skia2/include/private/SkVx.h

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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 <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
restore Op::round While I think trunc(mad(x, scale, 0.5)) is fine for doing our float to fixed point conversions, round(mul(x, scale)) was kind of better all around: - better rounding than +0.5 and trunc - faster when mad() is not an fma - often now no need to use the constant 0.5f or have it in a register - allows the mul() in to_unorm to use mul_f32_imm Those last two points are key... this actually frees up 2 registers in the x86 JIT when using to_unorm(). So I think maybe we can resurrect round and still guarantee our desired intra-machine stability by committing to using instructions that follow the current rounding mode, which is what [v]cvtps2dq inextricably uses. Left some notes on the ARM impl... we're rounding to nearest even there, which is probably the current mode anyway, but to be more correct we need a slightly longer impl that rounds float->float then "truncates". Unsure whether it matters in practice. Same deal in the unit test that I added back, now testing negative and 0.5 cases too. The expectations assume the current mode is nearest even. I had the idea to resurrect this when I was looking at adding _imm Ops for fma_f32. I noticed that the y and z arguments to an fma_f32 were by far most likely to be constants, and when they are, they're by far likely to both be constants, e.g. 255.0f & 0.5f from to_unorm(8,...). llvm disassembly for SkVM_round unit test looks good: ~ $ llc -mcpu=haswell /tmp/skvm-jit-1231521224.bc -o - .section __TEXT,__text,regular,pure_instructions .macosx_version_min 10, 15 .globl "_skvm-jit-1231521224" ## -- Begin function skvm-jit-1231521224 .p2align 4, 0x90 "_skvm-jit-1231521224": ## @skvm-jit-1231521224 .cfi_startproc cmpl $8, %edi jl LBB0_3 .p2align 4, 0x90 LBB0_2: ## %loopK ## =>This Inner Loop Header: Depth=1 vcvtps2dq (%rsi), %ymm0 vmovupd %ymm0, (%rdx) addl $-8, %edi addq $32, %rsi addq $32, %rdx cmpl $8, %edi jge LBB0_2 LBB0_3: ## %hoist1 xorl %eax, %eax testl %edi, %edi jle LBB0_6 .p2align 4, 0x90 LBB0_5: ## %loop1 ## =>This Inner Loop Header: Depth=1 vcvtss2si (%rsi,%rax), %ecx movl %ecx, (%rdx,%rax) decl %edi addq $4, %rax testl %edi, %edi jg LBB0_5 LBB0_6: ## %leave vzeroupper retq .cfi_endproc ## -- End function Change-Id: Ib59eb3fd8a6805397850d93226c6c6d37cc3ab84 Reviewed-on: https://skia-review.googlesource.com/c/skia/+/276738 Auto-Submit: Mike Klein <mtklein@google.com> Commit-Queue: Herb Derby <herb@google.com> Reviewed-by: Herb Derby <herb@google.com>
2020-03-12 16:05:46 +00:00
#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
// 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 {
// All Vec have the same simple memory layout, the same as `T vec[N]`.
template <int N, typename T>
struct alignas(N*sizeof(T)) Vec {
static_assert((N & (N-1)) == 0, "N must be a power of 2.");
static_assert(sizeof(T) >= alignof(T), "What kind of crazy T is this?");
Vec<N/2,T> lo, hi;
// 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;
template <typename U, typename=std::enable_if_t<std::is_convertible<U,T>::value>>
SKVX_ALWAYS_INLINE
Vec(U x) : lo(x), hi(x) {}
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));
lo = Vec<N/2,T>::Load(vals + 0);
hi = Vec<N/2,T>::Load(vals + N/2);
}
SKVX_ALWAYS_INLINE T operator[](int i) const { return i < N/2 ? lo[i] : hi[i-N/2]; }
SKVX_ALWAYS_INLINE T& operator[](int i) { return i < N/2 ? lo[i] : 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;
template <typename U, typename=std::enable_if_t<std::is_convertible<U,T>::value>>
SKVX_ALWAYS_INLINE
Vec(U x) : val(x) {}
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 !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.
#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.
// (This header is used in C++14 contexts, so we have to kind of fake constexpr if.)
