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 "deconvolutiondepthwise_arm.h"
  
  #include "layer_type.h"
  
  #if __ARM_NEON
  #include <arm_neon.h>
  #endif // __ARM_NEON
  
  #include "arm_activation.h"
  
  #include "cpu.h"
  
  namespace ncnn {
  
  DeconvolutionDepthWise_arm::DeconvolutionDepthWise_arm()
  {
  #if __ARM_NEON
      support_packing = true;
  #if NCNN_ARM82
      support_fp16_storage = cpu_support_arm_asimdhp();
  #endif
  #endif // __ARM_NEON
  
  #if NCNN_BF16
      support_bf16_storage = true;
  #endif
  }
  
  int DeconvolutionDepthWise_arm::create_pipeline(const Option& opt)
  {
  #if NCNN_ARM82
      if (support_fp16_storage && opt.use_fp16_storage)
      {
          return create_pipeline_fp16s(opt);
      }
  #endif
  
      // create Deconvolution op for each group
      const int maxk = kernel_w * kernel_h;
      int channels = (weight_data_size / group) / maxk / (num_output / group) * group;
  
      // depth-wise
      if (channels == group && group == num_output)
      {
          int elempack = 1;
  #if __ARM_NEON
          if (opt.use_packing_layout)
          {
              elempack = channels % 4 == 0 ? 4 : 1;
          }
  #endif
  
          Mat weight_data_transposed(weight_data.w);
          {
              float* pt = weight_data_transposed;
              const float* p = weight_data;
  
              for (int i = 0; i < (channels / group) * (num_output / group) * group; i++)
              {
                  for (int k = 0; k < maxk; k++)
                  {
                      pt[maxk - 1 - k] = p[k];
                  }
  
                  p += maxk;
                  pt += maxk;
              }
          }
  
  #if NCNN_BF16
          if (opt.use_bf16_storage)
          {
  #if __ARM_NEON
              if (elempack == 4)
              {
                  Mat weight_data_r2 = weight_data_transposed.reshape(maxk, group);
                  Mat weight_data_r2_packed;
                  convert_packing(weight_data_r2, weight_data_r2_packed, 4, opt);
  
                  ncnn::cast_float32_to_bfloat16(weight_data_r2_packed, weight_data_tm, opt);
              }
  #endif // __ARM_NEON
  
              if (elempack == 1)
              {
                  ncnn::cast_float32_to_bfloat16(weight_data_transposed, weight_data_tm, opt);
              }
  
              if (opt.lightmode)
              {
                  weight_data.release();
              }
  
              return 0;
          }
  #endif // NCNN_BF16
  
  #if __ARM_NEON
          // pack4
          if (elempack == 4)
          {
              Mat weight_data_r2 = weight_data_transposed.reshape(maxk, group);
              convert_packing(weight_data_r2, weight_data_tm, 4, opt);
          }
  #endif // __ARM_NEON
  
          // pack1
          if (elempack == 1)
          {
              weight_data_tm = weight_data_transposed;
          }
      }
      else
      {
          // group deconvolution
          for (int i = 0; i < (int)group_ops.size(); i++)
              delete group_ops[i];
  
          group_ops.clear();
  
          const int channels_g = channels / group;
          const int num_output_g = num_output / group;
  
          group_ops.resize(group);
  
          for (int g = 0; g < group; g++)
          {
              Mat weight_data_g = weight_data.range(maxk * channels_g * num_output_g * g, maxk * channels_g * num_output_g).clone();
              Mat bias_data_g;
              if (bias_term)
                  bias_data_g = bias_data.range(num_output_g * g, num_output_g);
  
              ncnn::Layer* op = ncnn::create_layer(ncnn::LayerType::Deconvolution);
  
              // set param
              ncnn::ParamDict pd;
              pd.set(0, num_output_g); // num_output
              pd.set(1, kernel_w);
              pd.set(11, kernel_h);
              pd.set(2, dilation_w);
              pd.set(12, dilation_h);
              pd.set(3, stride_w);
              pd.set(13, stride_h);
              pd.set(4, 0);  // pad_w
              pd.set(14, 0); // pad_h
              pd.set(18, output_pad_right);
              pd.set(19, output_pad_bottom);
              pd.set(5, bias_term);
              pd.set(6, maxk * channels_g * num_output_g); // weight_data_size
              pd.set(9, activation_type);
              pd.set(10, activation_params);
  
              op->load_param(pd);
  
