text: /*
   * Copyright (c) 2017-2021 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.
   */
  #ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
  #error "This example needs to be built with -DARM_COMPUTE_CL"
  #endif /* ARM_COMPUTE_CL */
  
  #include "arm_compute/core/Types.h"
  #include "arm_compute/core/Utils.h"
  #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
  #include "arm_compute/runtime/CL/CLScheduler.h"
  #include "arm_compute/runtime/CL/functions/CLGEMM.h"
  #include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
  #include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
  #include "src/core/CL/kernels/CLDepthConvertLayerKernel.h"
  #include "src/core/CL/kernels/CLFillBorderKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
  #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
  #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
  #include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
  #include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
  #include "src/core/CL/kernels/CLIm2ColKernel.h"
  #include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
  #include "tests/AssetsLibrary.h"
  #include "tests/CL/CLAccessor.h"
  #include "tests/Globals.h"
  #include "tests/IAccessor.h"
  #include "tests/SimpleTensor.h"
  #include "tests/validation/Validation.h"
  #include "tests/validation/reference/GEMM.h"
  #include "tests/validation/reference/GEMMLowp.h"
  
  #include "utils/TypePrinter.h"
  #include "utils/Utils.h"
  #include "utils/command_line/CommandLineOptions.h"
  #include "utils/command_line/CommandLineParser.h"
  
  #include "ValidateExample.h"
  
  #include <cstdlib>
  
  using namespace arm_compute;
  using namespace utils;
  using namespace arm_compute::test;
  using namespace arm_compute::test::validation;
  
  constexpr float                     abs_tolerance_f32(0.0001f); /**< F32 Absolute tolerance value for comparing reference's output against implementation's output for
                                                                 * floating point data types in case using relative tolerance fails because of small values */
  RelativeTolerance<float>            tolerance_f32(0.001f);      /**< F32 Tolerance value for comparing reference's output against implementation's output for floating point data types */
  RelativeTolerance<half_float::half> tolerance_f16(half(0.2));   /**< F16 Tolerance value for comparing reference's output against implementation's output for floating point data types */
  constexpr float                     tolerance_num_f16 = 0.02f;  /**< F16 Tolerance number */
  
  namespace
  {
  class GEMMCommandLineOptions final
  {
  public:
      explicit GEMMCommandLineOptions(CommandLineParser &parser) noexcept
          : help(parser.add_option<ToggleOption>("help")),
            add_bias(parser.add_option<ToggleOption>("add_bias")),
            M(parser.add_option<SimpleOption<int>>("m", 7)),
            N(parser.add_option<SimpleOption<int>>("n", 3)),
            K(parser.add_option<SimpleOption<int>>("k", 5)),
            B(parser.add_option<SimpleOption<int>>("b", 1)),
            alpha(parser.add_option<SimpleOption<float>>("alpha", 1.f)),
            beta(parser.add_option<SimpleOption<float>>("beta", 0.f)),
            offset_src0(parser.add_option<SimpleOption<int>>("offset_i0", 10)),
            offset_src1(parser.add_option<SimpleOption<int>>("offset_i1", 10)),
            offset_dst(parser.add_option<SimpleOption<int>>("offset_o", 10)),
            scale_src0(parser.add_option<SimpleOption<float>>("scale_i0", 1.f / 255)),
            scale_src1(parser.add_option<SimpleOption<float>>("scale_i1", 1.f / 255)),
            scale_dst(parser.add_option<SimpleOption<float>>("scale_o", 1.f / 255)),
            data_type()
      {
          // Setup data type
          const std::set<arm_compute::DataType> supported_data_types
          {
              DataType::F16,
              DataType::F32,
              DataType::QASYMM8,
          };
          data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
  
