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android/platform/external/executorch/50baff184/./kernels/optimized/vec/vec256/vec256_int.h
blob: c6982641fafcbcf3a2af1add5fd8173a9ff7ca40 [file]
/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
* All rights reserved.
*
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#pragma once
// DO NOT DEFINE STATIC DATA IN THIS HEADER!
// See Note [Do not compile initializers with AVX]
#include <executorch/kernels/optimized/vec/intrinsics.h>
#include <executorch/kernels/optimized/vec/vec_base.h>
#include <iostream>
namespace executorch {
namespace vec {
inline namespace CPU_CAPABILITY {
#ifdef CPU_CAPABILITY_AVX2
struct Vectorizedi {
protected:
__m256i values;
static inline __m256i invert(const __m256i& v) {
const auto ones = _mm256_set1_epi64x(-1);
return _mm256_xor_si256(ones, v);
}
public:
Vectorizedi() {}
Vectorizedi(__m256i v) : values(v) {}
operator __m256i() const {
return values;
}
};
#else
struct Vectorizedi {}; // dummy definition to make Vectorizedi always defined
#endif // CPU_CAPABILITY_AVX2
#ifdef CPU_CAPABILITY_AVX2
template <>
class Vectorized<int64_t> : public Vectorizedi {
private:
static const Vectorized<int64_t> ones;
public:
using value_type = int64_t;
using size_type = int;
static constexpr size_type size() {
return 4;
}
using Vectorizedi::Vectorizedi;
Vectorized() {}
Vectorized(int64_t v) { values = _mm256_set1_epi64x(v); }
Vectorized(int64_t val1, int64_t val2, int64_t val3, int64_t val4) {
values = _mm256_setr_epi64x(val1, val2, val3, val4);
}
template <int64_t mask>
static Vectorized<int64_t> blend(Vectorized<int64_t> a, Vectorized<int64_t> b) {
__at_align__ int64_t tmp_values[size()];
a.store(tmp_values);
if (mask & 0x01)
tmp_values[0] = _mm256_extract_epi64(b.values, 0);
if (mask & 0x02)
tmp_values[1] = _mm256_extract_epi64(b.values, 1);
if (mask & 0x04)
tmp_values[2] = _mm256_extract_epi64(b.values, 2);
if (mask & 0x08)
tmp_values[3] = _mm256_extract_epi64(b.values, 3);
return loadu(tmp_values);
}
static Vectorized<int64_t> blendv(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b,
const Vectorized<int64_t>& mask) {
return _mm256_blendv_epi8(a.values, b.values, mask.values);
}
template <typename step_t>
static Vectorized<int64_t> arange(int64_t base = 0, step_t step = static_cast<step_t>(1)) {
return Vectorized<int64_t>(base, base + step, base + 2 * step, base + 3 * step);
}
static Vectorized<int64_t>
set(Vectorized<int64_t> a, Vectorized<int64_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);
}
return b;
}
static Vectorized<int64_t> loadu(const void* ptr) {
return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
}
static Vectorized<int64_t> loadu(const void* ptr, int64_t count) {
__at_align__ int64_t tmp_values[size()];
// Ensure uninitialized memory does not change the output value See https://github.com/pytorch/pytorch/issues/32502
// for more details. We do not initialize arrays to zero using "={0}" because gcc would compile it to two
// instructions while a loop would be compiled to one instruction.
for (size_t i = 0; i < size(); ++i) {
tmp_values[i] = 0;
}
std::memcpy(tmp_values, ptr, count * sizeof(int64_t));
return loadu(tmp_values);
}
void store(void* ptr, int count = size()) const {
if (count == size()) {
// ptr need not to be aligned here. See
// https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-intel-advanced-vector-extensions/intrinsics-for-load-and-store-operations-1/mm256-storeu-si256.html
_mm256_storeu_si256(reinterpret_cast<__m256i*>(ptr), values);
} else if (count > 0) {
__at_align__ int64_t tmp_values[size()];
_mm256_storeu_si256(reinterpret_cast<__m256i*>(tmp_values), values);
std::memcpy(ptr, tmp_values, count * sizeof(int64_t));
}
}
const int64_t& operator[](int idx) const = delete;
int64_t& operator[](int idx) = delete;
Vectorized<int64_t> abs() const {
auto zero = _mm256_set1_epi64x(0);
auto is_larger = _mm256_cmpgt_epi64(zero, values);
auto inverse = _mm256_xor_si256(values, is_larger);
return _mm256_sub_epi64(inverse, is_larger);
}
Vectorized<int64_t> real() const {
return *this;
}
Vectorized<int64_t> imag() const {
return _mm256_set1_epi64x(0);
}
Vectorized<int64_t> conj() const {
return *this;
}
Vectorized<int64_t> neg() const;
Vectorized<int64_t> operator==(const Vectorized<int64_t>& other) const {
return _mm256_cmpeq_epi64(values, other.values);
}
Vectorized<int64_t> operator!=(const Vectorized<int64_t>& other) const {
return invert(_mm256_cmpeq_epi64(values, other.values));
}
Vectorized<int64_t> operator<(const Vectorized<int64_t>& other) const {
return _mm256_cmpgt_epi64(other.values, values);
}
Vectorized<int64_t> operator<=(const Vectorized<int64_t>& other) const {
return invert(_mm256_cmpgt_epi64(values, other.values));
}
Vectorized<int64_t> operator>(const Vectorized<int64_t>& other) const {
return _mm256_cmpgt_epi64(values, other.values);
}
Vectorized<int64_t> operator>=(const Vectorized<int64_t>& other) const {
return invert(_mm256_cmpgt_epi64(other.values, values));
}
Vectorized<int64_t> eq(const Vectorized<int64_t>& other) const;
Vectorized<int64_t> ne(const Vectorized<int64_t>& other) const;
Vectorized<int64_t> gt(const Vectorized<int64_t>& other) const;
Vectorized<int64_t> ge(const Vectorized<int64_t>& other) const;
Vectorized<int64_t> lt(const Vectorized<int64_t>& other) const;
Vectorized<int64_t> le(const Vectorized<int64_t>& other) const;
};
template <>
class Vectorized<int32_t> : public Vectorizedi {
private:
static const Vectorized<int32_t> ones;
public:
using value_type = int32_t;
using size_type = int;
static constexpr int size() {
return 8;
}
using Vectorizedi::Vectorizedi;
Vectorized() {}
Vectorized(int32_t v) { values = _mm256_set1_epi32(v); }
Vectorized(int32_t val1, int32_t val2, int32_t val3, int32_t val4,
int32_t val5, int32_t val6, int32_t val7, int32_t val8) {
values = _mm256_setr_epi32(val1, val2, val3, val4, val5, val6, val7, val8);
}
template <int64_t mask>
static Vectorized<int32_t> blend(Vectorized<int32_t> a, Vectorized<int32_t> b) {
return _mm256_blend_epi32(a, b, mask);
}
static Vectorized<int32_t> blendv(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b,
const Vectorized<int32_t>& mask) {
return _mm256_blendv_epi8(a.values, b.values, mask.values);
}
template <typename step_t>
static Vectorized<int32_t> arange(int32_t base = 0, step_t step = static_cast<step_t>(1)) {
return Vectorized<int32_t>(
base, base + step, base + 2 * step, base + 3 * step,
base + 4 * step, base + 5 * step, base + 6 * step, base + 7 * step);
}
static Vectorized<int32_t>
set(Vectorized<int32_t> a, Vectorized<int32_t> b, int32_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);
}
return b;
}
static Vectorized<int32_t> loadu(const void* ptr) {
return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
}
static Vectorized<int32_t> loadu(const void* ptr, int32_t count) {
__at_align__ int32_t tmp_values[size()];
// Ensure uninitialized memory does not change the output value See https://github.com/pytorch/pytorch/issues/32502
// for more details. We do not initialize arrays to zero using "={0}" because gcc would compile it to two
// instructions while a loop would be compiled to one instruction.
