[WIP][MXNET-107] Fused LSTM implementation for CPU (apache#10104)

* register RNN fused-API with nnvm, finish single-layer && undirection LSTM forward function

* fix coding style and lint complains

* add single-layer && undirectional LSTM backward function

* make interface universal for other RNN mode

* share intermediate result between forward and backward in a trick way

* add comments for important parameters

* modify testcase

* Fix coding style and error message

* fix openmp collapse error

* fix const

* remove rnn.cu and skip related testcases temporarily for building on GPU

* support multi-layer and bidirectional for lstm inference

* remove some testcaseS in test_gluon_rnn.py to build on GPU

* remove testcase between fp32 and fp64 temporarily

* retrigger ci

* fix some logs

* use a better way to share memory

* fix cudnn registration

* fix invariant calculations and enable some gpu testcases

* add thread local cache for cudnn rnn op

* add thread local cache for rnn op

* fix bugs

* remove some testcases to check segmentfault

* remove cudnn registeration to check segmentfault

* support multi-layer for LSTM Training

* modify lstm testcase

* add bidirectional support for lstm

* fix gluon and coding style

* fix bugs

* remove nnvm registration

* enable gpu testcases

* add detailed descriptions

* add dropout check

* fix workspace size

* dropout is not supported, add unit test for it

* fix review comments

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;
     }
   }