[pten]move reduce files and dev_api (#38715)

* move eigen/reduce.h imple into cpu/reduce.h

* ctx to dev_ctx

paddle/pten/include/math.h

@@ -18,7 +18,6 @@ limitations under the License. */
 #include "paddle/pten/api/lib/utils/storage.h"
 #include "paddle/pten/include/infermeta.h"
 #include "paddle/pten/kernels/complex_kernel.h"
-#include "paddle/pten/kernels/math_kernel.h"
 #include "paddle/pten/kernels/scale_kernel.h"
 
 namespace pten {
@@ -34,42 +33,6 @@ DenseTensor Sign(const ContextT& dev_ctx, const DenseTensor& x) {
   return dense_out;
 }
 
-template <typename T, typename ContextT>
-DenseTensor Mean(const ContextT& dev_ctx,
-                 const DenseTensor& x,
-                 const std::vector<int64_t>& axis,
-                 bool keep_dim) {
-  auto out_meta = ReduceInferMeta(x.meta(), axis, keep_dim);
-  pten::DenseTensor dense_out(
-      pten::make_intrusive<paddle::experimental::SharedStorage>(
-          dev_ctx.GetPlace()),
-      std::move(out_meta));
-  bool reduce_all = false;
-  MeanKernel<T, ContextT>(dev_ctx, x, axis, keep_dim, reduce_all, &dense_out);
-  return dense_out;
-}
-
-template <typename T, typename ContextT>
-DenseTensor Sum(const ContextT& dev_ctx,
-                const DenseTensor& x,
-                const std::vector<int64_t>& axis,
-                DataType dtype,
-                bool keep_dim) {
-  auto out_meta = ReduceInferMeta(x.meta(), axis, keep_dim, dtype);
-  pten::DenseTensor dense_out(
-      pten::make_intrusive<paddle::experimental::SharedStorage>(
-          dev_ctx.GetPlace()),
-      out_meta);
-
-  // The real value of reduce_all will be get in kernel
-  // so use default value(false) is OK.
-  bool reduce_all = false;
-
-  SumKernel<T, ContextT>(
-      dev_ctx, x, axis, keep_dim, reduce_all, out_meta.dtype, &dense_out);
-  return dense_out;
-}
-
 template <typename T, typename ContextT>
 DenseTensor Scale(const ContextT& dev_ctx,
                   const DenseTensor& x,

paddle/pten/kernels/cpu/math_kernel.cc

@@ -21,6 +21,7 @@
 #include "paddle/pten/kernels/cpu/elementwise.h"
 #include "paddle/pten/kernels/cpu/reduce.h"
 #include "paddle/pten/kernels/funcs/elementwise_functor.h"
+#include "paddle/pten/kernels/funcs/reduce_functor.h"
 
 // See Note [ Why still include the fluid headers? ]
 #include "paddle/fluid/framework/eigen.h"
@@ -61,7 +62,7 @@ void MeanKernel(const Context& dev_ctx,
                 bool reduce_all,
                 DenseTensor* out) {
   auto out_dtype = x.dtype();
-  pten::Reduce<CPUContext, T, pten::eigen::MeanFunctor>(
+  pten::Reduce<CPUContext, T, pten::funcs::MeanFunctor>(
       dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
 }
 
@@ -97,7 +98,7 @@ void SumKernel(const Context& dev_ctx,
                bool reduce_all,
                DataType out_dtype,
                DenseTensor* out) {
-  pten::Reduce<CPUContext, T, pten::eigen::SumFunctor>(
+  pten::Reduce<CPUContext, T, pten::funcs::SumFunctor>(
       dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
 }
 

paddle/pten/kernels/cpu/reduce.h

@@ -19,10 +19,184 @@
 #include "paddle/pten/api/ext/dispatch.h"
 #include "paddle/pten/backends/cpu/cpu_context.h"
 #include "paddle/pten/kernels/cast_kernel.h"
-#include "paddle/pten/kernels/hybird/eigen/reduce.h"
 
