在下面python代码中加入注意力机制模型

这是一个简单的示例代码，演示如何在一个Transformer模型中加入注意力机制。

首先，我们需要导入需要的库：

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

接下来，我们定义一个Transformer模型，该模型包含了一个多头注意力机制：

class TransformerModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super().__init__()
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.pos_enc = PositionalEncoding(hidden_size, dropout)
        self.transformer = nn.Transformer(d_model=hidden_size, nhead=num_heads, num_encoder_layers=num_layers,
                                           num_decoder_layers=num_layers, dim_feedforward=hidden_size*4, dropout=dropout)
        self.fc = nn.Linear(hidden_size, input_size)

    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_enc(x)
        output = self.transformer(x, x)
        output = self.fc(output)
        return output

我们可以看到，在这个模型中，我们首先对输入进行嵌入（embedding），然后添加位置编码（Positional Encoding）。然后，我们使用`nn.Transformer`来进行多头注意力机制的计算，最后通过一个全连接层进行输出。

下面是位置编码的实现：

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super().__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)

这个模型包含了一个包含多个头的注意力机制，具体实现如下：

class MultiHeadAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout):
        super().__init__()
        self.num_heads = num_heads
        self.hidden_size = hidden_size
        self.head_size = int(hidden_size / num_heads)

        self.q_linear = nn.Linear(hidden_size, hidden_size)
        self.v_linear = nn.Linear(hidden_size, hidden_size)
        self.k_linear = nn.Linear(hidden_size, hidden_size)

        self.dropout = nn.Dropout(p=dropout)
        self.out = nn.Linear(hidden_size, hidden_size)

    def forward(self, q, k, v, mask=None):
        bs = q.size(0)

        # Perform linear operation and split into h heads
        k = self.k_linear(k).view(bs, -1, self.num_heads, self.head_size)
        q = self.q_linear(q).view(bs, -1, self.num_heads, self.head_size)
        v = self.v_linear(v).view(bs, -1, self.num_heads, self.head_size)

        # Transpose to get dimensions bs * h * sl * d_model
        k = k.transpose(1, 2)
        q = q.transpose(1, 2)
        v = v.transpose(1, 2)

        # Calculate attention using function we will define next
        scores = self.calculate_attention(q, k, v, mask)

        # Concatenate heads and put through final linear layer
        concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.hidden_size)
        output = self.out(concat)

        return output

    def calculate_attention(self, q, k, v, mask=None):
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_size)
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == 0, -1e9)
        scores = F.softmax(scores, dim=-1)
        scores = self.dropout(scores)
        output = torch.matmul(scores, v)
        return output

在这个模型中，我们使用了三个线性层分别对输入进行处理，然后将其拆分成多个头。接着，我们使用`calculate_attention`函数计算注意力分数，并将其应用于值向量（value vector）。最后，我们将所有头的输出连接在一起，并通过一个线性层进行输出。

最后，在`TransformerModel`中调用多头注意力机制：

class TransformerModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super().__init__()
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.pos_enc = PositionalEncoding(hidden_size, dropout)
        self.attention = MultiHeadAttention(hidden_size, num_heads, dropout)
        self.fc = nn.Linear(hidden_size, input_size)

    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_enc(x)
        output = self.attention(x, x, x)
        output = self.fc(output)
        return output

在这里，我们将输入向量传递给注意力模型，并将其输出作为最终输出。