The Tor Browser: gfx/2d/convolver.cpp@97036ab72558 (annotated)

gfx/2d/convolver.cpp@97036ab72558 (annotated)

gfx/2d/convolver.cpp

Tue, 06 Jan 2015 21:39:09 +0100

author: Michael Schloh von Bennewitz <michael@schloh.com>
date: Tue, 06 Jan 2015 21:39:09 +0100
branch: TOR_BUG_9701
changeset 8: 97036ab72558
permissions: -rw-r--r--

Conditionally force memory storage according to privacy.thirdparty.isolate;
This solves Tor bug #9701, complying with disk avoidance documented in
https://www.torproject.org/projects/torbrowser/design/#disk-avoidance.

 // Copyright (c) 2006-2011 The Chromium Authors. All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions
 // are met:
 //  * Redistributions of source code must retain the above copyright
 //    notice, this list of conditions and the following disclaimer.
 //  * Redistributions in binary form must reproduce the above copyright
 //    notice, this list of conditions and the following disclaimer in
 //    the documentation and/or other materials provided with the
 //    distribution.
 //  * Neither the name of Google, Inc. nor the names of its contributors
 //    may be used to endorse or promote products derived from this
 //    software without specific prior written permission.
 //
 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 // FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 // COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 // INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 // BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
 // OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
 // AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 // OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
 // OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
 // SUCH DAMAGE.
 #include "convolver.h"
 #include <algorithm>
 #include "skia/SkTypes.h"
 // note: SIMD_SSE2 is not enabled because of bugs, apparently
 #if defined(SIMD_SSE2)
 #include <emmintrin.h>  // ARCH_CPU_X86_FAMILY was defined in build/config.h
 #endif
 #if defined(SK_CPU_LENDIAN)
 #define R_OFFSET_IDX 0
 #define G_OFFSET_IDX 1
 #define B_OFFSET_IDX 2
 #define A_OFFSET_IDX 3
 #else
 #define R_OFFSET_IDX 3
 #define G_OFFSET_IDX 2
 #define B_OFFSET_IDX 1
 #define A_OFFSET_IDX 0
 #endif
 namespace skia {
 namespace {
 // Converts the argument to an 8-bit unsigned value by clamping to the range
 // 0-255.
 inline unsigned char ClampTo8(int a) {
   if (static_cast<unsigned>(a) < 256)
     return a;  // Avoid the extra check in the common case.
   if (a < 0)
     return 0;
   return 255;
 }
 // Stores a list of rows in a circular buffer. The usage is you write into it
 // by calling AdvanceRow. It will keep track of which row in the buffer it
 // should use next, and the total number of rows added.
 class CircularRowBuffer {
  public:
   // The number of pixels in each row is given in |source_row_pixel_width|.
   // The maximum number of rows needed in the buffer is |max_y_filter_size|
   // (we only need to store enough rows for the biggest filter).
   //
   // We use the |first_input_row| to compute the coordinates of all of the
   // following rows returned by Advance().
   CircularRowBuffer(int dest_row_pixel_width, int max_y_filter_size,
                     int first_input_row)
       : row_byte_width_(dest_row_pixel_width * 4),
         num_rows_(max_y_filter_size),
         next_row_(0),
         next_row_coordinate_(first_input_row) {
     buffer_.resize(row_byte_width_ * max_y_filter_size);
     row_addresses_.resize(num_rows_);
   }
   // Moves to the next row in the buffer, returning a pointer to the beginning
   // of it.
   unsigned char* AdvanceRow() {
     unsigned char* row = &buffer_[next_row_ * row_byte_width_];
     next_row_coordinate_++;
     // Set the pointer to the next row to use, wrapping around if necessary.
     next_row_++;
     if (next_row_ == num_rows_)
       next_row_ = 0;
     return row;
   }
   // Returns a pointer to an "unrolled" array of rows. These rows will start
   // at the y coordinate placed into |*first_row_index| and will continue in
   // order for the maximum number of rows in this circular buffer.
   //
   // The |first_row_index_| may be negative. This means the circular buffer
   // starts before the top of the image (it hasn't been filled yet).
   unsigned char* const* GetRowAddresses(int* first_row_index) {
     // Example for a 4-element circular buffer holding coords 6-9.
     //   Row 0   Coord 8
     //   Row 1   Coord 9
     //   Row 2   Coord 6  <- next_row_ = 2, next_row_coordinate_ = 10.
     //   Row 3   Coord 7
     //
     // The "next" row is also the first (lowest) coordinate. This computation
     // may yield a negative value, but that's OK, the math will work out
     // since the user of this buffer will compute the offset relative
     // to the first_row_index and the negative rows will never be used.
