text: // 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.
  
  #include "convolutiondepthwise_riscv.h"
  
  #include "cpu.h"
  #include "layer_type.h"
  
  #if __riscv_vector
  #ifdef RVV_SPEC_0_7
  #include "riscv_v_071_fix.h"
  #else
  #include <riscv_vector.h>
  #endif
  #endif // __riscv_vector
  
  #include "riscv_activation.h"
  #include "riscv_usability.h"
  
  namespace ncnn {
  
  #include "convolutiondepthwise_3x3.h"
  
  #if __riscv_vector
  #include "convolutiondepthwise_3x3_packn.h"
  #include "convolutiondepthwise_5x5_packn.h"
  
  #if __riscv_zfh
  #include "convolutiondepthwise_3x3_packn_fp16s.h"
  #include "convolutiondepthwise_5x5_packn_fp16s.h"
  #endif
  #endif // __riscv_vector
  
  ConvolutionDepthWise_riscv::ConvolutionDepthWise_riscv()
  {
  #if __riscv_vector
      support_packing = true;
  #if __riscv_zfh
      support_fp16_storage = true;
  #endif
  #endif // __riscv_vector
  
      activation = 0;
  }
  
  int ConvolutionDepthWise_riscv::create_pipeline(const Option& opt)
  {
      if (dynamic_weight)
          return 0;
  
      activation = create_activation_layer(activation_type, activation_params, opt);
  
  #if NCNN_INT8
      if (opt.use_int8_inference && weight_data.elemsize == (size_t)1u)
      {
          // TODO implement int8
          return 0;
      }
  #endif
  
  #if __riscv_vector && __riscv_zfh
      if (opt.use_fp16_storage)
      {
          return create_pipeline_fp16s(opt);
      }
  #endif
  
  #if __riscv_vector
      const int packn = csrr_vlenb() / 4;
  #endif
  
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          int elempack = 1;
  #if __riscv_vector
          if (opt.use_packing_layout)
          {
              elempack = channels % packn == 0 ? packn : 1;
          }
  #endif
  
  #if __riscv_vector
          // packn
          if (elempack == packn)
          {
              Mat weight_data_r2 = weight_data.reshape(maxk, group);
              convert_packing(weight_data_r2, weight_data_tm, packn, opt);
          }
  #endif // __riscv_vector
  
          if (elempack == 1)
          {
              weight_data_tm = weight_data;
          }
  
          if (opt.lightmode)
          {
              weight_data.release();
          }
  
          return 0;
      }
  
      // group convolution
      create_group_ops(opt);
  
      if (opt.lightmode)
      {
          weight_data.release();
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::create_group_ops(const Option& opt)
  {
      // create Convolution op for each group
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      for (int i = 0; i < (int)group_ops.size(); i++)
          delete group_ops[i];
  
      group_ops.clear();
  
      const int channels_g = channels / group;
      const int num_output_g = num_output / group;
  
      group_ops.resize(group);
  
      for (int g = 0; g < group; g++)
      {
          Mat weight_data_g = weight_data.range(maxk * channels_g * num_output_g * g, maxk * channels_g * num_output_g).clone();
          Mat bias_data_g;
          if (bias_term)
              bias_data_g = bias_data.range(num_output_g * g, num_output_g);
  
          ncnn::Layer* op = ncnn::create_layer(ncnn::LayerType::Convolution);
  
          // set param
          ncnn::ParamDict pd;
          pd.set(0, num_output_g); // num_output
          pd.set(1, kernel_w);
          pd.set(11, kernel_h);
          pd.set(2, dilation_w);
          pd.set(12, dilation_h);
          pd.set(3, stride_w);
          pd.set(13, stride_h);
          pd.set(4, 0);  // pad_w
          pd.set(14, 0); // pad_h
          pd.set(5, bias_term);
          pd.set(6, maxk * channels_g * num_output_g); // weight_data_size
          pd.set(8, int8_scale_term);
          pd.set(9, activation_type);
          pd.set(10, activation_params);
  
          op->load_param(pd);
  
          // set weights
          if (bias_term)
          {
              ncnn::Mat weights[5];
              weights[0] = weight_data_g;
              weights[1] = bias_data_g;
  
