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//
//
// Copyright (C) 2022 yala <zhaojunchao@loongson.cn>;<junchao82@qq.com>. All rights reserved.
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
static void im2col_sgemm_pack4_lsx(const Mat& bottom_im2col, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
{
// Mat bottom_im2col(size, maxk, inch, 4u * 4, 4, opt.workspace_allocator);
const int size = bottom_im2col.w;
const int maxk = bottom_im2col.h;
const int inch = bottom_im2col.c;
const int outch = top_blob.c;
const float* bias = _bias;
// permute
Mat tmp;
if (size >= 12)
tmp.create(12 * maxk, inch, size / 12 + (size % 12) / 8 + (size % 12 % 8) / 4 + (size % 12 % 4) / 2 + size % 12 % 2, 4u * 4, 4, opt.workspace_allocator);
else if (size >= 8)
tmp.create(8 * maxk, inch, size / 8 + (size % 8) / 4 + (size % 4) / 2 + size % 2, 4u * 4, 4, opt.workspace_allocator);
else if (size >= 4)
tmp.create(4 * maxk, inch, size / 4 + (size % 4) / 2 + size % 2, 4u * 4, 4, opt.workspace_allocator);
else if (size >= 2)
tmp.create(2 * maxk, inch, size / 2 + size % 2, 4u * 4, 4, opt.workspace_allocator);
else
tmp.create(maxk, inch, size, 4u * 4, 4, opt.workspace_allocator);
{
int remain_size_start = 0;
int nn_size = size / 12;
#pragma omp parallel for num_threads(opt.num_threads)
for (int ii = 0; ii < nn_size; ii++)
{
int i = remain_size_start + ii * 12;
float* tmpptr = tmp.channel(i / 12);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * 4;
for (int k = 0; k < maxk; k++)
{
// transpose 4x12
__m128i _r0 = __lsx_vld(img0, 0);
__m128i _r1 = __lsx_vld(img0 + 4, 0);
__m128i _r2 = __lsx_vld(img0 + 4 * 2, 0);
__m128i _r3 = __lsx_vld(img0 + 4 * 3, 0);
__m128i _r4 = __lsx_vld(img0 + 4 * 4, 0);
__m128i _r5 = __lsx_vld(img0 + 4 * 5, 0);
__m128i _r6 = __lsx_vld(img0 + 4 * 6, 0);
__m128i _r7 = __lsx_vld(img0 + 4 * 7, 0);
__m128i _r8 = __lsx_vld(img0 + 4 * 8, 0);
__m128i _r9 = __lsx_vld(img0 + 4 * 9, 0);
__m128i _ra = __lsx_vld(img0 + 4 * 10, 0);
__m128i _rb = __lsx_vld(img0 + 4 * 11, 0);
__m128i _r01r = __lsx_vilvl_w(_r1, _r0);
__m128i _r01l = __lsx_vilvh_w(_r1, _r0);
__m128i _r23r = __lsx_vilvl_w(_r3, _r2);
__m128i _r23l = __lsx_vilvh_w(_r3, _r2);
__m128i _r45r = __lsx_vilvl_w(_r5, _r4);
__m128i _r45l = __lsx_vilvh_w(_r5, _r4);
__m128i _r67r = __lsx_vilvl_w(_r7, _r6);
__m128i _r67l = __lsx_vilvh_w(_r7, _r6);
__m128i _r89r = __lsx_vilvl_w(_r9, _r8);
__m128i _r89l = __lsx_vilvh_w(_r9, _r8);
__m128i _rabr = __lsx_vilvl_w(_rb, _ra);
__m128i _rabl = __lsx_vilvh_w(_rb, _ra);
__m128i _r0123_0 = __lsx_vilvl_d(_r23r, _r01r);
__m128i _r0123_1 = __lsx_vilvh_d(_r23r, _r01r);
__m128i _r0123_2 = __lsx_vilvl_d(_r23l, _r01l);
__m128i _r0123_3 = __lsx_vilvh_d(_r23l, _r01l);
__m128i _r4567_0 = __lsx_vilvl_d(_r67r, _r45r);
__m128i _r4567_1 = __lsx_vilvh_d(_r67r, _r45r);
__m128i _r4567_2 = __lsx_vilvl_d(_r67l, _r45l);
__m128i _r4567_3 = __lsx_vilvh_d(_r67l, _r45l);
__m128i _r89ab_0 = __lsx_vilvl_d(_rabr, _r89r);