     *first_row_index = next_row_coordinate_ - num_rows_;
     int cur_row = next_row_;
     for (int i = 0; i < num_rows_; i++) {
       row_addresses_[i] = &buffer_[cur_row * row_byte_width_];
       // Advance to the next row, wrapping if necessary.
       cur_row++;
       if (cur_row == num_rows_)
         cur_row = 0;
     }
     return &row_addresses_[0];
   }
  private:
   // The buffer storing the rows. They are packed, each one row_byte_width_.
   std::vector<unsigned char> buffer_;
   // Number of bytes per row in the |buffer_|.
   int row_byte_width_;
   // The number of rows available in the buffer.
   int num_rows_;
   // The next row index we should write into. This wraps around as the
   // circular buffer is used.
   int next_row_;
   // The y coordinate of the |next_row_|. This is incremented each time a
   // new row is appended and does not wrap.
   int next_row_coordinate_;
   // Buffer used by GetRowAddresses().
   std::vector<unsigned char*> row_addresses_;
 };
 // Convolves horizontally along a single row. The row data is given in
 // |src_data| and continues for the num_values() of the filter.
 template<bool has_alpha>
 // This function is miscompiled with gcc 4.5 with pgo. See bug 827946.
 #if defined(__GNUC__) && defined(MOZ_GCC_VERSION_AT_LEAST)
 #if MOZ_GCC_VERSION_AT_LEAST(4, 5, 0) && !MOZ_GCC_VERSION_AT_LEAST(4, 6, 0)
 __attribute__((optimize("-O1")))
 #endif
 #endif
 void ConvolveHorizontally(const unsigned char* src_data,
                           const ConvolutionFilter1D& filter,
                           unsigned char* out_row) {
   // Loop over each pixel on this row in the output image.
   int num_values = filter.num_values();
   for (int out_x = 0; out_x < num_values; out_x++) {
     // Get the filter that determines the current output pixel.
     int filter_offset, filter_length;
     const ConvolutionFilter1D::Fixed* filter_values =
         filter.FilterForValue(out_x, &filter_offset, &filter_length);
     // Compute the first pixel in this row that the filter affects. It will
     // touch |filter_length| pixels (4 bytes each) after this.
     const unsigned char* row_to_filter = &src_data[filter_offset * 4];
     // Apply the filter to the row to get the destination pixel in |accum|.
     int accum[4] = {0};
     for (int filter_x = 0; filter_x < filter_length; filter_x++) {
       ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_x];
       accum[0] += cur_filter * row_to_filter[filter_x * 4 + R_OFFSET_IDX];
       accum[1] += cur_filter * row_to_filter[filter_x * 4 + G_OFFSET_IDX];
       accum[2] += cur_filter * row_to_filter[filter_x * 4 + B_OFFSET_IDX];
       if (has_alpha)
         accum[3] += cur_filter * row_to_filter[filter_x * 4 + A_OFFSET_IDX];
     }
     // Bring this value back in range. All of the filter scaling factors
     // are in fixed point with kShiftBits bits of fractional part.
     accum[0] >>= ConvolutionFilter1D::kShiftBits;
     accum[1] >>= ConvolutionFilter1D::kShiftBits;
     accum[2] >>= ConvolutionFilter1D::kShiftBits;
     if (has_alpha)
       accum[3] >>= ConvolutionFilter1D::kShiftBits;
     // Store the new pixel.
     out_row[out_x * 4 + R_OFFSET_IDX] = ClampTo8(accum[0]);
     out_row[out_x * 4 + G_OFFSET_IDX] = ClampTo8(accum[1]);
     out_row[out_x * 4 + B_OFFSET_IDX] = ClampTo8(accum[2]);
     if (has_alpha)
       out_row[out_x * 4 + A_OFFSET_IDX] = ClampTo8(accum[3]);
   }
 }
 // Does vertical convolution to produce one output row. The filter values and
 // length are given in the first two parameters. These are applied to each
 // of the rows pointed to in the |source_data_rows| array, with each row
 // being |pixel_width| wide.
 //
 // The output must have room for |pixel_width * 4| bytes.
 template<bool has_alpha>
 void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values,
                         int filter_length,
                         unsigned char* const* source_data_rows,
                         int pixel_width,
                         unsigned char* out_row) {
   // We go through each column in the output and do a vertical convolution,
   // generating one output pixel each time.
   for (int out_x = 0; out_x < pixel_width; out_x++) {
     // Compute the number of bytes over in each row that the current column
     // we're convolving starts at. The pixel will cover the next 4 bytes.
     int byte_offset = out_x * 4;
     // Apply the filter to one column of pixels.
     int accum[4] = {0};
     for (int filter_y = 0; filter_y < filter_length; filter_y++) {
       ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_y];
       accum[0] += cur_filter
 	* source_data_rows[filter_y][byte_offset + R_OFFSET_IDX];
       accum[1] += cur_filter
 	* source_data_rows[filter_y][byte_offset + G_OFFSET_IDX];
       accum[2] += cur_filter
 	* source_data_rows[filter_y][byte_offset + B_OFFSET_IDX];
       if (has_alpha)
         accum[3] += cur_filter
 	  * source_data_rows[filter_y][byte_offset + A_OFFSET_IDX];
     }
     // Bring this value back in range. All of the filter scaling factors
     // are in fixed point with kShiftBits bits of precision.
     accum[0] >>= ConvolutionFilter1D::kShiftBits;
     accum[1] >>= ConvolutionFilter1D::kShiftBits;
     accum[2] >>= ConvolutionFilter1D::kShiftBits;
     if (has_alpha)
       accum[3] >>= ConvolutionFilter1D::kShiftBits;
     // Store the new pixel.
     out_row[byte_offset + R_OFFSET_IDX] = ClampTo8(accum[0]);
     out_row[byte_offset + G_OFFSET_IDX] = ClampTo8(accum[1]);
     out_row[byte_offset + B_OFFSET_IDX] = ClampTo8(accum[2]);
     if (has_alpha) {
       unsigned char alpha = ClampTo8(accum[3]);
       // Make sure the alpha channel doesn't come out smaller than any of the
       // color channels. We use premultipled alpha channels, so this should
       // never happen, but rounding errors will cause this from time to time.