              // set weights
              if (bias_term)
              {
                  ncnn::Mat weights[2];
                  weights[0] = weight_data_g;
                  weights[1] = bias_data_g;
  
                  op->load_model(ModelBinFromMatArray(weights));
              }
              else
              {
                  ncnn::Mat weights[1];
                  weights[0] = weight_data_g;
  
                  op->load_model(ModelBinFromMatArray(weights));
              }
  
              op->create_pipeline(opt);
  
              group_ops[g] = op;
          }
      }
  
      if (opt.lightmode)
      {
          weight_data.release();
      }
  
      return 0;
  }
  
  int DeconvolutionDepthWise_arm::destroy_pipeline(const Option& opt)
  {
      for (int i = 0; i < (int)group_ops.size(); i++)
      {
          group_ops[i]->destroy_pipeline(opt);
          delete group_ops[i];
      }
      group_ops.clear();
  
      return 0;
  }
  
  int DeconvolutionDepthWise_arm::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      int elembits = bottom_blob.elembits();
  
  #if NCNN_ARM82
      if (support_fp16_storage && 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 NCNN_BF16
      if (opt.use_bf16_storage && elembits == 16)
          return forward_bf16s(bottom_blob, top_blob, opt);
  #endif
  
      // convolv with NxN kernel
      // value = value + bias
  
      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;
  
      int outw = (w - 1) * stride_w + kernel_extent_w + output_pad_right;
      int outh = (h - 1) * stride_h + kernel_extent_h + output_pad_bottom;
      int out_elempack = 1;
  #if __ARM_NEON
      if (opt.use_packing_layout)
      {
          out_elempack = num_output % 4 == 0 ? 4 : 1;
      }
  #endif
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      Mat top_blob_bordered;
      if (pad_left > 0 || pad_right > 0 || pad_top > 0 || pad_bottom > 0 || (output_w > 0 && output_h > 0))
      {
          top_blob_bordered.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.workspace_allocator);
      }
      else
      {
          top_blob_bordered = top_blob;
          top_blob_bordered.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
      }
      if (top_blob_bordered.empty())
          return -100;
  
      const int maxk = kernel_w * kernel_h;
  
      // depth-wise
      if (channels * elempack == group && group == num_output)
      {
  #if __ARM_NEON
          if (elempack == 4)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int g = 0; g < channels; g++)
              {
                  float* outptr = top_blob_bordered.channel(g);
                  const float* kptr = (const float*)weight_data_tm + maxk * g * 4;
                  const Mat m = bottom_blob.channel(g);
  
                  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 + g * 4);
                          }
  
                          for (int y = 0; y < kernel_h; y++)
                          {
                              int sys = (i + y * dilation_h - (kernel_extent_h - 1));
                              if (sys < 0 || sys % stride_h != 0)
                                  continue;
  
                              int sy = sys / stride_h;
                              if (sy >= h)
                                  continue;
  
                              for (int x = 0; x < kernel_w; x++)
                              {
                                  int sxs = (j + x * dilation_w - (kernel_extent_w - 1));
                                  if (sxs < 0 || sxs % stride_w != 0)
                                      continue;
  
                                  int sx = sxs / stride_w;
                                  if (sx >= w)
                                      continue;
  
                                  const float* sptr = m.row(sy) + sx * 4;
  
                                  float32x4_t _val = vld1q_f32(sptr);
  
                                  int k = y * kernel_w + x;
  
                                  float32x4_t _w = vld1q_f32(kptr + k * 4);
  
                                  _sum = vmlaq_f32(_sum, _val, _w);
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1q_f32(outptr + j * 4, _sum);
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
  #endif // __ARM_NEON
  
          if (elempack == 1)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int g = 0; g < channels; g++)
              {
                  float* outptr = top_blob_bordered.channel(g);
                  const float* kptr = (const float*)weight_data_tm + maxk * g;
                  const Mat m = bottom_blob.channel(g);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[g];
                          }
  
                          for (int y = 0; y < kernel_h; y++)
                          {
                              int sys = (i + y * dilation_h - (kernel_extent_h - 1));
                              if (sys < 0 || sys % stride_h != 0)
                                  continue;
  
                              int sy = sys / stride_h;
                              if (sy >= h)
                                  continue;
  
                              const float* sptr = m.row(sy);
  
                              for (int x = 0; x < kernel_w; x++)
                              {
                                  int sxs = (j + x * dilation_w - (kernel_extent_w - 1));
                                  if (sxs < 0 || sxs % stride_w != 0)
                                      continue;
  
                                  int sx = sxs / stride_w;
                                  if (sx >= w)
                                      continue;
  
