Not sure how this implemented in Torch CUDA, but I believe a combination of Parallelism and Optimization should play their role to get us the fastest and at the same time the correct answer. Since the cpu time for this is quite fast  I am sure the optimization does its job for overlapping indices either for Torch (CPU) or Numpy.
In my view if overlapping indices are allowed for Torch (CUDA) the behaviour should be deterministic and the same as CPU or it should raise run-time errors.