diff --git a/paddle/phi/kernels/funcs/segmented_array.h b/paddle/phi/kernels/funcs/segmented_array.h
index 0f03dbac591ec745eb55408ad67f267200e74e3a..aa03eb4e9fcd21e1c5ac6901bb7a30d7fd1f895a 100644
--- a/paddle/phi/kernels/funcs/segmented_array.h
+++ b/paddle/phi/kernels/funcs/segmented_array.h
@@ -14,7 +14,7 @@
 
 #pragma once
 
-#include "paddle/phi/kernels/funcs/fast_divmod.h"
+#include "paddle/phi/core/dense_tensor.h"
 
 namespace phi {
 namespace funcs {
@@ -89,12 +89,11 @@ struct ArraySetterBase {
         ctx.GetPlace(),
         num_bytes,
         phi::Stream(reinterpret_cast<phi::StreamId>(ctx.stream())));
-    paddle::memory::Copy(ctx.GetPlace(),
-                         allocation->ptr(),
-                         phi::CPUPlace(),
-                         src,
-                         num_bytes,
-                         ctx.stream());
+    phi::backends::gpu::GpuMemcpyAsync(allocation->ptr(),
+                                       src,
+                                       num_bytes,
+                                       phi::gpuMemcpyHostToDevice,
+                                       ctx.stream());
     return allocation->ptr();
   }
 
