diff --git a/fastdeploy/function/functions.h b/fastdeploy/function/functions.h
index f789a17ce..d2ffe6a0c 100644
--- a/fastdeploy/function/functions.h
+++ b/fastdeploy/function/functions.h
@@ -17,7 +17,6 @@
 #include "fastdeploy/function/cast.h"
 #include "fastdeploy/function/clip.h"
 #include "fastdeploy/function/concat.h"
-#include "fastdeploy/function/cuda_cast.h"
 #include "fastdeploy/function/cumprod.h"
 #include "fastdeploy/function/elementwise.h"
 #include "fastdeploy/function/full.h"
@@ -28,6 +27,7 @@
 #include "fastdeploy/function/pad.h"
 #include "fastdeploy/function/quantile.h"
 #include "fastdeploy/function/reduce.h"
+#include "fastdeploy/function/slice.h"
 #include "fastdeploy/function/softmax.h"
 #include "fastdeploy/function/sort.h"
 #include "fastdeploy/function/split.h"
diff --git a/fastdeploy/function/math.cc b/fastdeploy/function/math.cc
index 0b3446947..751b79083 100644
--- a/fastdeploy/function/math.cc
+++ b/fastdeploy/function/math.cc
@@ -28,11 +28,13 @@ namespace function {
 template <typename T, typename Functor>
 void ActivationImpl(const FDTensor& X, FDTensor* Out, const Functor& functor) {
   FDASSERT(Out != nullptr, "Output Out should not be nullptr");
+  FDTensor out_tmp;
   auto x = EigenVector<T>::Flatten(X);
-  Out->Allocate(X.Shape(), X.Dtype());
-  auto out = EigenVector<T>::Flatten(*Out);
+  out_tmp.Allocate(X.Shape(), X.Dtype());
+  auto out = EigenVector<T>::Flatten(out_tmp);
   const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
   functor(dev, x, out);
+  *Out = std::move(out_tmp);
 }
 
 DEFINE_ACTIVATION_KERNEL(Sqrt, SqrtFunctor)
diff --git a/fastdeploy/function/slice.cc b/fastdeploy/function/slice.cc
new file mode 100644
index 000000000..8b50bf422
--- /dev/null
+++ b/fastdeploy/function/slice.cc
@@ -0,0 +1,167 @@
+// Copyright (c) 2022 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.
+
+#include "fastdeploy/function/slice.h"
+#include "fastdeploy/function/eigen.h"
+
+#include <algorithm>
+
+namespace fastdeploy {
+namespace function {
+
+std::vector<int64_t> GetSliceDims(const std::vector<int64_t>& in_dims,
+                                  const std::vector<int64_t>& axes,
+                                  const std::vector<int64_t>& starts,
+                                  const std::vector<int64_t>& ends,
+                                  std::vector<int64_t>* steps = nullptr) {
+  std::vector<int64_t> slice_dims(in_dims);
+
+  for (size_t i = 0; i < axes.size(); ++i) {
+    int64_t axis = axes[i];
+    if (in_dims[axis] == -1) {
+      continue;
+    }
+
+    int64_t start = starts[i];
+    int64_t end = ends[i];
+    int64_t step = steps == nullptr ? 1 : (*steps)[i];
+
+    if (step > 0) {
+      slice_dims[axis] = (end - start + step - 1) / step;
+    } else {
+      slice_dims[axis] = (end - start + step + 1) / step;
+    }
+  }
+  return slice_dims;
+}
+
+void CheckAndUpdateSliceAttrs(const std::vector<int64_t>& in_dims,
+                              const std::vector<int64_t>& axes,
+                              std::vector<int64_t>* starts,
+                              std::vector<int64_t>* ends,
+                              std::vector<int64_t>* steps = nullptr) {
+  for (size_t i = 0; i < axes.size(); ++i) {
+    int64_t axis = axes[i];
+    FDASSERT(axis < in_dims.size(),
+             "The axis value should be less than the rank of input, "
+             "but received axes[%d] = %d, rank of input is %d.",
+             i, axis, in_dims.size());
+    int64_t dim_value = in_dims[axis];
+
+    if (dim_value > 0) {
+      int64_t step = steps == nullptr ? 1 : (*steps)[i];
+      FDASSERT(step != 0, "Step should not be 0, but received step = %d.",
+               step);
+      int64_t start =
+          (*starts)[i] < 0 ? ((*starts)[i] + dim_value) : (*starts)[i];
