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Diffstat (limited to 'src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp | 950 |
1 files changed, 950 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp b/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp new file mode 100644 index 0000000000..71c247de79 --- /dev/null +++ b/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp @@ -0,0 +1,950 @@ +/* + * Copyright (c) 2017-2022 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" + +#include "arm_compute/core/Log.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/kernels/ClCastKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyNativeKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpOffsetContributionOutputStageKernel.h" +#include "src/gpu/cl/kernels/ClGemmLowpReductionKernel.h" +#include "src/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h" +#include "src/gpu/cl/utils/ClAuxTensorHandler.h" +#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" + +namespace arm_compute +{ +namespace opencl +{ +using namespace arm_compute::misc::shape_calculator; +using namespace arm_compute::cl_gemm; +using namespace arm_compute::opencl::kernels; +using namespace arm_compute::experimental; + +namespace +{ +inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type) +{ + switch (kernel_type) + { + case CLGEMMKernelType::NATIVE: + case CLGEMMKernelType::RESHAPED_ONLY_RHS: + case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: + { + return true; + } + default: + { + return false; + } + } +} + +//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type +inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run) +{ + auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run); + if (bool(gemm_kernel)) + { + if (validate_gemm_kernel(gemm_kernel.gemm_type)) + { + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", + to_string(gemm_kernel.gemm_type).c_str()); + return gemm_kernel.gemm_type; + } + } + gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", + to_string(gemm_kernel.gemm_type).c_str()); + return gemm_kernel.gemm_type; +} + +// Validate lhs_info and rhs_info for native kernel +inline bool validate_lhs_rhs_info_native(const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const ITensorInfo *a, + const ITensorInfo *b, + const GEMMReshapeInfo &reshape_info) +{ + // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel + TensorInfo mm_result_s32_info{}; + // Output tensor auto initialization if not yet initialized + auto_init_if_empty( + mm_result_s32_info, + a->clone()->set_tensor_shape(compute_mm_shape(*a, *b, false, reshape_info)).set_data_type(DataType::S32)); + // Validate mm kernel + // NOTE: Ignore all other parameters (eg. output stage etc.) and only validate lhs and rhs info + // NOTE: This assumes: + // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_arguments). + // 2. lhs and rhs info does not cause window and padding issues through side effects (in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_and_configure_window). + if (!bool(ClGemmLowpMatrixMultiplyNativeKernel::validate(a, b, &mm_result_s32_info, lhs_info, rhs_info, + reshape_info))) + { + return false; + } + return true; +} + +// Automatically select between mlgo (prioritized) and default heuristics for native kernel configs +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_native(auto_heuristics::CommonQuery query, + const ITensorInfo *a, + const ITensorInfo *b, + const GEMMReshapeInfo &reshape_info) +{ + auto config = auto_heuristics::select_mlgo_gemm_config_native(query); + if (config) + { + if (validate_lhs_rhs_info_native(config.lhs_info, config.rhs_info, a, b, reshape_info)) + { + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use native config from mlgo heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; + } + } + config = auto_heuristics::select_default_gemm_config_native(query); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from default heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; +} + +// Validate lhs_info and rhs_info for reshaped only rhs kernel +inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *output, + unsigned int m, + unsigned int n, + unsigned int k, + bool reinterpret_input_as_3d, + int depth_output_gemm3d) +{ + // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel + TensorInfo tmp_b_info{}; + // Validate reshape RHS kernel + auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); + if (!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) + { + return false; + } + // Validate mm kernel + // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info + // NOTE: This assumes: + // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments). + // 2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window). + GEMMKernelInfo gemm_kernel_info; + gemm_kernel_info.m = m; + gemm_kernel_info.n = n; + gemm_kernel_info.k = k; + gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; + gemm_kernel_info.lhs_info = lhs_info; + gemm_kernel_info.rhs_info = rhs_info; + // Since we ignore the output stage, output data type has to be S32 to pass the validation + TensorInfo output_info_copy(*output); + output_info_copy.set_data_type(DataType::S32); + if (!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, &output_info_copy, + gemm_kernel_info))) + { + return false; + } + return true; +} + +// Validate lhs_info and rhs_info for reshaped only rhs kernel +inline bool validate_lhs_rhs_info_reshaped_only_rhs_mmul(const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *output, + unsigned int m, + unsigned int n, + unsigned int k, + bool reinterpret_input_as_3d, + int depth_output_gemm3d) +{ + // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel + TensorInfo tmp_b_info{}; + // Validate reshape RHS kernel + auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); + if (!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) + { + return false; + } + // Validate mm kernel + // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info + // NOTE: This assumes: + // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments). + // 2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window). + GEMMKernelInfo gemm_kernel_info; + gemm_kernel_info.m = m; + gemm_kernel_info.n = n; + gemm_kernel_info.k = k; + gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; + gemm_kernel_info.lhs_info = lhs_info; + gemm_kernel_info.rhs_info = rhs_info; + // