riscv v optimization for convolution sgemm pack1

src/layer/riscv/convolution_1x1.h

@@ -0,0 +1,26 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// 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 conv1x1s1_sgemm_rvv(const Mat& bottom_blob, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
+{
+    int w = bottom_blob.w;
+    int h = bottom_blob.h;
+    const int size = w * h;
+
+    Mat bottom_im2col = bottom_blob;
+    bottom_im2col.w = size;
+    bottom_im2col.h = 1;
+
+    im2col_sgemm_rvv(bottom_im2col, top_blob, kernel, _bias, opt);
+}

src/layer/riscv/convolution_1x1_fp16s.h

@@ -0,0 +1,26 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// 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 conv1x1s1_sgemm_fp16sa_rvv(const Mat& bottom_blob, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
+{
+    int w = bottom_blob.w;
+    int h = bottom_blob.h;
+    const int size = w * h;
+
+    Mat bottom_im2col = bottom_blob;
+    bottom_im2col.w = size;
+    bottom_im2col.h = 1;
+
+    im2col_sgemm_fp16sa_rvv(bottom_im2col, top_blob, kernel, _bias, opt);
+}

src/layer/riscv/convolution_riscv.cpp

@@ -33,6 +33,9 @@
 
 namespace ncnn {
 
+#include "convolution_sgemm.h"
+#include "convolution_1x1.h"
+
 #if __riscv_vector
 #include "convolution_packn.h"
 #include "convolution_pack1ton.h"
@@ -48,6 +51,9 @@ namespace ncnn {
 #include "convolution_pack1ton_fp16s.h"
 #include "convolution_packnto1_fp16s.h"
 
+#include "convolution_sgemm_fp16s.h"
+#include "convolution_1x1_fp16s.h"
+
 #include "convolution_sgemm_packn_fp16s.h"
 #include "convolution_1x1_packn_fp16s.h"
 #include "convolution_3x3_packn_fp16s.h"
@@ -194,6 +200,10 @@ int Convolution_riscv::create_pipeline(const Option& opt)
     // pack1
     if (elempack == 1 && out_elempack == 1)
     {
+        if (opt.use_sgemm_convolution)
+        {
+            convolution_im2col_sgemm_transform_kernel_rvv(weight_data, weight_data_packed, num_input, num_output, kernel_w, kernel_h);
+        }
     }
 
     return 0;
@@ -385,6 +395,25 @@ int Convolution_riscv::forward(const Mat& bottom_blob, Mat& top_blob, const Opti
 
     if (elempack == 1 && out_elempack == 1)
     {
+        if (opt.use_sgemm_convolution && kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
+        {
+            conv1x1s1_sgemm_rvv(bottom_blob_bordered, top_blob, weight_data_packed, bias_data, opt);
+
+            if (activation)
+            {
+                activation->forward_inplace(top_blob, opt);
+            }
+        }
+        else if (opt.use_sgemm_convolution)
+        {
+            convolution_im2col_sgemm_rvv(bottom_blob_bordered, top_blob, weight_data_packed, bias_data, kernel_w, kernel_h, dilation_w, dilation_h, stride_w, stride_h, opt);
+
+            if (activation)
+            {
+                activation->forward_inplace(top_blob, opt);
+            }
+        }
+        else
         {
             const int maxk = kernel_w * kernel_h;
 
@@ -529,6 +558,10 @@ int Convolution_riscv::create_pipeline_fp16s(const Option& opt)
     // pack1
     if (elempack == 1 && out_elempack == 1)
     {
+        if (opt.use_fp16_arithmetic && opt.use_sgemm_convolution)
+        {
+            convolution_im2col_sgemm_transform_kernel_fp16sa_rvv(weight_data, weight_data_fp16, num_input, num_output, kernel_w, kernel_h);
+        }
     }
 
     ncnn::cast_float32_to_float16(bias_data, bias_data_fp16, opt);
@@ -700,6 +733,25 @@ int Convolution_riscv::forward_fp16sa(const Mat& bottom_blob, Mat& top_blob, con
 
     if (elempack == 1 && out_elempack == 1)
     {
+        if (opt.use_sgemm_convolution && kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
+        {
+            conv1x1s1_sgemm_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
+
+            if (activation)
+            {
+                activation->forward_inplace(top_blob, opt);
+            }
+        }
+        else if (opt.use_sgemm_convolution)
+        {
+            convolution_im2col_sgemm_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, kernel_w, kernel_h, dilation_w, dilation_h, stride_w, stride_h, opt);
+
+            if (activation)
+            {
+                activation->forward_inplace(top_blob, opt);
+            }
+        }
+        else
         {
             convolution_fp16s(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data, kernel_w, kernel_h, dilation_w, dilation_h, stride_w, stride_h, activation_type, activation_params, opt);
         }

src/layer/riscv/convolution_sgemm.h

@@ -0,0 +1,540 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// 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_rvv(const Mat& bottom_im2col, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
+{
+#if __riscv_vector
+    const int packn = csrr_vlenb() / 4;
+    const word_type vl = vsetvl_e32m1(packn);
+#endif
+
+    // Mat bottom_im2col(size, maxk, inch, 4u, 1, 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 __riscv_vector
+    if (size >= packn)
+        tmp.create(packn * maxk, inch, size / packn + size % packn, 4u, 1, opt.workspace_allocator);
+    else
+        tmp.create(maxk, inch, size, 4u, 1, opt.workspace_allocator);
+    {
+        int nn_size = size / packn;
+
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int ii = 0; ii < nn_size; ii++)
+        {
+            int i = ii * packn;
+
+            float* tmpptr = tmp.channel(i / packn);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const float* img0 = (const float*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    vse32_v_f32m1(tmpptr, vle32_v_f32m1(img0, vl), vl);
+                    img0 += size;
+                    tmpptr += packn;
+                }
+            }
+        }
+
+        int remain_size_start = nn_size * packn;
+
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int i = remain_size_start; i < size; i++)
+        {
+            float* tmpptr = tmp.channel(i / packn + i % packn);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const float* img0 = (const float*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    tmpptr[0] = img0[0];
+                    img0 += size;
+                    tmpptr += 1;
+                }
+            }
+        }
+    }
+#else // __riscv_vector
+    tmp.create(maxk, inch, size, 4u, 1, opt.workspace_allocator);
+    {
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int i = 0; i < size; i++)
+        {
+            float* tmpptr = tmp.channel(i);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const float* img0 = (const float*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    tmpptr[0] = img0[0];
+                    img0 += size;
+                    tmpptr += 1;
+                }
+            }
+        }
+    }
+#endif // __riscv_vector
+
+#if __riscv_vector
+    int nn_outch = outch >> 3;
+    int remain_outch_start = nn_outch << 3;
+
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int pp = 0; pp < nn_outch; pp++)
+    {
+        int p = pp * 8;
+
+        float* outptr0 = top_blob.channel(p);
+        float* outptr1 = top_blob.channel(p + 1);
+        float* outptr2 = top_blob.channel(p + 2);
+        float* outptr3 = top_blob.channel(p + 3);
+        float* outptr4 = top_blob.channel(p + 4);
+        float* outptr5 = top_blob.channel(p + 5);
+        float* outptr6 = top_blob.channel(p + 6);
+        float* outptr7 = top_blob.channel(p + 7);
+
+        const float zeros[8] = {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f};
+        const float* biasptr = bias ? bias + p : zeros;
+
+        int i = 0;
+        for (; i + (packn - 1) < size; i += packn)
+        {
+            const float* tmpptr = tmp.channel(i / packn);
+            const float* kptr = kernel.channel(p / 8);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat32m1_t _sum0 = vfmv_v_f_f32m1(biasptr[0], vl);
+            vfloat32m1_t _sum1 = vfmv_v_f_f32m1(biasptr[1], vl);
+            vfloat32m1_t _sum2 = vfmv_v_f_f32m1(biasptr[2], vl);
+            vfloat32m1_t _sum3 = vfmv_v_f_f32m1(biasptr[3], vl);
+            vfloat32m1_t _sum4 = vfmv_v_f_f32m1(biasptr[4], vl);
+            vfloat32m1_t _sum5 = vfmv_v_f_f32m1(biasptr[5], vl);
+            vfloat32m1_t _sum6 = vfmv_v_f_f32m1(biasptr[6], vl);
+            vfloat32m1_t _sum7 = vfmv_v_f_f32m1(biasptr[7], vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                vfloat32m1_t _val = vle32_v_f32m1(tmpptr, vl);
+                _sum0 = vfmacc_vf_f32m1(_sum0, kptr[0], _val, vl);
+                _sum1 = vfmacc_vf_f32m1(_sum1, kptr[1], _val, vl);
+                _sum2 = vfmacc_vf_f32m1(_sum2, kptr[2], _val, vl);
+                _sum3 = vfmacc_vf_f32m1(_sum3, kptr[3], _val, vl);
+                _sum4 = vfmacc_vf_f32m1(_sum4, kptr[4], _val, vl);
+                _sum5 = vfmacc_vf_f32m1(_sum5, kptr[5], _val, vl);
+                _sum6 = vfmacc_vf_f32m1(_sum6, kptr[6], _val, vl);
+                _sum7 = vfmacc_vf_f32m1(_sum7, kptr[7], _val, vl);
+                tmpptr += packn;
+                kptr += 8;
+            }
+
+            vse32_v_f32m1(outptr0, _sum0, vl);
+            vse32_v_f32m1(outptr1, _sum1, vl);
+            vse32_v_f32m1(outptr2, _sum2, vl);
+            vse32_v_f32m1(outptr3, _sum3, vl);
+            vse32_v_f32m1(outptr4, _sum4, vl);
+            vse32_v_f32m1(outptr5, _sum5, vl);
+            vse32_v_f32m1(outptr6, _sum6, vl);
+            vse32_v_f32m1(outptr7, _sum7, vl);
+
+            outptr0 += packn;
+            outptr1 += packn;
+            outptr2 += packn;
+            outptr3 += packn;
+            outptr4 += packn;
+            outptr5 += packn;
+            outptr6 += packn;
+            outptr7 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const float* tmpptr = tmp.channel(i / packn + i % packn);
+            const float* kptr = kernel.channel(p / 8);
+
+            int nn = inch * maxk; // inch always > 0
+
+            float sum0 = biasptr[0];
+            float sum1 = biasptr[1];
+            float sum2 = biasptr[2];
+            float sum3 = biasptr[3];
+            float sum4 = biasptr[4];
+            float sum5 = biasptr[5];
+            float sum6 = biasptr[6];
+            float sum7 = biasptr[7];
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                sum1 += tmpptr[0] * kptr[1];
+                sum2 += tmpptr[0] * kptr[2];
+                sum3 += tmpptr[0] * kptr[3];
+                sum4 += tmpptr[0] * kptr[4];
+                sum5 += tmpptr[0] * kptr[5];
+                sum6 += tmpptr[0] * kptr[6];
+                sum7 += tmpptr[0] * kptr[7];
+                tmpptr++;
+                kptr += 8;
+            }
+
+            outptr0[0] = sum0;
+            outptr1[0] = sum1;
+            outptr2[0] = sum2;
+            outptr3[0] = sum3;
+            outptr4[0] = sum4;
+            outptr5[0] = sum5;
+            outptr6[0] = sum6;
+            outptr7[0] = sum7;
+
+            outptr0++;
+            outptr1++;
+            outptr2++;
+            outptr3++;
+            outptr4++;
+            outptr5++;
+            outptr6++;
+            outptr7++;
+        }
+    }
+
+    nn_outch = (outch - remain_outch_start) >> 2;
+
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int pp = 0; pp < nn_outch; pp++)
+    {
+        int p = remain_outch_start + pp * 4;
+
+        float* outptr0 = top_blob.channel(p);
+        float* outptr1 = top_blob.channel(p + 1);
+        float* outptr2 = top_blob.channel(p + 2);
+        float* outptr3 = top_blob.channel(p + 3);
+
+        const float zeros[4] = {0.f, 0.f, 0.f, 0.f};
+        const float* biasptr = bias ? bias + p : zeros;
+
+        int i = 0;
+        for (; i + (packn - 1) < size; i += packn)
+        {
+            const float* tmpptr = tmp.channel(i / packn);
+            const float* kptr = kernel.channel(p / 8 + (p % 8) / 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat32m1_t _sum0 = vfmv_v_f_f32m1(biasptr[0], vl);
+            vfloat32m1_t _sum1 = vfmv_v_f_f32m1(biasptr[1], vl);
+            vfloat32m1_t _sum2 = vfmv_v_f_f32m1(biasptr[2], vl);
+            vfloat32m1_t _sum3 = vfmv_v_f_f32m1(biasptr[3], vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                vfloat32m1_t _val = vle32_v_f32m1(tmpptr, vl);
+                _sum0 = vfmacc_vf_f32m1(_sum0, kptr[0], _val, vl);
+                _sum1 = vfmacc_vf_f32m1(_sum1, kptr[1], _val, vl);
+                _sum2 = vfmacc_vf_f32m1(_sum2, kptr[2], _val, vl);
+                _sum3 = vfmacc_vf_f32m1(_sum3, kptr[3], _val, vl);
+                tmpptr += packn;
+                kptr += 4;
+            }
+
+            vse32_v_f32m1(outptr0, _sum0, vl);
+            vse32_v_f32m1(outptr1, _sum1, vl);
+            vse32_v_f32m1(outptr2, _sum2, vl);
+            vse32_v_f32m1(outptr3, _sum3, vl);
+
+            outptr0 += packn;
+            outptr1 += packn;
+            outptr2 += packn;
+            outptr3 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const float* tmpptr = tmp.channel(i / packn + i % packn);
+            const float* kptr = kernel.channel(p / 8 + (p % 8) / 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            float sum0 = biasptr[0];
+            float sum1 = biasptr[1];
+            float sum2 = biasptr[2];
+            float sum3 = biasptr[3];
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                sum1 += tmpptr[0] * kptr[1];
+                sum2 += tmpptr[0] * kptr[2];
+                sum3 += tmpptr[0] * kptr[3];
+                tmpptr++;
+                kptr += 4;
+            }
+
+            outptr0[0] = sum0;
+            outptr1[0] = sum1;
+            outptr2[0] = sum2;
+            outptr3[0] = sum3;
+
+            outptr0++;
+            outptr1++;
+            outptr2++;
+            outptr3++;
+        }
+    }
+
+    remain_outch_start += nn_outch << 2;
+
+    #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 + (packn - 1) < size; i += packn)
+        {
+            const float* tmpptr = tmp.channel(i / packn);
+            const float* kptr = kernel.channel(p / 8 + (p % 8) / 4 + p % 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat32m1_t _sum0 = vfmv_v_f_f32m1(bias0, vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                _sum0 = vfmacc_vf_f32m1(_sum0, kptr[0], vle32_v_f32m1(tmpptr, vl), vl);