  #if NCNN_INT8
              if (int8_scale_term)
              {
                  Mat weight_data_int8_scales_g(num_output_g);
                  weight_data_int8_scales_g.fill(weight_data_int8_scales[g]);
                  weights[2] = weight_data_int8_scales_g;
                  weights[3] = bottom_blob_int8_scales.range(g, 1);
              }
              if (int8_scale_term > 100)
              {
                  weights[4] = top_blob_int8_scales.range(g, 1);
              }
  #endif
  
              op->load_model(ModelBinFromMatArray(weights));
          }
          else
          {
              ncnn::Mat weights[4];
              weights[0] = weight_data_g;
  
  #if NCNN_INT8
              if (int8_scale_term)
              {
                  Mat weight_data_int8_scales_g(num_output_g);
                  weight_data_int8_scales_g.fill(weight_data_int8_scales[g]);
                  weights[1] = weight_data_int8_scales_g;
                  weights[2] = bottom_blob_int8_scales.range(g, 1);
              }
              if (int8_scale_term > 100)
              {
                  weights[3] = top_blob_int8_scales.range(g, 1);
              }
  #endif
  
              op->load_model(ModelBinFromMatArray(weights));
          }
  
          op->create_pipeline(opt);
  
          group_ops[g] = op;
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::destroy_pipeline(const Option& opt)
  {
      if (activation)
      {
          activation->destroy_pipeline(opt);
          delete activation;
          activation = 0;
      }
  
      for (int i = 0; i < (int)group_ops.size(); i++)
      {
          group_ops[i]->destroy_pipeline(opt);
          delete group_ops[i];
      }
      group_ops.clear();
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
  #if NCNN_INT8
      if (opt.use_int8_inference && int8_scale_term)
      {
          Mat bottom_blob_unpacked = bottom_blob;
          if (bottom_blob.elempack != 1)
          {
              Option opt_pack1 = opt;
              opt_pack1.blob_allocator = opt.workspace_allocator;
  
              convert_packing(bottom_blob, bottom_blob_unpacked, 1, opt_pack1);
          }
  
          Mat bottom_blob_unpacked_fp32 = bottom_blob_unpacked;
          if (bottom_blob_unpacked.elembits() == 16)
          {
              Option opt_pack1 = opt;
              opt_pack1.blob_allocator = opt.workspace_allocator;
  
              cast_float16_to_float32(bottom_blob_unpacked, bottom_blob_unpacked_fp32, opt_pack1);
          }
  
          Option opt_unpacked = opt;
          opt_unpacked.use_packing_layout = false;
          return ConvolutionDepthWise::forward_int8(bottom_blob_unpacked_fp32, top_blob, opt_unpacked);
      }
  #endif
  
      int elembits = bottom_blob.elembits();
  
  #if __riscv_vector && __riscv_zfh
      if (opt.use_fp16_storage && elembits == 16)
      {
          if (opt.use_fp16_arithmetic)
              return forward_fp16sa(bottom_blob, top_blob, opt);
          else
              return forward_fp16s(bottom_blob, top_blob, opt);
      }
  #endif
  
  #if __riscv_vector
      const int packn = csrr_vlenb() / 4;
      const word_type vl = vsetvl_e32m1(packn);
  #endif
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
      int elempack = bottom_blob.elempack;
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      Mat bottom_blob_bordered;
      make_padding(bottom_blob, bottom_blob_bordered, opt);
      if (bottom_blob_bordered.empty())
          return -100;
  
      w = bottom_blob_bordered.w;
      h = bottom_blob_bordered.h;
  
      int outw = (w - kernel_extent_w) / stride_w + 1;
      int outh = (h - kernel_extent_h) / stride_h + 1;
      int out_elempack = 1;
  #if __riscv_vector
      if (opt.use_packing_layout)
      {
          out_elempack = num_output % packn == 0 ? packn : 1;
      }
  #endif
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      top_blob.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // depth-wise
      if (channels * elempack == group && group == num_output)
      {
  #if __riscv_vector
          if (elempack == packn)
          {
              if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
              {
                  convdw3x3s1_packn_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
              {
                  convdw3x3s2_packn_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 5 && kernel_h == 5 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
              {
                  convdw5x5s1_packn_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 5 && kernel_h == 5 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
              {
                  convdw5x5s2_packn_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < channels; g++)
                  {
                      float* outptr = top_blob.channel(g);
                      const float* kptr = (const float*)weight_data_tm + maxk * g * packn;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              vfloat32m1_t _sum = vfmv_v_f_f32m1(0.f, vl);
  
                              if (bias_term)
                              {
                                  _sum = vle32_v_f32m1((const float*)bias_data + g * packn, vl);
                              }
  
