text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
  //
  // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  // in compliance with the License. You may obtain a copy of the License at
  //
  // https://opensource.org/licenses/BSD-3-Clause
  //
  // Unless required by applicable law or agreed to in writing, software distributed
  // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  // CONDITIONS OF ANY KIND, either express or implied. See the License for the
  // specific language governing permissions and limitations under the License.
  
  #include "convolutiondepthwise_vulkan.h"
  #include <algorithm>
  #include "layer_type.h"
  
  namespace ncnn {
  
  DEFINE_LAYER_CREATOR(ConvolutionDepthWise_vulkan)
  
  ConvolutionDepthWise_vulkan::ConvolutionDepthWise_vulkan()
  {
      support_vulkan = true;
  
      padding = 0;
      packing_unpack = 0;
      packing_pack = 0;
  
      pipeline_convolutiondepthwise = 0;
      pipeline_convolutiondepthwise_pack4 = 0;
      pipeline_convolutiondepthwise_pack8 = 0;
  
      pipeline_convolutiondepthwise_group = 0;
      pipeline_convolutiondepthwise_group_pack4 = 0;
      pipeline_convolutiondepthwise_group_pack1to4 = 0;
      pipeline_convolutiondepthwise_group_pack4to1 = 0;
      pipeline_convolutiondepthwise_group_pack8 = 0;
      pipeline_convolutiondepthwise_group_pack1to8 = 0;
      pipeline_convolutiondepthwise_group_pack4to8 = 0;
      pipeline_convolutiondepthwise_group_pack8to4 = 0;
      pipeline_convolutiondepthwise_group_pack8to1 = 0;
  }
  
  int ConvolutionDepthWise_vulkan::create_pipeline(const Option& opt)
  {
      const Mat& shape = bottom_shapes.empty() ? Mat() : bottom_shapes[0];
      const Mat& out_shape = top_shapes.empty() ? Mat() : top_shapes[0];
  
      // the shape after padding
      Mat shape_bordered;
      if (shape.dims != 0)
      {
          if (pad_left > 0 || pad_right > 0 || pad_top > 0 || pad_bottom > 0)
          {
              shape_bordered = Mat(shape.w + pad_left + pad_right, shape.h + pad_top + pad_bottom, shape.c, (void*)0);
          }
          else if ((pad_left == -233 && pad_right == -233 && pad_top == -233 && pad_bottom == -233)
              || (pad_left == -234 && pad_right == -234 && pad_top == -234 && pad_bottom == -234))
          {
              const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
              const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
              int wpad = kernel_extent_w + (shape.w - 1) / stride_w * stride_w - shape.w;
              int hpad = kernel_extent_h + (shape.h - 1) / stride_h * stride_h - shape.h;
              if (wpad > 0 || hpad > 0)
              {
                  shape_bordered = Mat(shape.w + wpad, shape.h + hpad, shape.c, (void*)0);
              }
          }
          else
          {
              shape_bordered = shape;
          }
      }
  
      {
          padding = ncnn::create_layer(ncnn::LayerType::Padding);
          padding->vkdev = vkdev;
  
          padding->bottom_shapes.resize(1);
          padding->bottom_shapes[0] = shape;
          padding->top_shapes.resize(1);
          padding->top_shapes[0] = shape_bordered;
  
          ncnn::ParamDict pd;
          pd.set(0, pad_top);
          pd.set(1, pad_bottom);
          pd.set(2, pad_left);
          pd.set(3, pad_right);
          pd.set(4, 0);
          pd.set(5, pad_value);
  
          padding->load_param(pd);
  
          padding->create_pipeline(opt);
      }
  
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      int elempack = opt.use_shader_pack8 && channels % 8 == 0 ? 8 : channels % 4 == 0 ? 4 : 1;
      int out_elempack = opt.use_shader_pack8 && num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
  
      Mat shape_bordered_packed;
      convert_shape_packing(shape_bordered, shape_bordered_packed, elempack);
  
