Why did I get the same two ids when using id() function on two different indexes of a pytorch tensor?

[Matias_Vasquez]

This is well explained in this answer.

Id function of tensor changes all the time

You’re using id() on wrapper objects, that are created on the fly. E.g. when you do a[100000] you get a new object that shares storage with “a”, but also records storage offset. Repeated call a[100000] creates a new wrapper, as it is impossible to cache these.

So when you do this ↓ you might get the same wrapper and just update the offset. This is not always the case. If you run your code multiple times, you will see that sometimes you get different addresses.

@shelvey_jiang

a = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(id(a[0]))
print(id(a[1]))

As mentioned in the previous link, what remains stable is the address of tensor´s storage, which can be accessed with a.storage().data_ptr().

# Here we are referencing a new wrapper that shares memory with 'a'
a = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(a[0].data_ptr())
print(a[1].data_ptr())
# Output:
# That is why we get two different results
# a[0]: 114012224
# a[1]: 114012248

# Here we are referencing where the actual tensor is stored
a = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(a[0].storage().data_ptr())
print(a[1].storage().data_ptr())
# Output:
# a[0]: 114011904
# a[1]: 114011904

But now, when we use data_ptr we get the address of the first element of the tensor. Since it is the same tensor, the first element must be the same.

Here are some other useful answers

https://discuss.pytorch.org/t/why-is-id-tensors-storage-different-every-time/100478

https://discuss.pytorch.org/t/any-way-to-check-if-two-tensors-have-the-same-base/44310/8