+#include "paddle/pten/api/lib/utils/storage.h"
+#include "paddle/pten/core/dense_tensor.h"
+#include "paddle/pten/kernels/hybird/eigen/common.h"
+#include "paddle/pten/kernels/hybird/transpose.h"
+// See Note [ Why still include the fluid headers? ]
+#include "paddle/fluid/operators/eigen/eigen_function.h"
 namespace pten {
 
+template <typename DeviceContext,
+          typename T,
+          size_t D,
+          size_t R_D,
+          typename Functor>
+void ReduceFunctor(const DeviceContext& context,
+                   const pten::DenseTensor& input,
+                   pten::DenseTensor* output,
+                   const std::vector<int64_t>& dims,
+                   bool keep_dim) {
+  auto x = EigenTensor<T, D>::From(input);
+  auto x_rank = static_cast<int>(x.dimensions().size());
+  auto reduce_dim = Eigen::array<int, R_D>();
+  std::vector<int64_t> dims_ref = dims;
+  for (size_t i = 0; i < dims_ref.size(); ++i) {
+    if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
+    reduce_dim[i] = dims_ref[i];
+  }
+  // construct the squeezed output tensor
+  DDim out_dims = output->dims();
+  if (keep_dim && x_rank > 1) {
+    const int kDelFlag = -2;
+    auto dims_vector = paddle::framework::vectorize(out_dims);
+    for (size_t i = 0; i < dims_ref.size(); ++i) {
+      dims_vector[dims_ref[i]] = kDelFlag;
+    }
+    dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
+                      dims_vector.end());
+    out_dims = paddle::framework::make_ddim(dims_vector);
+  }
+  auto& place = *context.eigen_device();
+  Functor functor;
+
+  if (D == 1) {
+    auto out = EigenScalar<T>::From(*output);
+    functor(place, &x, &out, reduce_dim);
+  } else {
+    auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
+    functor(place, &x, &out, reduce_dim);
+  }
+}
+
+#define HANDLE_REDUCE_DIM(NDIM, RDIM)                        \
+  if (ndim == NDIM && rdim == RDIM) {                        \
+    ReduceFunctor<DeviceContext, OutT, NDIM, RDIM, Functor>( \
+        dev_ctx, input, output, dims, keep_dim);             \
+  }
+//////////////// HandleLargeDim
+
+inline void GetShuffledDim(const DDim& src_dims,
+                           DDim* dst_dims,
+                           const std::vector<int64_t>& reduced_dims,
+                           std::vector<int64_t>* perm_axis) {
+  // check if it's a reduced dim
+  std::vector<bool> src_dims_check(src_dims.size(), false);
+  size_t src_size = src_dims.size();
+  size_t reduce_size = reduced_dims.size();
+  std::vector<int64_t> regular_reduced_dims = reduced_dims;
+  for (size_t i = 0; i < regular_reduced_dims.size(); i++) {
+    if (regular_reduced_dims[i] < 0) {
+      regular_reduced_dims[i] = src_size + regular_reduced_dims[i];
+    }
+  }
+
+  for (size_t i = 0; i < reduce_size; ++i) {
+    dst_dims->at(src_size - reduce_size + i) =
+        src_dims[regular_reduced_dims[i]];
+    (*perm_axis)[src_size - reduce_size + i] = regular_reduced_dims[i];
+    src_dims_check[regular_reduced_dims[i]] = true;
+  }
+
+  size_t offset = 0;
+  for (size_t i = 0; i < src_dims_check.size(); ++i) {
+    bool is_reduced = src_dims_check[i];
+    if (!is_reduced) {
+      (*perm_axis)[offset] = i;
+      dst_dims->at(offset++) = src_dims[i];
+    }
+  }
+}
+
+template <typename DeviceContext, typename OutT>
+void GetShuffledInput(const DeviceContext& dev_ctx,
+                      const pten::DenseTensor& input,
+                      pten::DenseTensor* shuffled_input,
+                      const std::vector<int64_t>& dims) {
+  DDim shuffled_dims(input.dims());
+  std::vector<int64_t> perm_axis(input.dims().size());
+  GetShuffledDim(input.dims(), &shuffled_dims, dims, &perm_axis);