                              const float* sptr = m.row(i * stride_h) + j * stride_w * packn;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  vfloat32m1_t _val = vle32_v_f32m1(sptr + space_ofs[k] * packn, vl);
                                  vfloat32m1_t _w = vle32_v_f32m1(kptr + k * packn, vl);
                                  _sum = vfmacc_vv_f32m1(_sum, _val, _w, vl);
                              }
  
                              _sum = activation_ps(_sum, activation_type, activation_params, vl);
  
                              vse32_v_f32m1(outptr + j * packn, _sum, vl);
                          }
  
                          outptr += outw * packn;
                      }
                  }
              }
          }
  #endif // __riscv_vector
  
          if (elempack == 1)
          {
              if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
              {
                  convdw3x3s1_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
              {
                  convdw3x3s2_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < group; g++)
                  {
                      float* outptr = top_blob.channel(g);
                      const float* kptr = (const float*)weight_data_tm + maxk * g;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              float sum = 0.f;
  
                              if (bias_term)
                                  sum = bias_data[g];
  
                              const float* sptr = m.row(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float val = (float)sptr[space_ofs[k]];
                                  float w = (float)kptr[k];
                                  sum += val * w;
                              }
  
                              sum = activation_ss(sum, activation_type, activation_params);
  
                              outptr[j] = sum;
                          }
  
                          outptr += outw;
                      }
                  }
              }
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels * elempack / group;
      const int num_output_g = num_output / group;
  
      int g_elempack = 1;
      int out_g_elempack = 1;
  #if __riscv_vector
      if (opt.use_packing_layout)
      {
          g_elempack = channels_g % packn == 0 ? packn : 1;
          out_g_elempack = num_output_g % packn == 0 ? packn : 1;
      }
  #endif
  
      // unpacking
      Mat bottom_blob_bordered_unpacked = bottom_blob_bordered;
      if (elempack > g_elempack)
      {
          Option opt_p = opt;
          opt_p.blob_allocator = opt.workspace_allocator;
          convert_packing(bottom_blob_bordered, bottom_blob_bordered_unpacked, 1, opt_p);
      }
  
      Mat top_blob_unpacked = top_blob;
      if (out_g_elempack < out_elempack)
      {
          top_blob_unpacked.create(outw, outh, num_output, out_elemsize / out_elempack, 1, opt.workspace_allocator);
          if (top_blob_unpacked.empty())
              return -100;
      }
  
      for (int g = 0; g < group; g++)
      {
          const Mat bottom_blob_bordered_g = bottom_blob_bordered_unpacked.channel_range(channels_g * g / g_elempack, channels_g / g_elempack);
          Mat top_blob_g = top_blob_unpacked.channel_range(num_output_g * g / out_g_elempack, num_output_g / out_g_elempack);
  
          const ncnn::Layer* op = group_ops[g];
  
          Option opt_g = opt;
          opt_g.blob_allocator = top_blob_unpacked.allocator;
  
          // forward
          op->forward(bottom_blob_bordered_g, top_blob_g, opt_g);
      }
  
      // packing
      if (out_g_elempack < out_elempack)
      {
          convert_packing(top_blob_unpacked, top_blob, out_elempack, opt);
      }
      else
      {
          top_blob = top_blob_unpacked;
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const
  {
      const Mat& bottom_blob = bottom_blobs[0];
      const Mat& _weight_data = bottom_blobs[1];
      Mat& top_blob = top_blobs[0];
  
      const int _kernel_w = _weight_data.w;
      const int _kernel_h = _weight_data.h;
      const int _num_output = _weight_data.c * _weight_data.elempack;
  
      Mat weight_data_flattened;
      flatten(_weight_data, weight_data_flattened, opt);
      if (weight_data_flattened.empty())
          return -100;
  
  #if NCNN_RVV
      if (opt.use_fp16_storage && cpu_support_riscv_v() && cpu_support_riscv_zfh() && weight_data_flattened.elembits() == 16)
      {
          Mat weight_data_flattened_fp32;
          cast_float16_to_float32(weight_data_flattened, weight_data_flattened_fp32, opt);
          weight_data_flattened = weight_data_flattened_fp32;
      }
  #endif // NCNN_RVV
  
      // weight_data_flattened as pack1
      weight_data_flattened.w *= weight_data_flattened.elempack;
      weight_data_flattened.elemsize /= weight_data_flattened.elempack;
      weight_data_flattened.elempack = 1;
  
