text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
  //
  // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  // in compliance with the License. You may obtain a copy of the License at
  //
  // https://opensource.org/licenses/BSD-3-Clause
  //
  // Unless required by applicable law or agreed to in writing, software distributed
  // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  // CONDITIONS OF ANY KIND, either express or implied. See the License for the
  // specific language governing permissions and limitations under the License.
  
  static void convolution_bf16s(const Mat& bottom_blob, Mat& top_blob, const Mat& weight_data_bf16, const Mat& bias_data, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, int activation_type, const Mat& activation_params, const Option& opt)
  {
      int w = bottom_blob.w;
      int channels = bottom_blob.c;
  
      int outw = top_blob.w;
      int outh = top_blob.h;
      int outch = top_blob.c;
  
      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;
          }
      }
  
      const float* bias_data_ptr = bias_data;
  
      // num_output
      #pragma omp parallel for num_threads(opt.num_threads)
      for (int p = 0; p < outch; 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_data_ptr)
                  {
                      sum = bias_data_ptr[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.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;
                  }
  
                  sum = activation_ss(sum, activation_type, activation_params);
  
                  outptr[j] = float32_to_bfloat16(sum);
              }
  
              outptr += outw;
          }
      }
  }
  