I suppose that model.cuda() and model.to(device) are the same, but they actually gave me different running time.
I have the follwoing:
device = torch.device("cuda")
model = model_name.from_pretrained("./my_module")  # load my saved model
tokenizer = tokenizer_name.from_pretrained("./my_module")  # load tokenizer
model.to(device)  # I think no assignment is needed since it's not a tensor
model.eval()  # I run my model for testing

However, later testing process takes 2 min 19 sec, which is different from if I do model.cuda() instead of model.to(device), while the latter takes 1 min 08 sec. I know they both are fast, but I don’t understand why their running times are quite different while the two ways of coding should be the same thing.
I was wondering due to the data amount, time might fluctuate. I can understand that. But I just wanna make sure the the two ways of coding a the same.