diff --git a/python/paddle/fluid/tests/custom_op/custom_relu_op.cc b/python/paddle/fluid/tests/custom_op/custom_relu_op.cc
index 04399a9826cfea94fdefd070c0c1143c84972a26..f1860635ed5f4c9b1f15e2834201d77e5c30f60d 100644
--- a/python/paddle/fluid/tests/custom_op/custom_relu_op.cc
+++ b/python/paddle/fluid/tests/custom_op/custom_relu_op.cc
@@ -17,8 +17,7 @@
 
 #include "paddle/extension.h"
 
-#define CHECK_CPU_INPUT(x) \
-  PD_CHECK(x.place() == paddle::PlaceType::kCPU, #x " must be a CPU Tensor.")
+#define CHECK_CPU_INPUT(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
 
 template <typename data_t>
 void relu_cpu_forward_kernel(const data_t* x_data,
@@ -26,7 +25,7 @@ void relu_cpu_forward_kernel(const data_t* x_data,
                              int64_t x_numel) {
   PD_CHECK(x_data != nullptr, "x_data is nullptr.");
   PD_CHECK(out_data != nullptr, "out_data is nullptr.");
-  for (int i = 0; i < x_numel; ++i) {
+  for (int64_t i = 0; i < x_numel; ++i) {
     out_data[i] = std::max(static_cast<data_t>(0.), x_data[i]);
   }
 }
@@ -36,7 +35,7 @@ void relu_cpu_backward_kernel(const data_t* grad_out_data,
                               const data_t* out_data,
                               data_t* grad_x_data,
                               int64_t out_numel) {
-  for (int i = 0; i < out_numel; ++i) {
+  for (int64_t i = 0; i < out_numel; ++i) {
     grad_x_data[i] =
         grad_out_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
   }
@@ -54,12 +53,12 @@ void relu_cpu_double_backward_kernel(const data_t* out_data,
 }
 
 std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
-  auto out = paddle::empty(x.shape(), x.dtype(), x.place());
+  auto out = paddle::empty_like(x);
 
   PD_DISPATCH_FLOATING_TYPES(
       x.type(), "relu_cpu_forward", ([&] {
         relu_cpu_forward_kernel<data_t>(
-            x.data<data_t>(), out.mutable_data<data_t>(x.place()), x.size());
+            x.data<data_t>(), out.data<data_t>(), x.numel());
       }));
 
   return {out};
@@ -68,13 +67,13 @@ std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
 std::vector<paddle::Tensor> relu_cpu_backward(const paddle::Tensor& x,
                                               const paddle::Tensor& out,
                                               const paddle::Tensor& grad_out) {
-  auto grad_x = paddle::empty(x.shape(), x.dtype(), x.place());
+  auto grad_x = paddle::empty_like(x);
 
   PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
                                relu_cpu_backward_kernel<data_t>(
                                    grad_out.data<data_t>(),
                                    out.data<data_t>(),
-                                   grad_x.mutable_data<data_t>(x.place()),
+                                   grad_x.data<data_t>(),
                                    out.size());
                              }));
 
@@ -108,9 +107,9 @@ std::vector<paddle::Tensor> relu_cuda_double_backward(
     const paddle::Tensor& out, const paddle::Tensor& ddx);
 
 std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
-  if (x.place() == paddle::PlaceType::kCPU) {
+  if (x.is_cpu()) {
     return relu_cpu_forward(x);
-  } else if (x.place() == paddle::PlaceType::kGPU) {
+  } else if (x.is_gpu()) {
     return relu_cuda_forward(x);
   } else {
     PD_THROW("Not implemented.");
@@ -120,10 +119,9 @@ std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
 std::vector<paddle::Tensor> ReluBackward(const paddle::Tensor& x,
                                          const paddle::Tensor& out,
                                          const paddle::Tensor& grad_out) {
-  // TODO(chenweihang): Check Input
-  if (x.place() == paddle::PlaceType::kCPU) {
+  if (x.is_cpu()) {
     return relu_cpu_backward(x, out, grad_out);
-  } else if (x.place() == paddle::PlaceType::kGPU) {
+  } else if (x.is_gpu()) {
     return relu_cuda_backward(x, out, grad_out);
   } else {
     PD_THROW("Not implemented.");
@@ -214,7 +212,7 @@ void relu_cpu_forward_out(const paddle::Tensor& x, paddle::Tensor* out) {
   PD_DISPATCH_FLOATING_TYPES(
       x.type(), "relu_cpu_forward", ([&] {
         relu_cpu_forward_kernel<data_t>(
-            x.data<data_t>(), out->mutable_data<data_t>(x.place()), x.size());
+            x.data<data_t>(), out->mutable_data<data_t>(x.place()), x.numel());
       }));
 }
 
diff --git a/python/paddle/fluid/tests/custom_op/custom_relu_op.cu b/python/paddle/fluid/tests/custom_op/custom_relu_op.cu
index 18f1a2b95c2ee5de557a15844310f403f9d545d6..f9314ea4b10663182e114d784db295eb6f532c27 100644
--- a/python/paddle/fluid/tests/custom_op/custom_relu_op.cu
+++ b/python/paddle/fluid/tests/custom_op/custom_relu_op.cu
@@ -14,15 +14,14 @@
 