      Mat bias_data_flattened;
      if (bias_term)
      {
          const Mat& _bias_data = bottom_blobs[2];
          flatten(_bias_data, bias_data_flattened, opt);
          if (bias_data_flattened.empty())
              return -100;
  
  #if NCNN_RVV
          if (opt.use_fp16_storage && cpu_support_riscv_v() && cpu_support_riscv_zfh() && bias_data_flattened.elembits() == 16)
          {
              Mat bias_data_flattened_fp32;
              cast_float16_to_float32(bias_data_flattened, bias_data_flattened_fp32, opt);
              bias_data_flattened = bias_data_flattened_fp32;
          }
  #endif // NCNN_RVV
  
          // bias_data_flattened as pack1
          bias_data_flattened.w *= bias_data_flattened.elempack;
          bias_data_flattened.elemsize /= bias_data_flattened.elempack;
          bias_data_flattened.elempack = 1;
      }
  
      ncnn::Layer* op = ncnn::create_layer(ncnn::LayerType::ConvolutionDepthWise);
  
      ncnn::ParamDict pd;
      pd.set(0, _num_output);
      pd.set(1, _kernel_w);
      pd.set(11, _kernel_h);
      pd.set(2, dilation_w);
      pd.set(12, dilation_h);
      pd.set(3, stride_w);
      pd.set(13, stride_h);
      pd.set(4, pad_left);
      pd.set(15, pad_right);
      pd.set(14, pad_top);
      pd.set(16, pad_bottom);
      pd.set(18, pad_value);
      pd.set(5, bias_term);
      pd.set(6, weight_data_flattened.w);
      pd.set(7, group);
      pd.set(8, int8_scale_term);
      pd.set(9, activation_type);
      pd.set(10, activation_params);
  
      op->load_param(pd);
  
      ncnn::Mat weights[2];
      weights[0] = weight_data_flattened;
      weights[1] = bias_data_flattened;
  
      op->load_model(ncnn::ModelBinFromMatArray(weights));
  
      op->create_pipeline(opt);
  
      op->forward(bottom_blob, top_blob, opt);
  
      op->destroy_pipeline(opt);
  
      delete op;
  
      return 0;
  }
  
  #if __riscv_vector && __riscv_zfh
  int ConvolutionDepthWise_riscv::create_pipeline_fp16s(const Option& opt)
  {
      const int packn = csrr_vlenb() / 2;
  
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          int elempack = 1;
          if (opt.use_packing_layout)
          {
              elempack = channels % packn == 0 ? packn : 1;
          }
  
          // packn
          if (elempack == packn)
          {
              Mat weight_data_r2 = weight_data.reshape(maxk, group);
              Mat weight_data_r2_packed;
              convert_packing(weight_data_r2, weight_data_r2_packed, packn, opt);
  
              ncnn::cast_float32_to_float16(weight_data_r2_packed, weight_data_tm, opt);
          }
  
          if (elempack == 1)
          {
              ncnn::cast_float32_to_float16(weight_data, weight_data_tm, opt);
          }
  
          ncnn::cast_float32_to_float16(bias_data, bias_data_fp16, opt);
  
          if (opt.lightmode)
          {
              weight_data.release();
          }
  
          return 0;
      }
  
      // group convolution
      create_group_ops(opt);
  
      if (opt.lightmode)
      {
          weight_data.release();
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::forward_fp16s(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      const int packn = csrr_vlenb() / 2;
      const word_type vl = vsetvl_e16m1(packn);
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
      int elempack = bottom_blob.elempack;
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      Mat bottom_blob_bordered;
      make_padding(bottom_blob, bottom_blob_bordered, opt);
      if (bottom_blob_bordered.empty())
          return -100;
  
      w = bottom_blob_bordered.w;
      h = bottom_blob_bordered.h;
  
      int outw = (w - kernel_extent_w) / stride_w + 1;
      int outh = (h - kernel_extent_h) / stride_h + 1;
      int out_elempack = (opt.use_packing_layout && num_output % packn == 0) ? packn : 1;
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      top_blob.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // depth-wise
      if (channels * elempack == group && group == num_output)
      {
          if (elempack == packn)
          {
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < channels; g++)
                  {
                      __fp16* outptr = top_blob.channel(g);
                      const __fp16* kptr = (const __fp16*)weight_data_tm + maxk * g * packn;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              vfloat32m2_t _sum = vfmv_v_f_f32m2(0.f, vl);
  
                              if (bias_term)
                              {
                                  _sum = vle32_v_f32m2((const float*)bias_data + g * packn, vl);
                              }
  