                                  float val = sptr[sx];
  
                                  int k = y * kernel_w + x;
  
                                  float w = kptr[k];
  
                                  sum += val * w;
                              }
                          }
  
                          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)));
                          }
  
                          outptr[j] = sum;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
      else
      {
          // group deconvolution
          const int channels_g = channels * elempack / group;
          const int num_output_g = num_output / group;
  
          int g_elempack = 1;
          int out_g_elempack = 1;
  #if __ARM_NEON
          if (opt.use_packing_layout)
          {
              g_elempack = channels_g % 4 == 0 ? 4 : 1;
              out_g_elempack = num_output_g % 4 == 0 ? 4 : 1;
          }
  #endif
  
          // unpacking
          Mat bottom_blob_unpacked = bottom_blob;
          if (elempack == 4 && g_elempack == 1)
          {
              Option opt_p = opt;
              opt_p.blob_allocator = opt.workspace_allocator;
              convert_packing(bottom_blob, bottom_blob_unpacked, 1, opt_p);
          }
  
          Mat top_blob_bordered_unpacked = top_blob_bordered;
          if (out_g_elempack == 1 && out_elempack == 4)
          {
              top_blob_bordered_unpacked.create(outw, outh, num_output, out_elemsize / out_elempack, 1, opt.workspace_allocator);
              if (top_blob_bordered_unpacked.empty())
                  return -100;
          }
  
          for (int g = 0; g < group; g++)
          {
              const Mat bottom_blob_g = bottom_blob_unpacked.channel_range(channels_g * g / g_elempack, channels_g / g_elempack);
              Mat top_blob_bordered_g = top_blob_bordered_unpacked.channel_range(num_output_g * g / out_g_elempack, num_output_g / out_g_elempack);
  
              const ncnn::Layer* op = group_ops[g];
  
              Option opt_g = opt;
              opt_g.blob_allocator = top_blob_bordered_unpacked.allocator;
  
              // forward
              op->forward(bottom_blob_g, top_blob_bordered_g, opt_g);
          }
  
          // packing
          if (out_g_elempack == 1 && out_elempack == 4)
          {
              convert_packing(top_blob_bordered_unpacked, top_blob_bordered, 4, opt);
          }
          else
          {
              top_blob_bordered = top_blob_bordered_unpacked;
          }
      }
  
      cut_padding(top_blob_bordered, top_blob, opt);
      if (top_blob.empty())
          return -100;
  
      return 0;
  }
  
  #if NCNN_BF16
  int DeconvolutionDepthWise_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;
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      int outw = (w - 1) * stride_w + kernel_extent_w + output_pad_right;
      int outh = (h - 1) * stride_h + kernel_extent_h + output_pad_bottom;
      int out_elempack = 1;
  #if __ARM_NEON
      if (opt.use_packing_layout)
      {
          out_elempack = num_output % 4 == 0 ? 4 : 1;
      }
  #endif
      size_t out_elemsize = elemsize / elempack * out_elempack;
  
      Mat top_blob_bordered;
      if (pad_left > 0 || pad_right > 0 || pad_top > 0 || pad_bottom > 0 || (output_w > 0 && output_h > 0))
      {
          top_blob_bordered.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.workspace_allocator);
      }
      else
      {
          top_blob_bordered = top_blob;
          top_blob_bordered.create(outw, outh, num_output / out_elempack, out_elemsize, out_elempack, opt.blob_allocator);
      }
      if (top_blob_bordered.empty())
          return -100;
  
      const int maxk = kernel_w * kernel_h;
  
      // depth-wise
      if (channels * elempack == group && group == num_output)
      {
  #if __ARM_NEON
          if (elempack == 4)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int g = 0; g < channels; g++)
              {
                  unsigned short* outptr = top_blob_bordered.channel(g);
                  const unsigned short* kptr = (const unsigned short*)weight_data_tm + maxk * g * 4;
                  const Mat m = bottom_blob.channel(g);
  
                  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 + g * 4);
                          }
  
                          for (int y = 0; y < kernel_h; y++)
                          {
                              int sys = (i + y * dilation_h - (kernel_extent_h - 1));
                              if (sys < 0 || sys % stride_h != 0)
                                  continue;
  
                              int sy = sys / stride_h;
                              if (sy >= h)
                                  continue;
  
                              for (int x = 0; x < kernel_w; x++)
                              {
                                  int sxs = (j + x * dilation_w - (kernel_extent_w - 1));
                                  if (sxs < 0 || sxs % stride_w != 0)
                                      continue;
  
