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414 lines
17 KiB
414 lines
17 KiB
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
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#error "This example needs to be built with -DARM_COMPUTE_CL"
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#endif /* ARM_COMPUTE_CL */
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/Utils.h"
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#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
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#include "arm_compute/runtime/CL/CLScheduler.h"
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#include "arm_compute/runtime/CL/functions/CLGEMM.h"
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#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
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#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
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#include "src/core/CL/kernels/CLDepthConvertLayerKernel.h"
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#include "src/core/CL/kernels/CLFillBorderKernel.h"
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#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
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#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
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#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
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#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
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#include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
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#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
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#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
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#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
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#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
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#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
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#include "src/core/CL/kernels/CLIm2ColKernel.h"
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#include "src/core/CL/kernels/CLWeightsReshapeKernel.h"
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#include "tests/AssetsLibrary.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/Globals.h"
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#include "tests/IAccessor.h"
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#include "tests/SimpleTensor.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/reference/GEMM.h"
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#include "tests/validation/reference/GEMMLowp.h"
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#include "utils/TypePrinter.h"
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#include "utils/Utils.h"
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#include "utils/command_line/CommandLineOptions.h"
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#include "utils/command_line/CommandLineParser.h"
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#include "ValidateExample.h"
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#include <cstdlib>
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using namespace arm_compute;
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using namespace utils;
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using namespace arm_compute::test;
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using namespace arm_compute::test::validation;
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constexpr float abs_tolerance_f32(0.0001f); /**< F32 Absolute tolerance value for comparing reference's output against implementation's output for
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* floating point data types in case using relative tolerance fails because of small values */
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RelativeTolerance<float> tolerance_f32(0.001f); /**< F32 Tolerance value for comparing reference's output against implementation's output for floating point data types */
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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 */
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constexpr float tolerance_num_f16 = 0.02f; /**< F16 Tolerance number */
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namespace
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{
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class GEMMCommandLineOptions final
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{
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public:
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explicit GEMMCommandLineOptions(CommandLineParser &parser) noexcept
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: help(parser.add_option<ToggleOption>("help")),
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add_bias(parser.add_option<ToggleOption>("add_bias")),
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M(parser.add_option<SimpleOption<int>>("m", 7)),
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N(parser.add_option<SimpleOption<int>>("n", 3)),
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K(parser.add_option<SimpleOption<int>>("k", 5)),
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B(parser.add_option<SimpleOption<int>>("b", 1)),
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alpha(parser.add_option<SimpleOption<float>>("alpha", 1.f)),
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beta(parser.add_option<SimpleOption<float>>("beta", 0.f)),
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offset_src0(parser.add_option<SimpleOption<int>>("offset_i0", 10)),
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offset_src1(parser.add_option<SimpleOption<int>>("offset_i1", 10)),
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offset_dst(parser.add_option<SimpleOption<int>>("offset_o", 10)),
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scale_src0(parser.add_option<SimpleOption<float>>("scale_i0", 1.f / 255)),
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scale_src1(parser.add_option<SimpleOption<float>>("scale_i1", 1.f / 255)),
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scale_dst(parser.add_option<SimpleOption<float>>("scale_o", 1.f / 255)),
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data_type()
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{
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// Setup data type
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const std::set<arm_compute::DataType> supported_data_types
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{
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DataType::F16,
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DataType::F32,
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DataType::QASYMM8,
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};
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data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
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// Setup help strings
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help->set_help("Show this help message");
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add_bias->set_help("Add bias to the GEMM. Used when running in QASYMM8");
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M->set_help("M value");
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N->set_help("N value");
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K->set_help("K value");
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B->set_help("B value - number of batches");
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alpha->set_help("Alpha value");
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beta->set_help("Beta value");
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offset_src0->set_help("Offset of first input. Used when running in QASYMM8");
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offset_src1->set_help("Offset of second input. Used when running in QASYMM8");
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offset_dst->set_help("Offset of output. Used when running in QASYMM8");
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scale_src0->set_help("Scale of first input. Used when running in QASYMM8");
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scale_src1->set_help("Scale of second input. Used when running in QASYMM8");
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scale_dst->set_help("Scale of output. Used when running in QASYMM8");
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data_type->set_help("Data type to use");
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}
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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GEMMCommandLineOptions(const GEMMCommandLineOptions &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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GEMMCommandLineOptions &operator=(const GEMMCommandLineOptions &) = delete;
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/** Allow instances of this class to be moved */
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GEMMCommandLineOptions(GEMMCommandLineOptions &&) noexcept(true) = default;
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/** Allow instances of this class to be moved */
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GEMMCommandLineOptions &operator=(GEMMCommandLineOptions &&) noexcept(true) = default;
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/** Default destructor */
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~GEMMCommandLineOptions() = default;
