Pytorch preferred way to copy a tensor

Question

There seems to be several ways to create a copy of a tensor in Pytorch, including

y = tensor.new_tensor(x) #a

y = x.clone().detach() #b

y = torch.empty_lik         


        

Answer 1

One example to check if the tensor is copied:

import torch
def samestorage(x,y):
    if x.storage().data_ptr()==y.storage().data_ptr():
        print("same storage")
    else:
        print("different storage")
a = torch.ones((1,2), requires_grad=True)
print(a)
b = a
c = a.data
d = a.detach()
e = a.data.clone()
f = a.clone()
g = a.detach().clone()
i = torch.empty_like(a).copy_(a)
j = torch.tensor(a) # UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).


print("a:",end='');samestorage(a,a)
print("b:",end='');samestorage(a,b)
print("c:",end='');samestorage(a,c)
print("d:",end='');samestorage(a,d)
print("e:",end='');samestorage(a,e)
print("f:",end='');samestorage(a,f)
print("g:",end='');samestorage(a,g)
print("i:",end='');samestorage(a,i)

Out:

tensor([[1., 1.]], requires_grad=True)
a:same storage
b:same storage
c:same storage
d:same storage
e:different storage
f:different storage
g:different storage
i:different storage
j:different storage

The tensor is copied if the different storage shows up. PyTorch has almost 100 different constructors, so you may add many more ways.

If I would need to copy a tensor I would just use copy(), this copies also the AD related info, so if I would need to remove AD related info I would use:

y = x.clone().detach()