text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2017 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 "convolution_arm.h"
  
  #include "benchmark.h"
  #include "cpu.h"
  #include "layer_type.h"
  
  #if __ARM_NEON
  #include <arm_neon.h>
  #include "neon_mathfun.h"
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  #include "neon_mathfun_fp16s.h"
  #endif
  #endif // __ARM_NEON
  
  #include "neon_activation.h"
  
  namespace ncnn {
  
  #include "convolution_sgemm.h"
  #include "convolution_sgemm_int8.h"
  
  #include "convolution_1x1.h"
  #include "convolution_1x1_bf16s.h"
  #include "convolution_1x1_int8.h"
  #include "convolution_2x2.h"
  #include "convolution_3x3.h"
  #include "convolution_3x3_int8.h"
  #include "convolution_4x4.h"
  #include "convolution_5x5.h"
  #include "convolution_7x7.h"
  
  #if __ARM_NEON
  #include "convolution_1x1_pack4.h"
  #include "convolution_1x1_pack4_bf16s.h"
  #include "convolution_1x1_pack4to1.h"
  #include "convolution_1x1_pack4to1_bf16s.h"
  #include "convolution_3x3_pack1to4.h"
  #include "convolution_3x3_pack1to4_bf16s.h"
  #include "convolution_3x3_pack4.h"
  #include "convolution_3x3_pack4_bf16s.h"
  #include "convolution_3x3_pack4to1.h"
  #include "convolution_3x3_pack4to1_bf16s.h"
  #include "convolution_5x5_pack4.h"
  #include "convolution_5x5_pack4_bf16s.h"
  #include "convolution_7x7_pack1to4.h"
  #include "convolution_7x7_pack1to4_bf16s.h"
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  #include "convolution_1x1_fp16s.h"
  #include "convolution_1x1_pack4_fp16s.h"
  #include "convolution_1x1_pack8_fp16s.h"
  #include "convolution_1x1_pack4to8_fp16s.h"
  #include "convolution_1x1_pack8to1_fp16s.h"
  #include "convolution_1x1_pack8to4_fp16s.h"
  #include "convolution_3x3_pack4_fp16s.h"
  #include "convolution_3x3_pack1to8_fp16s.h"
  #include "convolution_3x3_pack8_fp16s.h"
  #include "convolution_3x3_pack8to1_fp16s.h"
  #include "convolution_3x3_pack8to4_fp16s.h"
  #include "convolution_5x5_pack8_fp16s.h"
  #include "convolution_7x7_pack1to8_fp16s.h"
  #endif
  #endif // __ARM_NEON
  
  Convolution_arm::Convolution_arm()
  {
  #if __ARM_NEON
      support_packing = true;
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
      support_fp16_storage = true;
  #endif
  #endif // __ARM_NEON
  
      support_bf16_storage = true;
  
      activation = 0;
      convolution_dilation1 = 0;
  }
  
  int Convolution_arm::create_pipeline(const Option& opt)
  {
      if (activation_type == 1)
      {
          activation = ncnn::create_layer(ncnn::LayerType::ReLU);
  
          ncnn::ParamDict pd;
          activation->load_param(pd);
      }
      else if (activation_type == 2)
      {
          activation = ncnn::create_layer(ncnn::LayerType::ReLU);
  
          ncnn::ParamDict pd;
          pd.set(0, activation_params[0]); // slope
          activation->load_param(pd);
      }
      else if (activation_type == 3)
      {
          activation = ncnn::create_layer(ncnn::LayerType::Clip);
  
          ncnn::ParamDict pd;
          pd.set(0, activation_params[0]); // min
          pd.set(1, activation_params[1]); // max
          activation->load_param(pd);
      }
      else if (activation_type == 4)
      {
          activation = ncnn::create_layer(ncnn::LayerType::Sigmoid);
  
          ncnn::ParamDict pd;
          activation->load_param(pd);
      }
      else if (activation_type == 5)
      {
          activation = ncnn::create_layer(ncnn::LayerType::Mish);
  
          ncnn::ParamDict pd;
          activation->load_param(pd);
      }
  
      if (activation)
      {
          activation->create_pipeline(opt);
      }
  
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
      if (opt.use_fp16_storage)
      {
          return create_pipeline_fp16s(opt);
      }
  #endif
  
      if (opt.use_bf16_storage)
      {
          return create_pipeline_bf16s(opt);
      }
  
      if (opt.use_int8_inference && weight_data.elemsize == (size_t)1u)
      {
          support_packing = false;
  
          return create_pipeline_int8_arm(opt);
      }
  
      if ((!support_packing || !opt.use_packing_layout) && !opt.use_bf16_storage && kernel_w == kernel_h && dilation_w != 1 && dilation_h == dilation_w && stride_w == 1 && stride_h == 1)
      {
          convolution_dilation1 = ncnn::create_layer(ncnn::LayerType::Convolution);
  
          // set param
          ncnn::ParamDict pd;
          pd.set(0, num_output); // num_output
          pd.set(1, kernel_w);
          pd.set(11, kernel_h);
          pd.set(2, 1);
          pd.set(12, 1);
          pd.set(3, 1);  // stride_w
          pd.set(13, 1); // stride_h
          pd.set(4, 0);  // pad_w
          pd.set(14, 0); // pad_h
          pd.set(5, bias_term);
          pd.set(6, weight_data_size);
  
          convolution_dilation1->load_param(pd);
  
          // set weights
          if (bias_term)
          {
              ncnn::Mat weights[2];
              weights[0] = weight_data;
              weights[1] = bias_data;
  
              convolution_dilation1->load_model(ModelBinFromMatArray(weights));
          }
          else
          {
              ncnn::Mat weights[1];
              weights[0] = weight_data;
  
              convolution_dilation1->load_model(ModelBinFromMatArray(weights));
          }
  
          convolution_dilation1->create_pipeline(opt);
  
          return 0;
      }
  
      const int maxk = kernel_w * kernel_h;
      const int num_input = weight_data_size / maxk / num_output;
  
      int elempack = (support_packing && opt.use_packing_layout && num_input % 4 == 0) ? 4 : 1;
      int out_elempack = (support_packing && opt.use_packing_layout && num_output % 4 == 0) ? 4 : 1;
  
  #if __ARM_NEON
      // pack4
      if (elempack == 4 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_neon(weight_data, weight_data_pack4, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_neon(weight_data, weight_data_pack4, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack4_neon(weight_data, weight_data_pack4, num_input, num_output);
              conv3x3s1_winograd42_transform_kernel_pack4_neon(weight_data, weight_3x3_winograd42_data_pack4, num_input, num_output);
          }
          else
          {
              // src = kw-kh-inch-outch
              // dst = 4b-4a-kw-kh-inch/4a-outch/4b
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack4.create(maxk, num_input / 4, num_output / 4, (size_t)4 * 16, 16);
  
              for (int q = 0; q + 3 < num_output; q += 4)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  const Mat k1 = weight_data_r2.channel(q + 1);
                  const Mat k2 = weight_data_r2.channel(q + 2);
                  const Mat k3 = weight_data_r2.channel(q + 3);
  
