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skia2/include/private/SkVx.h
Mike Klein 53a5298a2f add mad() and shuffle() to SkVx 
Change-Id: Ie3e5b353f84e74d398a5350dc0baff5541789119
Reviewed-on: https://skia-review.googlesource.com/c/189982
Commit-Queue: Mike Klein <mtklein@google.com>
Commit-Queue: Herb Derby <herb@google.com>
Auto-Submit: Mike Klein <mtklein@google.com>
Reviewed-by: Herb Derby <herb@google.com>
2019-02-06 21:12:48 +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 alignof(T) alignment and is safe to use across translation units freely.
// It'd be nice to not pull in any Skia headers here, in case we want to spin this file off.
#include <algorithm> // std::accumulate, std::copy, std::fill, std::transform, etc.
#include <cstdint> // intXX_t
#include <cstring> // memcpy()
#include <cmath> // std::ceil, std::floor, std::trunc, std::round, std::sqrt, etc.
#include <functional> // std::plus, std::minus, std::multiplies, etc.
#include <initializer_list> // std::initializer_list
// We try to use <algorithm> and <functional> where natural so that the more
// idiosyncratic parts that can't use them stand out. This is an experiment.
namespace skvx {
// All Vec have the same simple memory layout, the same as `T vec[N]`.
// This gives Vec a consistent ABI, letting them pass between files compiled with
// different instruction sets (e.g. SSE2 and AVX2) without fear of ODR violation.
template <int N, typename T>
struct Vec {
static_assert((N & (N-1)) == 0, "N must be a power of 2.");
T vals[N];
// 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.
Vec() = default;
Vec(T x) { std::fill(vals,vals+N, x); }
Vec(std::initializer_list<T> xs) : Vec(0) {
std::copy(xs.begin(), xs.begin() + std::min(xs.size(), (size_t)N), vals);
}
T operator[](int i) const { return vals[i]; }
T& operator[](int i) { return vals[i]; }
static Vec Load(const void* ptr) {
Vec v;
memcpy(&v, ptr, sizeof(Vec));
return v;
}
void store(void* ptr) const {
memcpy(ptr, this, sizeof(Vec));
}
};
#if defined(_MSC_VER)
#define ALWAYS_INLINE __forceinline
#else
#define ALWAYS_INLINE __attribute__((always_inline))
#endif
// Helps tamp down on the repetitive boilerplate.
#define ___ template <int N, typename T> static inline ALWAYS_INLINE
#if defined(__GNUC__) && !defined(__clang__) && defined(__SSE__)
// GCC warns about ABI changes when returning >= 32 byte vectors when -mavx is not enabled.
// The functions that do that (BitPun::operator U() and to_vext()) are marked ALWAYS_INLINE,
// so we can just stifle the warning.
#pragma GCC diagnostic ignored "-Wpsabi"
#endif
// BitPun<V> holds a V and can implicitly bit-pun that V to any other equal sized type U.
template <typename V>
struct BitPun {
V v;
template <typename U>
ALWAYS_INLINE operator U() const {
static_assert(sizeof(U) == sizeof(V), "");
U u;
memcpy(&u, &v, sizeof(U));
return u;
}
};
template <typename V>
static inline ALWAYS_INLINE BitPun<V> bit_pun(V v) { return {v}; }
// Translate from a value type T to its corresponding Mask, the result of a comparison.
template <typename T> struct MaskHelper { using type = T; };
template <> struct MaskHelper<float > { using type = int32_t; };
template <> struct MaskHelper<double> { using type = int64_t; };
template <typename T> using Mask = typename MaskHelper<T>::type;
// Apply op() to each lane of one or two input vectors, returning a new vector of the results.
template <int N, typename T, typename Op>
static inline auto map(Vec<N,T> x, Op op) -> Vec<N, decltype(op(x[0]))> {
Vec<N, decltype(op(x[0]))> results;
std::transform(x.vals, x.vals+N, results.vals, op);
return results;
}
template <int N, typename T, typename Op>
static inline auto map(Vec<N,T> x, Vec<N,T> y, Op op) -> Vec<N, decltype(op(x[0], y[0]))> {
Vec<N, decltype(op(x[0], y[0]))> results;
std::transform(x.vals, x.vals+N, y.vals, results.vals, op);
return results;
}
// We have two default strategies for implementing most operations:
// 1) lean on Clang/GCC vector extensions when available;
// 2) fall back to portable implementations when not.
// At the end we can drop in platform-specific implementations that override these defaults.
#if !defined(SKNX_NO_SIMD) && (defined(__clang__) || defined(__GNUC__))
// VExt<N,T> types have the same size as Vec<N,T> and support most operations directly.