for (size_t i = 0; i < size(); ++i) {
tmp_values[i] = 0;
}
std::memcpy(tmp_values, ptr, count * sizeof(int32_t));
return loadu(tmp_values);
}
void store(void* ptr, int count = size()) const {
if (count == size()) {
// ptr need not to be aligned here. See
// https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-intel-advanced-vector-extensions/intrinsics-for-load-and-store-operations-1/mm256-storeu-si256.html
_mm256_storeu_si256(reinterpret_cast<__m256i*>(ptr), values);
} else if (count > 0) {
__at_align__ int32_t tmp_values[size()];
_mm256_storeu_si256(reinterpret_cast<__m256i*>(tmp_values), values);
std::memcpy(ptr, tmp_values, count * sizeof(int32_t));
}
}
const int32_t& operator[](int idx) const = delete;
int32_t& operator[](int idx) = delete;
Vectorized<int32_t> abs() const {
return _mm256_abs_epi32(values);
}
Vectorized<int32_t> real() const {
return *this;
}
Vectorized<int32_t> imag() const {
return _mm256_set1_epi32(0);
}
Vectorized<int32_t> conj() const {
return *this;
}
Vectorized<int32_t> neg() const;
Vectorized<int32_t> operator==(const Vectorized<int32_t>& other) const {
return _mm256_cmpeq_epi32(values, other.values);
}
Vectorized<int32_t> operator!=(const Vectorized<int32_t>& other) const {
return invert(_mm256_cmpeq_epi32(values, other.values));
}
Vectorized<int32_t> operator<(const Vectorized<int32_t>& other) const {
return _mm256_cmpgt_epi32(other.values, values);
}
Vectorized<int32_t> operator<=(const Vectorized<int32_t>& other) const {
return invert(_mm256_cmpgt_epi32(values, other.values));
}
Vectorized<int32_t> operator>(const Vectorized<int32_t>& other) const {
return _mm256_cmpgt_epi32(values, other.values);
}
Vectorized<int32_t> operator>=(const Vectorized<int32_t>& other) const {
return invert(_mm256_cmpgt_epi32(other.values, values));
}
Vectorized<int32_t> eq(const Vectorized<int32_t>& other) const;
Vectorized<int32_t> ne(const Vectorized<int32_t>& other) const;
Vectorized<int32_t> gt(const Vectorized<int32_t>& other) const;
Vectorized<int32_t> ge(const Vectorized<int32_t>& other) const;
Vectorized<int32_t> lt(const Vectorized<int32_t>& other) const;
Vectorized<int32_t> le(const Vectorized<int32_t>& other) const;
};
template <>
inline void convert(const int32_t *src, float *dst, int64_t n) {
int64_t i;
// int32_t and float have same size
#ifndef _MSC_VER
# pragma unroll
#endif
for (i = 0; i <= (n - Vectorized<int32_t>::size()); i += Vectorized<int32_t>::size()) {
auto input_vec = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(src + i));
auto output_vec = _mm256_cvtepi32_ps(input_vec);
_mm256_storeu_ps(reinterpret_cast<float*>(dst + i), output_vec);
}
#ifndef _MSC_VER
# pragma unroll
#endif
for (; i < n; i++) {
dst[i] = static_cast<float>(src[i]);
}
}
template <>
inline void convert(const int32_t *src, double *dst, int64_t n) {
int64_t i;
// int32_t has half the size of double
#ifndef _MSC_VER
# pragma unroll
#endif
for (i = 0; i <= (n - Vectorized<double>::size()); i += Vectorized<double>::size()) {
auto input_128_vec = _mm_loadu_si128(reinterpret_cast<const __m128i*>(src + i));
auto output_vec = _mm256_cvtepi32_pd(input_128_vec);
_mm256_storeu_pd(reinterpret_cast<double*>(dst + i), output_vec);
}
#ifndef _MSC_VER
# pragma unroll
#endif
for (; i < n; i++) {
dst[i] = static_cast<double>(src[i]);
}
}
template <>
class Vectorized<int16_t> : public Vectorizedi {
private:
static const Vectorized<int16_t> ones;
public:
using value_type = int16_t;
using size_type = int;
static constexpr int size() {
return 16;
}
using Vectorizedi::Vectorizedi;
Vectorized() {}
Vectorized(int16_t v) { values = _mm256_set1_epi16(v); }
Vectorized(int16_t val1, int16_t val2, int16_t val3, int16_t val4,
int16_t val5, int16_t val6, int16_t val7, int16_t val8,
int16_t val9, int16_t val10, int16_t val11, int16_t val12,
int16_t val13, int16_t val14, int16_t val15, int16_t val16) {
values = _mm256_setr_epi16(val1, val2, val3, val4, val5, val6, val7, val8,
val9, val10, val11, val12, val13, val14, val15, val16);
}
template <int64_t mask>
static Vectorized<int16_t> blend(Vectorized<int16_t> a, Vectorized<int16_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<int16_t> blendv(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b,
const Vectorized<int16_t>& mask) {
return _mm256_blendv_epi8(a.values, b.values, mask.values);
}
template <typename step_t>
static Vectorized<int16_t> arange(int16_t base = 0, step_t step = static_cast<step_t>(1)) {
return Vectorized<int16_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<int16_t>
set(Vectorized<int16_t> a, Vectorized<int16_t> b, int16_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<int16_t> loadu(const void* ptr) {
return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
}
static Vectorized<int16_t> loadu(const void* ptr, int16_t count) {
__at_align__ int16_t tmp_values[size()];
// Ensure uninitialized memory does not change the output value See https://github.com/pytorch/pytorch/issues/32502
// for more details. We do not initialize arrays to zero using "={0}" because gcc would compile it to two
// instructions while a loop would be compiled to one instruction.