       // These "impossible" colors will cause overflows (and hence random pixel
       // values) when the resulting bitmap is drawn to the screen.
       //
       // We only need to do this when generating the final output row (here).
       int max_color_channel = std::max(out_row[byte_offset + R_OFFSET_IDX],
           std::max(out_row[byte_offset + G_OFFSET_IDX], out_row[byte_offset + B_OFFSET_IDX]));
       if (alpha < max_color_channel)
         out_row[byte_offset + A_OFFSET_IDX] = max_color_channel;
       else
         out_row[byte_offset + A_OFFSET_IDX] = alpha;
     } else {
       // No alpha channel, the image is opaque.
       out_row[byte_offset + A_OFFSET_IDX] = 0xff;
     }
   }
 }
 // Convolves horizontally along a single row. The row data is given in
 // |src_data| and continues for the num_values() of the filter.
 void ConvolveHorizontally_SSE2(const unsigned char* src_data,
                                const ConvolutionFilter1D& filter,
                                unsigned char* out_row) {
 #if defined(SIMD_SSE2)
   int num_values = filter.num_values();
   int filter_offset, filter_length;
   __m128i zero = _mm_setzero_si128();
   __m128i mask[4];
   // |mask| will be used to decimate all extra filter coefficients that are
   // loaded by SIMD when |filter_length| is not divisible by 4.
   // mask[0] is not used in following algorithm.
   mask[1] = _mm_set_epi16(0, 0, 0, 0, 0, 0, 0, -1);
   mask[2] = _mm_set_epi16(0, 0, 0, 0, 0, 0, -1, -1);
   mask[3] = _mm_set_epi16(0, 0, 0, 0, 0, -1, -1, -1);
   // Output one pixel each iteration, calculating all channels (RGBA) together.
   for (int out_x = 0; out_x < num_values; out_x++) {
     const ConvolutionFilter1D::Fixed* filter_values =
         filter.FilterForValue(out_x, &filter_offset, &filter_length);
     __m128i accum = _mm_setzero_si128();
     // Compute the first pixel in this row that the filter affects. It will
     // touch |filter_length| pixels (4 bytes each) after this.
     const __m128i* row_to_filter =
         reinterpret_cast<const __m128i*>(&src_data[filter_offset << 2]);
     // We will load and accumulate with four coefficients per iteration.
     for (int filter_x = 0; filter_x < filter_length >> 2; filter_x++) {
       // Load 4 coefficients => duplicate 1st and 2nd of them for all channels.
       __m128i coeff, coeff16;
       // [16] xx xx xx xx c3 c2 c1 c0
       coeff = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(filter_values));
       // [16] xx xx xx xx c1 c1 c0 c0
       coeff16 = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(1, 1, 0, 0));
       // [16] c1 c1 c1 c1 c0 c0 c0 c0
       coeff16 = _mm_unpacklo_epi16(coeff16, coeff16);
       // Load four pixels => unpack the first two pixels to 16 bits =>
       // multiply with coefficients => accumulate the convolution result.
       // [8] a3 b3 g3 r3 a2 b2 g2 r2 a1 b1 g1 r1 a0 b0 g0 r0
       __m128i src8 = _mm_loadu_si128(row_to_filter);
       // [16] a1 b1 g1 r1 a0 b0 g0 r0
       __m128i src16 = _mm_unpacklo_epi8(src8, zero);
       __m128i mul_hi = _mm_mulhi_epi16(src16, coeff16);
       __m128i mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32]  a0*c0 b0*c0 g0*c0 r0*c0
       __m128i t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       // [32]  a1*c1 b1*c1 g1*c1 r1*c1
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       // Duplicate 3rd and 4th coefficients for all channels =>
       // unpack the 3rd and 4th pixels to 16 bits => multiply with coefficients
       // => accumulate the convolution results.
       // [16] xx xx xx xx c3 c3 c2 c2
       coeff16 = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(3, 3, 2, 2));
       // [16] c3 c3 c3 c3 c2 c2 c2 c2
       coeff16 = _mm_unpacklo_epi16(coeff16, coeff16);
       // [16] a3 g3 b3 r3 a2 g2 b2 r2
       src16 = _mm_unpackhi_epi8(src8, zero);
       mul_hi = _mm_mulhi_epi16(src16, coeff16);
       mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32]  a2*c2 b2*c2 g2*c2 r2*c2
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       // [32]  a3*c3 b3*c3 g3*c3 r3*c3
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       // Advance the pixel and coefficients pointers.
       row_to_filter += 1;
       filter_values += 4;
     }
     // When |filter_length| is not divisible by 4, we need to decimate some of
     // the filter coefficient that was loaded incorrectly to zero; Other than
     // that the algorithm is same with above, exceot that the 4th pixel will be
     // always absent.
     int r = filter_length&3;
     if (r) {
       // Note: filter_values must be padded to align_up(filter_offset, 8).
