tor-browser

convolve_separable5.cc (9723B)

---

// 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