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#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>
#if !(defined(__VSX__) || defined(CPU_CAPABILITY_VSX) || defined(CPU_CAPABILITY_ZVECTOR))
#include <ATen/cpu/vec/vec256/vec256_float.h>
#include <ATen/cpu/vec/vec256/vec256_float_neon.h>
#include <ATen/cpu/vec/vec256/vec256_bfloat16.h>
#include <ATen/cpu/vec/vec256/vec256_double.h>
#include <ATen/cpu/vec/vec256/vec256_int.h>
#include <ATen/cpu/vec/vec256/vec256_qint.h>
#include <ATen/cpu/vec/vec256/vec256_complex_float.h>
#include <ATen/cpu/vec/vec256/vec256_complex_double.h>
#elif defined(__VSX__) || defined(CPU_CAPABILITY_VSX)
#include <ATen/cpu/vec/vec256/vsx/vec256_common_vsx.h>
#else
#include <ATen/cpu/vec/vec256/zarch/vec256_zarch.h>
#include <ATen/cpu/vec/vec256/vec256_bfloat16.h>
#endif
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <cstring>
#include <ostream>
namespace at {
namespace vec {
// Note [CPU_CAPABILITY namespace]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// This header, and all of its subheaders, will be compiled with
// different architecture flags for each supported set of vector
// intrinsics. So we need to make sure they aren't inadvertently
// linked together. We do this by declaring objects in an `inline
// namespace` which changes the name mangling, but can still be
// accessed as `at::vec`.
inline namespace CPU_CAPABILITY {
inline std::ostream& operator<<(std::ostream& stream, const c10::qint32& val) {
stream << val.val_;
return stream;
}
inline std::ostream& operator<<(std::ostream& stream, const c10::qint8& val) {
stream << static_cast<int>(val.val_);
return stream;
}
inline std::ostream& operator<<(std::ostream& stream, const c10::quint8& val) {
stream << static_cast<unsigned int>(val.val_);
return stream;
}
template <typename T>
std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
T buf[Vectorized<T>::size()];
vec.store(buf);
stream << "vec[";
for (int i = 0; i != Vectorized<T>::size(); i++) {
if (i != 0) {
stream << ", ";
}
stream << buf[i];
}
stream << "]";
return stream;
}
#if defined(CPU_CAPABILITY_AVX2) && !defined(_MSC_VER)
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CAST (AVX2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
template<>
inline Vectorized<float> cast<float, double>(const Vectorized<double>& src) {
return _mm256_castpd_ps(src);
}
template<>
inline Vectorized<double> cast<double, float>(const Vectorized<float>& src) {
return _mm256_castps_pd(src);
}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GATHER ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
template<int64_t scale = 1>
std::enable_if_t<scale == 1 || scale == 2 || scale == 4 || scale == 8, Vectorized<double>>
inline gather(const double* base_addr, const Vectorized<int64_t>& vindex) {
return _mm256_i64gather_pd(base_addr, vindex, scale);
}
template<int64_t scale = 1>
std::enable_if_t<scale == 1 || scale == 2 || scale == 4 || scale == 8, Vectorized<float>>
inline gather(const float* base_addr, const Vectorized<int32_t>& vindex) {
return _mm256_i32gather_ps(base_addr, vindex, scale);
}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MASK GATHER ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
template<int64_t scale = 1>
std::enable_if_t<scale == 1 || scale == 2 || scale == 4 || scale == 8, Vectorized<double>>
inline mask_gather(const Vectorized<double>& src, const double* base_addr,
const Vectorized<int64_t>& vindex, const Vectorized<double>& mask) {
return _mm256_mask_i64gather_pd(src, base_addr, vindex, mask, scale);
}
template<int64_t scale = 1>
std::enable_if_t<scale == 1 || scale == 2 || scale == 4 || scale == 8, Vectorized<float>>
inline mask_gather(const Vectorized<float>& src, const float* base_addr,
const Vectorized<int32_t>& vindex, const Vectorized<float>& mask) {
return _mm256_mask_i32gather_ps(src, base_addr, vindex, mask, scale);
}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CONVERT ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Only works for inputs in the range: [-2^51, 2^51]
