diff --git a/paddle/operators/clip_op.cc b/paddle/operators/clip_op.cc
index 8d576bae2d28ac870c058a43b171ea3ba35cb297..d3d8cf176de67bfbd4f8c95b780f6f0284903da7 100644
--- a/paddle/operators/clip_op.cc
+++ b/paddle/operators/clip_op.cc
@@ -68,8 +68,9 @@ class ClipOpGrad : public framework::OperatorWithKernel {
                             "Input(Out@GRAD) should not be null");
     auto x_dims = ctx.Input<LoDTensor>("X")->dims();
     auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
-
-    x_grad->Resize(x_dims);
+    if (x_grad != nullptr) {
+      x_grad->Resize(x_dims);
+    }
   }
 };
 
diff --git a/paddle/operators/clip_op.cu b/paddle/operators/clip_op.cu
index ac6a062f6d9fc84b43d1fe74c0d4e922f16be494..7e9c6c23c2c46336a115ec36cdcba003d4667c24 100644
--- a/paddle/operators/clip_op.cu
+++ b/paddle/operators/clip_op.cu
@@ -43,22 +43,24 @@ class ClipGradientOpCUDAKernel : public framework::OpKernel {
     auto min = context.Attr<float>("min");
     auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
-    auto* x = context.Input<LoDTensor>("X");
-    auto dims = d_x->dims();
-    int64_t count = d_out->numel();
-    auto d_x_data = d_x->mutable_data<T>(context.GetPlace());
-    auto d_out_data = d_out->data<T>();
-    auto x_data = x->data<T>();
+    if (d_x != nullptr) {
+      auto* x = context.Input<LoDTensor>("X");
+      auto dims = d_x->dims();
+      int64_t count = d_out->numel();
+      auto d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      auto d_out_data = d_out->data<T>();
+      auto x_data = x->data<T>();
 
-    int N = d_x->dims()[0];
-    int D = d_x->dims()[1];
-    int block = 512;
-    int grid = (N * D + block - 1) / block;
-    ClipGradientKernel<T><<<
-        grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
-                            context.device_context())
-                            .stream()>>>(count, min, max, x_data, d_out_data,
-                                         d_x_data);
+      int N = d_x->dims()[0];
+      int D = d_x->dims()[1];
+      int block = 512;
+      int grid = (N * D + block - 1) / block;
+      ClipGradientKernel<T><<<
+          grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
+                              context.device_context())
+                              .stream()>>>(count, min, max, x_data, d_out_data,
+                                           d_x_data);
+    }
   }
 };
 
diff --git a/paddle/operators/clip_op.h b/paddle/operators/clip_op.h
index ba0aa7416f6c2c354d4fee6cae12c318db31395b..47bfe1b7f8ef84e4896b6a5fef839d582adc9599 100644
--- a/paddle/operators/clip_op.h
+++ b/paddle/operators/clip_op.h
@@ -78,17 +78,19 @@ class ClipGradKernel : public framework::OpKernel {
     auto min = context.op().Attr<float>("min");
     auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
-    auto* x = context.Input<LoDTensor>("X");
-    auto dims = d_x->dims();
-    int64_t count = d_out->numel();
-    auto d_x_data = d_x->mutable_data<T>(context.GetPlace());
-    auto d_out_data = d_out->data<T>();
-    auto x_data = x->data<T>();
-    for (int i = 0; i < count; ++i) {
-      if (x_data[i] > min && x_data[i] < max) {
-        d_x_data[i] = d_out_data[i];
-      } else {
-        d_x_data[i] = 0;
+    if (d_x != nullptr) {
+      auto* x = context.Input<LoDTensor>("X");
+      auto dims = d_x->dims();
+      int64_t count = d_out->numel();
+      auto d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      auto d_out_data = d_out->data<T>();
+      auto x_data = x->data<T>();
+      for (int i = 0; i < count; ++i) {
+        if (x_data[i] > min && x_data[i] < max) {
+          d_x_data[i] = d_out_data[i];
+        } else {
+          d_x_data[i] = 0;
+        }
       }
     }
   }