+
+  shuffled_input->Resize(shuffled_dims);
+  shuffled_input->mutable_data<OutT>();
+
+  pten::math::TransposeNormal<DeviceContext, OutT> trans;
+  trans(dev_ctx, input, shuffled_input, perm_axis);
+}
+
+template <typename DeviceContext, typename OutT, typename Functor>
+void HandleLargeDim(const DeviceContext& dev_ctx,
+                    const pten::DenseTensor& input,
+                    pten::DenseTensor* output,
+                    const std::vector<int64_t>& dims,
+                    bool keep_dim) {
+  //  shuffle the reduced dim to the end
+  pten::DenseTensor shuffled_input = pten::DenseTensor(
+      pten::make_intrusive<paddle::experimental::SharedStorage>(input.place()),
+      input.meta());
+
+  GetShuffledInput<DeviceContext, OutT>(dev_ctx, input, &shuffled_input, dims);
+
+  // transpose to 2D tensor whose shape is {unreduced, reduced}.
+  const int64_t unreduced = output->numel();
+  const int64_t reduced = shuffled_input.numel() / unreduced;
+  shuffled_input.Resize({unreduced, reduced});
+  DDim output_dim = output->dims();
+  output->Resize({unreduced});
+  ReduceFunctor<DeviceContext, OutT, 2, 1, Functor>(
+      dev_ctx, shuffled_input, output, {1}, keep_dim);
+  output->Resize(output_dim);
+}
+
+////////////// ReduceKernel
+
+template <typename DeviceContext, typename T, typename OutT, typename Functor>
+void ReduceKernelImpl(const DeviceContext& dev_ctx,
+                      const pten::DenseTensor& input,
+                      pten::DenseTensor* output,
+                      const std::vector<int64_t>& dims,
+                      bool keep_dim,
+                      bool reduce_all) {
+  output->mutable_data<OutT>();
+
+  if (reduce_all) {
+    // Flatten and reduce 1-D tensor
+    auto x = EigenVector<OutT>::Flatten(input);
+    auto out = EigenScalar<OutT>::From(*output);
+    auto& dev = *dev_ctx.eigen_device();
+    auto reduce_dim = Eigen::array<int, 1>({{0}});
+
+    Functor functor;
+    functor(dev, &x, &out, reduce_dim);
+  } else {
+    int ndim = input.dims().size();
+    int rdim = dims.size();
+    if (ndim > 6) {
+      HandleLargeDim<DeviceContext, OutT, Functor>(
+          dev_ctx, input, output, dims, keep_dim);
+
+    } else {
+      HANDLE_REDUCE_DIM(6, 5);
+      HANDLE_REDUCE_DIM(6, 4);
+      HANDLE_REDUCE_DIM(6, 3);
+      HANDLE_REDUCE_DIM(6, 2);
+      HANDLE_REDUCE_DIM(6, 1);
+      HANDLE_REDUCE_DIM(5, 4);
+      HANDLE_REDUCE_DIM(5, 3);
+      HANDLE_REDUCE_DIM(5, 2);
+      HANDLE_REDUCE_DIM(5, 1);
+      HANDLE_REDUCE_DIM(4, 3);
+      HANDLE_REDUCE_DIM(4, 2);
+      HANDLE_REDUCE_DIM(4, 1);
+      HANDLE_REDUCE_DIM(3, 2);
+      HANDLE_REDUCE_DIM(3, 1);
+      HANDLE_REDUCE_DIM(2, 1);
+      HANDLE_REDUCE_DIM(1, 1);
+    }
+  }
+}
+
 template <typename DeviceContext, typename T, typename Functor>
 void Reduce(const DeviceContext& dev_ctx,
             const DenseTensor& x,
@@ -52,7 +226,7 @@ void Reduce(const DeviceContext& dev_ctx,
     // do reduce sum
     PD_VISIT_ALL_TYPES(
         out_dtype, "ReduceKernelImpl", ([&] {
-          pten::eigen::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
+          pten::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
               dev_ctx, x, out, dims, keep_dim, reduce_all);
         }));
   } else {
@@ -66,7 +240,7 @@ void Reduce(const DeviceContext& dev_ctx,
     // do reduce sum
     PD_VISIT_ALL_TYPES(
         out_dtype, "ReduceKernelImpl", ([&] {
-          pten::eigen::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
+          pten::ReduceKernelImpl<DeviceContext, T, data_t, Functor>(
               dev_ctx, tmp_tensor, out, dims, keep_dim, reduce_all);
         }));
   }