                  Mat g0 = weight_data_pack4.channel(q / 4);
  
                  for (int p = 0; p + 3 < num_input; p += 4)
                  {
                      const float* k00 = k0.row(p);
                      const float* k01 = k0.row(p + 1);
                      const float* k02 = k0.row(p + 2);
                      const float* k03 = k0.row(p + 3);
  
                      const float* k10 = k1.row(p);
                      const float* k11 = k1.row(p + 1);
                      const float* k12 = k1.row(p + 2);
                      const float* k13 = k1.row(p + 3);
  
                      const float* k20 = k2.row(p);
                      const float* k21 = k2.row(p + 1);
                      const float* k22 = k2.row(p + 2);
                      const float* k23 = k2.row(p + 3);
  
                      const float* k30 = k3.row(p);
                      const float* k31 = k3.row(p + 1);
                      const float* k32 = k3.row(p + 2);
                      const float* k33 = k3.row(p + 3);
  
                      float* g00 = g0.row(p / 4);
  
                      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] = k01[k];
                          g00[5] = k11[k];
                          g00[6] = k21[k];
                          g00[7] = k31[k];
  
                          g00[8] = k02[k];
                          g00[9] = k12[k];
                          g00[10] = k22[k];
                          g00[11] = k32[k];
  
                          g00[12] = k03[k];
                          g00[13] = k13[k];
                          g00[14] = k23[k];
                          g00[15] = k33[k];
  
                          g00 += 16;
                      }
                  }
              }
          }
      }
  
      // pack1to4
      if (elempack == 1 && out_elempack == 4)
      {
          // src = kw-kh-inch-outch
          // dst = 4b-kw-kh-inch-outch/4b
          {
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack1to4.create(maxk, num_input, num_output / 4, (size_t)4 * 4, 4);
  
              for (int q = 0; q + 3 < num_output; q += 4)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  const Mat k1 = weight_data_r2.channel(q + 1);
                  const Mat k2 = weight_data_r2.channel(q + 2);
                  const Mat k3 = weight_data_r2.channel(q + 3);
  
                  Mat g0 = weight_data_pack1to4.channel(q / 4);
  
                  for (int p = 0; p < num_input; 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);
  
                      float* g00 = g0.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;
                      }
                  }
              }
          }
      }
  
      // pack4to1
      if (elempack == 4 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to1_neon(weight_data, weight_data_pack4to1, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to1_neon(weight_data, weight_data_pack4to1, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack4to1_neon(weight_data, weight_data_pack4to1, num_input, num_output);
          }
          else
          {
              // src = kw-kh-inch-outch
              // dst = 4a-kw-kh-inch/4a-outch
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack4to1.create(maxk, num_input / 4, num_output, (size_t)4 * 4, 4);
  
              for (int q = 0; q < num_output; q++)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  Mat g0 = weight_data_pack4to1.channel(q);
  
                  for (int p = 0; p + 3 < num_input; p += 4)
                  {
                      const float* k00 = k0.row(p);
                      const float* k01 = k0.row(p + 1);
                      const float* k02 = k0.row(p + 2);
                      const float* k03 = k0.row(p + 3);
  
                      float* g00 = g0.row(p / 4);
  
                      for (int k = 0; k < maxk; k++)
                      {
                          g00[0] = k00[k];
                          g00[1] = k01[k];
                          g00[2] = k02[k];
                          g00[3] = k03[k];
  
                          g00 += 4;
                      }
                  }
              }
          }
      }
  #endif // __ARM_NEON
  
      // pack1
      if (elempack == 1 && out_elempack == 1)
      {
          use_winograd3x3 = false;
          use_sgemm1x1 = false;
  
          if (opt.use_winograd_convolution && kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              // winograd is slow on small channel count
              if (num_input >= 16 && num_output >= 16)
                  use_winograd3x3 = true;
  
              if (use_winograd3x3)
              {
                  //                 conv3x3s1_winograd64_transform_kernel_neon(weight_data, weight_3x3_winograd64_data, num_input, num_output);
                  conv3x3s1_winograd64_transform_kernel_neon5(weight_data, weight_3x3_winograd64_data, num_input, num_output);
              }
          }
  
          // TODO assume more proper condition
          if (opt.use_sgemm_convolution && kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              if (num_input >= 64 && num_output >= 64)
                  use_sgemm1x1 = true;
  
              if (use_sgemm1x1)
              {
                  conv1x1s1_sgemm_transform_kernel_neon(weight_data, weight_1x1_sgemm_data, num_input, num_output);
              }
          }
  
          if (impl_type > 0 && impl_type < 6 && impl_type != 4)
          {
              switch (impl_type)
              {
              case 1:
                  // winograd
                  conv3x3s1_winograd64_transform_kernel_neon5(weight_data, weight_3x3_winograd64_data, num_input, num_output);
                  break;
              case 2:
                  // pointwise
                  conv1x1s1_sgemm_transform_kernel_neon(weight_data, weight_1x1_sgemm_data, num_input, num_output);
                  break;
              case 3:
                  // im2col
                  conv_im2col_sgemm_transform_kernel_neon(weight_data, weight_sgemm_data, num_input, num_output, maxk);
                  break;
              //                 case 4:
              //                     // direct
              //                     break;
              case 5:
                  // conv3x3s2
                  conv3x3s2_transform_kernel_neon(weight_data, weight_3x3s2_data, num_input, num_output);
                  break;
              }
          }
  
          if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv3x3s2_transform_kernel_neon(weight_data, weight_3x3s2_data, num_input, num_output);
          }
  
          if (opt.use_sgemm_convolution && kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv_im2col_sgemm_transform_kernel_neon(weight_data, weight_sgemm_data, num_input, num_output, maxk);
          }
  
          if (opt.use_sgemm_convolution && kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv_im2col_sgemm_transform_kernel_neon(weight_data, weight_sgemm_data, num_input, num_output, maxk);
          }
      }
  
      return 0;
  }
  
  int Convolution_arm::destroy_pipeline(const Option& opt)
  {
      if (activation)
      {
          activation->destroy_pipeline(opt);
          delete activation;
          activation = 0;
      }
  
      if (convolution_dilation1)
      {
          convolution_dilation1->destroy_pipeline(opt);
          delete convolution_dilation1;
          convolution_dilation1 = 0;
      }
  
      return 0;
  }
  
  int Convolution_arm::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      if (opt.use_int8_inference && weight_data.elemsize == (size_t)1u)
      {
          return forward_int8_arm(bottom_blob, top_blob, opt);
      }
  
      if (bottom_blob.dims != 3)
      {
          return Convolution::forward(bottom_blob, top_blob, opt);
      }
  
      int elembits = bottom_blob.elembits();
  
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
      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 (opt.use_bf16_storage && elembits == 16)
          return forward_bf16s(bottom_blob, top_blob, opt);
  
      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;
  
      //     NCNN_LOGE("Convolution input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
  
      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 = (support_packing && opt.use_packing_layout && num_output % 4 == 0) ? 4 : 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;
  
      if ((!support_packing || !opt.use_packing_layout) && kernel_w == kernel_h && dilation_w != 1 && dilation_h == dilation_w && stride_w == 1 && stride_h == 1)
      {
          if (outw >= dilation_w && outh >= dilation_h)
          {
              return forwardDilation_arm(bottom_blob_bordered, top_blob, opt);
          }
      }
  
      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;
          }
      }
  
  #if __ARM_NEON
      if (elempack == 4 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, 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 == 1 && stride_h == 1)
          {
              // we need more proper conditions
              if ((w <= 10 || (w >= 15 && w <= 18) || w == 21 || w == 22) && (h <= 10 || (h >= 15 && h <= 18) || h == 21 || h == 22))
              {
                  conv3x3s1_winograd42_pack4_neon(bottom_blob_bordered, top_blob, weight_3x3_winograd42_data_pack4, bias_data, opt);
              }
              else
              {
                  conv3x3s1_winograd64_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, 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)
          {
              conv3x3s2_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, 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)
          {
              conv5x5s1_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, 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)
          {
              conv5x5s2_pack4_neon(bottom_blob_bordered, top_blob, weight_data_pack4, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  float* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32(((const float*)bias_data) + p * 4);
                          }
  