+                tmpptr += packn;
+                kptr++;
+            }
+
+            vse32_v_f32m1(outptr0, _sum0, vl);
+
+            outptr0 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const float* tmpptr = tmp.channel(i / packn + i % packn);
+            const float* kptr = kernel.channel(p / 8 + (p % 8) / 4 + p % 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            float sum0 = bias0;
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                tmpptr++;
+                kptr++;
+            }
+
+            outptr0[0] = sum0;
+
+            outptr0++;
+        }
+    }
+#else // __riscv_vector
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int p = 0; p < outch; p++)
+    {
+        float* outptr0 = top_blob.channel(p);
+
+        const float bias0 = bias ? bias[p] : 0.f;
+
+        for (int i = 0; i < size; i++)
+        {
+            const float* tmpptr = tmp.channel(i);
+            const float* kptr = kernel.channel(p);
+
+            int nn = inch * maxk; // inch always > 0
+
+            float sum0 = bias0;
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                tmpptr++;
+                kptr++;
+            }
+
+            outptr0[0] = sum0;
+
+            outptr0++;
+        }
+    }
+#endif // __riscv_vector
+}
+
+static void convolution_im2col_sgemm_transform_kernel_rvv(const Mat& _kernel, Mat& kernel_tm, int inch, int outch, int kernel_w, int kernel_h)
+{
+    const int maxk = kernel_w * kernel_h;
+
+    // interleave
+    // src = maxk-inch-outch
+    // dst = 8b-maxk-inch-outch/8b
+    Mat kernel = _kernel.reshape(maxk, inch, outch);
+#if __riscv_vector
+    kernel_tm.create(8 * maxk, inch, outch / 8 + (outch % 8) / 4 + outch % 4);
+
+    int q = 0;
+    for (; q + 7 < outch; q += 8)
+    {
+        const Mat k0 = kernel.channel(q);
+        const Mat k1 = kernel.channel(q + 1);
+        const Mat k2 = kernel.channel(q + 2);
+        const Mat k3 = kernel.channel(q + 3);
+        const Mat k4 = kernel.channel(q + 4);
+        const Mat k5 = kernel.channel(q + 5);
+        const Mat k6 = kernel.channel(q + 6);
+        const Mat k7 = kernel.channel(q + 7);
+
+        float* g00 = kernel_tm.channel(q / 8);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+            const float* k10 = k1.row(p);
+            const float* k20 = k2.row(p);
+            const float* k30 = k3.row(p);
+            const float* k40 = k4.row(p);
+            const float* k50 = k5.row(p);
+            const float* k60 = k6.row(p);
+            const float* k70 = k7.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = k00[k];
+                g00[1] = k10[k];
+                g00[2] = k20[k];
+                g00[3] = k30[k];
+                g00[4] = k40[k];
+                g00[5] = k50[k];
+                g00[6] = k60[k];
+                g00[7] = k70[k];
+
+                g00 += 8;
+            }
+        }
+    }
+    for (; q + 3 < outch; q += 4)
+    {
+        const Mat k0 = kernel.channel(q);
+        const Mat k1 = kernel.channel(q + 1);
+        const Mat k2 = kernel.channel(q + 2);
+        const Mat k3 = kernel.channel(q + 3);
+
+        float* g00 = kernel_tm.channel(q / 8 + (q % 8) / 4);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+            const float* k10 = k1.row(p);
+            const float* k20 = k2.row(p);
+            const float* k30 = k3.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = k00[k];
+                g00[1] = k10[k];
+                g00[2] = k20[k];
+                g00[3] = k30[k];
+
+                g00 += 4;
+            }
+        }
+    }
+    for (; q < outch; q++)
+    {
+        const Mat k0 = kernel.channel(q);
+
+        float* g00 = kernel_tm.channel(q / 8 + (q % 8) / 4 + q % 4);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = k00[k];
+
+                g00 += 1;
+            }
+        }
+    }
+#else
+    kernel_tm = kernel;
+#endif // __riscv_vector
+}
+
+static void convolution_im2col_sgemm_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)
+{
+    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, 1, opt.workspace_allocator);
+    {
+        const int gap = w * stride_h - outw * stride_w;
+
+        #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;
+
+                    for (int i = 0; i < outh; i++)
+                    {
+                        int j = 0;
+                        for (; j < outw; j++)
+                        {
+                            ptr[0] = sptr[0];
+
+                            sptr += stride_w;
+                            ptr += 1;
+                        }
+
+                        sptr += gap;
+                    }
+                }
+            }
+        }
+    }
+
+    im2col_sgemm_rvv(bottom_im2col, top_blob, kernel, _bias, opt);
+}

