foo.py

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

batch_size = 32
model = Net()
input = torch.randn(batch_size, 1, 28, 28)

################### forward ################

# forward on TPU
xmodel = torch.jit.trace(torch.zeros(batch_size, 1, 28, 28))(model)
output = xmodel(input)

# forward on CPU
output_cpu = model(input)

# print difference
print((output - output_cpu).abs().max().item())