text: /*
   * Copyright (c) 2018-2020 ARM Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h"
  
  #include "arm_compute/core/AccessWindowStatic.h"
  #include "arm_compute/core/CL/CLHelpers.h"
  #include "arm_compute/core/CL/CLKernelLibrary.h"
  #include "arm_compute/core/CL/CLValidate.h"
  #include "arm_compute/core/CL/ICLKernel.h"
  #include "arm_compute/core/CL/ICLTensor.h"
  #include "arm_compute/core/Error.h"
  #include "arm_compute/core/Helpers.h"
  #include "arm_compute/core/TensorInfo.h"
  #include "arm_compute/core/Types.h"
  #include "arm_compute/core/Utils.h"
  #include "arm_compute/core/utils/misc/ShapeCalculator.h"
  #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
  #include "support/StringSupport.h"
  
  namespace arm_compute
  {
  using namespace arm_compute::misc::shape_calculator;
  
  namespace
  {
  Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
                            const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation,
                            const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::QASYMM8 || input->data_type() == DataType::QASYMM8_SIGNED)
                                      && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
                                      && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
                                      && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
                                      && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC),
                                      "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported");
      ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3);
      ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3);
  
      ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
  
      const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
  
      if(biases != nullptr)
      {
          if(is_qasymm)
          {
              ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
          }
          else
          {
              ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
          }
          ARM_COMPUTE_RETURN_ERROR_ON((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3)));
          ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
      }
  
      if(is_qasymm)
      {
          ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts);
          ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
          ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
          ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
          ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
  
          if(is_data_type_quantized_per_channel(weights->data_type()))
          {
              ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
              ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0));
              ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0));
          }
          else
          {
              ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
              ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0));
              ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0));
          }
      }
      else
      {
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
      }
  
      if(output->total_size() != 0)
      {
          const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
      }
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info,
                                                          unsigned int depth_multiplier, GPUTarget gpu_target, std::string &kernel_name, const Size2D dilation)
  {
      // Output auto inizialitation if not yet initialized
      const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
      auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info()));
  
      const unsigned int conv_stride_x = conv_info.stride().first;
      const unsigned int conv_stride_y = conv_info.stride().second;
      const bool         is_qasymm     = is_data_type_quantized_asymmetric(input->data_type());
      const bool         is_bifrost    = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
  
      // Configure kernel window
      unsigned int num_elems_read_per_iteration_x    = 0;
      unsigned int num_elems_read_per_iteration_y    = 0;
      unsigned int num_elems_written_per_iteration_x = 0;
      unsigned int num_elems_written_per_iteration_y = 0;
  
      if(input->data_type() == DataType::F16)
      {
          kernel_name                       = "depthwise_convolution_3x3_f16";
          num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
          num_elems_written_per_iteration_y = 1;
          num_elems_read_per_iteration_y    = 3;
          switch(conv_stride_x)
          {
              case 1:
                  num_elems_read_per_iteration_x = 8;
                  break;
              case 2:
                  num_elems_read_per_iteration_x = 9;
                  break;
              case 3:
                  num_elems_read_per_iteration_x = 16;
                  break;
              default:
                  num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
                  break;
          }
          if(is_bifrost)
          {
              if(conv_stride_x == 1 && conv_stride_y == 1)
              {
                  kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16";
                  num_elems_read_per_iteration_x    = 8;
                  num_elems_written_per_iteration_x = 4;
                  num_elems_read_per_iteration_y    = 6;
                  num_elems_written_per_iteration_y = 4;
              }
              else if(conv_stride_x == 2 && conv_stride_y == 2)
              {
                  kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16";
                  num_elems_read_per_iteration_x    = 10;
                  num_elems_written_per_iteration_x = 4;
                  num_elems_read_per_iteration_y    = 5;
                  num_elems_written_per_iteration_y = 2;
              }
          }
      }
      else if(input->data_type() == DataType::F32 && is_bifrost)
      {
          if(conv_stride_x == 1 && conv_stride_y == 1)
          {
              kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32";
              num_elems_read_per_iteration_x    = 4;
              num_elems_read_per_iteration_y    = 6;
              num_elems_written_per_iteration_x = 2;
              num_elems_written_per_iteration_y = 4;
          }
          else if(conv_stride_x == 2 && conv_stride_y == 2)
          {
              kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32";
              num_elems_read_per_iteration_x    = 6;
              num_elems_read_per_iteration_y    = 5;
              num_elems_written_per_iteration_x = 2;
              num_elems_written_per_iteration_y = 2;
          }
          else
          {
              kernel_name                       = "depthwise_convolution_3x3";
              num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
              num_elems_written_per_iteration_y = 1;
              num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
              num_elems_read_per_iteration_y    = 3;
          }
      }
      else
      {
          const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type());
  
          kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3";
          kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : "");
          kernel_name += (is_qasymm ? "_nchw" : "");
  
          num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
          num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1;
          num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0);
          num_elems_read_per_iteration_y    = num_elems_written_per_iteration_y + 2;
      }
      num_elems_read_per_iteration_x += (num_elems_read_per_iteration_x - 1) * (dilation.x() - 1);
      num_elems_read_per_iteration_y += (num_elems_read_per_iteration_y - 1) * (dilation.y() - 1);
  
