This is very straightforward to do with Gluon. You need to set the grad_req in your network Parameter instances to 'add' and manually set the gradient to zero using zero_grad() after each Trainer.step() (see here). To set grad_req to 'add':
for p in net.collect_params().values():
p.grad_req = 'add'
And similarly call zero_grad() on each parameter after calling Trainer.step(). Remember to modify batch_size argument of trainer.step() accordingly.