Summary:
Added option to recurrent_net and RNNCell's for forward_only. If this is set, the backward_step_net is not passed to the operator.
When backward_step_net is not available, operator knows it is in forward_only mode and does not create workspaces for each step but cycles
through only one private workspace.
Note: we could avoid doing a lot of work in recurrent.py:recurrent_network call when backward step is not needed, but doing that nicely requires
more refactoring that I did not want to do now. Thus, we create the backward step nets etc, but just don't pass it to the op.
This can be used to create more efficient inference models. You can also sanitize existing inference nets and remove the backward_step_net argument to
get the benefits.
Reviewed By: salexspb
Differential Revision: D4916482
fbshipit-source-id: c99b93c9cb897c32b0f449253f7f6d6a942618ad
Summary: CuDNN LSTM weights were incorrectly sized for layers > 0: there was assumption that the input size to middle layers is same as for the first layer, but actually the middle layer will get input from a layer below, which will have dimension equal to the output dimension (hidden dimension). This worked fine when input_dim and hidden_dim were equal, as are the default params for lstm_benchmark.
Reviewed By: salexspb
Differential Revision: D4922824
fbshipit-source-id: 3ed05529dcb0a4e66ad440084a55df1c5932fd33
Summary: This is the nice way to re-use RNN layers for training and for inference.
Reviewed By: salexspb
Differential Revision: D4825894
fbshipit-source-id: 779c69758cee8caca6f36bc507e3ea0566f7652a
