[WIP][MXNET-107] Fused LSTM implementation for CPU

python/mxnet/gluon/rnn/rnn_layer.py

@@ -23,7 +23,6 @@
 from __future__ import print_function
 __all__ = ['RNN', 'LSTM', 'GRU']
 
-from ...autograd import is_training
 from ... import ndarray
 from .. import Block
 from . import rnn_cell
@@ -186,8 +185,7 @@ def forward(self, inputs, states=None):
             for i in range(self._dir):
                 self.i2h_weight[i].shape = (self._gates*self._hidden_size, inputs.shape[2])
                 self.i2h_weight[i]._finish_deferred_init()
-        if inputs.context.device_type == 'gpu' or \
-            (not is_training() and self._mode == 'lstm'):
+        if inputs.context.device_type == 'gpu' or self._mode == 'lstm':
             out = self._forward_kernel(inputs, states)
         else:
             out = self._forward(inputs, states)

src/operator/cudnn_rnn-inl.h

@@ -38,7 +38,7 @@ namespace mxnet {
 namespace op {
 #if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5
 template<typename DType>
-class CuDNNRNNOp : public Operator {
+class CuDNNRNNOp : public Operator{
  public:
   explicit CuDNNRNNOp(RNNParam param) {
     this->param_ = param;
@@ -101,6 +101,7 @@ class CuDNNRNNOp : public Operator {
       CUDNN_CALL(cudnnDestroyDropoutDescriptor(dropout_desc_));
       Storage::Get()->Free(dropout_states_);
       Storage::Get()->Free(reserve_space_);
+      init_cudnn_ = false;
     }
   }