       __m128i coeff, coeff16;
       coeff = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(filter_values));
       // Mask out extra filter taps.
       coeff = _mm_and_si128(coeff, mask[r]);
       coeff16 = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(1, 1, 0, 0));
       coeff16 = _mm_unpacklo_epi16(coeff16, coeff16);
       // Note: line buffer must be padded to align_up(filter_offset, 16).
       // We resolve this by use C-version for the last horizontal line.
       __m128i src8 = _mm_loadu_si128(row_to_filter);
       __m128i src16 = _mm_unpacklo_epi8(src8, zero);
       __m128i mul_hi = _mm_mulhi_epi16(src16, coeff16);
       __m128i mul_lo = _mm_mullo_epi16(src16, coeff16);
       __m128i t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
       src16 = _mm_unpackhi_epi8(src8, zero);
       coeff16 = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(3, 3, 2, 2));
       coeff16 = _mm_unpacklo_epi16(coeff16, coeff16);
       mul_hi = _mm_mulhi_epi16(src16, coeff16);
       mul_lo = _mm_mullo_epi16(src16, coeff16);
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum = _mm_add_epi32(accum, t);
     }
     // Shift right for fixed point implementation.
     accum = _mm_srai_epi32(accum, ConvolutionFilter1D::kShiftBits);
     // Packing 32 bits |accum| to 16 bits per channel (signed saturation).
     accum = _mm_packs_epi32(accum, zero);
     // Packing 16 bits |accum| to 8 bits per channel (unsigned saturation).
     accum = _mm_packus_epi16(accum, zero);
     // Store the pixel value of 32 bits.
     *(reinterpret_cast<int*>(out_row)) = _mm_cvtsi128_si32(accum);
     out_row += 4;
   }
 #endif
 }
 // Convolves horizontally along four rows. The row data is given in
 // |src_data| and continues for the num_values() of the filter.
 // The algorithm is almost same as |ConvolveHorizontally_SSE2|. Please
 // refer to that function for detailed comments.
 void ConvolveHorizontally4_SSE2(const unsigned char* src_data[4],
                                 const ConvolutionFilter1D& filter,
                                 unsigned char* out_row[4]) {
 #if defined(SIMD_SSE2)
   int num_values = filter.num_values();
   int filter_offset, filter_length;
   __m128i zero = _mm_setzero_si128();
   __m128i mask[4];
   // |mask| will be used to decimate all extra filter coefficients that are
   // loaded by SIMD when |filter_length| is not divisible by 4.
   // mask[0] is not used in following algorithm.
   mask[1] = _mm_set_epi16(0, 0, 0, 0, 0, 0, 0, -1);
   mask[2] = _mm_set_epi16(0, 0, 0, 0, 0, 0, -1, -1);
   mask[3] = _mm_set_epi16(0, 0, 0, 0, 0, -1, -1, -1);
   // Output one pixel each iteration, calculating all channels (RGBA) together.
   for (int out_x = 0; out_x < num_values; out_x++) {
     const ConvolutionFilter1D::Fixed* filter_values =
         filter.FilterForValue(out_x, &filter_offset, &filter_length);
     // four pixels in a column per iteration.
     __m128i accum0 = _mm_setzero_si128();
     __m128i accum1 = _mm_setzero_si128();
     __m128i accum2 = _mm_setzero_si128();
     __m128i accum3 = _mm_setzero_si128();
     int start = (filter_offset<<2);
     // We will load and accumulate with four coefficients per iteration.
     for (int filter_x = 0; filter_x < (filter_length >> 2); filter_x++) {
       __m128i coeff, coeff16lo, coeff16hi;
       // [16] xx xx xx xx c3 c2 c1 c0
       coeff = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(filter_values));
       // [16] xx xx xx xx c1 c1 c0 c0
       coeff16lo = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(1, 1, 0, 0));
       // [16] c1 c1 c1 c1 c0 c0 c0 c0
       coeff16lo = _mm_unpacklo_epi16(coeff16lo, coeff16lo);
       // [16] xx xx xx xx c3 c3 c2 c2
       coeff16hi = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(3, 3, 2, 2));
       // [16] c3 c3 c3 c3 c2 c2 c2 c2
       coeff16hi = _mm_unpacklo_epi16(coeff16hi, coeff16hi);
       __m128i src8, src16, mul_hi, mul_lo, t;
 #define ITERATION(src, accum)                                          \
       src8 = _mm_loadu_si128(reinterpret_cast<const __m128i*>(src));   \
       src16 = _mm_unpacklo_epi8(src8, zero);                           \
       mul_hi = _mm_mulhi_epi16(src16, coeff16lo);                      \
       mul_lo = _mm_mullo_epi16(src16, coeff16lo);                      \
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);                          \
       accum = _mm_add_epi32(accum, t);                                 \
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);                          \
       accum = _mm_add_epi32(accum, t);                                 \
       src16 = _mm_unpackhi_epi8(src8, zero);                           \
       mul_hi = _mm_mulhi_epi16(src16, coeff16hi);                      \
       mul_lo = _mm_mullo_epi16(src16, coeff16hi);                      \
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);                          \
       accum = _mm_add_epi32(accum, t);                                 \
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);                          \
       accum = _mm_add_epi32(accum, t)
       ITERATION(src_data[0] + start, accum0);
       ITERATION(src_data[1] + start, accum1);
       ITERATION(src_data[2] + start, accum2);
       ITERATION(src_data[3] + start, accum3);
       start += 16;
       filter_values += 4;
     }
     int r = filter_length & 3;
     if (r) {
       // Note: filter_values must be padded to align_up(filter_offset, 8);
       __m128i coeff;
       coeff = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(filter_values));
       // Mask out extra filter taps.