      // Create window and update padding
      Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
  
      AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(),
                                         num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
                                         conv_stride_x, conv_stride_y);
      AccessWindowStatic    weights_access(weights, 0, 0, 3, 3);
      AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
  
      bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
  
      output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
  
      Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
      return std::make_pair(err, win);
  }
  } // namespace
  
  CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
      : _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0)
  {
  }
  
  BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const
  {
      return _border_size;
  }
  
  void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
                                                           const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation,
                                                           const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
  {
      configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts);
  }
  
  void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
                                                           const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation,
                                                           const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
                                                    conv_info, depth_multiplier, act_info, dilation,
                                                    (output_multipliers != nullptr) ? output_multipliers->info() : nullptr,
                                                    (output_shifts != nullptr) ? output_shifts->info() : nullptr));
  
      _input              = input;
      _output             = output;
      _weights            = weights;
      _biases             = biases;
      _conv_stride_x      = conv_info.stride().first;
      _conv_stride_y      = conv_info.stride().second;
      _conv_pad_left      = conv_info.pad_left();
      _conv_pad_top       = conv_info.pad_top();
      _border_size        = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
      _output_multipliers = output_multipliers;
      _output_shifts      = output_shifts;
      _is_quantized       = is_data_type_quantized_asymmetric(input->info()->data_type());
  
      // Configure kernel window
      std::string     kernel_name;
      const GPUTarget gpu_target = get_target();
  
      auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name, dilation);
      ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
      ICLKernel::configure_internal(win_config.second);
  
      // Set build options
      CLBuildOptions build_opts;
      build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
      build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z()));
      build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
      build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
      build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
      build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
      build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
  
      if(_is_quantized)
      {
          const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform();
          const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform();
          const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform();
  
          const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type());
          const bool is_dot8_supported        = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel;
          build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
          build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset));
          build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset));
          build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset));
          build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset));
          build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
          build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8");
  
          // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler
          float multiplier        = iq_info.scale * wq_info.scale / oq_info.scale;
          int   output_multiplier = 0;
          int   output_shift      = 0;
          quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
          build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
          build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
  
          if(act_info.enabled())
          {
              const int a_val = quantize_qasymm8(act_info.a(), oq_info);
              const int b_val = quantize_qasymm8(act_info.b(), oq_info);
              const int o1    = oq_info.offset;
  
              build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
              build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
              build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
  
              const float s1 = iq_info.scale;
              build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
              build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
          }
  
          build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
          build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type()));
          build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type()));
      }
      else
      {
          build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
          build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
          build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
          build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step()));
      }
  
      build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16");
      build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32");
  
      _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
  
      // Set config_id for enabling LWS tuning
      _config_id = kernel_name;
      _config_id += "_";
      _config_id += lower_string(string_from_data_type(input->info()->data_type()));
      _config_id += "_";
      _config_id += support::cpp11::to_string(input->info()->dimension(0));
      _config_id += "_";
      _config_id += support::cpp11::to_string(input->info()->dimension(1));
      _config_id += "_";
      _config_id += support::cpp11::to_string(input->info()->dimension(2));
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(0));
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(1));
  }
  
  Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
                                                            const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target,
                                                            const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
  {
      std::string kernel_name;
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(),
                                                                conv_info, depth_multiplier, gpu_target, kernel_name, dilation)
                                  .first);
  
      return Status{};
  }
  
  void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue)
  {
      ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
      ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
  
      Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
  
      // Create input window and adjust
      Window collapsed_in = collapsed;
      collapsed_in.adjust(Window::DimX, -_conv_pad_left, true);
      collapsed_in.adjust(Window::DimY, -_conv_pad_top, true);
      collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x);
      collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y);
  
      Window slice_in      = collapsed_in.first_slice_window_3D();
      Window slice_out     = collapsed.first_slice_window_3D();
      Window slice_weights = window.first_slice_window_3D();
      slice_weights.set_dimension_step(Window::DimX, 0);
      slice_weights.set_dimension_step(Window::DimY, 0);
  
      unsigned int idx = 3 * num_arguments_per_3D_tensor();
  
      // Set output multipliers in case of quantized data type
      if(_is_quantized)
      {
          Window slice;
          slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape());
          add_1D_tensor_argument(idx, _output_multipliers, slice);
          add_1D_tensor_argument(idx, _output_shifts, slice);
      }
  
      // Set biases
      if(_biases != nullptr)
      {
          Window slice_biases;
          slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
          add_1D_tensor_argument(idx, _biases, slice_biases);
      }
  
      do
      {
          idx = 0;
          add_3D_tensor_argument(idx, _input, slice_in);
          add_3D_tensor_argument(idx, _output, slice_out);
          add_3D_tensor_argument(idx, _weights, slice_weights);
  
          enqueue(queue, *this, slice_out, lws_hint());
      }
      while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in));
  }
  } // namespace arm_compute
  