#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once

// DO NOT DEFINE STATIC DATA IN THIS HEADER!
// See Note [Do not compile initializers with AVX]

// Used for shared functions and classes for vec256_bfloat16.h and
// vec256_half.h. Any functions/classes that are common between those two files
// should be defined here. Any non-shared functions/classes should be defined in
// the respective files.

#include <ATen/cpu/vec/intrinsics.h>
#include <ATen/cpu/vec/vec_base.h>

#if defined(CPU_CAPABILITY_AVX2)
#define SLEEF_STATIC_LIBS
#include <sleef.h>
#endif

namespace at::vec {
// See Note [CPU_CAPABILITY namespace]
inline namespace CPU_CAPABILITY {

#if defined(CPU_CAPABILITY_AVX2)

#ifndef SLEEF_CONST
#if (defined(__GNUC__) || defined(__CLANG__)) && !defined(__INTEL_COMPILER)
#define SLEEF_CONST const
#else
#define SLEEF_CONST
#endif
#define SLEEF_CONST_OLD SLEEF_CONST
#else
#define SLEEF_CONST_OLD
#endif

// bfloat16 conversion
static inline void cvtbf16_fp32(const __m128i& a, __m256& o) {
  o = _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(a), 16));
}

static inline void cvtbf16_fp32(const __m256i& a, __m256& o1, __m256& o2) {
  __m128i lo = _mm256_extractf128_si256(a, 0);
  __m128i hi = _mm256_extractf128_si256(a, 1);
  cvtbf16_fp32(lo, o1);
  cvtbf16_fp32(hi, o2);
}

static inline __m128i cvtfp32_bf16(const __m256& src) {
  __m256i value = _mm256_castps_si256(src);
  __m256i nan = _mm256_set1_epi32(0xffff);
  __m256i mask = _mm256_castps_si256(_mm256_cmp_ps(src, src, _CMP_ORD_Q));
  __m256i ones = _mm256_set1_epi32(0x1);
  __m256i vec_bias = _mm256_set1_epi32(0x7fff);
  // uint32_t lsb = (input >> 16) & 1;
  auto t_value = _mm256_and_si256(_mm256_srli_epi32(value, 16), ones);
  // uint32_t rounding_bias = 0x7fff + lsb;
  t_value = _mm256_add_epi32(t_value, vec_bias);
  // input += rounding_bias;
  t_value = _mm256_add_epi32(t_value, value);
  // input = input >> 16;
  t_value = _mm256_srli_epi32(t_value, 16);
  // Check NaN before converting back to bf16
  t_value = _mm256_blendv_epi8(nan, t_value, mask);
  t_value =
      _mm256_packus_epi32(t_value, t_value); // t[4-7] t[4-7] t[0-4] t[0-4]
  t_value = _mm256_permute4x64_epi64(t_value, 0xd8); // 11     01     10     00
  return _mm256_castsi256_si128(t_value);
}

static inline __m256i cvtfp32_bf16(const __m256& a, const __m256& b) {
  __m256i lo = _mm256_castps_si256(a);
  __m256i hi = _mm256_castps_si256(b);
  __m256i nan = _mm256_set1_epi32(0xffff);
  __m256i mask_lo = _mm256_castps_si256(_mm256_cmp_ps(a, a, _CMP_ORD_Q));
  __m256i mask_hi = _mm256_castps_si256(_mm256_cmp_ps(b, b, _CMP_ORD_Q));
  __m256i ones = _mm256_set1_epi32(0x1);
  __m256i vec_bias = _mm256_set1_epi32(0x7fff);
  // uint32_t lsb = (input >> 16) & 1;
  auto t_lo = _mm256_and_si256(_mm256_srli_epi32(lo, 16), ones);
  auto t_hi = _mm256_and_si256(_mm256_srli_epi32(hi, 16), ones);
  // uint32_t rounding_bias = 0x7fff + lsb;
  t_lo = _mm256_add_epi32(t_lo, vec_bias);
  t_hi = _mm256_add_epi32(t_hi, vec_bias);
  // input += rounding_bias;
  t_lo = _mm256_add_epi32(t_lo, lo);
  t_hi = _mm256_add_epi32(t_hi, hi);
  // input = input >> 16;
  t_lo = _mm256_srli_epi32(t_lo, 16);
  t_hi = _mm256_srli_epi32(t_hi, 16);
  // Check NaN before converting back to bf16
  t_lo = _mm256_blendv_epi8(nan, t_lo, mask_lo);
  t_hi = _mm256_blendv_epi8(nan, t_hi, mask_hi);

  t_lo = _mm256_packus_epi32(
      t_lo, t_hi); // t_hi[4-7] t_lo[4-7] t_hi[0-4] t_lo[0-4]
  return _mm256_permute4x64_epi64(t_lo, 0xd8); // 11        01        10 00
}

static inline __m256i merge_compare_result(const __m256& a, const __m256& b) {
  __m256i lo = _mm256_castps_si256(a);
  __m256i hi = _mm256_castps_si256(b);
  lo = _mm256_srli_epi32(lo, 16);
  hi = _mm256_srli_epi32(hi, 16);
  auto out = _mm256_packus_epi32(lo, hi);
  return _mm256_permute4x64_epi64(out, 0xd8);
}

// float16 conversion
static inline void cvtfp16_fp32(const __m128i& a, __m256& o) {
  o = _mm256_cvtph_ps(a);
}

static inline void cvtfp16_fp32(const __m256i& a, __m256& o1, __m256& o2) {
  __m128i lo = _mm256_extractf128_si256(a, 0);
  __m128i hi = _mm256_extractf128_si256(a, 1);
  cvtfp16_fp32(lo, o1);
  cvtfp16_fp32(hi, o2);
}

static inline __m128i cvtfp32_fp16(const __m256& src) {
  return _mm256_cvtps_ph(src, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
}

static inline __m256i cvtfp32_fp16(const __m256& a, const __m256& b) {
  __m128i lo =
      _mm256_cvtps_ph(a, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
  __m128i hi =
      _mm256_cvtps_ph(b, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1);
}

// dtype conversion between float16/bfloat16 and float32
template <
    typename T,
    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
inline void cvt_to_fp32(const __m128i& a, __m256& o);
template <>
inline void cvt_to_fp32<BFloat16>(const __m128i& a, __m256& o) {
  cvtbf16_fp32(a, o);
}
template <>
inline void cvt_to_fp32<Half>(const __m128i& a, __m256& o) {
  cvtfp16_fp32(a, o);
}

template <
    typename T,
    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
inline void cvt_to_fp32(const __m256i& a, __m256& o1, __m256& o2);
template <>
inline void cvt_to_fp32<BFloat16>(const __m256i& a, __m256& o1, __m256& o2) {
  cvtbf16_fp32(a, o1, o2);
}
template <>
inline void cvt_to_fp32<Half>(const __m256i& a, __m256& o1, __m256& o2) {
  cvtfp16_fp32(a, o1, o2);
}

template <
    typename T,
    bool is_compare_op = false,
    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
inline __m256i cvt_from_fp32(const __m256& a, const __m256& b);
template <>
inline __m256i cvt_from_fp32<BFloat16, false>(
    const __m256& a,
    const __m256& b) {
  return cvtfp32_bf16(a, b);
}
template <>
inline __m256i cvt_from_fp32<BFloat16, true>(const __m256& a, const __m256& b) {
  return merge_compare_result(a, b);
}
template <>
inline __m256i cvt_from_fp32<Half, false>(const __m256& a, const __m256& b) {
  return cvtfp32_fp16(a, b);
}
template <>
inline __m256i cvt_from_fp32<Half, true>(const __m256& a, const __m256& b) {
  return cvtfp32_fp16(a, b);
}

template <typename T>
class Vectorized16 {
  static_assert(
      is_reduced_floating_point_v<T>,
      "Support only float16 and bfloat16.");

 protected:
  __m256i values;

 public:
  using value_type = uint16_t;
  using size_type = int;
  static constexpr size_type size() {
    return 16;
  }
  Vectorized16() {}
  Vectorized16(__m256i v) : values(v) {}
  Vectorized16(T val) {
    value_type uw = val.x;
    values = _mm256_set1_epi16(uw);
  }
  Vectorized16(
      T val1,
      T val2,
      T val3,
      T val4,
      T val5,
      T val6,
      T val7,
      T val8,
      T val9,
      T val10,
      T val11,
      T val12,
      T val13,
      T val14,
      T val15,
      T val16) {
    values = _mm256_setr_epi16(
        val1.x,
        val2.x,
        val3.x,
        val4.x,
        val5.x,
        val6.x,
        val7.x,
        val8.x,
        val9.x,
        val10.x,
        val11.x,
        val12.x,
        val13.x,
        val14.x,
        val15.x,
        val16.x);
  }
  operator __m256i() const {
    return values;
  }
  T& operator[](int idx) = delete;
  const T& operator[](int idx) const = delete;
  int zero_mask() const {
    // returns an integer mask where all zero elements are translated to 1-bit
    // and others are translated to 0-bit
    __m256i cmp = _mm256_cmpeq_epi16(values, _mm256_set1_epi16(0));
    return _mm256_movemask_epi8(cmp);
  }
  static Vectorized<T> loadu(const void* ptr, int16_t count = size()) {
    if (count == size())
      return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));

    __at_align__ int16_t tmp_values[size()];
#ifndef __msvc_cl__
#pragma unroll
#endif
    for (const auto i : c10::irange(count, size())) {
      tmp_values[i] = 0;
    }
    std::memcpy(tmp_values, ptr, count * sizeof(int16_t));
    return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(tmp_values));
  }
  void store(void* ptr, int count = size()) const {
    if (count == size()) {
      _mm256_storeu_si256(reinterpret_cast<__m256i*>(ptr), values);
    } else if (count > 0) {
      __at_align__ int16_t tmp_values[size()];
      _mm256_storeu_si256(reinterpret_cast<__m256i*>(tmp_values), values);
      std::memcpy(ptr, tmp_values, count * sizeof(int16_t));
    }
  }
  template <int64_t mask>
  static Vectorized<T> blend(const Vectorized<T>& a, const Vectorized<T>& b) {
    __at_align__ int16_t tmp_values[size()];
    a.store(tmp_values);
    if (mask & 0x01)
      tmp_values[0] = _mm256_extract_epi16(b.values, 0);
    if (mask & 0x02)
      tmp_values[1] = _mm256_extract_epi16(b.values, 1);
    if (mask & 0x04)
      tmp_values[2] = _mm256_extract_epi16(b.values, 2);
    if (mask & 0x08)
      tmp_values[3] = _mm256_extract_epi16(b.values, 3);
    if (mask & 0x10)
      tmp_values[4] = _mm256_extract_epi16(b.values, 4);
    if (mask & 0x20)
      tmp_values[5] = _mm256_extract_epi16(b.values, 5);
    if (mask & 0x40)
      tmp_values[6] = _mm256_extract_epi16(b.values, 6);
    if (mask & 0x80)
      tmp_values[7] = _mm256_extract_epi16(b.values, 7);
    if (mask & 0x100)
      tmp_values[8] = _mm256_extract_epi16(b.values, 8);
    if (mask & 0x200)
      tmp_values[9] = _mm256_extract_epi16(b.values, 9);
    if (mask & 0x400)
      tmp_values[10] = _mm256_extract_epi16(b.values, 10);
    if (mask & 0x800)
      tmp_values[11] = _mm256_extract_epi16(b.values, 11);
    if (mask & 0x1000)
      tmp_values[12] = _mm256_extract_epi16(b.values, 12);
    if (mask & 0x2000)
      tmp_values[13] = _mm256_extract_epi16(b.values, 13);
    if (mask & 0x4000)
      tmp_values[14] = _mm256_extract_epi16(b.values, 14);
    if (mask & 0x8000)
      tmp_values[15] = _mm256_extract_epi16(b.values, 15);
    return loadu(tmp_values);
  }
  static Vectorized<T> blendv(
      const Vectorized<T>& a,
      const Vectorized<T>& b,
      const Vectorized<T>& mask) {
    return _mm256_blendv_epi8(a.values, b.values, mask.values);
  }
  template <typename step_t>
  static Vectorized<T> arange(
      T base = 0.f,
      step_t step = static_cast<step_t>(1)) {
    return Vectorized<T>(
        base,
        base + step,
        base + 2 * step,
        base + 3 * step,
        base + 4 * step,
        base + 5 * step,
        base + 6 * step,
        base + 7 * step,
        base + 8 * step,
        base + 9 * step,
        base + 10 * step,
        base + 11 * step,
        base + 12 * step,
        base + 13 * step,
        base + 14 * step,
        base + 15 * step);
  }
  static Vectorized<T> set(
      const Vectorized<T>& a,
      const Vectorized<T>& b,
      int64_t count = size()) {
    switch (count) {
      case 0:
        return a;
      case 1:
        return blend<1>(a, b);
      case 2:
        return blend<3>(a, b);
      case 3:
        return blend<7>(a, b);
      case 4:
        return blend<15>(a, b);
      case 5:
        return blend<31>(a, b);
      case 6:
        return blend<63>(a, b);
      case 7:
        return blend<127>(a, b);
      case 8:
        return blend<255>(a, b);
      case 9:
        return blend<511>(a, b);
      case 10:
        return blend<1023>(a, b);
      case 11:
        return blend<2047>(a, b);
      case 12:
        return blend<4095>(a, b);
      case 13:
        return blend<8191>(a, b);
      case 14:
        return blend<16383>(a, b);
      case 15:
        return blend<32767>(a, b);
    }
    return b;
  }

  // 'const' type qualifier on return type has no effect, but sleef defines this
  // this way For example `Sleef_exp2f8_u10` signature is `const __m256
  // (__m256)`
  C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wignored-qualifiers")
  Vectorized<T> map(SLEEF_CONST __m256 (*SLEEF_CONST_OLD vop)(__m256)) const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    const auto o1 = vop(lo);
    const auto o2 = vop(hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  C10_DIAGNOSTIC_POP()
  Vectorized<T> isnan() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    lo = _mm256_cmp_ps(lo, _mm256_set1_ps(0.0f), _CMP_UNORD_Q);
    hi = _mm256_cmp_ps(hi, _mm256_set1_ps(0.0f), _CMP_UNORD_Q);
    return merge_compare_result(lo, hi);
  }
  Vectorized<T> abs() const {
    return _mm256_andnot_si256(_mm256_set1_epi16(0x8000), values);
  }
  Vectorized<T> angle() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto angle_lambda = [](__m256 values_2) {
      const auto zero_vec = _mm256_set1_ps(0.f);
      const auto nan_vec = _mm256_set1_ps(NAN);
      const auto not_nan_mask = _mm256_cmp_ps(values_2, values_2, _CMP_EQ_OQ);
      const auto nan_mask = _mm256_cmp_ps(not_nan_mask, zero_vec, _CMP_EQ_OQ);
      const auto pi = _mm256_set1_ps(c10::pi<float>);

      const auto neg_mask = _mm256_cmp_ps(values_2, zero_vec, _CMP_LT_OQ);
      auto angle = _mm256_blendv_ps(zero_vec, pi, neg_mask);
      angle = _mm256_blendv_ps(angle, nan_vec, nan_mask);
      return angle;
    };
    auto o1 = angle_lambda(lo);
    auto o2 = angle_lambda(hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> real() const {
    return *this;
  }
  Vectorized<T> imag() const {
    return _mm256_set1_epi16(0);
  }
  Vectorized<T> conj() const {
    return *this;
  }
  Vectorized<T> acos() const {
    return map(Sleef_acosf8_u10);
  }
  Vectorized<T> acosh() const {
    return map(Sleef_acoshf8_u10);
  }
  Vectorized<T> asin() const {
    return map(Sleef_asinf8_u10);
  }
  Vectorized<T> atan() const {
    return map(Sleef_atanf8_u10);
  }
  Vectorized<T> atanh() const {
    return map(Sleef_atanhf8_u10);
  }
  Vectorized<T> atan2(const Vectorized<T>& b) const {
    __m256 lo, hi;
    __m256 b1, b2;
    cvt_to_fp32<T>(values, lo, hi);
    cvt_to_fp32<T>(b.values, b1, b2);
    auto o1 = Sleef_atan2f8_u10(lo, b1);
    auto o2 = Sleef_atan2f8_u10(hi, b2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> copysign(const Vectorized<T>& sign) const {
    // copy sign bit (0x8000) from sign and remaining bits from values
    __m256i mask_value = _mm256_set1_epi32(~0x80008000);
    __m256i mask_signbit = _mm256_set1_epi32(0x80008000);
    return Vectorized<T>(_mm256_or_si256(
        _mm256_and_si256(values, mask_value),
        _mm256_and_si256(sign, mask_signbit)));
  }
  Vectorized<T> erf() const {
    return map(Sleef_erff8_u10);
  }
  Vectorized<T> erfc() const {
    return map(Sleef_erfcf8_u15);
  }
  Vectorized<T> erfinv() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    __at_align__ float tmp1[size() / 2], tmp2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);
    for (int64_t i = 0; i < size() / 2; i++) {
      tmp1[i] = calc_erfinv(tmp1[i]);
      tmp2[i] = calc_erfinv(tmp2[i]);
    }
    auto o1 = _mm256_loadu_ps(tmp1);
    auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> exp() const {
    return map(Sleef_expf8_u10);
  }
  Vectorized<T> exp2() const {
    return map(Sleef_exp2f8_u10);
  }
  Vectorized<T> expm1() const {
    return map(Sleef_expm1f8_u10);
  }
  Vectorized<T> fexp_u20() const {
    return exp();
  }
  Vectorized<T> exp_u20() const {
    return exp();
  }
  Vectorized<T> fmod(const Vectorized<T>& q) const {
    __m256 x_lo, x_hi;
    cvt_to_fp32<T>(values, x_lo, x_hi);
    __m256 q_lo, q_hi;
    cvt_to_fp32<T>(q.values, q_lo, q_hi);
    auto o1 = Sleef_fmodf8(x_lo, q_lo);
    auto o2 = Sleef_fmodf8(x_hi, q_hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> hypot(const Vectorized<T>& b) const {
    __m256 lo, hi;
    __m256 b1, b2;
    cvt_to_fp32<T>(values, lo, hi);
    cvt_to_fp32<T>(b.values, b1, b2);
    auto o1 = Sleef_hypotf8_u05(lo, b1);
    auto o2 = Sleef_hypotf8_u05(hi, b2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> i0() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    __at_align__ float tmp1[size() / 2], tmp2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);
    for (int64_t i = 0; i < size() / 2; i++) {
      tmp1[i] = calc_i0(tmp1[i]);
      tmp2[i] = calc_i0(tmp2[i]);
    }
    auto o1 = _mm256_loadu_ps(tmp1);
    auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> i0e() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    constexpr auto sz = size();
    __at_align__ float tmp1[sz / 2], tmp2[sz / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);

    for (auto i = decltype(sz){0}; i < sz / 2; i++) {
      tmp1[i] = calc_i0e(tmp1[i]);
      tmp2[i] = calc_i0e(tmp2[i]);
    }
    const auto o1 = _mm256_loadu_ps(tmp1);
    const auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> digamma() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    constexpr auto sz = size();
    __at_align__ float tmp1[sz / 2], tmp2[sz / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);

    for (auto i = decltype(sz){0}; i < sz / 2; i++) {
      tmp1[i] = calc_digamma(tmp1[i]);
      tmp2[i] = calc_digamma(tmp2[i]);
    }
    const auto o1 = _mm256_loadu_ps(tmp1);
    const auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> igamma(const Vectorized<T>& x) const {
    __m256 lo, hi;
    __m256 xlo, xhi;
    cvt_to_fp32<T>(values, lo, hi);
    cvt_to_fp32<T>(x.values, xlo, xhi);
    __at_align__ float tmp1[size() / 2], tmp2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);
    __at_align__ float tmpx1[size() / 2], tmpx2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmpx1), xlo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmpx2), xhi);
    for (int64_t i = 0; i < size() / 2; ++i) {
      tmp1[i] = calc_igamma(tmp1[i], tmpx1[i]);
      tmp2[i] = calc_igamma(tmp2[i], tmpx2[i]);
    }
    auto o1 = _mm256_loadu_ps(tmp1);
    auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }

  Vectorized<T> igammac(const Vectorized<T>& x) const {
    __m256 lo, hi;
    __m256 xlo, xhi;
    cvt_to_fp32<T>(values, lo, hi);
    cvt_to_fp32<T>(x.values, xlo, xhi);
    __at_align__ float tmp1[size() / 2], tmp2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp1), lo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmp2), hi);
    __at_align__ float tmpx1[size() / 2], tmpx2[size() / 2];
    _mm256_storeu_ps(reinterpret_cast<float*>(tmpx1), xlo);
    _mm256_storeu_ps(reinterpret_cast<float*>(tmpx2), xhi);
    for (int64_t i = 0; i < size() / 2; ++i) {
      tmp1[i] = calc_igammac(tmp1[i], tmpx1[i]);
      tmp2[i] = calc_igammac(tmp2[i], tmpx2[i]);
    }
    auto o1 = _mm256_loadu_ps(tmp1);
    auto o2 = _mm256_loadu_ps(tmp2);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> log() const {
    return map(Sleef_logf8_u10);
  }
  Vectorized<T> log2() const {
    return map(Sleef_log2f8_u10);
  }
  Vectorized<T> log10() const {
    return map(Sleef_log10f8_u10);
  }
  Vectorized<T> log1p() const {
    return map(Sleef_log1pf8_u10);
  }
  Vectorized<T> sin() const {
    return map(Sleef_sinf8_u10);
  }
  Vectorized<T> sinh() const {
    return map(Sleef_sinhf8_u10);
  }
  Vectorized<T> cos() const {
    return map(Sleef_cosf8_u10);
  }
  Vectorized<T> cosh() const {
    return map(Sleef_coshf8_u10);
  }
  Vectorized<T> ceil() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto o1 = _mm256_ceil_ps(lo);
    auto o2 = _mm256_ceil_ps(hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> floor() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto o1 = _mm256_floor_ps(lo);
    auto o2 = _mm256_floor_ps(hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> neg() const {
    return _mm256_xor_si256(values, _mm256_set1_epi16(0x8000));
  }
  Vectorized<T> round() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto o1 =
        _mm256_round_ps(lo, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
    auto o2 =
        _mm256_round_ps(hi, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> tan() const {
    return map(Sleef_tanf8_u10);
  }
  Vectorized<T> tanh() const {
    return map(Sleef_tanhf8_u10);
  }
  Vectorized<T> trunc() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto o1 = _mm256_round_ps(lo, (_MM_FROUND_TO_ZERO | _MM_FROUND_NO_EXC));
    auto o2 = _mm256_round_ps(hi, (_MM_FROUND_TO_ZERO | _MM_FROUND_NO_EXC));
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> lgamma() const {
    return map(Sleef_lgammaf8_u10);
  }
  Vectorized<T> sqrt() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto o1 = _mm256_sqrt_ps(lo);
    auto o2 = _mm256_sqrt_ps(hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> reciprocal() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto ones = _mm256_set1_ps(1);
    auto o1 = _mm256_div_ps(ones, lo);
    auto o2 = _mm256_div_ps(ones, hi);
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> rsqrt() const {
    __m256 lo, hi;
    cvt_to_fp32<T>(values, lo, hi);
    auto ones = _mm256_set1_ps(1);
    auto o1 = _mm256_div_ps(ones, _mm256_sqrt_ps(lo));
    auto o2 = _mm256_div_ps(ones, _mm256_sqrt_ps(hi));
    return cvt_from_fp32<T>(o1, o2);
  }
  Vectorized<T> pow(const Vectorized<T>& b) const {
    __m256 lo, hi;
    __m256 b1, b2;
    cvt_to_fp32<T>(values, lo, hi);
    cvt_to_fp32<T>(b.values, b1, b2);
    auto o1 = Sleef_powf8_u10(lo, b1);
    auto o2 = Sleef_powf8_u10(hi, b2);
    return cvt_from_fp32<T>(o1, o2);
  }

 private:
  template <typename Op, typename VectorizedType>
  Vectorized<T> inline binary_compare(const VectorizedType& b, Op op) const {
    __m256 a_lo, a_hi;
    __m256 b_lo, b_hi;
    cvt_to_fp32<T>(values, a_lo, a_hi);
    cvt_to_fp32<T>(b.values, b_lo, b_hi);
    auto o1 = op(a_lo, b_lo);
    auto o2 = op(a_hi, b_hi);
    return cvt_from_fp32<T, /*is_compare_op*/ true>(o1, o2);
  }

 public:
  Vectorized<T> inline operator>(const Vectorized<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_GT_OQ);
    });
  }
  Vectorized<T> inline operator<(const Vectorized<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_LT_OQ);
    });
  }
  Vectorized<T> inline operator>=(const Vectorized<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_GE_OQ);
    });
  }
  Vectorized<T> inline operator<=(const Vectorized<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_LE_OQ);
    });
  }
  Vectorized<T> inline operator==(const Vectorized16<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_EQ_OQ);
    });
  }
  Vectorized<T> inline operator!=(const Vectorized16<T>& other) const {
    return binary_compare(other, [](__m256 x, __m256 y) {
      return _mm256_cmp_ps(x, y, _CMP_NEQ_UQ);
    });
  }
};

template <typename T, typename Op>
static inline Vectorized<T> binary_op_as_fp32(
    const Vectorized<T>& a,
    const Vectorized<T>& b,
    Op op) {
  __m256 a_lo, a_hi;
  __m256 b_lo, b_hi;
  cvt_to_fp32<T>(__m256i(a), a_lo, a_hi);
  cvt_to_fp32<T>(__m256i(b), b_lo, b_hi);
  auto o1 = op(a_lo, b_lo);
  auto o2 = op(a_hi, b_hi);
  return cvt_from_fp32<T>(o1, o2);
}

#define CONVERT_VECTORIZED_INIT(type, name)                     \
  inline std::tuple<Vectorized<float>, Vectorized<float>>       \
      convert_##name##_float(const Vectorized<type>& a) {       \
    __m256 o1, o2;                                              \
    cvt_to_fp32<type>(__m256i(a), o1, o2);                      \
    return std::make_tuple(o1, o2);                             \
  }                                                             \
  inline Vectorized<type> convert_float_##name(                 \
      const Vectorized<float>& a, const Vectorized<float>& b) { \
    return cvt_from_fp32<type>(__m256(a), __m256(b));           \
  }

#define LOAD_FP32_VECTORIZED_INIT(type, name)                               \
  inline void load_fp32_from_##name(                                        \
      const type* data, Vectorized<float>& out) {                           \
    auto values = _mm_loadu_si128(reinterpret_cast<const __m128i*>(data));  \
    __m256 out_values;                                                      \
    cvt_to_fp32<type>(values, out_values);                                  \
    out = out_values;                                                       \
  }                                                                         \
                                                                            \
  inline void load_fp32_from_##name(                                        \
      const type* data, Vectorized<float>& out1, Vectorized<float>& out2) { \
    auto vec = Vectorized<type>::loadu(data);                               \
    __m256 out1_values, out2_values;                                        \
    cvt_to_fp32<type>(vec, out1_values, out2_values);                       \
    out1 = out1_values;                                                     \
    out2 = out2_values;                                                     \
  }

#else // CPU_CAPABILITY_AVX2

#define CONVERT_NON_VECTORIZED_INIT(type, name)                     \
  inline std::tuple<Vectorized<float>, Vectorized<float>>           \
      convert_##name##_float(const Vectorized<type>& a) {           \
    constexpr int64_t K = Vectorized<type>::size();                 \
    __at_align__ float arr[K];                                      \
    __at_align__ type arr2[K];                                      \
    a.store(arr2);                                                  \
    convert(arr2, arr, K);                                          \
    return std::make_tuple(                                         \
        Vectorized<float>::loadu(arr),                              \
        Vectorized<float>::loadu(arr + Vectorized<float>::size())); \
  }                                                                 \
  inline Vectorized<type> convert_float_##name(                     \
      const Vectorized<float>& a, const Vectorized<float>& b) {     \
    constexpr int64_t K = Vectorized<type>::size();                 \
    __at_align__ float arr[K];                                      \
    __at_align__ type arr2[K];                                      \
    a.store(arr);                                                   \
    b.store(arr + Vectorized<float>::size());                       \
    convert(arr, arr2, K);                                          \
    return Vectorized<type>::loadu(arr2);                           \
  }

#define LOAD_FP32_NON_VECTORIZED_INIT(type, name)                           \
  inline void load_fp32_from_##name(                                        \
      const type* data, Vectorized<float>& out) {                           \
    __at_align__ float values[Vectorized<float>::size()];                   \
    for (const auto k : c10::irange(Vectorized<float>::size())) {           \
      values[k] = data[k];                                                  \
    }                                                                       \
    out = Vectorized<float>::loadu(values);                                 \
  }                                                                         \
                                                                            \
  inline void load_fp32_from_##name(                                        \
      const type* data, Vectorized<float>& out1, Vectorized<float>& out2) { \
    load_fp32_from_##name(data, out1);                                      \
    data += Vectorized<float>::size();                                      \
    load_fp32_from_##name(data, out2);                                      \
  }

#endif // CPU_CAPABILITY_AVX2
} // namespace CPU_CAPABILITY
} // namespace at::vec

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