                          const float* kptr = (const float*)weight_data_pack4 + maxk * channels * p * 16;
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const float* sptr = m.row(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++) // 29.23
                              {
                                  float32x4_t _val = vld1q_f32(sptr + space_ofs[k] * 4);
  
                                  float32x4_t _w0 = vld1q_f32(kptr);
                                  float32x4_t _w1 = vld1q_f32(kptr + 4);
                                  float32x4_t _w2 = vld1q_f32(kptr + 8);
                                  float32x4_t _w3 = vld1q_f32(kptr + 12);
  
  #if __aarch64__
                                  _sum = vmlaq_laneq_f32(_sum, _w0, _val, 0);
                                  _sum = vmlaq_laneq_f32(_sum, _w1, _val, 1);
                                  _sum = vmlaq_laneq_f32(_sum, _w2, _val, 2);
                                  _sum = vmlaq_laneq_f32(_sum, _w3, _val, 3);
  #else
                                  _sum = vmlaq_lane_f32(_sum, _w0, vget_low_f32(_val), 0);
                                  _sum = vmlaq_lane_f32(_sum, _w1, vget_low_f32(_val), 1);
                                  _sum = vmlaq_lane_f32(_sum, _w2, vget_high_f32(_val), 0);
                                  _sum = vmlaq_lane_f32(_sum, _w3, vget_high_f32(_val), 1);
  #endif
  
                                  kptr += 16;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f32(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 4)
      {
          if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_pack1to4_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4, 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)
          {
              conv3x3s2_pack1to4_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 7 && kernel_h == 7 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv7x7s2_pack1to4_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  float* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32(((const float*)bias_data) + p * 4);
                          }
  
                          const float* kptr = (const float*)weight_data_pack1to4 + maxk * channels * p * 4;
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const float* sptr = m.row(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++) // 29.23
                              {
                                  float32x4_t _val = vdupq_n_f32(sptr[space_ofs[k]]);
                                  float32x4_t _w = vld1q_f32(kptr);
                                  _sum = vmlaq_f32(_sum, _val, _w);
  
                                  kptr += 4;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f32(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4to1_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4to1_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1, 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 == 1 && stride_h == 1)
          {
              // TODO more proper condition
              conv3x3s1_winograd64_pack4to1_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1, bias_data, opt);
  
              //             conv3x3s1_pack4to1_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  float* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const float* kptr = (const float*)weight_data_pack4to1 + maxk * channels * p * 4;
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const float* sptr = m.row(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++) // 29.23
                              {
                                  float32x4_t _val = vld1q_f32(sptr + space_ofs[k] * 4);
                                  float32x4_t _w = vld1q_f32(kptr);
                                  float32x4_t _s4 = vmulq_f32(_val, _w);
  #if __aarch64__
                                  sum += vaddvq_f32(_s4); // dot
  #else
                                  float32x2_t _ss = vadd_f32(vget_low_f32(_s4), vget_high_f32(_s4));
                                  _ss = vpadd_f32(_ss, _ss);
                                  sum += vget_lane_f32(_ss, 0);
  #endif
  
                                  kptr += 4;
                              }
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  #endif // __ARM_NEON
  
      if (elempack == 1 && out_elempack == 1)
      {
          if (impl_type > 0 && impl_type < 6 && impl_type != 4)
          {
              // engineering is magic.
              switch (impl_type)
              {
              case 1:
                  conv3x3s1_winograd64_neon5(bottom_blob_bordered, top_blob, weight_3x3_winograd64_data, bias_data, opt);
                  break;
              case 2:
                  conv1x1s1_sgemm_neon(bottom_blob_bordered, top_blob, weight_1x1_sgemm_data, bias_data, opt);
                  break;
              case 3:
                  conv_im2col_sgemm_neon(bottom_blob_bordered, top_blob, weight_sgemm_data, bias_data, kernel_w, kernel_h, stride_w, stride_h, opt);
                  break;
              //                 case 4: FIXME fallback to auto path
              //                     conv(bottom_blob_bordered, top_blob, weight_data, bias_data, opt);
              //                     break;
              case 5:
                  conv3x3s2_packed_neon(bottom_blob_bordered, top_blob, weight_3x3s2_data, bias_data, opt);
                  break;
              }
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              if (use_sgemm1x1)
              {
                  conv1x1s1_sgemm_neon(bottom_blob_bordered, top_blob, weight_1x1_sgemm_data, bias_data, opt);
              }
              else
              {
                  conv1x1s1_neon(bottom_blob_bordered, top_blob, weight_data, bias_data, opt);
              }
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              if (opt.use_sgemm_convolution)
                  conv_im2col_sgemm_neon(bottom_blob_bordered, top_blob, weight_sgemm_data, bias_data, kernel_w, kernel_h, stride_w, stride_h, opt);
              else
                  conv1x1s2_neon(bottom_blob_bordered, top_blob, weight_data, 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 == 1 && stride_h == 1)
          {
              if (use_winograd3x3 && w <= 120 && h <= 120)
              {
                  //                 conv3x3s1_winograd64_neon4(bottom_blob_bordered, top_blob, weight_3x3_winograd64_data, bias_data, opt);
                  conv3x3s1_winograd64_neon5(bottom_blob_bordered, top_blob, weight_3x3_winograd64_data, bias_data, opt);
              }
              else
              {
                  conv3x3s1_neon(bottom_blob_bordered, top_blob, weight_data, 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)
          {
              if (opt.use_sgemm_convolution && !(outw >= 8 && outh >= 8))
                  conv_im2col_sgemm_neon(bottom_blob_bordered, top_blob, weight_sgemm_data, bias_data, kernel_w, kernel_h, stride_w, stride_h, opt);
              else
                  conv3x3s2_packed_neon(bottom_blob_bordered, top_blob, weight_3x3s2_data, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 4 && kernel_h == 4 && dilation_w == 1 && dilation_h == 1 && stride_w == 4 && stride_h == 4)
          {
              conv4x4s4_neon(bottom_blob_bordered, top_blob, weight_data, 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)
          {
              conv5x5s1_neon(bottom_blob_bordered, top_blob, weight_data, 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)
          {
              conv5x5s2_neon(bottom_blob_bordered, top_blob, weight_data, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 7 && kernel_h == 7 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv7x7s1_neon(bottom_blob_bordered, top_blob, weight_data, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 7 && kernel_h == 7 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv7x7s2_neon(bottom_blob_bordered, top_blob, weight_data, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  float* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const float* kptr = (const float*)weight_data + maxk * channels * p;
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const float* sptr = m.row(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float val = sptr[space_ofs[k]];
                                  float wt = kptr[k];
                                  sum += val * wt;
                              }
  
                              kptr += maxk;
                          }
  
                          if (activation_type == 1)
                          {
                              sum = std::max(sum, 0.f);
                          }
                          else if (activation_type == 2)
                          {
                              float slope = activation_params[0];
                              sum = sum > 0.f ? sum : sum * slope;
                          }
                          else if (activation_type == 3)
                          {
                              float min = activation_params[0];
                              float max = activation_params[1];
                              if (sum < min)
                                  sum = min;
                              if (sum > max)
                                  sum = max;
                          }
                          else if (activation_type == 4)
                          {
                              sum = static_cast<float>(1.f / (1.f + exp(-sum)));
                          }
                          else if (activation_type == 5)
                          {
                              sum = static_cast<float>(sum * tanh(log(exp(sum) + 1.f)));
                          }
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      return 0;
  }
  