       coeff = _mm_and_si128(coeff, mask[r]);
       __m128i coeff16lo = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(1, 1, 0, 0));
       /* c1 c1 c1 c1 c0 c0 c0 c0 */
       coeff16lo = _mm_unpacklo_epi16(coeff16lo, coeff16lo);
       __m128i coeff16hi = _mm_shufflelo_epi16(coeff, _MM_SHUFFLE(3, 3, 2, 2));
       coeff16hi = _mm_unpacklo_epi16(coeff16hi, coeff16hi);
       __m128i src8, src16, mul_hi, mul_lo, t;
       ITERATION(src_data[0] + start, accum0);
       ITERATION(src_data[1] + start, accum1);
       ITERATION(src_data[2] + start, accum2);
       ITERATION(src_data[3] + start, accum3);
     }
     accum0 = _mm_srai_epi32(accum0, ConvolutionFilter1D::kShiftBits);
     accum0 = _mm_packs_epi32(accum0, zero);
     accum0 = _mm_packus_epi16(accum0, zero);
     accum1 = _mm_srai_epi32(accum1, ConvolutionFilter1D::kShiftBits);
     accum1 = _mm_packs_epi32(accum1, zero);
     accum1 = _mm_packus_epi16(accum1, zero);
     accum2 = _mm_srai_epi32(accum2, ConvolutionFilter1D::kShiftBits);
     accum2 = _mm_packs_epi32(accum2, zero);
     accum2 = _mm_packus_epi16(accum2, zero);
     accum3 = _mm_srai_epi32(accum3, ConvolutionFilter1D::kShiftBits);
     accum3 = _mm_packs_epi32(accum3, zero);
     accum3 = _mm_packus_epi16(accum3, zero);
     *(reinterpret_cast<int*>(out_row[0])) = _mm_cvtsi128_si32(accum0);
     *(reinterpret_cast<int*>(out_row[1])) = _mm_cvtsi128_si32(accum1);
     *(reinterpret_cast<int*>(out_row[2])) = _mm_cvtsi128_si32(accum2);
     *(reinterpret_cast<int*>(out_row[3])) = _mm_cvtsi128_si32(accum3);
     out_row[0] += 4;
     out_row[1] += 4;
     out_row[2] += 4;
     out_row[3] += 4;
   }
 #endif
 }
 // Does vertical convolution to produce one output row. The filter values and
 // length are given in the first two parameters. These are applied to each
 // of the rows pointed to in the |source_data_rows| array, with each row
 // being |pixel_width| wide.
 //
 // The output must have room for |pixel_width * 4| bytes.
 template<bool has_alpha>
 void ConvolveVertically_SSE2(const ConvolutionFilter1D::Fixed* filter_values,
                              int filter_length,
                              unsigned char* const* source_data_rows,
                              int pixel_width,
                              unsigned char* out_row) {
 #if defined(SIMD_SSE2)
   int width = pixel_width & ~3;
   __m128i zero = _mm_setzero_si128();
   __m128i accum0, accum1, accum2, accum3, coeff16;
   const __m128i* src;
   // Output four pixels per iteration (16 bytes).
   for (int out_x = 0; out_x < width; out_x += 4) {
     // Accumulated result for each pixel. 32 bits per RGBA channel.
     accum0 = _mm_setzero_si128();
     accum1 = _mm_setzero_si128();
     accum2 = _mm_setzero_si128();
     accum3 = _mm_setzero_si128();
     // Convolve with one filter coefficient per iteration.
     for (int filter_y = 0; filter_y < filter_length; filter_y++) {
       // Duplicate the filter coefficient 8 times.
       // [16] cj cj cj cj cj cj cj cj
       coeff16 = _mm_set1_epi16(filter_values[filter_y]);
       // Load four pixels (16 bytes) together.
       // [8] a3 b3 g3 r3 a2 b2 g2 r2 a1 b1 g1 r1 a0 b0 g0 r0
       src = reinterpret_cast<const __m128i*>(
           &source_data_rows[filter_y][out_x << 2]);
       __m128i src8 = _mm_loadu_si128(src);
       // Unpack 1st and 2nd pixels from 8 bits to 16 bits for each channels =>
       // multiply with current coefficient => accumulate the result.
