add csrc code

csrc/fastdeploy/function/eigen.cc

@@ -0,0 +1,32 @@
+// 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/eigen.h"
+
+namespace fastdeploy {
+
+std::shared_ptr<EigenDeviceWrapper> EigenDeviceWrapper::instance_ = nullptr;
+
+std::shared_ptr<EigenDeviceWrapper> EigenDeviceWrapper::GetInstance() {
+  if (instance_ == nullptr) {
+    instance_ = std::make_shared<EigenDeviceWrapper>();
+  }
+  return instance_;
+}
+
+const Eigen::DefaultDevice* EigenDeviceWrapper::GetDevice() const {
+  return &device_;
+}
+
+}  // namespace fastdeploy

csrc/fastdeploy/function/eigen.h

@@ -0,0 +1,109 @@
+// 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 <algorithm>
+#include <memory>
+#include <vector>
+#include "fastdeploy/core/fd_tensor.h"
+#include "unsupported/Eigen/CXX11/Tensor"
+
+namespace fastdeploy {
+// EigenDim converts shape into Eigen::DSizes.
+template <int D>
+struct EigenDim {
+  using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
+
+  static Type From(const std::vector<int64_t>& dims) {
+    Type ret;
+    for (int64_t d = 0; d < dims.size(); d++) {
+      ret[d] = dims[d];
+    }
+    return ret;
+  }
+};
+
+// Interpret FDTensor as EigenTensor and EigenConstTensor.
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+struct EigenTensor {
+  using Type = Eigen::TensorMap<Eigen::Tensor<T, D, MajorType, IndexType>>;
+
+  using ConstType =
+      Eigen::TensorMap<Eigen::Tensor<const T, D, MajorType, IndexType>>;
+
+  static Type From(FDTensor& tensor,
+                   const std::vector<int64_t>& dims) {  // NOLINT
+    return Type(reinterpret_cast<T*>(tensor.Data()), EigenDim<D>::From(dims));
+  }
+
+  static Type From(FDTensor& tensor) {  // NOLINT
+    return From(tensor, tensor.shape);
+  }  // NOLINT
+
+  static ConstType From(const FDTensor& tensor,
+                        const std::vector<int64_t>& dims) {
+    return ConstType(reinterpret_cast<const T*>(tensor.Data()),
+                     EigenDim<D>::From(dims));
+  }
+
+  static ConstType From(const FDTensor& tensor) {
+    return From(tensor, tensor.shape);
+  }
+};
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+struct EigenScalar {
+  // Scalar tensor (implemented as a rank-0 tensor) of scalar type T.
+  using Type = Eigen::TensorMap<
+      Eigen::TensorFixedSize<T, Eigen::Sizes<>, MajorType, IndexType>>;
+  using ConstType = Eigen::TensorMap<
+      Eigen::TensorFixedSize<const T, Eigen::Sizes<>, MajorType, IndexType>>;
+
+  static Type From(FDTensor& tensor) {
+    return Type(reinterpret_cast<T*>(tensor.Data()));
+  }  // NOLINT
+
+  static ConstType From(const FDTensor& tensor) {
+    return ConstType(reinterpret_cast<const T*>(tensor.Data()));
+  }
+};
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
+  // Flatten reshapes a Tensor into an EigenVector.
+  static typename EigenVector::Type Flatten(FDTensor& tensor) {  // NOLINT
+    return EigenVector::From(tensor, {tensor.Numel()});
+  }
+
+  static typename EigenVector::ConstType Flatten(
+      const FDTensor& tensor) {  // NOLINT
+    return EigenVector::From(tensor, {tensor.Numel()});
+  }
+};
+
+class EigenDeviceWrapper {
+ public:
+  static std::shared_ptr<EigenDeviceWrapper> GetInstance();
+  const Eigen::DefaultDevice* GetDevice() const;
+
+ private:
+  Eigen::DefaultDevice device_;
+  static std::shared_ptr<EigenDeviceWrapper> instance_;
+};
+
+}  // namespace fastdeploy

csrc/fastdeploy/function/reduce.cc

@@ -0,0 +1,246 @@
+// 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 <set>
+
+#include "fastdeploy/function/eigen.h"
+#include "fastdeploy/function/reduce.h"
+#include "fastdeploy/function/reduce_functor.h"
+#include "fastdeploy/utils/utils.h"
+
+namespace fastdeploy {
+
+#ifdef ENABLE_FDTENSOR_FUNC
+
+template <typename T, size_t D, size_t R_D, typename Functor>
+void ReduceFunctor(const FDTensor& input, FDTensor* 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;
+
+  auto out_dims = input.shape;
+  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];
+    out_dims[dims_ref[i]] = 1;
+  }
+  auto origin_output_dims = out_dims;
+  output->Allocate(origin_output_dims, TypeToDataType<T>::dtype);
+  // construct the squeezed output tensor
+  if (x_rank > 1) {
+    const int kDelFlag = -2;
+    for (size_t i = 0; i < dims_ref.size(); ++i) {
+      out_dims[dims_ref[i]] = kDelFlag;
+    }
+    out_dims.erase(remove(out_dims.begin(), out_dims.end(), kDelFlag),
+                   out_dims.end());
+  }
+
+  auto& place = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  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);
+    if (!keep_dim) {
+      output->shape = std::move(out_dims);
+    }
+  }
+}
+
+#define HANDLE_REDUCE_DIM(NDIM, RDIM)                                        \
+  if (ndim == NDIM && rdim == RDIM) {                                        \
+    ReduceFunctor<OutT, NDIM, RDIM, Functor>(input, output, dims, keep_dim); \
+  }
+
+inline void GetShuffledDim(const std::vector<int64_t>& src_dims,
+                           std::vector<int64_t>* dst_dims,
+                           const std::vector<int64_t>& reduced_dims,
+                           std::vector<int>* 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 OutT>
+void GetShuffledInput(const FDTensor& input, FDTensor* shuffled_input,
+                      const std::vector<int64_t>& dims) {
+  auto shuffled_dims = input.shape;
+  std::vector<int> perm_axis(input.shape.size());
+  GetShuffledDim(input.shape, &shuffled_dims, dims, &perm_axis);
+
+  shuffled_input->Allocate(shuffled_dims, input.dtype);
+  // TODO(zhoushunjie) : Need to implement trans function
+  // phi::funcs::TransposeNormal<DeviceContext, OutT> trans;
+  // trans(dev_ctx, input, shuffled_input, perm_axis);
+}
+
+//////////////// HandleLargeDim
+template <typename OutT, typename Functor>
+void HandleLargeDim(const FDTensor& input, FDTensor* output,
+                    const std::vector<int64_t>& dims, bool keep_dim) {
+  //  shuffle the reduced dim to the end
+  FDTensor shuffled_input;
+  GetShuffledInput<OutT>(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.Allocate({unreduced, reduced}, TypeToDataType<OutT>::dtype);
+
+  auto output_dim = output->shape;
+  output->Allocate({unreduced}, TypeToDataType<OutT>::dtype);
+
+  ReduceFunctor<OutT, 2, 1, Functor>(shuffled_input, output, {1}, keep_dim);
+  output->shape = output_dim;
+}
+
+////////////// ReduceKernel
+
+template <typename OutT, typename Functor>
+void ReduceKernelImpl(const FDTensor& input, FDTensor* output,
+                      const std::vector<int64_t>& dims, bool keep_dim,
+                      bool reduce_all) {
+  output->Allocate({1}, TypeToDataType<OutT>::dtype);
+  const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  if (reduce_all) {
+    // Flatten and reduce 1-D tensor
+    auto x = EigenVector<OutT>::Flatten(input);
+    auto out = EigenScalar<OutT>::From(*output);
+    auto reduce_dim = Eigen::array<int, 1>({{0}});
+
+    Functor functor;
+    functor(dev, &x, &out, reduce_dim);
+  } else {
+    int ndim = input.shape.size();
+    int rdim = dims.size();
+    if (ndim > 3) {
+      HandleLargeDim<OutT, Functor>(input, output, dims, keep_dim);
+    } else {
+      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 OutT, typename Functor>
+void BoolReduceKernel(const FDTensor& input, FDTensor* output,
+                      const std::vector<int64_t>& dims, bool keep_dim,
+                      bool reduce_all) {
+  // The dims has full dim, set the reduce_all is True
+  const auto& input_dim_size = input.shape.size();
+  std::set<int> dims_set(dims.begin(), dims.end());
+  bool full_dim = true;
+  for (auto i = 0; i < input_dim_size; i++) {
+    if (dims_set.find(i) == dims_set.end()) {
+      full_dim = false;
+      break;
+    }
+  }
+  reduce_all = (reduce_all || full_dim);
+
+  ReduceKernelImpl<bool, Functor>(input, output, dims, keep_dim, reduce_all);
+}
+
+template <typename Functor>
+void Reduce(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+            bool keep_dim, bool reduce_all) {
+  // If the dims has full dim, set the reduce_all is True
+  const int& input_dim_size = x.shape.size();
+  std::set<int> dims_set(dims.begin(), dims.end());
+  bool full_dim = true;
+  for (int i = 0; i < input_dim_size; ++i) {
+    if (dims_set.find(i) == dims_set.end() &&
+        dims_set.find(i - input_dim_size) == dims_set.end()) {
+      full_dim = false;
+      break;
+    }
+  }
+  reduce_all = (reduce_all || full_dim);
+
+  FD_VISIT_ALL_TYPES(x.dtype, "ReduceKernelImpl", ([&] {
+                       ReduceKernelImpl<data_t, Functor>(x, out, dims, keep_dim,
+                                                         reduce_all);
+                     }));
+}
+
+void Max(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+         bool keep_dim, bool reduce_all) {
+  Reduce<MaxFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void Min(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+         bool keep_dim, bool reduce_all) {
+  Reduce<MinFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void Sum(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+         bool keep_dim, bool reduce_all) {
+  Reduce<SumFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void All(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+         bool keep_dim, bool reduce_all) {
+  BoolReduceKernel<bool, AllFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void Any(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+         bool keep_dim, bool reduce_all) {
+  BoolReduceKernel<bool, AnyFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void Mean(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+          bool keep_dim, bool reduce_all) {
+  Reduce<MeanFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+
+void Prod(const FDTensor& x, FDTensor* out, const std::vector<int64_t>& dims,
+          bool keep_dim, bool reduce_all) {
+  Reduce<ProdFunctor>(x, out, dims, keep_dim, reduce_all);
+}
+#endif
+
+}  // namespace fastdeploy