for (size_t i = 0; i < size(); ++i) {
tmp_values[i] = 0;
}
std::memcpy(tmp_values, ptr, count * sizeof(int16_t));
return loadu(tmp_values);
}
void store(void* ptr, int count = size()) const {
if (count == size()) {
// ptr need not to be aligned here. See
// https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-intel-advanced-vector-extensions/intrinsics-for-load-and-store-operations-1/mm256-storeu-si256.html
_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));
}
}
const int16_t& operator[](int idx) const = delete;
int16_t& operator[](int idx) = delete;
Vectorized<int16_t> abs() const {
return _mm256_abs_epi16(values);
}
Vectorized<int16_t> real() const {
return *this;
}
Vectorized<int16_t> imag() const {
return _mm256_set1_epi16(0);
}
Vectorized<int16_t> conj() const {
return *this;
}
Vectorized<int16_t> neg() const;
Vectorized<int16_t> operator==(const Vectorized<int16_t>& other) const {
return _mm256_cmpeq_epi16(values, other.values);
}
Vectorized<int16_t> operator!=(const Vectorized<int16_t>& other) const {
return invert(_mm256_cmpeq_epi16(values, other.values));
}
Vectorized<int16_t> operator<(const Vectorized<int16_t>& other) const {
return _mm256_cmpgt_epi16(other.values, values);
}
Vectorized<int16_t> operator<=(const Vectorized<int16_t>& other) const {
return invert(_mm256_cmpgt_epi16(values, other.values));
}
Vectorized<int16_t> operator>(const Vectorized<int16_t>& other) const {
return _mm256_cmpgt_epi16(values, other.values);
}
Vectorized<int16_t> operator>=(const Vectorized<int16_t>& other) const {
return invert(_mm256_cmpgt_epi16(other.values, values));
}
Vectorized<int16_t> eq(const Vectorized<int16_t>& other) const;
Vectorized<int16_t> ne(const Vectorized<int16_t>& other) const;
Vectorized<int16_t> gt(const Vectorized<int16_t>& other) const;
Vectorized<int16_t> ge(const Vectorized<int16_t>& other) const;
Vectorized<int16_t> lt(const Vectorized<int16_t>& other) const;
Vectorized<int16_t> le(const Vectorized<int16_t>& other) const;
};
template <typename T>
class Vectorized8 : public Vectorizedi {
static_assert(
std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value,
"Only int8_t/uint8_t are supported");
protected:
static const Vectorized<T> ones;
public:
using value_type = T;
using size_type = int;
static constexpr int size() {
return 32;
}
using Vectorizedi::Vectorizedi;
Vectorized8() {}
Vectorized8(T v) { values = _mm256_set1_epi8(v); }
Vectorized8(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,
T val17, T val18, T val19, T val20,
T val21, T val22, T val23, T val24,
T val25, T val26, T val27, T val28,
T val29, T val30, T val31, T val32) {
values = _mm256_setr_epi8(val1, val2, val3, val4, val5, val6, val7, val8,
val9, val10, val11, val12, val13, val14, val15, val16,
val17, val18, val19, val20, val21, val22, val23, val24,
val25, val26, val27, val28, val29, val30, val31, val32);
}
template <int64_t mask>
static Vectorized<T> blend(Vectorized<T> a, Vectorized<T> b) {
__at_align__ T tmp_values[size()];
a.store(tmp_values);
if (mask & 0x01)
tmp_values[0] = _mm256_extract_epi8(b.values, 0);
if (mask & 0x02)
tmp_values[1] = _mm256_extract_epi8(b.values, 1);
if (mask & 0x04)
tmp_values[2] = _mm256_extract_epi8(b.values, 2);
if (mask & 0x08)
tmp_values[3] = _mm256_extract_epi8(b.values, 3);
if (mask & 0x10)
tmp_values[4] = _mm256_extract_epi8(b.values, 4);
if (mask & 0x20)
tmp_values[5] = _mm256_extract_epi8(b.values, 5);
if (mask & 0x40)
tmp_values[6] = _mm256_extract_epi8(b.values, 6);
if (mask & 0x80)
tmp_values[7] = _mm256_extract_epi8(b.values, 7);
if (mask & 0x100)
tmp_values[8] = _mm256_extract_epi8(b.values, 8);
if (mask & 0x200)
tmp_values[9] = _mm256_extract_epi8(b.values, 9);
if (mask & 0x400)
tmp_values[10] = _mm256_extract_epi8(b.values, 10);
if (mask & 0x800)
tmp_values[11] = _mm256_extract_epi8(b.values, 11);
if (mask & 0x1000)
tmp_values[12] = _mm256_extract_epi8(b.values, 12);
if (mask & 0x2000)
tmp_values[13] = _mm256_extract_epi8(b.values, 13);
if (mask & 0x4000)
tmp_values[14] = _mm256_extract_epi8(b.values, 14);
if (mask & 0x8000)
tmp_values[15] = _mm256_extract_epi8(b.values, 15);
if (mask & 0x010000)
tmp_values[16] = _mm256_extract_epi8(b.values, 16);
if (mask & 0x020000)
tmp_values[17] = _mm256_extract_epi8(b.values, 17);
if (mask & 0x040000)
tmp_values[18] = _mm256_extract_epi8(b.values, 18);
if (mask & 0x080000)
tmp_values[19] = _mm256_extract_epi8(b.values, 19);
if (mask & 0x100000)
tmp_values[20] = _mm256_extract_epi8(b.values, 20);
if (mask & 0x200000)
tmp_values[21] = _mm256_extract_epi8(b.values, 21);
if (mask & 0x400000)
tmp_values[22] = _mm256_extract_epi8(b.values, 22);
if (mask & 0x800000)
tmp_values[23] = _mm256_extract_epi8(b.values, 23);
if (mask & 0x1000000)
tmp_values[24] = _mm256_extract_epi8(b.values, 24);
if (mask & 0x2000000)
tmp_values[25] = _mm256_extract_epi8(b.values, 25);
if (mask & 0x4000000)
tmp_values[26] = _mm256_extract_epi8(b.values, 26);
if (mask & 0x8000000)
tmp_values[27] = _mm256_extract_epi8(b.values, 27);
if (mask & 0x10000000)
tmp_values[28] = _mm256_extract_epi8(b.values, 28);
if (mask & 0x20000000)
tmp_values[29] = _mm256_extract_epi8(b.values, 29);
if (mask & 0x40000000)
tmp_values[30] = _mm256_extract_epi8(b.values, 30);
if (mask & 0x80000000)
tmp_values[31] = _mm256_extract_epi8(b.values, 31);
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, 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,
base + 16 * step, base + 17 * step, base + 18 * step, base + 19 * step,
base + 20 * step, base + 21 * step, base + 22 * step, base + 23 * step,
base + 24 * step, base + 25 * step, base + 26 * step, base + 27 * step,
base + 28 * step, base + 29 * step, base + 30 * step, base + 31 * step);
}
static Vectorized<T>
set(Vectorized<T> a, Vectorized<T> b, T count = size()) {
switch (count) {
case 0:
return a;
case 1:
return blend<0x1>(a, b);
case 2:
return blend<0x3>(a, b);
case 3:
return blend<0x7>(a, b);
case 4:
return blend<0xF>(a, b);
case 5:
return blend<0x1F>(a, b);
case 6:
return blend<0x3F>(a, b);
case 7:
return blend<0x7F>(a, b);
case 8:
return blend<0xFF>(a, b);
case 9:
return blend<0x1FF>(a, b);
case 10:
return blend<0x3FF>(a, b);
case 11:
return blend<0x7FF>(a, b);
case 12:
return blend<0xFFF>(a, b);
case 13:
return blend<0x1FFF>(a, b);
case 14:
return blend<0x3FFF>(a, b);
case 15:
return blend<0x7FFF>(a, b);
case 16:
return blend<0xFFFF>(a, b);
case 17:
return blend<0x1FFFF>(a, b);
case 18:
return blend<0x3FFFF>(a, b);
case 19:
return blend<0x7FFFF>(a, b);
case 20:
return blend<0xFFFFF>(a, b);
case 21:
return blend<0x1FFFFF>(a, b);
case 22:
return blend<0x3FFFFF>(a, b);
case 23:
return blend<0x7FFFFF>(a, b);
case 24:
return blend<0xFFFFFF>(a, b);
case 25:
return blend<0x1FFFFFF>(a, b);
case 26:
return blend<0x3FFFFFF>(a, b);
case 27:
return blend<0x7FFFFFF>(a, b);
case 28:
return blend<0xFFFFFFF>(a, b);
case 29:
return blend<0x1FFFFFFF>(a, b);
case 30:
return blend<0x3FFFFFFF>(a, b);
case 31:
return blend<0x7FFFFFFF>(a, b);
}
return b;
}
static Vectorized<T> loadu(const void* ptr) {
return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
}
static Vectorized<T> loadu(const void* ptr, T count) {
__at_align__ T tmp_values[size()];
// Ensure uninitialized memory does not change the output value See https://github.com/pytorch/pytorch/issues/32502
// for more details. We do not initialize arrays to zero using "={0}" because gcc would compile it to two
// instructions while a loop would be compiled to one instruction.
