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1f150eb68c
skia2/include/private/SkVx.h
Michael Ludwig 767586b330 Update Sk4px to use skvx instead of SkNx 
Adds a saturated_add function that was on SkNx and used in
SkXfermode_opts, but hadn't been ported to skvx yet.

Removes the Sk4px_opts variants and simplifies some of its functions;
many were already defined skvx.

The largest change is that Sk4px does not extend skvx::byte16, since it
used to extend Sk16b. Now it just has a vector as a data type. This
was necessary so that we could define operators that were typed for
Sk4px and Wide w/o conflicting with the free operators that were
defined for the base skvx types.

Change-Id: I8c667ba86f662ccf07ad85aa32e78abfc0a8c7ae
Reviewed-on: https://skia-review.googlesource.com/c/skia/+/542645
Reviewed-by: Herb Derby <herb@google.com>
Commit-Queue: Michael Ludwig <michaelludwig@google.com>
2022-05-23 17:41:53 +00:00

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/*
* Copyright 2019 Google Inc.
*
* Use of this source code is governed by a BSD-style license that can be
* found in the LICENSE file.
*/
#ifndef SKVX_DEFINED
#define SKVX_DEFINED
// skvx::Vec<N,T> are SIMD vectors of N T's, a v1.5 successor to SkNx<N,T>.
//
// This time we're leaning a bit less on platform-specific intrinsics and a bit
// more on Clang/GCC vector extensions, but still keeping the option open to
// drop in platform-specific intrinsics, actually more easily than before.
//
// We've also fixed a few of the caveats that used to make SkNx awkward to work
// with across translation units. skvx::Vec<N,T> always has N*sizeof(T) size
// and alignment and is safe to use across translation units freely.
// (Ideally we'd only align to T, but that tanks ARMv7 NEON codegen.)
// Please try to keep this file independent of Skia headers.
#include <algorithm> // std::min, std::max
#include <cassert> // assert()
#include <cmath> // ceilf, floorf, truncf, roundf, sqrtf, etc.
#include <cstdint> // intXX_t
#include <cstring> // memcpy()
#include <initializer_list> // std::initializer_list
#include <utility> // std::index_sequence
// Users may disable SIMD with SKNX_NO_SIMD, which may be set via compiler flags.
// The gn build has no option which sets SKNX_NO_SIMD.
// Use SKVX_USE_SIMD internally to avoid confusing double negation.
// Do not use 'defined' in a macro expansion.
#if !defined(SKNX_NO_SIMD)
#define SKVX_USE_SIMD 1
#else
#define SKVX_USE_SIMD 0
#endif
#if SKVX_USE_SIMD
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__)
#include <immintrin.h>
#elif defined(__ARM_NEON)
#include <arm_neon.h>
#elif defined(__wasm_simd128__)
#include <wasm_simd128.h>
#endif
#endif
// To avoid ODR violations, all methods must be force-inlined...
#if defined(_MSC_VER)
#define SKVX_ALWAYS_INLINE __forceinline
#else
#define SKVX_ALWAYS_INLINE __attribute__((always_inline))
#endif
// ... and all standalone functions must be static. Please use these helpers:
#define SI static inline
#define SIT template < typename T> SI
#define SIN template <int N > SI
#define SINT template <int N, typename T> SI
#define SINTU template <int N, typename T, typename U, \
typename=std::enable_if_t<std::is_convertible<U,T>::value>> SI
namespace skvx {
template <int N, typename T>
struct alignas(N*sizeof(T)) Vec;
template <int... Ix, int N, typename T>
SI Vec<sizeof...(Ix),T> shuffle(const Vec<N,T>&);
template <typename D, typename S>
SI D bit_pun(const S&);
// All Vec have the same simple memory layout, the same as `T vec[N]`.
template <int N, typename T>
struct alignas(N*sizeof(T)) VecStorage {
SKVX_ALWAYS_INLINE VecStorage() = default;
SKVX_ALWAYS_INLINE VecStorage(T s) : lo(s), hi(s) {}
Vec<N/2,T> lo, hi;
};
template <typename T>
struct VecStorage<4,T> {
SKVX_ALWAYS_INLINE VecStorage() = default;
SKVX_ALWAYS_INLINE VecStorage(T s) : lo(s), hi(s) {}
SKVX_ALWAYS_INLINE VecStorage(T x, T y, T z, T w) : lo(x,y), hi(z, w) {}
SKVX_ALWAYS_INLINE VecStorage(Vec<2,T> xy, T z, T w) : lo(xy), hi(z,w) {}
SKVX_ALWAYS_INLINE VecStorage(T x, T y, Vec<2,T> zw) : lo(x,y), hi(zw) {}
SKVX_ALWAYS_INLINE VecStorage(Vec<2,T> xy, Vec<2,T> zw) : lo(xy), hi(zw) {}
SKVX_ALWAYS_INLINE Vec<2,T>& xy() { return lo; }
SKVX_ALWAYS_INLINE Vec<2,T>& zw() { return hi; }
SKVX_ALWAYS_INLINE T& x() { return lo.lo.val; }
SKVX_ALWAYS_INLINE T& y() { return lo.hi.val; }
SKVX_ALWAYS_INLINE T& z() { return hi.lo.val; }
SKVX_ALWAYS_INLINE T& w() { return hi.hi.val; }
SKVX_ALWAYS_INLINE Vec<2,T> xy() const { return lo; }
SKVX_ALWAYS_INLINE Vec<2,T> zw() const { return hi; }
SKVX_ALWAYS_INLINE T x() const { return lo.lo.val; }
SKVX_ALWAYS_INLINE T y() const { return lo.hi.val; }
SKVX_ALWAYS_INLINE T z() const { return hi.lo.val; }
SKVX_ALWAYS_INLINE T w() const { return hi.hi.val; }
// Exchange-based swizzles. These should take 1 cycle on NEON and 3 (pipelined) cycles on SSE.
SKVX_ALWAYS_INLINE Vec<4,T> yxwz() const { return shuffle<1,0,3,2>(bit_pun<Vec<4,T>>(*this)); }
SKVX_ALWAYS_INLINE Vec<4,T> zwxy() const { return shuffle<2,3,0,1>(bit_pun<Vec<4,T>>(*this)); }
Vec<2,T> lo, hi;
};
template <typename T>
struct VecStorage<2,T> {
SKVX_ALWAYS_INLINE VecStorage() = default;
SKVX_ALWAYS_INLINE VecStorage(T s) : lo(s), hi(s) {}
SKVX_ALWAYS_INLINE VecStorage(T x, T y) : lo(x), hi(y) {}
SKVX_ALWAYS_INLINE T& x() { return lo.val; }
SKVX_ALWAYS_INLINE T& y() { return hi.val; }
SKVX_ALWAYS_INLINE T x() const { return lo.val; }
SKVX_ALWAYS_INLINE T y() const { return hi.val; }
// This exchange-based swizzle should take 1 cycle on NEON and 3 (pipelined) cycles on SSE.
SKVX_ALWAYS_INLINE Vec<2,T> yx() const { return shuffle<1,0>(bit_pun<Vec<2,T>>(*this)); }
SKVX_ALWAYS_INLINE Vec<4,T> xyxy() const {
return Vec<4,T>(bit_pun<Vec<2,T>>(*this), bit_pun<Vec<2,T>>(*this));
}
Vec<1,T> lo, hi;
};
template <int N, typename T>
struct alignas(N*sizeof(T)) Vec : public VecStorage<N,T> {
static_assert((N & (N-1)) == 0, "N must be a power of 2.");
static_assert(sizeof(T) >= alignof(T), "What kind of unusual T is this?");
// Methods belong here in the class declaration of Vec only if:
// - they must be here, like constructors or operator[];
// - they'll definitely never want a specialized implementation.
// Other operations on Vec should be defined outside the type.
SKVX_ALWAYS_INLINE Vec() = default;
using VecStorage<N,T>::VecStorage;
// NOTE: Vec{x} produces x000..., whereas Vec(x) produces xxxx.... since this constructor fills
// unspecified lanes with 0s, whereas the single T constructor fills all lanes with the value.
SKVX_ALWAYS_INLINE Vec(std::initializer_list<T> xs) {
T vals[N] = {0};
memcpy(vals, xs.begin(), std::min(xs.size(), (size_t)N)*sizeof(T));
this->lo = Vec<N/2,T>::Load(vals + 0);
this->hi = Vec<N/2,T>::Load(vals + N/2);
}
SKVX_ALWAYS_INLINE T operator[](int i) const { return i<N/2 ? this->lo[i] : this->hi[i-N/2]; }
SKVX_ALWAYS_INLINE T& operator[](int i) { return i<N/2 ? this->lo[i] : this->hi[i-N/2]; }
SKVX_ALWAYS_INLINE static Vec Load(const void* ptr) {
Vec v;
memcpy(&v, ptr, sizeof(Vec));
return v;
}
SKVX_ALWAYS_INLINE void store(void* ptr) const {
memcpy(ptr, this, sizeof(Vec));
}
};
template <typename T>
struct Vec<1,T> {
T val;
SKVX_ALWAYS_INLINE Vec() = default;
Vec(T s) : val(s) {}
SKVX_ALWAYS_INLINE Vec(std::initializer_list<T> xs) : val(xs.size() ? *xs.begin() : 0) {}
SKVX_ALWAYS_INLINE T operator[](int) const { return val; }
SKVX_ALWAYS_INLINE T& operator[](int) { return val; }
SKVX_ALWAYS_INLINE static Vec Load(const void* ptr) {
Vec v;
memcpy(&v, ptr, sizeof(Vec));
return v;
}
SKVX_ALWAYS_INLINE void store(void* ptr) const {
memcpy(ptr, this, sizeof(Vec));
}
};
// Ideally we'd only use bit_pun(), but until this file is always built as C++17 with constexpr if,
// we'll sometimes find need to use unchecked_bit_pun(). Please do check the call sites yourself!
template <typename D, typename S>
SI D unchecked_bit_pun(const S& s) {
D d;
memcpy(&d, &s, sizeof(D));
return d;
}
template <typename D, typename S>
SI D bit_pun(const S& s) {
static_assert(sizeof(D) == sizeof(S), "");
return unchecked_bit_pun<D>(s);
}
// Translate from a value type T to its corresponding Mask, the result of a comparison.
template <typename T> struct Mask { using type = T; };
template <> struct Mask<float > { using type = int32_t; };
template <> struct Mask<double> { using type = int64_t; };
template <typename T> using M = typename Mask<T>::type;
// Join two Vec<N,T> into one Vec<2N,T>.
SINT Vec<2*N,T> join(const Vec<N,T>& lo, const Vec<N,T>& hi) {
Vec<2*N,T> v;
v.lo = lo;
v.hi = hi;
return v;
}
// We have three strategies for implementing Vec operations:
// 1) lean on Clang/GCC vector extensions when available;
// 2) use map() to apply a scalar function lane-wise;
// 3) recurse on lo/hi to scalar portable implementations.
// We can slot in platform-specific implementations as overloads for particular Vec<N,T>,
// or often integrate them directly into the recursion of style 3), allowing fine control.
#if SKVX_USE_SIMD && (defined(__clang__) || defined(__GNUC__))
// VExt<N,T> types have the same size as Vec<N,T> and support most operations directly.
#if defined(__clang__)
template <int N, typename T>
using VExt = T __attribute__((ext_vector_type(N)));
#elif defined(__GNUC__)
template <int N, typename T>
struct VExtHelper {
typedef T __attribute__((vector_size(N*sizeof(T)))) type;
};
template <int N, typename T>
using VExt = typename VExtHelper<N,T>::type;
// For some reason some (new!) versions of GCC cannot seem to deduce N in the generic
// to_vec<N,T>() below for N=4 and T=float. This workaround seems to help...
SI Vec<4,float> to_vec(VExt<4,float> v) { return bit_pun<Vec<4,float>>(v); }
#endif
SINT VExt<N,T> to_vext(const Vec<N,T>& v) { return bit_pun<VExt<N,T>>(v); }
SINT Vec <N,T> to_vec(const VExt<N,T>& v) { return bit_pun<Vec <N,T>>(v); }
SINT Vec<N,T> operator+(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) + to_vext(y));
}
SINT Vec<N,T> operator-(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) - to_vext(y));
}
SINT Vec<N,T> operator*(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) * to_vext(y));
}
SINT Vec<N,T> operator/(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) / to_vext(y));
}
SINT Vec<N,T> operator^(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) ^ to_vext(y));
}
SINT Vec<N,T> operator&(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) & to_vext(y));
}
SINT Vec<N,T> operator|(const Vec<N,T>& x, const Vec<N,T>& y) {
return to_vec<N,T>(to_vext(x) | to_vext(y));
}
SINT Vec<N,T> operator!(const Vec<N,T>& x) { return to_vec<N,T>(!to_vext(x)); }
SINT Vec<N,T> operator-(const Vec<N,T>& x) { return to_vec<N,T>(-to_vext(x)); }
SINT Vec<N,T> operator~(const Vec<N,T>& x) { return to_vec<N,T>(~to_vext(x)); }
SINT Vec<N,T> operator<<(const Vec<N,T>& x, int k) { return to_vec<N,T>(to_vext(x) << k); }
SINT Vec<N,T> operator>>(const Vec<N,T>& x, int k) { return to_vec<N,T>(to_vext(x) >> k); }
SINT Vec<N,M<T>> operator==(const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) == to_vext(y));
}
SINT Vec<N,M<T>> operator!=(const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) != to_vext(y));
}
SINT Vec<N,M<T>> operator<=(const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) <= to_vext(y));
}
SINT Vec<N,M<T>> operator>=(const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) >= to_vext(y));
}
SINT Vec<N,M<T>> operator< (const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) < to_vext(y));
}
SINT Vec<N,M<T>> operator> (const Vec<N,T>& x, const Vec<N,T>& y) {
return bit_pun<Vec<N,M<T>>>(to_vext(x) > to_vext(y));
}
#else
// Either SKNX_NO_SIMD is defined, or Clang/GCC vector extensions are not available.
// We'll implement things portably with N==1 scalar implementations and recursion onto them.
// N == 1 scalar implementations.
SIT Vec<1,T> operator+(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val + y.val; }
SIT Vec<1,T> operator-(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val - y.val; }
SIT Vec<1,T> operator*(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val * y.val; }
SIT Vec<1,T> operator/(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val / y.val; }
SIT Vec<1,T> operator^(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val ^ y.val; }
SIT Vec<1,T> operator&(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val & y.val; }
SIT Vec<1,T> operator|(const Vec<1,T>& x, const Vec<1,T>& y) { return x.val | y.val; }
SIT Vec<1,T> operator!(const Vec<1,T>& x) { return !x.val; }
SIT Vec<1,T> operator-(const Vec<1,T>& x) { return -x.val; }
SIT Vec<1,T> operator~(const Vec<1,T>& x) { return ~x.val; }
SIT Vec<1,T> operator<<(const Vec<1,T>& x, int k) { return x.val << k; }
SIT Vec<1,T> operator>>(const Vec<1,T>& x, int k) { return x.val >> k; }
SIT Vec<1,M<T>> operator==(const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val == y.val ? ~0 : 0;
}
SIT Vec<1,M<T>> operator!=(const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val != y.val ? ~0 : 0;
}
SIT Vec<1,M<T>> operator<=(const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val <= y.val ? ~0 : 0;
}
SIT Vec<1,M<T>> operator>=(const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val >= y.val ? ~0 : 0;
}
SIT Vec<1,M<T>> operator< (const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val < y.val ? ~0 : 0;
}
SIT Vec<1,M<T>> operator> (const Vec<1,T>& x, const Vec<1,T>& y) {
return x.val > y.val ? ~0 : 0;
}
// Recurse on lo/hi down to N==1 scalar implementations.
SINT Vec<N,T> operator+(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo + y.lo, x.hi + y.hi);
}
SINT Vec<N,T> operator-(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo - y.lo, x.hi - y.hi);
}
SINT Vec<N,T> operator*(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo * y.lo, x.hi * y.hi);
}
SINT Vec<N,T> operator/(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo / y.lo, x.hi / y.hi);
}
SINT Vec<N,T> operator^(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo ^ y.lo, x.hi ^ y.hi);
}
SINT Vec<N,T> operator&(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo & y.lo, x.hi & y.hi);
}
SINT Vec<N,T> operator|(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo | y.lo, x.hi | y.hi);
}
SINT Vec<N,T> operator!(const Vec<N,T>& x) { return join(!x.lo, !x.hi); }
SINT Vec<N,T> operator-(const Vec<N,T>& x) { return join(-x.lo, -x.hi); }
SINT Vec<N,T> operator~(const Vec<N,T>& x) { return join(~x.lo, ~x.hi); }
SINT Vec<N,T> operator<<(const Vec<N,T>& x, int k) { return join(x.lo << k, x.hi << k); }
SINT Vec<N,T> operator>>(const Vec<N,T>& x, int k) { return join(x.lo >> k, x.hi >> k); }
SINT Vec<N,M<T>> operator==(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo == y.lo, x.hi == y.hi);
}
SINT Vec<N,M<T>> operator!=(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo != y.lo, x.hi != y.hi);
}
SINT Vec<N,M<T>> operator<=(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo <= y.lo, x.hi <= y.hi);
}
SINT Vec<N,M<T>> operator>=(const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo >= y.lo, x.hi >= y.hi);
}
SINT Vec<N,M<T>> operator< (const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo < y.lo, x.hi < y.hi);
}
SINT Vec<N,M<T>> operator> (const Vec<N,T>& x, const Vec<N,T>& y) {
return join(x.lo > y.lo, x.hi > y.hi);
}
#endif
// Scalar/vector operations splat the scalar to a vector.