__m128i _r89ab_1 = __lsx_vilvh_d(_rabr, _r89r);
__m128i _r89ab_2 = __lsx_vilvl_d(_rabl, _r89l);
__m128i _r89ab_3 = __lsx_vilvh_d(_rabl, _r89l);
__lsx_vst(_r0123_0, tmpptr, 0);
__lsx_vst(_r4567_0, tmpptr + 4, 0);
__lsx_vst(_r89ab_0, tmpptr + 4 * 2, 0);
__lsx_vst(_r0123_1, tmpptr + 4 * 3, 0);
__lsx_vst(_r4567_1, tmpptr + 4 * 4, 0);
__lsx_vst(_r89ab_1, tmpptr + 4 * 5, 0);
__lsx_vst(_r0123_2, tmpptr + 4 * 6, 0);
__lsx_vst(_r4567_2, tmpptr + 4 * 7, 0);
__lsx_vst(_r89ab_2, tmpptr + 4 * 8, 0);
__lsx_vst(_r0123_3, tmpptr + 4 * 9, 0);
__lsx_vst(_r4567_3, tmpptr + 4 * 10, 0);
__lsx_vst(_r89ab_3, tmpptr + 4 * 11, 0);
img0 += size * 4;
tmpptr += 48;
}
}
}
remain_size_start += nn_size * 12;
nn_size = (size - remain_size_start) >> 3;
#pragma omp parallel for num_threads(opt.num_threads)
for (int ii = 0; ii < nn_size; ii++)
{
int i = remain_size_start + ii * 8;
float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * 4;
for (int k = 0; k < maxk; k++)
{
// transpose 4x8
__m128i _r0 = __lsx_vld(img0, 0);
__m128i _r1 = __lsx_vld(img0 + 4, 0);
__m128i _r2 = __lsx_vld(img0 + 4 * 2, 0);
__m128i _r3 = __lsx_vld(img0 + 4 * 3, 0);
__m128i _r4 = __lsx_vld(img0 + 4 * 4, 0);
__m128i _r5 = __lsx_vld(img0 + 4 * 5, 0);
__m128i _r6 = __lsx_vld(img0 + 4 * 6, 0);
__m128i _r7 = __lsx_vld(img0 + 4 * 7, 0);
__m128i _r01r = __lsx_vilvl_w(_r1, _r0);
__m128i _r01l = __lsx_vilvh_w(_r1, _r0);
__m128i _r23r = __lsx_vilvl_w(_r3, _r2);
__m128i _r23l = __lsx_vilvh_w(_r3, _r2);
__m128i _r45r = __lsx_vilvl_w(_r5, _r4);
__m128i _r45l = __lsx_vilvh_w(_r5, _r4);
__m128i _r67r = __lsx_vilvl_w(_r7, _r6);
__m128i _r67l = __lsx_vilvh_w(_r7, _r6);
__m128i _r0123_0 = __lsx_vilvl_d(_r23r, _r01r);
__m128i _r0123_1 = __lsx_vilvh_d(_r23r, _r01r);
__m128i _r0123_2 = __lsx_vilvl_d(_r23l, _r01l);
__m128i _r0123_3 = __lsx_vilvh_d(_r23l, _r01l);
__m128i _r4567_0 = __lsx_vilvl_d(_r67r, _r45r);
__m128i _r4567_1 = __lsx_vilvh_d(_r67r, _r45r);
__m128i _r4567_2 = __lsx_vilvl_d(_r67l, _r45l);
__m128i _r4567_3 = __lsx_vilvh_d(_r67l, _r45l);
__lsx_vst(_r0123_0, tmpptr, 0);
__lsx_vst(_r4567_0, tmpptr + 4, 0);
__lsx_vst(_r0123_1, tmpptr + 4 * 2, 0);
__lsx_vst(_r4567_1, tmpptr + 4 * 3, 0);
__lsx_vst(_r0123_2, tmpptr + 4 * 4, 0);
__lsx_vst(_r4567_2, tmpptr + 4 * 5, 0);
__lsx_vst(_r0123_3, tmpptr + 4 * 6, 0);
__lsx_vst(_r4567_3, tmpptr + 4 * 7, 0);
img0 += size * 4;
tmpptr += 32;
}
}
}
remain_size_start += nn_size << 3;
nn_size = (size - remain_size_start) >> 2;
#pragma omp parallel for num_threads(opt.num_threads)
for (int ii = 0; ii < nn_size; ii++)
{
int i = remain_size_start + ii * 4;
float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * 4;
for (int k = 0; k < maxk; k++)
{
// transpose 4x4