 #include "paddle/extension.h"
 
-#define CHECK_GPU_INPUT(x) \
-  PD_CHECK(x.place() == paddle::PlaceType::kGPU, #x " must be a GPU Tensor.")
+#define CHECK_GPU_INPUT(x) PD_CHECK(x.is_gpu(), #x " must be a GPU Tensor.")
 
 template <typename data_t>
 __global__ void relu_cuda_forward_kernel(const data_t* x,
                                          data_t* y,
-                                         const int num) {
-  int gid = blockIdx.x * blockDim.x + threadIdx.x;
-  for (int i = gid; i < num; i += blockDim.x * gridDim.x) {
+                                         int64_t num) {
+  int64_t gid = blockIdx.x * blockDim.x + threadIdx.x;
+  for (int64_t i = gid; i < num; i += blockDim.x * gridDim.x) {
     y[i] = x[i] > static_cast<data_t>(0.) ? x[i] : static_cast<data_t>(0.);
   }
 }
@@ -31,9 +30,9 @@ template <typename data_t>
 __global__ void relu_cuda_backward_kernel(const data_t* dy,
                                           const data_t* y,
                                           data_t* dx,
-                                          const int num) {
-  int gid = blockIdx.x * blockDim.x + threadIdx.x;
-  for (int i = gid; i < num; i += blockDim.x * gridDim.x) {
+                                          int64_t num) {
+  int64_t gid = blockIdx.x * blockDim.x + threadIdx.x;
+  for (int64_t i = gid; i < num; i += blockDim.x * gridDim.x) {
     dx[i] = dy[i] * (y[i] > static_cast<data_t>(0.) ? static_cast<data_t>(1.)
                                                     : static_cast<data_t>(0.));
   }
@@ -54,15 +53,15 @@ __global__ void relu_cuda_double_backward_kernel(const data_t* out_data,
 
 std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x) {
   CHECK_GPU_INPUT(x);
-  auto out = paddle::empty(x.shape(), x.dtype(), x.place());
+  auto out = paddle::empty_like(x);
 
-  int numel = x.size();
-  int block = 512;
-  int grid = (numel + block - 1) / block;
+  int64_t numel = x.numel();
+  int64_t block = 512;
+  int64_t grid = (numel + block - 1) / block;
   PD_DISPATCH_FLOATING_AND_HALF_TYPES(
       x.type(), "relu_cuda_forward_kernel", ([&] {
         relu_cuda_forward_kernel<data_t><<<grid, block, 0, x.stream()>>>(
-            x.data<data_t>(), out.mutable_data<data_t>(x.place()), numel);
+            x.data<data_t>(), out.data<data_t>(), numel);
       }));
 
   return {out};
@@ -74,11 +73,11 @@ std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
   CHECK_GPU_INPUT(x);
   CHECK_GPU_INPUT(out);
   CHECK_GPU_INPUT(grad_out);
-  auto grad_x = paddle::empty(x.shape(), x.dtype(), x.place());
+  auto grad_x = paddle::empty_like(x);
 
-  int numel = out.size();
-  int block = 512;
-  int grid = (numel + block - 1) / block;
+  int64_t numel = out.numel();
+  int64_t block = 512;
+  int64_t grid = (numel + block - 1) / block;
   PD_DISPATCH_FLOATING_AND_HALF_TYPES(
       out.type(), "relu_cuda_backward_kernel", ([&] {
         relu_cuda_backward_kernel<data_t><<<grid, block, 0, x.stream()>>>(
@@ -97,7 +96,7 @@ std::vector<paddle::Tensor> relu_cuda_double_backward(
   CHECK_GPU_INPUT(ddx);
   auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
 
-  int64_t numel = out.size();
+  int64_t numel = out.numel();
   int64_t block = 512;
   int64_t grid = (numel + block - 1) / block;
   PD_DISPATCH_FLOATING_AND_HALF_TYPES(
@@ -119,7 +118,7 @@ std::vector<paddle::Tensor> relu_cuda_backward_without_x(
     const paddle::Tensor& out, const paddle::Tensor& grad_out) {
   auto grad_x = paddle::empty(out.shape(), out.dtype(), out.place());
 
-  int numel = out.size();
+  int numel = out.numel();
   int block = 512;
   int grid = (numel + block - 1) / block;
   PD_DISPATCH_FLOATING_AND_HALF_TYPES(
@@ -135,7 +134,7 @@ std::vector<paddle::Tensor> relu_cuda_backward_without_x(
 }
 
 void relu_cuda_forward_out(const paddle::Tensor& x, paddle::Tensor* out) {
-  int numel = x.size();
+  int numel = x.numel();
   int block = 512;
   int grid = (numel + block - 1) / block;
   out->reshape(x.shape());
@@ -150,7 +149,7 @@ void relu_cuda_backward_out(const paddle::Tensor& x,
                             const paddle::Tensor& out,
                             const paddle::Tensor& grad_out,
                             paddle::Tensor* grad_x) {
-  int numel = out.size();
+  int numel = out.numel();
   int block = 512;
   int grid = (numel + block - 1) / block;
   grad_x->reshape(x.shape());