ncnn / src /layer /loongarch /convolution_winograd_dot_int8.h
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// yala is pleased to support the open source community by making ncnn available.
//
//
// 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 convolution_winograd_dot_int8_lsx(Mat& bottom_blob_tm, int outch, const Mat& kernel_tm, Mat& top_blob_tm, const Option& opt)
{
// Mat bottom_blob_tm(tiles, 16/36/64, inch, 2u, 1, opt.workspace_allocator);
const int tiles = bottom_blob_tm.w;
const int batch = bottom_blob_tm.h;
const int inch = bottom_blob_tm.c;
// permute
Mat bottom_blob_tm2;
#if __loongarch_sx
if (inch >= 4)
{
if (tiles >= 2)
bottom_blob_tm2.create(inch / 4 + inch % 4, tiles / 2 + tiles % 2, batch, 16u, 8, opt.workspace_allocator);
else // if (tiles >= 1)
bottom_blob_tm2.create(inch / 4 + inch % 4, tiles, batch, 8u, 4, opt.workspace_allocator);
}
else
#endif // __loongarch_sx
{
if (tiles >= 2)
bottom_blob_tm2.create(inch, tiles / 2 + tiles % 2, batch, 4u, 2, opt.workspace_allocator);
else // if (tiles >= 1)
bottom_blob_tm2.create(inch, tiles, batch, 2u, 1, opt.workspace_allocator);
}
#pragma omp parallel for num_threads(opt.num_threads)
for (int r = 0; r < batch; r++)
{
Mat tm2 = bottom_blob_tm2.channel(r);
// tile
int i = 0;
for (; i + 1 < tiles; i += 2)
{
short* tmpptr = tm2.row<short>(i / 2);
const short* r0 = (const short*)bottom_blob_tm + r * tiles + i;
int q = 0;
#if __loongarch_sx
const short* r1 = (const short*)bottom_blob_tm.channel(1) + r * tiles + i;
const short* r2 = (const short*)bottom_blob_tm.channel(2) + r * tiles + i;
const short* r3 = (const short*)bottom_blob_tm.channel(3) + r * tiles + i;
for (; q + 3 < inch; q += 4)
{
tmpptr[0] = r0[0];
tmpptr[1] = r1[0];
tmpptr[2] = r2[0];
tmpptr[3] = r3[0];
tmpptr[4] = r0[1];
tmpptr[5] = r1[1];
tmpptr[6] = r2[1];
tmpptr[7] = r3[1];
r0 += bottom_blob_tm.cstep * 4;
r1 += bottom_blob_tm.cstep * 4;
r2 += bottom_blob_tm.cstep * 4;
r3 += bottom_blob_tm.cstep * 4;
tmpptr += 8;
}
#endif // __loongarch_sx
for (; q < inch; q++)
{
tmpptr[0] = r0[0];
tmpptr[1] = r0[1];
r0 += bottom_blob_tm.cstep;
tmpptr += 2;
}
}
for (; i < tiles; i++)
{
short* tmpptr = tm2.row<short>(i / 2 + i % 2);
const short* r0 = (const short*)bottom_blob_tm + r * tiles + i;
int q = 0;
#if __loongarch_sx
const short* r1 = (const short*)bottom_blob_tm.channel(1) + r * tiles + i;
const short* r2 = (const short*)bottom_blob_tm.channel(2) + r * tiles + i;
const short* r3 = (const short*)bottom_blob_tm.channel(3) + r * tiles + i;
for (; q + 3 < inch; q += 4)
{
tmpptr[0] = r0[0];
tmpptr[1] = r1[0];
tmpptr[2] = r2[0];