csrc/fastdeploy/function/reduce.h

@@ -0,0 +1,100 @@
+// 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 {
+
+#ifdef ENABLE_FDTENSOR_FUNC
+/** Excute the maximum operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Max(const FDTensor& x, FDTensor* out,
+                         const std::vector<int64_t>& dims,
+                         bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the minimum operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Min(const FDTensor& x, FDTensor* out,
+                         const std::vector<int64_t>& dims,
+                         bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the sum operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Sum(const FDTensor& x, FDTensor* out,
+                         const std::vector<int64_t>& dims,
+                         bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the all operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void All(const FDTensor& x, FDTensor* out,
+                         const std::vector<int64_t>& dims,
+                         bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the any operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Any(const FDTensor& x, FDTensor* out,
+                         const std::vector<int64_t>& dims,
+                         bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the mean operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Mean(const FDTensor& x, FDTensor* out,
+                          const std::vector<int64_t>& dims,
+                          bool keep_dim = false, bool reduce_all = false);
+
+/** Excute the product operation for input FDTensor along given dims.
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+    @param dims The vector of axis which will be reduced.
+    @param keep_dim Whether to keep the reduced dims, default false.
+    @param reduce_all Whether to reduce all dims, default false.
+*/
+FASTDEPLOY_DECL void Prod(const FDTensor& x, FDTensor* out,
+                          const std::vector<int64_t>& dims,
+                          bool keep_dim = false, bool reduce_all = false);
+
+#endif
+}  // namespace fastdeploy

csrc/fastdeploy/function/reduce_functor.h

@@ -0,0 +1,76 @@
+// 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/function/eigen.h"
+namespace fastdeploy {
+
+//////// Max Functor ///////
+struct MaxFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->maximum(dim);
+  }
+};
+
+//////// Min Functor ///////
+struct MinFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->minimum(dim);
+  }
+};
+
+//////// Sum Functor ///////
+struct SumFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->sum(dim);
+  }
+};
+
+//////// All Functor ///////
+struct AllFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->all(dim);
+  }
+};
+
+//////// Any Functor ///////
+struct AnyFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->any(dim);
+  }
+};
+
+//////// Mean Functor ///////
+struct MeanFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->mean(dim);
+  }
+};
+
+//////// Prod Functor ///////
+struct ProdFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(const Eigen::DefaultDevice& dev, X* x, Y* y, const Dim& dim) {
+    y->device(dev) = x->prod(dim);
+  }
+};
+
+}  // namespace fastdeploy