torch.Tensor.is_leaf

Tensor.is_leaf

所有requires_grad属性为False的张量将按惯例视为叶张量。

对于具有requires_grad属性且值为True的张量，如果它们是由用户创建的，则这些张量将被视为叶张量。这意味着它们不是某个操作的结果，因此grad_fn为None。

只有叶子 Tensor 在调用 backward() 时才会填充其 grad 属性。为了使非叶子 Tensor 的 grad 属性被填充，你可以使用 retain_grad()。

示例：

>>> a = torch.rand(10, requires_grad=True)
>>> a.is_leaf
True
>>> b = torch.rand(10, requires_grad=True).cuda()
>>> b.is_leaf
False
# b was created by the operation that cast a cpu Tensor into a cuda Tensor
>>> c = torch.rand(10, requires_grad=True) + 2
>>> c.is_leaf
False
# c was created by the addition operation
>>> d = torch.rand(10).cuda()
>>> d.is_leaf
True
# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
>>> e = torch.rand(10).cuda().requires_grad_()
>>> e.is_leaf
True
# e requires gradients and has no operations creating it
>>> f = torch.rand(10, requires_grad=True, device="cuda")
>>> f.is_leaf
True
# f requires grad, has no operation creating it