ncnn / src /layer /riscv /convolution_sgemm_packnto1.h
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//
// Copyright (C) 2021 THL A29 Limited, a Tencent company. 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_packnto1_rvv(const Mat& bottom_im2col, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
{
const int packn = csrr_vlenb() / 4;
const size_t vl = vsetvl_e32m1(packn);
// Mat bottom_im2col(size, maxk, inch, 4u * packn, packn, 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;
Mat tmp;
if (size >= 8)
tmp.create(8 * maxk, inch, size / 8 + (size % 8) / 4 + (size % 4) / 2 + size % 2, 4u * packn, packn, opt.workspace_allocator);
else if (size >= 4)
tmp.create(4 * maxk, inch, size / 4 + (size % 4) / 2 + size % 2, 4u * packn, packn, opt.workspace_allocator);
else if (size >= 2)
tmp.create(2 * maxk, inch, size / 2 + size % 2, 4u * packn, packn, opt.workspace_allocator);
else
tmp.create(maxk, inch, size, 4u * packn, packn, opt.workspace_allocator);
{
int remain_size_start = 0;
int nn_size = size >> 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 / 8);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * packn;
for (int k = 0; k < maxk; k++)
{
#if C906
for (int l = 0; l < packn; l++)
{
tmpptr[0] = img0[l];
tmpptr[1] = img0[l + packn];
tmpptr[2] = img0[l + packn * 2];
tmpptr[3] = img0[l + packn * 3];
tmpptr[4] = img0[l + packn * 4];
tmpptr[5] = img0[l + packn * 5];
tmpptr[6] = img0[l + packn * 6];
tmpptr[7] = img0[l + packn * 7];
tmpptr += 8;
}
img0 += size * packn;
#else
vfloat32m1_t _val0 = vle32_v_f32m1(img0, vl);
vfloat32m1_t _val1 = vle32_v_f32m1(img0 + packn, vl);
vfloat32m1_t _val2 = vle32_v_f32m1(img0 + packn * 2, vl);
vfloat32m1_t _val3 = vle32_v_f32m1(img0 + packn * 3, vl);
vfloat32m1_t _val4 = vle32_v_f32m1(img0 + packn * 4, vl);
vfloat32m1_t _val5 = vle32_v_f32m1(img0 + packn * 5, vl);
vfloat32m1_t _val6 = vle32_v_f32m1(img0 + packn * 6, vl);
vfloat32m1_t _val7 = vle32_v_f32m1(img0 + packn * 7, vl);
vsseg8e32_v_f32m1(tmpptr, _val0, _val1, _val2, _val3, _val4, _val5, _val6, _val7, vl);
img0 += size * packn;
tmpptr += packn * 8;
#endif
}
}
}
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 / 8 + (i % 8) / 4);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * packn;
for (int k = 0; k < maxk; k++)
{
#if C906
for (int l = 0; l < packn; l++)
{
tmpptr[0] = img0[l];
tmpptr[1] = img0[l + packn];
tmpptr[2] = img0[l + packn * 2];
tmpptr[3] = img0[l + packn * 3];
tmpptr += 4;
}
img0 += size * packn;
#else
vfloat32m1_t _val0 = vle32_v_f32m1(img0, vl);
vfloat32m1_t _val1 = vle32_v_f32m1(img0 + packn, vl);
vfloat32m1_t _val2 = vle32_v_f32m1(img0 + packn * 2, vl);
vfloat32m1_t _val3 = vle32_v_f32m1(img0 + packn * 3, vl);
vsseg4e32_v_f32m1(tmpptr, _val0, _val1, _val2, _val3, vl);
img0 += size * packn;
tmpptr += packn * 4;
#endif
}
}
}
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 / 8 + (i % 8) / 4 + (i % 4) / 2);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * packn;
for (int k = 0; k < maxk; k++)
{
#if C906
for (int l = 0; l < packn; l++)
{
tmpptr[0] = img0[l];
tmpptr[1] = img0[l + packn];
tmpptr += 2;
}
img0 += size * packn;
#else
vfloat32m1_t _val0 = vle32_v_f32m1(img0, vl);
vfloat32m1_t _val1 = vle32_v_f32m1(img0 + packn, vl);
vsseg2e32_v_f32m1(tmpptr, _val0, _val1, vl);
img0 += size * packn;
tmpptr += packn * 2;
#endif
}
}
}
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 / 8 + (i % 8) / 4 + (i % 4) / 2 + i % 2);
for (int q = 0; q < inch; q++)
{
const float* img0 = (const float*)bottom_im2col.channel(q) + i * packn;
for (int k = 0; k < maxk; k++)
{
vfloat32m1_t _val = vle32_v_f32m1(img0, vl);
