Merge branch 'rnn_operator' into gru_operator

src/rewrite_rnn.cpp

@@ -43,8 +43,8 @@ void rewrite_rnn::apply(program& prog) const
                 auto r_reverse = prog.insert_instruction(ins, op::slice{{0}, {1}, {2}}, args[2]);
 
                 // process bias
-                instruction_ref bias_forward, bias_reverse;
-                bias_forward = bias_reverse = prog.end();
+                instruction_ref bias_forward = prog.end();
+                instruction_ref bias_reverse = prog.end();
                 if(args.size() >= 4 && args[3]->get_operator().name() != "undefined")
                 {
                     bias_forward = prog.insert_instruction(ins, op::slice{{0}, {0}, {1}}, args[3]);
@@ -53,7 +53,8 @@ void rewrite_rnn::apply(program& prog) const
 
                 // process intial hidden state, it could be the 6th argument
                 // or the 5th one (if the sequence len argument is ignored)
-                instruction_ref ih_forward, ih_reverse;
+                instruction_ref ih_forward{};
+                instruction_ref ih_reverse{};
                 if(args.size() == 6 && args[5]->get_operator().name() != "undefined")
                 {
                     ih_forward = prog.insert_instruction(ins, op::slice{{0}, {0}, {1}}, args[5]);
@@ -215,7 +216,8 @@ std::vector<instruction_ref> rewrite_rnn::rnn_cell(bool is_forward,
         bias       = prog.insert_instruction(ins, op::broadcast{1, sih->get_shape()}, b);
     }
 
-    instruction_ref hidden_out = prog.end(), last_out;
+    instruction_ref hidden_out = prog.end();
+    instruction_ref last_out{};
     last_out            = prog.insert_instruction(ins, op::unsqueeze{{0, 1}}, sih);
     std::size_t seq_len = input->get_shape().lens()[0];
     for(std::size_t i = 0; i < seq_len; i++)