  #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  int Convolution_arm::create_pipeline_fp16s(const Option& opt)
  {
      const int maxk = kernel_w * kernel_h;
      const int num_input = weight_data_size / maxk / num_output;
  
      int elempack = 1;
      int out_elempack = 1;
  
      if (opt.use_packing_layout)
      {
          elempack = opt.use_fp16_arithmetic && num_input % 8 == 0 ? 8 : num_input % 4 == 0 ? 4 : 1;
          out_elempack = opt.use_fp16_arithmetic && num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
      }
  
      // src = kw-kh-inch-outch
      // dst = pb-pa-kw-kh-inch/pa-outch/pb
      {
          Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
          weight_data_fp16.create(maxk, num_input / elempack, num_output / out_elempack, (size_t)2u * elempack * out_elempack, elempack * out_elempack);
  
          for (int q = 0; q + (out_elempack - 1) < num_output; q += out_elempack)
          {
              Mat g0 = weight_data_fp16.channel(q / out_elempack);
  
              for (int p = 0; p + (elempack - 1) < num_input; p += elempack)
              {
                  __fp16* g00 = g0.row<__fp16>(p / elempack);
  
                  for (int k = 0; k < maxk; k++)
                  {
                      for (int i = 0; i < elempack; i++)
                      {
                          for (int j = 0; j < out_elempack; j++)
                          {
                              const float* k00 = weight_data_r2.channel(q + j).row(p + i);
  
                              g00[0] = (__fp16)k00[k];
  
                              g00++;
                          }
                      }
                  }
              }
          }
      }
  
      // pack8
      if (elempack == 8 && out_elempack == 8)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack8_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack8_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack8_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
              conv3x3s1_winograd42_transform_kernel_pack8_fp16sa_neon(weight_data, weight_3x3_winograd42_data_pack4, num_input, num_output);
          }
      }
  
      // pack4to8
      if (elempack == 4 && out_elempack == 8)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to8_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to8_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
      }
  
      // pack8to1
      if (elempack == 8 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack8to1_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack8to1_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack8to1_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
      }
  
      // pack8to4
      if (elempack == 8 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack8to4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack8to4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack8to4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
      }
  
      // pack4
      if (elempack == 4 && out_elempack == 4 && opt.use_fp16_arithmetic)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack4_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
      }
  
      // pack1
      if (elempack == 1 && out_elempack == 1 && opt.use_fp16_arithmetic)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_fp16sa_neon(weight_data, weight_data_fp16, num_input, num_output);
          }
      }
  
      ncnn::cast_float32_to_float16(bias_data, bias_data_fp16, opt);
  
      return 0;
  }
  
  int Convolution_arm::forward_fp16s(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      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;
  
      // NCNN_LOGE("Convolution input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
  
      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 = (support_packing && opt.use_packing_layout && num_output % 4 == 0) ? 4 : 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;
  
      // TODO dilated conv for bf16s
      //     if ((!support_packing || !opt.use_packing_layout) && kernel_w == kernel_h && dilation_w != 1 && dilation_h == dilation_w && stride_w == 1 && stride_h == 1)
      //     {
      //         return forwardDilation_arm(bottom_blob_bordered, top_blob, opt);
      //     }
  
      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;
          }
      }
  
      if (elempack == 4 && out_elempack == 4)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32((const float*)bias_data + p * 4);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vcvt_f32_f16(vld1_f16(sptr + space_ofs[k] * 4));
  
                                  float32x4_t _w0 = vcvt_f32_f16(vld1_f16(kptr));
                                  float32x4_t _w1 = vcvt_f32_f16(vld1_f16(kptr + 4));
                                  float32x4_t _w2 = vcvt_f32_f16(vld1_f16(kptr + 8));
                                  float32x4_t _w3 = vcvt_f32_f16(vld1_f16(kptr + 12));
  
                                  _sum = vfmaq_laneq_f32(_sum, _w0, _val, 0);
                                  _sum = vfmaq_laneq_f32(_sum, _w1, _val, 1);
                                  _sum = vfmaq_laneq_f32(_sum, _w2, _val, 2);
                                  _sum = vfmaq_laneq_f32(_sum, _w3, _val, 3);
  
                                  kptr += 16;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_f16(outptr + j * 4, vcvt_f16_f32(_sum));
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 4)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32((const float*)bias_data + p * 4);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vcvt_f32_f16(vdup_n_f16(sptr[space_ofs[k]]));
                                  float32x4_t _w = vcvt_f32_f16(vld1_f16(kptr));
                                  _sum = vfmaq_f32(_sum, _val, _w);
  
                                  kptr += 4;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_f16(outptr + j * 4, vcvt_f16_f32(_sum));
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 1)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vcvt_f32_f16(vld1_f16(sptr + space_ofs[k] * 4));
                                  float32x4_t _w = vcvt_f32_f16(vld1_f16(kptr));
                                  float32x4_t _s4 = vmulq_f32(_val, _w);
  
                                  sum += vaddvq_f32(_s4); // dot
  
                                  kptr += 4;
                              }
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = (__fp16)sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 1)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<__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;
                              }
  
                              kptr += maxk;
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = (__fp16)sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      return 0;
  }
  
  int Convolution_arm::forward_fp16sa(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      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;
  
      // NCNN_LOGE("Convolution input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
  
      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 (opt.use_packing_layout)
      {
          out_elempack = opt.use_fp16_arithmetic && num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 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;
  
      // TODO dilated conv for bf16s
      //     if ((!support_packing || !opt.use_packing_layout) && kernel_w == kernel_h && dilation_w != 1 && dilation_h == dilation_w && stride_w == 1 && stride_h == 1)
      //     {
      //         return forwardDilation_arm(bottom_blob_bordered, top_blob, opt);
      //     }
  
      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;
          }
      }
  
      if (elempack == 8 && out_elempack == 8)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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 == 1 && stride_h == 1)
          {
              // we need more proper conditions
              if ((w <= 10 || (w >= 15 && w <= 18) || w == 21 || w == 22) && (h <= 10 || (h >= 15 && h <= 18) || h == 21 || h == 22))
              {
                  conv3x3s1_winograd42_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_3x3_winograd42_data_pack4, bias_data_fp16, opt);
              }
              else
              {
                  conv3x3s1_winograd64_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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)
          {
              conv3x3s2_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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)
          {
              conv5x5s1_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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)
          {
              conv5x5s2_pack8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x8_t _sum = vdupq_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f16(((const __fp16*)bias_data_fp16) + p * 8);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 8;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x8_t _val = vld1q_f16(sptr + space_ofs[k] * 8);
  
                                  float16x8_t _w0 = vld1q_f16(kptr);
                                  float16x8_t _w1 = vld1q_f16(kptr + 8);
                                  float16x8_t _w2 = vld1q_f16(kptr + 16);
                                  float16x8_t _w3 = vld1q_f16(kptr + 24);
                                  float16x8_t _w4 = vld1q_f16(kptr + 32);
                                  float16x8_t _w5 = vld1q_f16(kptr + 40);
                                  float16x8_t _w6 = vld1q_f16(kptr + 48);
                                  float16x8_t _w7 = vld1q_f16(kptr + 56);
  