+      start = std::max(start, static_cast<int64_t>(0));
+
+      int64_t end =
+          0 < step && (*ends)[i] < 0 ? ((*ends)[i] + dim_value) : (*ends)[i];
+      end = std::min(end, dim_value);
+
+      if (step > 0) {
+        start = std::min(start, dim_value);
+        end = std::max(end, static_cast<int64_t>(0));
+        FDASSERT(end > start,
+                 "When step > 0, end should be greater than start, but "
+                 "received end = %d, start = %d.",
+                 end, start)
+      } else {
+        start = std::min(start, dim_value - 1);
+        if (end < -1) {
+          end += dim_value;
+        }
+        end = std::max(end, static_cast<int64_t>(-1));
+        FDASSERT(start >= end,
+                 "When step < 0, start should be greater than end, but "
+                 "received start = %d, end = %d.",
+                 start, end);
+      }
+
+      (*starts)[i] = start;
+      (*ends)[i] = end;
+    } else if (dim_value == 0) {
+      (*starts)[i] = 0;
+      (*ends)[i] = 0;
+    }
+  }
+}
+
+template <typename T, size_t D>
+void SliceKernel(const FDTensor& x, const std::vector<int64_t>& axes,
+                 const std::vector<int64_t>& starts,
+                 const std::vector<int64_t>& ends, FDTensor* out) {
+  FDASSERT(starts.size() == axes.size(),
+           "The size of starts must be equal to the size of axes.");
+  FDASSERT(ends.size() == axes.size(),
+           "The size of ends must be equal to the size of axes.");
+  auto starts_idx = starts;
+  auto end_idx = ends;
+  auto in_dims = x.Shape();
+  CheckAndUpdateSliceAttrs(in_dims, axes, &starts_idx, &end_idx);
+  auto slice_dims = GetSliceDims(in_dims, axes, starts, ends);
+
+  auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
+  auto extents = Eigen::DSizes<Eigen::DenseIndex, D>();
+  for (size_t i = 0; i < D; ++i) {
+    offsets[i] = 0;
+    extents[i] = slice_dims[i];
+  }
+  for (size_t i = 0; i < axes.size(); ++i) {
+    offsets[axes[i]] = starts[i];
+  }
+
+  out->Allocate(slice_dims, x.Dtype());
+  auto in_t = EigenTensor<T, D>::From(x, in_dims);
+  auto out_t = EigenTensor<T, D>::From(*out, slice_dims);
+  const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  out_t.device(dev) = in_t.slice(offsets, extents);
+}
+
+void Slice(const FDTensor& x, const std::vector<int64_t>& axes,
+           const std::vector<int64_t>& starts, const std::vector<int64_t>& ends,
+           FDTensor* out) {
+  FD_VISIT_ALL_TYPES(
+      x.dtype, "SliceKernel", ([&] {
+        int rank = x.Shape().size();
+        switch (rank) {
+        case 1:
+          SliceKernel<data_t, 1>(x, axes, starts, ends, out);
+          break;
+        case 2:
+          SliceKernel<data_t, 2>(x, axes, starts, ends, out);
+          break;
+        case 3:
+          SliceKernel<data_t, 3>(x, axes, starts, ends, out);
+          break;
+        case 4:
+          SliceKernel<data_t, 4>(x, axes, starts, ends, out);
+          break;
+        case 5:
+          SliceKernel<data_t, 5>(x, axes, starts, ends, out);
+          break;
+        case 6:
+          SliceKernel<data_t, 6>(x, axes, starts, ends, out);
+          break;
+        default:
+          FDASSERT(false,
+                   "The rank of input should be less than 7, but received %d.",
+                   rank);
+        }
+      }));
+}
+
+}  // namespace function
+}  // namespace fastdeploy
\ No newline at end of file
diff --git a/fastdeploy/function/slice.h b/fastdeploy/function/slice.h
new file mode 100644
index 000000000..d676a232e
--- /dev/null
+++ b/fastdeploy/function/slice.h
@@ -0,0 +1,41 @@
+// Copyright (c) 2022 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 "fastdeploy/core/fd_tensor.h"
+
+namespace fastdeploy {
+namespace function {
+
+/** This operator produces a slice of input along multiple axes.
