I tested .cpu() speed with the following code.
import torch
import torch.nn as nn
from time import time
import numpy as np

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10000, 1000)
        self.fc2 = nn.Linear(1000, 1000)
        self.fc3 = nn.Linear(1000, 1000)
        self.fc4 = nn.Linear(1000, 1000)
    def forward(self,x):
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        x = self.fc4(x)
        return x

net = Net()
net.cuda()
net.eval()

try1_time = []
try2_time = []
try3_time = []

for _ in range(10):
    x = torch.FloatTensor(10,10000).random_().cuda()
    with torch.no_grad():
        y1 = net(x)

    #try1 
    st = time()
    y1_numpy = y1.cpu().numpy()
    try1_time.append(time()-st)


    y1 = y1*10
    #try2
    st = time()
    y1_numpy = y1.cpu().numpy()
    try2_time.append(time()-st)

    with torch.no_grad():
        y2 = net(x) # y2, not y1
    #try3
    st = time()
    y1_numpy = y1.cpu().numpy() # y1
    try3_time.append(time()-st)


print ("try1:%fs" %(np.mean(try1_time)))
print ("try2:%fs" %(np.mean(try2_time)))
print ("try3:%fs" %(np.mean(try3_time)))

result is
try1:0.001630s
try2:0.000073s
try3:0.001641s

I expected try2 time = try3 time, because y1 is not affected by y2 = net(x).
How can I speed up try3?