examples/gemm_tuner/cl_gemmlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2020-2021, 2023 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/Helpers.h"
 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "arm_compute/runtime/CL/CLTuner.h"

 #include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h"
 #include "src/gpu/cl/kernels/ClGemmLowpReductionKernel.h"
 #include "tests/CL/Helper.h"
 #include "utils/command_line/CommandLineOptions.h"
 #include "utils/command_line/CommandLineParser.h"
 #include "utils/Utils.h"

 #include "CommonGemmExampleOptions.h"
 #include "GemmTunerHelpers.h"
 #include <cstdlib>
 #include <memory>

 using namespace arm_compute;
 using namespace utils;
 using namespace arm_compute::opencl::kernels;
 using namespace arm_compute::misc::shape_calculator;
 using namespace gemm_tuner;

 namespace
 {
 /** Structure holding all tunable gemm configs specific to this example/strategy */
 struct GemmConfigs
 {
     size_t m0{4};                /**< Number of rows processed by the matrix multiplication */
     size_t n0{4};                /**< Number of columns processed by the matrix multiplication */
     size_t k0{4};                /**< Number of partial accumulations performed by the matrix multiplication */
     size_t h0{1};                /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */
     bool   interleave_rhs{true}; /**< Interleave rhs matrix */
     bool   transpose_rhs{true};  /**< Transpose rhs matrix */
 };

 /** Formatted output of the GemmConfigs type
  *
  * @param[out] os      Output stream.
  * @param[in]  configs Tunable configurations to output
  *
  * @return Modified output stream.
  */
 ::std::ostream &operator<<(::std::ostream &os, const GemmConfigs &configs)
 {
     std::string false_str = std::string("false");
     std::string true_str  = std::string("true");

     os << "m0 : " << configs.m0 << std::endl;
     os << "n0 : " << configs.n0 << std::endl;
     os << "k0 : " << configs.k0 << std::endl;
     os << "h0 : " << configs.h0 << std::endl;
     os << "interleave_rhs : " << (configs.interleave_rhs ? true_str : false_str) << std::endl;
     os << "transpose_rhs : " << (configs.transpose_rhs ? true_str : false_str) << std::endl;
     return os;
 }

 /** Command line options for gemm configs */
 class GemmConfigOptions
 {
 public:
     /** Constructor
      *
      * @param[in,out] parser A parser on which "parse()" hasn't been called yet.
      */
     GemmConfigOptions(CommandLineParser &parser)
         : m0(parser.add_positional_option<SimpleOption<size_t>>("m0", 4)),
           n0(parser.add_positional_option<SimpleOption<size_t>>("n0", 4)),
           k0(parser.add_positional_option<SimpleOption<size_t>>("k0", 4)),
           h0(parser.add_positional_option<SimpleOption<size_t>>("h0", 1)),
           interleave_rhs(parser.add_positional_option<SimpleOption<size_t>>("interleave_rhs", 1)),
           transpose_rhs(parser.add_positional_option<SimpleOption<size_t>>("transpose_rhs", 1))
     {
         m0->set_help("Number of rows processed by the matrix multiplication");
         n0->set_help("Number of columns processed by the matrix multiplication");
         k0->set_help("Number of partial accumulations performed by the matrix multiplication");
         h0->set_help("Number of horizontal blocks of size (k0xn0) stored on the same output row");
         interleave_rhs->set_help("Interleave rhs matrix (1) / Do not interleave rhs matrix (0)");
         transpose_rhs->set_help("Transpose rhs matrix (1) / Do not transpose rhs matrix (0)");
     }
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     GemmConfigOptions(const GemmConfigOptions &) = delete;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     GemmConfigOptions &operator=(const GemmConfigOptions &) = delete;
     /** Allow instances of this class to be moved */
     GemmConfigOptions(GemmConfigOptions &&) = default;
     /** Allow instances of this class to be moved */
     GemmConfigOptions &operator=(GemmConfigOptions &&) = default;
     /** Default destructor */
     ~GemmConfigOptions() = default;

     SimpleOption<size_t> *m0; /**< Number of rows processed by the matrix multiplication option */
     SimpleOption<size_t> *n0; /**< Number of columns processed by the matrix multiplication option */
     SimpleOption<size_t> *k0; /**< Number of partial accumulations performed by the matrix multiplication option */
     SimpleOption<size_t> *h0; /**< Number of horizontal blocks of size (k0xn0) stored on the same output row option */
     SimpleOption<size_t> *interleave_rhs; /**< Interleave rhs matrix option (1 enable; 0 disable) */
     SimpleOption<size_t> *transpose_rhs;  /**< Transpose rhs matrix option (1 enable; 0 disable) */
 };