diff --git a/paddle/phi/kernels/funcs/stack_and_unstack.h b/paddle/phi/kernels/funcs/stack_and_unstack.h
new file mode 100644
index 0000000000000000000000000000000000000000..c516d4892bf629226bed9bb8f93cd3436792d639
--- /dev/null
+++ b/paddle/phi/kernels/funcs/stack_and_unstack.h
@@ -0,0 +1,276 @@
+// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/phi/backends/gpu/gpu_launch_config.h"
+#include "paddle/phi/kernels/funcs/fast_divmod.h"
+#include "paddle/phi/kernels/funcs/segmented_array.h"
+
+namespace phi {
+namespace funcs {
+
+template <typename T, typename IndexT, typename ArrayT>
+__global__ void StackCudaKernel(ArrayT array,
+                                GeneralDivMod<IndexT> divmoder,
+                                IndexT split_size,
+                                IndexT rows,
+                                IndexT cols,
+                                T* __restrict__ output) {
+  IndexT grid_x = static_cast<IndexT>(blockIdx.x) * blockDim.x + threadIdx.x;
+  IndexT grid_x_stride = static_cast<IndexT>(blockDim.x) * gridDim.x;
+  IndexT grid_y_stride = static_cast<IndexT>(blockDim.y) * gridDim.y;
+
+  for (; grid_x < cols; grid_x += grid_x_stride) {
+    IndexT grid_y = static_cast<IndexT>(blockIdx.y) * blockDim.y + threadIdx.y;
+
+    auto divmod_rslt = divmoder.div_mod(grid_x);
+    IndexT split = divmod_rslt[0];       // grid_x / split_size
+    IndexT col_offset = divmod_rslt[1];  // grid_x % split_size
+    const T* input_ptr = array.data[split];
+#pragma unroll
+    for (; grid_y < rows; grid_y += grid_y_stride) {
+      output[grid_y * cols + grid_x] =
+          input_ptr[grid_y * split_size + col_offset];
+    }
+  }
+}
+
+template <typename Context,
+          typename T,
+          typename IndexT,
+          SegmentedArraySize Size>
+void LaunchStackKernel(const Context& ctx,
+                       const IndexT x_col,
+                       const IndexT x_row,
+                       const IndexT out_col,
+                       const std::vector<const DenseTensor*>& x,
+                       DenseTensor* out) {
+  T* out_ptr = ctx.template Alloc<T>(out);
+  auto config = phi::backends::gpu::GetGpuLaunchConfig2D(ctx, out_col, x_row);
+
+  ConstPointerArraySetter<Context, T, Size> setter(ctx, x);
+  GeneralDivMod<IndexT> divmoder(x_col);
+  StackCudaKernel<T, IndexT, decltype(setter.array)>
+      <<<config.block_per_grid, config.thread_per_block, 0, ctx.stream()>>>(
+          setter.array, divmoder, x_col, x_row, out_col, out_ptr);
+}
+
+template <typename T, typename Context>
+void StackRawKernel(const Context& ctx,
+                    const std::vector<const DenseTensor*>& x,
+                    int axis,
+                    DenseTensor* out) {
+  if (axis < 0) axis += (x[0]->dims().size() + 1);
+  int num = static_cast<int>(x.size());
+
+  // Split x dim from axis to matrix of shape [x_row, x_col], and the output
+  // tensor's shape is [x_row, out_col].
+  int64_t x_row = 1;
+  for (int i = 0; i < axis; ++i) {
+    x_row *= x[0]->dims()[i];
+  }
+  int64_t x_col = x[0]->numel() / x_row;
+  int64_t out_col = x_col * num;
+
+  if (out->numel() < std::numeric_limits<int32_t>::max()) {
+    switch (CalcArraySize(num)) {
+      SEGMENTED_ARRAY_KERNEL_HELPER(
+          LaunchStackKernel<Context, T, int32_t, kArraySize>(
+              ctx, x_col, x_row, out_col, x, out));
+    }
+  } else {
+    switch (CalcArraySize(num)) {
+      SEGMENTED_ARRAY_KERNEL_HELPER(
+          LaunchStackKernel<Context, T, int64_t, kArraySize>(
+              ctx, x_col, x_row, out_col, x, out));
+    }
+  }
+}
+
+template <typename T, typename IndexT, typename ArrayT>
+__global__ void UnStackCudaKernel(const T* __restrict__ input,
+                                  IndexT out_row,
+                                  IndexT split_dim,
+                                  IndexT out_col,
+                                  IndexT num_splits,
+                                  GeneralDivMod<IndexT> col_divmoder,
+                                  ArrayT array) {
+  assert(blockDim.y == 1);
+  assert(blockDim.z == 1);
+  // In this case they are equal
+  assert(split_dim % num_splits == 0);
+
+  IndexT numel = out_row * split_dim * out_col;
+  IndexT each_dim_size = split_dim / num_splits;
+  IndexT split_dim_with_out_col = split_dim * out_col;
+
+  IndexT offset = blockIdx.x * blockDim.x + threadIdx.x;
+  if (each_dim_size == 1) {
+    for (; offset < numel; offset += blockDim.x * gridDim.x) {
+      auto col_divmod_rslt = col_divmoder.div_mod(offset);
+
+      IndexT i = offset / split_dim_with_out_col;
+      IndexT j = col_divmod_rslt[0] - i * split_dim;
+      IndexT k = col_divmod_rslt[1];  // offset % out_col
+
+      T* output = array.data[j];
+      if (output) {
+        IndexT output_idx = i * out_col + k;
+        *(output + output_idx) = input[offset];
+      }
+    }
+  } else {
+    for (; offset < numel; offset += blockDim.x * gridDim.x) {
+      auto col_divmod_rslt = col_divmoder.div_mod(offset);
+
+      IndexT i = offset / split_dim_with_out_col;
+      IndexT j = col_divmod_rslt[0] - i * split_dim;
+      IndexT k = col_divmod_rslt[1];  // offset % out_col
+
+      T* output = array.data[j / each_dim_size];
+      if (output) {
+        IndexT output_idx = (i + j % each_dim_size) * out_col + k;