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * packn;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  vfloat16m1_t _val = vle16_v_f16m1(sptr + space_ofs[k] * packn, vl);
                                  vfloat16m1_t _w = vle16_v_f16m1(kptr + k * packn, vl);
                                  _sum = vfwmacc_vv_f32m2(_sum, _val, _w, vl);
                              }
  
                              _sum = activation_ps(_sum, activation_type, activation_params, vl);
  
                              vse16_v_f16m1(outptr + j * packn, vfncvt_f_f_w_f16m1(_sum, vl), vl);
                          }
  
                          outptr += outw * packn;
                      }
                  }
              }
          }
  
          if (elempack == 1)
          {
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < group; g++)
                  {
                      __fp16* outptr = top_blob.channel(g);
                      const __fp16* kptr = (const __fp16*)weight_data_tm + maxk * g;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              float sum = 0.f;
  
                              if (bias_term)
                                  sum = bias_data[g];
  
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float val = (float)sptr[space_ofs[k]];
                                  float w = (float)kptr[k];
                                  sum += val * w;
                              }
  
                              sum = activation_ss(sum, activation_type, activation_params);
  
                              outptr[j] = (__fp16)sum;
                          }
  
                          outptr += outw;
                      }
                  }
              }
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels * elempack / group;
      const int num_output_g = num_output / group;
  
      int g_elempack = (opt.use_packing_layout && channels_g % packn == 0) ? packn : 1;
      int out_g_elempack = (opt.use_packing_layout && num_output_g % packn == 0) ? packn : 1;
  
      // unpacking
      Mat bottom_blob_bordered_unpacked = bottom_blob_bordered;
      if (elempack > g_elempack)
      {
          Option opt_p = opt;
          opt_p.blob_allocator = opt.workspace_allocator;
          convert_packing(bottom_blob_bordered, bottom_blob_bordered_unpacked, 1, opt_p);
      }
  
      Mat top_blob_unpacked = top_blob;
      if (out_g_elempack < out_elempack)
      {
          top_blob_unpacked.create(outw, outh, num_output, out_elemsize / out_elempack, 1, opt.workspace_allocator);
          if (top_blob_unpacked.empty())
              return -100;
      }
  
      for (int g = 0; g < group; g++)
      {
          const Mat bottom_blob_bordered_g = bottom_blob_bordered_unpacked.channel_range(channels_g * g / g_elempack, channels_g / g_elempack);
          Mat top_blob_g = top_blob_unpacked.channel_range(num_output_g * g / out_g_elempack, num_output_g / out_g_elempack);
  
          const ncnn::Layer* op = group_ops[g];
  
          Option opt_g = opt;
          opt_g.blob_allocator = top_blob_unpacked.allocator;
  
          // forward
          op->forward(bottom_blob_bordered_g, top_blob_g, opt_g);
      }
  
      // packing
      if (out_g_elempack < out_elempack)
      {
          convert_packing(top_blob_unpacked, top_blob, out_elempack, opt);
      }
      else
      {
          top_blob = top_blob_unpacked;
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_riscv::forward_fp16sa(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      const int packn = csrr_vlenb() / 2;
      const word_type vl = vsetvl_e16m1(packn);
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
      int elempack = bottom_blob.elempack;
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      Mat bottom_blob_bordered;
      make_padding(bottom_blob, bottom_blob_bordered, opt);
      if (bottom_blob_bordered.empty())
          return -100;
  
      w = bottom_blob_bordered.w;
      h = bottom_blob_bordered.h;
  
      int outw = (w - kernel_extent_w) / stride_w + 1;
      int outh = (h - kernel_extent_h) / stride_h + 1;
      int out_elempack = (opt.use_packing_layout && num_output % packn == 0) ? packn : 1;
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      top_blob.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // depth-wise
      if (channels * elempack == group && group == num_output)
      {
          if (elempack == packn)
          {
              if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
              {
                  convdw3x3s1_packn_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data_fp16, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
              {
                  convdw3x3s2_packn_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data_fp16, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 5 && kernel_h == 5 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
              {
                  convdw5x5s1_packn_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data_fp16, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else if (kernel_w == 5 && kernel_h == 5 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
              {
                  convdw5x5s2_packn_fp16sa_rvv(bottom_blob_bordered, top_blob, weight_data_tm, bias_data_fp16, opt);
  
                  if (activation)
                  {
                      activation->forward_inplace(top_blob, opt);
                  }
              }
              else
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < channels; g++)
                  {
                      __fp16* outptr = top_blob.channel(g);
                      const __fp16* kptr = (const __fp16*)weight_data_tm + maxk * g * packn;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              vfloat16m1_t _sum = vfmv_v_f_f16m1((__fp16)0.f, vl);
  
