diff --git a/paddle/fluid/operators/filter_by_instag_op.h b/paddle/fluid/operators/filter_by_instag_op.h
index 77bc9e466e808ceff61d0c2b46f4ce60d60dda61..fd0f42df11875e7d343f5593e406dc0a254ab406 100644
--- a/paddle/fluid/operators/filter_by_instag_op.h
+++ b/paddle/fluid/operators/filter_by_instag_op.h
@@ -65,19 +65,26 @@ class FilterByInstagKernel : public framework::OpKernel<T> {
     // expected auto = const int64_t
     auto* x2_data = x2->data<int64_t>();
     // e.g get [0, 1, 2, 3, ...]
-    auto x2_lods = x2->lod()[0];
+    size_t x2_lods_size = x2->dims()[0];
     Vector<size_t> x1_lods(1, 0);
     if (!is_x1_lod) {
       for (int i = 0; i < x1->dims()[0]; i++) {
         x1_lods.push_back(i + 1);
       }
     } else {
-      x1_lods = context.Input<LoDTensor>("Ins")->lod()[0];
+      // new: lod_level=0 => lod() return {}
+      if (x1->lod().size() != 0) {
+        x1_lods = x1->lod()[0];
+      } else {
+        for (int i = 0; i < x1->dims()[0]; i++) {
+          x1_lods.push_back(i + 1);
+        }
+      }
     }
     std::unordered_map<int64_t, int64_t> mmap_aux;
     Vector<size_t> out_lods(1, 0);
-    for (size_t i = 0; i < x2_lods.size() - 1; i++) {
-      for (size_t j = x2_lods[i]; j < x2_lods[i + 1]; j++) {
+    for (size_t i = 0; i < x2_lods_size; i++) {
+      for (size_t j = i; j < i + 1; j++) {
         if (filter_tag.find(x2_data[j]) != filter_tag.end()) {
           size_t batch_len = x1_lods[i + 1] - x1_lods[i];
           mmap_aux[out_lods.back()] = x1_lods[i];