// N.B. VExt<N,T> alignment is N*alignof(T), stricter than Vec<N,T>'s alignof(T).
#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;
#endif
___ VExt<N,T> to_vext(Vec<N,T> v) { return bit_pun(v); }
___ Vec<N,T> operator+(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) + to_vext(y)); }
___ Vec<N,T> operator-(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) - to_vext(y)); }
___ Vec<N,T> operator*(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) * to_vext(y)); }
___ Vec<N,T> operator/(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) / to_vext(y)); }
___ Vec<N,T> operator^(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) ^ to_vext(y)); }
___ Vec<N,T> operator&(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) & to_vext(y)); }
___ Vec<N,T> operator|(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) | to_vext(y)); }
___ Vec<N,T> operator!(Vec<N,T> x) { return bit_pun(!to_vext(x)); }
___ Vec<N,T> operator-(Vec<N,T> x) { return bit_pun(-to_vext(x)); }
___ Vec<N,T> operator~(Vec<N,T> x) { return bit_pun(~to_vext(x)); }
___ Vec<N,T> operator<<(Vec<N,T> x, int bits) { return bit_pun(to_vext(x) << bits); }
___ Vec<N,T> operator>>(Vec<N,T> x, int bits) { return bit_pun(to_vext(x) >> bits); }
___ Vec<N, Mask<T>> operator==(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) == to_vext(y)); }
___ Vec<N, Mask<T>> operator!=(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) != to_vext(y)); }
___ Vec<N, Mask<T>> operator<=(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) <= to_vext(y)); }
___ Vec<N, Mask<T>> operator>=(Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) >= to_vext(y)); }
___ Vec<N, Mask<T>> operator< (Vec<N,T> x, Vec<N,T> y) { return bit_pun(to_vext(x) < to_vext(y)); }
___ Vec<N, Mask<T>> operator> (Vec<N,T> x, Vec<N,T> y) { return bit_pun(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, in a way that should be easily autovectorizable.
___ Vec<N,T> operator+(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::plus <T>{}); }
___ Vec<N,T> operator-(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::minus <T>{}); }
___ Vec<N,T> operator*(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::multiplies<T>{}); }
___ Vec<N,T> operator/(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::divides <T>{}); }
___ Vec<N,T> operator^(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::bit_xor<T>{}); }
___ Vec<N,T> operator&(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::bit_and<T>{}); }
___ Vec<N,T> operator|(Vec<N,T> x, Vec<N,T> y) { return map(x,y, std::bit_or <T>{}); }
___ Vec<N,T> operator!(Vec<N,T> x) { return map(x, std::logical_not<T>{}); }
___ Vec<N,T> operator-(Vec<N,T> x) { return map(x, std::negate <T>{}); }
___ Vec<N,T> operator~(Vec<N,T> x) { return map(x, std::bit_not <T>{}); }
___ Vec<N,T> operator<<(Vec<N,T> x, int bits) { return map(x, [bits](T a) { return a << bits; }); }
___ Vec<N,T> operator>>(Vec<N,T> x, int bits) { return map(x, [bits](T a) { return a >> bits; }); }
___ Vec<N, Mask<T>> operator==(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a == b ? ~0 : 0; }); }
___ Vec<N, Mask<T>> operator!=(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a != b ? ~0 : 0; }); }
___ Vec<N, Mask<T>> operator<=(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a <= b ? ~0 : 0; }); }
___ Vec<N, Mask<T>> operator>=(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a >= b ? ~0 : 0; }); }
___ Vec<N, Mask<T>> operator< (Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a < b ? ~0 : 0; }); }
___ Vec<N, Mask<T>> operator> (Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) -> Mask<T> { return a > b ? ~0 : 0; }); }
#endif
// Some operations we want are not expressible with Clang/GCC vector extensions,
// so we implement them using the same approach as the alternate path above.
___ Vec<N,T> if_then_else(Vec<N,Mask<T>> cond, Vec<N,T> t, Vec<N,T> e) {
Vec<N,Mask<T>> t_bits = bit_pun(t),
e_bits = bit_pun(e);
return bit_pun( (cond & t_bits) | (~cond & e_bits) );
}
___ const T* begin(const Vec<N,T>& x) { return x.vals ; }
___ T* begin( Vec<N,T>& x) { return x.vals ; }
___ const T* end(const Vec<N,T>& x) { return x.vals+N; }
___ T* end( Vec<N,T>& x) { return x.vals+N; }
___ Vec<N,T> min(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) { return std::min(a,b); }); }
___ Vec<N,T> max(Vec<N,T> x, Vec<N,T> y) { return map(x,y, [](T a, T b) { return std::max(a,b); }); }
// Scalar/vector operations just splat the scalar to a vector...
