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多头注意力机制（Multi-Head Attention, MHA），是 Transformer 模型的核心组件之一。以下是对代码的逐行解析和详细说明：
attention-is-all-you-need-pytorch-master\transformer\SubLayers.py
class MultiHeadAttention(nn.Module):''' Multi-Head Attention module ''''''n_head: 多头注意力head数量 8d_model: 输入向量的维度 512d_k : 单head中 Q, k 向量的维度 512 / 8 = 64d_v : 单head中V向量的维度 d_k, 与d_v是独立的，可以相等也可以不等。在这里d_k = d_v 64'''def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):super().__init__()self.n_head = n_headself.d_k = d_kself.d_v = d_vself.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)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)def forward(self, q, k, v, mask=None):'''q, k, v 的形状为 (32, 10, 512)（batch_size=32，seq_len=10，d_model=512）'''d_k, d_v, n_head = self.d_k, self.d_v, self.n_headsz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)residual = q# Pass through the pre-attention projection: b x lq x (n*dv)# Separate different heads: b x lq x n x dvq = self.w_qs(q).view(sz_b, len_q, n_head, d_k) #view 为多头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, w的维度为(32, 10, 8, 64) 转换后维度为(32, 8, 10, 64)q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)if mask is not None:mask = mask.unsqueeze(1) # For head axis broadcasting.'''attn: (32, 8, 10, 10)输出q: (32, 8, 10, 64)'''q, attn = self.attention(q, k, v, mask=mask)# 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) #(32, 10, 512)q = self.dropout(self.fc(q))q += residualq = self.layer_norm(q)return q, attnattention-is-all-you-need-pytorch-master\transformer\Modules.py
class ScaledDotProductAttention(nn.Module):''' Scaled Dot-Product Attention '''def __init__(self, temperature, attn_dropout=0.1):super().__init__()self.temperature = temperatureself.dropout = nn.Dropout(attn_dropout)def forward(self, q, k, v, mask=None):'''q: (32, 8, 10, 64) -> 缩放 -> (32, 8, 10, 64)k: (32, 8, 10, 64) -> 转置 -> (32, 8, 64, 10)attn = q @ k.transpose(2, 3): (32, 8, 10, 10)attn: (32, 8, 10, 10) -> softmax -> (32, 8, 10, 10)v: (32, 8, 10, 64)output = attn @ v: (32, 8, 10, 64)'''attn = torch.matmul(q / self.temperature, k.transpose(2, 3))if mask is not None:attn = attn.masked_fill(mask == 0, -1e9)attn = self.dropout(F.softmax(attn, dim=-1))output = torch.matmul(attn, v)return output, attn