for (size_t i = 0; i < size(); ++i) {
tmp_values[i] = 0;
}
std::memcpy(tmp_values, ptr, count * sizeof(T));
return loadu(tmp_values);
}
void store(void* ptr, int count = size()) const {
if (count == size()) {
// ptr need not to be aligned here. See
// https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-intel-advanced-vector-extensions/intrinsics-for-load-and-store-operations-1/mm256-storeu-si256.html
_mm256_storeu_si256(reinterpret_cast<__m256i*>(ptr), values);
} else if (count > 0) {
__at_align__ T tmp_values[size()];
_mm256_storeu_si256(reinterpret_cast<__m256i*>(tmp_values), values);
std::memcpy(ptr, tmp_values, count * sizeof(T));
}
}
const T& operator[](int idx) const = delete;
T& operator[](int idx) = delete;
Vectorized<T> real() const {
return *this;
}
Vectorized<T> imag() const {
return _mm256_set1_epi8(0);
}
Vectorized<T> conj() const {
return *this;
}
};
template<>
class Vectorized<int8_t>: public Vectorized8<int8_t> {
public:
using Vectorized8::Vectorized8;
Vectorized<int8_t> neg() const;
Vectorized<int8_t> abs() const {
return _mm256_abs_epi8(values);
}
Vectorized<int8_t> operator==(const Vectorized<int8_t>& other) const {
return _mm256_cmpeq_epi8(values, other.values);
}
Vectorized<int8_t> operator!=(const Vectorized<int8_t>& other) const {
return invert(_mm256_cmpeq_epi8(values, other.values));
}
Vectorized<int8_t> operator<(const Vectorized<int8_t>& other) const {
return _mm256_cmpgt_epi8(other.values, values);
}
Vectorized<int8_t> operator<=(const Vectorized<int8_t>& other) const {
return invert(_mm256_cmpgt_epi8(values, other.values));
}
Vectorized<int8_t> operator>(const Vectorized<int8_t>& other) const {
return other < *this;
}
Vectorized<int8_t> operator>=(const Vectorized<int8_t>& other) const {
return other <= *this;
}
Vectorized<int8_t> eq(const Vectorized<int8_t>& other) const;
Vectorized<int8_t> ne(const Vectorized<int8_t>& other) const;
Vectorized<int8_t> gt(const Vectorized<int8_t>& other) const;
Vectorized<int8_t> ge(const Vectorized<int8_t>& other) const;
Vectorized<int8_t> lt(const Vectorized<int8_t>& other) const;
Vectorized<int8_t> le(const Vectorized<int8_t>& other) const;
};
template<>
class Vectorized<uint8_t>: public Vectorized8<uint8_t> {
public:
using Vectorized8::Vectorized8;
Vectorized<uint8_t> neg() const;
Vectorized<uint8_t> abs() const {
return *this;
}
Vectorized<uint8_t> operator==(const Vectorized<uint8_t>& other) const {
return _mm256_cmpeq_epi8(values, other.values);
}
Vectorized<uint8_t> operator!=(const Vectorized<uint8_t>& other) const {
return invert(_mm256_cmpeq_epi8(values, other.values));
}
Vectorized<uint8_t> operator<(const Vectorized<uint8_t>& other) const {
__m256i max = _mm256_max_epu8(values, other.values);
return invert(_mm256_cmpeq_epi8(max, values));
}
Vectorized<uint8_t> operator<=(const Vectorized<uint8_t>& other) const {
__m256i max = _mm256_max_epu8(values, other.values);
return _mm256_cmpeq_epi8(max, other.values);
}
Vectorized<uint8_t> operator>(const Vectorized<uint8_t>& other) const {
return other < *this;
}
Vectorized<uint8_t> operator>=(const Vectorized<uint8_t>& other) const {
return other <= *this;
}
Vectorized<uint8_t> eq(const Vectorized<uint8_t>& other) const;
Vectorized<uint8_t> ne(const Vectorized<uint8_t>& other) const;
Vectorized<uint8_t> gt(const Vectorized<uint8_t>& other) const;
Vectorized<uint8_t> ge(const Vectorized<uint8_t>& other) const;
Vectorized<uint8_t> lt(const Vectorized<uint8_t>& other) const;
Vectorized<uint8_t> le(const Vectorized<uint8_t>& other) const;
};
template <>
Vectorized<int64_t> inline operator+(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return _mm256_add_epi64(a, b);
}
template <>
Vectorized<int32_t> inline operator+(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_add_epi32(a, b);
}
template <>
Vectorized<int16_t> inline operator+(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return _mm256_add_epi16(a, b);
}
template <>
Vectorized<int8_t> inline operator+(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return _mm256_add_epi8(a, b);
}
template <>
Vectorized<uint8_t> inline operator+(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return _mm256_add_epi8(a, b);
}
template <>
Vectorized<int64_t> inline operator-(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return _mm256_sub_epi64(a, b);
}
template <>
Vectorized<int32_t> inline operator-(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_sub_epi32(a, b);
}
template <>
Vectorized<int16_t> inline operator-(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return _mm256_sub_epi16(a, b);
}
template <>
Vectorized<int8_t> inline operator-(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return _mm256_sub_epi8(a, b);
}
template <>
Vectorized<uint8_t> inline operator-(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return _mm256_sub_epi8(a, b);
}
// Negation. Defined here so we can utilize operator-
inline Vectorized<int64_t> Vectorized<int64_t>::neg() const {
return Vectorized<int64_t>(0) - *this;
}
inline Vectorized<int32_t> Vectorized<int32_t>::neg() const {
return Vectorized<int32_t>(0) - *this;
}
inline Vectorized<int16_t> Vectorized<int16_t>::neg() const {
return Vectorized<int16_t>(0) - *this;
}
inline Vectorized<int8_t> Vectorized<int8_t>::neg() const {
return Vectorized<int8_t>(0) - *this;
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::neg() const {
return Vectorized<uint8_t>(0) - *this;
}
// Emulate operations with no native 64-bit support in avx,
// by extracting each element, performing the operation pointwise,
// then combining the results into a vector.
