// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

#ifndef CORE_PROVIDERS_CPU_REDUCTION_OPS_H
#define CORE_PROVIDERS_CPU_REDUCTION_OPS_H

#ifndef SHARED_PROVIDER
#include "core/common/common.h"
#include "core/common/optional.h"
#include "core/framework/op_kernel.h"
#include "core/providers/cpu/containers.h"
#include "core/util/math.h"
#endif
#include "core/util/math_cpuonly.h"
#include "core/platform/threadpool.h"
#include "core/common/safeint.h"
#include <cmath>

namespace onnxruntime {

enum FastReduceKind {
  kNone = 0,   // no fast implementation
  kK = 1,      // kept dim = no reduce
  kR = 2,      // reduced dim = all reduced
  kKR = 4,     // kept dim, reduced dim
  kRK = 8,     // reduced dim, kept dim
  kKRK = 16,   // kept dim, reduced dim, kept dim
  kRKR = 32,   // reduced dim, kept dim, reduced dim
  kEmpty = 64  // empty reduce
};

FastReduceKind operator|(FastReduceKind a, FastReduceKind b);

bool operator==(FastReduceKind a, FastReduceKind b);

bool operator!=(FastReduceKind a, FastReduceKind b);

constexpr bool IsFastReduceKindAvailable(FastReduceKind scenario, FastReduceKind available) {
  return (static_cast<uint8_t>(scenario) & static_cast<uint8_t>(available)) > 0;
}
/* Evaluate the cost of parallelized FastReduce implementations. */
constexpr TensorOpCost ParallelReduceFastCost(int64_t n_row, int64_t n_col, int64_t element_size, int n_ops) {
  return TensorOpCost{static_cast<double>(n_row * n_col * element_size),
                      static_cast<double>(n_row * element_size),
                      static_cast<double>(n_row * n_col * element_size * n_ops)};
}

/**
  This only improves reduce function when reduced axes are contiguous:
  if len(shape) == 4, any single axis is ok, axes=(0, 1) or (1, 2) or (2, 3) is ok,
  axes=(0, 2) is not covered by this change, former implementation prevails.
  In that case, the shape can be compressed into three cases:
  (K = axis not reduced, R = reduced axis):

  *  KR - reduction on the last dimensions
  *  RK - reduction on the first dimensions
  *  KRK - reduction on the middle dimensions.
  *  RKR - reduction on all dimensions but the middle ones

  For these three configuration, the reduction may be optimized
  with vectors operations. Method WhichFastReduce() returns which case
  case be optimized for which aggregator.
*/
FastReduceKind OptimizeShapeForFastReduce(gsl::span<const int64_t> input_shape,
                                          gsl::span<const int64_t> reduced_axes,
                                          TensorShapeVector& fast_shape,
                                          TensorShapeVector& fast_output_shape,
                                          TensorShapeVector& fast_axes,
                                          bool keep_dims, bool noop_with_empty_axes = false);

class ResultsNoTransposePrepareForReduce {
 public:
  TensorShapeVector input_shape;
  TensorShapeVector reduced_axes;
  TensorShapeVector projected_index;
  int64_t last_loop_red_size;
  int64_t last_loop_red_inc;
  TensorShapeVector unprojected_index;
  int64_t last_loop_size;
  int64_t last_loop_inc;

  ResultsNoTransposePrepareForReduce() : input_shape(), reduced_axes(), projected_index(), unprojected_index() {
    last_loop_red_size = 0;
    last_loop_red_inc = 0;
    last_loop_size = 0;
    last_loop_inc = 0;
  }

  bool equal(gsl::span<const int64_t> local_input_shape, gsl::span<const int64_t> local_reduced_axes);
  void ValidateNotEmpty();
};

template <typename T>
inline T reduce_sqrt(T value) { return std::sqrt(value); }

template <>
inline int64_t reduce_sqrt<int64_t>(int64_t value) {
  return static_cast<int64_t>(std::sqrt(static_cast<double>(value)));
}

template <>
inline int32_t reduce_sqrt<int32_t>(int32_t value) {
  return static_cast<int32_t>(std::sqrt(static_cast<double>(value)));
}

