Follow GLOG enforcing style.

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();

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],
+      PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
                         "First dimension of gradient must be greater or "
-                     "equal than first dimension of target");
+                        "equal than first dimension of target.");
 
       if (sub_result.dimensions()[0] == y_dims[0]) {
         y_grad.device(eigen_place) = -1 * grad_mat;