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// Copyright (c) the JPEG XL Project Authors. All rights reserved.
//
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
#include "lib/jxl/convolve.h"
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/convolve_separable5.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include "lib/jxl/base/rect.h"
#include "lib/jxl/convolve-inl.h"
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::Add;
using hwy::HWY_NAMESPACE::Mul;
using hwy::HWY_NAMESPACE::MulAdd;
using hwy::HWY_NAMESPACE::Vec;
// 5x5 convolution by separable kernel with a single scan through the input.
// This is more cache-efficient than separate horizontal/vertical passes, and
// possibly faster (given enough registers) than tiling and/or transposing.
//
// Overview: imagine a 5x5 window around a central pixel. First convolve the
// rows by multiplying the pixels with the corresponding weights from
// WeightsSeparable5.horz[abs(x_offset) * 4]. Then multiply each of these
// intermediate results by the corresponding vertical weight, i.e.
// vert[abs(y_offset) * 4]. Finally, store the sum of these values as the
// convolution result at the position of the central pixel in the output.
//
// Each of these operations uses SIMD vectors. The central pixel and most
// importantly the output are aligned, so neighnoring pixels (e.g. x_offset=1)
// require unaligned loads. Because weights are supplied in identical groups of
// 4, we can use LoadDup128 to load them (slightly faster).
//
// Uses mirrored boundary handling. Until x >= kRadius, the horizontal
// convolution uses Neighbors class to shuffle vectors as if each of its lanes
// had been loaded from the mirrored offset. Similarly, the last full vector to
// write uses mirroring. In the case of scalar vectors, Neighbors is not usable
// and the value is loaded directly. Otherwise, the number of valid pixels
// modulo the vector size enables a small optimization: for smaller offsets,
// a non-mirrored load is sufficient.
class Separable5Strategy {
using D = HWY_CAPPED(float, 16);
using V = Vec<D>;
public:
static constexpr int64_t kRadius = 2;
template <size_t kSizeModN, class WrapRow>
static JXL_MAYBE_INLINE void ConvolveRow(
const float* const JXL_RESTRICT row_m, const size_t xsize,
const int64_t stride, const WrapRow& wrap_row,
const WeightsSeparable5& weights, float* const JXL_RESTRICT row_out) {
const D d;
const int64_t neg_stride = -stride; // allows LEA addressing.
const float* const JXL_RESTRICT row_t2 =
wrap_row(row_m + 2 * neg_stride, stride);
const float* const JXL_RESTRICT row_t1 =
wrap_row(row_m + 1 * neg_stride, stride);
const float* const JXL_RESTRICT row_b1 =
wrap_row(row_m + 1 * stride, stride);
const float* const JXL_RESTRICT row_b2 =
wrap_row(row_m + 2 * stride, stride);
const V wh0 = LoadDup128(d, weights.horz + 0 * 4);
const V wh1 = LoadDup128(d, weights.horz + 1 * 4);
const V wh2 = LoadDup128(d, weights.horz + 2 * 4);
const V wv0 = LoadDup128(d, weights.vert + 0 * 4);
const V wv1 = LoadDup128(d, weights.vert + 1 * 4);
const V wv2 = LoadDup128(d, weights.vert + 2 * 4);
size_t x = 0;
// More than one iteration for scalars.
for (; x < kRadius; x += Lanes(d)) {
const V conv0 =
Mul(HorzConvolveFirst(row_m, x, xsize, wh0, wh1, wh2), wv0);
const V conv1t = HorzConvolveFirst(row_t1, x, xsize, wh0, wh1, wh2);
const V conv1b = HorzConvolveFirst(row_b1, x, xsize, wh0, wh1, wh2);
const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
const V conv2t = HorzConvolveFirst(row_t2, x, xsize, wh0, wh1, wh2);
const V conv2b = HorzConvolveFirst(row_b2, x, xsize, wh0, wh1, wh2);
const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
Store(conv2, d, row_out + x);
}
// Main loop: load inputs without padding
for (; x + Lanes(d) + kRadius <= xsize; x += Lanes(d)) {
const V conv0 = Mul(HorzConvolve(row_m + x, wh0, wh1, wh2), wv0);
const V conv1t = HorzConvolve(row_t1 + x, wh0, wh1, wh2);
const V conv1b = HorzConvolve(row_b1 + x, wh0, wh1, wh2);
const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
const V conv2t = HorzConvolve(row_t2 + x, wh0, wh1, wh2);
const V conv2b = HorzConvolve(row_b2 + x, wh0, wh1, wh2);
const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
Store(conv2, d, row_out + x);
}
// Last full vector to write (the above loop handled mod >= kRadius)
#if HWY_TARGET == HWY_SCALAR
while (x < xsize) {
#else
if (kSizeModN < kRadius) {
#endif
const V conv0 =
Mul(HorzConvolveLast<kSizeModN>(row_m, x, xsize, wh0, wh1, wh2), wv0);
const V conv1t =
HorzConvolveLast<kSizeModN>(row_t1, x, xsize, wh0, wh1, wh2);
const V conv1b =
HorzConvolveLast<kSizeModN>(row_b1, x, xsize, wh0, wh1, wh2);
const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
const V conv2t =
HorzConvolveLast<kSizeModN>(row_t2, x, xsize, wh0, wh1, wh2);
const V conv2b =
HorzConvolveLast<kSizeModN>(row_b2, x, xsize, wh0, wh1, wh2);
const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
Store(conv2, d, row_out + x);
x += Lanes(d);
}
// If mod = 0, the above vector was the last.
