text: /*
   * Copyright (c) 2018-2021 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 "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
  
  #include "arm_compute/core/CL/CLHelpers.h"
  #include "arm_compute/core/CL/CLKernelLibrary.h"
  #include "arm_compute/core/CL/ICLTensor.h"
  #include "arm_compute/core/CL/OpenCL.h"
  #include "arm_compute/core/Helpers.h"
  #include "arm_compute/core/TensorInfo.h"
  #include "arm_compute/core/Utils.h"
  #include "arm_compute/core/Validate.h"
  #include "arm_compute/core/utils/misc/ShapeCalculator.h"
  #include "src/core/AccessWindowStatic.h"
  #include "src/core/CL/CLUtils.h"
  #include "src/core/CL/CLValidate.h"
  #include "src/core/CL/gemm/CLGEMMHelpers.h"
  #include "src/core/helpers/AutoConfiguration.h"
  #include "src/core/helpers/WindowHelpers.h"
  #include "src/core/utils/helpers/float_ops.h"
  #include "support/StringSupport.h"
  
  #include <cstddef>
  #include <cstdint>
  #include <tuple>
  
  using namespace arm_compute;
  using namespace arm_compute::misc::shape_calculator;
  
  namespace arm_compute
  {
  class Coordinates;
  } // namespace arm_compute
  
  namespace
  {
  using ElementsProcessed = Steps;
  
  Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
                            const GEMMRHSMatrixInfo &rhs_info,
                            const GEMMKernelInfo    &gemm_info)
  {
      ARM_COMPUTE_UNUSED(alpha);
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
      ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
      ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 != rhs_info.k0);
      ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.transpose == rhs_info.transpose);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(((lhs_info.k0 & (lhs_info.k0 - 1)) && lhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
      ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 > 16);
      ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((lhs_info.transpose) && ((lhs_info.m0 & (lhs_info.m0 - 1)) && lhs_info.m0 != 3), "Only 2,3,4,8,16 are supported for m0");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((rhs_info.transpose) && ((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr)
                                      && (!gemm_info.broadcast_bias),
                                      "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision && (input0->data_type() == DataType::F32), "Mixed precision only supported for F16 data type");
      ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info));
  
      const unsigned int m = gemm_info.m;
      const unsigned int n = gemm_info.n;
      const unsigned int k = gemm_info.k;
  
      TensorShape tensor_shape0{ input0->tensor_shape() };
      tensor_shape0.set(0, k);
      tensor_shape0.set(1, m);
  
      TensorShape tensor_shape1{ input1->tensor_shape() };
      tensor_shape1.set(0, n);
      tensor_shape1.set(1, k);
  
      if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
      {
          const unsigned int input2_dim0 = input2->dimension(0);
          const unsigned int input2_dim1 = input2->dimension(1);
  
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
          if(gemm_info.broadcast_bias)
          {
              ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
          }
          else
          {
              ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
          }
      }
  
      const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
      const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
  
      const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info));
      const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
  
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
  
      if(output->total_size() != 0)
      {
          const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
      }
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
                                                          const GEMMRHSMatrixInfo &rhs_info,
                                                          const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
  {
      unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
      unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
      bool          reinterpret_output_as_3d            = gemm_info.depth_output_gemm3d != 0;
  
      Window win{};
      Window win_out{};
      bool   window_changed = false;
  
      // Output tensor auto initialization if not yet initialized
      auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));
  
      TensorInfo tmp_info(*output);
  
      if(reinterpret_output_as_3d)
      {
          // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
          // the window needs to be constructed on the 2D collapsed version of the tensor
          TensorShape tmp_shape(output->tensor_shape());
          tmp_shape.collapse(2U, 1U);
          tmp_info.set_tensor_shape(tmp_shape);
      }
  
      // Configure kernel window
      num_elems_processed_per_iteration_x = rhs_info.n0;
      num_elems_processed_per_iteration_y = lhs_info.m0;
  
      win     = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
      win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
  
      if(input2 != nullptr)
      {
          const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
  
          const int bias_processed_per_iteration_y = gemm_info.broadcast_bias ? 1 : num_elems_processed_per_iteration_y;
  
          AccessWindowStatic input2_access(input2, 0, 0,
                                           ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
                                           ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y));
  
          window_changed = update_window_and_padding(win, input2_access); // window used by the execute_window_loop
      }
  
      // Collapse along the Z direction
      // This collapse needs to be here in order to tune the Z dimension of LWS
      Window             collapsed             = win;
      const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
      collapsed                                = win.collapse(win, dimension_to_collapse);
  
      Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
      return std::make_pair(err, collapsed);
  }
  } // namespace
  
  CLGEMMMatrixMultiplyReshapedKernel::CLGEMMMatrixMultiplyReshapedKernel()
      : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), _add_bias(false),
        _broadcast_bias(false), _export_to_cl_image(false), _k(1)
  {
  }
  
  void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
                                                     const GEMMLHSMatrixInfo &lhs_info,
                                                     const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
  {
      configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info);
  }
  
  void CLGEMMMatrixMultiplyReshapedKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha,
                                                     float                    beta,
                                                     const GEMMLHSMatrixInfo &lhs_info,
                                                     const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
  
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
  
      auto padding_info         = get_padding_info({ input0, output });
      _input0                   = input0;
      _input1                   = input1;
      _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
      _output                   = output;
      _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
      _use_dummy_work_items     = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
      _add_bias                 = _input2 != nullptr;
      _broadcast_bias           = gemm_info.broadcast_bias;
      _export_to_cl_image       = rhs_info.export_to_cl_image;
      _k                        = gemm_info.k;
  
