refine fc and gru pattern

Author: tensor-tang

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -519,50 +519,41 @@ bool VarLinksFromOp(Node* node, const std::string& op_type) {
 
 PDNode* patterns::FC(PDPattern* pattern, const std::string& name_scope,
                      PDNode* x, bool with_bias) {
-  // Create Operators
-  PDNode* elementwise_add_op{nullptr};
+  // mul op
   auto* mul_op = pattern->NewNode(name_scope, "mul")->assert_is_op("mul");
-  if (with_bias) {
-    elementwise_add_op = pattern->NewNode(name_scope, "elementwise_add")
-                             ->assert_is_op("elementwise_add");
-  }
-  // Create variables
-  // w
   auto* mul_weight_var = pattern->NewNode(name_scope, "w")
                              ->AsInput()
                              ->assert_is_persistable_var()
-                             ->assert_is_op_nth_input("mul", "Y", 0);
-  PDNode* mul_out_var{nullptr};
+                             ->assert_is_op_input("mul", "Y");
+
+  PDNode* fc_out{nullptr};
   if (with_bias) {
+    PDNode* elementwise_add_op{nullptr};
+    PDNode *mul_out_var{nullptr}, *bias{nullptr};
+    elementwise_add_op = pattern->NewNode(name_scope, "elementwise_add")
+                             ->assert_is_op("elementwise_add");
     // intermediate variable, will be removed in the IR after fuse.
     mul_out_var = pattern->NewNode(name_scope, "mul_out")
                       ->AsIntermediate()
                       ->assert_is_only_output_of_op("mul")
-                      ->assert_is_op_input("elementwise_add");
-  }
-  PDNode *bias{nullptr}, *fc_out{nullptr};
-  if (with_bias) {
+                      ->assert_is_op_input("elementwise_add", "X");
     // bias
     bias = pattern->NewNode(name_scope, "fc_bias")
-               ->assert_is_op_input("elementwise_add")
-               ->AsInput();
+               ->AsInput()
+               ->assert_is_persistable_var()
+               ->assert_is_op_input("elementwise_add", "Y");
     // output
     fc_out = pattern->NewNode(name_scope, "fc_out")
                  ->AsOutput()
-                 ->assert_is_op_output("elementwise_add");
+                 ->assert_is_op_output("elementwise_add", "Out");
+    mul_op->LinksFrom({x, mul_weight_var}).LinksTo({mul_out_var});
+    elementwise_add_op->LinksFrom({mul_out_var, bias}).LinksTo({fc_out});
   } else {
     fc_out = pattern->NewNode(name_scope, "fc_out")
                  ->AsOutput()
-                 ->assert_is_op_output("mul");
-  }
-
-  if (with_bias) {
-    mul_op->LinksFrom({mul_weight_var, x}).LinksTo({mul_out_var});
-    elementwise_add_op->LinksFrom({mul_out_var, bias}).LinksTo({fc_out});
-  } else {
+                 ->assert_is_op_output("mul", "Out");
     mul_op->LinksFrom({mul_weight_var, x}).LinksTo({fc_out});
   }
-
   return fc_out;
 }
 
@@ -609,6 +600,10 @@ PDNode* patterns::GRU(PDPattern* pattern, const std::string& name_scope,
   NEW_NODE(gru, BatchResetHiddenPrev, output);
   NEW_NODE(gru, BatchHidden, output);
 
+  BatchGate->AsIntermediate();
+  BatchResetHiddenPrev->AsIntermediate();
+  BatchHidden->AsIntermediate();
+
   gru_op->LinksFrom({x, Weight, Bias});
   gru_op->LinksTo({Hidden, BatchGate, BatchResetHiddenPrev, BatchHidden});
   return Hidden;