+        *(output + output_idx) = input[offset];
+      }
+    }
+  }
+}
+
+template <typename T, typename IndexT, typename ArrayT>
+__global__ void UnStackCudaKernelForLastDim(const T* __restrict__ in_data,
+                                            const IndexT cols,
+                                            const IndexT rows,
+                                            const IndexT tile_x_num,
+                                            ArrayT array) {
+  constexpr int buffer_size = 512;
+  __shared__ T s_buf[buffer_size];
+
+  for (IndexT tile_x = blockIdx.x; tile_x < tile_x_num; tile_x += gridDim.x) {
+    IndexT row_idx = tile_x * blockDim.x + threadIdx.x;
+    IndexT col_idx = blockIdx.y * blockDim.y + threadIdx.y;
+    int s_idx = threadIdx.y * blockDim.x + threadIdx.x;
+    bool is_valid = (col_idx < cols && row_idx < rows);
+
+    if (is_valid) {
+      T data = in_data[row_idx * cols + col_idx];
+      s_buf[s_idx] = data;
+    }
+    __syncthreads();
+    if (is_valid) {
+      if (array.data[col_idx]) {
+        array.data[col_idx][row_idx] = s_buf[s_idx];
+      }
+    }
+  }
+}
+
+template <typename Context,
+          typename T,
+          typename IndexT,
+          SegmentedArraySize Size>
+void LaunchUnStackKernel(const Context& ctx,
+                         const IndexT out_row,
+                         const IndexT split_dim,
+                         const IndexT out_col,
+                         const IndexT num_splits,
+                         const DenseTensor& x,
+                         std::vector<DenseTensor*>* outs) {
+  // each tensor in outs should have same shape.
+  VLOG(6) << "out_row=" << out_row << ", split_dim=" << split_dim
+          << ", out_col=" << out_col << ", num_splits=" << num_splits;
+
+  auto x_ptr = x.data<T>();
+  PointerArraySetter<Context, T, Size> setter(ctx, outs);
+
+  if (out_col == 1) {
+    // For the case axis == (x.dims().size() - 1)
+    constexpr int kThreads = 512;
+    constexpr int kWarpSize = 32;
+    constexpr int kMaxOut = 16;
+
+    int tid_x = 0, tid_y = 0, bid_x = 0, bid_y = 1;
+    if (split_dim < kMaxOut) {
+      tid_y = split_dim;
+      tid_x =
+          std::min(backends::gpu::RoundToNextHighPowOfTwo(out_row, kWarpSize),
+                   kThreads / backends::gpu::RoundToNextHighPowOfTwo(tid_y));
+    } else {
+      tid_y = kMaxOut;
+      tid_x = kWarpSize;
+      bid_y = backends::gpu::DivUp<int>(split_dim, kMaxOut);
+    }
+    int tile_x_num = backends::gpu::DivUp<int>(out_row, tid_x);
+    bid_x = std::min(tile_x_num, backends::gpu::kMultiDimslimit);
+    dim3 blocks(tid_x, tid_y, 1);
+    dim3 grids(bid_x, bid_y, 1);
+
+    UnStackCudaKernelForLastDim<T, IndexT, decltype(setter.array)>
+        <<<grids, blocks, 0, ctx.stream()>>>(
+            x_ptr, split_dim, out_row, tile_x_num, setter.array);
+  } else {
+    GeneralDivMod<IndexT> col_divmoder(out_col);
+    auto config = phi::backends::gpu::GetGpuLaunchConfig1D(
+        ctx, out_row * split_dim * out_col);
+
+    UnStackCudaKernel<T, IndexT, decltype(setter.array)>
+        <<<config.block_per_grid.x,
+           config.thread_per_block.x,
+           0,
+           ctx.stream()>>>(x_ptr,
+                           out_row,
+                           split_dim,
+                           out_col,
+                           num_splits,
+                           col_divmoder,
+                           setter.array);
+  }
+}
+
+template <typename T, typename Context>
+void UnStackRawKernel(const Context& ctx,
+                      const DenseTensor& x,
+                      int axis,
+                      std::vector<DenseTensor*>* outs) {
+  auto x_dims = x.dims();
+
+  // Input tensor is splited to split_dim tensors along split_dim dimension.
+  int64_t split_dim = x_dims[axis];
+
+  // Treat outs[i] as [out_row, out_col], and x as [out_row, split_dim,
+  // out_col].
+  int64_t out_row = 1;
+  for (int i = 0; i < axis; ++i) {
+    out_row *= x_dims[i];
+  }
+
+  int64_t out_col = x.numel() / (split_dim * out_row);
+
+  if (x.numel() < std::numeric_limits<int32_t>::max()) {
+    switch (CalcArraySize(split_dim)) {
+      SEGMENTED_ARRAY_KERNEL_HELPER(
+          LaunchUnStackKernel<Context, T, int32_t, kArraySize>(
+              ctx, out_row, split_dim, out_col, split_dim, x, outs));
+    }
+  } else {
+    switch (CalcArraySize(split_dim)) {
+      SEGMENTED_ARRAY_KERNEL_HELPER(
+          LaunchUnStackKernel<Context, T, int64_t, kArraySize>(
+              ctx, out_row, split_dim, out_col, split_dim, x, outs));
+    }
+  }
+}
+
+}  // namespace funcs
+}  // namespace phi
diff --git a/paddle/phi/kernels/gpu/stack_grad_kernel.cu b/paddle/phi/kernels/gpu/stack_grad_kernel.cu
index 572ed4a361b4e6730a743c811d5292dd2728d50e..6c72a3562e6a7521bbd436569414ba52bb2d00ae 100644
--- a/paddle/phi/kernels/gpu/stack_grad_kernel.cu
+++ b/paddle/phi/kernels/gpu/stack_grad_kernel.cu
@@ -13,125 +13,13 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/stack_grad_kernel.h"
-#include "paddle/fluid/memory/memory.h"
-#include "paddle/phi/backends/gpu/gpu_launch_config.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
-#include "paddle/phi/kernels/funcs/segmented_array.h"
+#include "paddle/phi/kernels/funcs/stack_and_unstack.h"
 
