Eh., if you have a PyTorch model that uses non-deterministic tensor operations like matrix multiplications, I think it is fair to call the model non-deterministic, since the matmul is not guaranteed to be deterministic - the non determinism of a matmul isn't a bug but a feature.
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My point is, your inference process is the non-deterministic part; not the model itself.
Eh., if you have a PyTorch model that uses non-deterministic tensor operations like matrix multiplications, I think it is fair to call the model non-deterministic, since the matmul is not guaranteed to be deterministic - the non determinism of a matmul isn't a bug but a feature.
See e.g.https://discuss.pytorch.org/t/why-is-torch-mm-non-determinis...