__m128i _r0 = __lsx_vld(img0, 0);
__m128i _r1 = __lsx_vld(img0 + 4, 0);
__m128i _r2 = __lsx_vld(img0 + 4 * 2, 0);
__m128i _r3 = __lsx_vld(img0 + 4 * 3, 0);
__m128i _r01r = __lsx_vilvl_w(_r1, _r0);
__m128i _r01l = __lsx_vilvh_w(_r1, _r0);
__m128i _r23r = __lsx_vilvl_w(_r3, _r2);
__m128i _r23l = __lsx_vilvh_w(_r3, _r2);
__m128i _r0123_0 = __lsx_vilvl_d(_r23r, _r01r);
__m128i _r0123_1 = __lsx_vilvh_d(_r23r, _r01r);
__m128i _r0123_2 = __lsx_vilvl_d(_r23l, _r01l);
__m128i _r0123_3 = __lsx_vilvh_d(_r23l, _r01l);
__lsx_vst(_r0123_0, tmpptr, 0);
__lsx_vst(_r0123_1, tmpptr + 4, 0);
__lsx_vst(_r0123_2, tmpptr + 4 * 2, 0);
__lsx_vst(_r0123_3, tmpptr + 4 * 3, 0);
img0 += size * 4;
tmpptr += 16;
}
}
}
remain_size_start += nn_size << 2;
nn_size = (size - remain_size_start) >> 1;
#pragma omp parallel for num_threads(opt.num_threads)
for (int ii = 0; ii < nn_size; ii++)
{
int i = remain_size_start + ii * 2;
float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4 + (i % 12 % 4) / 2);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * 4;
for (int k = 0; k < maxk; k++)
{
// transpose 4x2
__m128i _r0 = __lsx_vld(img0, 0);
__m128i _r1 = __lsx_vld(img0 + 4, 0);
__m128i _r01_0 = __lsx_vilvl_w(_r1, _r0);
__m128i _r01_1 = __lsx_vilvh_w(_r1, _r0);
__lsx_vst(_r01_0, tmpptr, 0);
__lsx_vst(_r01_1, tmpptr + 4, 0);
img0 += size * 4;
tmpptr += 8;
}
}
}
remain_size_start += nn_size << 1;
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = remain_size_start; i < size; i++)
{
float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4 + (i % 12 % 4) / 2 + i % 12 % 2);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * 4;
for (int k = 0; k < maxk; k++)
{
__m128i _val = __lsx_vld(img0, 0);
__lsx_vst(_val, tmpptr, 0);
img0 += size * 4;
tmpptr += 4;
}
}
}
}
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = 0; p < outch; p++)
{
float* outptr0 = top_blob.channel(p);
int i = 0;
for (; i + 11 < size; i += 12)
{
const float* tmpptr = tmp.channel(i / 12);
const float* kptr0 = kernel.channel(p);
int nn = inch * maxk * 4; // inch always > 0
__m128 _sum0 = bias ? (__m128)__lsx_vld(bias + p * 4, 0) : (__m128)__lsx_vreplgr2vr_w(0);
__m128 _sum1 = _sum0;
__m128 _sum2 = _sum0;
__m128 _sum3 = _sum0;
__m128 _sum4 = _sum0;
__m128 _sum5 = _sum0;
__m128 _sum6 = _sum0;
__m128 _sum7 = _sum0;
__m128 _sum8 = _sum0;
__m128 _sum9 = _sum0;
__m128 _suma = _sum0;
__m128 _sumb = _sum0;
for (int j = 0; j < nn; j++)
{
__builtin_prefetch(tmpptr + 48);
__builtin_prefetch(kptr0 + 16);
__m128i _val0123 = __lsx_vld(tmpptr, 0);
__m128i _val4567 = __lsx_vld(tmpptr + 4, 0);
__m128i _val89ab = __lsx_vld(tmpptr + 8, 0);
__m128 _w0 = (__m128)__lsx_vld(kptr0, 0);
_sum0 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 0), _sum0);
_sum1 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 1), _sum1);
_sum2 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 2), _sum2);
_sum3 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 3), _sum3);
_sum4 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 0), _sum4);
_sum5 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 1), _sum5);
_sum6 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 2), _sum6);
_sum7 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 3), _sum7);
