基于pytorch写一段自注意力机制（self-attention）的模型实现，再讲解一下如何对这个模型进行优化，并写一个例子

好的，下面是一个基于 PyTorch 实现的自注意力机制模型：

import torch.nn as nn
import torch

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        
        assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"
        
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(self.head_dim * heads, embed_size)
        
    def forward(self, values, keys, query, mask):
        # split embedding into self.heads pieces
        N = query.shape[0]
        value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
        
        # Split the embedding into self.heads pieces
        values = values.reshape(N, value_len, self.heads, self.head_dim)
        keys = keys.reshape(N, key_len, self.heads, self.head_dim)
        query = query.reshape(N, query_len, self.heads, self.head_dim)
        
        # Transpose to get dimensions batch_size * heads * sequence_length * head_dim
        values = values.transpose(1, 2)
        keys = keys.transpose(1, 2)
        query = query.transpose(1, 2)
        
        # Calculate the energy
        energy = torch.matmul(query, keys.transpose(-2, -1))
        
        # Apply the mask
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float('-1e20'))
        
        # Normalize the attention scores
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=-1)
        
        # Multiply the attention scores with the values
        out = torch.matmul(attention, values)
        
        # Concatenate heads and put through final linear layer
        out = out.transpose(1, 2).reshape(N, query_len, self.heads * self.head_dim)
        out = self.fc_out(out)
        
        return out

上面的模型是一个简单的自注意力机制模型，由多头注意力实现，接受三个输入：values、keys 和 query，并返回注意力得分计算后的输出。

接下来，我们可以使用 Adam 优化器对这个模型进行优化，如下所示：

import torch.optim as optim

model = SelfAttention(embed_size=128, heads=8)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# training loop
for epoch in range(num_epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # forward pass
        output = model(data)
        
        # calculate loss
        loss = criterion(output, target)
        
        # backward pass and optimization
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

在这个例子中，我们使用交叉熵损失作为损失函数，并使用 Adam 优化器对模型进行优化，同时使用 DataLoader 加载数据。以上是一个简单的对自注意力机制模型进行优化的例子。