Assuming you are using cudnn, then the answer is: yes, more fast kernel will be implemented for more workloads in the future.
You could use torch.backends.cudnn.benchmark = True at the beginning of your script to use the cudnn heuristics, which should pick the currently fastest available kernel.
I would also recommend to use the latest cudnn version (7.6.5.32).
Note that benchmark=True will profile the kernels in the first run for each new input shape, so your profiling should start after a warmup phase.
You could also compare the current speed to the native implementation by disabling cudnn via torch.backends.cudnn.enabled = False.