diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc
index 694b00e493149f332e94d2353d3c13501a59ebd0..dc30644a5e7e33d4289e48cac093aa5fde7e75e7 100644
--- a/paddle/operators/squared_l2_distance_op.cc
+++ b/paddle/operators/squared_l2_distance_op.cc
@@ -40,7 +40,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
                       "inputs must be same.");
 
     int rank = framework::arity(x_dims);
-    PADDLE_ENFORCE(rank >= 2, "Tensor rank should be at least equal to 2.");
+    PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
     PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0],
                       framework::product(y_dims) / y_dims[0],
                       "Product of dimensions expcet the first dimension of "
@@ -87,7 +87,6 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext& ctx) const override {
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                             "Gradient of Out should not be null");
-    // check out grad dimensions
     auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
     auto x_dims = ctx.Input<Tensor>("X")->dims();
     auto y_dims = ctx.Input<Tensor>("Y")->dims();
diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h
index 1015513bdf51525b9ff47c90625ff32ec4495b41..77c5a0a5c91c93fa9cd8760873f88af657475b2a 100644
--- a/paddle/operators/squared_l2_distance_op.h
+++ b/paddle/operators/squared_l2_distance_op.h
@@ -101,9 +101,9 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel {
       auto y_grad =
           EigenMatrix<T>::From(*y_g, framework::make_ddim({y_dims[0], cols}));
 
-      PADDLE_ENFORCE(sub_result.dimensions()[0] >= y_dims[0],
-                     "First dimension of gradient must be greater or "
-                     "equal than first dimension of target");
+      PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
+                        "First dimension of gradient must be greater or "
+                        "equal than first dimension of target.");
 
       if (sub_result.dimensions()[0] == y_dims[0]) {
         y_grad.device(eigen_place) = -1 * grad_mat;