template <typename op_t>
Vectorized<int64_t> inline emulate(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b, const op_t& op) {
int64_t a0 = _mm256_extract_epi64(a, 0);
int64_t a1 = _mm256_extract_epi64(a, 1);
int64_t a2 = _mm256_extract_epi64(a, 2);
int64_t a3 = _mm256_extract_epi64(a, 3);
int64_t b0 = _mm256_extract_epi64(b, 0);
int64_t b1 = _mm256_extract_epi64(b, 1);
int64_t b2 = _mm256_extract_epi64(b, 2);
int64_t b3 = _mm256_extract_epi64(b, 3);
int64_t c0 = op(a0, b0);
int64_t c1 = op(a1, b1);
int64_t c2 = op(a2, b2);
int64_t c3 = op(a3, b3);
return _mm256_set_epi64x(c3, c2, c1, c0);
}
template <typename op_t>
Vectorized<int64_t> inline emulate(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b, const Vectorized<int64_t>& c, const op_t& op) {
int64_t a0 = _mm256_extract_epi64(a, 0);
int64_t a1 = _mm256_extract_epi64(a, 1);
int64_t a2 = _mm256_extract_epi64(a, 2);
int64_t a3 = _mm256_extract_epi64(a, 3);
int64_t b0 = _mm256_extract_epi64(b, 0);
int64_t b1 = _mm256_extract_epi64(b, 1);
int64_t b2 = _mm256_extract_epi64(b, 2);
int64_t b3 = _mm256_extract_epi64(b, 3);
int64_t c0 = _mm256_extract_epi64(c, 0);
int64_t c1 = _mm256_extract_epi64(c, 1);
int64_t c2 = _mm256_extract_epi64(c, 2);
int64_t c3 = _mm256_extract_epi64(c, 3);
int64_t d0 = op(a0, b0, c0);
int64_t d1 = op(a1, b1, c1);
int64_t d2 = op(a2, b2, c2);
int64_t d3 = op(a3, b3, c3);
return _mm256_set_epi64x(d3, d2, d1, d0);
}
// AVX2 has no intrinsic for int64_t multiply so it needs to be emulated
// This could be implemented more efficiently using epi32 instructions
// This is also technically avx compatible, but then we'll need AVX
// code for add as well.
// Note: intentionally ignores undefined behavior like (-lowest * -1).
template <>
Vectorized<int64_t> inline operator*(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return emulate(a, b, [](int64_t a_point, int64_t b_point) {return a_point * b_point;});
}
template <>
Vectorized<int32_t> inline operator*(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_mullo_epi32(a, b);
}
template <>
Vectorized<int16_t> inline operator*(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return _mm256_mullo_epi16(a, b);
}
template <typename T, typename Op>
Vectorized<T> inline int_elementwise_binary_256(const Vectorized<T>& a, const Vectorized<T>& b, Op op) {
T values_a[Vectorized<T>::size()];
T values_b[Vectorized<T>::size()];
a.store(values_a);
b.store(values_b);
for (size_t i = 0; i != Vectorized<T>::size(); i++) {
values_a[i] = op(values_a[i], values_b[i]);
}
return Vectorized<T>::loadu(values_a);
}
template <>
Vectorized<int8_t> inline operator*(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
// We don't have an instruction for multiplying int8_t
return int_elementwise_binary_256(a, b, std::multiplies<int8_t>());
}
template <>
Vectorized<uint8_t> inline operator*(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
// We don't have an instruction for multiplying uint8_t
return int_elementwise_binary_256(a, b, std::multiplies<uint8_t>());
}
template <>
Vectorized<int64_t> inline minimum(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return emulate(a, b, [](int64_t a_point, int64_t b_point) {return std::min(a_point, b_point);});
}
template <>
Vectorized<int32_t> inline minimum(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_min_epi32(a, b);
}
template <>
Vectorized<int16_t> inline minimum(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return _mm256_min_epi16(a, b);
}
template <>
Vectorized<int8_t> inline minimum(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return _mm256_min_epi8(a, b);
}
template <>
Vectorized<uint8_t> inline minimum(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return _mm256_min_epu8(a, b);
}
template <>
Vectorized<int64_t> inline maximum(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return emulate(a, b, [](int64_t a_point, int64_t b_point) {return std::max(a_point, b_point);});
}
template <>
Vectorized<int32_t> inline maximum(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_max_epi32(a, b);
}
template <>
Vectorized<int16_t> inline maximum(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return _mm256_max_epi16(a, b);
}
template <>
Vectorized<int8_t> inline maximum(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return _mm256_max_epi8(a, b);
}
template <>
Vectorized<uint8_t> inline maximum(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return _mm256_max_epu8(a, b);
}
template <>
Vectorized<int64_t> inline clamp(const Vectorized<int64_t>& a, const Vectorized<int64_t>& min_val, const Vectorized<int64_t>& max_val) {
return emulate(a, min_val, max_val, [](int64_t a_point, int64_t min_point, int64_t max_point) {return std::min(max_point, std::max(a_point, min_point));});
}
template <>
Vectorized<int32_t> inline clamp(const Vectorized<int32_t>& a, const Vectorized<int32_t>& min_val, const Vectorized<int32_t>& max_val) {
return _mm256_min_epi32(max_val, _mm256_max_epi32(a, min_val));
}
template <>
Vectorized<int16_t> inline clamp(const Vectorized<int16_t>& a, const Vectorized<int16_t>& min_val, const Vectorized<int16_t>& max_val) {
return _mm256_min_epi16(max_val, _mm256_max_epi16(a, min_val));
}
template <>
Vectorized<int8_t> inline clamp(const Vectorized<int8_t>& a, const Vectorized<int8_t>& min_val, const Vectorized<int8_t>& max_val) {
return _mm256_min_epi8(max_val, _mm256_max_epi8(a, min_val));
}
template <>
Vectorized<uint8_t> inline clamp(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& min_val, const Vectorized<uint8_t>& max_val) {
return _mm256_min_epu8(max_val, _mm256_max_epu8(a, min_val));
}
template <>
Vectorized<int64_t> inline clamp_max(const Vectorized<int64_t>& a, const Vectorized<int64_t>& max_val) {
return emulate(a, max_val, [](int64_t a_point, int64_t max_point) {return std::min(max_point, a_point);});
}
template <>
Vectorized<int32_t> inline clamp_max(const Vectorized<int32_t>& a, const Vectorized<int32_t>& max_val) {
return _mm256_min_epi32(max_val, a);
}
template <>
Vectorized<int16_t> inline clamp_max(const Vectorized<int16_t>& a, const Vectorized<int16_t>& max_val) {
return _mm256_min_epi16(max_val, a);
}
template <>
Vectorized<int8_t> inline clamp_max(const Vectorized<int8_t>& a, const Vectorized<int8_t>& max_val) {
return _mm256_min_epi8(max_val, a);
}
template <>
Vectorized<uint8_t> inline clamp_max(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& max_val) {
return _mm256_min_epu8(max_val, a);
}
template <>
Vectorized<int64_t> inline clamp_min(const Vectorized<int64_t>& a, const Vectorized<int64_t>& min_val) {
return emulate(a, min_val, [](int64_t a_point, int64_t min_point) {return std::max(min_point, a_point);});
}
template <>
Vectorized<int32_t> inline clamp_min(const Vectorized<int32_t>& a, const Vectorized<int32_t>& min_val) {
return _mm256_max_epi32(min_val, a);
}
template <>
Vectorized<int16_t> inline clamp_min(const Vectorized<int16_t>& a, const Vectorized<int16_t>& min_val) {
return _mm256_max_epi16(min_val, a);