 /** Consumes the gemm configuration options and creates a structure containing all information
  *
  * @param[in] options Options to consume
  *
  * @return Structure containing the gemm configurations
  */
 GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
 {
     GemmConfigs configs;
     configs.m0             = options.m0->value();
     configs.n0             = options.n0->value();
     configs.k0             = options.k0->value();
     configs.h0             = options.h0->value();
     configs.interleave_rhs = options.interleave_rhs->value() != 0;
     configs.transpose_rhs  = options.transpose_rhs->value() != 0;
     return configs;
 }

 } // namespace

 using ClGemmLowpMatrixMultiplyReshapedOnlyRhs =
     test::CLSynthetizeOperator<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>;
 using ClGemmLowpMatrixAReduction = test::CLSynthetizeOperator<ClGemmLowpMatrixAReductionKernel>;

 class CLGEMMLowpMatrixMultiplyReshapedOnlyRHSFusedOutputStageFixedpointExample : public Example
 {
 public:
     bool do_setup(int argc, char **argv) override
     {
         // Default parameters
         CommonGemmExampleParams params;
         GemmConfigs             configs;

         // Parse command line options
         CommandLineParser        parser;
         CommonGemmExampleOptions param_options(parser, DataType::QASYMM8);
         GemmConfigOptions        config_options(parser);

         parser.parse(argc, argv);
         if (param_options.help->is_set() && param_options.help->value())
         {
             parser.print_help(argv[0]);
             return false;
         }
         if (!parser.validate())
         {
             // Invalid arguments. Use default parameters and configs
             std::cerr << "Invalid arguments." << std::endl;
             parser.print_help(argv[0]);
             std::cerr << "Falling back to default parameters and configs" << std::endl;
         }
         else
         {
             params  = consume_common_gemm_example_parameters(param_options);
             configs = consume_gemm_configs(config_options);
         }

         std::cout << "Gemm parameters:" << std::endl;
         std::cout << params << std::endl;
         std::cout << "Gemm configurations:" << std::endl;
         std::cout << configs << std::endl;

         tuner.set_tuner_mode(params.tuner_mode);

         CLScheduler::get().default_init(&tuner);

         lhs.allocator()->init(TensorInfo(TensorShape(params.K, params.M, params.B), 1, params.data_type));
         rhs.allocator()->init(TensorInfo(TensorShape(params.N, params.K, params.B), 1, params.data_type));
         bias.allocator()->init(TensorInfo(TensorShape(params.N), 1, DataType::S32));
         dst.allocator()->init(TensorInfo(TensorShape(params.N, params.M, params.B), 1, params.data_type));

         // Set arbitrary quantization information (non-zero offset to ensure offset contribution stage is included)
         // Could be extended in the future to include a user-controlled option for offset == 0
         const QuantizationInfo q_info{0.012, 3};
         lhs.info()->set_quantization_info(q_info);
         rhs.info()->set_quantization_info(q_info);
         bias.info()->set_quantization_info(q_info);
         dst.info()->set_quantization_info(q_info);

         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0 = configs.m0;
         lhs_info.k0 = configs.k0;

         GEMMRHSMatrixInfo rhs_info;
         rhs_info.n0                 = configs.n0;
         rhs_info.k0                 = configs.k0;
         rhs_info.h0                 = configs.h0;
         rhs_info.interleave         = configs.interleave_rhs;
         rhs_info.transpose          = configs.transpose_rhs;
         rhs_info.export_to_cl_image = false; // CL image not supported for quantized cases yet

         if (rhs_info.h0 == 0)
         {
             rhs_info.h0 = std::max(static_cast<unsigned int>(params.N) / rhs_info.n0, 1U);
         }

         rhs_reshaped.allocator()->init(
             TensorInfo(compute_rhs_reshaped_shape(*rhs.info(), rhs_info), 1, params.data_type));
         rhs_reshaped.info()->set_quantization_info(q_info);
         if (rhs_info.export_to_cl_image)
         {
             if (!examples::gemm_tuner_helpers::update_padding_for_cl_image(rhs_reshaped.info()))
             {
                 std::cerr << "cl_image is not supported on the device, disable export_to_cl_image" << std::endl;
                 return false;
             }
         }