template <typename T>
inline T reduce_log(T value) { return static_cast<T>(std::log(value)); }

template <>
inline int64_t reduce_log<int64_t>(int64_t value) {
  return static_cast<int64_t>(std::log(static_cast<double>(value)));
}

template <>
inline int32_t reduce_log<int32_t>(int32_t value) {
  return static_cast<int32_t>(std::log(static_cast<double>(value)));
}

template <typename T>
inline T reduce_exp(T value) { return static_cast<T>(std::exp(value)); }

template <typename T>
inline bool reduce_isinf(T value) { return std::isinf(value); }

template <>
inline bool reduce_isinf<int8_t>(int8_t) { return false; }

template <>
inline bool reduce_isinf<uint8_t>(uint8_t) { return false; }

template <>
inline bool reduce_isinf<int32_t>(int32_t) { return false; }

template <>
inline bool reduce_isinf<int64_t>(int64_t) { return false; }

template <typename T>
inline bool reduce_isnan(T value) { return std::isnan(value); }

template <>
inline bool reduce_isnan<int8_t>(int8_t) { return false; }

template <>
inline bool reduce_isnan<uint8_t>(uint8_t) { return false; }

template <>
inline bool reduce_isnan<int32_t>(int32_t) { return false; }

template <>
inline bool reduce_isnan<int64_t>(int64_t) { return false; }

class ReduceAggregatorBase {
 public:
  // Fast reduction: see OptimizeShapeForFastReduce's comment.
  static inline FastReduceKind WhichFastReduce() { return FastReduceKind::kNone; }
  static void FastReduceKR(const Tensor&, const gsl::span<const int64_t>&, Tensor&, concurrency::ThreadPool*);
  static void FastReduceRK(const Tensor&, const gsl::span<const int64_t>&, Tensor&, concurrency::ThreadPool*);
  static void FastReduceKRK(const Tensor&, const gsl::span<const int64_t>&, Tensor&, concurrency::ThreadPool*);
  static void FastReduceRKR(const Tensor&, const gsl::span<const int64_t>&, Tensor&, concurrency::ThreadPool*);
};

template <typename T, typename TVAL = T>
class ReduceAggregator : public ReduceAggregatorBase {
 public:
  typedef T input_type;
  typedef TVAL value_type;

 protected:
  int64_t N_;
  T accumulator_;

 public:
  inline ReduceAggregator(int64_t N, const T& init) {
    N_ = N;
    accumulator_ = init;
  }
  inline void update(const T&) {}
  inline void update0(const T&) {}
  inline TVAL aggall(const T*) {}
  inline TVAL get_value() { return accumulator_; }

 protected:
  static void CommonFastReduceRKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                                  Tensor& output, concurrency::ThreadPool* tp,
                                  std::function<TVAL(const T*)> f_init,
                                  std::function<void(TVAL&, const T*, int64_t)> f_update) {
    const T* data = input.Data<T>();
    TVAL* out = output.MutableData<TVAL>();
    int64_t d0 = fast_shape[0];
    int64_t d2 = fast_shape[2];
    int64_t inc = d2 * fast_shape[1];

    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[1], ParallelReduceFastCost(fast_shape[1], fast_shape[0] * fast_shape[2], sizeof(T), 6),
        [data, out, d0, d2, inc, f_init, f_update](ptrdiff_t begin, ptrdiff_t last) {
          const T* p;
          for (ptrdiff_t d = begin; d < last; ++d) {
            p = data + d * d2;
            out[d] = f_init(p);
            for (int64_t i = 0; i < d0; ++i, p += inc) {
              f_update(out[d], p, d2);
            }
          }
        });
  }
};

template <typename T>
class ReduceAggregatorSum : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorSum(int64_t N, const T&) : ReduceAggregator<T, T>(N, 0) {}
  inline void update(const T& v) { this->accumulator_ += v; }
  static T aggall(const T* from_data, int64_t size) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, size).sum();
  }
  inline T aggall(const T* from_data) {
    return aggall(from_data, this->N_);
  }