SINTU Vec<N,T> operator+ (U x, const Vec<N,T>& y) { return Vec<N,T>(x) + y; }
SINTU Vec<N,T> operator- (U x, const Vec<N,T>& y) { return Vec<N,T>(x) - y; }
SINTU Vec<N,T> operator* (U x, const Vec<N,T>& y) { return Vec<N,T>(x) * y; }
SINTU Vec<N,T> operator/ (U x, const Vec<N,T>& y) { return Vec<N,T>(x) / y; }
SINTU Vec<N,T> operator^ (U x, const Vec<N,T>& y) { return Vec<N,T>(x) ^ y; }
SINTU Vec<N,T> operator& (U x, const Vec<N,T>& y) { return Vec<N,T>(x) & y; }
SINTU Vec<N,T> operator| (U x, const Vec<N,T>& y) { return Vec<N,T>(x) | y; }
SINTU Vec<N,M<T>> operator==(U x, const Vec<N,T>& y) { return Vec<N,T>(x) == y; }
SINTU Vec<N,M<T>> operator!=(U x, const Vec<N,T>& y) { return Vec<N,T>(x) != y; }
SINTU Vec<N,M<T>> operator<=(U x, const Vec<N,T>& y) { return Vec<N,T>(x) <= y; }
SINTU Vec<N,M<T>> operator>=(U x, const Vec<N,T>& y) { return Vec<N,T>(x) >= y; }
SINTU Vec<N,M<T>> operator< (U x, const Vec<N,T>& y) { return Vec<N,T>(x) < y; }
SINTU Vec<N,M<T>> operator> (U x, const Vec<N,T>& y) { return Vec<N,T>(x) > y; }
SINTU Vec<N,T> operator+ (const Vec<N,T>& x, U y) { return x + Vec<N,T>(y); }
SINTU Vec<N,T> operator- (const Vec<N,T>& x, U y) { return x - Vec<N,T>(y); }
SINTU Vec<N,T> operator* (const Vec<N,T>& x, U y) { return x * Vec<N,T>(y); }
SINTU Vec<N,T> operator/ (const Vec<N,T>& x, U y) { return x / Vec<N,T>(y); }
SINTU Vec<N,T> operator^ (const Vec<N,T>& x, U y) { return x ^ Vec<N,T>(y); }
SINTU Vec<N,T> operator& (const Vec<N,T>& x, U y) { return x & Vec<N,T>(y); }
SINTU Vec<N,T> operator| (const Vec<N,T>& x, U y) { return x | Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator==(const Vec<N,T>& x, U y) { return x == Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator!=(const Vec<N,T>& x, U y) { return x != Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator<=(const Vec<N,T>& x, U y) { return x <= Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator>=(const Vec<N,T>& x, U y) { return x >= Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator< (const Vec<N,T>& x, U y) { return x < Vec<N,T>(y); }
SINTU Vec<N,M<T>> operator> (const Vec<N,T>& x, U y) { return x > Vec<N,T>(y); }
SINT Vec<N,T>& operator+=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x + y); }
SINT Vec<N,T>& operator-=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x - y); }
SINT Vec<N,T>& operator*=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x * y); }
SINT Vec<N,T>& operator/=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x / y); }
SINT Vec<N,T>& operator^=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x ^ y); }
SINT Vec<N,T>& operator&=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x & y); }
SINT Vec<N,T>& operator|=(Vec<N,T>& x, const Vec<N,T>& y) { return (x = x | y); }
SINTU Vec<N,T>& operator+=(Vec<N,T>& x, U y) { return (x = x + Vec<N,T>(y)); }
SINTU Vec<N,T>& operator-=(Vec<N,T>& x, U y) { return (x = x - Vec<N,T>(y)); }
SINTU Vec<N,T>& operator*=(Vec<N,T>& x, U y) { return (x = x * Vec<N,T>(y)); }
SINTU Vec<N,T>& operator/=(Vec<N,T>& x, U y) { return (x = x / Vec<N,T>(y)); }
SINTU Vec<N,T>& operator^=(Vec<N,T>& x, U y) { return (x = x ^ Vec<N,T>(y)); }
SINTU Vec<N,T>& operator&=(Vec<N,T>& x, U y) { return (x = x & Vec<N,T>(y)); }
SINTU Vec<N,T>& operator|=(Vec<N,T>& x, U y) { return (x = x | Vec<N,T>(y)); }
SINT Vec<N,T>& operator<<=(Vec<N,T>& x, int bits) { return (x = x << bits); }
SINT Vec<N,T>& operator>>=(Vec<N,T>& x, int bits) { return (x = x >> bits); }
// Some operations we want are not expressible with Clang/GCC vector extensions.
// Clang can reason about naive_if_then_else() and optimize through it better
// than if_then_else(), so it's sometimes useful to call it directly when we
// think an entire expression should optimize away, e.g. min()/max().
SINT Vec<N,T> naive_if_then_else(const Vec<N,M<T>>& cond, const Vec<N,T>& t, const Vec<N,T>& e) {
return bit_pun<Vec<N,T>>(( cond & bit_pun<Vec<N, M<T>>>(t)) |
(~cond & bit_pun<Vec<N, M<T>>>(e)) );
}
SIT Vec<1,T> if_then_else(const Vec<1,M<T>>& cond, const Vec<1,T>& t, const Vec<1,T>& e) {
// In practice this scalar implementation is unlikely to be used. See next if_then_else().
return bit_pun<Vec<1,T>>(( cond & bit_pun<Vec<1, M<T>>>(t)) |
(~cond & bit_pun<Vec<1, M<T>>>(e)) );
}
SINT Vec<N,T> if_then_else(const Vec<N,M<T>>& cond, const Vec<N,T>& t, const Vec<N,T>& e) {
// Specializations inline here so they can generalize what types the apply to.
#if SKVX_USE_SIMD && defined(__AVX2__)
if constexpr (N*sizeof(T) == 32) {
return unchecked_bit_pun<Vec<N,T>>(_mm256_blendv_epi8(unchecked_bit_pun<__m256i>(e),
unchecked_bit_pun<__m256i>(t),
unchecked_bit_pun<__m256i>(cond)));
}
#endif
#if SKVX_USE_SIMD && defined(__SSE4_1__)
if constexpr (N*sizeof(T) == 16) {
return unchecked_bit_pun<Vec<N,T>>(_mm_blendv_epi8(unchecked_bit_pun<__m128i>(e),
unchecked_bit_pun<__m128i>(t),
unchecked_bit_pun<__m128i>(cond)));
}
#endif
#if SKVX_USE_SIMD && defined(__ARM_NEON)
if constexpr (N*sizeof(T) == 16) {
return unchecked_bit_pun<Vec<N,T>>(vbslq_u8(unchecked_bit_pun<uint8x16_t>(cond),
unchecked_bit_pun<uint8x16_t>(t),
unchecked_bit_pun<uint8x16_t>(e)));
}
#endif
// Recurse for large vectors to try to hit the specializations above.
if constexpr (N*sizeof(T) > 16) {
return join(if_then_else(cond.lo, t.lo, e.lo),
if_then_else(cond.hi, t.hi, e.hi));
}
// This default can lead to better code than the recursing onto scalars.
return naive_if_then_else(cond, t, e);
}
SIT bool any(const Vec<1,T>& x) { return x.val != 0; }
SINT bool any(const Vec<N,T>& x) {
// For any(), the _mm_testz intrinsics are correct and don't require comparing 'x' to 0, so it's
// lower latency compared to _mm_movemask + _mm_compneq on plain SSE.
#if SKVX_USE_SIMD && defined(__AVX2__)
if constexpr (N*sizeof(T) == 32) {
return !_mm256_testz_si256(unchecked_bit_pun<__m256i>(x), _mm256_set1_epi32(-1));
}
#endif
#if SKVX_USE_SIMD && defined(__SSE_4_1__)
if constexpr (N*sizeof(T) == 16) {
return !_mm_testz_si128(unchecked_bit_pun<__m128i>(x), _mm_set1_epi32(-1));
}
#endif
#if SKVX_USE_SIMD && defined(__SSE__)
if constexpr (N*sizeof(T) == 16) {
// On SSE, movemask checks only the MSB in each lane, which is fine if the lanes were set
// directly from a comparison op (which sets all bits to 1 when true), but skvx::Vec<>
// treats any non-zero value as true, so we have to compare 'x' to 0 before calling movemask
return _mm_movemask_ps(_mm_cmpneq_ps(unchecked_bit_pun<__m128>(x),
_mm_set1_ps(0))) != 0b0000;
}
#endif
#if SKVX_USE_SIMD && defined(__aarch64__)
// On 64-bit NEON, take the max across lanes, which will be non-zero if any lane was true.
