import torch
from torch.autograd import Variable
import torch.optim
from torch.autograd._functions.linalg import Potrf


def reproduce():
	n_data = 100
	ndim = 100

	x_input = Variable(torch.rand(n_data, ndim) * 2 - 1)
	x_input.data[0, :] = 0
	output = Variable(torch.randn(n_data, 1))

	n_init = 1
	b = Variable(torch.randn(ndim, n_data))
	A = Variable(torch.randn(ndim, ndim))

	chol = Potrf.apply(A.mm(A.t()) + Variable(torch.eye(ndim)))

	pool = torch.multiprocessing.Pool(n_init)
	res = pool.apply_async(torch.gesv, args=(b, A))
	return res.get()


if __name__ == '__main__':
	print(reproduce())

Using Potrf.apply seems to be a source of error,
I am not sure potrf is in stable release,
but you can find source torch.autograd._function.linalg in github.
Using torch.potrf to tensor still causes the same problem.
Thanks for you help!