      Mat out_shape_packed;
      convert_shape_packing(out_shape, out_shape_packed, out_elempack);
  
      std::vector<vk_specialization_type> specializations(11 + 10);
      specializations[0].i = kernel_w;
      specializations[1].i = kernel_h;
      specializations[2].i = dilation_w;
      specializations[3].i = dilation_h;
      specializations[4].i = stride_w;
      specializations[5].i = stride_h;
      specializations[6].i = bias_term;
      specializations[7].i = group;
      specializations[8].i = activation_type;
      specializations[9].f = activation_params.w == 1 ? activation_params[0] : 0.f;
      specializations[10].f = activation_params.w == 2 ? activation_params[1] : 0.f;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          specializations[11 + 0].i = shape_bordered_packed.dims;
          specializations[11 + 1].i = shape_bordered_packed.w;
          specializations[11 + 2].i = shape_bordered_packed.h;
          specializations[11 + 3].i = shape_bordered_packed.c;
          specializations[11 + 4].i = shape_bordered_packed.cstep;
          specializations[11 + 5].i = out_shape_packed.dims;
          specializations[11 + 6].i = out_shape_packed.w;
          specializations[11 + 7].i = out_shape_packed.h;
          specializations[11 + 8].i = out_shape_packed.c;
          specializations[11 + 9].i = out_shape_packed.cstep;
  
          Mat local_size_xyz(8, 8, std::min(4, num_output / out_elempack), (void*)0);
          if (out_shape_packed.dims != 0)
          {
              local_size_xyz.w = std::min(8, out_shape_packed.w);
              local_size_xyz.h = std::min(8, out_shape_packed.h);
              local_size_xyz.c = std::min(4, out_shape_packed.c);
          }
  
          // pack1
          if (elempack == 1)
          {
              pipeline_convolutiondepthwise = new Pipeline(vkdev);
              pipeline_convolutiondepthwise->set_optimal_local_size_xyz(local_size_xyz);
              pipeline_convolutiondepthwise->create("convolutiondepthwise", opt, specializations, 4, 10);
          }
  
          // pack4
          if (elempack == 4)
          {
              pipeline_convolutiondepthwise_pack4 = new Pipeline(vkdev);
              pipeline_convolutiondepthwise_pack4->set_optimal_local_size_xyz(local_size_xyz);
              pipeline_convolutiondepthwise_pack4->create("convolutiondepthwise_pack4", opt, specializations, 4, 10);
          }
  
          // pack8
          if (elempack == 8)
          {
              pipeline_convolutiondepthwise_pack8 = new Pipeline(vkdev);
              pipeline_convolutiondepthwise_pack8->set_optimal_local_size_xyz(local_size_xyz);
              pipeline_convolutiondepthwise_pack8->create("convolutiondepthwise_pack8", opt, specializations, 4, 10);
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels / group;
      const int num_output_g = num_output / group;
  
      int elempack_g = opt.use_shader_pack8 && channels_g % 8 == 0 ? 8 : channels_g % 4 == 0 ? 4 : 1;
      int out_elempack_g = opt.use_shader_pack8 && num_output_g % 8 == 0 ? 8 : num_output_g % 4 == 0 ? 4 : 1;
  
      Mat shape_bordered_g_packed;
      convert_shape_packing(shape_bordered, shape_bordered_g_packed, elempack_g);
  
      Mat out_shape_g_packed;
      convert_shape_packing(out_shape, out_shape_g_packed, out_elempack_g);
  
      if (elempack > elempack_g)
      {
          packing_unpack = ncnn::create_layer(ncnn::LayerType::Packing);
          packing_unpack->vkdev = vkdev;
  
          packing_unpack->bottom_shapes.resize(1);
          packing_unpack->bottom_shapes[0] = shape_bordered_packed;
          packing_unpack->top_shapes.resize(1);
          packing_unpack->top_shapes[0] = shape_bordered_g_packed;
  
          ncnn::ParamDict pd;
          pd.set(0, elempack_g);
  
          packing_unpack->load_param(pd);
  
          packing_unpack->create_pipeline(opt);
      }
  
      if (out_elempack_g < out_elempack)
      {
          packing_pack = ncnn::create_layer(ncnn::LayerType::Packing);
          packing_pack->vkdev = vkdev;
  
          packing_pack->bottom_shapes.resize(1);
          packing_pack->bottom_shapes[0] = out_shape_g_packed;
          packing_pack->top_shapes.resize(1);
          packing_pack->top_shapes[0] = out_shape;
  