         // Configure output stage for quantized case
         GEMMLowpOutputStageInfo gemmlowp_output_stage;
         gemmlowp_output_stage.type             = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
         gemmlowp_output_stage.output_data_type = dst.info()->data_type();
         gemmlowp_output_stage.gemmlowp_offset  = 0;
         {
             gemmlowp_output_stage.is_quantized_per_channel = false;
             // Num_filters is 1 unless quantized type is of per_channel type. Could be extended in the future to support per-channel quantization.
             const unsigned int num_filters = 1;

             dst_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
             dst_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));

             gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
             gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
             quantization::compute_quantized_multipliers_and_shifts(lhs.info(), rhs.info(), dst.info(),
                                                                    gemmlowp_output_stage.gemmlowp_multipliers.data(),
                                                                    gemmlowp_output_stage.gemmlowp_shifts.data());
             gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
             gemmlowp_output_stage.gemmlowp_shift      = gemmlowp_output_stage.gemmlowp_shifts[0];

             // No fused activation
             PixelValue min_val{};
             PixelValue max_val{};
             std::tie(min_val, max_val) = get_min_max(dst.info()->data_type());

             auto min_activation = min_val.get<int32_t>();
             auto max_activation = max_val.get<int32_t>();

             // Set the GEMMLowp output stage info
             gemmlowp_output_stage.gemmlowp_offset    = dst.info()->quantization_info().uniform().offset;
             gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
             gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
         }

         GEMMKernelInfo gemm_info;
         gemm_info.m                       = params.M;
         gemm_info.n                       = params.N;
         gemm_info.k                       = params.K;
         gemm_info.depth_output_gemm3d     = 0;
         gemm_info.reinterpret_input_as_3d = false;
         gemm_info.broadcast_bias          = true;
         gemm_info.fp_mixed_precision      = false;
         gemm_info.has_pad_y               = false;
         gemm_info.mult_transpose1xW_width = configs.h0;
         gemm_info.lhs_info                = lhs_info;
         gemm_info.rhs_info                = rhs_info;
         gemm_info.a_offset                = lhs.info()->quantization_info().uniform().offset;
         gemm_info.b_offset                = rhs.info()->quantization_info().uniform().offset;
         gemm_info.output_stage            = gemmlowp_output_stage;

         // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
         if (gemm_info.b_offset != 0)
         {
             const TensorInfo info_vector_sum_row(compute_reductionB_shape(*lhs.info()), 1, DataType::S32);
             vector_sum_row.allocator()->init(info_vector_sum_row);

             mtx_a_reduction = std::make_unique<ClGemmLowpMatrixAReduction>();

             if (!mtx_a_reduction->validate(lhs.info(), vector_sum_row.info(), GEMMLowpReductionKernelInfo{}))
             {
                 std::cerr << "Invalid arguments for CLGEMMLowpMatrixAReductionKernel." << std::endl;
                 return false;
             }

             mtx_a_reduction->configure(lhs.info(), vector_sum_row.info(), GEMMLowpReductionKernelInfo{});
         }
         // Initialize matrix B reduction kernel only if _a_offset is not equal to 0
         if (gemm_info.a_offset != 0)
         {
             const TensorInfo info_vector_sum_col(compute_reductionA_shape(*rhs.info()), 1, DataType::S32);
             vector_sum_col.allocator()->init(info_vector_sum_col);
             // There's no need for a Matrix B reduction kernel as this is assumed to be run only once in the prepare stage
         }

         // Validate argments
         if (!gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info,
                            gemm_info.a_offset == 0 ? nullptr : vector_sum_col.info(),
                            gemm_info.b_offset == 0 ? nullptr : vector_sum_row.info(), bias.info(),
                            dst_multipliers.info(), dst_shifts.info()))
         {
             std::cerr << "Invalid arguments for ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel." << std::endl;
             return false;
         }

         // Configure function
         gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info,
                        gemm_info.a_offset == 0 ? nullptr : vector_sum_col.info(),
                        gemm_info.b_offset == 0 ? nullptr : vector_sum_row.info(), bias.info(), dst_multipliers.info(),
                        dst_shifts.info());