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public:
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ToggleOption *help;
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ToggleOption *add_bias;
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SimpleOption<int> *M;
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SimpleOption<int> *N;
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SimpleOption<int> *K;
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SimpleOption<int> *B;
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SimpleOption<float> *alpha;
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SimpleOption<float> *beta;
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SimpleOption<int> *offset_src0;
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SimpleOption<int> *offset_src1;
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SimpleOption<int> *offset_dst;
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SimpleOption<float> *scale_src0;
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SimpleOption<float> *scale_src1;
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SimpleOption<float> *scale_dst;
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EnumOption<arm_compute::DataType> *data_type;
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};
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} // namespace
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class CLGEMMValidateExample : public ValidateExample
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{
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public:
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bool do_setup(int argc, char **argv) override
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{
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CLScheduler::get().default_init();
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// Parse options
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CommandLineParser parser;
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GEMMCommandLineOptions gemm_options(parser);
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parser.parse(argc, argv);
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// Print help
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const bool print_help = gemm_options.help->is_set() ? gemm_options.help->value() : false;
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if(print_help)
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{
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parser.print_help(argv[0]);
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return false;
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}
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// Consume parameters
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consume_params(gemm_options);
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print_parameters_internal();
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const bool is_quantized = is_data_type_quantized(data_type);
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// Calculate re-quantization parameters
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if(is_quantized)
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{
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float multiplier = scale_src0 * scale_src1 / scale_dst;
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quantization::calculate_quantized_multiplier(multiplier, &dst_multiplier, &dst_shift);
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}
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// Initialize GEMM inputs/outputs
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src0.allocator()->init(TensorInfo(TensorShape(K, M, B), 1, data_type));
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src1.allocator()->init(TensorInfo(TensorShape(N, K, B), 1, data_type));
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src2.allocator()->init(TensorInfo(TensorShape(N, M, B), 1, data_type));
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init_sgemm_output(dst, src0, src1, data_type);
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// Configure function
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if(is_quantized)
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{
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src0.info()->set_quantization_info(QuantizationInfo(scale_src0, offset_src0));
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src1.info()->set_quantization_info(QuantizationInfo(scale_src1, offset_src1));
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dst.info()->set_quantization_info(QuantizationInfo(scale_dst, offset_dst));
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biases.allocator()->init(TensorInfo(TensorShape(N), 1, DataType::S32));
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init_sgemm_output(tmp_dst, src0, src1, DataType::S32);
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// Configure GEMMlowp matrix multiply function
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mm_gemmlowp.configure(&src0, &src1, nullptr, &tmp_dst);
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// Configure GEMMlowp output stage
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mm_gemmlowp_output_stage.configure(&tmp_dst, add_bias ? &biases : nullptr, &dst, dst_multiplier, dst_shift, offset_dst);
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tmp_dst.allocator()->allocate();
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biases.allocator()->allocate();
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fill(CLAccessor(biases), 3);
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}
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else
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{
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// Configure matrix multiply function
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mm_gemm.configure(&src0, &src1, &src2, &dst, alpha, beta);
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}
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// Allocate all the tensors
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src0.allocator()->allocate();
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src1.allocator()->allocate();
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dst.allocator()->allocate();
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src2.allocator()->allocate();
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fill(CLAccessor(src0), 0);
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fill(CLAccessor(src1), 1);
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fill(CLAccessor(src2), 2);
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return true;
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}
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void print_parameters_internal()
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{
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std::cout << "Datatype : " << string_from_data_type(data_type) << "\n";
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std::cout << "M : " << support::cpp11::to_string(M) << "\n";
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std::cout << "N : " << support::cpp11::to_string(N) << "\n";
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std::cout << "K : " << support::cpp11::to_string(K) << "\n";
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std::cout << "B : " << support::cpp11::to_string(B) << "\n";
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if(data_type == DataType::QASYMM8)
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{
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std::cout << "Scale_Src0 : " << support::cpp11::to_string(scale_src0) << "\n";
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std::cout << "Offset_Src0 : " << support::cpp11::to_string(offset_src0) << "\n";
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std::cout << "Scale_Scr1 : " << support::cpp11::to_string(scale_src1) << "\n";
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std::cout << "Offset_Src1 : " << support::cpp11::to_string(offset_src1) << "\n";
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std::cout << "Scale_Dst : " << support::cpp11::to_string(scale_dst) << "\n";
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std::cout << "Offset_Dst : " << support::cpp11::to_string(offset_dst) << "\n";
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std::cout << "Bias : " << support::cpp11::to_string(add_bias) << "\n";
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}
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else
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{
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std::cout << "Alpha : " << support::cpp11::to_string(alpha) << "\n";
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std::cout << "Beta : " << support::cpp11::to_string(beta) << "\n";
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}
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}
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void do_validate() override
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{
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switch(data_type)
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{
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case DataType::F16:
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{
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SimpleTensor<half> ref_src0 = { TensorShape(K, M, B), data_type, 1 };
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SimpleTensor<half> ref_src1 = { TensorShape(N, K, B), data_type, 1 };
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SimpleTensor<half> ref_src2 = { TensorShape(N, M, B), data_type, 1 };