}
template <>
Vectorized<int8_t> inline clamp_min(const Vectorized<int8_t>& a, const Vectorized<int8_t>& min_val) {
return _mm256_max_epi8(min_val, a);
}
template <>
Vectorized<uint8_t> inline clamp_min(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& min_val) {
return _mm256_max_epu8(min_val, a);
}
template<typename T>
Vectorized<int32_t> inline convert_to_int32(const T* ptr) {
return Vectorized<int32_t>::loadu(ptr);
}
template<>
Vectorized<int32_t> inline convert_to_int32<int8_t>(const int8_t* ptr) {
return _mm256_cvtepi8_epi32(_mm_loadl_epi64(reinterpret_cast<const __m128i*>(ptr)));
}
template<>
Vectorized<int32_t> inline convert_to_int32<uint8_t>(const uint8_t* ptr) {
return _mm256_cvtepu8_epi32(_mm_loadl_epi64(reinterpret_cast<const __m128i*>(ptr)));
}
template <>
Vectorized<int64_t> inline operator/(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return int_elementwise_binary_256(a, b, std::divides<int64_t>());
}
template <>
Vectorized<int32_t> inline operator/(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return int_elementwise_binary_256(a, b, std::divides<int32_t>());
}
template <>
Vectorized<int16_t> inline operator/(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return int_elementwise_binary_256(a, b, std::divides<int16_t>());
}
template <>
Vectorized<int8_t> inline operator/(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return int_elementwise_binary_256(a, b, std::divides<int8_t>());
}
template <>
Vectorized<uint8_t> inline operator/(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return int_elementwise_binary_256(a, b, std::divides<uint8_t>());
}
template<class T, typename std::enable_if_t<std::is_base_of<Vectorizedi, Vectorized<T>>::value, int> = 0>
inline Vectorized<T> operator&(const Vectorized<T>& a, const Vectorized<T>& b) {
return _mm256_and_si256(a, b);
}
template<class T, typename std::enable_if_t<std::is_base_of<Vectorizedi, Vectorized<T>>::value, int> = 0>
inline Vectorized<T> operator|(const Vectorized<T>& a, const Vectorized<T>& b) {
return _mm256_or_si256(a, b);
}
template<class T, typename std::enable_if_t<std::is_base_of<Vectorizedi, Vectorized<T>>::value, int> = 0>
inline Vectorized<T> operator^(const Vectorized<T>& a, const Vectorized<T>& b) {
return _mm256_xor_si256(a, b);
}
template<class T, typename std::enable_if_t<std::is_base_of<Vectorizedi, Vectorized<T>>::value, int> = 0>
inline Vectorized<T> operator~(const Vectorized<T>& a) {
return _mm256_xor_si256(a, _mm256_set1_epi32(-1));
}
inline Vectorized<int64_t> Vectorized<int64_t>::eq(const Vectorized<int64_t>& other) const {
return (*this == other) & Vectorized<int64_t>(1);
}
inline Vectorized<int64_t> Vectorized<int64_t>::ne(const Vectorized<int64_t>& other) const {
return (*this != other) & Vectorized<int64_t>(1);
}
inline Vectorized<int64_t> Vectorized<int64_t>::gt(const Vectorized<int64_t>& other) const {
return (*this > other) & Vectorized<int64_t>(1);
}
inline Vectorized<int64_t> Vectorized<int64_t>::ge(const Vectorized<int64_t>& other) const {
return (*this >= other) & Vectorized<int64_t>(1);
}
inline Vectorized<int64_t> Vectorized<int64_t>::lt(const Vectorized<int64_t>& other) const {
return (*this < other) & Vectorized<int64_t>(1);
}
inline Vectorized<int64_t> Vectorized<int64_t>::le(const Vectorized<int64_t>& other) const {
return (*this <= other) & Vectorized<int64_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::eq(const Vectorized<int32_t>& other) const {
return (*this == other) & Vectorized<int32_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::ne(const Vectorized<int32_t>& other) const {
return (*this != other) & Vectorized<int32_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::gt(const Vectorized<int32_t>& other) const {
return (*this > other) & Vectorized<int32_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::ge(const Vectorized<int32_t>& other) const {
return (*this >= other) & Vectorized<int32_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::lt(const Vectorized<int32_t>& other) const {
return (*this < other) & Vectorized<int32_t>(1);
}
inline Vectorized<int32_t> Vectorized<int32_t>::le(const Vectorized<int32_t>& other) const {
return (*this <= other) & Vectorized<int32_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::eq(const Vectorized<int16_t>& other) const {
return (*this == other) & Vectorized<int16_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::ne(const Vectorized<int16_t>& other) const {
return (*this != other) & Vectorized<int16_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::gt(const Vectorized<int16_t>& other) const {
return (*this > other) & Vectorized<int16_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::ge(const Vectorized<int16_t>& other) const {
return (*this >= other) & Vectorized<int16_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::lt(const Vectorized<int16_t>& other) const {
return (*this < other) & Vectorized<int16_t>(1);
}
inline Vectorized<int16_t> Vectorized<int16_t>::le(const Vectorized<int16_t>& other) const {
return (*this <= other) & Vectorized<int16_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::eq(const Vectorized<int8_t>& other) const {
return (*this == other) & Vectorized<int8_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::ne(const Vectorized<int8_t>& other) const {
return (*this != other) & Vectorized<int8_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::gt(const Vectorized<int8_t>& other) const {
return (*this > other) & Vectorized<int8_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::ge(const Vectorized<int8_t>& other) const {
return (*this >= other) & Vectorized<int8_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::lt(const Vectorized<int8_t>& other) const {
return (*this < other) & Vectorized<int8_t>(1);
}
inline Vectorized<int8_t> Vectorized<int8_t>::le(const Vectorized<int8_t>& other) const {
return (*this <= other) & Vectorized<int8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::eq(const Vectorized<uint8_t>& other) const {
return (*this == other) & Vectorized<uint8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::ne(const Vectorized<uint8_t>& other) const {
return (*this != other) & Vectorized<uint8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::gt(const Vectorized<uint8_t>& other) const {
return (*this > other) & Vectorized<uint8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::ge(const Vectorized<uint8_t>& other) const {
return (*this >= other) & Vectorized<uint8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::lt(const Vectorized<uint8_t>& other) const {
return (*this < other) & Vectorized<uint8_t>(1);
}
inline Vectorized<uint8_t> Vectorized<uint8_t>::le(const Vectorized<uint8_t>& other) const {
return (*this <= other) & Vectorized<uint8_t>(1);
}
template <bool left_shift>
Vectorized<int16_t> inline shift_256_16(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
// No vector instruction for shifting int16_t, so emulating it instead.