 namespace phi {
 
-template <typename T, typename IndexT, typename ArrayT>
-__global__ void UnStackCudaKernel(const T* __restrict__ input,
-                                  IndexT pre_dim_size,
-                                  IndexT split_dim_size,
-                                  IndexT suf_dim_size,
-                                  IndexT num_split,
-                                  ArrayT array) {
-  assert(blockDim.y == 1);
-  assert(blockDim.z == 1);
-  // In this case they are equal
-  assert(split_dim_size % num_split == 0);
-
-  IndexT size = pre_dim_size * split_dim_size * suf_dim_size;
-  IndexT each_dim_size = split_dim_size / num_split;
-
-  for (IndexT offset = blockIdx.x * blockDim.x + threadIdx.x; offset < size;
-       offset += blockDim.x * gridDim.x) {
-    IndexT i = offset / (split_dim_size * suf_dim_size);
-    IndexT j = (offset % (split_dim_size * suf_dim_size)) / suf_dim_size;
-    IndexT k = offset % suf_dim_size;
-
-    T* output = array.data[j / each_dim_size];
-    if (output == nullptr) {
-      return;
-    }
-    IndexT output_ind = i * each_dim_size * suf_dim_size +
-                        (j % each_dim_size) * suf_dim_size + k;
-    *(output + output_ind) = input[offset];
-  }
-}
-
-template <typename T, typename IndexT, typename ArrayT>
-__global__ void UnStackCudaKernelForLastDim(const T* __restrict__ in_data,
-                                            const IndexT cols,
-                                            const IndexT rows,
-                                            const IndexT tile_x_num,
-                                            ArrayT array) {
-  constexpr int buffer_size = 512;
-  __shared__ T s_buf[buffer_size];
-
-  for (IndexT tile_x = blockIdx.x; tile_x < tile_x_num; tile_x += gridDim.x) {
-    IndexT row_idx = tile_x * blockDim.x + threadIdx.x;
-    IndexT col_idx = blockIdx.y * blockDim.y + threadIdx.y;
-    int s_idx = threadIdx.y * blockDim.x + threadIdx.x;
-    bool is_valid = (col_idx < cols && row_idx < rows);
-
-    if (is_valid) {
-      T data = in_data[row_idx * cols + col_idx];
-      s_buf[s_idx] = data;
-    }
-    __syncthreads();
-    if (is_valid) {
-      if (array.data[col_idx]) {
-        array.data[col_idx][row_idx] = s_buf[s_idx];
-      }
-    }
-  }
-}
-
-template <typename Context,
-          typename T,
-          typename IndexT,
-          funcs::SegmentedArraySize Size>
-void LaunchUnStackKernel(const Context& ctx,
-                         const IndexT pre_dim,
-                         const IndexT split_dim,
-                         const IndexT suf_dim,
-                         const IndexT num_splits,
-                         const DenseTensor& out_grad,
-                         std::vector<DenseTensor*>* x_grad) {
-  // each x_grad should have same shape
-  auto dout_ptr = out_grad.data<T>();
-  funcs::PointerArraySetter<Context, T, Size> setter(ctx, x_grad);
-
-  if (suf_dim == 1) {
-    // For the case axis == (out_grad.dims().size() - 1)
-    constexpr int kThreads = 512;
-    constexpr int kWarpSize = 32;
-    constexpr int kMaxOut = 16;
-
-    int tid_x = 0, tid_y = 0, bid_x = 0, bid_y = 1;
-    if (split_dim < kMaxOut) {
-      tid_y = split_dim;
-      tid_x =
-          std::min(backends::gpu::RoundToNextHighPowOfTwo(pre_dim, kWarpSize),
-                   kThreads / backends::gpu::RoundToNextHighPowOfTwo(tid_y));
-    } else {
-      tid_y = kMaxOut;
-      tid_x = kWarpSize;
-      bid_y = backends::gpu::DivUp<int>(split_dim, kMaxOut);
-    }
-    int tile_x_num = backends::gpu::DivUp<int>(pre_dim, tid_x);
-    bid_x = std::min(tile_x_num, backends::gpu::kMultiDimslimit);
-    dim3 blocks(tid_x, tid_y, 1);
-    dim3 grids(bid_x, bid_y, 1);
-
-    UnStackCudaKernelForLastDim<T, IndexT, decltype(setter.array)>
-        <<<grids, blocks, 0, ctx.stream()>>>(
-            dout_ptr, split_dim, pre_dim, tile_x_num, setter.array);
-  } else {
-    auto config = phi::backends::gpu::GetGpuLaunchConfig1D(
-        ctx, pre_dim * split_dim * suf_dim);
-
-    UnStackCudaKernel<T, IndexT, decltype(setter.array)>
-        <<<config.block_per_grid.x,
-           config.thread_per_block.x,
-           0,
-           ctx.stream()>>>(
-            dout_ptr, pre_dim, split_dim, suf_dim, num_splits, setter.array);
-  }
-}
-
 template <typename T, typename Context>
 void StackGradKernel(const Context& ctx,
                      const DenseTensor& out_grad,
@@ -144,41 +32,12 @@ void StackGradKernel(const Context& ctx,
       split_dim,
       x_grad.size(),
       phi::errors::InvalidArgument(
-          "Output x_grad size should be equal to the split_dim, but"
-          " received split_dim is:%d x_grad size is:%d.",
+          "Output x_grad's size should be equal to the split_dim, but"
+          " received split_dim is:%d x_grad's size is:%d.",
           split_dim,
           x_grad.size()));
 
