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/CLRangeKernel.h"
  
  #include "arm_compute/core/CL/CLHelpers.h"
  #include "arm_compute/core/CL/CLValidate.h"
  #include "arm_compute/core/CL/ICLTensor.h"
  #include "arm_compute/core/Utils.h"
  #include "support/StringSupport.h"
  
  using namespace arm_compute;
  
  namespace
  {
  unsigned int get_num_elems_processed_per_iteration(const DataType dt)
  {
      unsigned int num_elems_processed_per_iteration = preferred_vector_width(CLKernelLibrary::get().get_device(), dt);
      if(num_elems_processed_per_iteration > 8)
      {
          num_elems_processed_per_iteration = 8; //kernel uses only 8 lanes.
      }
      return num_elems_processed_per_iteration;
  }
  
  Status validate_arguments(const ITensorInfo &output, const float start, const float end, const float step)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output,
                                                           1,
                                                           DataType::U8, DataType::S8, DataType::QASYMM8,
                                                           DataType::U16, DataType::S16,
                                                           DataType::U32, DataType::S32,
                                                           DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((start == end), "start of the requested sequence must not be equal to the end");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(((start < end) && (step <= 0)), "step must be greater than 0 when start < end");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(((start > end) && (step >= 0)), "step must be less than 0 when start > end");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(start, output.data_type(), output.quantization_info()), "start value is outside the range of the data type");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(end, output.data_type(), output.quantization_info()), "end value is outside the range of the data type");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(step, output.data_type(), output.quantization_info()), "step value is outside the range of the data type");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((start == end), "start of the requested sequence must not be equal to the end");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(output.num_dimensions() != 1, "Output has to be a 1-D tensor");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(output.tensor_shape().total_size() < num_of_elements_in_range(start, end, step), "Output tensor size is incorrect");
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window(ITensorInfo &output, const float start, const float end, const float step)
  {
      unsigned int num_elems_processed_per_iteration = get_num_elems_processed_per_iteration(output.data_type());
      // Auto initialize output if not initialized
      auto_init_if_empty(output, TensorShape(num_of_elements_in_range(start, end, step)), 1, output.data_type(), output.quantization_info());
  
      // Configure kernel window
      Window win = calculate_max_window(output, Steps(num_elems_processed_per_iteration));
  
      AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
      bool                   window_changed = update_window_and_padding(win, output_access);
      output_access.set_valid_region(win, ValidRegion(Coordinates(), TensorShape(num_of_elements_in_range(start, end, step))));
      Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
      return std::make_pair(err, win);
  }
  } // namespace
  
  CLRangeKernel::CLRangeKernel()
      : _start(0), _end(1), _step(1), _output(nullptr)
  {
  }
  
  void CLRangeKernel::configure(ICLTensor *output, const float start, const float end, const float step)
  {
      configure(CLKernelLibrary::get().get_compile_context(), output, start, end, step);
  }
  
  void CLRangeKernel::configure(const CLCompileContext &compile_context, ICLTensor *output, const float start, const float end, const float step)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(output);
  
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*(output->info()), start, end, step));
  
      // Configure kernel window
      auto win_config = validate_and_configure_window(*(output->info()), start, end, step);
      ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
  
      _start  = start;
      _end    = end;
      _step   = step;
      _output = output;
  
      std::string kernel_name = "range";
  
      unsigned int num_elems_processed_per_iteration = get_num_elems_processed_per_iteration(output->info()->data_type());
      // Set build options
      CLBuildOptions build_opts;
      build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));
      build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
      build_opts.add_option("-DSTART=" + support::cpp11::to_string(start));
      build_opts.add_option("-DSTEP=" + support::cpp11::to_string(step));
      if(is_data_type_quantized_asymmetric(output->info()->data_type()))
      {
          const UniformQuantizationInfo qinfo = output->info()->quantization_info().uniform();
          build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(qinfo.offset));
          build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(qinfo.scale));
          kernel_name += "_quantized";
      }
      // Create kernel
      _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
      ICLKernel::configure_internal(win_config.second);
  
      // Set config_id for enabling LWS tuning
      _config_id = kernel_name;
      _config_id += "_";
      _config_id += lower_string(string_from_data_type(output->info()->data_type()));
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(0));
  }
  
  Status CLRangeKernel::validate(const ITensorInfo *output, const float start, const float end, const float step)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
  
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*output, start, end, step));
      ARM_COMPUTE_RETURN_ON_ERROR((validate_and_configure_window(*(output->clone()), start, end, step)).first);
  
      return Status{};
  }
  
  void CLRangeKernel::run(const Window &window, cl::CommandQueue &queue)
  {
      ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
      ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
      unsigned int idx = 0;
      add_1D_tensor_argument(idx, _output, window);
  
      enqueue(queue, *this, window, lws_hint());
  }
  