Transformer模型的自注意力机制的代码是什么

自注意力机制的代码可以使用PyTorch实现如下：

class SelfAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout_prob):
        super(SelfAttention, self).__init__()

        self.num_heads = num_heads
        self.head_size = hidden_size // num_heads

        self.query = nn.Linear(hidden_size, hidden_size)
        self.key = nn.Linear(hidden_size, hidden_size)
        self.value = nn.Linear(hidden_size, hidden_size)

        self.dropout = nn.Dropout(dropout_prob)

    def forward(self, input_tensor):
        batch_size = input_tensor.size(0)
        query = self.query(input_tensor).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        key = self.key(input_tensor).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        value = self.value(input_tensor).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)

        attention_scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.head_size)
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        context = torch.matmul(self.dropout(attention_probs), value)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.head_size)

        return context

这段代码定义了一个SelfAttention模块，其中包含了query、key和value三个线性层，并且使用了多头注意力机制。在forward方法中，将输入张量通过query、key和value三个线性层，然后将结果进行reshape、transpose等操作，最后计算注意力分数，然后通过softmax函数归一化得到注意力分布，然后将分布与value相乘得到上下文向量。最后将多头的上下文向量拼接起来，得到最终的输出