-  auto dout_dims = out_grad.dims();
-  int64_t dout_pre = 1;
-  for (int i = 0; i < axis; ++i) {
-    dout_pre *= dout_dims[i];
-  }
-  int64_t dout_suf = out_grad.numel() / (split_dim * dout_pre);
-
-  if (out_grad.numel() < std::numeric_limits<int32_t>::max()) {
-    switch (funcs::CalcArraySize(split_dim)) {
-      SEGMENTED_ARRAY_KERNEL_HELPER(
-          LaunchUnStackKernel<Context, T, int32_t, kArraySize>(ctx,
-                                                               dout_pre,
-                                                               split_dim,
-                                                               dout_suf,
-                                                               split_dim,
-                                                               out_grad,
-                                                               &x_grad));
-    }
-  } else {
-    switch (funcs::CalcArraySize(split_dim)) {
-      SEGMENTED_ARRAY_KERNEL_HELPER(
-          LaunchUnStackKernel<Context, T, int64_t, kArraySize>(ctx,
-                                                               dout_pre,
-                                                               split_dim,
-                                                               dout_suf,
-                                                               split_dim,
-                                                               out_grad,
-                                                               &x_grad));
-    }
-  }
+  funcs::UnStackRawKernel<T, Context>(ctx, out_grad, axis, &x_grad);
 }
 
