version: pytorch==1.3.0
code：
import torch input = torch.randn((2, 128, 10, 6), dtype=torch.float32) out = input.sum() print("%3.10f" % out.data) << 0.0181007385
Compute comparison tests using the same data：
sum：0.0179971529
numpy： 0.017990112. (RE=0.04%)
torch cuda：0.0179862976. (RE=0.06%)
torch cpu：0.0181007385 （RE=0.57%）
The error of summation of float32 type data is normal, and the precision is close compared with other computed data. However, the calculation results of the Torch CPU vary greatly. How to determine the accuracy of the data?
Is it because the accumulated errors are processed by other computing libraries, while the Torch CPU does not have higher errors? Or is the processing of TorchCPU a special process?
Where can I see the implementation of the cumulative calculation?