tmpptr[3] = r3[0];
r0 += bottom_blob_tm.cstep * 4;
r1 += bottom_blob_tm.cstep * 4;
r2 += bottom_blob_tm.cstep * 4;
r3 += bottom_blob_tm.cstep * 4;
tmpptr += 4;
}
#endif // __loongarch_sx
for (; q < inch; q++)
{
tmpptr[0] = r0[0];
r0 += bottom_blob_tm.cstep;
tmpptr += 1;
}
}
}
bottom_blob_tm = Mat();
// permute end
top_blob_tm.create(tiles, batch, outch, 4u, 1, opt.workspace_allocator);
#if __loongarch_sx
int nn_outch = outch >> 2;
int remain_outch_start = nn_outch << 2;
#pragma omp parallel for num_threads(opt.num_threads)
for (int pp = 0; pp < nn_outch; pp++)
{
int p = pp * 4;
int* output0_tm = top_blob_tm.channel(p);
int* output1_tm = top_blob_tm.channel(p + 1);
int* output2_tm = top_blob_tm.channel(p + 2);
int* output3_tm = top_blob_tm.channel(p + 3);
const Mat kernel0_tm = kernel_tm.channel(p / 4);
for (int r = 0; r < batch; r++)
{
const Mat bb2 = bottom_blob_tm2.channel(r);
int i = 0;
for (; i + 1 < tiles; i += 2)
{
const short* r0 = bb2.row<const short>(i / 2);
const short* k0 = kernel0_tm.row<const short>(r);
int nn4 = inch / 4;
int nn1 = inch % 4;
__m128i _sum00 = __lsx_vreplgr2vr_w(0);
__m128i _sum10 = __lsx_vreplgr2vr_w(0);
if (nn4 > 0)
{
__m128i _sum01 = __lsx_vreplgr2vr_w(0);
__m128i _sum02 = __lsx_vreplgr2vr_w(0);
__m128i _sum03 = __lsx_vreplgr2vr_w(0);
__m128i _sum11 = __lsx_vreplgr2vr_w(0);
__m128i _sum12 = __lsx_vreplgr2vr_w(0);
__m128i _sum13 = __lsx_vreplgr2vr_w(0);
int j = 0;
for (; j < nn4; j++)
{
__m128i _val01 = __lsx_vld(r0, 0);
__m128i _val0 = __lsx_vilvl_d(_val01, _val01);
__m128i _val1 = __lsx_vilvh_d(_val01, _val01);
__m128i _w0 = __lsx_vld(k0, 0);
__m128i _w1 = __lsx_vld(k0 + 8, 0);
__m128i _extval0 = __lsx_vslti_h(_val0, 0);
__m128i _extval1 = __lsx_vslti_h(_val1, 0);
__m128i _extw0 = __lsx_vslti_h(_w0, 0);
__m128i _extw1 = __lsx_vslti_h(_w1, 0);
__m128i _val0l = __lsx_vilvl_h(_extval0, _val0);
__m128i _val0h = __lsx_vilvh_h(_extval0, _val0);
__m128i _val1l = __lsx_vilvl_h(_extval1, _val1);
__m128i _val1h = __lsx_vilvh_h(_extval1, _val1);
__m128i _w0l = __lsx_vilvl_h(_extw0, _w0);
__m128i _w0h = __lsx_vilvh_h(_extw0, _w0);
__m128i _w1l = __lsx_vilvl_h(_extw1, _w1);
__m128i _w1h = __lsx_vilvh_h(_extw1, _w1);
_sum00 = __lsx_vmadd_w(_sum00, _val0l, _w0l);
_sum01 = __lsx_vmadd_w(_sum01, _val0h, _w0h);
_sum02 = __lsx_vmadd_w(_sum02, _val0l, _w1l);
_sum03 = __lsx_vmadd_w(_sum03, _val0h, _w1h);
_sum10 = __lsx_vmadd_w(_sum10, _val1l, _w0l);
_sum11 = __lsx_vmadd_w(_sum11, _val1h, _w0h);
_sum12 = __lsx_vmadd_w(_sum12, _val1l, _w1l);
_sum13 = __lsx_vmadd_w(_sum13, _val1h, _w1h);
r0 += 8;
k0 += 16;
}
// transpose 4x4
{