          ncnn::ParamDict pd;
          pd.set(0, out_elempack);
  
          packing_pack->load_param(pd);
  
          packing_pack->create_pipeline(opt);
      }
  
      specializations[11 + 0].i = shape_bordered_g_packed.dims;
      specializations[11 + 1].i = shape_bordered_g_packed.w;
      specializations[11 + 2].i = shape_bordered_g_packed.h;
      specializations[11 + 3].i = shape_bordered_g_packed.c;
      specializations[11 + 4].i = shape_bordered_g_packed.cstep;
      specializations[11 + 5].i = out_shape_g_packed.dims;
      specializations[11 + 6].i = out_shape_g_packed.w;
      specializations[11 + 7].i = out_shape_g_packed.h;
      specializations[11 + 8].i = out_shape_g_packed.c;
      specializations[11 + 9].i = out_shape_g_packed.cstep;
  
      Mat local_size_xyz(8, 8, std::min(4, num_output / out_elempack_g), (void*)0);
      if (out_shape_g_packed.dims != 0)
      {
          local_size_xyz.w = std::min(8, out_shape_g_packed.w);
          local_size_xyz.h = std::min(8, out_shape_g_packed.h);
          local_size_xyz.c = std::min(4, out_shape_g_packed.c);
      }
  
      // pack1
      if (elempack_g == 1 && out_elempack_g == 1)
      {
          pipeline_convolutiondepthwise_group = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group->create("convolutiondepthwise_group", opt, specializations, 4, 10);
      }
  
      // pack4
      if (elempack_g == 4 && out_elempack_g == 4)
      {
          pipeline_convolutiondepthwise_group_pack4 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack4->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack4->create("convolutiondepthwise_group_pack4", opt, specializations, 4, 10);
      }
  
      // pack1to4
      if (elempack_g == 1 && out_elempack_g == 4)
      {
          pipeline_convolutiondepthwise_group_pack1to4 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack1to4->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack1to4->create("convolutiondepthwise_group_pack1to4", opt, specializations, 4, 10);
      }
  
      // pack4to1
      if (elempack_g == 4 && out_elempack_g == 1)
      {
          pipeline_convolutiondepthwise_group_pack4to1 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack4to1->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack4to1->create("convolutiondepthwise_group_pack4to1", opt, specializations, 4, 10);
      }
  
      // pack8
      if (elempack_g == 8 && out_elempack_g == 8)
      {
          pipeline_convolutiondepthwise_group_pack8 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack8->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack8->create("convolutiondepthwise_group_pack8", opt, specializations, 4, 10);
      }
  
      // pack1to8
      if (elempack_g == 1 && out_elempack_g == 8)
      {
          pipeline_convolutiondepthwise_group_pack1to8 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack1to8->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack1to8->create("convolutiondepthwise_group_pack1to8", opt, specializations, 4, 10);
      }
  
      // pack4to8
      if (elempack_g == 4 && out_elempack_g == 8)
      {
          pipeline_convolutiondepthwise_group_pack4to8 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack4to8->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack4to8->create("convolutiondepthwise_group_pack4to8", opt, specializations, 4, 10);
      }
  
      // pack8to4
      if (elempack_g == 8 && out_elempack_g == 4)
      {
          pipeline_convolutiondepthwise_group_pack8to4 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack8to4->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack8to4->create("convolutiondepthwise_group_pack8to4", opt, specializations, 4, 10);
      }
  
      // pack8to1
      if (elempack_g == 8 && out_elempack_g == 1)
      {
          pipeline_convolutiondepthwise_group_pack8to1 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack8to1->set_optimal_local_size_xyz(local_size_xyz);
          pipeline_convolutiondepthwise_group_pack8to1->create("convolutiondepthwise_group_pack8to1", opt, specializations, 4, 10);
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_vulkan::destroy_pipeline(const Option& opt)
  {
      if (padding)
      {
          padding->destroy_pipeline(opt);
          delete padding;
          padding = 0;
      }
  
      if (packing_unpack)
      {
          packing_unpack->destroy_pipeline(opt);
          delete packing_unpack;
          packing_unpack = 0;
      }
  