#if 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 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 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) {
#if 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) {
#if defined(__AVX2__)
if /*constexpr*/ (N*sizeof(T) == 32) {
return _mm256_testc_si256(unchecked_bit_pun<__m256i>(x),
_mm256_set1_epi32(-1));
}
#endif
#if defined(__SSE4_1__)
if /*constexpr*/ (N*sizeof(T) == 16) {
return _mm_testc_si128(unchecked_bit_pun<__m128i>(x),
_mm_set1_epi32(-1));
}
#endif
#if 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 !defined(SKNX_NO_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); }
// 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 !defined(SKNX_NO_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...);
Reland "update skvx scalar-fallback strategy" This is a reland of 4985db413d3ee27b944936c71c0cd04740cd28da ...with a better implementation of map(). I don't understand why we had to revert, but it had something with calling the function pointer in map_(), so maybe this will help. I've flattened the map_() / map() merge CL into this one, and marked the resulting map() as no_sanitize("cfi"). I don't see anything wrong, so I think it's a false positive. Original change's description: > update skvx scalar-fallback strategy > > Turns out Clang's a lot better at auto-vectorizing "obvious" scalar code > into obvious vector code when it's written out the long way, e.g. > > F32x4 x = ...; > x = { sqrtf(x[0]), sqrtf(x[1]), sqrtf(x[2]), sqrtf(x[3]) }; > > vectorizes into sqrtps a lot more reliably than our recurse-onto-scalars > strategy, and also better than the other naive approach, > > F32x4 x = ...; > for (int i = 0; i < 4; i++) { x[i] = sqrtf(x[i]); } > > So here I've added a map(V, fn) -> V' using C++14 tricks to let the > compiler handle the expansion of x = { fn(x[0]), fn(x[1]), ... > fn(x[N-1]) } for any N, and implemented most skvx scalar fallback code > using that. > > With these now vectorizing well at any N, we can remove any > specializations we'd written for particular N, really tidying up. > > Over in the SkVM interpreter, this is a big improvement for ceil and > floor, which were being done 2 floats at a time instead of 8. They're > now slimmed way down to > > shlq $6, %r13 > vroundps $K, (%r12,%r13), %ymm0 > vroundps $K, 32(%r12,%r13), %ymm1 > jmp ... > > where K is 9 or 10 depending on the op. > > I haven't found a scalar function that Clang will vectorize to vcvtps2pd > (the rounding one, not truncating vcvttps2pd), so I've kept lrint() > written the long way, updated to the style I've been using lately with > specializations inline. > > Change-Id: Ia97abe3c876008228bf62b1daacd6f6140408fc4 > Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317375 > Reviewed-by: Herb Derby <herb@google.com> > Commit-Queue: Mike Klein <mtklein@google.com> Cq-Include-Trybots: luci.chromium.try:linux_chromium_cfi_rel_ng Bug: chromium:1129408 Change-Id: Ia9c14074b9a14a67dd221f4925894d35a551f9d7 Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317551 Commit-Queue: Mike Klein <mtklein@google.com> Reviewed-by: Herb Derby <herb@google.com>
2020-09-16 19:33:37 +00:00
}
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);
}
Reland "update skvx scalar-fallback strategy" This is a reland of 4985db413d3ee27b944936c71c0cd04740cd28da ...with a better implementation of map(). I don't understand why we had to revert, but it had something with calling the function pointer in map_(), so maybe this will help. I've flattened the map_() / map() merge CL into this one, and marked the resulting map() as no_sanitize("cfi"). I don't see anything wrong, so I think it's a false positive. Original change's description: > update skvx scalar-fallback strategy > > Turns out Clang's a lot better at auto-vectorizing "obvious" scalar code > into obvious vector code when it's written out the long way, e.g. > > F32x4 x = ...; > x = { sqrtf(x[0]), sqrtf(x[1]), sqrtf(x[2]), sqrtf(x[3]) }; > > vectorizes into sqrtps a lot more reliably than our recurse-onto-scalars > strategy, and also better than the other naive approach, > > F32x4 x = ...; > for (int i = 0; i < 4; i++) { x[i] = sqrtf(x[i]); } > > So here I've added a map(V, fn) -> V' using C++14 tricks to let the > compiler handle the expansion of x = { fn(x[0]), fn(x[1]), ... > fn(x[N-1]) } for any N, and implemented most skvx scalar fallback code > using that. > > With these now vectorizing well at any N, we can remove any > specializations we'd written for particular N, really tidying up. > > Over in the SkVM interpreter, this is a big improvement for ceil and > floor, which were being done 2 floats at a time instead of 8. They're > now slimmed way down to > > shlq $6, %r13 > vroundps $K, (%r12,%r13), %ymm0 > vroundps $K, 32(%r12,%r13), %ymm1 > jmp ... > > where K is 9 or 10 depending on the op. > > I haven't found a scalar function that Clang will vectorize to vcvtps2pd > (the rounding one, not truncating vcvttps2pd), so I've kept lrint() > written the long way, updated to the style I've been using lately with > specializations inline. > > Change-Id: Ia97abe3c876008228bf62b1daacd6f6140408fc4 > Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317375 > Reviewed-by: Herb Derby <herb@google.com> > Commit-Queue: Mike Klein <mtklein@google.com> Cq-Include-Trybots: luci.chromium.try:linux_chromium_cfi_rel_ng Bug: chromium:1129408 Change-Id: Ia9c14074b9a14a67dd221f4925894d35a551f9d7 Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317551 Commit-Queue: Mike Klein <mtklein@google.com> Reviewed-by: Herb Derby <herb@google.com>
2020-09-16 19:33:37 +00:00
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 defined(__AVX__)
if /*constexpr*/ (N == 8) {
return unchecked_bit_pun<Vec<N,int>>(_mm256_cvtps_epi32(unchecked_bit_pun<__m256>(x)));
}
#endif
#if 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));
}
Reland "update skvx scalar-fallback strategy" This is a reland of 4985db413d3ee27b944936c71c0cd04740cd28da ...with a better implementation of map(). I don't understand why we had to revert, but it had something with calling the function pointer in map_(), so maybe this will help. I've flattened the map_() / map() merge CL into this one, and marked the resulting map() as no_sanitize("cfi"). I don't see anything wrong, so I think it's a false positive. Original change's description: > update skvx scalar-fallback strategy > > Turns out Clang's a lot better at auto-vectorizing "obvious" scalar code > into obvious vector code when it's written out the long way, e.g. > > F32x4 x = ...; > x = { sqrtf(x[0]), sqrtf(x[1]), sqrtf(x[2]), sqrtf(x[3]) }; > > vectorizes into sqrtps a lot more reliably than our recurse-onto-scalars > strategy, and also better than the other naive approach, > > F32x4 x = ...; > for (int i = 0; i < 4; i++) { x[i] = sqrtf(x[i]); } > > So here I've added a map(V, fn) -> V' using C++14 tricks to let the > compiler handle the expansion of x = { fn(x[0]), fn(x[1]), ... > fn(x[N-1]) } for any N, and implemented most skvx scalar fallback code > using that. > > With these now vectorizing well at any N, we can remove any > specializations we'd written for particular N, really tidying up. > > Over in the SkVM interpreter, this is a big improvement for ceil and > floor, which were being done 2 floats at a time instead of 8. They're > now slimmed way down to > > shlq $6, %r13 > vroundps $K, (%r12,%r13), %ymm0 > vroundps $K, 32(%r12,%r13), %ymm1 > jmp ... > > where K is 9 or 10 depending on the op. > > I haven't found a scalar function that Clang will vectorize to vcvtps2pd > (the rounding one, not truncating vcvttps2pd), so I've kept lrint() > written the long way, updated to the style I've been using lately with > specializations inline. > > Change-Id: Ia97abe3c876008228bf62b1daacd6f6140408fc4 > Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317375 > Reviewed-by: Herb Derby <herb@google.com> > Commit-Queue: Mike Klein <mtklein@google.com> Cq-Include-Trybots: luci.chromium.try:linux_chromium_cfi_rel_ng Bug: chromium:1129408 Change-Id: Ia9c14074b9a14a67dd221f4925894d35a551f9d7 Reviewed-on: https://skia-review.googlesource.com/c/skia/+/317551 Commit-Queue: Mike Klein <mtklein@google.com> Reviewed-by: Herb Derby <herb@google.com>
2020-09-16 19:33:37 +00:00
SIN Vec<N,float> fract(const Vec<N,float>& x) { return x - floor(x); }
// The default logic for to_half/from_half is borrowed from skcms,
// and 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_denorm = em < 0x3880'0000;
return cast<uint16_t>(if_then_else(is_denorm, Vec<N,uint32_t>(0)
, (s>>16) + (em>>13) - ((127-15)<<10)));
}
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;
auto is_denorm = bit_pun<Vec<N,int32_t>>(em < 0x0400);
return if_then_else(is_denorm, Vec<N,float>(0)
, bit_pun<Vec<N,float>>( (s<<16) + (em<<13) + ((127-15)<<23) ));
}
// 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 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 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 defined(__F16C__)
if /*constexpr*/ (N == 8) {
return unchecked_bit_pun<Vec<N,float>>(_mm256_cvtph_ps(unchecked_bit_pun<__m128i>(x)));
}
#endif
#if 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 );
}
#if !defined(SKNX_NO_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));
}
#else
// Nothing special when we don't have NEON... just cast up to 16-bit 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);
}
#endif
} // namespace skvx
#undef SINTU
#undef SINT
#undef SIT
#undef SI
#endif//SKVX_DEFINED