raw_weights = torch.matmul(Q, K.transpose(-2, -1)) # 形状 (batch_size, num_heads, seq_len, seq_len) # 对自注意力原始权重进行缩放 scale_factor = K.size(-1) ** 0.5 scaled_weights = raw_weights / scale_factor # 形状 (batch_size, num_heads, seq_len, seq_len) # 对缩放后的...
def forward(self, Q, K, V, mask=None): batch_size = Q.size(0) # 线性投影 Q = self.Wq(Q).view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2) K = self.Wk(K).view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2) V = self.Wv(V).view(batch...
q = q.transpose(1,2) v = v.transpose(1,2) # calculate attention using function we will define next scores = attention(q, k, v, self.d_k, mask, self.dropout) # concatenate heads and put through final linear layer concat = scores.transpose(1,2).contiguous\ .view(bs, -1, self.d...
# 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 = attention(q, k, v, self.d_k, mask, self.dropout) # concatenate heads and put through fina...
1. 2. 3. '2.2.0' 1. 0 一键调用[官方案例] 复现完结 class paddle.nn.Transformer(d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation=‘relu’, attn_dropout=None, act_dropout=None, normalize_before=False, weight_attr=None, ...
k=self.key(x) v=self.value(x) scores=torch.matmul(q, k.transpose(1,2)) attention_weights=self.softmax(scores) weighted_sum=torch.matmul(attention_weights, v) output=weighted_sum+x returnoutput 在上面的代码中,我们定义了一个名为SelfAttention的类,继承自PyTorch的nn.Module类。该类包含了自注...
q=self.query(x) k=self.key(x) v=self.value(x) scores=torch.matmul(q, k.transpose(-2,-1)) attention_weights=torch.softmax(scores, dim=-1) output=torch.matmul(attention_weights, v) returnoutput # 使用示例 input_seq_length=10 input_dim=32 hidden_dim=64 model=SelfAttention(input_dim...
W_Q(input_Q) # 乘以 W(6*6) 变为 1*4*6 Q = Q.view(batch, -1, n_heads, d_k).transpose(1, 2) # 切开为2个Head 变为 1*2*4*3 1批 2个Head 4词 3编码 K = self.W_K(input_K).view(batch, -1, n_heads, d_k).transpose(1, 2) V = self.W_V(input_V).view(batch...
stdv = 1./ math.sqrt(self.v.size(0)) self.v.data.uniform_(-stdv, stdv) def forward(self, q, k, v, mask=None): # 计算 q 和 k 的点积 qk = torch.matmul(q, k.transpose(-1, -2)) / math.sqrt(self.v.size(0)) # 计算注意力权重 attention_weights = F.softmax(self.attn...
w_q(x) k = self.w_k(x) v = self.w_v(x) # 使用RoPE矩阵旋转Q和K q_rotated = (torch.bmm(q.transpose(0, 1), self.R[:m])).transpose(0, 1) k_rotated = (torch.bmm(k.transpose(0, 1), self.R[:m])).transpose(0, 1) # 执行缩放点积注意力 activations = F.scaled_dot_...