Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 189 insertions, 0 deletions

diff --git a/third_party/jpeg-xl/lib/jxl/convolve_symmetric5.cc b/third_party/jpeg-xl/lib/jxl/convolve_symmetric5.cc
new file mode 100644
index 0000000000..2e203fd08f
--- /dev/null
+++ b/third_party/jpeg-xl/lib/jxl/convolve_symmetric5.cc
@@ -0,0 +1,189 @@
+// 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_symmetric5.cc"
+#include <hwy/foreach_target.h>
+#include <hwy/highway.h>
+
+#include "lib/jxl/base/common.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::Vec;
+
+// Weighted sum of 1x5 pixels around ix, iy with [wx2 wx1 wx0 wx1 wx2].
+template <class WrapY>
+static float WeightedSumBorder(const ImageF& in, const WrapY wrap_y,
+                               const int64_t ix, const int64_t iy,
+                               const size_t xsize, const size_t ysize,
+                               const float wx0, const float wx1,
+                               const float wx2) {
+  const WrapMirror wrap_x;
+  const float* JXL_RESTRICT row = in.ConstRow(wrap_y(iy, ysize));
+  const float in_m2 = row[wrap_x(ix - 2, xsize)];
+  const float in_p2 = row[wrap_x(ix + 2, xsize)];
+  const float in_m1 = row[wrap_x(ix - 1, xsize)];
+  const float in_p1 = row[wrap_x(ix + 1, xsize)];
+  const float in_00 = row[ix];
+  const float sum_2 = wx2 * (in_m2 + in_p2);
+  const float sum_1 = wx1 * (in_m1 + in_p1);
+  const float sum_0 = wx0 * in_00;
+  return sum_2 + (sum_1 + sum_0);
+}
+
+template <class WrapY, class V>
+static V WeightedSum(const ImageF& in, const WrapY wrap_y, const size_t ix,
+                     const int64_t iy, const size_t ysize, const V wx0,
+                     const V wx1, const V wx2) {
+  const HWY_FULL(float) d;
+  const float* JXL_RESTRICT center = in.ConstRow(wrap_y(iy, ysize)) + ix;
+  const auto in_m2 = LoadU(d, center - 2);
+  const auto in_p2 = LoadU(d, center + 2);
+  const auto in_m1 = LoadU(d, center - 1);
+  const auto in_p1 = LoadU(d, center + 1);
+  const auto in_00 = LoadU(d, center);
+  const auto sum_2 = Mul(wx2, Add(in_m2, in_p2));
+  const auto sum_1 = Mul(wx1, Add(in_m1, in_p1));
+  const auto sum_0 = Mul(wx0, in_00);
+  return Add(sum_2, Add(sum_1, sum_0));
+}
+
+// Produces result for one pixel
+template <class WrapY>
+float Symmetric5Border(const ImageF& in, const int64_t ix, const int64_t iy,
+                       const WeightsSymmetric5& weights) {
+  const float w0 = weights.c[0];
+  const float w1 = weights.r[0];
+  const float w2 = weights.R[0];
+  const float w4 = weights.d[0];
+  const float w5 = weights.L[0];
+  const float w8 = weights.D[0];
+
+  const size_t xsize = in.xsize();
+  const size_t ysize = in.ysize();
+  const WrapY wrap_y;
+  // Unrolled loop over all 5 rows of the kernel.
+  float sum0 = WeightedSumBorder(in, wrap_y, ix, iy, xsize, ysize, w0, w1, w2);
+
+  sum0 += WeightedSumBorder(in, wrap_y, ix, iy - 2, xsize, ysize, w2, w5, w8);
+  float sum1 =
+      WeightedSumBorder(in, wrap_y, ix, iy + 2, xsize, ysize, w2, w5, w8);
+
+  sum0 += WeightedSumBorder(in, wrap_y, ix, iy - 1, xsize, ysize, w1, w4, w5);
+  sum1 += WeightedSumBorder(in, wrap_y, ix, iy + 1, xsize, ysize, w1, w4, w5);
+
+  return sum0 + sum1;
+}
+
+// Produces result for one vector's worth of pixels
+template <class WrapY>
+static void Symmetric5Interior(const ImageF& in, const int64_t ix,
+                               const int64_t rix, const int64_t iy,
+                               const WeightsSymmetric5& weights,
+                               float* JXL_RESTRICT row_out) {
+  const HWY_FULL(float) d;
+
+  const auto w0 = LoadDup128(d, weights.c);
+  const auto w1 = LoadDup128(d, weights.r);
+  const auto w2 = LoadDup128(d, weights.R);
+  const auto w4 = LoadDup128(d, weights.d);
+  const auto w5 = LoadDup128(d, weights.L);
+  const auto w8 = LoadDup128(d, weights.D);
+
+  const size_t ysize = in.ysize();
+  const WrapY wrap_y;
+  // Unrolled loop over all 5 rows of the kernel.
