I have a few questions regarding RNN (LSTM/GRU) implementation in Gluon and/or Symbolic.
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Is there a difference between how LSTM and GRU are implemented in Gluon vs Symbolic?
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Does Gluon follow the CudNN RNN implementation for LSTM/GRU?
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In case that’s true, how does it handle packing the input elements into contiguous memory? PyTorch has a method called pack_padded_sequence, does Gluon do something like this internally?
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Does Gluon RNN implementation support variable length input? In case it does, what’s the way to pass a variable length NDArray to it since MXNet NDArray requires elements to be of same dimensions?