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_pack1 = 0;
      packing_pack4 = 0;
  
      pipeline_convolutiondepthwise = 0;
      pipeline_convolutiondepthwise_pack4 = 0;
  
      pipeline_convolutiondepthwise_group = 0;
      pipeline_convolutiondepthwise_group_pack4 = 0;
      pipeline_convolutiondepthwise_group_pack1to4 = 0;
      pipeline_convolutiondepthwise_group_pack4to1 = 0;
  }
  
  int ConvolutionDepthWise_vulkan::create_pipeline(const Option& opt)
  {
      {
          padding = ncnn::create_layer(ncnn::LayerType::Padding);
          padding->vkdev = vkdev;
  
          ncnn::ParamDict pd;
          pd.set(0, pad_h);
          pd.set(1, pad_h);
          pd.set(2, pad_w);
          pd.set(3, pad_w);
          pd.set(4, 0);
          pd.set(5, 0.f);
  
          padding->load_param(pd);
  
          padding->create_pipeline(opt);
      }
  
      std::vector<vk_specialization_type> specializations(11);
      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;
  
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          // pack1
          if (num_output % 4 != 0)
          {
              pipeline_convolutiondepthwise = new Pipeline(vkdev);
              pipeline_convolutiondepthwise->set_optimal_local_size_xyz(32, 32, num_output);
              pipeline_convolutiondepthwise->create("convolutiondepthwise", opt, specializations, 4, 10);
          }
  
          // pack4
          if (num_output % 4 == 0)
          {
              pipeline_convolutiondepthwise_pack4 = new Pipeline(vkdev);
              pipeline_convolutiondepthwise_pack4->set_optimal_local_size_xyz(32, 32, std::max(1, num_output / 4));
              pipeline_convolutiondepthwise_pack4->create("convolutiondepthwise_pack4", opt, specializations, 4, 10);
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels / group;
      const int num_output_g = num_output / group;
  
      // pack1
      if (channels_g % 4 != 0 && num_output_g % 4 != 0)
      {
          pipeline_convolutiondepthwise_group = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group->set_optimal_local_size_xyz(32, 32, std::max(1, num_output / 8));
          pipeline_convolutiondepthwise_group->create("convolutiondepthwise_group", opt, specializations, 4, 10);
      }
  
      // pack4
      if (channels_g % 4 == 0 && num_output_g % 4 == 0)
      {
          pipeline_convolutiondepthwise_group_pack4 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack4->set_optimal_local_size_xyz(32, 32, std::max(1, num_output / 8));
          pipeline_convolutiondepthwise_group_pack4->create("convolutiondepthwise_group_pack4", opt, specializations, 4, 10);
      }
  
      // pack1to4
      if (channels_g % 4 != 0 && num_output_g % 4 == 0)
      {
          pipeline_convolutiondepthwise_group_pack1to4 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack1to4->set_optimal_local_size_xyz(32, 32, std::max(1, num_output / 8));
          pipeline_convolutiondepthwise_group_pack1to4->create("convolutiondepthwise_group_pack1to4", opt, specializations, 4, 10);
      }
  
      // pack4to1
      if (channels_g % 4 == 0 && num_output_g % 4 != 0)
      {
          pipeline_convolutiondepthwise_group_pack4to1 = new Pipeline(vkdev);
          pipeline_convolutiondepthwise_group_pack4to1->set_optimal_local_size_xyz(32, 32, std::max(1, num_output / 8));
          pipeline_convolutiondepthwise_group_pack4to1->create("convolutiondepthwise_group_pack4to1", opt, specializations, 4, 10);
      }
  
      if (channels % 4 == 0 && channels_g % 4 != 0)
      {
          packing_pack1 = ncnn::create_layer(ncnn::LayerType::Packing);
          packing_pack1->vkdev = vkdev;
  
          ncnn::ParamDict pd;
          pd.set(0, 1);
  
          packing_pack1->load_param(pd);
  
          packing_pack1->create_pipeline(opt);
      }
  
      if (num_output_g % 4 != 0 && num_output % 4 == 0)
      {
          packing_pack4 = ncnn::create_layer(ncnn::LayerType::Packing);
          packing_pack4->vkdev = vkdev;
  
          ncnn::ParamDict pd;
          pd.set(0, 4);
  
          packing_pack4->load_param(pd);
  
          packing_pack4->create_pipeline(opt);
      }
  
      return 0;
  }
  
  int ConvolutionDepthWise_vulkan::destroy_pipeline(const Option& opt)
  {
      if (padding)
      {
          padding->destroy_pipeline(opt);
          delete padding;
          padding = 0;
      }
  
      if (packing_pack1)
      {
          packing_pack1->destroy_pipeline(opt);
          delete packing_pack1;
          packing_pack1 = 0;
      }
  
      if (packing_pack4)
      {
          packing_pack4->destroy_pipeline(opt);
          delete packing_pack4;
          packing_pack4 = 0;
      }
  
      delete pipeline_convolutiondepthwise;
      pipeline_convolutiondepthwise = 0;
  
      delete pipeline_convolutiondepthwise_pack4;
      pipeline_convolutiondepthwise_pack4 = 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;
  
      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;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          // pack1
          if (num_output % 4 != 0)
          {
              cmd.record_upload(weight_data, weight_data_gpu, opt);
          }
  
          // pack4
          if (num_output % 4 == 0)
          {
              Mat weight_data_pack4;
              Mat weight_data_r2 = weight_data.reshape(maxk, group);
              convert_packing(weight_data_r2, weight_data_pack4, 4);
  
              cmd.record_upload(weight_data_pack4, weight_data_gpu_pack4, opt);
          }
  
          if (bias_term)
          {
              if (num_output % 4 != 0)
              {
                  cmd.record_upload(bias_data, bias_data_gpu, opt);
              }
  
              if (num_output % 4 == 0)
              {
                  Mat bias_data_pack4;
                  convert_packing(bias_data, bias_data_pack4, 4);
                  cmd.record_upload(bias_data_pack4, bias_data_gpu_pack4, opt);
              }
          }
  
          return 0;
      }
  
      // group convolution
      const int channels_g = channels / group;
      const int num_output_g = num_output / group;
  
      // pack1
      if (channels_g % 4 != 0 && num_output_g % 4 != 0)
      {
          cmd.record_upload(weight_data, weight_data_gpu, opt);
      }
  
      // pack4
      if (channels_g % 4 == 0 && num_output_g % 4 == 0)
      {
          // src = kw-kh-inch-outch
          // dst = 4a-4b-kw-kh-inch/4a-outch/4b
          Mat weight_data_pack4_groups;
          {
              Mat weight_data_r2_groups = weight_data.reshape(maxk, channels_g, num_output_g * group);
  
              weight_data_pack4_groups.create(maxk, channels_g/4, num_output_g/4 * group, (size_t)4*16, 16);
  
              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_pack4 = weight_data_pack4_groups.channel_range(num_output_g/4 * g, num_output_g/4);
  
                  for (int q=0; q+3<num_output_g; 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<channels_g; 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] = k01[k];
                              g00[2] = k02[k];
                              g00[3] = k03[k];
  
                              g00[4] = k10[k];
                              g00[5] = k11[k];
                              g00[6] = k12[k];
                              g00[7] = k13[k];
  
                              g00[8] = k20[k];
                              g00[9] = k21[k];
                              g00[10] = k22[k];
                              g00[11] = k23[k];
  
                              g00[12] = k30[k];
                              g00[13] = k31[k];
                              g00[14] = k32[k];
                              g00[15] = k33[k];
  
                              g00 += 16;
                          }
                      }
                  }
              }
          }
  
          cmd.record_upload(weight_data_pack4_groups, weight_data_gpu_pack4, opt);
      }
  
      // pack1to4
      if (channels_g % 4 != 0 && num_output_g % 4 == 0)
      {
          // src = kw-kh-inch-outch
          // dst = 4b-kw-kh-inch-outch/4b
          Mat weight_data_pack1to4_groups;
          {
              Mat weight_data_r2_groups = weight_data.reshape(maxk, channels_g, num_output_g * group);
  
              weight_data_pack1to4_groups.create(maxk, channels_g, num_output_g/4 * group, (size_t)4*4, 4);
  
              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_pack1to4 = weight_data_pack1to4_groups.channel_range(num_output_g/4 * g, num_output_g/4);
  
                  for (int q=0; q+3<num_output_g; 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<channels_g; 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;
                          }
                      }
                  }
              }
          }
  
          cmd.record_upload(weight_data_pack1to4_groups, weight_data_gpu_pack1to4, opt);
      }
  
