class Model(nn.Module):
    def __init__(self):
         pass
    def forward(self, inputs1, inputs2, inputs3):
         # inputs1, inputs2, inputs3 are already in gpu.
         pass

model = Model()
model = torch.nn.DataParallel(model)
model = model.cuda()

Here the inputs1, inputs2, inputs3 are in gpu even though i pass them as just tensor with cpu because of the model.cuda(). The inputs2 is so big that crashes the program with cuda out of memory . Is there a way to force inputs2 to be in cpu without converting them to cpu in the forward function. ?