帮我实现一个transformer的结构

好的，以下是一个简单的Transformer结构的实现代码：

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

class Transformer(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
        super(Transformer, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.num_heads = num_heads
        
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.pos_encoding = PositionalEncoding(hidden_dim)
        self.layers = nn.ModuleList([TransformerLayer(hidden_dim, num_heads) for _ in range(num_layers)])
        self.fc = nn.Linear(hidden_dim, input_dim)
        
    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoding(x)
        for layer in self.layers:
            x = layer(x)
        x = self.fc(x)
        return x
    
class TransformerLayer(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(TransformerLayer, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        
        self.self_attn = MultiHeadAttention(hidden_dim, num_heads)
        self.norm1 = nn.LayerNorm(hidden_dim)
        self.feed_forward = FeedForward(hidden_dim)
        self.norm2 = nn.LayerNorm(hidden_dim)
        
    def forward(self, x):
        residual = x
        x = self.self_attn(x)
        x = self.norm1(x + residual)
        residual = x
        x = self.feed_forward(x)
        x = self.norm2(x + residual)
        return x
    
class MultiHeadAttention(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        
        self.q_linear = nn.Linear(hidden_dim, hidden_dim)
        self.k_linear = nn.Linear(hidden_dim, hidden_dim)
        self.v_linear = nn.Linear(hidden_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, hidden_dim)
        
    def forward(self, x):
        batch_size, seq_len, hidden_dim = x.size()
        q = self.q_linear(x).view(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads).transpose(1, 2)
        k = self.k_linear(x).view(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads).transpose(1, 2)
        v = self.v_linear(x).view(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads).transpose(1, 2)
        
        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / (hidden_dim // self.num_heads) ** 0.5
        attn_probs = F.softmax(attn_scores, dim=-1)
        attn_output = torch.matmul(attn_probs, v)
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, hidden_dim)
        attn_output = self.fc(attn_output)
        return attn_output
    
class FeedForward(nn.Module):
    def __init__(self, hidden_dim):
        super(FeedForward, self).__init__()
        self.hidden_dim = hidden_dim
        
        self.fc1 = nn.Linear(hidden_dim, hidden_dim * 4)
        self.fc2 = nn.Linear(hidden_dim * 4, hidden_dim)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
    
class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.hidden_dim = hidden_dim
        
        pe = torch.zeros(max_len, hidden_dim)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim))
        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 x

这个Transformer结构包括了多头自注意力机制、前馈神经网络和位置编码等模块，可以用于序列到序列的任务，比如机器翻译、文本生成等。