// From: https://stackoverflow.com/a/41148578
template<>
Vectorized<int64_t>
inline convert_to_int_of_same_size<double>(const Vectorized<double> &src) {
auto x = _mm256_add_pd(src, _mm256_set1_pd(0x0018000000000000));
return _mm256_sub_epi64(
_mm256_castpd_si256(x),
_mm256_castpd_si256(_mm256_set1_pd(0x0018000000000000))
);
}
template<>
Vectorized<int32_t>
inline convert_to_int_of_same_size<float>(const Vectorized<float> &src) {
return _mm256_cvttps_epi32(src);
}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTERLEAVE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
template <>
std::pair<Vectorized<double>, Vectorized<double>>
inline interleave2<double>(const Vectorized<double>& a, const Vectorized<double>& b) {
// inputs:
// a = {a0, a1, a3, a3}
// b = {b0, b1, b2, b3}
// swap lanes:
// a_swapped = {a0, a1, b0, b1}
// b_swapped = {a2, a3, b2, b3}
auto a_swapped = _mm256_permute2f128_pd(a, b, 0b0100000); // 0, 2. 4 bits apart
auto b_swapped = _mm256_permute2f128_pd(a, b, 0b0110001); // 1, 3. 4 bits apart
// group cols crossing lanes:
// return {a0, b0, a1, b1}
// {a2, b2, a3, b3}
return std::make_pair(_mm256_permute4x64_pd(a_swapped, 0b11011000), // 0, 2, 1, 3
_mm256_permute4x64_pd(b_swapped, 0b11011000)); // 0, 2, 1, 3
}
template <>
std::pair<Vectorized<float>, Vectorized<float>>
inline interleave2<float>(const Vectorized<float>& a, const Vectorized<float>& b) {
// inputs:
// a = {a0, a1, a2, a3, a4, a5, a6, a7}
// b = {b0, b1, b2, b3, b4, b5, b6, b7}
// swap lanes:
// a_swapped = {a0, a1, a2, a3, b0, b1, b2, b3}
// b_swapped = {a4, a5, a6, a7, b4, b5, b6, b7}
// TODO: can we support caching this?
auto a_swapped = _mm256_permute2f128_ps(a, b, 0b0100000); // 0, 2. 4 bits apart
auto b_swapped = _mm256_permute2f128_ps(a, b, 0b0110001); // 1, 3. 4 bits apart
// group cols crossing lanes:
// return {a0, b0, a1, b1, a2, b2, a3, b3}
// {a4, b4, a5, b5, a6, b6, a7, b7}
const __m256i group_ctrl = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
return std::make_pair(_mm256_permutevar8x32_ps(a_swapped, group_ctrl),
_mm256_permutevar8x32_ps(b_swapped, group_ctrl));
}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DEINTERLEAVE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
template <>
std::pair<Vectorized<double>, Vectorized<double>>
inline deinterleave2<double>(const Vectorized<double>& a, const Vectorized<double>& b) {
// inputs:
// a = {a0, b0, a1, b1}
// b = {a2, b2, a3, b3}
// group cols crossing lanes:
// a_grouped = {a0, a1, b0, b1}
// b_grouped = {a2, a3, b2, b3}
auto a_grouped = _mm256_permute4x64_pd(a, 0b11011000); // 0, 2, 1, 3
auto b_grouped = _mm256_permute4x64_pd(b, 0b11011000); // 0, 2, 1, 3
// swap lanes:
// return {a0, a1, a2, a3}
// {b0, b1, b2, b3}
return std::make_pair(_mm256_permute2f128_pd(a_grouped, b_grouped, 0b0100000), // 0, 2. 4 bits apart
_mm256_permute2f128_pd(a_grouped, b_grouped, 0b0110001)); // 1, 3. 4 bits apart
}
template <>
std::pair<Vectorized<float>, Vectorized<float>>
inline deinterleave2<float>(const Vectorized<float>& a, const Vectorized<float>& b) {
// inputs:
// a = {a0, b0, a1, b1, a2, b2, a3, b3}
// b = {a4, b4, a5, b5, a6, b6, a7, b7}
// group cols crossing lanes:
// a_grouped = {a0, a1, a2, a3, b0, b1, b2, b3}
// b_grouped = {a4, a5, a6, a7, b4, b5, b6, b7}
// TODO: can we support caching this?
const __m256i group_ctrl = _mm256_setr_epi32(0, 2, 4, 6, 1, 3, 5, 7);
auto a_grouped = _mm256_permutevar8x32_ps(a, group_ctrl);
auto b_grouped = _mm256_permutevar8x32_ps(b, group_ctrl);
// swap lanes:
// return {a0, a1, a2, a3, a4, a5, a6, a7}
// {b0, b1, b2, b3, b4, b5, b6, b7}
return std::make_pair(_mm256_permute2f128_ps(a_grouped, b_grouped, 0b0100000), // 0, 2. 4 bits apart
_mm256_permute2f128_ps(a_grouped, b_grouped, 0b0110001)); // 1, 3. 4 bits apart
}
#endif // (defined(CPU_CAPABILITY_AVX2) && !defined(_MSC_VER)
}}}
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