 }  // namespace phi
diff --git a/paddle/phi/kernels/gpu/stack_kernel.cu b/paddle/phi/kernels/gpu/stack_kernel.cu
index a50396e7c97293e8a3b667f02544bcff93592c5f..e1d7d4e6f389c895198a00ece36c7ecb1da1a5b2 100644
--- a/paddle/phi/kernels/gpu/stack_kernel.cu
+++ b/paddle/phi/kernels/gpu/stack_kernel.cu
@@ -13,89 +13,19 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/stack_kernel.h"
-#include "paddle/fluid/memory/memory.h"
-#include "paddle/phi/backends/gpu/gpu_launch_config.h"
-#include "paddle/phi/core/dense_tensor.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
-#include "paddle/phi/kernels/funcs/segmented_array.h"
+#include "paddle/phi/kernels/funcs/stack_and_unstack.h"
 
 namespace phi {
 
-template <typename T, typename IndexT, typename ArrayT>
-__global__ void StackCUDAKernel(ArrayT array,
-                                funcs::GeneralDivMod<IndexT> divmoder,
-                                IndexT split_size,
-                                IndexT rows,
-                                IndexT cols,
-                                T* __restrict__ output) {
-  IndexT grid_x = static_cast<IndexT>(blockIdx.x) * blockDim.x + threadIdx.x;
-  IndexT grid_x_stride = static_cast<IndexT>(blockDim.x) * gridDim.x;
-  IndexT grid_y_stride = static_cast<IndexT>(blockDim.y) * gridDim.y;
-
-  for (; grid_x < cols; grid_x += grid_x_stride) {
-    IndexT grid_y = static_cast<IndexT>(blockIdx.y) * blockDim.y + threadIdx.y;
-
-    auto divmod_rslt = divmoder.div_mod(grid_x);
-    IndexT split = divmod_rslt[0];       // grid_x / split_size
-    IndexT col_offset = divmod_rslt[1];  // grid_x % split_size
-    const T* input_ptr = array.data[split];
-#pragma unroll
-    for (; grid_y < rows; grid_y += grid_y_stride) {
-      output[grid_y * cols + grid_x] =
-          input_ptr[grid_y * split_size + col_offset];
-    }
-  }
-}
-
-template <typename Context,
-          typename T,
-          typename IndexT,
-          funcs::SegmentedArraySize Size>
-void LaunchStackKernel(const Context& ctx,
-                       const IndexT x_col,
-                       const IndexT x_row,
-                       const IndexT out_col,
-                       const std::vector<const DenseTensor*>& x,
-                       DenseTensor* out) {
-  T* out_ptr = ctx.template Alloc<T>(out);
-  auto config = phi::backends::gpu::GetGpuLaunchConfig2D(ctx, out_col, x_row);
-
-  funcs::ConstPointerArraySetter<Context, T, Size> setter(ctx, x);
-  funcs::GeneralDivMod<IndexT> divmoder(x_col);
-  StackCUDAKernel<T, IndexT, decltype(setter.array)>
-      <<<config.block_per_grid, config.thread_per_block, 0, ctx.stream()>>>(
-          setter.array, divmoder, x_col, x_row, out_col, out_ptr);
-}
-
 template <typename T, typename Context>
 void StackKernel(const Context& ctx,
                  const std::vector<const DenseTensor*>& x,
                  int axis,
                  DenseTensor* out) {
-  if (axis < 0) axis += (x[0]->dims().size() + 1);
-  int num = static_cast<int>(x.size());
-
-  // Split x dim from axis to matrix
-  int64_t x_row = 1;
-  for (int i = 0; i < axis; ++i) {
-    x_row *= x[0]->dims()[i];
-  }
-  int64_t x_col = x[0]->numel() / x_row;
-  int64_t out_col = x_col * num;
-
-  if (out->numel() < std::numeric_limits<int32_t>::max()) {
-    switch (funcs::CalcArraySize(num)) {
-      SEGMENTED_ARRAY_KERNEL_HELPER(
-          LaunchStackKernel<Context, T, int32_t, kArraySize>(
-              ctx, x_col, x_row, out_col, x, out));
-    }
-  } else {
-    switch (funcs::CalcArraySize(num)) {
-      SEGMENTED_ARRAY_KERNEL_HELPER(
-          LaunchStackKernel<Context, T, int64_t, kArraySize>(
-              ctx, x_col, x_row, out_col, x, out));
-    }
-  }
+  funcs::StackRawKernel<T, Context>(ctx, x, axis, out);
 }
 