+  auto sum0 = WeightedSum(in, wrap_y, ix, iy, ysize, w0, w1, w2);
+
+  sum0 = Add(sum0, WeightedSum(in, wrap_y, ix, iy - 2, ysize, w2, w5, w8));
+  auto sum1 = WeightedSum(in, wrap_y, ix, iy + 2, ysize, w2, w5, w8);
+
+  sum0 = Add(sum0, WeightedSum(in, wrap_y, ix, iy - 1, ysize, w1, w4, w5));
+  sum1 = Add(sum1, WeightedSum(in, wrap_y, ix, iy + 1, ysize, w1, w4, w5));
+
+  StoreU(Add(sum0, sum1), d, row_out + rix);
+}
+
+template <class WrapY>
+static void Symmetric5Row(const ImageF& in, const Rect& rect, const int64_t iy,
+                          const WeightsSymmetric5& weights,
+                          float* JXL_RESTRICT row_out) {
+  const int64_t kRadius = 2;
+  const size_t xend = rect.x1();
+
+  size_t rix = 0;
+  size_t ix = rect.x0();
+  const HWY_FULL(float) d;
+  const size_t N = Lanes(d);
+  const size_t aligned_x = RoundUpTo(kRadius, N);
+  for (; ix < std::min(aligned_x, xend); ++ix, ++rix) {
+    row_out[rix] = Symmetric5Border<WrapY>(in, ix, iy, weights);
+  }
+  for (; ix + N + kRadius <= xend; ix += N, rix += N) {
+    Symmetric5Interior<WrapY>(in, ix, rix, iy, weights, row_out);
+  }
+  for (; ix < xend; ++ix, ++rix) {
+    row_out[rix] = Symmetric5Border<WrapY>(in, ix, iy, weights);
+  }
+}
+
+// Semi-vectorized (interior pixels Fonly); called directly like slow::, unlike
+// the fully vectorized strategies below.
+void Symmetric5(const ImageF& in, const Rect& in_rect,
+                const WeightsSymmetric5& weights, ThreadPool* pool,
+                ImageF* JXL_RESTRICT out, const Rect& out_rect) {
+  JXL_ASSERT(in_rect.xsize() == out_rect.xsize());
+  JXL_ASSERT(in_rect.ysize() == out_rect.ysize());
+  const size_t ysize = in_rect.ysize();
+  JXL_CHECK(RunOnPool(
+      pool, 0, static_cast<uint32_t>(ysize), ThreadPool::NoInit,
+      [&](const uint32_t task, size_t /*thread*/) {
+        const int64_t riy = task;
+        const int64_t iy = in_rect.y0() + riy;
+
+        if (iy < 2 || iy >= static_cast<ssize_t>(in.ysize()) - 2) {
+          Symmetric5Row<WrapMirror>(in, in_rect, iy, weights,
+                                    out_rect.Row(out, riy));
+        } else {
+          Symmetric5Row<WrapUnchanged>(in, in_rect, iy, weights,
+                                       out_rect.Row(out, riy));
+        }
+      },
+      "Symmetric5x5Convolution"));
+}
+
+// NOLINTNEXTLINE(google-readability-namespace-comments)
+}  // namespace HWY_NAMESPACE
+}  // namespace jxl
+HWY_AFTER_NAMESPACE();
+
+#if HWY_ONCE
+namespace jxl {
+
+HWY_EXPORT(Symmetric5);
+void Symmetric5(const ImageF& in, const Rect& in_rect,
+                const WeightsSymmetric5& weights, ThreadPool* pool,
+                ImageF* JXL_RESTRICT out, const Rect& out_rect) {
+  return HWY_DYNAMIC_DISPATCH(Symmetric5)(in, in_rect, weights, pool, out,
+                                          out_rect);
+}
+
+void Symmetric5(const ImageF& in, const Rect& rect,
+                const WeightsSymmetric5& weights, ThreadPool* pool,
+                ImageF* JXL_RESTRICT out) {
+  return Symmetric5(in, rect, weights, pool, out, Rect(*out));
+}
+
+}  // namespace jxl
+#endif  // HWY_ONCE