                                  _sum = vfmaq_laneq_f16(_sum, _w0, _val, 0);
                                  _sum = vfmaq_laneq_f16(_sum, _w1, _val, 1);
                                  _sum = vfmaq_laneq_f16(_sum, _w2, _val, 2);
                                  _sum = vfmaq_laneq_f16(_sum, _w3, _val, 3);
                                  _sum = vfmaq_laneq_f16(_sum, _w4, _val, 4);
                                  _sum = vfmaq_laneq_f16(_sum, _w5, _val, 5);
                                  _sum = vfmaq_laneq_f16(_sum, _w6, _val, 6);
                                  _sum = vfmaq_laneq_f16(_sum, _w7, _val, 7);
  
                                  kptr += 64;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f16(outptr + j * 8, _sum);
                      }
  
                      outptr += outw * 8;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 8)
      {
          if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_pack1to8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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)
          {
              conv3x3s2_pack1to8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 7 && kernel_h == 7 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv7x7s2_pack1to8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x8_t _sum = vdupq_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f16(((const __fp16*)bias_data_fp16) + p * 8);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x8_t _val = vdupq_n_f16(sptr[space_ofs[k]]);
                                  float16x8_t _w = vld1q_f16(kptr);
                                  _sum = vfmaq_f16(_sum, _val, _w);
  
                                  kptr += 8;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f16(outptr + j * 8, _sum);
                      }
  
                      outptr += outw * 8;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 8)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4to8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4to8_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x8_t _sum = vdupq_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f16(((const __fp16*)bias_data_fp16) + p * 8);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x4_t _val = vld1_f16(sptr + space_ofs[k] * 4);
  
                                  float16x8_t _w0 = vld1q_f16(kptr);
                                  float16x8_t _w1 = vld1q_f16(kptr + 8);
                                  float16x8_t _w2 = vld1q_f16(kptr + 16);
                                  float16x8_t _w3 = vld1q_f16(kptr + 24);
  
                                  _sum = vfmaq_lane_f16(_sum, _w0, _val, 0);
                                  _sum = vfmaq_lane_f16(_sum, _w1, _val, 1);
                                  _sum = vfmaq_lane_f16(_sum, _w2, _val, 2);
                                  _sum = vfmaq_lane_f16(_sum, _w3, _val, 3);
  
                                  kptr += 32;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f16(outptr + j * 8, _sum);
                      }
  
                      outptr += outw * 8;
                  }
              }
          }
      }
  
      if (elempack == 8 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack8to1_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack8to1_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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 == 1 && stride_h == 1)
          {
              // TODO more proper condition
              conv3x3s1_winograd64_pack8to1_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              //             conv3x3s1_pack8to1_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 8;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x8_t _val = vld1q_f16(sptr + space_ofs[k] * 8);
                                  float16x8_t _w = vld1q_f16(kptr);
                                  float16x8_t _s8 = vmulq_f16(_val, _w);
  
                                  float16x4_t _s4 = vadd_f16(vget_low_f16(_s8), vget_high_f16(_s8));
                                  sum += vaddvq_f32(vcvt_f32_f16(_s4)); // dot
  
                                  kptr += 8;
                              }
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      if (elempack == 8 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack8to4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack8to4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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 == 1 && stride_h == 1)
          {
              // TODO more proper condition
              conv3x3s1_winograd64_pack8to4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              //             conv3x3s1_pack8to4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x4_t _sum = vdup_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1_f16(((const __fp16*)bias_data_fp16) + p * 4);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 8;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x8_t _val = vld1q_f16(sptr + space_ofs[k] * 8);
  
                                  float16x4_t _w0 = vld1_f16(kptr);
                                  float16x4_t _w1 = vld1_f16(kptr + 4);
                                  float16x4_t _w2 = vld1_f16(kptr + 8);
                                  float16x4_t _w3 = vld1_f16(kptr + 12);
                                  float16x4_t _w4 = vld1_f16(kptr + 16);
                                  float16x4_t _w5 = vld1_f16(kptr + 20);
                                  float16x4_t _w6 = vld1_f16(kptr + 24);
                                  float16x4_t _w7 = vld1_f16(kptr + 28);
  
                                  _sum = vfma_laneq_f16(_sum, _w0, _val, 0);
                                  _sum = vfma_laneq_f16(_sum, _w1, _val, 1);
                                  _sum = vfma_laneq_f16(_sum, _w2, _val, 2);
                                  _sum = vfma_laneq_f16(_sum, _w3, _val, 3);
                                  _sum = vfma_laneq_f16(_sum, _w4, _val, 4);
                                  _sum = vfma_laneq_f16(_sum, _w5, _val, 5);
                                  _sum = vfma_laneq_f16(_sum, _w6, _val, 6);
                                  _sum = vfma_laneq_f16(_sum, _w7, _val, 7);
  
                                  kptr += 32;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_f16(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, 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 == 1 && stride_h == 1)
          {
              // TODO more proper condition
              conv3x3s1_winograd64_pack4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              //             conv3x3s1_pack4_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x4_t _sum = vdup_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1_f16(((const __fp16*)bias_data_fp16) + p * 4);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x4_t _val = vld1_f16(sptr + space_ofs[k] * 4);
  
                                  float16x4_t _w0 = vld1_f16(kptr);
                                  float16x4_t _w1 = vld1_f16(kptr + 4);
                                  float16x4_t _w2 = vld1_f16(kptr + 8);
                                  float16x4_t _w3 = vld1_f16(kptr + 12);
  
                                  _sum = vfma_lane_f16(_sum, _w0, _val, 0);
                                  _sum = vfma_lane_f16(_sum, _w1, _val, 1);
                                  _sum = vfma_lane_f16(_sum, _w2, _val, 2);
                                  _sum = vfma_lane_f16(_sum, _w3, _val, 3);
  
                                  kptr += 16;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_f16(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 4)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float16x4_t _sum = vdup_n_f16((__fp16)0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1_f16(((const __fp16*)bias_data_fp16) + p * 4);
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x4_t _val = vdup_n_f16(sptr[space_ofs[k]]);
                                  float16x4_t _w = vld1_f16(kptr);
                                  _sum = vfma_f16(_sum, _val, _w);
  
                                  kptr += 4;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_f16(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 1)
      {
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<const __fp16>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float16x4_t _val = vld1_f16(sptr + space_ofs[k] * 4);
                                  float16x4_t _w = vld1_f16(kptr);
                                  float16x4_t _s4 = vmul_f16(_val, _w);
  
                                  sum += vaddvq_f32(vcvt_f32_f16(_s4)); // dot
  
                                  kptr += 4;
                              }
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_fp16sa_neon(bottom_blob_bordered, top_blob, weight_data_fp16, bias_data_fp16, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  __fp16* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const __fp16* kptr = weight_data_fp16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const __fp16* sptr = m.row<__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;
                              }
  
                              kptr += maxk;
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      return 0;
  }
  #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  
  int Convolution_arm::create_pipeline_bf16s(const Option& opt)
  {
      const int maxk = kernel_w * kernel_h;
      const int num_input = weight_data_size / maxk / num_output;
  
      int elempack = (support_packing && opt.use_packing_layout && num_input % 4 == 0) ? 4 : 1;
      int out_elempack = (support_packing && opt.use_packing_layout && num_output % 4 == 0) ? 4 : 1;
  