src/layer/riscv/convolution_sgemm_fp16s.h

@@ -0,0 +1,540 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// 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_fp16sa_rvv(const Mat& bottom_im2col, Mat& top_blob, const Mat& kernel, const Mat& _bias, const Option& opt)
+{
+#if __riscv_vector
+    const int packn = csrr_vlenb() / 2;
+    const word_type vl = vsetvl_e16m1(packn);
+#endif
+
+    // Mat bottom_im2col(size, maxk, inch, 4u, 1, 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 __fp16* bias = _bias;
+
+    // permute
+    Mat tmp;
+#if __riscv_vector
+    if (size >= packn)
+        tmp.create(packn * maxk, inch, size / packn + size % packn, 4u, 1, opt.workspace_allocator);
+    else
+        tmp.create(maxk, inch, size, 4u, 1, opt.workspace_allocator);
+    {
+        int nn_size = size / packn;
+
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int ii = 0; ii < nn_size; ii++)
+        {
+            int i = ii * packn;
+
+            __fp16* tmpptr = tmp.channel(i / packn);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const __fp16* img0 = (const __fp16*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    vse16_v_f16m1(tmpptr, vle16_v_f16m1(img0, vl), vl);
+                    img0 += size;
+                    tmpptr += packn;
+                }
+            }
+        }
+
+        int remain_size_start = nn_size * packn;
+
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int i = remain_size_start; i < size; i++)
+        {
+            __fp16* tmpptr = tmp.channel(i / packn + i % packn);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const __fp16* img0 = (const __fp16*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    tmpptr[0] = img0[0];
+                    img0 += size;
+                    tmpptr += 1;
+                }
+            }
+        }
+    }
+#else // __riscv_vector
+    tmp.create(maxk, inch, size, 4u, 1, opt.workspace_allocator);
+    {
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int i = 0; i < size; i++)
+        {
+            __fp16* tmpptr = tmp.channel(i);
+
+            for (int q = 0; q < inch; q++)
+            {
+                const __fp16* img0 = (const __fp16*)bottom_im2col.channel(q) + i;
+
+                for (int k = 0; k < maxk; k++)
+                {
+                    tmpptr[0] = img0[0];
+                    img0 += size;
+                    tmpptr += 1;
+                }
+            }
+        }
+    }
+#endif // __riscv_vector
+
+#if __riscv_vector
+    int nn_outch = outch >> 3;
+    int remain_outch_start = nn_outch << 3;
+
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int pp = 0; pp < nn_outch; pp++)
+    {
+        int p = pp * 8;
+
+        __fp16* outptr0 = top_blob.channel(p);
+        __fp16* outptr1 = top_blob.channel(p + 1);
+        __fp16* outptr2 = top_blob.channel(p + 2);
+        __fp16* outptr3 = top_blob.channel(p + 3);
+        __fp16* outptr4 = top_blob.channel(p + 4);
+        __fp16* outptr5 = top_blob.channel(p + 5);
+        __fp16* outptr6 = top_blob.channel(p + 6);
+        __fp16* outptr7 = top_blob.channel(p + 7);
+
+        const __fp16 zeros[8] = {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f};
+        const __fp16* biasptr = bias ? bias + p : zeros;
+
+        int i = 0;
+        for (; i + (packn - 1) < size; i += packn)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn);
+            const __fp16* kptr = kernel.channel(p / 8);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat16m1_t _sum0 = vfmv_v_f_f16m1(biasptr[0], vl);
+            vfloat16m1_t _sum1 = vfmv_v_f_f16m1(biasptr[1], vl);
+            vfloat16m1_t _sum2 = vfmv_v_f_f16m1(biasptr[2], vl);
+            vfloat16m1_t _sum3 = vfmv_v_f_f16m1(biasptr[3], vl);
+            vfloat16m1_t _sum4 = vfmv_v_f_f16m1(biasptr[4], vl);
+            vfloat16m1_t _sum5 = vfmv_v_f_f16m1(biasptr[5], vl);
+            vfloat16m1_t _sum6 = vfmv_v_f_f16m1(biasptr[6], vl);
