談一談對transformer的理解（代碼）

關於transformer的原理，這裏就不多說，主要還是結合論文中的圖來對代碼進行一下講解。

看這張圖，其實可以看到最核心的部分就是下面這一塊：

關於講解，我就直接寫在代碼裏面，用中文來對其進行詳細的一個介紹。相對應的代碼如下：

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class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    def forward(self, q, k, v, mask=None):

        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))  # query和每個key進行相似度計算得到權重

        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)

        attn = self.dropout(F.softmax(attn, dim=-1))   # 使用一個softmax函數對這些權重進行歸一化
        output = torch.matmul(attn, v)    # 權重和相應的鍵值value進行加權求和得到最後的attention

        return output, attn

 相對應的公式和圖，看下面。

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 除了點積之外，還可以用cosine的相似性、mlp網絡等來計算score

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class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()

        self.n_head = n_head  # 注意力頭的數目，說白了就是你想吧hidden_size 分成幾部分來分別計算，一般取8/12
        self.d_k = d_k  # 每個注意力頭的大小   
        self.d_v = d_v  # 每個注意力頭的大小
        #  d_model == n_head*d_k
        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)   # 這裏的d_model=q的hidden_size
        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc = nn.Linear(n_head * d_v, d_model, bias=False) 

        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)

        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)   #  d_model=hidden_size

    def forward(self, q, k, v, mask=None):  # 一般q!=(k==v),如果是self_atten,就是q==k==v
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)  # q:(batch, seq_len, hidden_size)

        residual = q  # 保留原始的q,後面做完attention之後要把原始的q進行相加，具體看上圖最左邊的黑色箭頭
        q = self.layer_norm(q)  # q:(batch, seq_len, hidden_size)

        # Pass through the pre-attention projection: b x lq x (n*dv)
        # Separate different heads: b x lq x n x dv
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)  # (batch, seq_len, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # Transpose for attention dot product: b x n x lq x dv
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)   # (batch, n_head, seq_len, d_k)

        if mask is not None:
            mask = mask.unsqueeze(1)  # For head axis broadcasting.

        q, attn = self.attention(q, k, v, mask=mask)  # 要計算atten係數和更新之後的q

        # Transpose to move the head dimension back: b x lq x n x dv
        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)  #  (batch, seq_len, hidden_size)
        q = self.dropout(self.fc(q))
        q += residual  # 將原始的q進行相加

        return q, attn    # attn裏面是所有的係數，其實已經用過了，就沒啥作用了，主要保留的是經過atten之後的q

class PositionwiseFeedForward(nn.Module):
    ''' A two-feed-forward-layer module '''

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid)  # position-wise
        self.w_2 = nn.Linear(d_hid, d_in)  # position-wise
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):

        residual = x
        x = self.layer_norm(x)

        x = self.w_2(F.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual 

        return x