num_layers=num_layers)
        
        self.fc = nn.Linear(d_model, output_vocab_size)
        
    def forward(self, src, tgt):
        src_embedded = self.encoder_embedding(src)
        tgt_embedded = self.decoder_embedding(tgt)
        
        src_mask = self.generate_square_subsequent_mask(src.shape[0])
        tgt_mask = self.generate_square_subsequent_mask(tgt.shape[0])
        
        memory = self.encoder(src_embedded.transpose(0, 1), src_key_padding_mask=None, src_mask=src_mask)
        
        output = self.decoder(tgt_embedded.transpose(0, 1), memory, tgt_mask=tgt_mask)
        output = self.fc(output.transpose(0, 1))
        
        return output
    
    def generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask