#pragma once

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

#include <ATen/cpu/vec/intrinsics.h>
#include <ATen/cpu/vec/vec_base.h>
#include <c10/util/irange.h>
#if defined(CPU_CAPABILITY_AVX512)
#define SLEEF_STATIC_LIBS
#include <sleef.h>
#endif

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

#if defined(CPU_CAPABILITY_AVX512)

template <>
struct is_vec_specialized_for<float> : std::bool_constant<true> {};

template <>
class Vectorized<float> {
 private:
  static constexpr __m512i zero_vec{0, 0, 0, 0, 0, 0, 0, 0};

 public:
  __m512 values;
  using value_type = float;
  using size_type = int;
  static constexpr size_type size() {
    return 16;
  }
  Vectorized() {
    values = _mm512_setzero_ps();
  }
  Vectorized(__m512 v) : values(v) {}
  Vectorized(float val) {
    values = _mm512_set1_ps(val);
  }
  Vectorized(
      float val1,
      float val2,
      float val3,
      float val4,
      float val5,
      float val6,
      float val7,
      float val8,
      float val9,
      float val10,
      float val11,
      float val12,
      float val13,
      float val14,
      float val15,
      float val16) {
    values = _mm512_setr_ps(
        val1,
        val2,
        val3,
        val4,
        val5,
        val6,
        val7,
        val8,
        val9,
        val10,
        val11,
        val12,
        val13,
        val14,
        val15,
        val16);
  }
  Vectorized(const float (&arr)[16])
      : Vectorized(
            arr[0],
            arr[1],
            arr[2],
            arr[3],
            arr[4],
            arr[5],
            arr[6],
            arr[7],
            arr[8],
            arr[9],
            arr[10],
            arr[11],
            arr[12],
            arr[13],
            arr[14],
            arr[15]) {}
  operator __m512() const {
    return values;
  }
  template <int64_t mask>
  static Vectorized<float> blend(
      const Vectorized<float>& a,
      const Vectorized<float>& b) {
    return _mm512_mask_blend_ps(mask, a.values, b.values);
  }
  static Vectorized<float> blendv(
      const Vectorized<float>& a,
      const Vectorized<float>& b,
      const Vectorized<float>& mask) {
    auto all_ones = _mm512_set1_epi32(0xFFFFFFFF);
    auto mmask = _mm512_cmp_epi32_mask(
        _mm512_castps_si512(mask.values), all_ones, _MM_CMPINT_EQ);
    return _mm512_mask_blend_ps(mmask, a.values, b.values);
  }
  template <typename step_t>
  static Vectorized<float> arange(
      float base = 0.f,
      step_t step = static_cast<step_t>(1)) {
    return Vectorized<float>(
        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<float> set(
      const Vectorized<float>& a,
      const Vectorized<float>& 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;
  }
  static Vectorized<float> loadu(const void* ptr, int64_t count = size()) {
    if (count == size())
      return _mm512_loadu_ps(reinterpret_cast<const float*>(ptr));

    __mmask16 mask = (1ULL << count) - 1;
    return _mm512_maskz_loadu_ps(mask, ptr);
  }
  void store(void* ptr, int64_t count = size()) const {
    if (count == size()) {
      _mm512_storeu_ps(reinterpret_cast<float*>(ptr), values);
    } else if (count > 0) {
      __mmask16 mask = (1ULL << count) - 1;
      _mm512_mask_storeu_ps(reinterpret_cast<float*>(ptr), mask, values);
    }
  }
  const float& operator[](int idx) const = delete;
  float& operator[](int idx) = 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
    __mmask16 cmp = _mm512_cmp_ps_mask(values, _mm512_set1_ps(0.0), _CMP_EQ_OQ);
    return static_cast<int32_t>(cmp);
  }
  Vectorized<float> isnan() const {
    auto mask = _mm512_cmp_ps_mask(values, _mm512_set1_ps(0.0), _CMP_UNORD_Q);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }
  bool has_inf_nan() const {
    __m512 self_sub = _mm512_sub_ps(values, values);
    return (_mm512_movepi8_mask(_mm512_castps_si512(self_sub)) &
            0x7777777777777777) != 0;
  }
  Vectorized<float> map(float (*const f)(float)) const {
    __at_align__ float tmp[size()];
    store(tmp);
    for (const auto i : c10::irange(size())) {
      tmp[i] = f(tmp[i]);
    }
    return loadu(tmp);
  }
  Vectorized<float> abs() const {
    auto mask = _mm512_set1_ps(-0.f);
    return _mm512_andnot_ps(mask, values);
  }
  Vectorized<float> angle() const {
    __m512 zero_vec = _mm512_set1_ps(0.f);
    const auto nan_vec = _mm512_set1_ps(NAN);
    const auto not_nan_mask = _mm512_cmp_ps_mask(values, values, _CMP_EQ_OQ);
    const auto not_nan_vec = _mm512_mask_set1_epi32(
        _mm512_castps_si512(zero_vec), not_nan_mask, 0xFFFFFFFF);
    const auto nan_mask = _mm512_cmp_ps_mask(
        _mm512_castsi512_ps(not_nan_vec), zero_vec, _CMP_EQ_OQ);
    const auto pi = _mm512_set1_ps(c10::pi<double>);

    const auto neg_mask = _mm512_cmp_ps_mask(values, zero_vec, _CMP_LT_OQ);
    auto angle = _mm512_mask_blend_ps(neg_mask, zero_vec, pi);
    angle = _mm512_mask_blend_ps(nan_mask, angle, nan_vec);
    return angle;
  }
  Vectorized<float> real() const {
    return *this;
  }
  Vectorized<float> imag() const {
    return _mm512_set1_ps(0);
  }
  Vectorized<float> conj() const {
    return *this;
  }
  Vectorized<float> acos() const {
    return Vectorized<float>(Sleef_acosf16_u10(values));
  }
  Vectorized<float> acosh() const {
    return Vectorized<float>(Sleef_acoshf16_u10(values));
  }
  Vectorized<float> asin() const {
    return Vectorized<float>(Sleef_asinf16_u10(values));
  }
  Vectorized<float> asinh() const {
    return Vectorized<float>(Sleef_asinhf16_u10(values));
  }
  Vectorized<float> atan() const {
    return Vectorized<float>(Sleef_atanf16_u10(values));
  }
  Vectorized<float> atanh() const {
    return Vectorized<float>(Sleef_atanhf16_u10(values));
  }
  Vectorized<float> atan2(const Vectorized<float>& b) const {
    return Vectorized<float>(Sleef_atan2f16_u10(values, b));
  }
  Vectorized<float> copysign(const Vectorized<float>& sign) const {
    return Vectorized<float>(Sleef_copysignf16(values, sign));
  }
  Vectorized<float> erf() const {
    // constants
    const auto neg_zero_vec = _mm512_set1_ps(-0.f);
    const auto one_vec = _mm512_set1_ps(1.0f);
    const auto p = _mm512_set1_ps(0.3275911f);
    const auto p1 = _mm512_set1_ps(0.254829592f);
    const auto p2 = _mm512_set1_ps(-0.284496736f);
    const auto p3 = _mm512_set1_ps(1.421413741f);
    const auto p4 = _mm512_set1_ps(-1.453152027f);
    const auto p5 = _mm512_set1_ps(1.061405429f);
    // sign(x)
    auto sign_mask = _mm512_and_ps(neg_zero_vec, values);
    auto abs_vec = _mm512_abs_ps(values);
    // t = 1 / (p * abs(x) + 1)
    auto tmp0 = _mm512_fmadd_ps(p, abs_vec, one_vec);
    auto t = _mm512_div_ps(one_vec, tmp0);
    // r = p5 * t ^ 4 + p4 * t ^ 3 + p3 * t ^ 2 + p2 * t + p1
    auto tmp1 = _mm512_fmadd_ps(p5, t, p4);
    auto tmp2 = _mm512_fmadd_ps(tmp1, t, p3);
    auto tmp3 = _mm512_fmadd_ps(tmp2, t, p2);
    auto r = _mm512_fmadd_ps(tmp3, t, p1);
    // - exp(- x * x)
    auto pow_2 = _mm512_mul_ps(values, values);
    auto neg_pow_2 = _mm512_xor_ps(neg_zero_vec, pow_2);
    // auto tmp4 = exp(neg_pow_2);
    auto tmp4 = Vectorized<float>(Sleef_expf16_u10(neg_pow_2));
    auto tmp5 = _mm512_xor_ps(neg_zero_vec, tmp4);
    // erf(x) = sign(x) * (1 - r * t * exp(- x * x))
    auto tmp6 = _mm512_mul_ps(tmp5, t);
    auto tmp7 = _mm512_fmadd_ps(tmp6, r, one_vec);
    return _mm512_xor_ps(sign_mask, tmp7);
  }
  Vectorized<float> erfc() const {
    return Vectorized<float>(Sleef_erfcf16_u15(values));
  }
  Vectorized<float> erfinv() const {
    return map(calc_erfinv);
  }
  Vectorized<float> exp() const {
    return Vectorized<float>(Sleef_expf16_u10(values));
  }
  Vectorized<float> exp2() const {
    return Vectorized<float>(Sleef_exp2f16_u10(values));
  }
  Vectorized<float> expm1() const {
    return Vectorized<float>(Sleef_expm1f16_u10(values));
  }
  Vectorized<float> fexp_u20() const {
    const __m512 vec_c0 = _mm512_set1_ps(0.00010703434948458272f);
    const __m512 vec_c1 = _mm512_set1_ps(0.30354260500649682f);
    const __m512 vec_c2 = _mm512_set1_ps(-0.22433836478672356);
    const __m512 vec_c3 = _mm512_set1_ps(-0.079204240219773236);

    const __m512 vec_exp_log2ef =
        _mm512_castsi512_ps(_mm512_set1_epi32(0x3fb8aa3b)); // log2(e)

    const __m512 vec_a = _mm512_set1_ps(std::pow(2, 23) / std::log2(2));
    const __m512 vec_b = _mm512_set1_ps(std::pow(2, 23) * 127.f);

    const __m512 vec_ln_flt_min =
        _mm512_castsi512_ps(_mm512_set1_epi32(0xc2aeac50));
    const __m512 vec_ln_flt_max =
        _mm512_castsi512_ps(_mm512_set1_epi32(0x42b17218));
    __m512i vec_infinity = _mm512_set1_epi32(0x7F800000);
    __m512i vec_zero = _mm512_setzero_epi32();

    // Fast Exponential Computation on SIMD Architectures
    // A. Cristiano I. Malossi, Yves Ineichen, Costas Bekas, and Alessandro
    // Curioni exp(x) = 2**(x * log2(e))
    //        = 2**xi * 2**xf   - TIPS we are using  the EEEE floating point
    //        representation with identification to the exponent and the
    //        mentissa
    //  2**xf will be approximated to a polynomial of degree 3 computed with
    //  Horner method
    // mask for the boundary condition
    auto min_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_min, _CMP_LT_OS);
    auto max_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_max, _CMP_GT_OS);

    // transformation with log2(e)
    auto vec_src = _mm512_mul_ps(values, vec_exp_log2ef);
    auto vec_fractional = _mm512_sub_ps(vec_src, _mm512_floor_ps(vec_src));

    // compute polynomial using Horner Scheme, for superscalar processor
    auto vec_res = _mm512_fmadd_ps(vec_fractional, vec_c3, vec_c2);
    vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c1);
    vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c0);

    vec_src = _mm512_sub_ps(vec_src, vec_res);
    // the tips is here, headache in perspective
    auto tmp = _mm512_fmadd_ps(vec_a, vec_src, vec_b);
    // headache bis - we loose precision with the cast but it "fits", but ok
    // after f32 -> f16 later
    __m512i casted_integer = _mm512_cvttps_epi32(tmp);
    // boundary condition, lower than the min -> 0
    casted_integer = _mm512_mask_mov_epi32(casted_integer, min_mask, vec_zero);
    // boundary condition, larger than the max -> +oo
    casted_integer =
        _mm512_mask_mov_epi32(casted_integer, max_mask, vec_infinity);
    // final interpretation to float
    return _mm512_castsi512_ps(casted_integer);
  }
  Vectorized<float> exp_u20() const {
    // A faster version of exp with ULP=20
    const __m512 vec_factorial_1 =
        _mm512_set1_ps(0.999999701f); // 1/factorial(1)
    const __m512 vec_factorial_2 =
        _mm512_set1_ps(0.499991506f); // 1/factorial(2)
    const __m512 vec_factorial_3 =
        _mm512_set1_ps(0.166676521f); // 1/factorial(3)
    const __m512 vec_factorial_4 =
        _mm512_set1_ps(0.0418978221f); // 1/factorial(4)
    const __m512 vec_factorial_5 =
        _mm512_set1_ps(0.00828929059f); // 1/factorial(5)
    const __m512 vec_exp_log2ef =
        _mm512_castsi512_ps(_mm512_set1_epi32(0x3fb8aa3b)); // log2(e)
    const __m512 vec_half = _mm512_set1_ps(0.5f);
    const __m512 vec_one = _mm512_set1_ps(1.f);
    const __m512 vec_zero = _mm512_set1_ps(0.f);
    const __m512 vec_two = _mm512_set1_ps(2.f);
    const __m512 vec_ln2f =
        _mm512_castsi512_ps(_mm512_set1_epi32(0x3f317218)); // ln(2)
    const __m512 vec_ln_flt_min =
        _mm512_castsi512_ps(_mm512_set1_epi32(0xc2aeac50));
    const __m512 vec_ln_flt_max =
        _mm512_castsi512_ps(_mm512_set1_epi32(0x42b17218));
    const __m512i vec_127 = _mm512_set1_epi32(0x0000007f);
    const int n_mantissa_bits = 23;

    // exp(x) =
    // = exp(n * ln(2) + r) // divide x by ln(2) and get quot and rem
    // = 2^n * exp(r) // simplify the exp(n*ln(2)) expression

    auto less_ln_flt_min_mask =
        _mm512_cmp_ps_mask(values, vec_ln_flt_min, 1 /*_CMP_LT_OS*/);
    auto vec_src = _mm512_min_ps(values, vec_ln_flt_max);
    vec_src = _mm512_max_ps(vec_src, vec_ln_flt_min);

    // fx = floorf(x * log2ef + 0.5)
    auto vec_fx = _mm512_fmadd_ps(vec_src, vec_exp_log2ef, vec_half);
    auto vec_fx_i = _mm512_cvt_roundps_epi32(
        vec_fx, _MM_FROUND_TO_NEG_INF | _MM_FROUND_NO_EXC);
    vec_fx = _mm512_cvtepi32_ps(vec_fx_i);

    // x = x - fx * ln2
    auto vec_exp_poly = _mm512_fnmadd_ps(vec_fx, vec_ln2f, vec_src);

    // compute polynomial
    auto vec_res =
        _mm512_fmadd_ps(vec_exp_poly, vec_factorial_5, vec_factorial_4);
    vec_res = _mm512_fmadd_ps(vec_exp_poly, vec_res, vec_factorial_3);
    vec_res = _mm512_fmadd_ps(vec_exp_poly, vec_res, vec_factorial_2);
    vec_res = _mm512_fmadd_ps(vec_exp_poly, vec_res, vec_factorial_1);
    vec_res = _mm512_fmadd_ps(vec_exp_poly, vec_res, vec_one);

    // compute 2^(n-1)
    auto vec_exp_number = _mm512_sub_ps(vec_fx, vec_one);
    auto vec_exp_number_i = _mm512_cvtps_epi32(vec_exp_number);
    auto vec_two_pow_n_i = _mm512_add_epi32(vec_exp_number_i, vec_127);
    vec_two_pow_n_i = _mm512_slli_epi32(vec_two_pow_n_i, n_mantissa_bits);
    auto vec_two_pow_n = _mm512_castsi512_ps(vec_two_pow_n_i);
    vec_two_pow_n =
        _mm512_mask_blend_ps(less_ln_flt_min_mask, vec_two_pow_n, vec_zero);

    // y = y * 2^n
    vec_res = _mm512_mul_ps(vec_res, vec_two_pow_n);
    vec_res = _mm512_mul_ps(vec_res, vec_two);
    return vec_res;
  }
  Vectorized<float> fmod(const Vectorized<float>& q) const {
    return Vectorized<float>(Sleef_fmodf16(values, q));
  }
  Vectorized<float> log() const {
    return Vectorized<float>(Sleef_logf16_u10(values));
  }
  Vectorized<float> log2() const {
    return Vectorized<float>(Sleef_log2f16_u10(values));
  }
  Vectorized<float> log10() const {
    return Vectorized<float>(Sleef_log10f16_u10(values));
  }
  Vectorized<float> log1p() const {
    return Vectorized<float>(Sleef_log1pf16_u10(values));
  }
  Vectorized<float> frac() const;
  Vectorized<float> sin() const {
    return Vectorized<float>(Sleef_sinf16_u35(values));
  }
  Vectorized<float> sinh() const {
    return Vectorized<float>(Sleef_sinhf16_u10(values));
  }
  Vectorized<float> cos() const {
    return Vectorized<float>(Sleef_cosf16_u35(values));
  }
  Vectorized<float> cosh() const {
    return Vectorized<float>(Sleef_coshf16_u10(values));
  }
  Vectorized<float> ceil() const {
    return _mm512_ceil_ps(values);
  }
  Vectorized<float> floor() const {
    return _mm512_floor_ps(values);
  }
  Vectorized<float> hypot(const Vectorized<float>& b) const {
    return Vectorized<float>(Sleef_hypotf16_u05(values, b));
  }
  Vectorized<float> i0() const {
    return map(calc_i0);
  }
  Vectorized<float> i0e() const {
    return map(calc_i0e);
  }
  Vectorized<float> digamma() const {
    return map(calc_digamma);
  }
  Vectorized<float> igamma(const Vectorized<float>& x) const {
    __at_align__ float tmp[size()];
    __at_align__ float tmp_x[size()];
    store(tmp);
    x.store(tmp_x);
    for (const auto i : c10::irange(size())) {
      tmp[i] = calc_igamma(tmp[i], tmp_x[i]);
    }
    return loadu(tmp);
  }
  Vectorized<float> igammac(const Vectorized<float>& x) const {
    __at_align__ float tmp[size()];
    __at_align__ float tmp_x[size()];
    store(tmp);
    x.store(tmp_x);
    for (const auto i : c10::irange(size())) {
      tmp[i] = calc_igammac(tmp[i], tmp_x[i]);
    }
    return loadu(tmp);
  }
  Vectorized<float> neg() const {
    return _mm512_xor_ps(_mm512_set1_ps(-0.f), values);
  }
  Vectorized<float> nextafter(const Vectorized<float>& b) const {
    return Vectorized<float>(Sleef_nextafterf16(values, b));
  }
  Vectorized<float> round() const {
    return _mm512_roundscale_ps(
        values, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
  }
  Vectorized<float> tan() const {
    return Vectorized<float>(Sleef_tanf16_u10(values));
  }
  Vectorized<float> tanh() const {
    return Vectorized<float>(Sleef_tanhf16_u10(values));
  }
  Vectorized<float> trunc() const {
    return _mm512_roundscale_ps(
        values, (_MM_FROUND_TO_ZERO | _MM_FROUND_NO_EXC));
  }
  Vectorized<float> lgamma() const {
    return Vectorized<float>(Sleef_lgammaf16_u10(values));
  }
  Vectorized<float> sqrt() const {
    return _mm512_sqrt_ps(values);
  }
  Vectorized<float> reciprocal() const {
    return _mm512_div_ps(_mm512_set1_ps(1), values);
  }
  Vectorized<float> rsqrt() const {
    return _mm512_div_ps(_mm512_set1_ps(1), _mm512_sqrt_ps(values));
  }
  Vectorized<float> pow(const Vectorized<float>& b) const {
    return Vectorized<float>(Sleef_powf16_u10(values, b));
  }
  float reduce_add() const {
    return _mm512_reduce_add_ps(values);
  }
  float reduce_max() const {
    return _mm512_reduce_max_ps(values);
  }
  // Comparison using the _CMP_**_OQ predicate.
  //   `O`: get false if an operand is NaN
  //   `Q`: do not raise if an operand is NaN
  Vectorized<float> operator==(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_EQ_OQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> operator!=(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_NEQ_UQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> operator<(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_LT_OQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> operator<=(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_LE_OQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> operator>(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_GT_OQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> operator>=(const Vectorized<float>& other) const {
    auto mask = _mm512_cmp_ps_mask(values, other.values, _CMP_GE_OQ);
    return _mm512_castsi512_ps(
        _mm512_mask_set1_epi32(zero_vec, mask, 0xFFFFFFFF));
  }

  Vectorized<float> eq(const Vectorized<float>& other) const;
  Vectorized<float> ne(const Vectorized<float>& other) const;
  Vectorized<float> gt(const Vectorized<float>& other) const;
  Vectorized<float> ge(const Vectorized<float>& other) const;
  Vectorized<float> lt(const Vectorized<float>& other) const;
  Vectorized<float> le(const Vectorized<float>& other) const;
};

template <>
Vectorized<float> inline operator+(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_add_ps(a, b);
}

template <>
Vectorized<float> inline operator-(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_sub_ps(a, b);
}

template <>
Vectorized<float> inline operator*(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_mul_ps(a, b);
}

template <>
Vectorized<float> inline operator/(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_div_ps(a, b);
}

// frac. Implement this here so we can use subtraction
inline Vectorized<float> Vectorized<float>::frac() const {
  return *this - this->trunc();
}

// Implements the IEEE 754 201X `maximum` operation, which propagates NaN if
// either input is a NaN.
template <>
Vectorized<float> inline maximum(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  auto zero_vec = _mm512_set1_epi32(0);
  auto max = _mm512_max_ps(a, b);
  auto isnan_mask = _mm512_cmp_ps_mask(a, b, _CMP_UNORD_Q);
  auto isnan = _mm512_castsi512_ps(
      _mm512_mask_set1_epi32(zero_vec, isnan_mask, 0xFFFFFFFF));
  // Exploit the fact that all-ones is a NaN.
  return _mm512_or_ps(max, isnan);
}

// Implements the IEEE 754 201X `minimum` operation, which propagates NaN if
// either input is a NaN.
template <>
Vectorized<float> inline minimum(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  auto zero_vec = _mm512_set1_epi32(0);
  auto min = _mm512_min_ps(a, b);
  auto isnan_mask = _mm512_cmp_ps_mask(a, b, _CMP_UNORD_Q);
  auto isnan = _mm512_castsi512_ps(
      _mm512_mask_set1_epi32(zero_vec, isnan_mask, 0xFFFFFFFF));
  // Exploit the fact that all-ones is a NaN.
  return _mm512_or_ps(min, isnan);
}

template <>
Vectorized<float> inline clamp(
    const Vectorized<float>& a,
    const Vectorized<float>& min,
    const Vectorized<float>& max) {
  return _mm512_min_ps(max, _mm512_max_ps(min, a));
}

template <>
Vectorized<float> inline clamp_max(
    const Vectorized<float>& a,
    const Vectorized<float>& max) {
  return _mm512_min_ps(max, a);
}

template <>
Vectorized<float> inline clamp_min(
    const Vectorized<float>& a,
    const Vectorized<float>& min) {
  return _mm512_max_ps(min, a);
}

template <>
Vectorized<float> inline operator&(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_and_ps(a, b);
}

template <>
Vectorized<float> inline operator|(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_or_ps(a, b);
}

template <>
Vectorized<float> inline operator^(
    const Vectorized<float>& a,
    const Vectorized<float>& b) {
  return _mm512_xor_ps(a, b);
}

inline Vectorized<float> Vectorized<float>::eq(
    const Vectorized<float>& other) const {
  return (*this == other) & Vectorized<float>(1.0f);
}

inline Vectorized<float> Vectorized<float>::ne(
    const Vectorized<float>& other) const {
  return (*this != other) & Vectorized<float>(1.0f);
}

inline Vectorized<float> Vectorized<float>::gt(
    const Vectorized<float>& other) const {
  return (*this > other) & Vectorized<float>(1.0f);
}

inline Vectorized<float> Vectorized<float>::ge(
    const Vectorized<float>& other) const {
  return (*this >= other) & Vectorized<float>(1.0f);
}

inline Vectorized<float> Vectorized<float>::lt(
    const Vectorized<float>& other) const {
  return (*this < other) & Vectorized<float>(1.0f);
}

inline Vectorized<float> Vectorized<float>::le(
    const Vectorized<float>& other) const {
  return (*this <= other) & Vectorized<float>(1.0f);
}

template <>
inline void convert(const float* src, float* dst, int64_t n) {
  int64_t i;
#ifndef __msvc_cl__
#pragma unroll
#endif
  for (i = 0; i <= (n - Vectorized<float>::size());
       i += Vectorized<float>::size()) {
    _mm512_storeu_ps(dst + i, _mm512_loadu_ps(src + i));
  }
#ifndef __msvc_cl__
#pragma unroll
#endif
  for (; i < n; i++) {
    dst[i] = src[i];
  }
}