__m128i _tmp0, _tmp1, _tmp2, _tmp3;
_tmp0 = __lsx_vilvl_w(_sum01, _sum00);
_tmp1 = __lsx_vilvl_w(_sum03, _sum02);
_tmp2 = __lsx_vilvh_w(_sum01, _sum00);
_tmp3 = __lsx_vilvh_w(_sum03, _sum02);
_sum00 = __lsx_vilvl_d(_tmp1, _tmp0);
_sum01 = __lsx_vilvh_d(_tmp1, _tmp0);
_sum02 = __lsx_vilvl_d(_tmp3, _tmp2);
_sum03 = __lsx_vilvh_d(_tmp3, _tmp2);
}
{
__m128i _tmp0, _tmp1, _tmp2, _tmp3;
_tmp0 = __lsx_vilvl_w(_sum11, _sum10);
_tmp1 = __lsx_vilvl_w(_sum13, _sum12);
_tmp2 = __lsx_vilvh_w(_sum11, _sum10);
_tmp3 = __lsx_vilvh_w(_sum13, _sum12);
_sum10 = __lsx_vilvl_d(_tmp1, _tmp0);
_sum11 = __lsx_vilvh_d(_tmp1, _tmp0);
_sum12 = __lsx_vilvl_d(_tmp3, _tmp2);
_sum13 = __lsx_vilvh_d(_tmp3, _tmp2);
}
_sum00 = __lsx_vadd_w(_sum00, _sum01);
_sum02 = __lsx_vadd_w(_sum02, _sum03);
_sum10 = __lsx_vadd_w(_sum10, _sum11);
_sum12 = __lsx_vadd_w(_sum12, _sum13);
_sum00 = __lsx_vadd_w(_sum00, _sum02);
_sum10 = __lsx_vadd_w(_sum10, _sum12);
}
for (int j = 0; j < nn1; j++)
{
__m128i _val0 = __lsx_vreplgr2vr_h(r0[0]);
__m128i _val1 = __lsx_vreplgr2vr_h(r0[1]);
__m128i _val = __lsx_vilvl_d(_val1, _val0);
__m128i _w16 = __lsx_vld(k0, 0);
_w16 = __lsx_vilvl_d(_w16, _w16);
__m128i _extval = __lsx_vslti_h(_val, 0);
__m128i _extw16 = __lsx_vslti_h(_w16, 0);
__m128i _vall = __lsx_vilvl_h(_extval, _val);
__m128i _valh = __lsx_vilvh_h(_extval, _val);
__m128i _w0l = __lsx_vilvl_h(_extw16, _w16);
__m128i _w0h = __lsx_vilvh_h(_extw16, _w16);
_sum00 = __lsx_vmadd_w(_sum00, _vall, _w0l);
_sum10 = __lsx_vmadd_w(_sum10, _valh, _w0h);
r0 += 2;
k0 += 4;
}
int sum[8];
__lsx_vst(_sum00, sum, 0);
__lsx_vst(_sum10, sum + 4, 0);
output0_tm[0] = sum[0];
output1_tm[0] = sum[1];
output2_tm[0] = sum[2];
output3_tm[0] = sum[3];
output0_tm[1] = sum[4];
output1_tm[1] = sum[5];
output2_tm[1] = sum[6];
output3_tm[1] = sum[7];
output0_tm += 2;
output1_tm += 2;
output2_tm += 2;
output3_tm += 2;
}
for (; i < tiles; i++)
{
const short* r0 = bb2.row<const short>(i / 2 + i % 2);
const short* k0 = kernel0_tm.row<const short>(r);
int nn4 = inch / 4;
int nn1 = inch % 4;
__m128i _sum0 = __lsx_vreplgr2vr_w(0);
if (nn4 > 0)
{
__m128i _sum1 = __lsx_vreplgr2vr_w(0);
__m128i _sum2 = __lsx_vreplgr2vr_w(0);
__m128i _sum3 = __lsx_vreplgr2vr_w(0);
int j = 0;
for (; j < nn4; j++)
{
__m128i _val16 = __lsx_vld(r0, 0);
_val16 = __lsx_vilvl_d(_val16, _val16);
__m128i _w0 = __lsx_vld(k0, 0);
__m128i _w1 = __lsx_vld(k0 + 8, 0);
__m128i _extval16 = __lsx_vslti_h(_val16, 0);
__m128i _extw0 = __lsx_vslti_h(_w0, 0);
__m128i _extw1 = __lsx_vslti_h(_w1, 0);
__m128i _val0l = __lsx_vilvl_h(_extval16, _val16);
__m128i _val0h = __lsx_vilvh_h(_extval16, _val16);
__m128i _w0l = __lsx_vilvl_h(_extw0, _w0);
__m128i _w0h = __lsx_vilvh_h(_extw0, _w0);
__m128i _w1l = __lsx_vilvl_h(_extw1, _w1);
__m128i _w1h = __lsx_vilvh_h(_extw1, _w1);
_sum0 = __lsx_vmadd_w(_sum0, _val0l, _w0l);
