While looking at the definition of the LSTM implemented in PyTorch (http://pytorch.org/docs/master/nn.html#lstm 25), I realized that the cells i, f, g and o have two biases each, instead of a single one. Clearly, a definition with a single bias for each of these cells would be equivalent, in the sense that the two biases could be merged into one by summing them. Is there any implementation advantage in considering two instead of one?