                                  int sx = sxs / stride_w;
                                  if (sx >= w)
                                      continue;
  
                                  const unsigned short* sptr = m.row<const unsigned short>(sy) + sx * 4;
  
                                  float32x4_t _val = vcvt_f32_bf16(vld1_u16(sptr));
  
                                  int k = y * kernel_w + x;
  
                                  float32x4_t _w = vcvt_f32_bf16(vld1_u16(kptr + k * 4));
  
                                  _sum = vmlaq_f32(_sum, _val, _w);
                              }
                          }
  
                          _sum = activation_ps(_sum, activation_type, activation_params);
  
                          vst1_u16(outptr + j * 4, vcvt_bf16_f32(_sum));
                      }
  
                      outptr += outw * 4;
                  }
              }
          }
  #endif // __ARM_NEON
  
          if (elempack == 1)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int g = 0; g < channels; g++)
              {
                  unsigned short* outptr = top_blob_bordered.channel(g);
                  const unsigned short* kptr = (const unsigned short*)weight_data_tm + maxk * g;
                  const Mat m = bottom_blob.channel(g);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          float sum = 0.f;
  
                          if (bias_term)
                          {
                              sum = bias_data[g];
                          }
  
                          for (int y = 0; y < kernel_h; y++)
                          {
                              int sys = (i + y * dilation_h - (kernel_extent_h - 1));
                              if (sys < 0 || sys % stride_h != 0)
                                  continue;
  
                              int sy = sys / stride_h;
                              if (sy >= h)
                                  continue;
  
                              const unsigned short* sptr = m.row<const unsigned short>(sy);
  
                              for (int x = 0; x < kernel_w; x++)
                              {
                                  int sxs = (j + x * dilation_w - (kernel_extent_w - 1));
                                  if (sxs < 0 || sxs % stride_w != 0)
                                      continue;
  
                                  int sx = sxs / stride_w;
                                  if (sx >= w)
                                      continue;
  
                                  float val = bfloat16_to_float32(sptr[sx]);
  
                                  int k = y * kernel_w + x;
  
                                  float w = bfloat16_to_float32(kptr[k]);
  
                                  sum += val * w;
                              }
                          }
  
                          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)));
                          }
  
                          outptr[j] = float32_to_bfloat16(sum);
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
      else
      {
          // group deconvolution
          const int channels_g = channels * elempack / group;
          const int num_output_g = num_output / group;
  
          int g_elempack = 1;
          int out_g_elempack = 1;
  #if __ARM_NEON
          if (opt.use_packing_layout)
          {
              g_elempack = channels_g % 4 == 0 ? 4 : 1;
              out_g_elempack = num_output_g % 4 == 0 ? 4 : 1;
          }
  #endif
  
          // unpacking
          Mat bottom_blob_unpacked = bottom_blob;
          if (elempack == 4 && g_elempack == 1)
          {
              Option opt_p = opt;
              opt_p.blob_allocator = opt.workspace_allocator;
              convert_packing(bottom_blob, bottom_blob_unpacked, 1, opt_p);
          }
  
          Mat top_blob_bordered_unpacked = top_blob_bordered;
          if (out_g_elempack == 1 && out_elempack == 4)
          {
              top_blob_bordered_unpacked.create(outw, outh, num_output, out_elemsize / out_elempack, 1, opt.workspace_allocator);
              if (top_blob_bordered_unpacked.empty())
                  return -100;
          }
  
          for (int g = 0; g < group; g++)
          {
              const Mat bottom_blob_g = bottom_blob_unpacked.channel_range(channels_g * g / g_elempack, channels_g / g_elempack);
              Mat top_blob_bordered_g = top_blob_bordered_unpacked.channel_range(num_output_g * g / out_g_elempack, num_output_g / out_g_elempack);
  
              const ncnn::Layer* op = group_ops[g];
  
              Option opt_g = opt;
              opt_g.blob_allocator = top_blob_bordered_unpacked.allocator;
  
              // forward
              op->forward(bottom_blob_g, top_blob_bordered_g, opt_g);
          }
  
          // packing
          if (out_g_elempack == 1 && out_elempack == 4)
          {
              convert_packing(top_blob_bordered_unpacked, top_blob_bordered, 4, opt);
          }
          else
          {
              top_blob_bordered = top_blob_bordered_unpacked;
          }
      }
  
      cut_padding(top_blob_bordered, top_blob, opt);
      if (top_blob.empty())
          return -100;
  
      return 0;
  }
  #endif // NCNN_BF16
  
  } // namespace ncnn
  