c = np.arange(1,37).reshape(3,4,3) c =torch.Tensor(c) torch.matmul(a,c) RuntimeError Traceback (most recent call last) <ipython-input-24-3e3273a3a8c4> in <cell line: 4>() 2 c =torch.Tensor(c) 3 ---> 4 torch.matmul(a,c) 希望自己可以坚持用文章记录自己的算法成长之路。欢...
Python 是动态输入类型的语言,像 Matlab 一样,变量类型是动态推断的;静态类型的 C 语言须声明变量类型,如 int a = 1,而 Python 只需要 a = 1; Python中代码包含关系使用缩进来表示,而不是使用括号来进行包含。 一、变量类型与输出语句 1. 变量类型 基本变量类型:字符串、数字、布尔型; 高级变量类型:集合、...
torch.matmul(query, rel_pos.transpose(-1, -2)) 其中数学上的依据可以参考苏神的博客,俺觉得说的通俗易懂(苏神牛逼) 让研究人员绞尽脑汁的Transformer位置编码 - 科学空间|Scientific Spaces 从直观角度理解这个求注意力方法怎么理解呢? 这里query就是bert里的Q,relpos就是上文中好不容易求出来的相对距离矩阵,...
matmul(q, k) if self.scale: w = w / math.sqrt(v.size(-1)) b_subset = self.b[:, :, :w.size(-2), :w.size(-1)] if sequence_mask is not None: b_subset = b_subset * sequence_mask.view( sequence_mask.size(0), 1, -1) b_subset = b_subset.permute(1, 0, 2, 3) ...
matmul(q, k.transpose(-2, -1)) / math.sqrt(q.size(-1)) attn_weight = torch.nn.functional.softmax(div, dim=-1) output = torch.matmul(attn_weight, v) return output func = Model() x = torch.randn(1, 4, 2, 2) func = Model() with torch.no_grad(): print(func(x.clone()...
matmul(querys, keys.transpose(2, 3)) # [h, N, T_q, T_k] scores = scores / (self.key_dim ** 0.5) scores = F.softmax(scores, dim=3) # out = score * V out = torch.matmul(scores, values) # [h, N, T_q, num_units/h] out = torch.cat(torch.split(out, 1, dim=0)...
defforward(self,Q,K,V,attn_mask=None): defforward(self,Q,K,V,attn_mask): scores=torch.matmul(Q,K.transpose(-1,-2))/np.sqrt(d_k)# scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)] ifattn_maskisnotNone: ...
torch.ormqr(input, input2, input3, left=True, transpose=False) → Tensor torch.pinverse(input, rcond=1e-15) → Tensor torch.qr(input, some=True, out=None) -> (Tensor, Tensor) torch.solve(input, A, out=None) -> (Tensor, Tensor) ...
(self, query, key, value, atten_mask): scale = 0.08838 qk = torch.matmul(query, key.transpose(2, 3)).mul(scale) qk = qk + atten_mask * (-10000.0) softmax_res = torch.nn.functional.softmax(qk, dim=-1) attention_out = torch.matmul(softmax_res, value) return attention_out def...
r =1.0/ (torch.matmul(ex, c.transpose(-1,-2)) + eps)whileiter < max_iter: iter +=1cinv = torch.matmul(r.transpose(-1,-2), ex)iftorch.max(torch.abs(cinv * c -1)) <= tol:breakc =1.0/ (cinv + eps) r =1.0/ ((ex @ c.transpose(-1,-2)) + eps) ...