diff --git a/paddle/fluid/operators/graph_khop_sampler_op.cu b/paddle/fluid/operators/graph_khop_sampler_op.cu
index 39767b5e20a8783568f1fb66bbd46fa08f79726b..c9e4dac74a85a919999a96186c83d64fc3d1c0d3 100644
--- a/paddle/fluid/operators/graph_khop_sampler_op.cu
+++ b/paddle/fluid/operators/graph_khop_sampler_op.cu
@@ -423,6 +423,31 @@ class GraphKhopSamplerOpCUDAKernel : public framework::OpKernel<T> {
     std::vector<int> sample_sizes = ctx.Attr<std::vector<int>>("sample_sizes");
     bool return_eids = ctx.Attr<bool>("return_eids");
 
+    auto row_dims = src->dims();
+    auto row_dims_lens = row_dims.size();
+    auto col_dims = dst_count->dims();
+    auto col_dims_lens = col_dims.size();
+    auto x_dims = vertices->dims();
+    auto x_dims_lens = x_dims.size();
+    for (int i = 0; i < row_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(
+          row_dims[i],
+          0,
+          phi::errors::InvalidArgument("The size of Row(X) should not be 0."));
+    }
+    for (int i = 0; i < col_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(col_dims[i],
+                        0,
+                        phi::errors::InvalidArgument(
+                            "The size of Col_Ptr(X) should not be 0."));
+    }
+    for (int i = 0; i < x_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(x_dims[i],
+                        0,
+                        phi::errors::InvalidArgument(
+                            "The size of Input_Node(X) should not be 0."));
+    }
+
     const T* src_data = src->data<T>();
     const T* dst_count_data = dst_count->data<T>();
     const T* p_vertices = vertices->data<T>();
diff --git a/paddle/fluid/operators/graph_khop_sampler_op.h b/paddle/fluid/operators/graph_khop_sampler_op.h
index f5ec87f23c88bf93a6e7ac9ffb95199d5bbc0430..a22b7a6ee20d8f4531728ffbaabd0a37e2cecbec 100644
--- a/paddle/fluid/operators/graph_khop_sampler_op.h
+++ b/paddle/fluid/operators/graph_khop_sampler_op.h
@@ -199,6 +199,31 @@ class GraphKhopSamplerOpKernel : public framework::OpKernel<T> {
     auto* src = ctx.Input<phi::DenseTensor>("Row");
     auto* dst_count = ctx.Input<phi::DenseTensor>("Col_Ptr");
     auto* vertices = ctx.Input<phi::DenseTensor>("X");
+    auto row_dims = src->dims();
+    auto row_dims_lens = row_dims.size();
+    auto col_dims = dst_count->dims();
+    auto col_dims_lens = col_dims.size();
+    auto x_dims = vertices->dims();
+    auto x_dims_lens = x_dims.size();
+    for (int i = 0; i < row_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(
+          row_dims[i],
+          0,
+          phi::errors::InvalidArgument("The size of Row(X) should not be 0."));
+    }
+    for (int i = 0; i < col_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(col_dims[i],
+                        0,
+                        phi::errors::InvalidArgument(
+                            "The size of Col_Ptr(X) should not be 0."));
+    }
+    for (int i = 0; i < x_dims_lens; i++) {
+      PADDLE_ENFORCE_NE(x_dims[i],
+                        0,
+                        phi::errors::InvalidArgument(
+                            "The size of Input_Node(X) should not be 0."));
+    }
+
     std::vector<int> sample_sizes = ctx.Attr<std::vector<int>>("sample_sizes");
     bool return_eids = ctx.Attr<bool>("return_eids");
 
diff --git a/python/paddle/fluid/tests/unittests/test_graph_khop_sampler.py b/python/paddle/fluid/tests/unittests/test_graph_khop_sampler.py
index 019757518d6c9a28f341854dd0b572fb11113ada..4d4d42b0fa36133bae60a5317c59875bd527f3f0 100644
--- a/python/paddle/fluid/tests/unittests/test_graph_khop_sampler.py
+++ b/python/paddle/fluid/tests/unittests/test_graph_khop_sampler.py
@@ -259,6 +259,36 @@ class TestGraphKhopSampler(unittest.TestCase):
                 in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
                 self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
 
+    def test_for_null_pointer_error(self):
+        def test_in_row():
+            array = np.array([], dtype=np.float32)
+            x = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
+            y = paddle.to_tensor([10], dtype='int32')
+            layer = paddle.incubate.graph_khop_sampler(
+                row=x, colptr=x, input_nodes=y, sample_sizes=[0]
+            )
+
+        def test_in_col():
+            array = np.array([], dtype=np.float32)
+            x = paddle.to_tensor([10], dtype='int32')
+            col = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
+            y = paddle.to_tensor([10], dtype='int32')
+            layer = paddle.incubate.graph_khop_sampler(
+                row=x, colptr=col, input_nodes=y, sample_sizes=[0]
+            )
+
+        def test_in_input_nodes():
+            array = np.array([], dtype=np.float32)
+            x = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
+            y = paddle.to_tensor([10], dtype='int32')
+            layer = paddle.incubate.graph_khop_sampler(
+                row=y, colptr=y, input_nodes=x, sample_sizes=[0]
+            )
+
+        self.assertRaises(ValueError, test_in_row)
+        self.assertRaises(ValueError, test_in_col)
+        self.assertRaises(ValueError, test_in_input_nodes)
+
 
 if __name__ == "__main__":
     unittest.main()