_sum8 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val89ab, 0), _sum8);
_sum9 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val89ab, 1), _sum9);
_suma = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val89ab, 2), _suma);
_sumb = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val89ab, 3), _sumb);
tmpptr += 12;
kptr0 += 4;
}
__lsx_vst(_sum0, outptr0, 0);
__lsx_vst(_sum1, outptr0 + 4, 0);
__lsx_vst(_sum2, outptr0 + 4 * 2, 0);
__lsx_vst(_sum3, outptr0 + 4 * 3, 0);
__lsx_vst(_sum4, outptr0 + 4 * 4, 0);
__lsx_vst(_sum5, outptr0 + 4 * 5, 0);
__lsx_vst(_sum6, outptr0 + 4 * 6, 0);
__lsx_vst(_sum7, outptr0 + 4 * 7, 0);
__lsx_vst(_sum8, outptr0 + 4 * 8, 0);
__lsx_vst(_sum9, outptr0 + 4 * 9, 0);
__lsx_vst(_suma, outptr0 + 4 * 10, 0);
__lsx_vst(_sumb, outptr0 + 4 * 11, 0);
outptr0 += 4 * 12;
}
for (; i + 7 < size; i += 8)
{
const float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8);
const float* kptr0 = kernel.channel(p);
int nn = inch * maxk * 4; // inch always > 0
__m128 _sum0 = bias ? (__m128)__lsx_vld(bias + p * 4, 0) : (__m128)__lsx_vreplgr2vr_w(0);
__m128 _sum1 = _sum0;
__m128 _sum2 = _sum0;
__m128 _sum3 = _sum0;
__m128 _sum4 = _sum0;
__m128 _sum5 = _sum0;
__m128 _sum6 = _sum0;
__m128 _sum7 = _sum0;
for (int j = 0; j < nn; j++)
{
__builtin_prefetch(tmpptr + 32);
__builtin_prefetch(kptr0 + 16);
__m128i _val0123 = __lsx_vld(tmpptr, 0);
__m128i _val4567 = __lsx_vld(tmpptr + 4, 0);
__m128 _w0 = (__m128)__lsx_vld(kptr0, 0);
_sum0 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 0), _sum0);
_sum1 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 1), _sum1);
_sum2 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 2), _sum2);
_sum3 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 3), _sum3);
_sum4 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 0), _sum4);
_sum5 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 1), _sum5);
_sum6 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 2), _sum6);
_sum7 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val4567, 3), _sum7);
tmpptr += 8;
kptr0 += 4;
}
__lsx_vst(_sum0, outptr0, 0);
__lsx_vst(_sum1, outptr0 + 4, 0);
__lsx_vst(_sum2, outptr0 + 4 * 2, 0);
__lsx_vst(_sum3, outptr0 + 4 * 3, 0);
__lsx_vst(_sum4, outptr0 + 4 * 4, 0);
__lsx_vst(_sum5, outptr0 + 4 * 5, 0);
__lsx_vst(_sum6, outptr0 + 4 * 6, 0);
__lsx_vst(_sum7, outptr0 + 4 * 7, 0);
outptr0 += 4 * 8;
}
for (; i + 3 < size; i += 4)
{
const float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4);
const float* kptr0 = kernel.channel(p);
int nn = inch * maxk * 4; // inch always > 0
__m128 _sum0 = bias ? (__m128)__lsx_vld(bias + p * 4, 0) : (__m128)__lsx_vreplgr2vr_w(0);
__m128 _sum1 = _sum0;
__m128 _sum2 = _sum0;
__m128 _sum3 = _sum0;
for (int j = 0; j < nn; j++)
{
__builtin_prefetch(tmpptr + 16);
__builtin_prefetch(kptr0 + 16);
__m128i _val0123 = __lsx_vld(tmpptr, 0);
__m128 _w0 = (__m128)__lsx_vld(kptr0, 0);
_sum0 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 0), _sum0);