template <>
Vectorized<float> inline fmadd(
    const Vectorized<float>& a,
    const Vectorized<float>& b,
    const Vectorized<float>& c) {
  return _mm512_fmadd_ps(a, b, c);
}

template <>
Vectorized<float> inline fnmadd(
    const Vectorized<float>& a,
    const Vectorized<float>& b,
    const Vectorized<float>& c) {
  return _mm512_fnmadd_ps(a, b, c);
}

template <>
Vectorized<float> inline fmsub(
    const Vectorized<float>& a,
    const Vectorized<float>& b,
    const Vectorized<float>& c) {
  return _mm512_fmsub_ps(a, b, c);
}

template <>
Vectorized<float> inline fnmsub(
    const Vectorized<float>& a,
    const Vectorized<float>& b,
    const Vectorized<float>& c) {
  return _mm512_fnmsub_ps(a, b, c);
}

// TODO: rewrite with ATEN vectorized (need to add unpack and shuffle)
// Used by Inductor CPP codegen for micro gemm
// Code referred to FBGEMM:
// https://github.com/pytorch/FBGEMM/blob/39a423e4ad1a04b77fea81c7d09c3e6f8984fae9/src/UtilsAvx512.cc#L230-L304
// kernel for transposing mxn where m, n <= 16
// (M + 1) / 2 * 2 + (M + 3) / 4 * 4 + (M + 7) / 8 * 8 + N instructions
inline void transpose_block(
    at::vec::VectorizedN<float, 16>& input,
    int M = 16,
    int N = 16) {
  TORCH_CHECK(M <= 16 && N <= 16, "transpose_block expects M, N <= 16.");
  // unpacking and interleaving 32-bit elements
  __m512 temp[16];
  int i;
  for (i = 0; i < (M + 1) / 2; ++i) {
    temp[2 * i] = _mm512_unpacklo_ps(input[2 * i], input[2 * i + 1]);
    temp[2 * i + 1] = _mm512_unpackhi_ps(input[2 * i], input[2 * i + 1]);
  }
  for (i = i * 2; i < 16; ++i) {
    temp[i] = _mm512_setzero_ps();
  }

  // unpacking and interleaving 64-bit elements
  for (i = 0; i < (M + 3) / 4; ++i) {
    input[4 * i] = _mm512_castpd_ps(_mm512_unpacklo_pd(
        _mm512_castps_pd(temp[4 * i]), _mm512_castps_pd(temp[4 * i + 2])));
    input[4 * i + 1] = _mm512_castpd_ps(_mm512_unpackhi_pd(
        _mm512_castps_pd(temp[4 * i]), _mm512_castps_pd(temp[4 * i + 2])));
    input[4 * i + 2] = _mm512_castpd_ps(_mm512_unpacklo_pd(
        _mm512_castps_pd(temp[4 * i + 1]), _mm512_castps_pd(temp[4 * i + 3])));
    input[4 * i + 3] = _mm512_castpd_ps(_mm512_unpackhi_pd(
        _mm512_castps_pd(temp[4 * i + 1]), _mm512_castps_pd(temp[4 * i + 3])));
  }

  //  shuffle 128-bits (composed of 4 32-bit elements)
  for (i = 0; i < (M + 7) / 8; ++i) {
    temp[8 * i] = _mm512_shuffle_f32x4(input[8 * i], input[8 * i + 4], 0x88);
    temp[8 * i + 1] =
        _mm512_shuffle_f32x4(input[8 * i + 1], input[8 * i + 5], 0x88);
    temp[8 * i + 2] =
        _mm512_shuffle_f32x4(input[8 * i + 2], input[8 * i + 6], 0x88);
    temp[8 * i + 3] =
        _mm512_shuffle_f32x4(input[8 * i + 3], input[8 * i + 7], 0x88);
    temp[8 * i + 4] =
        _mm512_shuffle_f32x4(input[8 * i], input[8 * i + 4], 0xdd);
    temp[8 * i + 5] =
        _mm512_shuffle_f32x4(input[8 * i + 1], input[8 * i + 5], 0xdd);
    temp[8 * i + 6] =
        _mm512_shuffle_f32x4(input[8 * i + 2], input[8 * i + 6], 0xdd);
    temp[8 * i + 7] =
        _mm512_shuffle_f32x4(input[8 * i + 3], input[8 * i + 7], 0xdd);
  }

  for (i = 0; i < N; ++i) {
    if (i < 8) {
      input[i] = _mm512_shuffle_f32x4(temp[i], temp[8 + i], 0x88);
    } else {
      input[i] = _mm512_shuffle_f32x4(temp[i - 8], temp[i], 0xdd);
    }
  }
}