  // Fast reduction
  static inline FastReduceKind WhichFastReduce() {
    return FastReduceKind::kKR | FastReduceKind::kRK | FastReduceKind::kKRK | FastReduceKind::kRKR;
  }

  static void FastReduceKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    int64_t stridei = fast_shape[1];
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(1, stridei, sizeof(T), 6),
        [data, stridei, out](ptrdiff_t first, ptrdiff_t last) {
          for (ptrdiff_t d = first; d < last; ++d) {
            out[d] = aggall(data + d * stridei, stridei);
          }
        });
  }

  static void FastReduceRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    int64_t N = fast_shape[1];
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();

    int64_t n_rows = fast_shape[0];
    memcpy(out, data, N * sizeof(T));
    concurrency::ThreadPool::TryParallelFor(
        tp, N, ParallelReduceFastCost(1, n_rows, sizeof(T), 6),
        [data, out, N, n_rows](ptrdiff_t begin, ptrdiff_t end) {
          for (int64_t row = 1; row < n_rows; ++row) {
            EigenVectorArrayMap<T>(out + begin, end - begin) += ConstEigenVectorArrayMap<T>(
                data + row * N + begin, end - begin);
          }
        });
  }

  static void FastReduceKRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    int64_t N = fast_shape[2];
    const T* data = input.Data<T>();
    int64_t stridei = fast_shape[1] * fast_shape[2];
    int64_t strideo = fast_shape[2];
    T* out = output.MutableData<T>();
    std::vector<T> one(fast_shape[1], 1);
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(fast_shape[1], fast_shape[2], sizeof(T), 6),
        [one, data, fast_shape, stridei, strideo, out, N](ptrdiff_t begin, ptrdiff_t last) {
          for (ptrdiff_t d = begin; d < last; ++d) {
            math::MatMul<T>(1, N, fast_shape[1], one.data(), data + stridei * d, out + strideo * d, nullptr);
          }
        });
  }

  static void FastReduceRKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregator<T, T>::CommonFastReduceRKR(
        input, fast_shape, output, tp,
        [=](const T*) -> T { return 0; },
        [=](T& value, const T* p, int64_t size) {
          value += aggall(p, size);
        });
  }
};

template <typename T, typename TVAL = T>
class ReduceAggregatorSumSquare : public ReduceAggregator<T, TVAL> {
 public:
  inline ReduceAggregatorSumSquare(int64_t N, const T&) : ReduceAggregator<T, TVAL>(N, 0) {}
  inline TVAL aggall(const T* from_data) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).squaredNorm();
  }
  inline void update(const T& v) { this->accumulator_ += v * v; }
};

template <typename T>
class ReduceAggregatorMean : public ReduceAggregatorSum<T> {
 public:
  inline ReduceAggregatorMean(int64_t N, const T&) : ReduceAggregatorSum<T>(N, 0) {}
  static T aggall(const T* from_data, int64_t size) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, size).mean();
  }
  inline T aggall(const T* from_data) {
    return aggall(from_data, this->N_);
  }
  inline T get_value() { return this->accumulator_ / static_cast<T>(this->N_); }

  // Fast reduction
  // WhichFastReduce() already defined in ReduceAggregatorSum

  static void FastReduceKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregatorSum<T>::FastReduceKR(input, fast_shape, output, tp);
    // TODO: use MLAS or BLAS
    T* out = output.MutableData<T>();
    T* end = out + fast_shape[0];
    for (; out != end; ++out) {
      *out /= static_cast<T>(fast_shape[1]);
    }
  }

  static void FastReduceRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregatorSum<T>::FastReduceRK(input, fast_shape, output, tp);
    // TODO: use MLAS or BLAS
    T* out = output.MutableData<T>();
    T* end = out + fast_shape[1];
    for (; out != end; ++out) {
      *out /= static_cast<T>(fast_shape[0]);
    }
  }

  static void FastReduceKRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregatorSum<T>::FastReduceKRK(input, fast_shape, output, tp);
    int64_t strideo = fast_shape[2];
    T* out = output.MutableData<T>();
    T* begin;
    T* end;
    T div = static_cast<T>(fast_shape[1]);
    for (int64_t d = 0; d < fast_shape[0]; ++d) {
      begin = out + strideo * d;
      end = begin + strideo;
      for (; begin != end; ++begin) {
        *begin /= div;
      }
    }
  }

