Hey, thanks for your reply. The post have been flagged by error.
The thing is, the attention mechanism isn’t part of the LSTM anyway (both conceptually and in the code). In ONMT-py, the decoder LSTM feeds its output in the attention layer at each timestep, see https://github.com/pltrdy/OpenNMT-py/blob/master/onmt/Models.py#L108 55
Therefore, I don’t think that the difference comes from attention.