      // pack4to1
      if (channels_g % 4 == 0 && num_output_g % 4 != 0)
      {
          // src = kw-kh-inch-outch
          // dst = 4a-kw-kh-inch/4a-outch
          Mat weight_data_pack4to1_groups;
          {
              Mat weight_data_r2_groups = weight_data.reshape(maxk, channels_g, num_output_g * group);
  
              weight_data_pack4to1_groups.create(maxk, channels_g/4, num_output_g * group, (size_t)4*4, 4);
  
              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_pack4to1 = weight_data_pack4to1_groups.channel_range(num_output_g * g, num_output_g);
  
                  for (int q=0; q<num_output_g; q++)
                  {
                      const Mat k0 = weight_data_r2.channel(q);
                      Mat g0 = weight_data_pack4to1.channel(q);
  
                      for (int p=0; p+3<channels_g; 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;
                          }
                      }
                  }
              }
          }
  
          cmd.record_upload(weight_data_pack4to1_groups, weight_data_gpu_pack4to1, opt);
      }
  
      if (bias_term)
      {
          if (num_output_g % 4 != 0)
          {
              cmd.record_upload(bias_data, bias_data_gpu, opt);
          }
  
          if (num_output_g % 4 == 0)
          {
              Mat bias_data_pack4;
              convert_packing(bias_data, bias_data_pack4, 4);
              cmd.record_upload(bias_data_pack4, bias_data_gpu_pack4, 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_w > 0 || pad_h > 0)
      {
          ncnn::Option opt_pad = opt;
          opt_pad.blob_vkallocator = opt.workspace_vkallocator;
  
          padding->forward(bottom_blob, bottom_blob_bordered, cmd, opt_pad);
  
          w = bottom_blob_bordered.w;
          h = bottom_blob_bordered.h;
      }
      else if (pad_w == -233 && pad_h == -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)
          {
              ncnn::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];
          }
  
          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 = 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 == 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] = elempack == 4 ? weight_data_gpu_pack4 : weight_data_gpu;
          bindings[3] = bias_term ? (elempack == 4 ? bias_data_gpu_pack4 : 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 == 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;
  
      // unpacking
      VkMat bottom_blob_bordered_unpacked = bottom_blob_bordered;
      if (elempack == 4 && channels_g % 4 != 0)
      {
          ncnn::Option opt_pack1 = opt;
          opt_pack1.blob_vkallocator = opt.workspace_vkallocator;
  
          packing_pack1->forward(bottom_blob_bordered, bottom_blob_bordered_unpacked, cmd, opt_pack1);
      }
  
      VkMat top_blob_unpacked = top_blob;
      if (num_output_g % 4 != 0 && out_elempack == 4)
      {
          top_blob_unpacked.create(outw, outh, num_output, out_elemsize / out_elempack, 1, 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;
      if (channels_g % 4 != 0 && num_output_g % 4 != 0)
      {
          bindings[2] = weight_data_gpu;
          bindings[3] = bias_term ? bias_data_gpu : bindings[2];// TODO use dummy buffer
      }
      else if (channels_g % 4 == 0 && num_output_g % 4 == 0)
      {
          bindings[2] = weight_data_gpu_pack4;
          bindings[3] = bias_term ? bias_data_gpu_pack4 : bindings[2];// TODO use dummy buffer
      }
      else if (channels_g % 4 != 0 && num_output_g % 4 == 0)
      {
          bindings[2] = weight_data_gpu_pack1to4;
          bindings[3] = bias_term ? bias_data_gpu_pack4 : bindings[2];// TODO use dummy buffer
      }
      else if (channels_g % 4 == 0 && num_output_g % 4 != 0)
      {
          bindings[2] = weight_data_gpu_pack4to1;
          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 (channels_g % 4 != 0 && num_output_g % 4 != 0)
      {
          pipeline = pipeline_convolutiondepthwise_group;
      }
      else if (channels_g % 4 == 0 && num_output_g % 4 == 0)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack4;
      }
      else if (channels_g % 4 != 0 && num_output_g % 4 == 0)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack1to4;
      }
      else if (channels_g % 4 == 0 && num_output_g % 4 != 0)
      {
          pipeline = pipeline_convolutiondepthwise_group_pack4to1;
      }
  
      cmd.record_pipeline(pipeline, bindings, constants, top_blob_unpacked);
  
      // packing
      if (num_output_g % 4 != 0 && out_elempack == 4)
      {
          packing_pack4->forward(top_blob_unpacked, top_blob, cmd, opt);
      }
      else
      {
          top_blob = top_blob_unpacked;
      }
  
      return 0;
  }
  
  } // namespace ncnn
  