vse32_v_f32m1(tmpptr, _val, vl);
img0 += size * packn;
tmpptr += packn;
}
}
}
}
int nn_outch = outch / packn;
int remain_outch_start = nn_outch * packn;
#ifdef __clang__
// clang complains about VLA in the following loop
float* _zero_tmp = new float[packn]();
for (int _zero_clean_idx = 0; _zero_clean_idx < packn; _zero_clean_idx++)
{
_zero_tmp[_zero_clean_idx] = 0.f;
}
#endif // __clang__
#pragma omp parallel for num_threads(opt.num_threads)
for (int pp = 0; pp < nn_outch; pp++)
{
int p = pp * packn;
float* outptr0 = top_blob.channel(p);
#ifdef __clang__
const float* zeros = _zero_tmp;
#else
const float zeros[packn] = {0.f};
#endif // __clang__
const float* biasptr = bias ? bias + p : zeros;
int i = 0;
for (; i + 7 < size; i += 8)
{
const float* tmpptr = tmp.channel(i / 8);
const float* kptr0 = kernel.channel(p / packn);
int nn = inch * maxk * packn; // inch always > 0
vfloat32m1_t _sum0 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum1 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum2 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum3 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum4 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum5 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum6 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum7 = vle32_v_f32m1(biasptr, vl);
for (int j = 0; j < nn; j++)
{
float val0 = *tmpptr++;
float val1 = *tmpptr++;
float val2 = *tmpptr++;
float val3 = *tmpptr++;
float val4 = *tmpptr++;
float val5 = *tmpptr++;
float val6 = *tmpptr++;
float val7 = *tmpptr++;
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vf_f32m1(_sum0, val0, _w0, vl);
_sum1 = vfmacc_vf_f32m1(_sum1, val1, _w0, vl);
_sum2 = vfmacc_vf_f32m1(_sum2, val2, _w0, vl);
_sum3 = vfmacc_vf_f32m1(_sum3, val3, _w0, vl);
_sum4 = vfmacc_vf_f32m1(_sum4, val4, _w0, vl);
_sum5 = vfmacc_vf_f32m1(_sum5, val5, _w0, vl);
_sum6 = vfmacc_vf_f32m1(_sum6, val6, _w0, vl);
_sum7 = vfmacc_vf_f32m1(_sum7, val7, _w0, vl);
kptr0 += packn;
}
#if C906
vsse32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, vl);
vsse32_v_f32m1(outptr0 + 1, top_blob.cstep * sizeof(float), _sum1, vl);
vsse32_v_f32m1(outptr0 + 2, top_blob.cstep * sizeof(float), _sum2, vl);
vsse32_v_f32m1(outptr0 + 3, top_blob.cstep * sizeof(float), _sum3, vl);
vsse32_v_f32m1(outptr0 + 4, top_blob.cstep * sizeof(float), _sum4, vl);
vsse32_v_f32m1(outptr0 + 5, top_blob.cstep * sizeof(float), _sum5, vl);
vsse32_v_f32m1(outptr0 + 6, top_blob.cstep * sizeof(float), _sum6, vl);
vsse32_v_f32m1(outptr0 + 7, top_blob.cstep * sizeof(float), _sum7, vl);
#else
vssseg8e32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, _sum1, _sum2, _sum3, _sum4, _sum5, _sum6, _sum7, vl);
#endif
outptr0 += 8;
}
for (; i + 3 < size; i += 4)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4);
const float* kptr0 = kernel.channel(p / packn);
int nn = inch * maxk * packn; // inch always > 0
vfloat32m1_t _sum0 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum1 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum2 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum3 = vle32_v_f32m1(biasptr, vl);
for (int j = 0; j < nn; j++)
{
float val0 = *tmpptr++;
float val1 = *tmpptr++;
float val2 = *tmpptr++;
float val3 = *tmpptr++;
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vf_f32m1(_sum0, val0, _w0, vl);
_sum1 = vfmacc_vf_f32m1(_sum1, val1, _w0, vl);
_sum2 = vfmacc_vf_f32m1(_sum2, val2, _w0, vl);
_sum3 = vfmacc_vf_f32m1(_sum3, val3, _w0, vl);
kptr0 += packn;
}
#if C906
vsse32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, vl);
vsse32_v_f32m1(outptr0 + 1, top_blob.cstep * sizeof(float), _sum1, vl);