  #if __ARM_NEON
      // pack4
      if (elempack == 4 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_bf16s_neon(weight_data, weight_data_pack4_bf16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4_bf16s_neon(weight_data, weight_data_pack4_bf16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack4_neon(weight_data, weight_data_pack4_bf16, num_input, num_output);
              conv3x3s1_winograd42_transform_kernel_pack4_neon(weight_data, weight_3x3_winograd42_data_pack4, num_input, num_output);
          }
          else
          {
              // src = kw-kh-inch-outch
              // dst = 4b-4a-kw-kh-inch/4a-outch/4b
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack4_bf16.create(maxk, num_input / 4, num_output / 4, (size_t)2 * 16, 16);
  
              for (int q = 0; q + 3 < num_output; q += 4)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  const Mat k1 = weight_data_r2.channel(q + 1);
                  const Mat k2 = weight_data_r2.channel(q + 2);
                  const Mat k3 = weight_data_r2.channel(q + 3);
  
                  Mat g0 = weight_data_pack4_bf16.channel(q / 4);
  
                  for (int p = 0; p + 3 < num_input; p += 4)
                  {
                      const float* k00 = k0.row(p);
                      const float* k01 = k0.row(p + 1);
                      const float* k02 = k0.row(p + 2);
                      const float* k03 = k0.row(p + 3);
  
                      const float* k10 = k1.row(p);
                      const float* k11 = k1.row(p + 1);
                      const float* k12 = k1.row(p + 2);
                      const float* k13 = k1.row(p + 3);
  
                      const float* k20 = k2.row(p);
                      const float* k21 = k2.row(p + 1);
                      const float* k22 = k2.row(p + 2);
                      const float* k23 = k2.row(p + 3);
  
                      const float* k30 = k3.row(p);
                      const float* k31 = k3.row(p + 1);
                      const float* k32 = k3.row(p + 2);
                      const float* k33 = k3.row(p + 3);
  
                      unsigned short* g00 = g0.row<unsigned short>(p / 4);
  
                      for (int k = 0; k < maxk; k++)
                      {
                          g00[0] = float32_to_bfloat16(k00[k]);
                          g00[1] = float32_to_bfloat16(k10[k]);
                          g00[2] = float32_to_bfloat16(k20[k]);
                          g00[3] = float32_to_bfloat16(k30[k]);
  
                          g00[4] = float32_to_bfloat16(k01[k]);
                          g00[5] = float32_to_bfloat16(k11[k]);
                          g00[6] = float32_to_bfloat16(k21[k]);
                          g00[7] = float32_to_bfloat16(k31[k]);
  
                          g00[8] = float32_to_bfloat16(k02[k]);
                          g00[9] = float32_to_bfloat16(k12[k]);
                          g00[10] = float32_to_bfloat16(k22[k]);
                          g00[11] = float32_to_bfloat16(k32[k]);
  
                          g00[12] = float32_to_bfloat16(k03[k]);
                          g00[13] = float32_to_bfloat16(k13[k]);
                          g00[14] = float32_to_bfloat16(k23[k]);
                          g00[15] = float32_to_bfloat16(k33[k]);
  
                          g00 += 16;
                      }
                  }
              }
          }
      }
  
      // pack1to4
      if (elempack == 1 && out_elempack == 4)
      {
          // src = kw-kh-inch-outch
          // dst = 4b-kw-kh-inch-outch/4b
          {
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack1to4_bf16.create(maxk, num_input, num_output / 4, (size_t)2 * 4, 4);
  
              for (int q = 0; q + 3 < num_output; q += 4)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  const Mat k1 = weight_data_r2.channel(q + 1);
                  const Mat k2 = weight_data_r2.channel(q + 2);
                  const Mat k3 = weight_data_r2.channel(q + 3);
  
                  Mat g0 = weight_data_pack1to4_bf16.channel(q / 4);
  
                  for (int p = 0; p < num_input; 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);
  
                      unsigned short* g00 = g0.row<unsigned short>(p);
  
                      for (int k = 0; k < maxk; k++)
                      {
                          g00[0] = float32_to_bfloat16(k00[k]);
                          g00[1] = float32_to_bfloat16(k10[k]);
                          g00[2] = float32_to_bfloat16(k20[k]);
                          g00[3] = float32_to_bfloat16(k30[k]);
  
                          g00 += 4;
                      }
                  }
              }
          }
      }
  
      // pack4to1
      if (elempack == 4 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to1_bf16s_neon(weight_data, weight_data_pack4to1_bf16, num_input, num_output);
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s1_sgemm_transform_kernel_pack4to1_bf16s_neon(weight_data, weight_data_pack4to1_bf16, num_input, num_output);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_winograd64_transform_kernel_pack4to1_neon(weight_data, weight_data_pack4to1_bf16, num_input, num_output);
          }
          else
          {
              // src = kw-kh-inch-outch
              // dst = 4a-kw-kh-inch/4a-outch
              Mat weight_data_r2 = weight_data.reshape(maxk, num_input, num_output);
  
              weight_data_pack4to1_bf16.create(maxk, num_input / 4, num_output, (size_t)2 * 4, 4);
  
              for (int q = 0; q < num_output; q++)
              {
                  const Mat k0 = weight_data_r2.channel(q);
                  Mat g0 = weight_data_pack4to1_bf16.channel(q);
  
                  for (int p = 0; p + 3 < num_input; p += 4)
                  {
                      const float* k00 = k0.row(p);
                      const float* k01 = k0.row(p + 1);
                      const float* k02 = k0.row(p + 2);
                      const float* k03 = k0.row(p + 3);
  
                      unsigned short* g00 = g0.row<unsigned short>(p / 4);
  
                      for (int k = 0; k < maxk; k++)
                      {
                          g00[0] = float32_to_bfloat16(k00[k]);
                          g00[1] = float32_to_bfloat16(k01[k]);
                          g00[2] = float32_to_bfloat16(k02[k]);
                          g00[3] = float32_to_bfloat16(k03[k]);
  
                          g00 += 4;
                      }
                  }
              }
          }
      }
  #endif // __ARM_NEON
  
      // pack1
      if (elempack == 1 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_transform_kernel_bf16s_neon(weight_data, weight_data_bf16, num_input, num_output);
          }
          else
          {
              ncnn::cast_float32_to_bfloat16(weight_data, weight_data_bf16, opt);
          }
      }
  
      return 0;
  }
  
  int Convolution_arm::forward_bf16s(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      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;
  
      //     NCNN_LOGE("Convolution input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
  
      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 = (support_packing && opt.use_packing_layout && num_output % 4 == 0) ? 4 : 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;
  
      // TODO dilated conv for bf16s
      //     if ((!support_packing || !opt.use_packing_layout) && kernel_w == kernel_h && dilation_w != 1 && dilation_h == dilation_w && stride_w == 1 && stride_h == 1)
      //     {
      //         return forwardDilation_arm(bottom_blob_bordered, top_blob, opt);
      //     }
  
      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;
          }
      }
  
  #if __ARM_NEON
      if (elempack == 4 && out_elempack == 4)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, 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 == 1 && stride_h == 1)
          {
              // we need more proper conditions
              if ((w <= 10 || (w >= 15 && w <= 18) || w == 21 || w == 22) && (h <= 10 || (h >= 15 && h <= 18) || h == 21 || h == 22))
              {
                  conv3x3s1_winograd42_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_3x3_winograd42_data_pack4, bias_data, opt);
              }
              else
              {
                  conv3x3s1_winograd64_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, 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)
          {
              conv3x3s2_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, 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)
          {
              conv5x5s1_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, 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)
          {
              conv5x5s2_pack4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  unsigned short* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32(((const float*)bias_data) + p * 4);
                          }
  
                          const unsigned short* kptr = weight_data_pack4_bf16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const unsigned short* sptr = m.row<const unsigned short>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vcvt_f32_bf16(vld1_u16(sptr + space_ofs[k] * 4));
  
