03 Transformer 中的多头注意力（Multi-Head Attention）Pytorch代码实现

3：20 来个赞

24：43 弹幕，是否懂了

QKV 相乘（QKV 同源），QK 相乘得到相似度A，AV 相乘得到注意力值 Z

1. 第一步实现一个自注意力机制

自注意力计算

def self_attention(query, key, value, dropout=None, mask=None):
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
    # mask的操作在QK之后，softmax之前
    if mask is not None:
        mask.cuda()
        scores = scores.masked_fill(mask == 0, -1e9)
    self_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        self_attn = dropout(self_attn)
    return torch.matmul(self_attn, value), self_attn

多头注意力

# PYthon/PYtorch/你看的这个模型的理论
class MultiHeadAttention(nn.Module):

    def __init__(self):
        super(MultiHeadAttention, self).__init__()

    def forward(self,  head, d_model, query, key, value, dropout=0.1,mask=None):
        """

        :param head: 头数，默认 8
        :param d_model: 输入的维度 512
        :param query: Q
        :param key: K
        :param value: V
        :param dropout:
        :param mask:
        :return:
        """
        assert (d_model % head == 0)
        self.d_k = d_model // head
        self.head = head
        self.d_model = d_model

        self.linear_query = nn.Linear(d_model, d_model)
        self.linear_key = nn.Linear(d_model, d_model)
        self.linear_value = nn.Linear(d_model, d_model)

        # 自注意力机制的 QKV 同源，线性变换

        self.linear_out = nn.Linear(d_model, d_model)

        self.dropout = nn.Dropout(p=dropout)
        self.attn = None

        # if mask is not None:
        #     # 多头注意力机制的线性变换层是4维，是把query[batch, frame_num, d_model]变成[batch, -1, head, d_k]
        #     # 再1，2维交换变成[batch, head, -1, d_k], 所以mask要在第一维添加一维，与后面的self attention计算维度一样
        #     mask = mask.unsqueeze(1)

        n_batch = query.size(0)

        # 多头需要对这个 X 切分成多头

        # query==key==value
        # [b,1,512]
        # [b,8,1,64]

        # [b,32,512]
        # [b,8,32,64]

        query = self.linear_query(query).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 32, 64]
        key = self.linear_key(key).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 32, 64]
        value = self.linear_value(value).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 32, 64]

        x, self.attn = self_attention(query, key, value, dropout=self.dropout, mask=mask)
        # [b,8,32,64]
        # [b,32,512]
        # 变为三维， 或者说是concat head
        x = x.transpose(1, 2).contiguous().view(n_batch, -1, self.head * self.d_k)

        return self.linear_out(x)