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: Add CUDA version of GRU operator
Reviewed By: jamesr66a
Differential Revision: D5571043
fbshipit-source-id: 332aa64fc8a9116cc33382f2b2907080e58c13b3
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
Summary:
Just an implementation of the forward pass of the GRU Unit Op, not the full RNNCell.
Functions were created to mimic LSTM implementation as closely as possible.
Backwards pass implementations are defined in GRU_unit_op.{h, cc}
assertGradientChecks call added to gru_cell_test.py
Reviewed By: salexspb
Differential Revision: D5364856
fbshipit-source-id: 09cff4478091827763b40cc331e4e0abf0ec258f
Summary:
Just an implementation of the forward pass of the GRU Unit Op, not the full RNNCell.
Functions were created to mimic LSTM implementation as closely as possible.
Implementation defined in GRU_unit_op.{h, cc}
tests put in gru_cell_test.py, which import rnn_cell_test_util.py for sigmoid, tanh, and _prepare_rnn functions.
Reviewed By: jamesr66a
Differential Revision: D5363697
fbshipit-source-id: f9ba9fe0be01ffc868dd22027be8be4975b84998