  static void FastReduceRKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregatorSum<T>::FastReduceRKR(input, fast_shape, output, tp);
    T* out = output.MutableData<T>();
    T div = static_cast<T>(fast_shape[0] * fast_shape[2]);
    T* end = out + fast_shape[1];
    for (; out != end; ++out) {
      *out /= div;
    }
  }
};

template <typename T>
class ReduceAggregatorMax : public ReduceAggregator<T> {
 public:
  inline ReduceAggregatorMax(int64_t N, const T& init) : ReduceAggregator<T, T>(N, init) {}
  static T aggall(const T* from_data, int64_t size) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, size).maxCoeff();
  }
  inline T aggall(const T* from_data) {
    return aggall(from_data, this->N_);
  }
  inline void update(const T& v) { this->accumulator_ = v > this->accumulator_ ? v : this->accumulator_; }

  // Fast reduction
  static inline FastReduceKind WhichFastReduce() {
    return FastReduceKind::kKR | FastReduceKind::kRK | FastReduceKind::kKRK | FastReduceKind::kRKR;
  }

  static void FastReduceKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    int64_t stridei = fast_shape[1];
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(1, stridei, sizeof(T), 6),
        [data, stridei, out](std::ptrdiff_t first, std::ptrdiff_t last) {
          EigenVectorMap<T>(out + first, last - first) = ConstEigenMatrixMap<T>(
                                                             data + first * stridei, stridei, last - first)
                                                             .colwise()
                                                             .maxCoeff();
        });
  }

  static void FastReduceRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    int64_t n_rows = fast_shape[0];
    int64_t N = fast_shape[1];
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    memcpy(out, data, N * sizeof(T));

    concurrency::ThreadPool::TryParallelFor(
        tp, N, ParallelReduceFastCost(1, n_rows, sizeof(T), 6),
        [data, out, N, n_rows](ptrdiff_t begin, ptrdiff_t end) {
          const T* p;
          for (int64_t row = 1; row < n_rows; ++row) {
            p = data + row * N;
            for (int64_t j = begin; j < end; ++j) {
              if (out[j] < p[j])
                out[j] = p[j];
            }
          }
        });
  }

  static void FastReduceKRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    int64_t stridei = fast_shape[1] * fast_shape[2];
    int64_t strideo = fast_shape[2];
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(fast_shape[1], fast_shape[2], sizeof(T), 6),
        [data, fast_shape, stridei, strideo, out](ptrdiff_t begin, ptrdiff_t end) {
          for (ptrdiff_t j = begin; j < end; ++j) {
            EigenVectorMap<T>(out + j * strideo, strideo) =
                ConstEigenMatrixMap<T>(
                    data + j * stridei, fast_shape[2], fast_shape[1])
                    .rowwise()
                    .maxCoeff();
          }
        });
  }

  static void FastReduceRKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregator<T, T>::CommonFastReduceRKR(
        input, fast_shape, output, tp,
        [=](const T* p) -> T { return p[0]; },
        [=](T& value, const T* p, int64_t size) {
          T v = aggall(p, size);
          if (v > value)
            value = v;
        });
  }
};

template <typename T, typename TVAL = int64_t>
class ReduceAggregatorArgMinMax : public ReduceAggregator<T, TVAL> {
 protected:
  int64_t arg_;
  int64_t index_;

 public:
  inline ReduceAggregatorArgMinMax(int64_t N, const T& init) : ReduceAggregator<T, TVAL>(N, init) {
    arg_ = 0;
    index_ = 0;
  }
  inline TVAL get_value() { return arg_; }
  inline void enforce(const ResultsNoTransposePrepareForReduce& res) {
    ORT_ENFORCE(res.projected_index.size() == 0, "Only one axis is allowed for reduction.");
  }
};