// Control masks for shuffle operation, treating 256 bits as an
// array of 16-bit elements, and considering pairs of neighboring
// elements. Specifially, a mask named "ctl_M_N" (M,N in [0,1], and
// M!=N) is set so that shuffle will move element with index M from
// input pair into element with index N in output pair, and element
// with index M in output pair will be set to all 0s.
__m256i ctl_0_1 = _mm256_set_epi8(29, 28, 0x80, 0x80, 25, 24, 0x80, 0x80,
21, 20, 0x80, 0x80, 17, 16, 0x80, 0x80,
13, 12, 0x80, 0x80, 9, 8, 0x80, 0x80,
5, 4, 0x80, 0x80, 1, 0, 0x80, 0x80);
__m256i ctl_1_0 = _mm256_set_epi8(0x80, 0x80, 31, 30, 0x80, 0x80, 27, 26,
0x80, 0x80, 23, 22, 0x80, 0x80, 19, 18,
0x80, 0x80, 15, 14, 0x80, 0x80, 11, 10,
0x80, 0x80, 7, 6, 0x80, 0x80, 3, 2);
// Masks for bitwise and operation, treating 256 bits as an array of
// 16-bit elements, and considering them in pairs of neighboring
// elements. A mask named "keep_M" (M in [0,1]) is set so that
// bitwise and will copy element with index M from input pair into
// element with the same index in output pair, while the other
// element in output pair will be set to all 0s.
__m256i keep_0 = _mm256_set1_epi32(0xFFFF);
__m256i keep_1 = _mm256_set1_epi32(0xFFFF0000);
// Take each 16-bit element with idx%2==0 from input array to be
// shifted and extend it to 32 bits so that 0s are added to the
// right. Then, perform shifting on this 32-bit number. Upper 16
// bits will be proper result of shifting original 16-bit number, so
// write them to result array, into the same position from which
// corresponding input element is taken. Also, make sure that
// result array elements with idx%2!=0 are set to all 0s.
//
// Note that number of bits to shift for is extended to 32 bits by
// adding 0s to the left. That means this number is not properly
// sign-extended for negative values. However, number of bits to
// shift is treated as an unsigned integer by respective shift
// intrinsics anyway so if negative then either with or without
// proper sign extension, it will be interpreted as a number greater
// than 32, and the shifting result will be the same.
__m256i a0 = _mm256_shuffle_epi8(a, ctl_0_1);
__m256i b0 = _mm256_and_si256(b, keep_0);
__m256i c0;
if (left_shift)
c0 = _mm256_sllv_epi32(a0, b0);
else
c0 = _mm256_srav_epi32(a0, b0);
c0 = _mm256_shuffle_epi8(c0, ctl_1_0);
// Peform shifting the same way for input array elements with
// idx%2==1.
__m256i a1 = _mm256_and_si256(a, keep_1);
__m256i b1 = _mm256_shuffle_epi8(b, ctl_1_0);
__m256i c1;
if (left_shift)
c1 = _mm256_sllv_epi32(a1, b1);
else
c1 = _mm256_srav_epi32(a1, b1);
c1 = _mm256_and_si256(c1, keep_1);
// Merge partial results into the final result.
__m256i c = _mm256_or_si256(c0, c1);
return c;
}
template <bool left_shift, typename T, typename std::enable_if_t<std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value, int> = 0>
Vectorized<T> inline shift_256_8(const Vectorized<T>& a, const Vectorized<T>& b) {
// No vector instruction for shifting int8_t/uint8_t, so emulating
// it instead.
// Control masks for shuffle operation, treating 256 bits as an
// array of 8-bit elements, and considering quadruples of
// neighboring elements. Specifially, a mask named "ctl_M_N" (M,N
// in [0,1,2,3], and M!=N) is set so that shuffle will move element
// with index M from input quadruple into element with index N in
// output quadruple, and other elements in output quadruple will be
// set to all 0s.
__m256i ctl_0_3 = _mm256_set_epi8(28, 0x80, 0x80, 0x80, 24, 0x80, 0x80, 0x80,
20, 0x80, 0x80, 0x80, 16, 0x80, 0x80, 0x80,
12, 0x80, 0x80, 0x80, 8, 0x80, 0x80, 0x80,
4, 0x80, 0x80, 0x80, 0, 0x80, 0x80, 0x80);
__m256i ctl_1_0 = _mm256_set_epi8(0x80, 0x80, 0x80, 29, 0x80, 0x80, 0x80, 25,
0x80, 0x80, 0x80, 21, 0x80, 0x80, 0x80, 17,
0x80, 0x80, 0x80, 13, 0x80, 0x80, 0x80, 9,
0x80, 0x80, 0x80, 5, 0x80, 0x80, 0x80, 1);
__m256i ctl_1_3 = _mm256_set_epi8(29, 0x80, 0x80, 0x80, 25, 0x80, 0x80, 0x80,
21, 0x80, 0x80, 0x80, 17, 0x80, 0x80, 0x80,
13, 0x80, 0x80, 0x80, 9, 0x80, 0x80, 0x80,
5, 0x80, 0x80, 0x80, 1, 0x80, 0x80, 0x80);
__m256i ctl_2_0 = _mm256_set_epi8(0x80, 0x80, 0x80, 30, 0x80, 0x80, 0x80, 26,
0x80, 0x80, 0x80, 22, 0x80, 0x80, 0x80, 18,
0x80, 0x80, 0x80, 14, 0x80, 0x80, 0x80, 10,
0x80, 0x80, 0x80, 6, 0x80, 0x80, 0x80, 2);
__m256i ctl_2_3 = _mm256_set_epi8(30, 0x80, 0x80, 0x80, 26, 0x80, 0x80, 0x80,
22, 0x80, 0x80, 0x80, 18, 0x80, 0x80, 0x80,
14, 0x80, 0x80, 0x80, 10, 0x80, 0x80, 0x80,
6, 0x80, 0x80, 0x80, 2, 0x80, 0x80, 0x80);
__m256i ctl_3_0 = _mm256_set_epi8(0x80, 0x80, 0x80, 31, 0x80, 0x80, 0x80, 27,
0x80, 0x80, 0x80, 23, 0x80, 0x80, 0x80, 19,
0x80, 0x80, 0x80, 15, 0x80, 0x80, 0x80, 11,
0x80, 0x80, 0x80, 7, 0x80, 0x80, 0x80, 3);
__m256i ctl_3_1 = _mm256_set_epi8(0x80, 0x80, 31, 0x80, 0x80, 0x80, 27, 0x80,
0x80, 0x80, 23, 0x80, 0x80, 0x80, 19, 0x80,
0x80, 0x80, 15, 0x80, 0x80, 0x80, 11, 0x80,
0x80, 0x80, 7, 0x80, 0x80, 0x80, 3, 0x80);
__m256i ctl_3_2 = _mm256_set_epi8(0x80, 31, 0x80, 0x80, 0x80, 27, 0x80, 0x80,
0x80, 23, 0x80, 0x80, 0x80, 19, 0x80, 0x80,
0x80, 15, 0x80, 0x80, 0x80, 11, 0x80, 0x80,
0x80, 7, 0x80, 0x80, 0x80, 3, 0x80, 0x80);
// Masks for bitwise and operation, treating 256 bits as an array of
// 8-bit elements, and considering them in quadruples of neighboring
// elements. A mask named "keep_M" (M in [0,1,2,3]) is set so that
// bitwise and will copy element with index M from input quadruple
// into element with the same index in output quadruple, while the
// other elements in output quadruple will be set to all 0s.