                                  float32x4_t _w0 = vcvt_f32_bf16(vld1_u16(kptr));
                                  float32x4_t _w1 = vcvt_f32_bf16(vld1_u16(kptr + 4));
                                  float32x4_t _w2 = vcvt_f32_bf16(vld1_u16(kptr + 8));
                                  float32x4_t _w3 = vcvt_f32_bf16(vld1_u16(kptr + 12));
  
  #if __aarch64__
                                  _sum = vmlaq_laneq_f32(_sum, _w0, _val, 0);
                                  _sum = vmlaq_laneq_f32(_sum, _w1, _val, 1);
                                  _sum = vmlaq_laneq_f32(_sum, _w2, _val, 2);
                                  _sum = vmlaq_laneq_f32(_sum, _w3, _val, 3);
  #else
                                  _sum = vmlaq_lane_f32(_sum, _w0, vget_low_f32(_val), 0);
                                  _sum = vmlaq_lane_f32(_sum, _w1, vget_low_f32(_val), 1);
                                  _sum = vmlaq_lane_f32(_sum, _w2, vget_high_f32(_val), 0);
                                  _sum = vmlaq_lane_f32(_sum, _w3, vget_high_f32(_val), 1);
  #endif
  
                                  kptr += 16;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_u16(outptr + j * 4, vcvt_bf16_f32(_sum));
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 1 && out_elempack == 4)
      {
          if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv3x3s1_pack1to4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4_bf16, 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)
          {
              conv3x3s2_pack1to4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 7 && kernel_h == 7 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv7x7s2_pack1to4_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack1to4_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output / out_elempack; p++)
              {
                  unsigned short* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float32x4_t _sum = vdupq_n_f32(0.f);
  
                          if (bias_term)
                          {
                              _sum = vld1q_f32(((const float*)bias_data) + p * 4);
                          }
  
                          const unsigned short* kptr = weight_data_pack1to4_bf16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const unsigned short* sptr = m.row<const unsigned short>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vdupq_n_f32(bfloat16_to_float32(sptr[space_ofs[k]]));
                                  float32x4_t _w = vcvt_f32_bf16(vld1_u16(kptr));
                                  _sum = vmlaq_f32(_sum, _val, _w);
  
                                  kptr += 4;
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_u16(outptr + j * 4, vcvt_bf16_f32(_sum));
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
      }
  
      if (elempack == 4 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_pack4to1_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv1x1s2_pack4to1_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1_bf16, 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 == 1 && stride_h == 1)
          {
              // TODO more proper condition
              conv3x3s1_winograd64_pack4to1_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1_bf16, bias_data, opt);
  
              //             conv3x3s1_pack4to1_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_pack4to1_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  unsigned short* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const unsigned short* kptr = weight_data_pack4to1_bf16.channel(p);
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const unsigned short* sptr = m.row<const unsigned short>(i * stride_h) + j * stride_w * 4;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float32x4_t _val = vcvt_f32_bf16(vld1_u16(sptr + space_ofs[k] * 4));
                                  float32x4_t _w = vcvt_f32_bf16(vld1_u16(kptr));
                                  float32x4_t _s4 = vmulq_f32(_val, _w);
  #if __aarch64__
                                  sum += vaddvq_f32(_s4); // dot
  #else
                                  float32x2_t _ss = vadd_f32(vget_low_f32(_s4), vget_high_f32(_s4));
                                  _ss = vpadd_f32(_ss, _ss);
                                  sum += vget_lane_f32(_ss, 0);
  #endif
  
                                  kptr += 4;
                              }
                          }
  
                          sum = activation_ss(sum, activation_type, activation_params);
  
                          outptr[j] = float32_to_bfloat16(sum);
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  #endif // __ARM_NEON
  
      if (elempack == 1 && out_elempack == 1)
      {
          if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
          {
              conv1x1s1_sgemm_bf16s_neon(bottom_blob_bordered, top_blob, weight_data_bf16, bias_data, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
          }
          else
          {
              // num_output
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int p = 0; p < num_output; p++)
              {
                  unsigned short* outptr = top_blob.channel(p);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[p];
                          }
  
                          const unsigned short* kptr = (const unsigned short*)weight_data_bf16 + maxk * channels * p;
  
                          // channels
                          for (int q = 0; q < channels; q++)
                          {
                              const Mat m = bottom_blob_bordered.channel(q);
                              const unsigned short* sptr = m.row<unsigned short>(i * stride_h) + j * stride_w;
  
                              for (int k = 0; k < maxk; k++)
                              {
                                  float val = bfloat16_to_float32(sptr[space_ofs[k]]);
                                  float wt = bfloat16_to_float32(kptr[k]);
                                  sum += val * wt;
                              }
  
                              kptr += maxk;
                          }
  
                          if (activation_type == 1)
                          {
                              sum = std::max(sum, 0.f);
                          }
                          else if (activation_type == 2)
                          {
                              float slope = activation_params[0];
                              sum = sum > 0.f ? sum : sum * slope;
                          }
                          else if (activation_type == 3)
                          {
                              float min = activation_params[0];
                              float max = activation_params[1];
                              if (sum < min)
                                  sum = min;
                              if (sum > max)
                                  sum = max;
                          }
                          else if (activation_type == 4)
                          {
                              sum = static_cast<float>(1.f / (1.f + exp(-sum)));
                          }
                          else if (activation_type == 5)
                          {
                              sum = static_cast<float>(sum * tanh(log(exp(sum) + 1.f)));
                          }
  
                          outptr[j] = float32_to_bfloat16(sum);
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      return 0;
  }
  
  int Convolution_arm::create_pipeline_int8_arm(const Option& opt)
  {
      const int maxk = kernel_w * kernel_h;
      const int num_input = weight_data_size / maxk / num_output;
  
      use_winograd3x3_int8 = false;
      use_sgemm1x1_int8 = false;
  
      if (opt.use_winograd_convolution && kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
      {
          use_winograd3x3_int8 = true;
          //         conv3x3s1_winograd23_transform_kernel_int8_neon(weight_data, weight_3x3_winograd23_data_int8, num_input, num_output);
          conv3x3s1_winograd43_transform_kernel_int8_neon(weight_data, weight_3x3_winograd23_data_int8, num_input, num_output);
      }
  
      if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
      {
          conv3x3s2_transform_kernel_int8_neon(weight_data, weight_3x3s2_data_int8, num_input, num_output);
      }
      else if (kernel_w == 1 && kernel_h == 1 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
      {
          use_sgemm1x1_int8 = true;
          conv1x1s1_sgemm_transform_kernel_int8_neon(weight_data, weight_1x1s1_sgemm_data_int8, num_input, num_output);
      }
      else
      {
          conv_im2col_sgemm_transform_kernel_int8_neon(weight_data, weight_sgemm_data_int8, num_input, num_output, maxk);
      }
  
      return 0;
  }
  
  int Convolution_arm::forward_int8_arm(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      if (dilation_w > 1 || dilation_h > 1)
      {
          return Convolution::forward(bottom_blob, top_blob, opt);
      }
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      // int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
  
      //     NCNN_LOGE("Convolution_arm input %d x %d  ksize=%d %d  stride=%d %d", w, h, kernel_w, kernel_h, stride_w, stride_h);
  
      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_unbordered = bottom_blob;
      if (elemsize != 1)
      {
          Option opt_g = opt;
          opt_g.blob_allocator = opt.workspace_allocator;
  
          quantize_float32_to_int8(bottom_blob, bottom_blob_unbordered, bottom_blob_int8_scale, opt_g);
      }
  