 }  // namespace phi
diff --git a/paddle/phi/kernels/gpu/unstack_grad_kernel.cu b/paddle/phi/kernels/gpu/unstack_grad_kernel.cu
index b7c349de0df32b0a0687264c7504e600306068fd..88bf155606c1b3b5437deac226752941bcbe6906 100644
--- a/paddle/phi/kernels/gpu/unstack_grad_kernel.cu
+++ b/paddle/phi/kernels/gpu/unstack_grad_kernel.cu
@@ -16,7 +16,19 @@
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
-#include "paddle/phi/kernels/impl/unstack_grad_kernel_impl.h"
+#include "paddle/phi/kernels/funcs/stack_and_unstack.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void UnStackGradKernel(const Context& ctx,
+                       const std::vector<const DenseTensor*>& out_grad,
+                       int axis,
+                       DenseTensor* x_grad) {
+  funcs::StackRawKernel<T, Context>(ctx, out_grad, axis, x_grad);
+}
+
+}  // namespace phi
 
 PD_REGISTER_KERNEL(unstack_grad,
                    GPU,
@@ -26,4 +38,5 @@ PD_REGISTER_KERNEL(unstack_grad,
                    double,
                    int64_t,
                    int,
-                   phi::dtype::float16) {}
+                   phi::dtype::float16,
+                   phi::dtype::bfloat16) {}
diff --git a/paddle/phi/kernels/gpu/unstack_kernel.cu b/paddle/phi/kernels/gpu/unstack_kernel.cu
index f147f4c0f0edfd4af38070f3c221cdd70aef7c9c..4331322bdc202c2c3641fe7653d88377050baaea 100644
--- a/paddle/phi/kernels/gpu/unstack_kernel.cu
+++ b/paddle/phi/kernels/gpu/unstack_kernel.cu
@@ -16,7 +16,33 @@
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
-#include "paddle/phi/kernels/impl/unstack_kernel_impl.h"
+#include "paddle/phi/kernels/funcs/stack_and_unstack.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void UnStackKernel(const Context& ctx,
+                   const DenseTensor& x,
+                   int axis,
+                   int num,
+                   std::vector<DenseTensor*> outs) {
+  if (x.numel() == 0) return;
+  if (axis < 0) axis += x.dims().size();
+
+  int64_t split_dim = x.dims()[axis];
+  PADDLE_ENFORCE_EQ(
+      split_dim,
+      outs.size(),
+      phi::errors::InvalidArgument(
+          "Output outs's size should be equal to the split_dim, but"
+          " received split_dim is:%d outs's size is:%d.",
+          split_dim,
+          outs.size()));
+
+  funcs::UnStackRawKernel<T, Context>(ctx, x, axis, &outs);
+}
+
+}  // namespace phi
 
 PD_REGISTER_KERNEL(unstack,
                    GPU,
@@ -26,4 +52,5 @@ PD_REGISTER_KERNEL(unstack,
                    double,
                    int64_t,
                    int,
-                   phi::dtype::float16) {}
+                   phi::dtype::float16,
+                   phi::dtype::bfloat16) {}
diff --git a/paddle/phi/kernels/stack_grad_kernel.h b/paddle/phi/kernels/stack_grad_kernel.h
index 32451e606f26ab207c932e39846fc3ad91960bc7..1e8f2d68399f87b240fd530defe8fd18c1b92b04 100644
--- a/paddle/phi/kernels/stack_grad_kernel.h
+++ b/paddle/phi/kernels/stack_grad_kernel.h
@@ -20,7 +20,7 @@ namespace phi {
 
 template <typename T, typename Context>
 void StackGradKernel(const Context& dev_ctx,
-                     const DenseTensor& out,
+                     const DenseTensor& out_grad,
                      int axis,
                      std::vector<DenseTensor*> x_grad);
 
diff --git a/paddle/phi/kernels/unstack_grad_kernel.h b/paddle/phi/kernels/unstack_grad_kernel.h
index de0e3004d8038dea3114920739ee099546fbcb68..cb50f5ec9240c0de0f4c2f44672d9ca8c7818019 100644
--- a/paddle/phi/kernels/unstack_grad_kernel.h
+++ b/paddle/phi/kernels/unstack_grad_kernel.h
@@ -20,7 +20,7 @@ namespace phi {
 
 template <typename T, typename Context>
 void UnStackGradKernel(const Context& dev_ctx,
-                       const std::vector<const DenseTensor*>& x,
+                       const std::vector<const DenseTensor*>& out_grad,
                        int axis,
                        DenseTensor* x_grad);