I have a neuromorphic board which appears to use the same PCG64 Generator used by numpy as part of the dynamics of the neural network. I can replicate the randomness seen on the board with numpy in a forward sense assuming I seed the numpy Generator properly.
I’d like to train a network to run on this board making use of pytorch’s autograd capability, and using the same seeded PCG64 generated sequence. When I use:
gen1 = torch.Generator() gen1.manual_seed(seed) torch.rand(1, generator=gen1)
I get different results than if I do:
gen2 = numpy.random.default_rng(seed=seed) gen2.random(1)
Is there a way to get the same PCG64 based pseudorandom sequence from both numpy and torch?