// The specific lane-size doesn't really matter in this case since it's really any set bit
// that we're looking for.
if constexpr (N*sizeof(T) == 8 ) { return vmaxv_u8 (unchecked_bit_pun<uint8x8_t> (x)) > 0; }
if constexpr (N*sizeof(T) == 16) { return vmaxvq_u8(unchecked_bit_pun<uint8x16_t>(x)) > 0; }
#endif
#if SKVX_USE_SIMD && defined(__wasm_simd128__)
if constexpr (N == 4 && sizeof(T) == 4) {
return wasm_i32x4_any_true(unchecked_bit_pun<VExt<4,int>>(x));
}
#endif
return any(x.lo)
|| any(x.hi);
}
SIT bool all(const Vec<1,T>& x) { return x.val != 0; }
SINT bool all(const Vec<N,T>& x) {
// Unlike any(), we have to respect the lane layout, or we'll miss cases where a
// true lane has a mix of 0 and 1 bits.
#if SKVX_USE_SIMD && defined(__SSE__)
// Unfortunately, the _mm_testc intrinsics don't let us avoid the comparison to 0 for all()'s
// correctness, so always just use the plain SSE version.
if constexpr (N == 4 && sizeof(T) == 4) {
return _mm_movemask_ps(_mm_cmpneq_ps(unchecked_bit_pun<__m128>(x),
_mm_set1_ps(0))) == 0b1111;
}
#endif
#if SKVX_USE_SIMD && defined(__aarch64__)
// On 64-bit NEON, take the min across the lanes, which will be non-zero if all lanes are != 0.
if constexpr (sizeof(T)==1 && N==8) {return vminv_u8 (unchecked_bit_pun<uint8x8_t> (x)) > 0;}
if constexpr (sizeof(T)==1 && N==16) {return vminvq_u8 (unchecked_bit_pun<uint8x16_t>(x)) > 0;}
if constexpr (sizeof(T)==2 && N==4) {return vminv_u16 (unchecked_bit_pun<uint16x4_t>(x)) > 0;}
if constexpr (sizeof(T)==2 && N==8) {return vminvq_u16(unchecked_bit_pun<uint16x8_t>(x)) > 0;}
if constexpr (sizeof(T)==4 && N==2) {return vminv_u32 (unchecked_bit_pun<uint32x2_t>(x)) > 0;}
if constexpr (sizeof(T)==4 && N==4) {return vminvq_u32(unchecked_bit_pun<uint32x4_t>(x)) > 0;}
#endif
#if SKVX_USE_SIMD && defined(__wasm_simd128__)
if constexpr (N == 4 && sizeof(T) == 4) {
return wasm_i32x4_all_true(unchecked_bit_pun<VExt<4,int>>(x));
}
#endif
return all(x.lo)
&& all(x.hi);
}
// cast() Vec<N,S> to Vec<N,D>, as if applying a C-cast to each lane.
// TODO: implement with map()?
template <typename D, typename S>
SI Vec<1,D> cast(const Vec<1,S>& src) { return (D)src.val; }
template <typename D, int N, typename S>
SI Vec<N,D> cast(const Vec<N,S>& src) {
#if SKVX_USE_SIMD && defined(__clang__)
return to_vec(__builtin_convertvector(to_vext(src), VExt<N,D>));
#else
return join(cast<D>(src.lo), cast<D>(src.hi));
#endif
}
// min/max match logic of std::min/std::max, which is important when NaN is involved.
SIT T min(const Vec<1,T>& x) { return x.val; }
SIT T max(const Vec<1,T>& x) { return x.val; }
SINT T min(const Vec<N,T>& x) { return std::min(min(x.lo), min(x.hi)); }
SINT T max(const Vec<N,T>& x) { return std::max(max(x.lo), max(x.hi)); }
SINT Vec<N,T> min(const Vec<N,T>& x, const Vec<N,T>& y) { return naive_if_then_else(y < x, y, x); }
SINT Vec<N,T> max(const Vec<N,T>& x, const Vec<N,T>& y) { return naive_if_then_else(x < y, y, x); }
SINTU Vec<N,T> min(const Vec<N,T>& x, U y) { return min(x, Vec<N,T>(y)); }
SINTU Vec<N,T> max(const Vec<N,T>& x, U y) { return max(x, Vec<N,T>(y)); }
SINTU Vec<N,T> min(U x, const Vec<N,T>& y) { return min(Vec<N,T>(x), y); }
SINTU Vec<N,T> max(U x, const Vec<N,T>& y) { return max(Vec<N,T>(x), y); }
// pin matches the logic of SkTPin, which is important when NaN is involved. It always returns
// values in the range lo..hi, and if x is NaN, it returns lo.
SINT Vec<N,T> pin(const Vec<N,T>& x, const Vec<N,T>& lo, const Vec<N,T>& hi) {
return max(lo, min(x, hi));
}
// Shuffle values from a vector pretty arbitrarily:
// skvx::Vec<4,float> rgba = {R,G,B,A};
// shuffle<2,1,0,3> (rgba) ~> {B,G,R,A}
// shuffle<2,1> (rgba) ~> {B,G}
// shuffle<2,1,2,1,2,1,2,1>(rgba) ~> {B,G,B,G,B,G,B,G}
// shuffle<3,3,3,3> (rgba) ~> {A,A,A,A}
// The only real restriction is that the output also be a legal N=power-of-two sknx::Vec.
template <int... Ix, int N, typename T>
SI Vec<sizeof...(Ix),T> shuffle(const Vec<N,T>& x) {
#if SKVX_USE_SIMD && defined(__clang__)
// TODO: can we just always use { x[Ix]... }?
return to_vec<sizeof...(Ix),T>(__builtin_shufflevector(to_vext(x), to_vext(x), Ix...));
#else
return { x[Ix]... };
#endif
}
// Call map(fn, x) for a vector with fn() applied to each lane of x, { fn(x[0]), fn(x[1]), ... },
// or map(fn, x,y) for a vector of fn(x[i], y[i]), etc.
template <typename Fn, typename... Args, size_t... I>
SI auto map(std::index_sequence<I...>,
Fn&& fn, const Args&... args) -> skvx::Vec<sizeof...(I), decltype(fn(args[0]...))> {
auto lane = [&](size_t i)
#if defined(__clang__)
// CFI, specifically -fsanitize=cfi-icall, seems to give a false positive here,
// with errors like "control flow integrity check for type 'float (float)
// noexcept' failed during indirect function call... note: sqrtf.cfi_jt defined
// here". But we can be quite sure fn is the right type: it's all inferred!