          // Setup help strings
          help->set_help("Show this help message");
          add_bias->set_help("Add bias to the GEMM. Used when running in QASYMM8");
          M->set_help("M value");
          N->set_help("N value");
          K->set_help("K value");
          B->set_help("B value - number of batches");
          alpha->set_help("Alpha value");
          beta->set_help("Beta value");
          offset_src0->set_help("Offset of first input. Used when running in QASYMM8");
          offset_src1->set_help("Offset of second input. Used when running in QASYMM8");
          offset_dst->set_help("Offset of output. Used when running in QASYMM8");
          scale_src0->set_help("Scale of first input. Used when running in QASYMM8");
          scale_src1->set_help("Scale of second input. Used when running in QASYMM8");
          scale_dst->set_help("Scale of output. Used when running in QASYMM8");
          data_type->set_help("Data type to use");
      }
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      GEMMCommandLineOptions(const GEMMCommandLineOptions &) = delete;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      GEMMCommandLineOptions &operator=(const GEMMCommandLineOptions &) = delete;
      /** Allow instances of this class to be moved */
      GEMMCommandLineOptions(GEMMCommandLineOptions &&) noexcept(true) = default;
      /** Allow instances of this class to be moved */
      GEMMCommandLineOptions &operator=(GEMMCommandLineOptions &&) noexcept(true) = default;
      /** Default destructor */
      ~GEMMCommandLineOptions() = default;
  
  public:
      ToggleOption                      *help;
      ToggleOption                      *add_bias;
      SimpleOption<int>                 *M;
      SimpleOption<int>                 *N;
      SimpleOption<int>                 *K;
      SimpleOption<int>                 *B;
      SimpleOption<float>               *alpha;
      SimpleOption<float>               *beta;
      SimpleOption<int>                 *offset_src0;
      SimpleOption<int>                 *offset_src1;
      SimpleOption<int>                 *offset_dst;
      SimpleOption<float>               *scale_src0;
      SimpleOption<float>               *scale_src1;
      SimpleOption<float>               *scale_dst;
      EnumOption<arm_compute::DataType> *data_type;
  };
  } // namespace
  
  class CLGEMMValidateExample : public ValidateExample
  {
  public:
      bool do_setup(int argc, char **argv) override
      {
          CLScheduler::get().default_init();
  
          // Parse options
          CommandLineParser      parser;
          GEMMCommandLineOptions gemm_options(parser);
          parser.parse(argc, argv);
  
          // Print help
          const bool print_help = gemm_options.help->is_set() ? gemm_options.help->value() : false;
          if(print_help)
          {
              parser.print_help(argv[0]);
              return false;
          }
  
          // Consume parameters
          consume_params(gemm_options);
          print_parameters_internal();
  
          const bool is_quantized = is_data_type_quantized(data_type);
  
          // Calculate re-quantization parameters
          if(is_quantized)
          {
              float multiplier = scale_src0 * scale_src1 / scale_dst;
              quantization::calculate_quantized_multiplier(multiplier, &dst_multiplier, &dst_shift);
          }
  
          // Initialize GEMM inputs/outputs
          src0.allocator()->init(TensorInfo(TensorShape(K, M, B), 1, data_type));
          src1.allocator()->init(TensorInfo(TensorShape(N, K, B), 1, data_type));
          src2.allocator()->init(TensorInfo(TensorShape(N, M, B), 1, data_type));
          init_sgemm_output(dst, src0, src1, data_type);
  
          // Configure function
          if(is_quantized)
          {
              src0.info()->set_quantization_info(QuantizationInfo(scale_src0, offset_src0));
              src1.info()->set_quantization_info(QuantizationInfo(scale_src1, offset_src1));
              dst.info()->set_quantization_info(QuantizationInfo(scale_dst, offset_dst));
              biases.allocator()->init(TensorInfo(TensorShape(N), 1, DataType::S32));
              init_sgemm_output(tmp_dst, src0, src1, DataType::S32);
  
              // Configure GEMMlowp matrix multiply function
              mm_gemmlowp.configure(&src0, &src1, nullptr, &tmp_dst);
  