      if (packing_pack)
      {
          packing_pack->destroy_pipeline(opt);
          delete packing_pack;
          packing_pack = 0;
      }
  
      delete pipeline_convolutiondepthwise;
      pipeline_convolutiondepthwise = 0;
  
      delete pipeline_convolutiondepthwise_pack4;
      pipeline_convolutiondepthwise_pack4 = 0;
  
      delete pipeline_convolutiondepthwise_pack8;
      pipeline_convolutiondepthwise_pack8 = 0;
  
      delete pipeline_convolutiondepthwise_group;
      pipeline_convolutiondepthwise_group = 0;
  
      delete pipeline_convolutiondepthwise_group_pack4;
      pipeline_convolutiondepthwise_group_pack4 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack1to4;
      pipeline_convolutiondepthwise_group_pack1to4 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack4to1;
      pipeline_convolutiondepthwise_group_pack4to1 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack8;
      pipeline_convolutiondepthwise_group_pack8 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack1to8;
      pipeline_convolutiondepthwise_group_pack1to8 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack4to8;
      pipeline_convolutiondepthwise_group_pack4to8 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack8to4;
      pipeline_convolutiondepthwise_group_pack8to4 = 0;
  
      delete pipeline_convolutiondepthwise_group_pack8to1;
      pipeline_convolutiondepthwise_group_pack8to1 = 0;
  
      return 0;
  }
  
  int ConvolutionDepthWise_vulkan::upload_model(VkTransfer& cmd, const Option& opt)
  {
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      int elempack = opt.use_shader_pack8 && channels % 8 == 0 ? 8 : channels % 4 == 0 ? 4 : 1;
  //     int out_elempack = opt.use_shader_pack8 && num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          Mat weight_data_packed;
          Mat weight_data_r2 = weight_data.reshape(maxk, group);
          convert_packing(weight_data_r2, weight_data_packed, elempack);
  
          cmd.record_upload(weight_data_packed, weight_data_gpu, opt);
  
          if (bias_term)
          {
              cmd.record_upload(bias_data, bias_data_gpu, opt);
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels / group;
      const int num_output_g = num_output / group;
  
      int elempack_g = opt.use_shader_pack8 && channels_g % 8 == 0 ? 8 : channels_g % 4 == 0 ? 4 : 1;
      int out_elempack_g = opt.use_shader_pack8 && num_output_g % 8 == 0 ? 8 : num_output_g % 4 == 0 ? 4 : 1;
  
      // src = kw-kh-inch-outch
      // dst = pa-pb-kw-kh-inch/pa-outch/pb
      Mat weight_data_packed_groups;
      {
          Mat weight_data_r2_groups = weight_data.reshape(maxk, channels_g, num_output_g * group);
  
          weight_data_packed_groups.create(maxk, channels_g/elempack_g, num_output_g/out_elempack_g * group, (size_t)4*elempack_g*out_elempack_g, elempack_g*out_elempack_g);
  
          for (int g=0; g<group; g++)
          {
              const Mat weight_data_r2 = weight_data_r2_groups.channel_range(num_output_g * g, num_output_g);
  
              Mat weight_data_packed = weight_data_packed_groups.channel_range(num_output_g/out_elempack_g * g, num_output_g/out_elempack_g);
  
              for (int q=0; q+(out_elempack_g-1)<num_output_g; q+=out_elempack_g)
              {
                  Mat g0 = weight_data_packed.channel(q/out_elempack_g);
  
                  for (int p=0; p+(elempack_g-1)<channels_g; p+=elempack_g)
                  {
                      float* g00 = g0.row(p/elempack_g);
  
                      for (int k=0; k<maxk; k++)
                      {
  
                          for (int i=0; i<out_elempack_g; i++)
                          {
                              const Mat k0 = weight_data_r2.channel(q+i);
  
                              for (int j=0; j<elempack_g; j++)
                              {
                                  const float* k00 = k0.row(p+j);
  
                                  g00[0] = k00[k];
  