// So, stifle CFI in this function.
__attribute__((no_sanitize("cfi")))
#endif
{ return fn(args[i]...); };
return { lane(I)... };
}
template <typename Fn, int N, typename T, typename... Rest>
auto map(Fn&& fn, const Vec<N,T>& first, const Rest&... rest) {
// Derive an {0...N-1} index_sequence from the size of the first arg: N lanes in, N lanes out.
return map(std::make_index_sequence<N>{}, fn, first,rest...);
}
SIN Vec<N,float> ceil(const Vec<N,float>& x) { return map( ceilf, x); }
SIN Vec<N,float> floor(const Vec<N,float>& x) { return map(floorf, x); }
SIN Vec<N,float> trunc(const Vec<N,float>& x) { return map(truncf, x); }
SIN Vec<N,float> round(const Vec<N,float>& x) { return map(roundf, x); }
SIN Vec<N,float> sqrt(const Vec<N,float>& x) { return map( sqrtf, x); }
SIN Vec<N,float> abs(const Vec<N,float>& x) { return map( fabsf, x); }
SIN Vec<N,float> fma(const Vec<N,float>& x,
const Vec<N,float>& y,
const Vec<N,float>& z) {
// I don't understand why Clang's codegen is terrible if we write map(fmaf, x,y,z) directly.
auto fn = [](float x, float y, float z) { return fmaf(x,y,z); };
return map(fn, x,y,z);
}
SI Vec<1,int> lrint(const Vec<1,float>& x) {
return (int)lrintf(x.val);
}
SIN Vec<N,int> lrint(const Vec<N,float>& x) {
#if SKVX_USE_SIMD && defined(__AVX__)
if constexpr (N == 8) {
return unchecked_bit_pun<Vec<N,int>>(_mm256_cvtps_epi32(unchecked_bit_pun<__m256>(x)));
}
#endif
#if SKVX_USE_SIMD && defined(__SSE__)
if constexpr (N == 4) {
return unchecked_bit_pun<Vec<N,int>>(_mm_cvtps_epi32(unchecked_bit_pun<__m128>(x)));
}
#endif
return join(lrint(x.lo),
lrint(x.hi));
}
SIN Vec<N,float> fract(const Vec<N,float>& x) { return x - floor(x); }
// Assumes inputs are finite and treat/flush denorm half floats as/to zero.
// Key constants to watch for:
// - a float is 32-bit, 1-8-23 sign-exponent-mantissa, with 127 exponent bias;
// - a half is 16-bit, 1-5-10 sign-exponent-mantissa, with 15 exponent bias.
SIN Vec<N,uint16_t> to_half_finite_ftz(const Vec<N,float>& x) {
Vec<N,uint32_t> sem = bit_pun<Vec<N,uint32_t>>(x),
s = sem & 0x8000'0000,
em = sem ^ s,
is_norm = em > 0x387f'd000, // halfway between largest f16 denorm and smallest norm
norm = (em>>13) - ((127-15)<<10);
return cast<uint16_t>((s>>16) | (is_norm & norm));
}
SIN Vec<N,float> from_half_finite_ftz(const Vec<N,uint16_t>& x) {
Vec<N,uint32_t> wide = cast<uint32_t>(x),
s = wide & 0x8000,
em = wide ^ s,
is_norm = em > 0x3ff,
norm = (em<<13) + ((127-15)<<23);
return bit_pun<Vec<N,float>>((s<<16) | (is_norm & norm));
}
// Like if_then_else(), these N=1 base cases won't actually be used unless explicitly called.
SI Vec<1,uint16_t> to_half(const Vec<1,float>& x) { return to_half_finite_ftz(x); }
SI Vec<1,float> from_half(const Vec<1,uint16_t>& x) { return from_half_finite_ftz(x); }
SIN Vec<N,uint16_t> to_half(const Vec<N,float>& x) {
#if SKVX_USE_SIMD && defined(__F16C__)
if constexpr (N == 8) {
return unchecked_bit_pun<Vec<N,uint16_t>>(_mm256_cvtps_ph(unchecked_bit_pun<__m256>(x),
_MM_FROUND_CUR_DIRECTION));
}
#endif
#if SKVX_USE_SIMD && defined(__aarch64__)
if constexpr (N == 4) {
return unchecked_bit_pun<Vec<N,uint16_t>>(vcvt_f16_f32(unchecked_bit_pun<float32x4_t>(x)));
}
#endif
if constexpr (N > 4) {
return join(to_half(x.lo),
to_half(x.hi));
}
return to_half_finite_ftz(x);
}
SIN Vec<N,float> from_half(const Vec<N,uint16_t>& x) {
#if SKVX_USE_SIMD && defined(__F16C__)
if constexpr (N == 8) {
return unchecked_bit_pun<Vec<N,float>>(_mm256_cvtph_ps(unchecked_bit_pun<__m128i>(x)));
}
#endif
#if SKVX_USE_SIMD && defined(__aarch64__)
if constexpr (N == 4) {
return unchecked_bit_pun<Vec<N,float>>(vcvt_f32_f16(unchecked_bit_pun<float16x4_t>(x)));
}
#endif
if constexpr (N > 4) {
return join(from_half(x.lo),
from_half(x.hi));
}
return from_half_finite_ftz(x);
}
// div255(x) = (x + 127) / 255 is a bit-exact rounding divide-by-255, packing down to 8-bit.
SIN Vec<N,uint8_t> div255(const Vec<N,uint16_t>& x) {
return cast<uint8_t>( (x+127)/255 );
}
// approx_scale(x,y) approximates div255(cast<uint16_t>(x)*cast<uint16_t>(y)) within a bit,
// and is always perfect when x or y is 0 or 255.
SIN Vec<N,uint8_t> approx_scale(const Vec<N,uint8_t>& x, const Vec<N,uint8_t>& y) {
// All of (x*y+x)/256, (x*y+y)/256, and (x*y+255)/256 meet the criteria above.
// We happen to have historically picked (x*y+x)/256.
auto X = cast<uint16_t>(x),
Y = cast<uint16_t>(y);
return cast<uint8_t>( (X*Y+X)/256 );
}
// saturated_add(x,y) sums values and clamps to the maximum value instead of overflowing.