              // Configure GEMMlowp output stage
              mm_gemmlowp_output_stage.configure(&tmp_dst, add_bias ? &biases : nullptr, &dst, dst_multiplier, dst_shift, offset_dst);
              tmp_dst.allocator()->allocate();
              biases.allocator()->allocate();
              fill(CLAccessor(biases), 3);
          }
          else
          {
              // Configure matrix multiply function
              mm_gemm.configure(&src0, &src1, &src2, &dst, alpha, beta);
          }
  
          // Allocate all the tensors
          src0.allocator()->allocate();
          src1.allocator()->allocate();
          dst.allocator()->allocate();
          src2.allocator()->allocate();
  
          fill(CLAccessor(src0), 0);
          fill(CLAccessor(src1), 1);
          fill(CLAccessor(src2), 2);
  
          return true;
      }
  
      void print_parameters_internal()
      {
          std::cout << "Datatype : " << string_from_data_type(data_type) << "\n";
          std::cout << "M : " << support::cpp11::to_string(M) << "\n";
          std::cout << "N : " << support::cpp11::to_string(N) << "\n";
          std::cout << "K : " << support::cpp11::to_string(K) << "\n";
          std::cout << "B : " << support::cpp11::to_string(B) << "\n";
          if(data_type == DataType::QASYMM8)
          {
              std::cout << "Scale_Src0 : " << support::cpp11::to_string(scale_src0) << "\n";
              std::cout << "Offset_Src0 : " << support::cpp11::to_string(offset_src0) << "\n";
              std::cout << "Scale_Scr1 : " << support::cpp11::to_string(scale_src1) << "\n";
              std::cout << "Offset_Src1 : " << support::cpp11::to_string(offset_src1) << "\n";
              std::cout << "Scale_Dst : " << support::cpp11::to_string(scale_dst) << "\n";
              std::cout << "Offset_Dst : " << support::cpp11::to_string(offset_dst) << "\n";
              std::cout << "Bias : " << support::cpp11::to_string(add_bias) << "\n";
          }
          else
          {
              std::cout << "Alpha : " << support::cpp11::to_string(alpha) << "\n";
              std::cout << "Beta : " << support::cpp11::to_string(beta) << "\n";
          }
      }
  
      void do_validate() override
      {
          switch(data_type)
          {
              case DataType::F16:
              {
                  SimpleTensor<half> ref_src0 = { TensorShape(K, M, B), data_type, 1 };
                  SimpleTensor<half> ref_src1 = { TensorShape(N, K, B), data_type, 1 };
                  SimpleTensor<half> ref_src2 = { TensorShape(N, M, B), data_type, 1 };
  
                  fill(ref_src0, 0);
                  fill(ref_src1, 1);
                  fill(ref_src2, 2);
  
                  SimpleTensor<half> ref_dst = reference::gemm<half>(ref_src0, ref_src1, ref_src2, alpha, beta);
                  validate(CLAccessor(dst), ref_dst, tolerance_f16, tolerance_num_f16);
                  break;
              }
              case DataType::F32:
              {
                  SimpleTensor<float> ref_src0 = { TensorShape(K, M, B), data_type, 1 };
                  SimpleTensor<float> ref_src1 = { TensorShape(N, K, B), data_type, 1 };
                  SimpleTensor<float> ref_src2 = { TensorShape(N, M, B), data_type, 1 };
  
                  fill(ref_src0, 0);
                  fill(ref_src1, 1);
                  fill(ref_src2, 2);
  
                  SimpleTensor<float> ref_dst = reference::gemm<float>(ref_src0, ref_src1, ref_src2, alpha, beta);
                  validate(CLAccessor(dst), ref_dst, tolerance_f32, 0.f, abs_tolerance_f32);
                  break;
              }
              case DataType::QASYMM8:
              {
                  SimpleTensor<uint8_t> ref_src0{ TensorShape(K, M, B), data_type, 1 };
                  SimpleTensor<uint8_t> ref_src1{ TensorShape(N, K, B), data_type, 1 };
                  SimpleTensor<uint8_t> ref_dst;
  
                  // Fill reference
                  fill(ref_src0, 0);
                  fill(ref_src1, 1);
  