                                  g00++;
                              }
                          }
  
                      }
                  }
              }
          }
      }
  
      cmd.record_upload(weight_data_packed_groups, weight_data_gpu, opt);
  
      if (bias_term)
      {
          cmd.record_upload(bias_data, bias_data_gpu, opt);
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_vulkan::forward(const VkMat& bottom_blob, VkMat& top_blob, VkCompute& cmd, 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;
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      VkMat bottom_blob_bordered = bottom_blob;
      if (pad_left > 0 || pad_right > 0 || pad_top > 0 || pad_bottom > 0)
      {
          Option opt_pad = opt;
          opt_pad.blob_vkallocator = opt.workspace_vkallocator;
  
          padding->forward(bottom_blob, bottom_blob_bordered, cmd, opt_pad);
      }
      else if (pad_left == -233 && pad_right == -233 && pad_top == -233 && pad_bottom == -233)
      {
          int wpad = kernel_extent_w + (w - 1) / stride_w * stride_w - w;
          int hpad = kernel_extent_h + (h - 1) / stride_h * stride_h - h;
          if (wpad > 0 || hpad > 0)
          {
              Option opt_pad = opt;
              opt_pad.blob_vkallocator = opt.workspace_vkallocator;
  
              VkMat padding_param_blob(4, (size_t)4u, 1, opt.staging_vkallocator, opt.staging_vkallocator);
              padding_param_blob.prepare_staging_buffer();
              int* padding_params = padding_param_blob.mapped();
  
              padding_params[0] = hpad / 2;
              padding_params[1] = hpad - hpad / 2;
              padding_params[2] = wpad / 2;
              padding_params[3] = wpad - wpad / 2;
  
              std::vector<VkMat> padding_inputs(2);
              padding_inputs[0] = bottom_blob;
              padding_inputs[1] = padding_param_blob;
  
              std::vector<VkMat> padding_outputs(1);
              padding->forward(padding_inputs, padding_outputs, cmd, opt_pad);
              bottom_blob_bordered = padding_outputs[0];
          }
      }
      else if (pad_left == -234 && pad_right == -234 && pad_top == -234 && pad_bottom == -234)
      {
          int wpad = kernel_extent_w + (w - 1) / stride_w * stride_w - w;
          int hpad = kernel_extent_h + (h - 1) / stride_h * stride_h - h;
          if (wpad > 0 || hpad > 0)
          {
              Option opt_pad = opt;
              opt_pad.blob_vkallocator = opt.workspace_vkallocator;
  
              VkMat padding_param_blob(4, (size_t)4u, 1, opt.staging_vkallocator, opt.staging_vkallocator);
              padding_param_blob.prepare_staging_buffer();
              int* padding_params = padding_param_blob.mapped();
  
              padding_params[0] = hpad - hpad / 2;
              padding_params[1] = hpad / 2;
              padding_params[2] = wpad - wpad / 2;
              padding_params[3] = wpad / 2;
  
              std::vector<VkMat> padding_inputs(2);
              padding_inputs[0] = bottom_blob;
              padding_inputs[1] = padding_param_blob;
  
              std::vector<VkMat> padding_outputs(1);
              padding->forward(padding_inputs, padding_outputs, cmd, opt_pad);
              bottom_blob_bordered = padding_outputs[0];
          }
      }
  
      w = bottom_blob_bordered.w;
      h = bottom_blob_bordered.h;
  
      int outw = (w - kernel_extent_w) / stride_w + 1;
      int outh = (h - kernel_extent_h) / stride_h + 1;
      int out_elempack = opt.use_shader_pack8 && num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      if (opt.use_fp16_packed && !opt.use_fp16_storage)
      {
          if (out_elempack == 8) out_elemsize = 8*2u;
          if (out_elempack == 4) out_elemsize = 4*2u;
          if (out_elempack == 1) out_elemsize = 4u;
      }
  
      top_blob.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_vkallocator, opt.staging_vkallocator);
      if (top_blob.empty())
          return -100;
  
