addcdiv(self, tensor1, tensor2, *args, **kwargs) addcdiv_(self, tensor1, tensor2, *args, **kwargs) addcmul(self, tensor1, tensor2, *args, **kwargs) addcmul_(self, tensor1, tensor2, *args, **kwargs) addmm(self,
print("Elements number along axis 0 of Tensor:", Tensor.shape[0]) print("Elements number along the last axis of Tensor:", Tensor.shape[-1]) print('Number of elements in Tensor: ', Tensor.numel()) #用.numel表示元素个数 1. 2. 3. 4. 5. 6. 7. Tensor的axis、shape、dimension、ndi...
完整的MOE模型: classMoE(nn.Module):def__init__(self,trained_experts):super(MoE,self).__init__()self.experts = nn.ModuleList(trained_experts)num_experts = len(trained_experts)# Assuming all experts have the same input dimensioninput_dim = trained...
Support aten::sum with bool tensor input by @mfeliz-cruise in #1512 [fix]Disambiguate cast layer names by @mfeliz-cruise in #1513 feat: Add functionality for easily benchmarking fx code on key models by @gs-olive in #1506 [feat]Canonicalize aten::multiply to aten::mul by @mfeliz-crui...
创建Creating Tensor 标量、向量、矩阵、tensor 标量scalar scalar=torch.tensor(7)scalar.ndim# 查看维度scalar.item()# 转换成 python中的整数 1. 2. 3. 向量vector vector=torch.tensor([7,7])vector.shape#查看形状 1. 2. 矩阵matrix MATRIX=torch.tensor([[7,8],[9,10]]) ...
outputs=torch.stack([expert(x)forexpertinself.experts],dim=2)# Adjust the weights tensor shape to match the expert outputs weights=weights.unsqueeze(1).expand_as(outputs)# Multiply the expert outputswiththe weights and # sum along the third dimensionreturntorch.sum(outputs*weights,dim=2) ...
( [expert(x) for expert in self.experts], dim=2) # Adjust the weights tensor shape to match the expert outputs weights = weights.unsqueeze(1).expand_as(outputs) # Multiply the expert outputs with the weights and # sum along the third dimension return torch.sum(outputs * weights, dim=...
PyTorch 中的乘法:mul()、multiply()、matmul()、mm()、mv()、dot() torch.mul() 函数功能:逐个对 input 和 other 中对应的元素相乘. 本操作支持广播,因此 input 和 other 均可以是张量或者数字. 举例如下: >>> import torch >>> a = torch.randn(3) >>> a tensor([-1.7095, 1.7837, 1.1865]) ...
Returns a new tensor that is anarrowed version ofinputtensor. The dimensiondimis input fromstarttostart+length. The returned tensor andinputtensor share the same underlying storage. Parameters input (Tensor) – the tensor tonarrow dim (int) – the dimension along which tonarrow ...
For example, if your first tensor has the shape of (b1, m, n) and your second tensor has the shape of (b2, n, p), where b1 != b2 but b1 or b2 is 1, you can like this: if b1 == 1: # expand the first tensor along the batch dimension input1 = input1.expand(b2, m, ...