Your code seems to work fine:
class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.params = [torch.randn(10, 10) for i in range(10)]
        for i, tensor in enumerate(self.params):
             self.register_buffer("tensors%d"%i, tensor)

model = MyModule()
print(model.tensors0.type())
> torch.FloatTensor

model = model.to('cuda')
print(model.tensors0.type())
> torch.cuda.FloatTensor

Is this not working in your case?