text: /*
   * Copyright (c) 2017-2020 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/CLGEMMMatrixMultiplyKernel.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/ICLTensor.h"
  #include "arm_compute/core/CL/OpenCL.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/Window.h"
  #include "arm_compute/core/utils/helpers/float_ops.h"
  #include "arm_compute/core/utils/misc/ShapeCalculator.h"
  #include "support/StringSupport.h"
  
  #include <set>
  #include <string>
  
  namespace arm_compute
  {
  using namespace arm_compute::misc::shape_calculator;
  
  namespace
  {
  using ElementsProcessed = Steps;
  
  inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float beta,
                                   bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision)
  {
      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((fp_mixed_precision && (input0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (input2 != nullptr)
                                      && (!reshape_info.broadcast_bias()),
                                      "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D");
  
      if(!is_interleaved_transposed)
      {
          ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
  
          if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
          {
              const unsigned int m           = reshape_info.reinterpret_input_as_3d() ? input0->dimension(1) * input0->dimension(2) : input0->dimension(1);
              const unsigned int n           = input1->dimension(0);
              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(reshape_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");
              }
          }
      }
      else
      {
          GEMMRHSMatrixInfo rhs_info;
          GEMMLHSMatrixInfo lhs_info;
          const auto        m                         = static_cast<unsigned int>(reshape_info.m());
          const auto        n                         = static_cast<unsigned int>(reshape_info.n());
          const int         k                         = reshape_info.k();
          const int         mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
          const int         mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
          rhs_info.n0                                 = 16 / input1->element_size();
          rhs_info.k0                                 = 1;
          rhs_info.h0                                 = mult_transpose1xW_width;
          rhs_info.interleave                         = false;
          rhs_info.transpose                          = false;
          lhs_info.m0                                 = 4;
          lhs_info.k0                                 = 4;
          lhs_info.v0                                 = mult_interleave4x4_height;
          lhs_info.interleave                         = true;
          lhs_info.transpose                          = true;
  
          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);
  
          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(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(reshape_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");
              }
          }
      }
  
      if(output->total_size() != 0)
      {
          const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info));
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
      }
  
      return Status{};
  }
  
  inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output,
                                                                 float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
                                                                 ElementsProcessed &num_elements_processed)
  {
      ARM_COMPUTE_UNUSED(beta);
      bool   window_changed = false;
      Window win{};
      Window win_out{};
  
      const DataType data_type                           = input0->data_type();
      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_input_as_3d             = reshape_info.reinterpret_input_as_3d();
      bool           reinterpret_output_as_3d            = (reshape_info.depth_output_gemm3d() != 0);
  
      // In case both input and output have to be reinterpreted as 3D tensors,
      // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
      if(reinterpret_input_as_3d == reinterpret_output_as_3d)
      {
          reinterpret_input_as_3d  = false;
          reinterpret_output_as_3d = false;
      }
  
      // Output tensor auto inizialitation if not yet initialized
      auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_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);
      }
  
      if(is_interleaved_transposed)
      {
          // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
          ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
  
          // Configure kernel window
          num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
          num_elems_processed_per_iteration_y = 4;
  
          // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
          // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
          const int m          = reshape_info.m();
          const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
  
          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));
  
          AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1));
          AccessWindowStatic input1_access(input1, 0, 0,
                                           ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),
                                           ceil_to_multiple(input1->dimension(1), num_elems_processed_per_iteration_y));
          AccessWindowStatic output_access(output, 0, 0,
                                           ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
                                           output->dimension(1) + bottom_pad);
  
          if(input2 != nullptr)
          {
              const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
  
              const int bias_processed_per_iteration_y = reshape_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, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
                               update_window_and_padding(win_out, output_access);                             // window used to update the padding requirements of output tensor
          }
          else
          {
              window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
                               update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
          }
  
          output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
      }
      else // The input tensors have not been reshaped
      {
          // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
          num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
          num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
  
          // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
          // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
          const int m          = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1];
          const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
  
          // Create kernels according to the architecture, data type and input size.
          GPUTarget arch_target = get_arch_from_target(gpu_target);
          if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
          {
              num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4;
          }
  
          // Configure window
          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));
  
          AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad);
          AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
          AccessWindowStatic output_access(output, 0, 0,
                                           ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
                                           output->dimension(1) + bottom_pad);
  
          if(input2 != nullptr)
          {
              const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
  
              const int bias_processed_per_iteration_y = reshape_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, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
                               update_window_and_padding(win_out, output_access);                             // window used to update the padding requirements of output tensor
          }
          else
          {
              window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
                               update_window_and_padding(win_out, output_access);              // window used to update the padding requirements of output tensor
          }
  
          Coordinates coord;
          coord.set_num_dimensions(output->num_dimensions());
          output_access.set_valid_region(win_out, ValidRegion(coord, output->tensor_shape()));
      }
  
