diff --git a/src/operator/nn/mkldnn/mkldnn_rnn.cc b/src/operator/nn/mkldnn/mkldnn_rnn.cc
index f713c497a077..05401b2b80f9 100644
--- a/src/operator/nn/mkldnn/mkldnn_rnn.cc
+++ b/src/operator/nn/mkldnn/mkldnn_rnn.cc
@@ -483,8 +483,10 @@ void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_
   char *weights_ptr = static_cast<char *>(w_ptr);
   size_t wx_bytes = GetRnnGatesNum(param_.mode) * param_.state_size *
         param_.input_size * dtype_bytes;  //* DIMS: ngates x state_size x input_size
+  size_t wh_bytes = GetRnnGatesNum(param_.mode) * param_.state_size *
+        param_.state_size * dtype_bytes;  //* DIMS: ngates x state_size x state_size
   char *l2r_wx = weights_ptr;
-  char *l2r_wh = l2r_wx + wx_bytes;  //* DIMS: ngates x state_size * state_size
+  char *l2r_wh = l2r_wx + wx_bytes;       //* DIMS: ngates x state_size * state_size
 
   if (param_.num_layer == 1 && param_.bidirectional) {
     //* single bidirectinal layer, concat weights on direction axis
@@ -494,8 +496,8 @@ void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_
     ConcatWeights(*weights_iter_r_, 1, {l2r_wh, r2l_wh}, format_tag::ldgoi);
   } else if (param_.num_layer == 1 && !param_.bidirectional) {
     //* single uni-directional layer, no concatenate operator needed
-    weights_layer_r_->set_data_handle(l2r_wx);
-    weights_iter_r_->set_data_handle(l2r_wh);
+    std::memcpy(weights_layer_r_->get_data_handle(), l2r_wx, wx_bytes);
+    std::memcpy(weights_iter_r_->get_data_handle(), l2r_wh, wh_bytes);
   } else if (param_.num_layer > 1 && !param_.bidirectional) {
     //* concat fused multi-layer weights on layer axis
     std::vector<void *> l2r_wx_ptrs;
@@ -514,8 +516,6 @@ void MKLDNNRnnForward::SetWeightsMem(MKLDNNRnnMemMgr* mgr, void *w_ptr, void *b_
   }
 
   // Adjust gates order of LBR-GRU among concatenated memory inplace.
-  //* DIMS: ngates x state_size x state_size (ngates = 3, when mode == gru)
-  size_t wh_bytes = 3 * param_.state_size * param_.state_size * dtype_bytes;
   char* fused_wx = static_cast<char*>(weights_layer_r_->get_data_handle());
   char* fused_wh = static_cast<char*>(weights_iter_r_->get_data_handle());
   if (param_.mode == rnn_enum::kGru) {
@@ -928,7 +928,7 @@ void MKLDNNRnnOp::Forward(const OpContext &ctx,
     weights_version_ = inputs[rnn_enum::kParams].version();
   }
 
-  if (!initialized_ || is_training || fwd_trn_vec_.size() == 0) {
+  if (!initialized_ || is_training || fwd_inf_vec_.size() == 0) {
     Init(ctx, inputs, req, outputs);
   }