      // Check if we need to slide the matrix B
      const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
      _slide_matrix_b                          = (_input1->info()->num_dimensions() >= num_dimensions_input0);
  
      ElementsProcessed num_elements_processed{};
  
      // Configure kernel window
      auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
      ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
      ICLKernel::configure_internal(win_config.second);
  
      const bool     enable_mixed_precision = gemm_info.fp_mixed_precision;
      const DataType data_type              = input0->info()->data_type();
  
      // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
      const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1);
  
      const unsigned int partial_store_m0 = internal_m % lhs_info.m0;
      const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
  
      // Create build options
      CLBuildOptions build_opts;
      build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
      build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
      build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
      build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
      build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
      build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
      build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
      build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
      build_opts.add_option_if(lhs_info.interleave, "-DLHS_INTERLEAVE");
      build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
      build_opts.add_option_if(lhs_info.transpose, "-DLHS_TRANSPOSE");
      build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
      build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
      build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
      build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
      build_opts.add_option_if(enable_mixed_precision, "-DMIXED_PRECISION");
      build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT");
      build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1)));
      build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
      build_opts.add_option("-DDATA_TYPE_ACCUMULATOR=" + (enable_mixed_precision ? get_cl_type_from_data_type(DataType::F32) : get_cl_type_from_data_type(data_type)));
      build_opts.add_option("-DM=" + support::cpp11::to_string(gemm_info.m));
      build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
      build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
      build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
      build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
      build_opts.add_option("-DK0=" + support::cpp11::to_string(lhs_info.k0));
      build_opts.add_option("-DV0=" + support::cpp11::to_string(lhs_info.v0));
      build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
      build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
      build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
  
      std::string kernel_name("gemm_mm_reshaped_");
      kernel_name += lhs_info.transpose ? "lhs_t_" : "lhs_nt_";
      kernel_name += rhs_info.transpose ? "rhs_t" : "rhs_nt";
      kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
  
      // Create kernel
      _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 += (_add_bias ? "add_bias_" : "");
      _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
      _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
      _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
      _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
      _config_id += "_";
      _config_id += (enable_mixed_precision ? "mixed_precision_" : "");
      _config_id += support::cpp11::to_string(output->info()->dimension(1));
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(0));
      _config_id += "_";
      _config_id += support::cpp11::to_string(gemm_info.k);
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(2));
      _config_id += "_";
      _config_id += support::cpp11::to_string(lhs_info.m0);
      _config_id += "_";
      _config_id += support::cpp11::to_string(rhs_info.n0);
      _config_id += "_";
      _config_id += support::cpp11::to_string(lhs_info.k0);
      _config_id += "_";
      _config_id += support::cpp11::to_string(lhs_info.v0);
      _config_id += "_";
      _config_id += support::cpp11::to_string(rhs_info.h0);
      _config_id += "_";
      _config_id += support::cpp11::to_string(lhs_info.interleave);
      _config_id += "_";
      _config_id += support::cpp11::to_string(rhs_info.interleave);
  
      ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
  }
  
  Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
                                                      const GEMMLHSMatrixInfo &lhs_info,
                                                      const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
  {
      ElementsProcessed num_elements_processed{};
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
                                                                input1->clone().get(),
                                                                input2 != nullptr ? input2->clone().get() : nullptr,
                                                                output->clone().get(),
                                                                lhs_info,
                                                                rhs_info,
                                                                gemm_info,
                                                                num_elements_processed)
                                  .first);
  
      return Status{};
  }
  
  void CLGEMMMatrixMultiplyReshapedKernel::run(const Window &window, cl::CommandQueue &queue)
  {
      ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
      ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
  
      if(_input1->info()->num_dimensions() < 3)
      {
          // The stride_z for matrix B must be zero if we do not slice
          ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
      }
  
      Window slice          = window.first_slice_window_3D();
      Window slice_matrix_b = slice;
  
      slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
      slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
  
      const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
  
      cl::Image2D input1_image2d;
  
      if(_export_to_cl_image)
      {
          const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2));
          const size_t      image_row_pitch = _input1->info()->strides_in_bytes()[1];
  
          input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, _input1->info()->data_type(), image_row_pitch);
      }
  
      do
      {
          Window slice_b = slice;
          // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
          // This scenario can happen when the matrix multiplication is used to perform a convolution operation
          if(!_slide_matrix_b)
          {
              slice_b = slice_matrix_b;
          }
  
          unsigned int idx = 0;
  
          // LHS buffer
          add_2D_tensor_argument(idx, _input0, slice);
  
          // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
          if(_export_to_cl_image)
          {
              _kernel.setArg(idx++, input1_image2d);
          }
          else
          {
              add_2D_tensor_argument(idx, _input1, slice_b);
          }
  
          // Bias buffer (_add_bias == true)
          add_2D_tensor_argument_if(_add_bias, idx, _input2, slice);
  
          // Output buffer
          add_2D_tensor_argument(idx, _output, slice);
  
          // K dimension (not used if _export_to_cl_image == true)
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_k));
  
          // LHS stride_z
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
  
          // RHS stride_z (not used if _export_to_cl_image == true)
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
  
          // Bias stride_z (if _add_bias == true)
          if(_add_bias)
          {
              _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
          }
  
          // Output stride_z
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
  
          // Cross-plan padding (if _reinterpret_output_as_3d = true)
          if(_reinterpret_output_as_3d)
          {
              _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad));
          }
  
          // Dispatch kernel
          enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
      }
      while(window.slide_window_slice_3D(slice));
  }
  