__m256i keep_0 = _mm256_set1_epi32(0xFF);
__m256i keep_3 = _mm256_set1_epi32(0xFF000000);
// Take each 8-bit element with idx%4==0 from input array to be
// shifted and extend it to 32 bits so that 0s are added to the
// right. Then, perform shifting on this 32-bit number. Upper 8
// bits will be proper result of shifting original 8-bit number, so
// write them to result array, into the same position from which
// corresponding input element is taken. Also, make sure that
// result array elements with idx%4!=0 are set to all 0s.
//
// Note that number of bits to shift for is extended to 32 bits by
// adding 0s to the left. That means this number is not properly
// sign-extended for negative values. However, number of bits to
// shift is treated as an unsigned integer by respective shift
// intrinsics anyway so if negative then either with or without
// proper sign extension, it will be interpreted as a number greater
// than 32, and the shifting result will be the same.
__m256i a0 = _mm256_shuffle_epi8(a, ctl_0_3);
__m256i b0 = _mm256_and_si256(b, keep_0);
__m256i c0;
if (left_shift)
c0 = _mm256_sllv_epi32(a0, b0);
else
if (std::is_same<T, int8_t>::value)
c0 = _mm256_srav_epi32(a0, b0);
else
c0 = _mm256_srlv_epi32(a0, b0);
c0 = _mm256_shuffle_epi8(c0, ctl_3_0);
// Peform shifting the same way for input array elements with
// idx%4==1.
__m256i a1 = _mm256_shuffle_epi8(a, ctl_1_3);
__m256i b1 = _mm256_shuffle_epi8(b, ctl_1_0);
__m256i c1;
if (left_shift)
c1 = _mm256_sllv_epi32(a1, b1);
else
if (std::is_same<T, int8_t>::value)
c1 = _mm256_srav_epi32(a1, b1);
else
c1 = _mm256_srlv_epi32(a1, b1);
c1 = _mm256_shuffle_epi8(c1, ctl_3_1);
// Peform shifting the same way for input array elements with
// idx%4==2.
__m256i a2 = _mm256_shuffle_epi8(a, ctl_2_3);
__m256i b2 = _mm256_shuffle_epi8(b, ctl_2_0);
__m256i c2;
if (left_shift)
c2 = _mm256_sllv_epi32(a2, b2);
else
if (std::is_same<T, int8_t>::value)
c2 = _mm256_srav_epi32(a2, b2);
else
c2 = _mm256_srlv_epi32(a2, b2);
c2 = _mm256_shuffle_epi8(c2, ctl_3_2);
// Peform shifting the same way for input array elements with
// idx%4==3.
__m256i a3 = _mm256_and_si256(a, keep_3);
__m256i b3 = _mm256_shuffle_epi8(b, ctl_3_0);
__m256i c3;
if (left_shift)
c3 = _mm256_sllv_epi32(a3, b3);
else
if (std::is_same<T, int8_t>::value)
c3 = _mm256_srav_epi32(a3, b3);
else
c3 = _mm256_srlv_epi32(a3, b3);
c3 = _mm256_and_si256(c3, keep_3);
// Merge partial results into the final result.
__m256i c01 = _mm256_or_si256(c0, c1);
__m256i c23 = _mm256_or_si256(c2, c3);
__m256i c = _mm256_or_si256(c01, c23);
return c;
}
template <>
Vectorized<int64_t> inline operator<<(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
return _mm256_sllv_epi64(a, b);
}
template <>
Vectorized<int32_t> inline operator<<(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_sllv_epi32(a, b);
}
template <>
Vectorized<int16_t> inline operator<<(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return shift_256_16<true>(a, b);
}
template <>
Vectorized<int8_t> inline operator<<(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return shift_256_8<true>(a, b);
}
template <>
Vectorized<uint8_t> inline operator<<(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return shift_256_8<true>(a, b);
}
template <>
Vectorized<int64_t> inline operator>>(const Vectorized<int64_t>& a, const Vectorized<int64_t>& b) {
// No vector instruction for right arithmetic shifting int64_t, so emulating it
// instead.
// Clamp the shift values such that shift values < 0 and > 64 are changed to 64
// which results in -1 for negative input and 0 for non-negative input.
__m256i zero = _mm256_set1_epi64x(0);
__m256i max_shift = _mm256_set1_epi64x(64);
__m256i mask = _mm256_or_si256(_mm256_cmpgt_epi64(zero, b), _mm256_cmpgt_epi64(b, max_shift));
__m256i shift = _mm256_blendv_epi8(b, max_shift, mask);
// Shift the number logically to the right, thus filling the most
// significant bits with 0s. Then, replace these bits with the sign
// bit.
__m256i sign_bits = _mm256_cmpgt_epi64(zero, a);
__m256i sign_shift = _mm256_sub_epi64(max_shift, shift);
__m256i sign_ext = _mm256_sllv_epi64(sign_bits, sign_shift);
__m256i c = _mm256_srlv_epi64(a, shift);
c = _mm256_or_si256(c, sign_ext);
return c;
}
template <>
Vectorized<int32_t> inline operator>>(const Vectorized<int32_t>& a, const Vectorized<int32_t>& b) {
return _mm256_srav_epi32(a, b);
}
template <>
Vectorized<int16_t> inline operator>>(const Vectorized<int16_t>& a, const Vectorized<int16_t>& b) {
return shift_256_16<false>(a, b);
}
template <>
Vectorized<int8_t> inline operator>>(const Vectorized<int8_t>& a, const Vectorized<int8_t>& b) {
return shift_256_8<false>(a, b);
}
template <>
Vectorized<uint8_t> inline operator>>(const Vectorized<uint8_t>& a, const Vectorized<uint8_t>& b) {
return shift_256_8<false>(a, b);
}
#endif
}}}