                              if (bias_term)
                              {
                                  _sum = vle16_v_f16m1((const __fp16*)bias_data_fp16 + g * packn, vl);
                              }
  
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * packn;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  vfloat16m1_t _val = vle16_v_f16m1(sptr + space_ofs[k] * packn, vl);
                                  vfloat16m1_t _w = vle16_v_f16m1(kptr + k * packn, vl);
                                  _sum = vfmacc_vv_f16m1(_sum, _val, _w, vl);
                              }
  
                              _sum = activation_ps(_sum, activation_type, activation_params, vl);
  
                              vse16_v_f16m1(outptr + j * packn, _sum, vl);
                          }
  
                          outptr += outw * packn;
                      }
                  }
              }
          }
  
          if (elempack == 1)
          {
              {
                  const int maxk = kernel_w * kernel_h;
  
                  // kernel offsets
                  std::vector<int> _space_ofs(maxk);
                  int* space_ofs = &_space_ofs[0];
                  {
                      int p1 = 0;
                      int p2 = 0;
                      int gap = w * dilation_h - kernel_w * dilation_w;
                      for (int i = 0; i < kernel_h; i++)
                      {
                          for (int j = 0; j < kernel_w; j++)
                          {
                              space_ofs[p1] = p2;
                              p1++;
                              p2 += dilation_w;
                          }
                          p2 += gap;
                      }
                  }
  
                  #pragma omp parallel for num_threads(opt.num_threads)
                  for (int g = 0; g < group; g++)
                  {
                      __fp16* outptr = top_blob.channel(g);
                      const __fp16* kptr = (const __fp16*)weight_data_tm + maxk * g;
                      const Mat m = bottom_blob_bordered.channel(g);
  
                      for (int i = 0; i < outh; i++)
                      {
                          for (int j = 0; j < outw; j++)
                          {
                              float sum = 0.f;
  
                              if (bias_term)
                                  sum = bias_data[g];
  
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  __fp16 val = sptr[space_ofs[k]];
                                  __fp16 w = kptr[k];
                                  sum += val * w;
                              }
  
                              sum = activation_ss(sum, activation_type, activation_params);
  
                              outptr[j] = (__fp16)sum;
                          }
  
                          outptr += outw;
                      }
                  }
              }
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels * elempack / group;
      const int num_output_g = num_output / group;
  
      int g_elempack = (opt.use_packing_layout && channels_g % packn == 0) ? packn : 1;
      int out_g_elempack = (opt.use_packing_layout && num_output_g % packn == 0) ? packn : 1;
  
      // unpacking
      Mat bottom_blob_bordered_unpacked = bottom_blob_bordered;
      if (elempack > g_elempack)
      {
          Option opt_p = opt;
          opt_p.blob_allocator = opt.workspace_allocator;
          convert_packing(bottom_blob_bordered, bottom_blob_bordered_unpacked, g_elempack, opt_p);
      }
  
      Mat top_blob_unpacked = top_blob;
      if (out_g_elempack < out_elempack)
      {
          top_blob_unpacked.create(outw, outh, num_output / out_g_elempack, out_elemsize / out_elempack * out_g_elempack, out_g_elempack, opt.workspace_allocator);
          if (top_blob_unpacked.empty())
              return -100;
      }
  
      for (int g = 0; g < group; g++)
      {
          const Mat bottom_blob_bordered_g = bottom_blob_bordered_unpacked.channel_range(channels_g * g / g_elempack, channels_g / g_elempack);
          Mat top_blob_g = top_blob_unpacked.channel_range(num_output_g * g / out_g_elempack, num_output_g / out_g_elempack);
  
          const ncnn::Layer* op = group_ops[g];
  
          Option opt_g = opt;
          opt_g.blob_allocator = top_blob_unpacked.allocator;
  
          // forward
          op->forward(bottom_blob_bordered_g, top_blob_g, opt_g);
      }
  
      // packing
      if (out_g_elempack < out_elempack)
      {
          convert_packing(top_blob_unpacked, top_blob, out_elempack, opt);
      }
      else
      {
          top_blob = top_blob_unpacked;
      }
  
      return 0;
  }
  #endif // __riscv_vector && __riscv_zfh
  
  } // namespace ncnn
  