         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
         rhs_reshaped.allocator()->allocate();
         bias.allocator()->allocate();
         dst.allocator()->allocate();
         vector_sum_col.allocator()->allocate();
         vector_sum_row.allocator()->allocate();
         dst_multipliers.allocator()->allocate();
         dst_shifts.allocator()->allocate();

         return true;
     }
     void do_run() override
     {
         if (mtx_a_reduction != nullptr)
         {
             ITensorPack red_pack({{ACL_SRC, &lhs}, {ACL_DST, &dst}});
             mtx_a_reduction->run(red_pack);
         }

         ITensorPack gemm_pack({{ACL_SRC_0, &lhs},
                                {ACL_SRC_1, &rhs},
                                {ACL_BIAS, &bias},
                                {ACL_VEC_COL_SUM, &vector_sum_col},
                                {ACL_VEC_ROW_SUM, &vector_sum_row},
                                {ACL_SHIFTS, &dst_shifts},
                                {ACL_MULTIPLIERS, &dst_multipliers},
                                {ACL_DST, &dst}});
         gemm.run(gemm_pack);

         // Make sure all the OpenCL jobs are done executing:
         CLScheduler::get().sync();
     }

     void do_teardown() override
     {
     }

 private:
     CLTensor                                    lhs{};
     CLTensor                                    rhs{};
     CLTensor                                    rhs_reshaped{};
     CLTensor                                    bias{};
     CLTensor                                    dst{};
     CLTensor                                    vector_sum_col{};
     CLTensor                                    vector_sum_row{};
     CLTensor                                    dst_multipliers{};
     CLTensor                                    dst_shifts{};
     CLTuner                                     tuner{};
     ClGemmLowpMatrixMultiplyReshapedOnlyRhs     gemm{};
     std::unique_ptr<ClGemmLowpMatrixAReduction> mtx_a_reduction{nullptr};
 };

 /** Main test program for gemmlowp reshaped rhs only with fused output stage fixedpoint
  *
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] M, [optional] N, [optional] K, [optional] B, [optional] m0, [optional] n0, [optional] k0, [optional] h0, [optional] interleave_rhs, [optional] transpose_rhs )
  */
 int main(int argc, char **argv)
 {
     return run_example<CLGEMMLowpMatrixMultiplyReshapedOnlyRHSFusedOutputStageFixedpointExample>(argc, argv);
 }
	/*
	* Copyright (c) 2020-2021, 2023 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/Helpers.h"
	#include "arm_compute/core/KernelDescriptors.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"
	#include "arm_compute/runtime/CL/CLTuner.h"

	#include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h"
	#include "src/gpu/cl/kernels/ClGemmLowpReductionKernel.h"
	#include "tests/CL/Helper.h"
	#include "utils/command_line/CommandLineOptions.h"
	#include "utils/command_line/CommandLineParser.h"
	#include "utils/Utils.h"

	#include "CommonGemmExampleOptions.h"
	#include "GemmTunerHelpers.h"
	#include <cstdlib>
	#include <memory>

	using namespace arm_compute;
	using namespace utils;
	using namespace arm_compute::opencl::kernels;
	using namespace arm_compute::misc::shape_calculator;
	using namespace gemm_tuner;

	namespace
	{
	/** Structure holding all tunable gemm configs specific to this example/strategy */
	struct GemmConfigs
	{
	size_t m0{4}; /*< Number of rows processed by the matrix multiplication /
	size_t n0{4}; /*< Number of columns processed by the matrix multiplication /
	size_t k0{4}; /*< Number of partial accumulations performed by the matrix multiplication /
	size_t h0{1}; /*< Number of horizontal blocks of size (k0xn0) stored on the same output row /
	bool interleave_rhs{true}; /*< Interleave rhs matrix /
	bool transpose_rhs{true}; /*< Transpose rhs matrix /
	};

	/** Formatted output of the GemmConfigs type
	*
	* @param[out] os Output stream.
	* @param[in] configs Tunable configurations to output
	*
	* @return Modified output stream.
	*/
	::std::ostream &operator<<(::std::ostream &os, const GemmConfigs &configs)
	{
	std::string false_str = std::string("false");
	std::string true_str = std::string("true");

	os << "m0 : " << configs.m0 << std::endl;
	os << "n0 : " << configs.n0 << std::endl;
	os << "k0 : " << configs.k0 << std::endl;
	os << "h0 : " << configs.h0 << std::endl;
	os << "interleave_rhs : " << (configs.interleave_rhs ? true_str : false_str) << std::endl;
	os << "transpose_rhs : " << (configs.transpose_rhs ? true_str : false_str) << std::endl;
	return os;
	}