// TODO(jgong5): rewrite with ATEN vectorized (need to add unpack and shuffle)
// Used by Inductor CPP codegen
// Code referred to FBGEMM:
// https://github.com/pytorch/FBGEMM/blob/39a423e4ad1a04b77fea81c7d09c3e6f8984fae9/src/UtilsAvx512.cc#L230-L304
// kernel for transposing mxn where m, n <= 16
// M + (M + 1) / 2 * 2 + (M + 3) / 4 * 4 + (M + 7) / 8 * 8 + 2 * N instructions
inline void transpose_mxn_16x16(
    const float* src,
    int64_t ld_src,
    float* dst,
    int64_t ld_dst,
    int M,
    int N) {
  TORCH_CHECK(M <= 16 && N <= 16, "transpose_mxn<float> expects M, N <= 16.");
  // load from src to registers
  at::vec::VectorizedN<float, 16> input;
  int i;
  if (N == 16) {
    for (i = 0; i < M; ++i) {
      input[i] = _mm512_loadu_ps(&src[i * ld_src]);
    }
  } else {
    __mmask16 src_mask = (1 << N) - 1;
    for (i = 0; i < M; ++i) {
      input[i] = _mm512_maskz_loadu_ps(src_mask, &src[i * ld_src]);
    }
  }
  for (; i < 16; ++i) {
    // Not really needed but to avoid uninitialized variable warning.
    // Shouldn't be much overhead because xor can be executed in parallel with
    // other instructions.
    input[i] = _mm512_setzero_ps();
  }

  transpose_block(input, M, N);

  // store from registers to dst
  if (M == 16) {
    for (i = 0; i < N; ++i) {
      _mm512_storeu_ps(&dst[i * ld_dst], input[i]);
    }
  } else {
    __mmask16 dst_mask = (1 << M) - 1;
    for (i = 0; i < N; ++i) {
      _mm512_mask_storeu_ps(&dst[i * ld_dst], dst_mask, input[i]);
    }
  }
}

template <>
inline void transpose_mxn<float>(
    const float* src,
    int64_t ld_src,
    float* dst,
    int64_t ld_dst,
    int M,
    int N) {
  int64_t i = 0;
  for (; i < M / 16 * 16; i += 16) {
    int64_t j = 0;
    for (; j < N / 16 * 16; j += 16) {
      transpose_mxn_16x16(
          src + i * ld_src + j, ld_src, dst + j * ld_dst + i, ld_dst, 16, 16);
    }
    // handle remainder j
    int nrem = N - j;
    if (nrem > 0) {
      transpose_mxn_16x16(
          src + i * ld_src + j, ld_src, dst + j * ld_dst + i, ld_dst, 16, nrem);
    }
  }
  // handle remainder i
  int mrem = M - i;
  if (mrem > 0) {
    int j = 0;
    for (; j < N / 16 * 16; j += 16) {
      transpose_mxn_16x16(
          src + i * ld_src + j, ld_src, dst + j * ld_dst + i, ld_dst, mrem, 16);
    }
    // handle remainder j
    int nrem = N - j;
    transpose_mxn_16x16(
        src + i * ld_src + j, ld_src, dst + j * ld_dst + i, ld_dst, mrem, nrem);
  }
}

template <
    typename T,
    int M,
    int N,
    typename std::enable_if_t<std::is_same_v<T, float>, int> = 0>
inline void transpose_mxn(
    const float* src,
    int64_t ld_src,
    float* dst,
    int64_t ld_dst) {
  transpose_mxn<float>(src, ld_src, dst, ld_dst, M, N);
}

#endif

} // namespace CPU_CAPABILITY
} // namespace at::vec
