Lightstar/venv/Lib/site-packages/torch/include/ATen/native/cpu/ReduceUtils.h

#pragma once

#include <ATen/Parallel.h>
#include <ATen/NumericUtils.h>
#include <ATen/cpu/vec/vec.h>
#include <ATen/cpu/vec/functional.h>
#include <ATen/native/ReductionType.h>
#include <c10/util/irange.h>
#include <ATen/OpMathType.h>
#include <ATen/native/cpu/utils.h>
#include <ATen/OpMathType.h>

namespace at::native {
inline namespace CPU_CAPABILITY {

using namespace vec;

#define AT_DISPATCH_REDUCTION_TYPES(op, ...)                                   \
  [&] {                                                                        \
    switch (op) {                                                              \
      case ReductionType::SUM: {                                               \
        static constexpr auto reduce = ReductionType::SUM;                     \
        return __VA_ARGS__();                                                  \
      }                                                                        \
      case ReductionType::MEAN: {                                              \
        static constexpr auto reduce = ReductionType::MEAN;                    \
        return __VA_ARGS__();                                                  \
      }                                                                        \
      case ReductionType::MIN: {                                               \
        static constexpr auto reduce = ReductionType::MIN;                     \
        return __VA_ARGS__();                                                  \
      }                                                                        \
      case ReductionType::MAX: {                                               \
        static constexpr auto reduce = ReductionType::MAX;                     \
        return __VA_ARGS__();                                                  \
      }                                                                        \
      case ReductionType::PROD: {                                              \
        static constexpr auto reduce = ReductionType::PROD;                    \
        return __VA_ARGS__();                                                  \
      }                                                                        \
    }                                                                          \
  }()

template <typename scalar_t, ReductionType reduce>
inline vec_scalar_t<scalar_t> init_value() {
  using acc_t = vec_scalar_t<scalar_t>;
  acc_t val;
  if (reduce == ReductionType::SUM ||
      reduce == ReductionType::MEAN) {
    val = static_cast<acc_t>(0);
  } else if (reduce == ReductionType::PROD) {
    val = static_cast<acc_t>(1);
  } else if (reduce == ReductionType::MAX) {
    val = -std::numeric_limits<acc_t>::infinity();
  } else {
    TORCH_INTERNAL_ASSERT(reduce == ReductionType::MIN);
    val = std::numeric_limits<acc_t>::infinity();
  }
  return val;
}

template <typename scalar_t, ReductionType reduce>
inline vec_scalar_t<scalar_t> init_value(const std::optional<Scalar>& initial) {
  using acc_t = vec_scalar_t<scalar_t>;
  if (initial.has_value()) {
    return initial.value().to<acc_t>();
  } else {
    return init_value<scalar_t, reduce>();
  }
}

template <typename scalar_t>
inline void init(scalar_t* out, int64_t size, const vec_scalar_t<scalar_t>& val) {
  using Vec = Vectorized<vec_scalar_t<scalar_t>>;
  map<scalar_t>(
      [val](Vec x) { return Vec(val); },
      out,
      out,
      size);
}

template <typename scalar_t, ReductionType reduce>
inline void init(scalar_t* out, int64_t size, const std::optional<Scalar>& initial) {
  using acc_t = vec_scalar_t<scalar_t>;
  acc_t val = init_value<scalar_t, reduce>(initial);
  init(out, size, val);
}

// overload with `include_self`, used by scatter_reduce
template <typename scalar_t, ReductionType reduce>
inline void init(scalar_t* out, int64_t size, bool include_self = false) {
  using acc_t = vec_scalar_t<scalar_t>;
  if (!include_self) {
    acc_t val = init_value<scalar_t, reduce>();
    init(out, size, val);
  }
}

template <typename scalar_t, ReductionType reduce>
inline void _init(scalar_t* self_ptr, at::opmath_type<scalar_t>* buffer_ptr, int64_t size, bool include_self) {
  if (!include_self) {
    init<at::opmath_type<scalar_t>, reduce>(buffer_ptr, size, include_self);
  } else {
    vec::convert(self_ptr, buffer_ptr, size);
  }
}

template <typename scalar_t>
inline typename std::enable_if<!std::is_same<scalar_t, Vec2>::value, scalar_t>::type
_max(const scalar_t& x, const scalar_t& y) {
  return at::_isnan(y) ? y : std::max(x, y);
}

template <typename scalar_t>
inline Vectorized<scalar_t> _max(const Vectorized<scalar_t>& x, const Vectorized<scalar_t>& y) {
  // vec::maximum propagates NaN
  return vec::maximum(x, y);
}

template <typename vec_t>
inline typename std::enable_if<std::is_same<vec_t, Vec2>::value, Vec2>::type
_max(const vec_t& x, const vec_t& y) {
  // vec::maximum propagates NaN
  return maximum(x, y);
}

template <typename scalar_t>
inline typename std::enable_if<!std::is_same<scalar_t, Vec2>::value, scalar_t>::type
_min(const scalar_t& x, const scalar_t& y) {
  return at::_isnan(y) ? y : std::min(x, y);
}

template <typename scalar_t>
inline Vectorized<scalar_t> _min(const Vectorized<scalar_t>& x, const Vectorized<scalar_t>& y) {
  // vec::minimum propagates NaN
  return vec::minimum(x, y);
}

template <typename vec_t>
inline typename std::enable_if<std::is_same<vec_t, Vec2>::value, Vec2>::type
_min(const vec_t& x, const vec_t& y) {
  // vec::minimum propagates NaN
  return minimum(x, y);
}

template <typename scalar_t, typename accumut, typename Op,
          typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
inline void map_acc(
    const Op& vec_fun,
    accumut* output_data,
    const accumut* input_data,
    const scalar_t* input_data2,
    int64_t size) {
  using Vec = vec::Vectorized<scalar_t>;
  using aVec = vec::Vectorized<accumut>;
  int64_t d = 0;
  constexpr int64_t kVecSize = Vec::size();
  constexpr int64_t kaVecSize = aVec::size();
  for (d = 0; d < size - (size % kVecSize); d += kVecSize) {
    Vec data2_vec = Vec::loadu(input_data2 + d);
    auto [data2_avec0, data2_avec1] = convert_to_float<scalar_t>(data2_vec);
    aVec input_vec0 = aVec::loadu(input_data + d);
    aVec input_vec1 = aVec::loadu(input_data + d + kaVecSize);
    vec_fun(input_vec0, data2_avec0).store(output_data + d);
    vec_fun(input_vec1, data2_avec1).store(output_data + d + kaVecSize);
  }
  if (size - d > 0) {
    int64_t tail_size = size - d;
    Vec data2_vec = Vec::loadu(input_data2 + d, tail_size);
    auto [data2_avec0, data2_avec1] = convert_to_float<scalar_t>(data2_vec);
    if (tail_size > kaVecSize) {
      aVec input_vec0 = aVec::loadu(input_data + d);
      aVec input_vec1 = aVec::loadu(input_data + d + kaVecSize, tail_size - kaVecSize);
      vec_fun(input_vec0, data2_avec0).store(output_data + d);
      vec_fun(input_vec1, data2_avec1).store(output_data + d + kaVecSize, tail_size - kaVecSize);
    } else {
      aVec input_vec0 = aVec::loadu(input_data + d, tail_size);
      vec_fun(input_vec0, data2_avec0).store(output_data + d, tail_size);
    }
  }
}

// for Max and Min, propagate NaN:
template <typename T, ReductionType reduce>
inline T update(const T& x, const T& y) {
  if (reduce == ReductionType::SUM ||
      reduce == ReductionType::MEAN) {
    return x + y;
  } else if (reduce == ReductionType::PROD) {
    return x * y;
  } else if (reduce == ReductionType::MAX) {
    return _max(x, y);
  } else {
    TORCH_INTERNAL_ASSERT(reduce == ReductionType::MIN);
    return _min(x, y);
  }
}

template <typename scalar_t, ReductionType reduce>
inline void update(scalar_t* out, const scalar_t* data, int64_t K) {
  using Vec = vec::Vectorized<vec_scalar_t<scalar_t>>;
  map2<scalar_t>(
      [](Vec x, Vec y) { return update<Vec, reduce>(x, y); },
      out,
      out,
      data,
      K);
}

template <typename scalar_t, ReductionType reduce,
          typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
inline void update(at::opmath_type<scalar_t>* out, const scalar_t* data, int64_t K) {
  using opmath_t = at::opmath_type<scalar_t>;
  using Vec = vec::Vectorized<opmath_t>;
  map_acc<scalar_t, opmath_t>(
      [](Vec x, Vec y) { return update<Vec, reduce>(x, y); },
      out,
      out,
      data,
      K);
}

template <typename scalar_t, ReductionType reduce>
inline void write(scalar_t* out, int64_t count, int64_t K) {
  using Vec = vec::Vectorized<vec_scalar_t<scalar_t>>;
  if (reduce == ReductionType::MEAN) {
    if (count > 0) {
      vec::map<scalar_t>(
          [count](Vec x) { return x / Vec(count); },
          out,
          out,
          K);
    }
  }
}

} // namespace CPU_CAPABILITY
} // namespace at::native