       // [16] a1 b1 g1 r1 a0 b0 g0 r0
       __m128i src16 = _mm_unpacklo_epi8(src8, zero);
       __m128i mul_hi = _mm_mulhi_epi16(src16, coeff16);
       __m128i mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32] a0 b0 g0 r0
       __m128i t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum0 = _mm_add_epi32(accum0, t);
       // [32] a1 b1 g1 r1
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum1 = _mm_add_epi32(accum1, t);
       // Unpack 3rd and 4th pixels from 8 bits to 16 bits for each channels =>
       // multiply with current coefficient => accumulate the result.
       // [16] a3 b3 g3 r3 a2 b2 g2 r2
       src16 = _mm_unpackhi_epi8(src8, zero);
       mul_hi = _mm_mulhi_epi16(src16, coeff16);
       mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32] a2 b2 g2 r2
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum2 = _mm_add_epi32(accum2, t);
       // [32] a3 b3 g3 r3
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum3 = _mm_add_epi32(accum3, t);
     }
     // Shift right for fixed point implementation.
     accum0 = _mm_srai_epi32(accum0, ConvolutionFilter1D::kShiftBits);
     accum1 = _mm_srai_epi32(accum1, ConvolutionFilter1D::kShiftBits);
     accum2 = _mm_srai_epi32(accum2, ConvolutionFilter1D::kShiftBits);
     accum3 = _mm_srai_epi32(accum3, ConvolutionFilter1D::kShiftBits);
     // Packing 32 bits |accum| to 16 bits per channel (signed saturation).
     // [16] a1 b1 g1 r1 a0 b0 g0 r0
     accum0 = _mm_packs_epi32(accum0, accum1);
     // [16] a3 b3 g3 r3 a2 b2 g2 r2
     accum2 = _mm_packs_epi32(accum2, accum3);
     // Packing 16 bits |accum| to 8 bits per channel (unsigned saturation).
     // [8] a3 b3 g3 r3 a2 b2 g2 r2 a1 b1 g1 r1 a0 b0 g0 r0
     accum0 = _mm_packus_epi16(accum0, accum2);
     if (has_alpha) {
       // Compute the max(ri, gi, bi) for each pixel.
       // [8] xx a3 b3 g3 xx a2 b2 g2 xx a1 b1 g1 xx a0 b0 g0
       __m128i a = _mm_srli_epi32(accum0, 8);
       // [8] xx xx xx max3 xx xx xx max2 xx xx xx max1 xx xx xx max0
       __m128i b = _mm_max_epu8(a, accum0);  // Max of r and g.
       // [8] xx xx a3 b3 xx xx a2 b2 xx xx a1 b1 xx xx a0 b0
       a = _mm_srli_epi32(accum0, 16);
       // [8] xx xx xx max3 xx xx xx max2 xx xx xx max1 xx xx xx max0
       b = _mm_max_epu8(a, b);  // Max of r and g and b.
       // [8] max3 00 00 00 max2 00 00 00 max1 00 00 00 max0 00 00 00
       b = _mm_slli_epi32(b, 24);
       // Make sure the value of alpha channel is always larger than maximum
       // value of color channels.
       accum0 = _mm_max_epu8(b, accum0);
     } else {
       // Set value of alpha channels to 0xFF.
       __m128i mask = _mm_set1_epi32(0xff000000);
       accum0 = _mm_or_si128(accum0, mask);
     }
     // Store the convolution result (16 bytes) and advance the pixel pointers.
     _mm_storeu_si128(reinterpret_cast<__m128i*>(out_row), accum0);
     out_row += 16;
   }
   // When the width of the output is not divisible by 4, We need to save one
   // pixel (4 bytes) each time. And also the fourth pixel is always absent.
   if (pixel_width & 3) {
     accum0 = _mm_setzero_si128();
     accum1 = _mm_setzero_si128();
     accum2 = _mm_setzero_si128();
     for (int filter_y = 0; filter_y < filter_length; ++filter_y) {
       coeff16 = _mm_set1_epi16(filter_values[filter_y]);
       // [8] a3 b3 g3 r3 a2 b2 g2 r2 a1 b1 g1 r1 a0 b0 g0 r0
       src = reinterpret_cast<const __m128i*>(
           &source_data_rows[filter_y][width<<2]);
       __m128i src8 = _mm_loadu_si128(src);
       // [16] a1 b1 g1 r1 a0 b0 g0 r0
       __m128i src16 = _mm_unpacklo_epi8(src8, zero);
       __m128i mul_hi = _mm_mulhi_epi16(src16, coeff16);
       __m128i mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32] a0 b0 g0 r0
       __m128i t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum0 = _mm_add_epi32(accum0, t);
       // [32] a1 b1 g1 r1
       t = _mm_unpackhi_epi16(mul_lo, mul_hi);
       accum1 = _mm_add_epi32(accum1, t);
       // [16] a3 b3 g3 r3 a2 b2 g2 r2
       src16 = _mm_unpackhi_epi8(src8, zero);
       mul_hi = _mm_mulhi_epi16(src16, coeff16);
       mul_lo = _mm_mullo_epi16(src16, coeff16);
       // [32] a2 b2 g2 r2
       t = _mm_unpacklo_epi16(mul_lo, mul_hi);
       accum2 = _mm_add_epi32(accum2, t);
     }
     accum0 = _mm_srai_epi32(accum0, ConvolutionFilter1D::kShiftBits);
     accum1 = _mm_srai_epi32(accum1, ConvolutionFilter1D::kShiftBits);
     accum2 = _mm_srai_epi32(accum2, ConvolutionFilter1D::kShiftBits);
     // [16] a1 b1 g1 r1 a0 b0 g0 r0
     accum0 = _mm_packs_epi32(accum0, accum1);
     // [16] a3 b3 g3 r3 a2 b2 g2 r2
     accum2 = _mm_packs_epi32(accum2, zero);
     // [8] a3 b3 g3 r3 a2 b2 g2 r2 a1 b1 g1 r1 a0 b0 g0 r0
     accum0 = _mm_packus_epi16(accum0, accum2);