	/** Command line options for gemm configs */
	class GemmConfigOptions
	{
	public:
	/** Constructor
	*
	* @param[in,out] parser A parser on which "parse()" hasn't been called yet.
	*/
	GemmConfigOptions(CommandLineParser &parser)
	: m0(parser.add_positional_option<SimpleOption<size_t>>("m0", 4)),
	n0(parser.add_positional_option<SimpleOption<size_t>>("n0", 4)),
	k0(parser.add_positional_option<SimpleOption<size_t>>("k0", 4)),
	h0(parser.add_positional_option<SimpleOption<size_t>>("h0", 1)),
	interleave_rhs(parser.add_positional_option<SimpleOption<size_t>>("interleave_rhs", 1)),
	transpose_rhs(parser.add_positional_option<SimpleOption<size_t>>("transpose_rhs", 1))
	{
	m0->set_help("Number of rows processed by the matrix multiplication");
	n0->set_help("Number of columns processed by the matrix multiplication");
	k0->set_help("Number of partial accumulations performed by the matrix multiplication");
	h0->set_help("Number of horizontal blocks of size (k0xn0) stored on the same output row");
	interleave_rhs->set_help("Interleave rhs matrix (1) / Do not interleave rhs matrix (0)");
	transpose_rhs->set_help("Transpose rhs matrix (1) / Do not transpose rhs matrix (0)");
	}
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	GemmConfigOptions(const GemmConfigOptions &) = delete;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	GemmConfigOptions &operator=(const GemmConfigOptions &) = delete;
	/** Allow instances of this class to be moved */
	GemmConfigOptions(GemmConfigOptions &&) = default;
	/** Allow instances of this class to be moved */
	GemmConfigOptions &operator=(GemmConfigOptions &&) = default;
	/** Default destructor */
	~GemmConfigOptions() = default;

	SimpleOption<size_t> m0; /< Number of rows processed by the matrix multiplication option /
	SimpleOption<size_t> n0; /< Number of columns processed by the matrix multiplication option /
	SimpleOption<size_t> k0; /< Number of partial accumulations performed by the matrix multiplication option /
	SimpleOption<size_t> h0; /< Number of horizontal blocks of size (k0xn0) stored on the same output row option /
	SimpleOption<size_t> interleave_rhs; /< Interleave rhs matrix option (1 enable; 0 disable) /
	SimpleOption<size_t> transpose_rhs; /< Transpose rhs matrix option (1 enable; 0 disable) /
	};

	/** Consumes the gemm configuration options and creates a structure containing all information
	*
	* @param[in] options Options to consume
	*
	* @return Structure containing the gemm configurations
	*/
	GemmConfigs consume_gemm_configs(const GemmConfigOptions &options)
	{
	GemmConfigs configs;
	configs.m0 = options.m0->value();
	configs.n0 = options.n0->value();
	configs.k0 = options.k0->value();
	configs.h0 = options.h0->value();
	configs.interleave_rhs = options.interleave_rhs->value() != 0;
	configs.transpose_rhs = options.transpose_rhs->value() != 0;
	return configs;
	}

	} // namespace

	using ClGemmLowpMatrixMultiplyReshapedOnlyRhs =
	test::CLSynthetizeOperator<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>;
	using ClGemmLowpMatrixAReduction = test::CLSynthetizeOperator<ClGemmLowpMatrixAReductionKernel>;

	class CLGEMMLowpMatrixMultiplyReshapedOnlyRHSFusedOutputStageFixedpointExample : public Example
	{
	public:
	bool do_setup(int argc, char **argv) override
	{
	// Default parameters
	CommonGemmExampleParams params;
	GemmConfigs configs;

	// Parse command line options
	CommandLineParser parser;
	CommonGemmExampleOptions param_options(parser, DataType::QASYMM8);
	GemmConfigOptions config_options(parser);

	parser.parse(argc, argv);
	if (param_options.help->is_set() && param_options.help->value())
	{
	parser.print_help(argv[0]);
	return false;
	}
	if (!parser.validate())
	{
	// Invalid arguments. Use default parameters and configs
	std::cerr << "Invalid arguments." << std::endl;
	parser.print_help(argv[0]);
	std::cerr << "Falling back to default parameters and configs" << std::endl;
	}
	else
	{
	params = consume_common_gemm_example_parameters(param_options);
	configs = consume_gemm_configs(config_options);
	}

	std::cout << "Gemm parameters:" << std::endl;
	std::cout << params << std::endl;
	std::cout << "Gemm configurations:" << std::endl;
	std::cout << configs << std::endl;

	tuner.set_tuner_mode(params.tuner_mode);