      Mat bottom_blob_bordered;
      make_padding(bottom_blob_unbordered, 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;
  
      // int8
      size_t out_elemsize = use_int8_requantize ? 1u : 4u;
  
      top_blob.create(outw, outh, num_output, out_elemsize, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // int8
      if (use_int8_requantize == true)
      {
          Mat top_blob_tm;
          top_blob_tm.create(outw, outh, num_output, (size_t)4u, opt.workspace_allocator);
          if (top_blob_tm.empty())
              return -100;
  
          if (use_sgemm1x1_int8)
          {
              std::vector<float> requantize_scales;
              for (int p = 0; p < num_output; p++)
              {
                  float scale_in;
                  if (weight_data_int8_scales[p] == 0)
                      scale_in = 0;
                  else
                      scale_in = 1.f / (bottom_blob_int8_scale * weight_data_int8_scales[p]);
  
                  float scale_out = top_blob_int8_scale;
  
                  requantize_scales.push_back(scale_in);
                  requantize_scales.push_back(scale_out);
              }
  
              conv1x1s1_sgemm_int8_requant_neon(bottom_blob_bordered, top_blob, weight_1x1s1_sgemm_data_int8, bias_data, requantize_scales, opt);
  
              if (activation)
              {
                  activation->forward_inplace(top_blob, opt);
              }
  
              return 0;
          }
          else if (use_winograd3x3_int8)
          {
              //             conv3x3s1_winograd23_int8_neon(bottom_blob_bordered, top_blob_tm, weight_3x3_winograd23_data_int8, opt);
              conv3x3s1_winograd43_int8_neon(bottom_blob_bordered, top_blob_tm, weight_3x3_winograd23_data_int8, opt);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv3x3s2_packed_int8_neon(bottom_blob_bordered, top_blob_tm, weight_3x3s2_data_int8, opt);
          }
          else
          {
              conv_im2col_sgemm_int8_neon(bottom_blob_bordered, top_blob_tm, weight_sgemm_data_int8, kernel_w, kernel_h, stride_w, stride_h, opt);
          }
  
          // requantize, reverse scale inplace
          #pragma omp parallel for num_threads(opt.num_threads)
          for (int p = 0; p < num_output; p++)
          {
              Option opt_g = opt;
              opt_g.num_threads = 1;
              opt_g.blob_allocator = top_blob.allocator;
  
              Mat top_blob_tm_g = top_blob_tm.channel_range(p, 1);
              Mat top_blob_g = top_blob.channel_range(p, 1);
  
              // requantize and relu
              float scale_in;
              if (weight_data_int8_scales[p] == 0)
                  scale_in = 0;
              else
                  scale_in = 1.f / (bottom_blob_int8_scale * weight_data_int8_scales[p]);
  
              float scale_out = top_blob_int8_scale; //FIXME load param
  
              requantize_int8_to_int8(top_blob_tm_g, top_blob_g, scale_in, scale_out, bias_term ? (const float*)bias_data + p : 0, bias_term ? 1 : 0, 0, opt_g);
          }
      }
      else
      {
          if (use_sgemm1x1_int8)
          {
              conv1x1s1_sgemm_int8_neon(bottom_blob_bordered, top_blob, weight_1x1s1_sgemm_data_int8, opt);
          }
          else if (use_winograd3x3_int8)
          {
              //             conv3x3s1_winograd23_int8_neon(bottom_blob_bordered, top_blob, weight_3x3_winograd23_data_int8, opt);
              conv3x3s1_winograd43_int8_neon(bottom_blob_bordered, top_blob, weight_3x3_winograd23_data_int8, opt);
          }
          else if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 2 && stride_h == 2)
          {
              conv3x3s2_packed_int8_neon(bottom_blob_bordered, top_blob, weight_3x3s2_data_int8, opt);
          }
          else
          {
              conv_im2col_sgemm_int8_neon(bottom_blob_bordered, top_blob, weight_sgemm_data_int8, kernel_w, kernel_h, stride_w, stride_h, opt);
          }
  
          // dequantize, reverse scale inplace
          #pragma omp parallel for num_threads(opt.num_threads)
          for (int p = 0; p < num_output; p++)
          {
              Option opt_g = opt;
              opt_g.num_threads = 1;
              opt_g.blob_allocator = top_blob.allocator;
  
              Mat top_blob_g = top_blob.channel_range(p, 1);
  
              // dequantize
              float scale_in;
              if (weight_data_int8_scales[p] == 0)
                  scale_in = 0;
              else
                  scale_in = 1.f / (bottom_blob_int8_scale * weight_data_int8_scales[p]);
  
              dequantize_int32_to_float32(top_blob_g, scale_in, bias_term ? (const float*)bias_data + p : 0, bias_term ? 1 : 0, opt_g);
          }
      }
  
      if (activation)
      {
          activation->forward_inplace(top_blob, opt);
      }
  
      return 0;
  }
  
  int Convolution_arm::forwardDilation_arm(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      size_t elemsize = bottom_blob.elemsize;
  
      const int kernel_size = kernel_w;
      const int stride = stride_w;
      const int dilation = dilation_w;
      const int kernel_extent = dilation * (kernel_size - 1) + 1;
  
      int outw = (w - kernel_extent) / stride + 1;
      int outh = (h - kernel_extent) / stride + 1;
  
      top_blob.create(outw, outh, num_output, elemsize, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // Make (dilation * dilation) batches
      Mat inner_bottom_blob;
      Mat inner_top_blob;
      for (int x = 0; x < dilation; x++)
      {
          for (int y = 0; y < dilation; y++)
          {
              int inner_w = (w - y + dilation - 1) / dilation;
              int inner_h = (h - x + dilation - 1) / dilation;
  
              int inner_outw = (inner_w - kernel_size) / stride + 1;
              int inner_outh = (inner_h - kernel_size) / stride + 1;
  
              inner_bottom_blob.create(inner_w, inner_h, bottom_blob.c, elemsize, opt.workspace_allocator);
              if (inner_bottom_blob.empty())
                  return -100;
  
              inner_top_blob.create(inner_outw, inner_outh, num_output, elemsize, opt.workspace_allocator);
              if (inner_top_blob.empty())
                  return -100;
  
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int c = 0; c < bottom_blob.c; c++)
              {
                  float* outptr = inner_bottom_blob.channel(c);
  
                  for (int i = 0; i < inner_h; i++)
                  {
                      const float* ptr = (const float*)bottom_blob.channel(c) + dilation * i * w + x * w + y;
                      for (int j = 0; j < inner_w; j++)
                      {
                          outptr[j] = ptr[j * dilation];
                      }
                      outptr += inner_w;
                  }
              }
  
              Option opt_g = opt;
              opt_g.blob_allocator = inner_top_blob.allocator;
              convolution_dilation1->forward(inner_bottom_blob, inner_top_blob, opt_g);
  
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int c = 0; c < num_output; c++)
              {
                  float* outptr = (float*)top_blob.channel(c) + x * outw + y;
                  for (int i = 0; i < inner_outh; i++)
                  {
                      const float* ptr = (const float*)inner_top_blob.channel(c) + i * inner_outw;
                      for (int j = 0; j < inner_outw; j++)
                      {
                          outptr[j * dilation] = ptr[j];
                      }
                      outptr += dilation * outw;
                  }
              }
          }
      }
  
      if (activation)
      {
          activation->forward_inplace(top_blob, opt);
      }
  
      return 0;
  }
  
  } // namespace ncnn
  