SINT std::enable_if_t<std::is_unsigned_v<T>, Vec<N,T>> saturated_add(const Vec<N,T>& x,
const Vec<N,T>& y) {
#if SKVX_USE_SIMD && (defined(__SSE__) || defined(__ARM_NEON))
// Both SSE and ARM have 16-lane saturated adds, so use intrinsics for those and recurse down
// or join up to take advantage.
if constexpr (N == 16 && sizeof(T) == 1) {
#if defined(__SSE__)
return unchecked_bit_pun<Vec<N,T>>(_mm_adds_epu8(unchecked_bit_pun<__m128i>(x),
unchecked_bit_pun<__m128i>(y)));
#else // __ARM_NEON
return unchecked_bit_pun<Vec<N,T>>(vqaddq_u8(unchecked_bit_pun<uint8x16_t>(x),
unchecked_bit_pun<uint8x16_t>(y)));
#endif
} else if constexpr (N < 16 && sizeof(T) == 1) {
return saturated_add(join(x,x), join(y,y)).lo;
} else if constexpr (sizeof(T) == 1) {
return join(saturated_add(x.lo, y.lo), saturated_add(x.hi, y.hi));
}
#endif
// Otherwise saturate manually
auto sum = x + y;
return if_then_else(sum < x, Vec<N,T>(std::numeric_limits<T>::max()), sum);
}
// The ScaledDividerU32 takes a divisor > 1, and creates a function divide(numerator) that
// calculates a numerator / denominator. For this to be rounded properly, numerator should have
// half added in:
// divide(numerator + half) == floor(numerator/denominator + 1/2).
//
// This gives an answer within +/- 1 from the true value.
//
// Derivation of half:
// numerator/denominator + 1/2 = (numerator + half) / d
// numerator + denominator / 2 = numerator + half
// half = denominator / 2.
//
// Because half is divided by 2, that division must also be rounded.
// half == denominator / 2 = (denominator + 1) / 2.
//
// The divisorFactor is just a scaled value:
// divisorFactor = (1 / divisor) * 2 ^ 32.
// The maximum that can be divided and rounded is UINT_MAX - half.
class ScaledDividerU32 {
public:
explicit ScaledDividerU32(uint32_t divisor)
: fDivisorFactor{(uint32_t)(std::round((1.0 / divisor) * (1ull << 32)))}
, fHalf{(divisor + 1) >> 1} {
assert(divisor > 1);
}
Vec<4, uint32_t> divide(const Vec<4, uint32_t>& numerator) const {
#if SKVX_USE_SIMD && defined(__ARM_NEON)
uint64x2_t hi = vmull_n_u32(vget_high_u32(to_vext(numerator)), fDivisorFactor);
uint64x2_t lo = vmull_n_u32(vget_low_u32(to_vext(numerator)), fDivisorFactor);
return to_vec<4, uint32_t>(vcombine_u32(vshrn_n_u64(lo,32), vshrn_n_u64(hi,32)));
#else
return cast<uint32_t>((cast<uint64_t>(numerator) * fDivisorFactor) >> 32);
#endif
}
uint32_t half() const { return fHalf; }
private:
const uint32_t fDivisorFactor;
const uint32_t fHalf;
};
#if SKVX_USE_SIMD && defined(__ARM_NEON)
// With NEON we can do eight u8*u8 -> u16 in one instruction, vmull_u8 (read, mul-long).
SI Vec<8,uint16_t> mull(const Vec<8,uint8_t>& x,
const Vec<8,uint8_t>& y) {
return to_vec<8,uint16_t>(vmull_u8(to_vext(x),
to_vext(y)));
}
SIN std::enable_if_t<(N < 8), Vec<N,uint16_t>> mull(const Vec<N,uint8_t>& x,
const Vec<N,uint8_t>& y) {
// N < 8 --> double up data until N == 8, returning the part we need.
return mull(join(x,x),
join(y,y)).lo;
}
SIN std::enable_if_t<(N > 8), Vec<N,uint16_t>> mull(const Vec<N,uint8_t>& x,
const Vec<N,uint8_t>& y) {
// N > 8 --> usual join(lo,hi) strategy to recurse down to N == 8.
return join(mull(x.lo, y.lo),
mull(x.hi, y.hi));
}
// Or do four u16*u16 -> u32 in one instruction, vmull_u16
SI Vec<4,uint32_t> mull(const Vec<4,uint16_t>& x,
const Vec<4,uint16_t>& y) {
return to_vec<4,uint32_t>(vmull_u16(to_vext(x),
to_vext(y)));
}
SIN std::enable_if_t<(N < 4), Vec<N,uint32_t>> mull(const Vec<N,uint16_t>& x,
const Vec<N,uint16_t>& y) {
// N < 4 --> double up data until N == 4, returning the part we need.
return mull(join(x,x),
join(y,y)).lo;
}
SIN std::enable_if_t<(N > 4), Vec<N,uint32_t>> mull(const Vec<N,uint16_t>& x,
const Vec<N,uint16_t>& y) {
// N > 4 --> usual join(lo,hi) strategy to recurse down to N == 4.
return join(mull(x.lo, y.lo),
mull(x.hi, y.hi));
}
#else
// Nothing special when we don't have NEON... just cast up and multiply.
SIN Vec<N,uint16_t> mull(const Vec<N,uint8_t>& x,
const Vec<N,uint8_t>& y) {
return cast<uint16_t>(x) * cast<uint16_t>(y);
}
SIN Vec<N,uint32_t> mull(const Vec<N,uint16_t>& x,
const Vec<N,uint16_t>& y) {
return cast<uint32_t>(x) * cast<uint32_t>(y);
}
#endif
SINT T dot(const Vec<N, T>& a, const Vec<N, T>& b) {
auto ab = a*b;
if constexpr (N == 2) {
return ab[0] + ab[1];
} else if constexpr (N == 4) {
return ab[0] + ab[1] + ab[2] + ab[3];
} else {
T sum = ab[0];
for (int i = 1; i < N; ++i) {
sum += ab[i];
}
return sum;
}
}
SI float cross(const Vec<2, float>& a, const Vec<2, float>& b) {
auto x = a * shuffle<1,0>(b);
return x[0] - x[1];
}
// De-interleaving load of 4 vectors.
//
// WARNING: These are really only supported well on NEON. Consider restructuring your data before
// resorting to these methods.