_sum1 = __lsx_vmadd_w(_sum1, _val0h, _w0h);
_sum2 = __lsx_vmadd_w(_sum2, _val0l, _w1l);
_sum3 = __lsx_vmadd_w(_sum3, _val0h, _w1h);
r0 += 4;
k0 += 16;
}
// transpose 4x4
{
__m128i _tmp0, _tmp1, _tmp2, _tmp3;
_tmp0 = __lsx_vilvl_w(_sum1, _sum0);
_tmp1 = __lsx_vilvl_w(_sum3, _sum2);
_tmp2 = __lsx_vilvh_w(_sum1, _sum0);
_tmp3 = __lsx_vilvh_w(_sum3, _sum2);
_sum0 = __lsx_vilvl_d(_tmp1, _tmp0);
_sum1 = __lsx_vilvh_d(_tmp1, _tmp0);
_sum2 = __lsx_vilvl_d(_tmp3, _tmp2);
_sum3 = __lsx_vilvh_d(_tmp3, _tmp2);
}
_sum0 = __lsx_vadd_w(_sum0, _sum1);
_sum2 = __lsx_vadd_w(_sum2, _sum3);
_sum0 = __lsx_vadd_w(_sum0, _sum2);
}
for (int j = 0; j < nn1; j++)
{
__m128i _val = __lsx_vreplgr2vr_w(r0[0]);
__m128i _w16 = __lsx_vld(k0, 0);
__m128i _extw16 = __lsx_vslti_h(_w16, 0);
__m128i _w0l = __lsx_vilvl_h(_extw16, _w16);
_sum0 = __lsx_vmadd_w(_sum0, _val, _w0l);
r0 += 1;
k0 += 4;
}
int sum[4];
__lsx_vst(_sum0, sum, 0);
output0_tm[0] = sum[0];
output1_tm[0] = sum[1];
output2_tm[0] = sum[2];
output3_tm[0] = sum[3];
output0_tm += 1;
output1_tm += 1;
output2_tm += 1;
output3_tm += 1;
}
}
}
#else // __loongarch_sx
int nn_outch = outch >> 1;
int remain_outch_start = nn_outch << 1;
#pragma omp parallel for num_threads(opt.num_threads)
for (int pp = 0; pp < nn_outch; pp++)
{
int p = pp * 2;
int* output0_tm = top_blob_tm.channel(p);
int* output1_tm = top_blob_tm.channel(p + 1);
const Mat kernel0_tm = kernel_tm.channel(p / 2);
for (int r = 0; r < batch; r++)
{
const Mat bb2 = bottom_blob_tm2.channel(r);
int i = 0;
for (; i + 1 < tiles; i += 2)
{
const short* r0 = bb2.row<const short>(i / 2);
const short* k0 = kernel0_tm.row<const short>(r);
int sum00 = 0;
int sum01 = 0;
int sum10 = 0;
int sum11 = 0;
int nn1 = inch;
for (int j = 0; j < nn1; j++)
{
signed short val0 = r0[0];
signed short val1 = r0[1];
signed short w0 = k0[0];
signed short w1 = k0[1];
sum00 += val0 * w0;
sum01 += val1 * w0;
sum10 += val0 * w1;
sum11 += val1 * w1;
r0 += 2;
k0 += 2;
}
output0_tm[0] = sum00;
output0_tm[1] = sum01;
output1_tm[0] = sum10;
output1_tm[1] = sum11;
output0_tm += 2;
output1_tm += 2;
}
for (; i < tiles; i++)
{
const short* r0 = bb2.row<const short>(i / 2 + i % 2);
const short* k0 = kernel0_tm.row<const short>(r);
int sum0 = 0;
int sum1 = 0;
int nn1 = inch;
for (int j = 0; j < nn1; j++)
{
signed short val0 = r0[0];
signed short w0 = k0[0];
signed short w1 = k0[1];