     if (has_alpha) {
       // [8] xx a3 b3 g3 xx a2 b2 g2 xx a1 b1 g1 xx a0 b0 g0
       __m128i a = _mm_srli_epi32(accum0, 8);
       // [8] xx xx xx max3 xx xx xx max2 xx xx xx max1 xx xx xx max0
       __m128i b = _mm_max_epu8(a, accum0);  // Max of r and g.
       // [8] xx xx a3 b3 xx xx a2 b2 xx xx a1 b1 xx xx a0 b0
       a = _mm_srli_epi32(accum0, 16);
       // [8] xx xx xx max3 xx xx xx max2 xx xx xx max1 xx xx xx max0
       b = _mm_max_epu8(a, b);  // Max of r and g and b.
       // [8] max3 00 00 00 max2 00 00 00 max1 00 00 00 max0 00 00 00
       b = _mm_slli_epi32(b, 24);
       accum0 = _mm_max_epu8(b, accum0);
     } else {
       __m128i mask = _mm_set1_epi32(0xff000000);
       accum0 = _mm_or_si128(accum0, mask);
     }
     for (int out_x = width; out_x < pixel_width; out_x++) {
       *(reinterpret_cast<int*>(out_row)) = _mm_cvtsi128_si32(accum0);
       accum0 = _mm_srli_si128(accum0, 4);
       out_row += 4;
     }
   }
 #endif
 }
 }  // namespace
 // ConvolutionFilter1D ---------------------------------------------------------
 ConvolutionFilter1D::ConvolutionFilter1D()
     : max_filter_(0) {
 }
 ConvolutionFilter1D::~ConvolutionFilter1D() {
 }
 void ConvolutionFilter1D::AddFilter(int filter_offset,
                                     const float* filter_values,
                                     int filter_length) {
   SkASSERT(filter_length > 0);
   std::vector<Fixed> fixed_values;
   fixed_values.reserve(filter_length);
   for (int i = 0; i < filter_length; ++i)
     fixed_values.push_back(FloatToFixed(filter_values[i]));
   AddFilter(filter_offset, &fixed_values[0], filter_length);
 }
 void ConvolutionFilter1D::AddFilter(int filter_offset,
                                     const Fixed* filter_values,
                                     int filter_length) {
   // It is common for leading/trailing filter values to be zeros. In such
   // cases it is beneficial to only store the central factors.
   // For a scaling to 1/4th in each dimension using a Lanczos-2 filter on
   // a 1080p image this optimization gives a ~10% speed improvement.
   int first_non_zero = 0;
   while (first_non_zero < filter_length && filter_values[first_non_zero] == 0)
     first_non_zero++;
   if (first_non_zero < filter_length) {
     // Here we have at least one non-zero factor.
     int last_non_zero = filter_length - 1;
     while (last_non_zero >= 0 && filter_values[last_non_zero] == 0)
       last_non_zero--;
     filter_offset += first_non_zero;
     filter_length = last_non_zero + 1 - first_non_zero;
     SkASSERT(filter_length > 0);
     for (int i = first_non_zero; i <= last_non_zero; i++)
       filter_values_.push_back(filter_values[i]);
   } else {
     // Here all the factors were zeroes.
     filter_length = 0;
   }
   FilterInstance instance;
   // We pushed filter_length elements onto filter_values_
   instance.data_location = (static_cast<int>(filter_values_.size()) -
                             filter_length);
   instance.offset = filter_offset;
   instance.length = filter_length;
   filters_.push_back(instance);
   max_filter_ = std::max(max_filter_, filter_length);
 }
 void BGRAConvolve2D(const unsigned char* source_data,
                     int source_byte_row_stride,
                     bool source_has_alpha,
                     const ConvolutionFilter1D& filter_x,
                     const ConvolutionFilter1D& filter_y,
                     int output_byte_row_stride,
                     unsigned char* output,
                     bool use_sse2) {
 #if !defined(SIMD_SSE2)
   // Even we have runtime support for SSE2 instructions, since the binary
   // was not built with SSE2 support, we had to fallback to C version.
   use_sse2 = false;
 #endif
   int max_y_filter_size = filter_y.max_filter();
   // The next row in the input that we will generate a horizontally
   // convolved row for. If the filter doesn't start at the beginning of the
   // image (this is the case when we are only resizing a subset), then we
   // don't want to generate any output rows before that. Compute the starting
   // row for convolution as the first pixel for the first vertical filter.
   int filter_offset, filter_length;
   const ConvolutionFilter1D::Fixed* filter_values =
       filter_y.FilterForValue(0, &filter_offset, &filter_length);
   int next_x_row = filter_offset;
   // We loop over each row in the input doing a horizontal convolution. This
   // will result in a horizontally convolved image. We write the results into
   // a circular buffer of convolved rows and do vertical convolution as rows
   // are available. This prevents us from having to store the entire
   // intermediate image and helps cache coherency.
