写一个transformer的编码器

以下是一个简单的Transformer编码器的实现：

import torch
import torch.nn as nn

class TransformerEncoder(nn.Module):
    def __init__(self, d_model, nhead, num_layers, dim_feedforward, dropout):
        super(TransformerEncoder, self).__init__()
        
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        
        self.pos_encoding = PositionalEncoding(d_model, dropout)
        self.transformer_layers = nn.ModuleList([TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout) for _ in range(num_layers)])
        
    def forward(self, src, mask=None):
        src = self.pos_encoding(src)
        
        for i in range(self.num_layers):
            src = self.transformer_layers[i](src, mask)
        
        return src

class TransformerEncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward, dropout):
        super(TransformerEncoderLayer, self).__init__()
        
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, dim_feedforward),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(dim_feedforward, d_model),
            nn.Dropout(dropout)
        )
        
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src, mask=None):
        src2, _ = self.self_attn(src, src, src, attn_mask=mask)
        src = src + self.dropout(src2)
        src = self.norm1(src)
        
        src2 = self.feed_forward(src)
        src = src + self.dropout(src2)
        src = self.norm2(src)
        
        return src

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        
        self.dropout = nn.Dropout(p=dropout)
        
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

在这个实现中，我们定义了一个`TransformerEncoder`和一个`TransformerEncoderLayer`类。`TransformerEncoder`类包含了多个`TransformerEncoderLayer`层。每个`TransformerEncoderLayer`层有一个自注意力层和一个前馈网络层。我们还定义了一个`PositionalEncoding`类来添加位置编码。

在`TransformerEncoder`类的`forward`方法中，我们首先对输入进行位置编码，然后依次通过每个`TransformerEncoderLayer`层。最后，我们将编码后的结果返回。

在`TransformerEncoderLayer`类的`forward`方法中，我们首先通过自注意力层进行自注意力计算。然后，我们使用残差连接和层归一化将输入与自注意力输出相加，并进行一次Dropout操作。接下来，我们通过前馈网络层，并再次使用残差连接和层归一化将输入与前馈网络输出相加，并进行一次Dropout操作。最终，我们返回输出。

在`PositionalEncoding`类的`__init__`方法中，我们生成一个位置编码矩阵，并在`forward`方法中将其添加到输入中。我们还使用Dropout对输出进行正则化。