template <typename T, typename TVAL = int64_t>
class ReduceAggregatorArgMax : public ReduceAggregatorArgMinMax<T, TVAL> {
 public:
  inline ReduceAggregatorArgMax(int64_t N, const T& init) : ReduceAggregatorArgMinMax<T, TVAL>(N, init) {}
  inline TVAL aggall(const T* from_data) {
    Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).maxCoeff(&this->arg_);
    return this->get_value();
  }
  inline void update(const T& v) {
    if (v > this->accumulator_) {
      this->accumulator_ = v;
      this->arg_ = this->index_;
    }
    ++this->index_;
  }
};

template <typename T, typename TVAL = int64_t>
class ReduceAggregatorArgMaxLastIndex : public ReduceAggregatorArgMax<T, TVAL> {
 public:
  inline ReduceAggregatorArgMaxLastIndex(int64_t N, const T& init) : ReduceAggregatorArgMax<T, TVAL>(N, init) {}
  inline TVAL aggall(const T* from_data) {
    for (int64_t i = 0; i < this->N_; ++i) {
      update(from_data[i]);
    }
    return this->get_value();
  }
  inline void update(const T& v) {
    if (v >= this->accumulator_) {
      this->accumulator_ = v;
      this->arg_ = this->index_;
    }
    ++this->index_;
  }
};

template <typename T, typename TVAL = int64_t>
class ReduceAggregatorArgMin : public ReduceAggregatorArgMinMax<T, TVAL> {
 public:
  inline ReduceAggregatorArgMin(int64_t N, const T& init) : ReduceAggregatorArgMinMax<T, TVAL>(N, init) {}
  inline TVAL aggall(const T* from_data) {
    Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).minCoeff(&this->arg_);
    return this->get_value();
  }
  inline void update(const T& v) {
    if (v < this->accumulator_) {
      this->accumulator_ = v;
      this->arg_ = this->index_;
    }
    ++this->index_;
  }
};

template <typename T, typename TVAL = int64_t>
class ReduceAggregatorArgMinLastIndex : public ReduceAggregatorArgMin<T, TVAL> {
 public:
  inline ReduceAggregatorArgMinLastIndex(int64_t N, const T& init) : ReduceAggregatorArgMin<T, TVAL>(N, init) {}
  inline TVAL aggall(const T* from_data) {
    for (int64_t i = 0; i < this->N_; ++i) {
      update(from_data[i]);
    }
    return this->get_value();
  }
  inline void update(const T& v) {
    if (v <= this->accumulator_) {
      this->accumulator_ = v;
      this->arg_ = this->index_;
    }
    ++this->index_;
  }
};

template <typename T>
class ReduceAggregatorMin : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorMin(int64_t N, const T& init) : ReduceAggregator<T, T>(N, init) {}
  static T aggall(const T* from_data, int64_t size) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, size).minCoeff();
  }
  inline T aggall(const T* from_data) {
    return aggall(from_data, this->N_);
  }
  inline void update(const T& v) { this->accumulator_ = v < this->accumulator_ ? v : this->accumulator_; }

  // Fast reduction
  static inline FastReduceKind WhichFastReduce() {
    return FastReduceKind::kKR | FastReduceKind::kRK | FastReduceKind::kKRK | FastReduceKind::kRKR;
  }

  static void FastReduceKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    int64_t stridei = fast_shape[1];
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(1, stridei, sizeof(T), 6),
        [data, stridei, out](std::ptrdiff_t first, std::ptrdiff_t last) {
          EigenVectorMap<T>(out + first, last - first) = ConstEigenMatrixMap<T>(
                                                             data + first * stridei, stridei, last - first)
                                                             .colwise()
                                                             .minCoeff();
        });
  }

  static void FastReduceRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                           Tensor& output, concurrency::ThreadPool* tp) {
    int64_t n_rows = fast_shape[0];
    int64_t N = fast_shape[1];
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    memcpy(out, data, N * sizeof(T));

    concurrency::ThreadPool::TryParallelFor(
        tp, N, ParallelReduceFastCost(1, n_rows, sizeof(T), 6),
        [data, out, N, n_rows](ptrdiff_t begin, ptrdiff_t end) {
          const T* p;
          for (int64_t row = 1; row < n_rows; ++row) {
            p = data + row * N;
            for (int64_t j = begin; j < end; ++j) {
              if (out[j] > p[j])
                out[j] = p[j];
            }
          }
        });
  }

