您已经回答了自己的问题,即下划线表示PyTorch中的就地操作.但是,我想简要指出为什么就地操作会出现问题：

>首先,在大多数情况下,建议在PyTorch网站上不要使用就地操作.除非在沉重的内存压力下工作,否则在大多数情况下,不使用就地操作会更有效率.

https://pytorch.org/docs/stable/notes/autograd.html#in-place-operations-with-autograd

>其次,在使用就地操作时可能会出现计算梯度的问题：

Every tensor keeps a version counter, that is incremented every time

it is marked dirty in any operation. When a Function saves any tensors

for backward, a version counter of their containing Tensor is saved as

well. Once you access self.saved_tensors it is checked, and if it is

greater than the saved value an error is raised. This ensures that if

you’re using in-place functions and not seeing any errors, you can be

sure that the computed gradients are correct.

07001

这是从您发布的答案中摘录并经过稍微修改的示例：

首先是就地版本：

import torch

a = torch.tensor([2, 4, 6], requires_grad=True, dtype=torch.float)

adding_tensor = torch.rand(3)

b = a.add_(adding_tensor)

c = torch.sum(b)

c.backward()

print(c.grad_fn)

导致此错误：

---------------------------------------------------------------------------

RuntimeError Traceback (most recent call last)

in

2 a = torch.tensor([2, 4, 6], requires_grad=True, dtype=torch.float)

3 adding_tensor = torch.rand(3)

----> 4 b = a.add_(adding_tensor)

5 c = torch.sum(b)

6 c.backward()

RuntimeError: a leaf Variable that requires grad has been used in an in-place operation.

其次,非就地版本：

import torch

a = torch.tensor([2, 4, 6], requires_grad=True, dtype=torch.float)

adding_tensor = torch.rand(3)

b = a.add(adding_tensor)

c = torch.sum(b)

c.backward()

print(c.grad_fn)

哪个工作得很好-输出：

因此,作为总结,我只想指出要在PyTorch中谨慎使用就地操作.