diff --git a/paddle/fluid/operators/distributed/parameter_recv.cc b/paddle/fluid/operators/distributed/parameter_recv.cc
index ae6516b2464326eaa6e6731ae446090c8ab36042..f40f25c7573db10b195dcd6c9bbf1b6f30a3851b 100644
--- a/paddle/fluid/operators/distributed/parameter_recv.cc
+++ b/paddle/fluid/operators/distributed/parameter_recv.cc
@@ -52,16 +52,12 @@ void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
 
   auto *recv_var = scope.FindVar(rpc_ctx.var_name);
 
-  std::vector<framework::Tensor *> recved_tensors;
-
   // recv all vars to local scope
   if (recv_var->IsType<framework::LoDTensor>()) {
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < rpc_ctx.splited_var_names.size(); i++) {
       auto &recv_var_name = rpc_ctx.splited_var_names[i];
-      framework::Tensor *t =
-          local_scope->Var(recv_var_name)->GetMutable<framework::LoDTensor>();
-      recved_tensors.push_back(t);
+      local_scope->Var(recv_var_name);
       VLOG(3) << "recv " << recv_var_name << " from " << rpc_ctx.epmap[i];
       rets.push_back(rpc_client->AsyncGetVar(rpc_ctx.epmap[i], cpu_ctx,
                                              *local_scope, recv_var_name,
@@ -81,14 +77,34 @@ void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
         recv_var->GetMutable<framework::LoDTensor>();
     auto dev_ctx = paddle::platform::CPUDeviceContext();
     int64_t recv_numel = 0;
-    for (auto *in : recved_tensors) {
-      recv_numel += in->numel();
-      auto in_stride = framework::stride_numel(in->dims());
-      auto out_stride = framework::stride_numel(recv_tensor->dims());
-      StridedNumelCopyWithAxis<T>(
-          dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
-          in->data<T>(), in_stride, in_stride[0]);
-      output_offset += in_stride[0];
+    for (auto &recv_var_name : rpc_ctx.splited_var_names) {
+      auto *recv_var = local_scope->FindVar(recv_var_name);
+      if (recv_var->IsType<framework::LoDTensor>()) {
+        auto &in = recv_var->Get<framework::LoDTensor>();
+        recv_numel += in.numel();
+        auto in_stride = framework::stride_numel(in.dims());
+        auto out_stride = framework::stride_numel(recv_tensor->dims());
+        StridedNumelCopyWithAxis<T>(
+            dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
+            in.data<T>(), in_stride, in_stride[0]);
+        output_offset += in_stride[0];
+      } else if (recv_var->IsType<framework::SelectedRows>()) {
+        auto &recv_slr = recv_var->Get<framework::SelectedRows>();
+        auto &recv_dims = recv_tensor->dims();
+        int64_t width = recv_dims[1];
+        PADDLE_ENFORCE_EQ(recv_slr.height(), recv_dims[0]);
+        PADDLE_ENFORCE_EQ(recv_slr.value().dims()[1], width);
+        PADDLE_ENFORCE_EQ(recv_slr.value().dims()[0], recv_slr.rows().size());
+        VLOG(3) << "recv slr " << recv_var_name << " dims "
+                << recv_slr.value().dims();
+        for (auto i = 0; i < recv_slr.rows().size(); ++i) {
+          auto row_id = recv_slr.rows()[i];
+          memcpy(recv_tensor->data<T>() + row_id * width,
+                 recv_slr.value().data<T>() + i * width, sizeof(T) * width);
+        }
+      } else {
+        PADDLE_THROW("unsupported recieved var type");
+      }
     }
     PADDLE_ENFORCE_EQ(recv_numel, recv_tensor->numel());
   }