I did not benchmark this. It is just reading protocol buffers, which should be pretty fast. Running tf sess dataloader should be similar in speed, but then you incur transformation from TF tensor to numpy and  PyTorch tensors.
Converting to np offline and then using pytorch dataloader could be the best, as pytorch dataloader uses multiple workers.
PyTorch nightly has IterableDataset abstraction in works, that should allow plugging steraming readers into dataloader very easy, see https://pytorch.org/docs/master/data.html#dataset-types 83