paddle/pten/kernels/funcs/reduce_functor.h

+// 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
+
+namespace pten {
+namespace funcs {
+
+//////// Sum Functor ///////
+struct SumFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->sum(dim);
+  }
+};
+
+//////// Mean Functor ///////
+struct MeanFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->mean(dim);
+  }
+};
+
+}  // namespace funcs
+}  // namespace pten

paddle/pten/kernels/hybird/eigen/reduce.h

-// Copyright (c) 2021 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/pten/api/lib/utils/storage.h"
-#include "paddle/pten/core/dense_tensor.h"
-#include "paddle/pten/kernels/hybird/eigen/common.h"
-#include "paddle/pten/kernels/hybird/transpose.h"
-
-// See Note [ Why still include the fluid headers? ]
-#include "paddle/fluid/operators/eigen/eigen_function.h"
-
-namespace pten {
-namespace eigen {
-
-template <typename DeviceContext,
-          typename T,
-          size_t D,
-          size_t R_D,
-          typename Functor>
-void ReduceFunctor(const DeviceContext& context,
-                   const pten::DenseTensor& input,
-                   pten::DenseTensor* output,
-                   const std::vector<int64_t>& dims,
-                   bool keep_dim) {
-  auto x = EigenTensor<T, D>::From(input);
-  auto x_rank = static_cast<int>(x.dimensions().size());
-  auto reduce_dim = Eigen::array<int, R_D>();
-  std::vector<int64_t> dims_ref = dims;
-  for (size_t i = 0; i < dims_ref.size(); ++i) {
-    if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
-    reduce_dim[i] = dims_ref[i];
-  }
-  // construct the squeezed output tensor
-  DDim out_dims = output->dims();
-  if (keep_dim && x_rank > 1) {
-    const int kDelFlag = -2;
-    auto dims_vector = paddle::framework::vectorize(out_dims);
-    for (size_t i = 0; i < dims_ref.size(); ++i) {
-      dims_vector[dims_ref[i]] = kDelFlag;
-    }
-    dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
-                      dims_vector.end());
-    out_dims = paddle::framework::make_ddim(dims_vector);
-  }
-  auto& place = *context.eigen_device();
-  Functor functor;
-
-  if (D == 1) {
-    auto out = EigenScalar<T>::From(*output);
-    functor(place, &x, &out, reduce_dim);
-  } else {
-    auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
-    functor(place, &x, &out, reduce_dim);
-  }
-}
-
-#define HANDLE_REDUCE_DIM(NDIM, RDIM)                        \
-  if (ndim == NDIM && rdim == RDIM) {                        \
-    ReduceFunctor<DeviceContext, OutT, NDIM, RDIM, Functor>( \
-        dev_ctx, input, output, dims, keep_dim);             \
-  }
-//////////////// HandleLargeDim
-
-inline void GetShuffledDim(const DDim& src_dims,
-                           DDim* dst_dims,
-                           const std::vector<int64_t>& reduced_dims,
-                           std::vector<int64_t>* perm_axis) {
-  // check if it's a reduced dim
-  std::vector<bool> src_dims_check(src_dims.size(), false);
-  size_t src_size = src_dims.size();
-  size_t reduce_size = reduced_dims.size();
-  std::vector<int64_t> regular_reduced_dims = reduced_dims;
-  for (size_t i = 0; i < regular_reduced_dims.size(); i++) {
-    if (regular_reduced_dims[i] < 0) {
-      regular_reduced_dims[i] = src_size + regular_reduced_dims[i];
-    }
-  }
-
-  for (size_t i = 0; i < reduce_size; ++i) {
-    dst_dims->at(src_size - reduce_size + i) =
-        src_dims[regular_reduced_dims[i]];
-    (*perm_axis)[src_size - reduce_size + i] = regular_reduced_dims[i];
-    src_dims_check[regular_reduced_dims[i]] = true;
-  }
-
-  size_t offset = 0;
-  for (size_t i = 0; i < src_dims_check.size(); ++i) {
-    bool is_reduced = src_dims_check[i];
-    if (!is_reduced) {
-      (*perm_axis)[offset] = i;
