Hi,
These two should be equivalent even if they define different computational graphs (potentially doing broadcasting in a different way).
That means that, during training, because of floating point precision, they can end up giving noticeably different results as errors are amplified by the training.
Do you have a small code sample to reproduce the behavior you present?