vsse32_v_f32m1(outptr0 + 2, top_blob.cstep * sizeof(float), _sum2, vl);
vsse32_v_f32m1(outptr0 + 3, top_blob.cstep * sizeof(float), _sum3, vl);
#else
vssseg4e32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, _sum1, _sum2, _sum3, vl);
#endif
outptr0 += 4;
}
for (; i + 1 < size; i += 2)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4 + (i % 4) / 2);
const float* kptr0 = kernel.channel(p / packn);
int nn = inch * maxk * packn; // inch always > 0
vfloat32m1_t _sum0 = vle32_v_f32m1(biasptr, vl);
vfloat32m1_t _sum1 = vle32_v_f32m1(biasptr, vl);
for (int j = 0; j < nn; j++)
{
float val0 = *tmpptr++;
float val1 = *tmpptr++;
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vf_f32m1(_sum0, val0, _w0, vl);
_sum1 = vfmacc_vf_f32m1(_sum1, val1, _w0, vl);
kptr0 += packn;
}
#if C906
vsse32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, vl);
vsse32_v_f32m1(outptr0 + 1, top_blob.cstep * sizeof(float), _sum1, vl);
#else
vssseg2e32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum0, _sum1, vl);
#endif
outptr0 += 2;
}
for (; i < size; i++)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4 + (i % 4) / 2 + i % 2);
const float* kptr0 = kernel.channel(p / packn);
int nn = inch * maxk * packn; // inch always > 0
vfloat32m1_t _sum = vle32_v_f32m1(biasptr, vl);
for (int j = 0; j < nn; j++)
{
float val = *tmpptr++;
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum = vfmacc_vf_f32m1(_sum, val, _w0, vl);
kptr0 += packn;
}
vsse32_v_f32m1(outptr0, top_blob.cstep * sizeof(float), _sum, vl);
outptr0 += 1;
}
}
#ifdef __clang__
delete[] _zero_tmp;
#endif
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = remain_outch_start; p < outch; p++)
{
float* outptr0 = top_blob.channel(p);
const float bias0 = bias ? bias[p] : 0.f;
int i = 0;
for (; i + 7 < size; i += 8)
{
const float* tmpptr = tmp.channel(i / 8);
const float* kptr0 = kernel.channel(p / packn + p % packn);
int nn = inch * maxk; // inch always > 0
float sum0 = bias0;
float sum1 = bias0;
float sum2 = bias0;
float sum3 = bias0;
float sum4 = bias0;
float sum5 = bias0;
float sum6 = bias0;
float sum7 = bias0;
vfloat32m1_t _sum0 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum1 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum2 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum3 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum4 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum5 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum6 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum7 = vfmv_v_f_f32m1(0.f, vl);
for (int j = 0; j < nn; j++)
{
vfloat32m1_t _val0;
vfloat32m1_t _val1;
vfloat32m1_t _val2;
vfloat32m1_t _val3;
vfloat32m1_t _val4;
vfloat32m1_t _val5;
vfloat32m1_t _val6;
vfloat32m1_t _val7;
vlseg8e32_v_f32m1(&_val0, &_val1, &_val2, &_val3, &_val4, &_val5, &_val6, &_val7, tmpptr, vl);
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vv_f32m1(_sum0, _val0, _w0, vl);
_sum1 = vfmacc_vv_f32m1(_sum1, _val1, _w0, vl);
_sum2 = vfmacc_vv_f32m1(_sum2, _val2, _w0, vl);
_sum3 = vfmacc_vv_f32m1(_sum3, _val3, _w0, vl);
_sum4 = vfmacc_vv_f32m1(_sum4, _val4, _w0, vl);
_sum5 = vfmacc_vv_f32m1(_sum5, _val5, _w0, vl);
_sum6 = vfmacc_vv_f32m1(_sum6, _val6, _w0, vl);
_sum7 = vfmacc_vv_f32m1(_sum7, _val7, _w0, vl);
tmpptr += packn * 8;
kptr0 += packn;
}
#if C906
// TODO
std::vector<float> ss0(packn);
std::vector<float> ss1(packn);
std::vector<float> ss2(packn);
std::vector<float> ss3(packn);
std::vector<float> ss4(packn);
std::vector<float> ss5(packn);
std::vector<float> ss6(packn);
std::vector<float> ss7(packn);
vse32_v_f32m1((float*)ss0.data(), _sum0, vl);