  static void FastReduceKRK(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    const T* data = input.Data<T>();
    T* out = output.MutableData<T>();
    int64_t stridei = fast_shape[1] * fast_shape[2];
    int64_t strideo = fast_shape[2];
    concurrency::ThreadPool::TryParallelFor(
        tp, fast_shape[0], ParallelReduceFastCost(fast_shape[1], fast_shape[2], sizeof(T), 6),
        [data, fast_shape, stridei, strideo, out](ptrdiff_t begin, ptrdiff_t end) {
          for (ptrdiff_t j = begin; j < end; ++j) {
            EigenVectorMap<T>(out + j * strideo, strideo) =
                ConstEigenMatrixMap<T>(
                    data + j * stridei, fast_shape[2], fast_shape[1])
                    .rowwise()
                    .minCoeff();
          }
        });
  }

  static void FastReduceRKR(const Tensor& input, const gsl::span<const int64_t>& fast_shape,
                            Tensor& output, concurrency::ThreadPool* tp) {
    ReduceAggregator<T, T>::CommonFastReduceRKR(
        input, fast_shape, output, tp,
        [=](const T* p) -> T { return p[0]; },
        [=](T& value, const T* p, int64_t size) {
          T v = aggall(p, size);
          if (v < value)
            value = v;
        });
  }
};

template <typename T>
class ReduceAggregatorProd : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorProd(int64_t N, const T&) : ReduceAggregator<T, T>(N, 1) {}
  inline T aggall(const T* from_data) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).prod();
  }
  inline void update(const T& v) { this->accumulator_ *= v; }
};

template <typename T>
class ReduceAggregatorL1 : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorL1(int64_t N, const T&) : ReduceAggregator<T, T>(N, 0) {}
  inline T aggall(const T* from_data) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).cwiseAbs().sum();
  }
  inline void update(const T& v) { this->accumulator_ += v > 0 ? v : -v; }
};

template <typename T>
class ReduceAggregatorL2 : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorL2(int64_t N, const T&) : ReduceAggregator<T, T>(N, 0) {}
  inline T aggall(const T* from_data) {
    return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).norm();
  }
  inline void update(const T& v) { this->accumulator_ += v * v; }
  inline T get_value() { return reduce_sqrt<T>(this->accumulator_); }
};

template <typename T>
class ReduceAggregatorLogSum : public ReduceAggregator<T, T> {
 public:
  inline ReduceAggregatorLogSum(int64_t N, const T&) : ReduceAggregator<T, T>(N, 0) {}
  inline T aggall(const T* from_data) {
    return reduce_log<T>(Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).sum());
  }
  inline void update(const T& v) { this->accumulator_ += v; }
  inline T get_value() { return reduce_log<T>(this->accumulator_); }
};

template <typename T>
class ReduceAggregatorLogSumExp : public ReduceAggregator<T, T> {
 protected:
  T max_;

 public:
  inline ReduceAggregatorLogSumExp(int64_t N, const T& init) : ReduceAggregator<T, T>(N, 0) {
    max_ = reduce_isinf(init) ? this->accumulator_ : init;
  }
  inline T aggall(const T* from_data) {
    max_ = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>(from_data, this->N_).maxCoeff();
    for (int64_t i = 0; i < this->N_; ++i) {
      update(from_data[i]);
    }
    return get_value();
  }
  inline void update0(const T& v) {
    max_ = (reduce_isinf(v) || reduce_isnan(v) || v < max_) ? max_ : v;
  }
  inline void update(const T& v) { this->accumulator_ += reduce_exp(v - max_); }
  inline T get_value() { return reduce_log<T>(this->accumulator_) + max_; }
};

void NoTransposePrepareForReduce(const TensorShape& new_input_shape,
                                 gsl::span<const int64_t> reduced_axes,
                                 ResultsNoTransposePrepareForReduce& results);

template <typename AGG>
void NoTransposeReduce1Loop(Tensor* output, const TensorShape& new_input_shape, const Tensor& input,
                            gsl::span<const int64_t> reduced_axes, concurrency::ThreadPool* tp,
                            ResultsNoTransposePrepareForReduce& last_results);