+    @param x The input tensor.
+    @param axes Axes that starts and ends apply to.
+    @param starts If starts is a list or tuple, the elements of it should be
+      integers or Tensors with shape [1]. If starts is an Tensor, it should
+      be an 1-D Tensor. It represents starting indices of corresponding axis
+      in axes
+    @param ends If ends is a list or tuple, the elements of it should be
+      integers or Tensors with shape [1]. If ends is an Tensor, it should
+      be an 1-D Tensor . It represents ending indices of corresponding axis
+      in axes.
+    @param out The output tensor which stores the result.
+*/
+
+FASTDEPLOY_DECL void Slice(const FDTensor& x, const std::vector<int64_t>& axes,
+                           const std::vector<int64_t>& starts,
+                           const std::vector<int64_t>& ends, FDTensor* out);
+
+}  // namespace function
+}  // namespace fastdeploy
diff --git a/tests/function/test_slice.cc b/tests/function/test_slice.cc
new file mode 100644
index 000000000..03a7fdfbd
--- /dev/null
+++ b/tests/function/test_slice.cc
@@ -0,0 +1,69 @@
+// Copyright (c) 2022 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.
+
+#include "fastdeploy/core/fd_tensor.h"
+#include "fastdeploy/function/slice.h"
+#include "glog/logging.h"
+#include "gtest_utils.h"
+#include "gtest/gtest.h"
+#include <array>
+#include <vector>
+
+namespace fastdeploy {
+namespace function {
+
+std::vector<float> CreateTestData() {
+  // Shape: [2, 3, 4]
+  std::vector<float> x_data = {
+      1.8428625,  0.6461913, 0.13740455,  0.11430702, 0.659926,  0.535816,
+      0.7429162,  0.8456049, -1.21228176, 0.29970083, 0.8621713, 0.40894133,
+      0.12684688, 2.1566195, -9.42884097, 20.8476526, 0.2458633, 0.669046,
+      0.87888306, 0.6762589, 0.666453,    0.32523027, 0.4139388, 0.8341406};
+  return x_data;
+}
+
+TEST(fastdeploy, slice) {
+  CheckShape check_shape;
+  CheckData check_data;
+  FDTensor x, y;
+  auto test_data = CreateTestData();
+  x.SetExternalData({2, 3, 4}, FDDataType::FP32, test_data.data());
+
+  // x[0:1]
+  Slice(x, {0}, {0}, {1}, &y);
+  std::vector<float> result = {1.842862,  0.646191, 0.137405, 0.114307,
+                               0.659926,  0.535816, 0.742916, 0.845605,
+                               -1.212282, 0.299701, 0.862171, 0.408941};
+  check_shape(y.shape, {1, 3, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), result.data(),
+             result.size());
+
+  // x[:, 1:2]
+  Slice(x, {1}, {1}, {2}, &y);
+  result = {0.659926, 0.535816, 0.742916, 0.845605,
+            0.245863, 0.669046, 0.878883, 0.676259};
+  check_shape(y.shape, {2, 1, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), result.data(),
+             result.size());
+
+  // x[:, 0:1, 2:4]
+  Slice(x, {1, 2}, {0, 2}, {1, 4}, &y);
+  result = {0.137405, 0.114307, -9.428841, 20.847652};
+  check_shape(y.shape, {2, 1, 2});
+  check_data(reinterpret_cast<const float*>(y.Data()), result.data(),
+             result.size());
+}
+
+}  // namespace function
+}  // namespace fastdeploy