vse32_v_f32m1((float*)ss1.data(), _sum1, vl);
vse32_v_f32m1((float*)ss2.data(), _sum2, vl);
vse32_v_f32m1((float*)ss3.data(), _sum3, vl);
vse32_v_f32m1((float*)ss4.data(), _sum4, vl);
vse32_v_f32m1((float*)ss5.data(), _sum5, vl);
vse32_v_f32m1((float*)ss6.data(), _sum6, vl);
vse32_v_f32m1((float*)ss7.data(), _sum7, vl);
for (int i = 0; i < packn; i++)
{
sum0 += ss0[i];
sum1 += ss1[i];
sum2 += ss2[i];
sum3 += ss3[i];
sum4 += ss4[i];
sum5 += ss5[i];
sum6 += ss6[i];
sum7 += ss7[i];
}
#else
sum0 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum0, vfmv_s_f_f32m1(vfloat32m1_t(), sum0, vl), vl));
sum1 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum1, vfmv_s_f_f32m1(vfloat32m1_t(), sum1, vl), vl));
sum2 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum2, vfmv_s_f_f32m1(vfloat32m1_t(), sum2, vl), vl));
sum3 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum3, vfmv_s_f_f32m1(vfloat32m1_t(), sum3, vl), vl));
sum4 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum4, vfmv_s_f_f32m1(vfloat32m1_t(), sum4, vl), vl));
sum5 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum5, vfmv_s_f_f32m1(vfloat32m1_t(), sum5, vl), vl));
sum6 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum6, vfmv_s_f_f32m1(vfloat32m1_t(), sum6, vl), vl));
sum7 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum7, vfmv_s_f_f32m1(vfloat32m1_t(), sum7, vl), vl));
#endif
outptr0[0] = sum0;
outptr0[1] = sum1;
outptr0[2] = sum2;
outptr0[3] = sum3;
outptr0[4] = sum4;
outptr0[5] = sum5;
outptr0[6] = sum6;
outptr0[7] = sum7;
outptr0 += 8;
}
for (; i + 3 < size; i += 4)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4);
const float* kptr0 = kernel.channel(p / packn + p % packn);
int nn = inch * maxk; // inch always > 0
float sum0 = bias0;
float sum1 = bias0;
float sum2 = bias0;
float sum3 = bias0;
vfloat32m1_t _sum0 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum1 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum2 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum3 = vfmv_v_f_f32m1(0.f, vl);
for (int j = 0; j < nn; j++)
{
vfloat32m1_t _val0;
vfloat32m1_t _val1;
vfloat32m1_t _val2;
vfloat32m1_t _val3;
vlseg4e32_v_f32m1(&_val0, &_val1, &_val2, &_val3, tmpptr, vl);
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vv_f32m1(_sum0, _val0, _w0, vl);
_sum1 = vfmacc_vv_f32m1(_sum1, _val1, _w0, vl);
_sum2 = vfmacc_vv_f32m1(_sum2, _val2, _w0, vl);
_sum3 = vfmacc_vv_f32m1(_sum3, _val3, _w0, vl);
tmpptr += packn * 4;
kptr0 += packn;
}
#if C906
// TODO
std::vector<float> ss0(packn);
std::vector<float> ss1(packn);
std::vector<float> ss2(packn);
std::vector<float> ss3(packn);
vse32_v_f32m1((float*)ss0.data(), _sum0, vl);
vse32_v_f32m1((float*)ss1.data(), _sum1, vl);
vse32_v_f32m1((float*)ss2.data(), _sum2, vl);
vse32_v_f32m1((float*)ss3.data(), _sum3, vl);
for (int i = 0; i < packn; i++)
{
sum0 += ss0[i];
sum1 += ss1[i];
sum2 += ss2[i];
sum3 += ss3[i];
}
#else
sum0 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum0, vfmv_s_f_f32m1(vfloat32m1_t(), sum0, vl), vl));
sum1 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum1, vfmv_s_f_f32m1(vfloat32m1_t(), sum1, vl), vl));
sum2 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum2, vfmv_s_f_f32m1(vfloat32m1_t(), sum2, vl), vl));
sum3 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum3, vfmv_s_f_f32m1(vfloat32m1_t(), sum3, vl), vl));
#endif
outptr0[0] = sum0;
outptr0[1] = sum1;
outptr0[2] = sum2;
outptr0[3] = sum3;
outptr0 += 4;
}
for (; i + 1 < size; i += 2)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4 + (i % 4) / 2);
const float* kptr0 = kernel.channel(p / packn + p % packn);