if (kSizeModN != 0) {
for (; x < xsize; ++x) {
float mul = 0.0f;
for (int64_t dy = -kRadius; dy <= kRadius; ++dy) {
const float wy = weights.vert[std::abs(dy) * 4];
const float* clamped_row = wrap_row(row_m + dy * stride, stride);
for (int64_t dx = -kRadius; dx <= kRadius; ++dx) {
const float wx = weights.horz[std::abs(dx) * 4];
const int64_t clamped_x = Mirror(x + dx, xsize);
mul += clamped_row[clamped_x] * wx * wy;
}
}
row_out[x] = mul;
}
}
}
private:
// Same as HorzConvolve for the first/last vector in a row.
static JXL_MAYBE_INLINE V HorzConvolveFirst(
const float* const JXL_RESTRICT row, const int64_t x, const int64_t xsize,
const V wh0, const V wh1, const V wh2) {
const D d;
const V c = LoadU(d, row + x);
const V mul0 = Mul(c, wh0);
#if HWY_TARGET == HWY_SCALAR
const V l1 = LoadU(d, row + Mirror(x - 1, xsize));
const V l2 = LoadU(d, row + Mirror(x - 2, xsize));
#else
(void)xsize;
const V l1 = Neighbors::FirstL1(c);
const V l2 = Neighbors::FirstL2(c);
#endif
const V r1 = LoadU(d, row + x + 1);
const V r2 = LoadU(d, row + x + 2);
const V mul1 = MulAdd(Add(l1, r1), wh1, mul0);
const V mul2 = MulAdd(Add(l2, r2), wh2, mul1);
return mul2;
}
template <size_t kSizeModN>
static JXL_MAYBE_INLINE V
HorzConvolveLast(const float* const JXL_RESTRICT row, const int64_t x,
const int64_t xsize, const V wh0, const V wh1, const V wh2) {
const D d;
const V c = LoadU(d, row + x);
const V mul0 = Mul(c, wh0);
const V l1 = LoadU(d, row + x - 1);
const V l2 = LoadU(d, row + x - 2);
V r1;
V r2;
#if HWY_TARGET == HWY_SCALAR
r1 = LoadU(d, row + Mirror(x + 1, xsize));
r2 = LoadU(d, row + Mirror(x + 2, xsize));
#else
const size_t N = Lanes(d);
if (kSizeModN == 0) {
r2 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 2)));
r1 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 1)));
} else { // == 1
const auto last = LoadU(d, row + xsize - N);
r2 = TableLookupLanes(last, SetTableIndices(d, MirrorLanes(N - 1)));
r1 = last;
}
#endif
// Sum of pixels with Manhattan distance i, multiplied by weights[i].
const V sum1 = Add(l1, r1);
const V mul1 = MulAdd(sum1, wh1, mul0);
const V sum2 = Add(l2, r2);
const V mul2 = MulAdd(sum2, wh2, mul1);
return mul2;
}
// Requires kRadius valid pixels before/after pos.
static JXL_MAYBE_INLINE V HorzConvolve(const float* const JXL_RESTRICT pos,
const V wh0, const V wh1,
const V wh2) {
const D d;
const V c = LoadU(d, pos);
const V mul0 = Mul(c, wh0);
// Loading anew is faster than combining vectors.
const V l1 = LoadU(d, pos - 1);
const V r1 = LoadU(d, pos + 1);
const V l2 = LoadU(d, pos - 2);
const V r2 = LoadU(d, pos + 2);
// Sum of pixels with Manhattan distance i, multiplied by weights[i].
const V sum1 = Add(l1, r1);
const V mul1 = MulAdd(sum1, wh1, mul0);
const V sum2 = Add(l2, r2);
const V mul2 = MulAdd(sum2, wh2, mul1);
return mul2;
}
};
Status Separable5(const ImageF& in, const Rect& rect,
const WeightsSeparable5& weights, ThreadPool* pool,
ImageF* out) {
using Conv = ConvolveT<Separable5Strategy>;
if (rect.xsize() >= Conv::MinWidth()) {
JXL_ENSURE(SameSize(rect, *out));
JXL_ENSURE(rect.xsize() >= Conv::MinWidth());
Conv::Run(in, rect, weights, pool, out);
return true;
}
return SlowSeparable5(in, rect, weights, pool, out, Rect(*out));
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
HWY_EXPORT(Separable5);
Status Separable5(const ImageF& in, const Rect& rect,
const WeightsSeparable5& weights, ThreadPool* pool,
ImageF* out) {
return HWY_DYNAMIC_DISPATCH(Separable5)(in, rect, weights, pool, out);
}
} // namespace jxl
#endif // HWY_ONCE