_sum1 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 1), _sum1);
_sum2 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 2), _sum2);
_sum3 = __lsx_vfmadd_s(_w0, (__m128)__lsx_vreplvei_w(_val0123, 3), _sum3);
tmpptr += 4;
kptr0 += 4;
}
__lsx_vst(_sum0, outptr0, 0);
__lsx_vst(_sum1, outptr0 + 4, 0);
__lsx_vst(_sum2, outptr0 + 4 * 2, 0);
__lsx_vst(_sum3, outptr0 + 4 * 3, 0);
outptr0 += 4 * 4;
}
for (; i + 1 < size; i += 2)
{
const float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4 + (i % 12 % 4) / 2);
const float* kptr0 = kernel.channel(p);
int nn = inch * maxk * 4; // inch always > 0
__m128 _sum0 = bias ? (__m128)__lsx_vld(bias + p * 4, 0) : (__m128)__lsx_vreplgr2vr_w(0);
__m128 _sum1 = _sum0;
for (int j = 0; j < nn; j++)
{
__builtin_prefetch(tmpptr + 8);
__builtin_prefetch(kptr0 + 16);
__m128 _val0 = __lsx_vreplfr2vr_s(*tmpptr++);
__m128 _val1 = __lsx_vreplfr2vr_s(*tmpptr++);
__m128 _w0 = (__m128)__lsx_vld(kptr0, 0);
_sum0 = __lsx_vfmadd_s(_w0, _val0, _sum0);
_sum1 = __lsx_vfmadd_s(_w0, _val1, _sum1);
kptr0 += 4;
}
__lsx_vst(_sum0, outptr0, 0);
__lsx_vst(_sum1, outptr0 + 4, 0);
outptr0 += 4 * 2;
}
for (; i < size; i++)
{
const float* tmpptr = tmp.channel(i / 12 + (i % 12) / 8 + (i % 12 % 8) / 4 + (i % 12 % 4) / 2 + i % 12 % 2);
const float* kptr0 = kernel.channel(p);
int nn = inch * maxk * 4; // inch always > 0
__m128 _sum = bias ? (__m128)__lsx_vld(bias + p * 4, 0) : (__m128)__lsx_vreplgr2vr_w(0);
for (int j = 0; j < nn; j++)
{
__builtin_prefetch(tmpptr + 4);
__builtin_prefetch(kptr0 + 16);
__m128 _val0 = __lsx_vreplfr2vr_s(*tmpptr++);
__m128 _w0 = (__m128)__lsx_vld(kptr0, 0);
_sum = __lsx_vfmadd_s(_w0, _val0, _sum);
kptr0 += 4;
}
__lsx_vst(_sum, outptr0, 0);
outptr0 += 4;
}
}
}
static void convolution_im2col_sgemm_pack4_lsx(const Mat& bottom_blob, Mat& top_blob, const Mat& kernel, const Mat& _bias, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, const Option& opt)
{
int w = bottom_blob.w;
int inch = bottom_blob.c;
int outw = top_blob.w;
int outh = top_blob.h;
const int size = outw * outh;
const int maxk = kernel_w * kernel_h;
// im2col
Mat bottom_im2col(size, maxk, inch, 4u * 4, 4, opt.workspace_allocator);
{
const int gap = (w * stride_h - outw * stride_w) * 4;
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = 0; p < inch; p++)
{
const Mat img = bottom_blob.channel(p);
float* ptr = bottom_im2col.channel(p);
for (int u = 0; u < kernel_h; u++)
{
for (int v = 0; v < kernel_w; v++)
{
const float* sptr = img.row<const float>(dilation_h * u) + dilation_w * v * 4;
for (int i = 0; i < outh; i++)
{
int j = 0;
for (; j < outw; j++)
{
__m128 _val = (__m128)__lsx_vld(sptr, 0);
__lsx_vst(_val, ptr, 0);
sptr += stride_w * 4;
ptr += 4;
}
sptr += gap;
}
}
}
}
}
im2col_sgemm_pack4_lsx(bottom_im2col, top_blob, kernel, _bias, opt);
}
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