diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 8d1479bdd6311709baaf2a6c673db3d0de4610f8..f9f1c134d798a4e68a629364c17d1a59a94a7002 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -34,6 +34,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+constexpr char kOptimizeBlock[] = "OptimizeBlock";
 constexpr int kCondStart = 0;
 constexpr int kCondRunning = 1;
 constexpr int kCondDone = 2;
@@ -99,10 +100,8 @@ class RecvOp : public framework::OperatorBase {
     auto fan_in = Attr<int>("Fanin");
     size_t param_count = param_list.size();
 
-    std::string program_str = Attr<std::string>("OptimizeProgram");
-    framework::proto::ProgramDesc program_desc;
-    program_desc.ParseFromString(program_str);
-    framework::ProgramDesc program(program_desc);
+    auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
+    auto *program = block->Program();
     framework::Executor executor(dev_place);
 
     // TODO(typhoonzero): change this to a while_op for every cluster-batch.
@@ -142,8 +141,9 @@ class RecvOp : public framework::OperatorBase {
       if (exit_flag) {
         break;
       }
+
       try {
-        executor.Run(program, &recv_scope, 0, /*global_block*/
+        executor.Run(*program, &recv_scope, block->ID(), /*global_block*/
                      false /*create_local_scope*/, false /*create_vars*/);
       } catch (std::exception &e) {
         LOG(ERROR) << "run sub program error " << e.what();
@@ -175,8 +175,8 @@ This operator will recv tensor from send_op
                          "IP address to listen on.")
         .SetDefault("127.0.0.1:6164")
         .AddCustomChecker([](const std::string &ip) { return !ip.empty(); });
-    AddAttr<std::string>("OptimizeProgram", "type string",
-                         "Serialized ProgramDesc string for recv to run.");
+    AddAttr<framework::BlockDesc *>(
+        kOptimizeBlock, "Serialized ProgramDesc string for recv to run.");
     AddAttr<std::vector<std::string>>(
         "ParamList", "type list of string",
         "grad->param name mapping to find which param to optimize.")
diff --git a/paddle/operators/send_recv_op_test.cc b/paddle/operators/send_recv_op_test.cc
index ea091694798475dfd9631910a750405be950c20c..045a0f5434f339bab345d14881ed05450ce6588d 100644
--- a/paddle/operators/send_recv_op_test.cc
+++ b/paddle/operators/send_recv_op_test.cc
@@ -130,10 +130,7 @@ void StartServerNet(bool is_sparse) {
   attrs.insert({"endpoint", std::string("127.0.0.1:6174")});
   attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
   attrs.insert({"GradList", std::vector<std::string>({"x1"})});
-  std::string program_proto;
-  PADDLE_ENFORCE(program.Proto()->SerializeToString(&program_proto));
-
-  attrs.insert({"OptimizeProgram", program_proto});
+  attrs.insert({"OptimizeBlock", block});
   recv_op = f::OpRegistry::CreateOp("recv", {{"RX", {"x1"}}}, {}, attrs);
   recv_op->Run(scope, place);
 }
diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index bd957f88de5d51a2fa3e482284e2d8080f1be76b..02a0e4cd2639e857bce07afa9858531e8d177ad0 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -452,7 +452,7 @@ class DistributeTranspiler:
                     },  # grads to recv
             outputs={},
             attrs={
-                "OptimizeProgram": optimize_sub_program.desc,
+                "OptimizeBlock": optimize_sub_program.global_block(),
                 "endpoint": endpoint,
                 "ParamList": [
                     p.name
diff --git a/python/paddle/v2/fluid/distribute_transpiler_simple.py b/python/paddle/v2/fluid/distribute_transpiler_simple.py
index bd88f02bde0c6a58138e20db2b07cbd06cd40ba3..56ffb56b1247646903485e5859b60f63df9b97a2 100644
--- a/python/paddle/v2/fluid/distribute_transpiler_simple.py
+++ b/python/paddle/v2/fluid/distribute_transpiler_simple.py
@@ -243,7 +243,7 @@ class SimpleDistributeTranspiler:
                     self.param_grad_map[endpoint]["grads"]},  # grads to recv
             outputs={},
             attrs={
-                "OptimizeProgram": optimize_sub_program.desc,
+                "OptimizeBlock": optimize_sub_program.global_block(),
                 "endpoint": endpoint,
                 "ParamList":
                 [p.name for p in self.param_grad_map[endpoint]["params"]],