// Specific case for ReduceLogSumExp.
template <typename AGG>
void NoTransposeReduce2Loops(Tensor* output, const TensorShape& new_input_shape, const Tensor& input,
                             gsl::span<const int64_t> reduced_axes, concurrency::ThreadPool* tp,
                             ResultsNoTransposePrepareForReduce& last_results);

template <typename AGG>
void CommonReduce1Loop(OpKernelContext* ctx,
                       const gsl::span<const int64_t>& axes_, int64_t keepdims_,
                       bool noop_with_empty_axes = false);

// Specific case for ReduceLogSumExp.
template <typename AGG>
void CommonReduce2Loops(OpKernelContext* ctx,
                        const gsl::span<const int64_t>& axes_, int64_t keepdims_,
                        bool noop_with_empty_axes = false);

template <bool allow_multi_axes>
class ReduceKernelBase {
 protected:
  ReduceKernelBase(const OpKernelInfo& info, optional<int64_t> keepdims_override = {}) {
    if (allow_multi_axes) {
      axes_ = ToShapeVector(info.GetAttrsOrDefault<int64_t>("axes"));
    } else {
      auto v = info.GetAttrOrDefault<int64_t>("axis", 0);
      axes_.push_back(v);
    }
    int64_t keepdims = 1;
    if (keepdims_override.has_value()) {
      keepdims = *keepdims_override;
    } else {
      ORT_ENFORCE(info.GetAttr("keepdims", &keepdims).IsOK());
    }
    keepdims_ = (keepdims == 1);
    int64_t noop_with_empty_axes = info.GetAttrOrDefault<int64_t>("noop_with_empty_axes", 0);
    noop_with_empty_axes_ = (noop_with_empty_axes == 1);
    int64_t select_last_index = info.GetAttrOrDefault<int64_t>("select_last_index", 0);
    select_last_index_ = (select_last_index != 0);
  }

  TensorShapeVector axes_;
  bool keepdims_;
  bool noop_with_empty_axes_;
  bool select_last_index_;
};

template <bool allow_multi_axes>
class ReduceKernel : public OpKernel, public ReduceKernelBase<allow_multi_axes> {
 protected:
  ReduceKernel(const OpKernelInfo& info) : OpKernel(info), ReduceKernelBase<allow_multi_axes>(info) {}
};

template <typename T>
class ReduceL1 final : public ReduceKernel<true> {
 public:
  ReduceL1(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceL2 final : public ReduceKernel<true> {
 public:
  ReduceL2(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceLogSum final : public ReduceKernel<true> {
 public:
  ReduceLogSum(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceLogSumExp final : public ReduceKernel<true> {
 public:
  ReduceLogSumExp(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceMax final : public ReduceKernel<true> {
 public:
  ReduceMax(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceMean final : public ReduceKernel<true> {
 public:
  ReduceMean(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceMin final : public ReduceKernel<true> {
 public:
  ReduceMin(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceProd final : public ReduceKernel<true> {
 public:
  ReduceProd(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ReduceSum final : public ReduceKernel<true> {
 public:
  ReduceSum(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;

  // For external calls requiring ReduceSum implementation - will return the reduced output.
  //`input_shape_override` overrides the shape of `input` for compute purposes.
  static std::unique_ptr<Tensor> Impl(const Tensor& input, gsl::span<const int64_t> reduce_axes,
                                      AllocatorPtr allocator, concurrency::ThreadPool* tp, bool keep_dims,
                                      const TensorShape* input_shape_override = nullptr);
};

template <typename T>
class ReduceSumSquare final : public ReduceKernel<true> {
 public:
  ReduceSumSquare(const OpKernelInfo& info) : ReduceKernel<true>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ArgMax final : public ReduceKernel<false> {
 public:
  ArgMax(const OpKernelInfo& info) : ReduceKernel<false>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

template <typename T>
class ArgMin final : public ReduceKernel<false> {
 public:
  ArgMin(const OpKernelInfo& info) : ReduceKernel<false>(info) {
  }

  Status Compute(OpKernelContext* context) const override;
};

}  // namespace onnxruntime

#endif  // !CORE_PROVIDERS_CPU_REDUCTION_OPS_H