-      dst_dims->at(offset++) = src_dims[i];
-    }
-  }
-}
-
-template <typename DeviceContext, typename OutT>
-void GetShuffledInput(const DeviceContext& dev_ctx,
-                      const pten::DenseTensor& input,
-                      pten::DenseTensor* shuffled_input,
-                      const std::vector<int64_t>& dims) {
-  DDim shuffled_dims(input.dims());
-  std::vector<int64_t> perm_axis(input.dims().size());
-  GetShuffledDim(input.dims(), &shuffled_dims, dims, &perm_axis);
-
-  shuffled_input->Resize(shuffled_dims);
-  shuffled_input->mutable_data<OutT>();
-
-  pten::math::TransposeNormal<DeviceContext, OutT> trans;
-  trans(dev_ctx, input, shuffled_input, perm_axis);
-}
-
-template <typename DeviceContext, typename OutT, typename Functor>
-void HandleLargeDim(const DeviceContext& dev_ctx,
-                    const pten::DenseTensor& input,
-                    pten::DenseTensor* output,
-                    const std::vector<int64_t>& dims,
-                    bool keep_dim) {
-  //  shuffle the reduced dim to the end
-  pten::DenseTensor shuffled_input = pten::DenseTensor(
-      pten::make_intrusive<paddle::experimental::SharedStorage>(input.place()),
-      input.meta());
-
-  GetShuffledInput<DeviceContext, OutT>(dev_ctx, input, &shuffled_input, dims);
-
-  // transpose to 2D tensor whose shape is {unreduced, reduced}.
-  const int64_t unreduced = output->numel();
-  const int64_t reduced = shuffled_input.numel() / unreduced;
-  shuffled_input.Resize({unreduced, reduced});
-  DDim output_dim = output->dims();
-  output->Resize({unreduced});
-  ReduceFunctor<DeviceContext, OutT, 2, 1, Functor>(
-      dev_ctx, shuffled_input, output, {1}, keep_dim);
-  output->Resize(output_dim);
-}
-
-////////////// ReduceKernel
-
-template <typename DeviceContext, typename T, typename OutT, typename Functor>
-void ReduceKernelImpl(const DeviceContext& dev_ctx,
-                      const pten::DenseTensor& input,
-                      pten::DenseTensor* output,
-                      const std::vector<int64_t>& dims,
-                      bool keep_dim,
-                      bool reduce_all) {
-  output->mutable_data<OutT>();
-
-  if (reduce_all) {
-    // Flatten and reduce 1-D tensor
-    auto x = EigenVector<OutT>::Flatten(input);
-    auto out = EigenScalar<OutT>::From(*output);
-    auto& dev = *dev_ctx.eigen_device();
-    auto reduce_dim = Eigen::array<int, 1>({{0}});
-
-    Functor functor;
-    functor(dev, &x, &out, reduce_dim);
-  } else {
-    int ndim = input.dims().size();
-    int rdim = dims.size();
-    if (ndim > 6) {
-      HandleLargeDim<DeviceContext, OutT, Functor>(
-          dev_ctx, input, output, dims, keep_dim);
-
-    } else {
-      HANDLE_REDUCE_DIM(6, 5);
-      HANDLE_REDUCE_DIM(6, 4);
-      HANDLE_REDUCE_DIM(6, 3);
-      HANDLE_REDUCE_DIM(6, 2);
-      HANDLE_REDUCE_DIM(6, 1);
-      HANDLE_REDUCE_DIM(5, 4);
-      HANDLE_REDUCE_DIM(5, 3);
-      HANDLE_REDUCE_DIM(5, 2);
-      HANDLE_REDUCE_DIM(5, 1);
-      HANDLE_REDUCE_DIM(4, 3);
-      HANDLE_REDUCE_DIM(4, 2);
-      HANDLE_REDUCE_DIM(4, 1);
-      HANDLE_REDUCE_DIM(3, 2);
-      HANDLE_REDUCE_DIM(3, 1);
-      HANDLE_REDUCE_DIM(2, 1);
-      HANDLE_REDUCE_DIM(1, 1);
-    }
-  }
-}
-
-//////// Sum Functor ///////
-struct SumFunctor {
-  template <typename DeviceContext, typename X, typename Y, typename Dim>
-  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
-    y->device(place) = x->sum(dim);
-  }
-};
-
-//////// Mean Functor ///////
-struct MeanFunctor {
-  template <typename DeviceContext, typename X, typename Y, typename Dim>
-  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
-    y->device(place) = x->mean(dim);
-  }
-};
-
-}  // namespace eigen
-}  // namespace pten