      // 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
  
  CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel()
      : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _add_bias(false),
        _broadcast_bias(false)
  {
  }
  
  void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
                                             bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
  {
      configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision, activation_info);
  }
  
  void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
                                             bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
  
      // Perform validate step
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta,
                                                    is_interleaved_transposed, reshape_info, fp_mixed_precision));
  
      _input0                   = input0;
      _input1                   = input1;
      _input2                   = helpers::float_ops::is_zero(beta) ? nullptr : input2;
      _output                   = output;
      _reinterpret_input_as_3d  = reshape_info.reinterpret_input_as_3d();
      _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
      _add_bias                 = _input2 != nullptr;
      _broadcast_bias           = reshape_info.broadcast_bias();
  
      // In case both input and output have to be reinterpreted as 3D tensors,
      // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
      if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
      {
          _reinterpret_input_as_3d  = false;
          _reinterpret_output_as_3d = false;
      }
  
      // Check if we need to slide the matrix B
      const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions();
  
      _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);
  
      const DataType data_type = input0->info()->data_type();
  
      // Get target architecture
      GPUTarget gpu_target = get_target();
  
      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(), beta, is_interleaved_transposed, reshape_info,
                                                      gpu_target, num_elements_processed);
      ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
      ICLKernel::configure_internal(win_config.second);
  
      // 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(reshape_info.broadcast_bias(), "-DBROADCAST_BIAS");
      build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
      build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
      build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
      build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
      build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
      build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation())));
      build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a()));
      build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b()));
  
      const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
  
      std::string kernel_name;
      if(is_interleaved_transposed)
      {
          const int mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
          const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
  
          build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0)));
          build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width));
          build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height));
  
          if(is_data_type_float(data_type) && is_bifrost)
          {
              kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
          }
          else
          {
              kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type));
              if(fp_mixed_precision && data_type == DataType::F16)
              {
                  // currently wider accumulator is only supported for fp16 kernels.
                  kernel_name += "_acc32";
              }
          }
      }
      else // The input tensors have not been reshaped
      {
          build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
          build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
  
          // Create kernels according to the architecture, data type and input size.
          if(is_data_type_float(data_type) && is_bifrost)
          {
              kernel_name = "gemm_mm_floating_point";
  
              if(input0->info()->num_dimensions() != 1)
              {
                  kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
                  if(fp_mixed_precision && data_type == DataType::F16)
                  {
                      // currently wider accumulator is only supported for fp16 kernels.
                      kernel_name += "_acc32";
                  }
              }
              else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32)
              {
                  // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
                  // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
                  // FC6 and FC7 of AlexNet and VGG-16).
                  kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000";
              }
  
              // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
              // via exhaustive autotuning over a range of representative layer configurations.
              set_lws_hint(cl::NDRange(4));
          }
          else // (MIDGARD and F32) or (F16)
          {
              kernel_name = "gemm_mm_floating_point";
          }
          build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
          build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x()));
      }
  
      // Create kernel
      _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
  
      // Set config_id for enabling LWS tuning
      _config_id = "gemm_";
      _config_id += (is_interleaved_transposed ? "reshaped_" : "");
      _config_id += (_add_bias ? "add_bias_" : "");
      _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
      _config_id += (fp_mixed_precision ? "fp_mixed_" : "");
      _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
      _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
      _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
      _config_id += "_";
      _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(output->info()->dimension(2));
      _config_id += "_";
      _config_id += support::cpp11::to_string(output->info()->dimension(3));
      _config_id += "_";
      _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
  }
  
  Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
                                              bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
  {
      // Note: num_elements_processed will be set in validate_and_configure_window()
      ElementsProcessed num_elements_processed{};
      ARM_COMPUTE_UNUSED(alpha);
      ARM_COMPUTE_UNUSED(activation_info);
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
                                                                input1->clone().get(),
                                                                (input2 != nullptr) ? input2->clone().get() : nullptr,
                                                                output->clone().get(),
                                                                beta,
                                                                is_interleaved_transposed,
                                                                reshape_info,
                                                                gpu_target,
                                                                num_elements_processed)
                                  .first);
  
      return Status{};
  }
  
  void CLGEMMMatrixMultiplyKernel::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 num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0;
  
      if(_reinterpret_input_as_3d)
      {
          // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
          const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 3 + num_arguments_bias;
          const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
          _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
      }
  
      if(_reinterpret_output_as_3d)
      {
          // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
          const unsigned int idx0                  = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias;
          const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
          _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
      }
  
      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;
          add_2D_tensor_argument(idx, _input0, slice);
          add_2D_tensor_argument(idx, _input1, slice_b);
          if(_add_bias)
          {
              add_2D_tensor_argument(idx, _input2, slice);
          }
          add_2D_tensor_argument(idx, _output, slice);
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
          if(_add_bias)
          {
              _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2]));
          }
          _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
          enqueue(queue, *this, slice, lws_hint());
      }
      while(window.slide_window_slice_3D(slice));
  }
  } // namespace arm_compute
  