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fill(ref_src0, 0);
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fill(ref_src1, 1);
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fill(ref_src2, 2);
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SimpleTensor<half> ref_dst = reference::gemm<half>(ref_src0, ref_src1, ref_src2, alpha, beta);
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validate(CLAccessor(dst), ref_dst, tolerance_f16, tolerance_num_f16);
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break;
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}
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case DataType::F32:
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{
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SimpleTensor<float> ref_src0 = { TensorShape(K, M, B), data_type, 1 };
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SimpleTensor<float> ref_src1 = { TensorShape(N, K, B), data_type, 1 };
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SimpleTensor<float> ref_src2 = { TensorShape(N, M, B), data_type, 1 };
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fill(ref_src0, 0);
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fill(ref_src1, 1);
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fill(ref_src2, 2);
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SimpleTensor<float> ref_dst = reference::gemm<float>(ref_src0, ref_src1, ref_src2, alpha, beta);
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validate(CLAccessor(dst), ref_dst, tolerance_f32, 0.f, abs_tolerance_f32);
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break;
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}
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case DataType::QASYMM8:
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{
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SimpleTensor<uint8_t> ref_src0{ TensorShape(K, M, B), data_type, 1 };
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SimpleTensor<uint8_t> ref_src1{ TensorShape(N, K, B), data_type, 1 };
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SimpleTensor<uint8_t> ref_dst;
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// Fill reference
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fill(ref_src0, 0);
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fill(ref_src1, 1);
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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);
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const std::vector<int32_t> dst_multiplier_vec = { dst_multiplier };
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const std::vector<int32_t> dst_shift_vec = { dst_shift };
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if(add_bias)
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{
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SimpleTensor<int32_t> biases{ TensorShape(N), DataType::S32, 1 };
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// Fill bias
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fill(biases, 3);
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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);
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}
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else
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{
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ref_dst = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(ref_tmp_dst, dst_multiplier_vec, dst_shift_vec, offset_dst);
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}
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validate(CLAccessor(dst), ref_dst);
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break;
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}
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default:
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break;
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}
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}
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void do_run() override
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{
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// Execute the function
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if(data_type == DataType::QASYMM8)
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{
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// Run gemmlowp
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mm_gemmlowp.run();
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// Run output stage
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mm_gemmlowp_output_stage.run();
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}
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else
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{
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// Run gemm
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mm_gemm.run();
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}
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// Make sure all the OpenCL jobs are done executing:
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CLScheduler::get().sync();
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}
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private:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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switch(tensor.data_type())
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{
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case DataType::F16:
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::S32:
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case DataType::QASYMM8:
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{
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std::uniform_int_distribution<> distribution(-6000, 6000);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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library->fill_tensor_uniform(tensor, i);
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}
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}
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void consume_params(const GEMMCommandLineOptions &opts)
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{
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ARM_COMPUTE_ERROR_ON(opts.M->value() <= 0);
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ARM_COMPUTE_ERROR_ON(opts.N->value() <= 0);
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ARM_COMPUTE_ERROR_ON(opts.K->value() <= 0);
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ARM_COMPUTE_ERROR_ON(opts.B->value() <= 0);
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M = opts.M->value();
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N = opts.N->value();
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K = opts.K->value();
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B = opts.B->value();
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alpha = opts.alpha->value();
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beta = opts.beta->value();
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offset_src0 = opts.offset_src0->value();
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offset_src1 = opts.offset_src1->value();
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offset_dst = opts.offset_dst->value();
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scale_src0 = opts.scale_src0->value();
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scale_src1 = opts.scale_src1->value();
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scale_dst = opts.scale_dst->value();
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add_bias = opts.add_bias->is_set() ? opts.add_bias->value() : true;
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data_type = opts.data_type->value();
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}
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CLTensor src0{}, src1{}, src2{}, dst{};
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CLTensor tmp_dst{}, biases{};
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CLGEMM mm_gemm{};
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CLGEMMLowpMatrixMultiplyCore mm_gemmlowp{};
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CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint mm_gemmlowp_output_stage{};
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size_t M{ 7 }, N{ 3 }, K{ 5 }, B{ 1 };
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DataType data_type{ DataType::F32 };
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float alpha{ 1.0 }, beta{ 0.0 };
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int offset_src0{ 10 }, offset_src1{ 10 }, offset_dst{ 10 };
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float scale_src0{ 1.0f / 255 }, scale_src1{ 1.0f / 255 }, scale_dst{ 1.0f / 255 };
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int32_t dst_multiplier{ 0 }, dst_shift{ 0 };
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bool add_bias{ true };
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};
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/** Main program for gemm test
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*
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* @param[in] argc Number of arguments
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* @param[in] argv Arguments
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*
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*/
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int main(int argc, char **argv)
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{
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return utils::run_example<CLGEMMValidateExample>(argc, argv);
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}
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