sum0 += val0 * w0;
sum1 += val0 * w1;
r0 += 1;
k0 += 2;
}
output0_tm[0] = sum0;
output1_tm[0] = sum1;
output0_tm += 1;
output1_tm += 1;
}
}
}
#endif // __loongarch_sx
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = remain_outch_start; p < outch; p++)
{
int* output0_tm = top_blob_tm.channel(p);
#if __loongarch_sx
const Mat kernel0_tm = kernel_tm.channel(p / 4 + p % 4);
#else
const Mat kernel0_tm = kernel_tm.channel(p / 2 + p % 2);
#endif
for (int r = 0; r < batch; r++)
{
const Mat bb2 = bottom_blob_tm2.channel(r);
int i = 0;
for (; i + 1 < tiles; i += 2)
{
const short* r0 = bb2.row<const short>(i / 2);
const short* k0 = kernel0_tm.row<const short>(r);
int sum0 = 0;
int sum1 = 0;
#if __loongarch_sx
int nn4 = inch / 4;
int nn1 = inch % 4;
if (nn4 > 0)
{
__m128i _sum0 = __lsx_vreplgr2vr_w(0);
__m128i _sum1 = __lsx_vreplgr2vr_w(0);
int j = 0;
for (; j < nn4; j++)
{
__m128i _val16 = __lsx_vld(r0, 0);
__m128i _w16 = __lsx_vld(k0, 0);
_w16 = __lsx_vilvl_d(_w16, _w16);
__m128i _extval16 = __lsx_vslti_h(_val16, 0);
__m128i _extw16 = __lsx_vslti_h(_w16, 0);
__m128i _val0l = __lsx_vilvl_h(_extval16, _val16);
__m128i _val0h = __lsx_vilvh_h(_extval16, _val16);
__m128i _w0l = __lsx_vilvl_h(_extw16, _w16);
__m128i _w0h = __lsx_vilvh_h(_extw16, _w16);
_sum0 = __lsx_vmadd_w(_sum0, _val0l, _w0l);
_sum1 = __lsx_vmadd_w(_sum1, _val0h, _w0h);
r0 += 8;
k0 += 4;
}
sum0 = __lsx_reduce_add_w(_sum0);
sum1 = __lsx_reduce_add_w(_sum1);
}
#else // __loongarch_sx
int nn1 = inch;
#endif // __loongarch_sx
for (int q = 0; q < nn1; q++)
{
signed short val0 = r0[0];
signed short val1 = r0[1];
signed short w = k0[0];
sum0 += val0 * w;
sum1 += val1 * w;
k0 += 1;
r0 += 2;
}
output0_tm[0] = sum0;
output0_tm[1] = sum1;
output0_tm += 2;
}
for (; i < tiles; i++)
{
const short* r0 = bb2.row<const short>(i / 2 + i % 2);
const short* k0 = kernel0_tm.row<const short>(r);
int sum = 0;
#if __loongarch_sx
int nn4 = inch / 4;
int nn1 = inch % 4;
if (nn4 > 0)
{
__m128i _sum = __lsx_vreplgr2vr_w(0);
int j = 0;
for (; j < nn4; j++)
{
__m128i _val16 = __lsx_vld(r0, 0);
__m128i _w16 = __lsx_vld(k0, 0);
__m128i _extval16 = __lsx_vslti_h(_val16, 0);
__m128i _extw16 = __lsx_vslti_h(_w16, 0);
__m128i _val0l = __lsx_vilvl_h(_extval16, _val16);
__m128i _w0l = __lsx_vilvl_h(_extw16, _w16);
_sum = __lsx_vmadd_w(_sum, _val0l, _w0l);
r0 += 4;
k0 += 4;
}
sum = __lsx_reduce_add_w(_sum);
}
#else // __loongarch_sx
int nn1 = inch;
#endif // __loongarch_sx
for (int q = 0; q < nn1; q++)
{
signed short val = r0[0];
signed short w = k0[0];
sum += val * w;
k0 += 1;
r0 += 1;
}
output0_tm[0] = sum;
output0_tm++;
}
}
}
}