# Output of torch.norm(x) depends on size of x, not in a good way?

I am confused: can you help me understand why does torch.norm(x) depend on the size of x? Thanks!

``````N=10000
x=torch.ones(N)
n1=torch.norm(x)
n2=numpy.sqrt(N)
``````
``````[output]: n1=tensor(100.), n2=100.
``````
``````N=40000
x=torch.ones(N)
n1=torch.norm(x)
n2=numpy.sqrt(N)
``````
``````[output]: n1=tensor(266.0605) n2=200.
``````

We calculate the Vector 2-norm by default. And that is a function of the total sum, by definition:

http://mathworld.wolfram.com/L2-Norm.html

However, once you hit floating point precision limits, you get “inaccurate” results…

I’m taking a look at what these accuracy limits are, wrt floating point, and if that might be affecting things.

I know that, but for x=torch.ones(40000), the answer should be 200, not 266.0605 as I get with torch.norm(x)

I’m sorry, I posted my answer as I was typing it, by mistake.

I looked into this further, it looks like a bug that we introduced in 1.0.0, I’m taking a further look and filing an issue.

It is only affected on the CPU, and produces the correct result on GPU.

Got it, I think gpu is affected as well, though.

I checked the GPU implementation via:

``````>>> import torch
>>> x = torch.ones(40000)
>>> torch.norm(x)
tensor(266.0605)
>>> torch.norm(x.to(dtype=torch.float32, device='cuda'))
tensor(200., device='cuda:0')
``````

Seems to work fine.
Are you seeing incorrect result on GPU as well?

Double checked it – no, you are right, GPU result is correct. Thanks!

I filed an issue at https://github.com/pytorch/pytorch/issues/15602
It will for sure be fixed in our next minor release on Jan 15th, and will be fixed in our nightlies much sooner than that, I’m having it looked at with high priority.

Really sorry for the bug!

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just fyi, this is fixed now via https://github.com/pytorch/pytorch/pull/15885 and will go into the 1.0.1 release in the time window of < 1 week from now.

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