SIT void strided_load4(const T* v,
Vec<1,T>& a,
Vec<1,T>& b,
Vec<1,T>& c,
Vec<1,T>& d) {
a.val = v[0];
b.val = v[1];
c.val = v[2];
d.val = v[3];
}
SINT void strided_load4(const T* v,
Vec<N,T>& a,
Vec<N,T>& b,
Vec<N,T>& c,
Vec<N,T>& d) {
strided_load4(v, a.lo, b.lo, c.lo, d.lo);
strided_load4(v + 4*(N/2), a.hi, b.hi, c.hi, d.hi);
}
#if SKVX_USE_SIMD && defined(__ARM_NEON)
#define IMPL_LOAD4_TRANSPOSED(N, T, VLD) \
SI void strided_load4(const T* v, \
Vec<N,T>& a, \
Vec<N,T>& b, \
Vec<N,T>& c, \
Vec<N,T>& d) { \
auto mat = VLD(v); \
a = bit_pun<Vec<N,T>>(mat.val[0]); \
b = bit_pun<Vec<N,T>>(mat.val[1]); \
c = bit_pun<Vec<N,T>>(mat.val[2]); \
d = bit_pun<Vec<N,T>>(mat.val[3]); \
}
IMPL_LOAD4_TRANSPOSED(2, uint32_t, vld4_u32)
IMPL_LOAD4_TRANSPOSED(4, uint16_t, vld4_u16)
IMPL_LOAD4_TRANSPOSED(8, uint8_t, vld4_u8)
IMPL_LOAD4_TRANSPOSED(2, int32_t, vld4_s32)
IMPL_LOAD4_TRANSPOSED(4, int16_t, vld4_s16)
IMPL_LOAD4_TRANSPOSED(8, int8_t, vld4_s8)
IMPL_LOAD4_TRANSPOSED(2, float, vld4_f32)
IMPL_LOAD4_TRANSPOSED(4, uint32_t, vld4q_u32)
IMPL_LOAD4_TRANSPOSED(8, uint16_t, vld4q_u16)
IMPL_LOAD4_TRANSPOSED(16, uint8_t, vld4q_u8)
IMPL_LOAD4_TRANSPOSED(4, int32_t, vld4q_s32)
IMPL_LOAD4_TRANSPOSED(8, int16_t, vld4q_s16)
IMPL_LOAD4_TRANSPOSED(16, int8_t, vld4q_s8)
IMPL_LOAD4_TRANSPOSED(4, float, vld4q_f32)
#undef IMPL_LOAD4_TRANSPOSED
#elif SKVX_USE_SIMD && defined(__SSE__)
SI void strided_load4(const float* v,
Vec<4,float>& a,
Vec<4,float>& b,
Vec<4,float>& c,
Vec<4,float>& d) {
__m128 a_ = _mm_loadu_ps(v);
__m128 b_ = _mm_loadu_ps(v+4);
__m128 c_ = _mm_loadu_ps(v+8);
__m128 d_ = _mm_loadu_ps(v+12);
_MM_TRANSPOSE4_PS(a_, b_, c_, d_);
a = bit_pun<Vec<4,float>>(a_);
b = bit_pun<Vec<4,float>>(b_);
c = bit_pun<Vec<4,float>>(c_);
d = bit_pun<Vec<4,float>>(d_);
}
#endif
// De-interleaving load of 2 vectors.
//
// WARNING: These are really only supported well on NEON. Consider restructuring your data before
// resorting to these methods.
SIT void strided_load2(const T* v, Vec<1,T>& a, Vec<1,T>& b) {
a.val = v[0];
b.val = v[1];
}
SINT void strided_load2(const T* v, Vec<N,T>& a, Vec<N,T>& b) {
strided_load2(v, a.lo, b.lo);
strided_load2(v + 2*(N/2), a.hi, b.hi);
}
#if SKVX_USE_SIMD && defined(__ARM_NEON)
#define IMPL_LOAD2_TRANSPOSED(N, T, VLD) \
SI void strided_load2(const T* v, Vec<N,T>& a, Vec<N,T>& b) { \
auto mat = VLD(v); \
a = bit_pun<Vec<N,T>>(mat.val[0]); \
b = bit_pun<Vec<N,T>>(mat.val[1]); \
}
IMPL_LOAD2_TRANSPOSED(2, uint32_t, vld2_u32)
IMPL_LOAD2_TRANSPOSED(4, uint16_t, vld2_u16)
IMPL_LOAD2_TRANSPOSED(8, uint8_t, vld2_u8)
IMPL_LOAD2_TRANSPOSED(2, int32_t, vld2_s32)
IMPL_LOAD2_TRANSPOSED(4, int16_t, vld2_s16)
IMPL_LOAD2_TRANSPOSED(8, int8_t, vld2_s8)
IMPL_LOAD2_TRANSPOSED(2, float, vld2_f32)
IMPL_LOAD2_TRANSPOSED(4, uint32_t, vld2q_u32)
IMPL_LOAD2_TRANSPOSED(8, uint16_t, vld2q_u16)
IMPL_LOAD2_TRANSPOSED(16, uint8_t, vld2q_u8)
IMPL_LOAD2_TRANSPOSED(4, int32_t, vld2q_s32)
IMPL_LOAD2_TRANSPOSED(8, int16_t, vld2q_s16)
IMPL_LOAD2_TRANSPOSED(16, int8_t, vld2q_s8)
IMPL_LOAD2_TRANSPOSED(4, float, vld2q_f32)
#undef IMPL_LOAD2_TRANSPOSED
#endif
// Define commonly used aliases
using float2 = Vec< 2, float>;
using float4 = Vec< 4, float>;
using float8 = Vec< 8, float>;
using double2 = Vec< 2, double>;
using double4 = Vec< 4, double>;
using double8 = Vec< 8, double>;
using byte2 = Vec< 2, uint8_t>;
using byte4 = Vec< 4, uint8_t>;
using byte8 = Vec< 8, uint8_t>;
using byte16 = Vec<16, uint8_t>;
using int2 = Vec< 2, int32_t>;
using int4 = Vec< 4, int32_t>;
using int8 = Vec< 8, int32_t>;
using uint2 = Vec< 2, uint32_t>;
using uint4 = Vec< 4, uint32_t>;
using uint8 = Vec< 8, uint32_t>;
using long2 = Vec< 2, int64_t>;
using long4 = Vec< 4, int64_t>;
using long8 = Vec< 8, int64_t>;
// Use with from_half and to_half to convert between floatX, and use these for storage.
using half2 = Vec< 2, uint16_t>;
using half4 = Vec< 4, uint16_t>;
using half8 = Vec< 8, uint16_t>;
} // namespace skvx
#undef SINTU
#undef SINT
#undef SIN
#undef SIT
#undef SI
#undef SKVX_ALWAYS_INLINE
#undef SKVX_USE_SIMD
#endif//SKVX_DEFINED
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