Summary:
Fixed unit test failures for GRU cell first implemented in D5778202
- GRUCell implementation added to rnn_cell.py
- GRU with recurrent attention test added to seq2seq_model_caffe2.py
- seq2seq_rnn.py
- Added specific behavior for 'gru' cell type
- in LSTMWithAttentionDecoder, output_indices fix for GRU cells
- in build_initial_rnn_decoder_states, don't process cell state for GRU cells
Reviewed By: salexspb
Differential Revision: D6316441
fbshipit-source-id: 18668f3db62245c5cdaf3bfa473a40e0feba0473
Summary: GRU is different than LSTM that it only has hidden states but no cell states. So in this case, reusing the code of _LSTM is problematic, as we need to delete the part of creating cell state, and change many other places that use hard-coded 4 (hidden_all, hidden, cell_all, cell) into 2 (hidden_all, hidden). Otherwise GRU will break during the backward pass, when the optimizer tries to apply gradient to each of the parameters, because cell state is never used, so it does not have gradients for the corresponding parameters (i.e., cell_state_w, cell_state_b).
Differential Revision: D5589309
fbshipit-source-id: f5af67dfe0842acd68223f6da3e96a81639e8049
Summary: Implemented python logic and tests to create an RNNCell for GRU. Uses the preexisting GRU Unit Op code.
Reviewed By: salexspb
Differential Revision: D5364893
fbshipit-source-id: 2451d7ec8c2eacb8d8c9b7c893bfd21b65fb9d18