+            vfloat16m1_t _sum7 = vfmv_v_f_f16m1(biasptr[7], vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                vfloat16m1_t _val = vle16_v_f16m1(tmpptr, vl);
+                _sum0 = vfmacc_vf_f16m1(_sum0, kptr[0], _val, vl);
+                _sum1 = vfmacc_vf_f16m1(_sum1, kptr[1], _val, vl);
+                _sum2 = vfmacc_vf_f16m1(_sum2, kptr[2], _val, vl);
+                _sum3 = vfmacc_vf_f16m1(_sum3, kptr[3], _val, vl);
+                _sum4 = vfmacc_vf_f16m1(_sum4, kptr[4], _val, vl);
+                _sum5 = vfmacc_vf_f16m1(_sum5, kptr[5], _val, vl);
+                _sum6 = vfmacc_vf_f16m1(_sum6, kptr[6], _val, vl);
+                _sum7 = vfmacc_vf_f16m1(_sum7, kptr[7], _val, vl);
+                tmpptr += packn;
+                kptr += 8;
+            }
+
+            vse16_v_f16m1(outptr0, _sum0, vl);
+            vse16_v_f16m1(outptr1, _sum1, vl);
+            vse16_v_f16m1(outptr2, _sum2, vl);
+            vse16_v_f16m1(outptr3, _sum3, vl);
+            vse16_v_f16m1(outptr4, _sum4, vl);
+            vse16_v_f16m1(outptr5, _sum5, vl);
+            vse16_v_f16m1(outptr6, _sum6, vl);
+            vse16_v_f16m1(outptr7, _sum7, vl);
+
+            outptr0 += packn;
+            outptr1 += packn;
+            outptr2 += packn;
+            outptr3 += packn;
+            outptr4 += packn;
+            outptr5 += packn;
+            outptr6 += packn;
+            outptr7 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn + i % packn);
+            const __fp16* kptr = kernel.channel(p / 8);
+
+            int nn = inch * maxk; // inch always > 0
+
+            __fp16 sum0 = biasptr[0];
+            __fp16 sum1 = biasptr[1];
+            __fp16 sum2 = biasptr[2];
+            __fp16 sum3 = biasptr[3];
+            __fp16 sum4 = biasptr[4];
+            __fp16 sum5 = biasptr[5];
+            __fp16 sum6 = biasptr[6];
+            __fp16 sum7 = biasptr[7];
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                sum1 += tmpptr[0] * kptr[1];
+                sum2 += tmpptr[0] * kptr[2];
+                sum3 += tmpptr[0] * kptr[3];
+                sum4 += tmpptr[0] * kptr[4];
+                sum5 += tmpptr[0] * kptr[5];
+                sum6 += tmpptr[0] * kptr[6];
+                sum7 += tmpptr[0] * kptr[7];
+                tmpptr++;
+                kptr += 8;
+            }
+
+            outptr0[0] = sum0;
+            outptr1[0] = sum1;
+            outptr2[0] = sum2;
+            outptr3[0] = sum3;
+            outptr4[0] = sum4;
+            outptr5[0] = sum5;
+            outptr6[0] = sum6;
+            outptr7[0] = sum7;
+
+            outptr0++;
+            outptr1++;
+            outptr2++;
+            outptr3++;
+            outptr4++;
+            outptr5++;
+            outptr6++;
+            outptr7++;
+        }
+    }
+
+    nn_outch = (outch - remain_outch_start) >> 2;
+
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int pp = 0; pp < nn_outch; pp++)
+    {
+        int p = remain_outch_start + pp * 4;
+
+        __fp16* outptr0 = top_blob.channel(p);
+        __fp16* outptr1 = top_blob.channel(p + 1);
+        __fp16* outptr2 = top_blob.channel(p + 2);
+        __fp16* outptr3 = top_blob.channel(p + 3);
+
+        const __fp16 zeros[4] = {0.f, 0.f, 0.f, 0.f};
+        const __fp16* biasptr = bias ? bias + p : zeros;
+
+        int i = 0;
+        for (; i + (packn - 1) < size; i += packn)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn);
+            const __fp16* kptr = kernel.channel(p / 8 + (p % 8) / 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat16m1_t _sum0 = vfmv_v_f_f16m1(biasptr[0], vl);
+            vfloat16m1_t _sum1 = vfmv_v_f_f16m1(biasptr[1], vl);
+            vfloat16m1_t _sum2 = vfmv_v_f_f16m1(biasptr[2], vl);
+            vfloat16m1_t _sum3 = vfmv_v_f_f16m1(biasptr[3], vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                vfloat16m1_t _val = vle16_v_f16m1(tmpptr, vl);