   // We will need four extra rows to allow horizontal convolution could be done
   // simultaneously. We also padding each row in row buffer to be aligned-up to
   // 16 bytes.
   // TODO(jiesun): We do not use aligned load from row buffer in vertical
   // convolution pass yet. Somehow Windows does not like it.
   int row_buffer_width = (filter_x.num_values() + 15) & ~0xF;
   int row_buffer_height = max_y_filter_size + (use_sse2 ? 4 : 0);
   CircularRowBuffer row_buffer(row_buffer_width,
                                row_buffer_height,
                                filter_offset);
   // Loop over every possible output row, processing just enough horizontal
   // convolutions to run each subsequent vertical convolution.
   SkASSERT(output_byte_row_stride >= filter_x.num_values() * 4);
   int num_output_rows = filter_y.num_values();
   // We need to check which is the last line to convolve before we advance 4
   // lines in one iteration.
   int last_filter_offset, last_filter_length;
   filter_y.FilterForValue(num_output_rows - 1, &last_filter_offset,
                           &last_filter_length);
   for (int out_y = 0; out_y < num_output_rows; out_y++) {
     filter_values = filter_y.FilterForValue(out_y,
                                             &filter_offset, &filter_length);
     // Generate output rows until we have enough to run the current filter.
     if (use_sse2) {
       while (next_x_row < filter_offset + filter_length) {
         if (next_x_row + 3 < last_filter_offset + last_filter_length - 1) {
           const unsigned char* src[4];
           unsigned char* out_row[4];
           for (int i = 0; i < 4; ++i) {
             src[i] = &source_data[(next_x_row + i) * source_byte_row_stride];
             out_row[i] = row_buffer.AdvanceRow();
           }
           ConvolveHorizontally4_SSE2(src, filter_x, out_row);
           next_x_row += 4;
         } else {
           // For the last row, SSE2 load possibly to access data beyond the
           // image area. therefore we use C version here.
           if (next_x_row == last_filter_offset + last_filter_length - 1) {
             if (source_has_alpha) {
               ConvolveHorizontally<true>(
                   &source_data[next_x_row * source_byte_row_stride],
                   filter_x, row_buffer.AdvanceRow());
             } else {
               ConvolveHorizontally<false>(
                   &source_data[next_x_row * source_byte_row_stride],
                   filter_x, row_buffer.AdvanceRow());
             }
           } else {
             ConvolveHorizontally_SSE2(
                 &source_data[next_x_row * source_byte_row_stride],
                 filter_x, row_buffer.AdvanceRow());
           }
           next_x_row++;
         }
       }
     } else {
       while (next_x_row < filter_offset + filter_length) {
         if (source_has_alpha) {
           ConvolveHorizontally<true>(
               &source_data[next_x_row * source_byte_row_stride],
               filter_x, row_buffer.AdvanceRow());
         } else {
           ConvolveHorizontally<false>(
               &source_data[next_x_row * source_byte_row_stride],
               filter_x, row_buffer.AdvanceRow());
         }
         next_x_row++;
       }
     }
     // Compute where in the output image this row of final data will go.
     unsigned char* cur_output_row = &output[out_y * output_byte_row_stride];
     // Get the list of rows that the circular buffer has, in order.
     int first_row_in_circular_buffer;
     unsigned char* const* rows_to_convolve =
         row_buffer.GetRowAddresses(&first_row_in_circular_buffer);
     // Now compute the start of the subset of those rows that the filter
     // needs.
     unsigned char* const* first_row_for_filter =
         &rows_to_convolve[filter_offset - first_row_in_circular_buffer];
     if (source_has_alpha) {
       if (use_sse2) {
         ConvolveVertically_SSE2<true>(filter_values, filter_length,
                                       first_row_for_filter,
                                       filter_x.num_values(), cur_output_row);
       } else {
         ConvolveVertically<true>(filter_values, filter_length,
                                  first_row_for_filter,
                                  filter_x.num_values(), cur_output_row);
       }
     } else {
       if (use_sse2) {
         ConvolveVertically_SSE2<false>(filter_values, filter_length,
                                        first_row_for_filter,
                                        filter_x.num_values(), cur_output_row);
       } else {
         ConvolveVertically<false>(filter_values, filter_length,
                                  first_row_for_filter,
                                  filter_x.num_values(), cur_output_row);
       }
     }
   }
 }
 }  // namespace skia

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gfx/2d/convolver.cpp@97036ab72558 (annotated)

gfx/2d/convolver.cpp