      // depth-wise
      if (channels == group / elempack && group / elempack == num_output / elempack)
      {
          std::vector<VkMat> bindings(4);
          bindings[0] = bottom_blob_bordered;
          bindings[1] = top_blob;
          bindings[2] = weight_data_gpu;
          bindings[3] = bias_term ? bias_data_gpu : bindings[2];// TODO use dummy buffer
  
          std::vector<vk_constant_type> constants(10);
          constants[0].i = bottom_blob_bordered.dims;
          constants[1].i = bottom_blob_bordered.w;
          constants[2].i = bottom_blob_bordered.h;
          constants[3].i = bottom_blob_bordered.c;
          constants[4].i = bottom_blob_bordered.cstep;
          constants[5].i = top_blob.dims;
          constants[6].i = top_blob.w;
          constants[7].i = top_blob.h;
          constants[8].i = top_blob.c;
          constants[9].i = top_blob.cstep;
  
          const Pipeline* pipeline = elempack == 8 ? pipeline_convolutiondepthwise_pack8
                                   : elempack == 4 ? pipeline_convolutiondepthwise_pack4
                                   : pipeline_convolutiondepthwise;
  
          cmd.record_pipeline(pipeline, bindings, constants, top_blob);
  
          return 0;
      }
  
      const int channels_g = channels * elempack / group;
      const int num_output_g = num_output / group;
  
      int elempack_g = opt.use_shader_pack8 && channels_g % 8 == 0 ? 8 : channels_g % 4 == 0 ? 4 : 1;
      int out_elempack_g = opt.use_shader_pack8 && num_output_g % 8 == 0 ? 8 : num_output_g % 4 == 0 ? 4 : 1;
  
      // unpacking
      VkMat bottom_blob_bordered_unpacked = bottom_blob_bordered;
      if (elempack > elempack_g)
      {
          Option opt_pack1 = opt;
          opt_pack1.blob_vkallocator = opt.workspace_vkallocator;
  
          packing_unpack->forward(bottom_blob_bordered, bottom_blob_bordered_unpacked, cmd, opt_pack1);
      }
  
      VkMat top_blob_unpacked = top_blob;
      if (out_elempack_g < out_elempack)
      {
          top_blob_unpacked.create(outw, outh, num_output / out_elempack_g, out_elemsize / out_elempack * out_elempack_g, out_elempack_g, opt.workspace_vkallocator, opt.staging_vkallocator);
          if (top_blob_unpacked.empty())
              return -100;
      }
  
      std::vector<VkMat> bindings(4);
      bindings[0] = bottom_blob_bordered_unpacked;
      bindings[1] = top_blob_unpacked;
      bindings[2] = weight_data_gpu;
      bindings[3] = bias_term ? bias_data_gpu : bindings[2];// TODO use dummy buffer
  
      std::vector<vk_constant_type> constants(10);
      constants[0].i = bottom_blob_bordered_unpacked.dims;
      constants[1].i = bottom_blob_bordered_unpacked.w;
      constants[2].i = bottom_blob_bordered_unpacked.h;
      constants[3].i = bottom_blob_bordered_unpacked.c;
      constants[4].i = bottom_blob_bordered_unpacked.cstep;
      constants[5].i = top_blob_unpacked.dims;
      constants[6].i = top_blob_unpacked.w;
      constants[7].i = top_blob_unpacked.h;
      constants[8].i = top_blob_unpacked.c;
      constants[9].i = top_blob_unpacked.cstep;
  
      const Pipeline* pipeline = 0;
      if (elempack_g == 1 && out_elempack_g == 1)
      {
          pipeline = pipeline_convolutiondepthwise_group;
      }
      else if (elempack_g == 4 && out_elempack_g == 4)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack4;
      }
      else if (elempack_g == 1 && out_elempack_g == 4)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack1to4;
      }
      else if (elempack_g == 4 && out_elempack_g == 1)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack4to1;
      }
      else if (elempack_g == 8 && out_elempack_g == 8)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack8;
      }
      else if (elempack_g == 1 && out_elempack_g == 8)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack1to8;
      }
      else if (elempack_g == 4 && out_elempack_g == 8)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack4to8;
      }
      else if (elempack_g == 8 && out_elempack_g == 4)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack8to4;
      }
      else if (elempack_g == 8 && out_elempack_g == 1)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack8to1;
      }
  
      cmd.record_pipeline(pipeline, bindings, constants, top_blob_unpacked);
  
      // packing
      if (out_elempack_g < out_elempack)
      {
          packing_pack->forward(top_blob_unpacked, top_blob, cmd, opt);
      }
      else
      {
          top_blob = top_blob_unpacked;
      }
  
      return 0;
  }
  
  } // namespace ncnn
  