+                _sum0 = vfmacc_vf_f16m1(_sum0, kptr[0], _val, vl);
+                _sum1 = vfmacc_vf_f16m1(_sum1, kptr[1], _val, vl);
+                _sum2 = vfmacc_vf_f16m1(_sum2, kptr[2], _val, vl);
+                _sum3 = vfmacc_vf_f16m1(_sum3, kptr[3], _val, vl);
+                tmpptr += packn;
+                kptr += 4;
+            }
+
+            vse16_v_f16m1(outptr0, _sum0, vl);
+            vse16_v_f16m1(outptr1, _sum1, vl);
+            vse16_v_f16m1(outptr2, _sum2, vl);
+            vse16_v_f16m1(outptr3, _sum3, vl);
+
+            outptr0 += packn;
+            outptr1 += packn;
+            outptr2 += packn;
+            outptr3 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn + i % packn);
+            const __fp16* kptr = kernel.channel(p / 8 + (p % 8) / 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            __fp16 sum0 = biasptr[0];
+            __fp16 sum1 = biasptr[1];
+            __fp16 sum2 = biasptr[2];
+            __fp16 sum3 = biasptr[3];
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                sum1 += tmpptr[0] * kptr[1];
+                sum2 += tmpptr[0] * kptr[2];
+                sum3 += tmpptr[0] * kptr[3];
+                tmpptr++;
+                kptr += 4;
+            }
+
+            outptr0[0] = sum0;
+            outptr1[0] = sum1;
+            outptr2[0] = sum2;
+            outptr3[0] = sum3;
+
+            outptr0++;
+            outptr1++;
+            outptr2++;
+            outptr3++;
+        }
+    }
+
+    remain_outch_start += nn_outch << 2;
+
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int p = remain_outch_start; p < outch; p++)
+    {
+        __fp16* outptr0 = top_blob.channel(p);
+
+        const __fp16 bias0 = bias ? bias[p] : 0.f;
+
+        int i = 0;
+        for (; i + (packn - 1) < size; i += packn)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn);
+            const __fp16* kptr = kernel.channel(p / 8 + (p % 8) / 4 + p % 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            vfloat16m1_t _sum0 = vfmv_v_f_f16m1(bias0, vl);
+
+            for (int q = 0; q < nn; q++)
+            {
+                _sum0 = vfmacc_vf_f16m1(_sum0, kptr[0], vle16_v_f16m1(tmpptr, vl), vl);
+                tmpptr += packn;
+                kptr++;
+            }
+
+            vse16_v_f16m1(outptr0, _sum0, vl);
+
+            outptr0 += packn;
+        }
+        for (; i < size; i++)
+        {
+            const __fp16* tmpptr = tmp.channel(i / packn + i % packn);
+            const __fp16* kptr = kernel.channel(p / 8 + (p % 8) / 4 + p % 4);
+
+            int nn = inch * maxk; // inch always > 0
+
+            __fp16 sum0 = bias0;
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                tmpptr++;
+                kptr++;
+            }
+
+            outptr0[0] = sum0;
+
+            outptr0++;
+        }
+    }
+#else // __riscv_vector
+    #pragma omp parallel for num_threads(opt.num_threads)
+    for (int p = 0; p < outch; p++)
+    {
+        __fp16* outptr0 = top_blob.channel(p);
+
+        const __fp16 bias0 = bias ? bias[p] : 0.f;
+
+        for (int i = 0; i < size; i++)
+        {
+            const __fp16* tmpptr = tmp.channel(i);
+            const __fp16* kptr = kernel.channel(p);
+
+            int nn = inch * maxk; // inch always > 0
+
+            __fp16 sum0 = bias0;
+
+            for (int q = 0; q < nn; q++)
+            {
+                sum0 += tmpptr[0] * kptr[0];
+                tmpptr++;
+                kptr++;
+            }
+
+            outptr0[0] = sum0;
+
+            outptr0++;
+        }
+    }
+#endif // __riscv_vector
+}
+
+static void convolution_im2col_sgemm_transform_kernel_fp16sa_rvv(const Mat& _kernel, Mat& kernel_tm, int inch, int outch, int kernel_w, int kernel_h)
+{
+    const int maxk = kernel_w * kernel_h;
+
+    // interleave
