diff --git a/paddle/fluid/framework/details/async_ssa_graph_executor.cc b/paddle/fluid/framework/details/async_ssa_graph_executor.cc
index e9aad5d264d1745662848d1ba313b573d0974cb7..8fe4cdc7099bbf39fdfdb6dd969a42d4f2bb525a 100644
--- a/paddle/fluid/framework/details/async_ssa_graph_executor.cc
+++ b/paddle/fluid/framework/details/async_ssa_graph_executor.cc
@@ -81,6 +81,9 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
           nodes_to_delete.push_back(node);
           VLOG(3) << "find and remove an recv op: "
                   << recv_varname_to_ctx[recv_var_name];
+        } else if (node->Name() == "lookup_table") {
+          VLOG(0) << "set lookup_table op remote_prefetch to false";
+          node->Op()->SetAttr("remote_prefetch", false);
         }
       }
     }
diff --git a/paddle/fluid/operators/lookup_table_op.h b/paddle/fluid/operators/lookup_table_op.h
index 524565a439fec16e026f4b6414e78d0aff57ea29..62e298e066948c93a84a131a0dffc0a1d53f2a5b 100644
--- a/paddle/fluid/operators/lookup_table_op.h
+++ b/paddle/fluid/operators/lookup_table_op.h
@@ -50,11 +50,12 @@ class LookupTableKernel : public framework::OpKernel<T> {
 
     // for remote prefetch
     auto epmap = context.Attr<std::vector<std::string>>("epmap");
+    auto remote_prefetch = context.Attr<bool>("remote_prefetch");
     auto height_sections =
         context.Attr<std::vector<int64_t>>("height_sections");
     auto table_names = context.Attr<std::vector<std::string>>("table_names");
 
-    if (!epmap.empty()) {
+    if (remote_prefetch && !epmap.empty()) {
 // if epmap is not empty, then the parameter will be fetched from remote
 // parameter
 // server
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 9743cfa7272a90cef3fa0e95b18ae5fc49272060..f2413f603304f8262476ca3ae2b820c89d009c3d 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -341,8 +341,7 @@ def embedding(input,
               is_distributed=False,
               padding_idx=None,
               param_attr=None,
-              dtype='float32',
-              remote_prefetch=False):
+              dtype='float32'):
     """
     **Embedding Layer**
 
@@ -381,7 +380,7 @@ def embedding(input,
     """
 
     helper = LayerHelper('embedding', **locals())
-    remote_prefetch = is_sparse and (not is_distributed) and remote_prefetch
+    remote_prefetch = is_sparse and (not is_distributed)
     if remote_prefetch:
         assert is_sparse is True and is_distributed is False
     w = helper.create_parameter(