text: /*
   * Copyright (c) 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/CLGEMMLowpQuantizeDownInt32ScaleKernel.h"
  
  #include "arm_compute/core/AccessWindowStatic.h"
  #include "arm_compute/core/CL/CLHelpers.h"
  #include "arm_compute/core/CL/ICLTensor.h"
  #include "arm_compute/core/Error.h"
  #include "arm_compute/core/Helpers.h"
  #include "arm_compute/core/Types.h"
  #include "arm_compute/core/Validate.h"
  #include "arm_compute/core/Window.h"
  #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
  #include "support/StringSupport.h"
  
  namespace arm_compute
  {
  namespace
  {
  Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
      ARM_COMPUTE_RETURN_ERROR_ON((output_stage->output_data_type != DataType::QASYMM8) && (output_stage->output_data_type != DataType::QASYMM8_SIGNED));
      ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)));
      ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
                                  || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound);
  
      // Check biases if exist
      if(bias != nullptr)
      {
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
          ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
          ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
      }
  
      if(output->total_size() != 0)
      {
          ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() != output_stage->output_data_type, "Mismatching output data type");
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
      }
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, DataType output_data_type)
  {
      // Output auto inizialitation if not yet initialized
      auto_init_if_empty(*output, input->clone()->set_data_type(output_data_type));
  
      constexpr unsigned int num_elems_processed_per_iteration = 4;
  
      // Configure kernel window
      Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
  
      AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
  
      bool window_changed = update_window_and_padding(win,
                                                      input_access);
  
      AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
      window_changed = window_changed || update_window_and_padding(win, output_result_access);
      output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
  
      if(bias != nullptr)
      {
          AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]);
          window_changed = window_changed || update_window_and_padding(win, bias_access);
      }
  
      Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
      return std::make_pair(err, win);
  }
  } //namespace
  
  CLGEMMLowpQuantizeDownInt32ScaleKernel::CLGEMMLowpQuantizeDownInt32ScaleKernel()
      : _input(nullptr), _bias(nullptr), _output(nullptr), _output_stage(nullptr)
  {
  }
  Status CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, output_stage));
  
      return Status{};
  }
  
  void CLGEMMLowpQuantizeDownInt32ScaleKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage)
  {
      configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, output_stage);
  }
  
  void CLGEMMLowpQuantizeDownInt32ScaleKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo *output_stage)
  {
      // Perform validate step
      ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
  
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
                                                    (bias != nullptr) ? bias->info() : nullptr,
                                                    output->info(),
                                                    output_stage));
  
      _input        = input;
      _bias         = bias;
      _output       = output;
      _output_stage = output_stage;
  
      // Set the arguments to pass at compile time
      auto           min = output_stage->gemmlowp_min_bound;
      auto           max = output_stage->gemmlowp_max_bound;
      CLBuildOptions build_opts;
      build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage->gemmlowp_offset));
      build_opts.add_option("-DRESULT_MULT_INT=" + support::cpp11::to_string(output_stage->gemmlowp_multiplier));
      build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage->gemmlowp_shift));
      build_opts.add_option_if((min > std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))) && (min != max),
                               "-DMIN_BOUND=" + support::cpp11::to_string(min));
      build_opts.add_option_if((max < std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))) && (min != max),
                               "-DMAX_BOUND=" + support::cpp11::to_string(max));
      build_opts.add_option("-DOUTPUT_DATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));
      build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
  
      // Create kernel
      _kernel = create_kernel(compile_context, "gemmlowp_output_stage_quantize_down", build_opts.options());
  
      // Configure kernel window
      auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), output_stage->output_data_type);
      ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
      ICLKernel::configure_internal(win_config.second);
  }
  
  void CLGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window, cl::CommandQueue &queue)
  {
      ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
      ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
  
      Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
      Window slice     = collapsed.first_slice_window_3D();
  
      unsigned int idx1 = num_arguments_per_3D_tensor();
      if(_bias != nullptr)
      {
          Window biases_slice(slice);
          biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
          biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
          add_1D_tensor_argument(idx1, _bias, biases_slice);
      }
  
      do
      {
          unsigned int idx = 0;
          add_3D_tensor_argument(idx, _input, slice);
          add_3D_tensor_argument(idx1, _output, slice);
          enqueue(queue, *this, slice, lws_hint());
      }
      while(collapsed.slide_window_slice_3D(slice));
  }
  }