Hi Cbd!



 cbd:

Output is tensor([98310.1016])
after roundoff tensor([98310.1016])
I want exact “98310.1” value. I try to round off but its not working.


The core problem is that 98310.1 is not exactly representable by
the floating numbers that pytorch (and your cpu and gpu) use under
the hood for arithmetic.  (Just to be clear, nor is 0.1.)
Please consider carefully the following:
>>> import torch
>>> torch.__version__
'1.9.0'
>>> torch.set_printoptions (precision=20)
>>> bs = torch.tensor ([0.1], dtype = torch.float)
>>> bs
tensor([0.10000000149011611938])
>>> 1 + bs
tensor([1.10000002384185791016])
>>> (1 + bs) - 1
tensor([0.10000002384185791016])
>>> 98310 + bs
tensor([98310.10156250000000000000])
>>> (98310 + bs) - 98310
tensor([0.10156250000000000000])
>>> bd = torch.tensor ([0.1], dtype = torch.double)
>>> bd
tensor([0.10000000000000000555], dtype=torch.float64)
>>> 1 + bd
tensor([1.10000000000000008882], dtype=torch.float64)
>>> (1 + bd) - 1
tensor([0.10000000000000008882], dtype=torch.float64)
>>> 98310 + bd
tensor([98310.10000000000582076609], dtype=torch.float64)
>>> (98310 + bd) - 98310
tensor([0.10000000000582076609], dtype=torch.float64)

The Wikipedia article on round-off error gives a good introduction to
what is going on.
This is all directly relevant to the related question you raised in this
other thread of yours:

  
    
    
    Floating point addition 
  
  
    I found strange behaviour while adding two tensor. Here “mat” tensor is output of mathematical equation and its precision value is 0 after decimal point ( I checked by placing torch.set_printoptions(precision=10) before print). After that if i do addition for below code it gives me output “98310.1016” instead of “98310.1”. Its very simple but i don’t know from where “.1016” comes in answer instead of “.1”. 
##    mat tensor([[98310., 98310., 98310.],
        [98310., 98310., 98310.],
        [98…
  


Best.
K. Frank