paddle/pten/kernels/math_kernel.h

@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/pten/api/lib/utils/storage.h"
 #include "paddle/pten/core/dense_tensor.h"
 #include "paddle/pten/include/infermeta.h"
+#include "paddle/pten/kernels/empty_kernel.h"
 
 namespace pten {
 
@@ -121,4 +122,34 @@ DenseTensor Multiply(const ContextT& dev_ctx,
   return dense_out;
 }
 
+template <typename T, typename Context>
+DenseTensor Mean(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const std::vector<int64_t>& axis,
+                 bool keep_dim) {
+  auto out_meta = ReduceInferMeta(x.meta(), axis, keep_dim);
+  auto dense_out = pten::Empty<T, Context>(dev_ctx, std::move(out_meta));
+  bool reduce_all = false;
+  MeanKernel<T, Context>(dev_ctx, x, axis, keep_dim, reduce_all, &dense_out);
+  return dense_out;
+}
+
+template <typename T, typename Context>
+DenseTensor Sum(const Context& dev_ctx,
+                const DenseTensor& x,
+                const std::vector<int64_t>& axis,
+                DataType dtype,
+                bool keep_dim) {
+  auto out_meta = ReduceInferMeta(x.meta(), axis, keep_dim, dtype);
+  auto dense_out = pten::Empty<T, Context>(dev_ctx, std::move(out_meta));
+
+  // The real value of reduce_all will be get in kernel
+  // so use default value(false) is OK.
+  bool reduce_all = false;
+
+  SumKernel<T, Context>(
+      dev_ctx, x, axis, keep_dim, reduce_all, out_meta.dtype, &dense_out);
+  return dense_out;
+}
+
 }  // namespace pten

paddle/pten/tests/kernels/test_mean_dev_api.cc

@@ -15,7 +15,7 @@ limitations under the License. */
 #include <gtest/gtest.h>
 #include <memory>
 
-#include "paddle/pten/include/math.h"
+#include "paddle/pten/kernels/math_kernel.h"
 
 #include "paddle/pten/api/lib/utils/allocator.h"
 #include "paddle/pten/core/dense_tensor.h"

paddle/pten/tests/kernels/test_sum_dev_api.cc

@@ -15,7 +15,7 @@ limitations under the License. */
 #include <gtest/gtest.h>
 #include <memory>
 
-#include "paddle/pten/include/math.h"
+#include "paddle/pten/kernels/math_kernel.h"
 
 #include "paddle/pten/api/lib/utils/allocator.h"
 #include "paddle/pten/core/dense_tensor.h"