Adding upstream version 115.7.0esr.upstream/115.7.0esr

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

1 files changed, 285 insertions, 0 deletions

diff --git a/third_party/jpeg-xl/lib/jxl/convolve_separable7.cc b/third_party/jpeg-xl/lib/jxl/convolve_separable7.cc
new file mode 100644
index 0000000000..086dfd22b5
--- /dev/null
+++ b/third_party/jpeg-xl/lib/jxl/convolve_separable7.cc
@@ -0,0 +1,285 @@
+// 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_separable7.cc"
+#include <hwy/foreach_target.h>
+#include <hwy/highway.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;
+
+// 7x7 convolution by separable kernel with a single scan through the input.
+// Extended version of Separable5, see documentation there.
+class Separable7Strategy {
+  using D = HWY_CAPPED(float, 16);
+  using V = Vec<D>;
+
+ public:
+  static constexpr int64_t kRadius = 3;
+
+  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 WeightsSeparable7& 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_t3 =
+        wrap_row(row_m + 3 * neg_stride, stride);
+    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 float* const JXL_RESTRICT row_b3 =
+        wrap_row(row_m + 3 * 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 wh3 = LoadDup128(d, weights.horz + 3 * 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);
+    const V wv3 = LoadDup128(d, weights.vert + 3 * 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, wh3), wv0);
+
+      const V conv1t = HorzConvolveFirst(row_t1, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv1b = HorzConvolveFirst(row_b1, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
+
+      const V conv2t = HorzConvolveFirst(row_t2, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv2b = HorzConvolveFirst(row_b2, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
+
+      const V conv3t = HorzConvolveFirst(row_t3, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv3b = HorzConvolveFirst(row_b3, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv3 = MulAdd(Add(conv3t, conv3b), wv3, conv2);
+
+      Store(conv3, 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, wh3), wv0);
+
+      const V conv1t = HorzConvolve(row_t1 + x, wh0, wh1, wh2, wh3);
+      const V conv1b = HorzConvolve(row_b1 + x, wh0, wh1, wh2, wh3);
+      const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
+
+      const V conv2t = HorzConvolve(row_t2 + x, wh0, wh1, wh2, wh3);
+      const V conv2b = HorzConvolve(row_b2 + x, wh0, wh1, wh2, wh3);
+      const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
+
+      const V conv3t = HorzConvolve(row_t3 + x, wh0, wh1, wh2, wh3);
+      const V conv3b = HorzConvolve(row_b3 + x, wh0, wh1, wh2, wh3);
+      const V conv3 = MulAdd(Add(conv3t, conv3b), wv3, conv2);
+
+      Store(conv3, 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, wh3),
+              wv0);
+
+      const V conv1t =
+          HorzConvolveLast<kSizeModN>(row_t1, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv1b =
+          HorzConvolveLast<kSizeModN>(row_b1, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
+
+      const V conv2t =
+          HorzConvolveLast<kSizeModN>(row_t2, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv2b =
+          HorzConvolveLast<kSizeModN>(row_b2, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
+
+      const V conv3t =
+          HorzConvolveLast<kSizeModN>(row_t3, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv3b =
+          HorzConvolveLast<kSizeModN>(row_b3, x, xsize, wh0, wh1, wh2, wh3);
+      const V conv3 = MulAdd(Add(conv3t, conv3b), wv3, conv2);
+
+      Store(conv3, 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 V wh3) {
+    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));
+    const V l3 = LoadU(d, row + Mirror(x - 3, xsize));
+#else
+    (void)xsize;
+    const V l1 = Neighbors::FirstL1(c);
+    const V l2 = Neighbors::FirstL2(c);
+    const V l3 = Neighbors::FirstL3(c);
+#endif
+
+    const V r1 = LoadU(d, row + x + 1);
+    const V r2 = LoadU(d, row + x + 2);
+    const V r3 = LoadU(d, row + x + 3);
+
+    const V mul1 = MulAdd(Add(l1, r1), wh1, mul0);
+    const V mul2 = MulAdd(Add(l2, r2), wh2, mul1);
+    const V mul3 = MulAdd(Add(l3, r3), wh3, mul2);
+    return mul3;
+  }
+
+  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 V wh3) {
+    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);
+    const V l3 = LoadU(d, row + x - 3);
+
+    V r1, r2, r3;
+#if HWY_TARGET == HWY_SCALAR
+    r1 = LoadU(d, row + Mirror(x + 1, xsize));
+    r2 = LoadU(d, row + Mirror(x + 2, xsize));
+    r3 = LoadU(d, row + Mirror(x + 3, xsize));
+#else
+    const size_t N = Lanes(d);
+    if (kSizeModN == 0) {
+      r3 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 3)));
+      r2 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 2)));
+      r1 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 1)));
+    } else if (kSizeModN == 1) {
+      const auto last = LoadU(d, row + xsize - N);
+      r3 = TableLookupLanes(last, SetTableIndices(d, MirrorLanes(N - 2)));
+      r2 = TableLookupLanes(last, SetTableIndices(d, MirrorLanes(N - 1)));
+      r1 = last;
+    } else /* kSizeModN >= 2 */ {
+      const auto last = LoadU(d, row + xsize - N);
+      r3 = TableLookupLanes(last, SetTableIndices(d, MirrorLanes(N - 1)));
+      r2 = last;
+      r1 = LoadU(d, row + x + 1);
+    }
+#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);
+    const V sum3 = Add(l3, r3);
+    const V mul3 = MulAdd(sum3, wh3, mul2);
+    return mul3;
+  }
+
+  // Returns one vector of horizontal convolution results; lane i is the result
+  // for pixel pos + i. This is the fast path for interior pixels, i.e. 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 V wh3) {
+    const D d;
+    const V c = LoadU(d, pos);
+    const V mul0 = Mul(c, wh0);
+
+    // TODO(janwas): better to Combine
+    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);
+    const V l3 = LoadU(d, pos - 3);
+    const V r3 = LoadU(d, pos + 3);
+    // 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);
+    const V sum3 = Add(l3, r3);
+    const V mul3 = MulAdd(sum3, wh3, mul2);
+    return mul3;
+  }
+};
+
+void Separable7(const ImageF& in, const Rect& rect,
+                const WeightsSeparable7& weights, ThreadPool* pool,
+                ImageF* out) {
+  using Conv = ConvolveT<Separable7Strategy>;
+  if (rect.xsize() >= Conv::MinWidth()) {
+    return Conv::Run(in, rect, weights, pool, out);
+  }
+
+  return SlowSeparable7(in, rect, weights, pool, out);
+}
+
+// NOLINTNEXTLINE(google-readability-namespace-comments)
+}  // namespace HWY_NAMESPACE
+}  // namespace jxl
+HWY_AFTER_NAMESPACE();
+
+#if HWY_ONCE
+namespace jxl {
+
+HWY_EXPORT(Separable7);
+void Separable7(const ImageF& in, const Rect& rect,
+                const WeightsSeparable7& weights, ThreadPool* pool,
+                ImageF* out) {
+  return HWY_DYNAMIC_DISPATCH(Separable7)(in, rect, weights, pool, out);
+}
+
+}  // namespace jxl
+#endif  // HWY_ONCE