___ Vec<N,T> operator+ (T x, Vec<N,T> y) { return Vec<N,T>(x) + y; }
___ Vec<N,T> operator- (T x, Vec<N,T> y) { return Vec<N,T>(x) - y; }
___ Vec<N,T> operator* (T x, Vec<N,T> y) { return Vec<N,T>(x) * y; }
___ Vec<N,T> operator/ (T x, Vec<N,T> y) { return Vec<N,T>(x) / y; }
___ Vec<N,T> operator^ (T x, Vec<N,T> y) { return Vec<N,T>(x) ^ y; }
___ Vec<N,T> operator& (T x, Vec<N,T> y) { return Vec<N,T>(x) & y; }
___ Vec<N,T> operator| (T x, Vec<N,T> y) { return Vec<N,T>(x) | y; }
___ Vec<N,Mask<T>> operator==(T x, Vec<N,T> y) { return Vec<N,T>(x) == y; }
___ Vec<N,Mask<T>> operator!=(T x, Vec<N,T> y) { return Vec<N,T>(x) != y; }
___ Vec<N,Mask<T>> operator<=(T x, Vec<N,T> y) { return Vec<N,T>(x) <= y; }
___ Vec<N,Mask<T>> operator>=(T x, Vec<N,T> y) { return Vec<N,T>(x) >= y; }
___ Vec<N,Mask<T>> operator< (T x, Vec<N,T> y) { return Vec<N,T>(x) < y; }
___ Vec<N,Mask<T>> operator> (T x, Vec<N,T> y) { return Vec<N,T>(x) > y; }
___ Vec<N,T> min(T x, Vec<N,T> y) { return min(Vec<N,T>(x), y); }
___ Vec<N,T> max(T x, Vec<N,T> y) { return max(Vec<N,T>(x), y); }
// ... and same deal for vector/scalar operations.
___ Vec<N,T> operator+ (Vec<N,T> x, T y) { return x + Vec<N,T>(y); }
___ Vec<N,T> operator- (Vec<N,T> x, T y) { return x - Vec<N,T>(y); }
___ Vec<N,T> operator* (Vec<N,T> x, T y) { return x * Vec<N,T>(y); }
___ Vec<N,T> operator/ (Vec<N,T> x, T y) { return x / Vec<N,T>(y); }
___ Vec<N,T> operator^ (Vec<N,T> x, T y) { return x ^ Vec<N,T>(y); }
___ Vec<N,T> operator& (Vec<N,T> x, T y) { return x & Vec<N,T>(y); }
___ Vec<N,T> operator| (Vec<N,T> x, T y) { return x | Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator==(Vec<N,T> x, T y) { return x == Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator!=(Vec<N,T> x, T y) { return x != Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator<=(Vec<N,T> x, T y) { return x <= Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator>=(Vec<N,T> x, T y) { return x >= Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator< (Vec<N,T> x, T y) { return x < Vec<N,T>(y); }
___ Vec<N,Mask<T>> operator> (Vec<N,T> x, T y) { return x > Vec<N,T>(y); }
___ Vec<N,T> min(Vec<N,T> x, T y) { return min(x, Vec<N,T>(y)); }
___ Vec<N,T> max(Vec<N,T> x, T y) { return max(x, Vec<N,T>(y)); }
// The various op= operators, for vectors...
___ Vec<N,T>& operator+=(Vec<N,T>& x, Vec<N,T> y) { return (x = x + y); }
___ Vec<N,T>& operator-=(Vec<N,T>& x, Vec<N,T> y) { return (x = x - y); }
___ Vec<N,T>& operator*=(Vec<N,T>& x, Vec<N,T> y) { return (x = x * y); }
___ Vec<N,T>& operator/=(Vec<N,T>& x, Vec<N,T> y) { return (x = x / y); }
___ Vec<N,T>& operator^=(Vec<N,T>& x, Vec<N,T> y) { return (x = x ^ y); }
___ Vec<N,T>& operator&=(Vec<N,T>& x, Vec<N,T> y) { return (x = x & y); }
___ Vec<N,T>& operator|=(Vec<N,T>& x, Vec<N,T> y) { return (x = x | y); }
// ... for scalars...
