Torch and Numpy have different norm

It seems to me that np.linalg.norm and torch.norm have different outputs.

torch version: 1.0.0

import torch
import numpy as np

var_torch = torch.randn((3,333,333))
var_numpy = var_torch.detach().numpy()
>>> tensor(1884.0406)
>>> 577.75214

Is this expected?

I think they handle the dimensions differently. If I specify the order of dimensions, then I can get the same results using NumPy and PyTorch, so first taking the norm of dimensions 1, 2 and then the norm of the resulting tensor/array:

>>> np.linalg.norm(np.linalg.norm(var_numpy, axis=(1,2)))
>>> torch.norm(torch.norm(var_torch, dim=(1,2)))

This result is consistent with what NumPy gives without specifying dimensions. However, I don’t know how PyTorch calculates its result tensor(1883.6027):


@smth @ptrblck Any comment?

This issue might be related to this bug.
Soumith opened an issue here to track it.

Just to double check if these issues are related: Do you get the correct result on GPU?

Thanks @ptrblck. I get correct results on GPU:

>>> torch.norm(
tensor(577.3682, device='cuda:0')
1 Like

Thanks for testing it @vmirly1 (as I currently don’t have access to a GPU :wink: ).
In that case it looks like they are related.

As Soumith said, it’s a high priority bug and should be fixed before the next minor release.

1 Like

Ahh I see the duality with the previous thread.
Thanks so much.


Sorry to reopen this issue, I found that np.linalg.norm() and torch.norm give similar (I say similar is because the results have different decimal points) results for Frobenius norm, but for 2-norm, the results are more different:

Here is the code for reproduce:

import torch
from scipy.linalg import norm
import numpy as np
a = np.arange(9) - 4.0
a = a.reshape((3, 3))
test1 = np.linalg.norm(a)
test2 = torch.norm(torch.from_numpy(a).cuda())
tensor(7.7460, device=‘cuda:0’, dtype=torch.float64)
test1 = np.linalg.norm(a, ord=2)
test2 = torch.norm(torch.from_numpy(a).cuda(), p=2)
tensor(7.7460, device=‘cuda:0’, dtype=torch.float64)

Do you think this is normal? I’m using Pytorch 1.2.0 on Ubuntu 18.04. I need to obtain exactly the same results for 2-norm.