                  SimpleTensor<int32_t> ref_tmp_dst = reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(ref_src0, ref_src1, TensorShape(N, M, B), offset_src0, offset_src1);
  
                  const std::vector<int32_t> dst_multiplier_vec = { dst_multiplier };
                  const std::vector<int32_t> dst_shift_vec      = { dst_shift };
  
                  if(add_bias)
                  {
                      SimpleTensor<int32_t> biases{ TensorShape(N), DataType::S32, 1 };
                      // Fill bias
                      fill(biases, 3);
                      ref_dst = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(ref_tmp_dst, biases, dst_multiplier_vec, dst_shift_vec, offset_dst);
                  }
                  else
                  {
                      ref_dst = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(ref_tmp_dst, dst_multiplier_vec, dst_shift_vec, offset_dst);
                  }
                  validate(CLAccessor(dst), ref_dst);
                  break;
              }
              default:
                  break;
          }
      }
      void do_run() override
      {
          // Execute the function
          if(data_type == DataType::QASYMM8)
          {
              // Run gemmlowp
              mm_gemmlowp.run();
              // Run output stage
              mm_gemmlowp_output_stage.run();
          }
          else
          {
              // Run gemm
              mm_gemm.run();
          }
  
          // Make sure all the OpenCL jobs are done executing:
          CLScheduler::get().sync();
      }
  
  private:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          switch(tensor.data_type())
          {
              case DataType::F16:
              {
                  arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
                  library->fill(tensor, distribution, i);
                  break;
              }
              case DataType::F32:
              {
                  std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
                  library->fill(tensor, distribution, i);
                  break;
              }
              case DataType::S32:
              case DataType::QASYMM8:
              {
                  std::uniform_int_distribution<> distribution(-6000, 6000);
                  library->fill(tensor, distribution, i);
                  break;
              }
              default:
                  library->fill_tensor_uniform(tensor, i);
          }
      }
  
      void consume_params(const GEMMCommandLineOptions &opts)
      {
          ARM_COMPUTE_ERROR_ON(opts.M->value() <= 0);
          ARM_COMPUTE_ERROR_ON(opts.N->value() <= 0);
          ARM_COMPUTE_ERROR_ON(opts.K->value() <= 0);
          ARM_COMPUTE_ERROR_ON(opts.B->value() <= 0);
          M           = opts.M->value();
          N           = opts.N->value();
          K           = opts.K->value();
          B           = opts.B->value();
          alpha       = opts.alpha->value();
          beta        = opts.beta->value();
          offset_src0 = opts.offset_src0->value();
          offset_src1 = opts.offset_src1->value();
          offset_dst  = opts.offset_dst->value();
          scale_src0  = opts.scale_src0->value();
          scale_src1  = opts.scale_src1->value();
          scale_dst   = opts.scale_dst->value();
          add_bias    = opts.add_bias->is_set() ? opts.add_bias->value() : true;
          data_type   = opts.data_type->value();
      }
  
      CLTensor src0{}, src1{}, src2{}, dst{};
      CLTensor tmp_dst{}, biases{};
  
      CLGEMM                                              mm_gemm{};
      CLGEMMLowpMatrixMultiplyCore                        mm_gemmlowp{};
      CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint mm_gemmlowp_output_stage{};
  
      size_t   M{ 7 }, N{ 3 }, K{ 5 }, B{ 1 };
      DataType data_type{ DataType::F32 };
      float    alpha{ 1.0 }, beta{ 0.0 };
      int      offset_src0{ 10 }, offset_src1{ 10 }, offset_dst{ 10 };
      float    scale_src0{ 1.0f / 255 }, scale_src1{ 1.0f / 255 }, scale_dst{ 1.0f / 255 };
      int32_t  dst_multiplier{ 0 }, dst_shift{ 0 };
      bool     add_bias{ true };
  };
  
  /** Main program for gemm test
   *
   * @param[in] argc Number of arguments
   * @param[in] argv Arguments
   *
   */
  int main(int argc, char **argv)
  {
      return utils::run_example<CLGEMMValidateExample>(argc, argv);
  }
  