___ Vec<N,T>& operator+=(Vec<N,T>& x, T y) { return (x = x + Vec<N,T>(y)); }
___ Vec<N,T>& operator-=(Vec<N,T>& x, T y) { return (x = x - Vec<N,T>(y)); }
___ Vec<N,T>& operator*=(Vec<N,T>& x, T y) { return (x = x * Vec<N,T>(y)); }
___ Vec<N,T>& operator/=(Vec<N,T>& x, T y) { return (x = x / Vec<N,T>(y)); }
___ Vec<N,T>& operator^=(Vec<N,T>& x, T y) { return (x = x ^ Vec<N,T>(y)); }
___ Vec<N,T>& operator&=(Vec<N,T>& x, T y) { return (x = x & Vec<N,T>(y)); }
___ Vec<N,T>& operator|=(Vec<N,T>& x, T y) { return (x = x | Vec<N,T>(y)); }
// ... and for shifts.
___ Vec<N,T>& operator<<=(Vec<N,T>& x, int bits) { return (x = x << bits); }
___ Vec<N,T>& operator>>=(Vec<N,T>& x, int bits) { return (x = x >> bits); }
___ Vec<N,T> ceil(Vec<N,T> x) { return map(x, [](T a) { return std:: ceil(a); }); }
___ Vec<N,T> floor(Vec<N,T> x) { return map(x, [](T a) { return std::floor(a); }); }
___ Vec<N,T> trunc(Vec<N,T> x) { return map(x, [](T a) { return std::trunc(a); }); }
___ Vec<N,T> round(Vec<N,T> x) { return map(x, [](T a) { return std::round(a); }); }
___ Vec<N,T> sqrt(Vec<N,T> x) { return map(x, [](T a) { return std:: sqrt(a); }); }
___ Vec<N,T> abs(Vec<N,T> x) { return if_then_else(x < T(0), -x, x); }
___ T min(Vec<N,T> x) { return *std::min_element(x.vals, x.vals+N); }
___ T max(Vec<N,T> x) { return *std::max_element(x.vals, x.vals+N); }
___ bool any(Vec<N,T> x) { return std::any_of(x.vals, x.vals+N, [](T a) { return a != Mask<T>(0); }); }
___ bool all(Vec<N,T> x) { return std::all_of(x.vals, x.vals+N, [](T a) { return a != Mask<T>(0); }); }
// These operations may have platform-specific results:
// - mad() may fuse the mul-add if FMA instructions are available;
// - rcp() and rsqrt() have plaform-specific precision.
___ Vec<N,T> mad(Vec<N,T> f, Vec<N,T> m, Vec<N,T> a) { return f*m+a; }
___ Vec<N,T> rcp(Vec<N,T> x) { return T(1) / x; }
___ Vec<N,T> rsqrt(Vec<N,T> x) { return rcp(sqrt(x)); }
// All vector/scalar combinations for mad().
___ Vec<N,T> mad(T f, Vec<N,T> m, Vec<N,T> a) { return Vec<N,T>(f)*m + a; }
___ Vec<N,T> mad(Vec<N,T> f, T m, Vec<N,T> a) { return f*Vec<N,T>(m) + a; }
___ Vec<N,T> mad(Vec<N,T> f, Vec<N,T> m, T a) { return f*m + Vec<N,T>(a); }
___ Vec<N,T> mad(Vec<N,T> f, T m, T a) { return f*Vec<N,T>(m) + Vec<N,T>(a); }
___ Vec<N,T> mad(T f, Vec<N,T> m, T a) { return Vec<N,T>(f)*m + Vec<N,T>(a); }
___ Vec<N,T> mad(T f, T m, Vec<N,T> a) { return Vec<N,T>(f)*Vec<N,T>(m) + a; }
// Platform-specific specializations and overloads can now drop in here.
} // namespace skvx
// These next few routines take extra template arguments that prevent
// argument-dependent lookup. They must live outside the skvx namespace,
// but since they operate only on skvx::Vec, that shouldn't be a big deal.
// cast() Vec<N,S> to Vec<N,D>, as if applying a C-cast to each lane.
template <typename D, int N, typename S>
static inline ALWAYS_INLINE skvx::Vec<N,D> cast(skvx::Vec<N,S> src) {
#if !defined(SKNX_NO_SIMD) && defined(__clang__)
return skvx::bit_pun(__builtin_convertvector(skvx::to_vext(src), skvx::VExt<N,D>));
#else
return skvx::map(src, [](S a) { return (D)a; });
#endif
}
// 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>
static inline ALWAYS_INLINE skvx::Vec<sizeof...(Ix),T> shuffle(skvx::Vec<N,T> x) {
return { x[Ix]... };
}
#undef ALWAYS_INLINE
#undef ___
#endif//SKVX_DEFINED
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