int nn = inch * maxk; // inch always > 0
float sum0 = bias0;
float sum1 = bias0;
vfloat32m1_t _sum0 = vfmv_v_f_f32m1(0.f, vl);
vfloat32m1_t _sum1 = vfmv_v_f_f32m1(0.f, vl);
for (int j = 0; j < nn; j++)
{
vfloat32m1_t _val0;
vfloat32m1_t _val1;
vlseg2e32_v_f32m1(&_val0, &_val1, tmpptr, vl);
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vv_f32m1(_sum0, _val0, _w0, vl);
_sum1 = vfmacc_vv_f32m1(_sum1, _val1, _w0, vl);
tmpptr += packn * 2;
kptr0 += packn;
}
#if C906
// TODO
std::vector<float> ss0(packn);
std::vector<float> ss1(packn);
vse32_v_f32m1((float*)ss0.data(), _sum0, vl);
vse32_v_f32m1((float*)ss1.data(), _sum1, vl);
for (int i = 0; i < packn; i++)
{
sum0 += ss0[i];
sum1 += ss1[i];
}
#else
sum0 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum0, vfmv_s_f_f32m1(vfloat32m1_t(), sum0, vl), vl));
sum1 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum1, vfmv_s_f_f32m1(vfloat32m1_t(), sum1, vl), vl));
#endif
outptr0[0] = sum0;
outptr0[1] = sum1;
outptr0 += 2;
}
for (; i < size; i++)
{
const float* tmpptr = tmp.channel(i / 8 + (i % 8) / 4 + (i % 4) / 2 + i % 2);
const float* kptr0 = kernel.channel(p / packn + p % packn);
int nn = inch * maxk; // inch always > 0
float sum0 = bias0;
vfloat32m1_t _sum0 = vfmv_v_f_f32m1(0.f, vl);
for (int j = 0; j < nn; j++)
{
vfloat32m1_t _val0 = vle32_v_f32m1(tmpptr, vl);
vfloat32m1_t _w0 = vle32_v_f32m1(kptr0, vl);
_sum0 = vfmacc_vv_f32m1(_sum0, _val0, _w0, vl);
tmpptr += packn;
kptr0 += packn;
}
#if C906
// TODO
std::vector<float> ss0(packn);
vse32_v_f32m1((float*)ss0.data(), _sum0, vl);
for (int i = 0; i < packn; i++)
{
sum0 += ss0[i];
}
#else
sum0 = vfmv_f_s_f32m1_f32(vfredusum_vs_f32m1_f32m1(vfloat32m1_t(), _sum0, vfmv_s_f_f32m1(vfloat32m1_t(), sum0, vl), vl));
#endif
outptr0[0] = sum0;
outptr0 += 1;
}
}
}
static void convolution_im2col_sgemm_transform_kernel_packnto1_rvv(const Mat& _kernel, Mat& kernel_tm, int inch, int outch, int kernel_w, int kernel_h)
{
const int packn = csrr_vlenb() / 4;
const int maxk = kernel_w * kernel_h;
// interleave
// src = maxk-inch-outch
// dst = pb-pa-maxk-inch/pa-outch/pb
Mat kernel = _kernel.reshape(maxk, inch, outch);
kernel_tm.create(packn * packn * maxk, inch / packn, outch / packn + outch % packn);
int q = 0;
for (; q + (packn - 1) < outch; q += packn)
{
float* g00 = kernel_tm.channel(q / packn);
for (int p = 0; p + (packn - 1) < inch; p += packn)
{
for (int k = 0; k < maxk; k++)
{
for (int i = 0; i < packn; i++)
{
for (int j = 0; j < packn; j++)
{
const float* k00 = kernel.channel(q + j).row(p + i);
g00[0] = k00[k];
g00++;
}
}
}
}
}
for (; q < outch; q++)
{
const Mat k0 = kernel.channel(q);
float* g00 = kernel_tm.channel(q / packn + q % packn);
for (int p = 0; p + (packn - 1) < inch; p += packn)
{
for (int k = 0; k < maxk; k++)
{
for (int j = 0; j < packn; j++)
{
const float* k00 = k0.row(p + j);
g00[0] = k00[k];
g00++;
}
}
}
}
}
static void convolution_im2col_sgemm_packnto1_rvv(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)
{
const int packn = csrr_vlenb() / 4;
const size_t vl = vsetvl_e32m1(packn);
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 * packn, packn, opt.workspace_allocator);
{
const int gap = (w * stride_h - outw * stride_w) * packn;
#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(dilation_h * u) + dilation_w * v * packn;
for (int i = 0; i < outh; i++)
{
int j = 0;
for (; j < outw; j++)
{
vfloat32m1_t _val = vle32_v_f32m1(sptr, vl);
vse32_v_f32m1(ptr, _val, vl);
sptr += stride_w * packn;
ptr += packn;
}
sptr += gap;
}
}
}
}
}
im2col_sgemm_packnto1_rvv(bottom_im2col, top_blob, kernel, _bias, opt);
}