diff --git a/paddle/fluid/operators/expand_op.h b/paddle/fluid/operators/expand_op.h
index eb3b46f91348d1af097327bf2cbcd8285a61068d..4f167468ebdf4b5e3f82af535976c7d334ddcafe 100644
--- a/paddle/fluid/operators/expand_op.h
+++ b/paddle/fluid/operators/expand_op.h
@@ -34,9 +34,7 @@ limitations under the License. */
     break;                          \
   }
 #define REP_EXPAND_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE, ~)
-#define COND(n)                                               \
-  BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
-                         BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
+#define COND(n) BOOST_PP_GREATER_EQUAL(n, BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
 #define EXPAND_GRAD_CASE(n)                                        \
   case n: {                                                        \
     ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
@@ -145,33 +143,29 @@ class ExpandGradKernel : public framework::OpKernel<T> {
     // auto& expand_times = context.Attr<std::vector<int>>("expand_times");
     auto expand_times = get_expand_times(context);
     auto x_dims = in0->dims();
-    // 1. reshape_dims_vec is the broadcast parameter. For each dimension i,
-    //    if expand_times[i] > 1 and x_dims[i] > 1, i will be splitted to two
-    //    dimensions [expand_times[i], x_dims[i]].
+    // 1. reshape_dims_vec is the broadcast parameter.
     // 2. reduce_dims_vec is the dimension parameter to compute gradients. For
     //    each dimension expanded, the gradients should be summed to original
     //    size.
     std::vector<int> reshape_dims_vec;
     std::vector<int> reduce_dims_vec;
     for (size_t i = 0; i < expand_times.size(); ++i) {
-      if (expand_times[i] == 1) {
-        reshape_dims_vec.push_back(x_dims[i]);
-      } else {
-        if (x_dims[i] == 1) {
-          reduce_dims_vec.push_back(reshape_dims_vec.size());
-          reshape_dims_vec.push_back(expand_times[i]);
-        } else {
-          reduce_dims_vec.push_back(reshape_dims_vec.size());
-          reshape_dims_vec.push_back(expand_times[i]);
-          reshape_dims_vec.push_back(x_dims[i]);
-        }
-      }
+      reduce_dims_vec.push_back(reshape_dims_vec.size());
+      reshape_dims_vec.push_back(expand_times[i]);
+      reshape_dims_vec.push_back(x_dims[i]);
     }
 
-    int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
-               reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
+    int dims = reduce_dims_vec.size();
+
+    bool just_copy = true;
+    for (size_t i = 0; i < expand_times.size(); i++) {
+      if (expand_times[i] != 1) {
+        just_copy = false;
+        break;
+      }
+    }
     // no need reduce, just copy
-    if (reduce_dims_vec.size() == 0) {
+    if (just_copy) {
       auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
       auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
       out0->mutable_data<T>(context.GetPlace());
@@ -179,7 +173,7 @@ class ExpandGradKernel : public framework::OpKernel<T> {
                             out0);
     } else {
       switch (dims) {
-        REP_EXPAND_GRAD_TEMPLATE(72)
+        REP_EXPAND_GRAD_TEMPLATE(MAX_RANK_SUPPORTED)
         default:
           PADDLE_ENFORCE(
               false, "Only support tensor with rank being between 1 and 6.");
@@ -192,8 +186,8 @@ class ExpandGradKernel : public framework::OpKernel<T> {
   void ExpandBackward(const framework::ExecutionContext& context,
                       const std::vector<int>& reshape_dims_vec,
                       const std::vector<int>& reduce_dims_vec) const {
-    size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
-    size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
+    size_t reshape_size = reshape_dims_vec.size();
+    size_t reduce_size = reduce_dims_vec.size();
     PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
                       "Inconsistent size between template Dims and "
                       "reshape dimensions.");
@@ -204,11 +198,11 @@ class ExpandGradKernel : public framework::OpKernel<T> {
     auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
     out0->mutable_data<T>(context.GetPlace());
     auto x_grad = EigenVector<T>::Flatten(*out0);
-    Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 1> reshape_dims;
+    Eigen::DSizes<int, Dims * 2> reshape_dims;
     for (size_t i = 0; i < reshape_size; ++i) {
       reshape_dims[i] = reshape_dims_vec[i];
     }
-    Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
+    Eigen::DSizes<int, Dims> reduce_dims;
     for (size_t i = 0; i < reduce_size; ++i) {
       reduce_dims[i] = reduce_dims_vec[i];
     }