	CLScheduler::get().default_init(&tuner);

	lhs.allocator()->init(TensorInfo(TensorShape(params.K, params.M, params.B), 1, params.data_type));
	rhs.allocator()->init(TensorInfo(TensorShape(params.N, params.K, params.B), 1, params.data_type));
	bias.allocator()->init(TensorInfo(TensorShape(params.N), 1, DataType::S32));
	dst.allocator()->init(TensorInfo(TensorShape(params.N, params.M, params.B), 1, params.data_type));

	// Set arbitrary quantization information (non-zero offset to ensure offset contribution stage is included)
	// Could be extended in the future to include a user-controlled option for offset == 0
	const QuantizationInfo q_info{0.012, 3};
	lhs.info()->set_quantization_info(q_info);
	rhs.info()->set_quantization_info(q_info);
	bias.info()->set_quantization_info(q_info);
	dst.info()->set_quantization_info(q_info);

	GEMMLHSMatrixInfo lhs_info;
	lhs_info.m0 = configs.m0;
	lhs_info.k0 = configs.k0;

	GEMMRHSMatrixInfo rhs_info;
	rhs_info.n0 = configs.n0;
	rhs_info.k0 = configs.k0;
	rhs_info.h0 = configs.h0;
	rhs_info.interleave = configs.interleave_rhs;
	rhs_info.transpose = configs.transpose_rhs;
	rhs_info.export_to_cl_image = false; // CL image not supported for quantized cases yet

	if (rhs_info.h0 == 0)
	{
	rhs_info.h0 = std::max(static_cast<unsigned int>(params.N) / rhs_info.n0, 1U);
	}

	rhs_reshaped.allocator()->init(
	TensorInfo(compute_rhs_reshaped_shape(*rhs.info(), rhs_info), 1, params.data_type));
	rhs_reshaped.info()->set_quantization_info(q_info);
	if (rhs_info.export_to_cl_image)
	{
	if (!examples::gemm_tuner_helpers::update_padding_for_cl_image(rhs_reshaped.info()))
	{
	std::cerr << "cl_image is not supported on the device, disable export_to_cl_image" << std::endl;
	return false;
	}
	}

	// Configure output stage for quantized case
	GEMMLowpOutputStageInfo gemmlowp_output_stage;
	gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
	gemmlowp_output_stage.output_data_type = dst.info()->data_type();
	gemmlowp_output_stage.gemmlowp_offset = 0;
	{
	gemmlowp_output_stage.is_quantized_per_channel = false;
	// Num_filters is 1 unless quantized type is of per_channel type. Could be extended in the future to support per-channel quantization.
	const unsigned int num_filters = 1;

	dst_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
	dst_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));

	gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
	gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
	quantization::compute_quantized_multipliers_and_shifts(lhs.info(), rhs.info(), dst.info(),
	gemmlowp_output_stage.gemmlowp_multipliers.data(),
	gemmlowp_output_stage.gemmlowp_shifts.data());
	gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
	gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];

	// No fused activation
	PixelValue min_val{};
	PixelValue max_val{};
	std::tie(min_val, max_val) = get_min_max(dst.info()->data_type());

	auto min_activation = min_val.get<int32_t>();
	auto max_activation = max_val.get<int32_t>();

	// Set the GEMMLowp output stage info
	gemmlowp_output_stage.gemmlowp_offset = dst.info()->quantization_info().uniform().offset;
	gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
	gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
	}

	GEMMKernelInfo gemm_info;
	gemm_info.m = params.M;
	gemm_info.n = params.N;
	gemm_info.k = params.K;
	gemm_info.depth_output_gemm3d = 0;
	gemm_info.reinterpret_input_as_3d = false;
	gemm_info.broadcast_bias = true;
	gemm_info.fp_mixed_precision = false;
	gemm_info.has_pad_y = false;
	gemm_info.mult_transpose1xW_width = configs.h0;
	gemm_info.lhs_info = lhs_info;
	gemm_info.rhs_info = rhs_info;
	gemm_info.a_offset = lhs.info()->quantization_info().uniform().offset;
	gemm_info.b_offset = rhs.info()->quantization_info().uniform().offset;
	gemm_info.output_stage = gemmlowp_output_stage;

	// Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
	if (gemm_info.b_offset != 0)
	{
	const TensorInfo info_vector_sum_row(compute_reductionB_shape(*lhs.info()), 1, DataType::S32);
	vector_sum_row.allocator()->init(info_vector_sum_row);

	mtx_a_reduction = std::make_unique<ClGemmLowpMatrixAReduction>();

	if (!mtx_a_reduction->validate(lhs.info(), vector_sum_row.info(), GEMMLowpReductionKernelInfo{}))
	{
	std::cerr << "Invalid arguments for CLGEMMLowpMatrixAReductionKernel." << std::endl;
	return false;
	}

	mtx_a_reduction->configure(lhs.info(), vector_sum_row.info(), GEMMLowpReductionKernelInfo{});
	}
	// Initialize matrix B reduction kernel only if _a_offset is not equal to 0
	if (gemm_info.a_offset != 0)
	{
	const TensorInfo info_vector_sum_col(compute_reductionA_shape(*rhs.info()), 1, DataType::S32);
	vector_sum_col.allocator()->init(info_vector_sum_col);
	// There's no need for a Matrix B reduction kernel as this is assumed to be run only once in the prepare stage
	}

	// Validate argments
	if (!gemm.validate(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info,
	gemm_info.a_offset == 0 ? nullptr : vector_sum_col.info(),
	gemm_info.b_offset == 0 ? nullptr : vector_sum_row.info(), bias.info(),
	dst_multipliers.info(), dst_shifts.info()))
	{
	std::cerr << "Invalid arguments for ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel." << std::endl;
	return false;
	}

	// Configure function
	gemm.configure(lhs.info(), rhs_reshaped.info(), dst.info(), gemm_info,
	gemm_info.a_offset == 0 ? nullptr : vector_sum_col.info(),
	gemm_info.b_offset == 0 ? nullptr : vector_sum_row.info(), bias.info(), dst_multipliers.info(),
	dst_shifts.info());

	// Allocate tensors
	lhs.allocator()->allocate();
	rhs.allocator()->allocate();
	rhs_reshaped.allocator()->allocate();
	bias.allocator()->allocate();
	dst.allocator()->allocate();
	vector_sum_col.allocator()->allocate();
	vector_sum_row.allocator()->allocate();
	dst_multipliers.allocator()->allocate();
	dst_shifts.allocator()->allocate();

	return true;
	}
	void do_run() override
	{
	if (mtx_a_reduction != nullptr)
	{
	ITensorPack red_pack({{ACL_SRC, &lhs}, {ACL_DST, &dst}});
	mtx_a_reduction->run(red_pack);
	}

	ITensorPack gemm_pack({{ACL_SRC_0, &lhs},
	{ACL_SRC_1, &rhs},
	{ACL_BIAS, &bias},
	{ACL_VEC_COL_SUM, &vector_sum_col},
	{ACL_VEC_ROW_SUM, &vector_sum_row},
	{ACL_SHIFTS, &dst_shifts},
	{ACL_MULTIPLIERS, &dst_multipliers},
	{ACL_DST, &dst}});
	gemm.run(gemm_pack);

	// Make sure all the OpenCL jobs are done executing:
	CLScheduler::get().sync();
	}

	void do_teardown() override
	{
	}

	private:
	CLTensor lhs{};
	CLTensor rhs{};
	CLTensor rhs_reshaped{};
	CLTensor bias{};
	CLTensor dst{};
	CLTensor vector_sum_col{};
	CLTensor vector_sum_row{};
	CLTensor dst_multipliers{};
	CLTensor dst_shifts{};
	CLTuner tuner{};
	ClGemmLowpMatrixMultiplyReshapedOnlyRhs gemm{};
	std::unique_ptr<ClGemmLowpMatrixAReduction> mtx_a_reduction{nullptr};
	};

	/** Main test program for gemmlowp reshaped rhs only with fused output stage fixedpoint
	*
	* @param[in] argc Number of arguments
	* @param[in] argv Arguments ( [optional] M, [optional] N, [optional] K, [optional] B, [optional] m0, [optional] n0, [optional] k0, [optional] h0, [optional] interleave_rhs, [optional] transpose_rhs )
	*/
	int main(int argc, char **argv)
	{
	return run_example<CLGEMMLowpMatrixMultiplyReshapedOnlyRHSFusedOutputStageFixedpointExample>(argc, argv);
	}