+    // src = maxk-inch-outch
+    // dst = 8b-maxk-inch-outch/8b
+    Mat kernel = _kernel.reshape(maxk, inch, outch);
+#if __riscv_vector
+    kernel_tm.create(8 * maxk, inch, outch / 8 + (outch % 8) / 4 + outch % 4, 2u);
+
+    int q = 0;
+    for (; q + 7 < outch; q += 8)
+    {
+        const Mat k0 = kernel.channel(q);
+        const Mat k1 = kernel.channel(q + 1);
+        const Mat k2 = kernel.channel(q + 2);
+        const Mat k3 = kernel.channel(q + 3);
+        const Mat k4 = kernel.channel(q + 4);
+        const Mat k5 = kernel.channel(q + 5);
+        const Mat k6 = kernel.channel(q + 6);
+        const Mat k7 = kernel.channel(q + 7);
+
+        __fp16* g00 = kernel_tm.channel(q / 8);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+            const float* k10 = k1.row(p);
+            const float* k20 = k2.row(p);
+            const float* k30 = k3.row(p);
+            const float* k40 = k4.row(p);
+            const float* k50 = k5.row(p);
+            const float* k60 = k6.row(p);
+            const float* k70 = k7.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = (__fp16)k00[k];
+                g00[1] = (__fp16)k10[k];
+                g00[2] = (__fp16)k20[k];
+                g00[3] = (__fp16)k30[k];
+                g00[4] = (__fp16)k40[k];
+                g00[5] = (__fp16)k50[k];
+                g00[6] = (__fp16)k60[k];
+                g00[7] = (__fp16)k70[k];
+
+                g00 += 8;
+            }
+        }
+    }
+    for (; q + 3 < outch; q += 4)
+    {
+        const Mat k0 = kernel.channel(q);
+        const Mat k1 = kernel.channel(q + 1);
+        const Mat k2 = kernel.channel(q + 2);
+        const Mat k3 = kernel.channel(q + 3);
+
+        __fp16* g00 = kernel_tm.channel(q / 8 + (q % 8) / 4);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+            const float* k10 = k1.row(p);
+            const float* k20 = k2.row(p);
+            const float* k30 = k3.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = (__fp16)k00[k];
+                g00[1] = (__fp16)k10[k];
+                g00[2] = (__fp16)k20[k];
+                g00[3] = (__fp16)k30[k];
+
+                g00 += 4;
+            }
+        }
+    }
+    for (; q < outch; q++)
+    {
+        const Mat k0 = kernel.channel(q);
+
+        __fp16* g00 = kernel_tm.channel(q / 8 + (q % 8) / 4 + q % 4);
+
+        for (int p = 0; p < inch; p++)
+        {
+            const float* k00 = k0.row(p);
+
+            for (int k = 0; k < maxk; k++)
+            {
+                g00[0] = (__fp16)k00[k];
+
+                g00 += 1;
+            }
+        }
+    }
+#else
+    kernel_tm = kernel;
+#endif // __riscv_vector
+}
+
+static void convolution_im2col_sgemm_fp16sa_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)
+{
+    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, 1, opt.workspace_allocator);
+    {
+        const int gap = w * stride_h - outw * stride_w;
+
+        #pragma omp parallel for num_threads(opt.num_threads)
+        for (int p = 0; p < inch; p++)
+        {
+            const Mat img = bottom_blob.channel(p);
+            __fp16* ptr = bottom_im2col.channel(p);
+
+            for (int u = 0; u < kernel_h; u++)
+            {
+                for (int v = 0; v < kernel_w; v++)
+                {
+                    const __fp16* sptr = img.row<const __fp16>(dilation_h * u) + dilation_w * v;
+
+                    for (int i = 0; i < outh; i++)
+                    {
+                        int j = 0;
+                        for (; j < outw; j++)
+                        {
+                            ptr[0] = sptr[0];
+
+                            sptr += stride_w;
+                            ptr += 1;
+                        }
+
+                        sptr += gap;
+                    }
+                }
+            }
+        }
+    }
+
+    im2col_sgemm_fp16sa_rvv(bottom_im2col, top_blob, kernel, _bias, opt);
+}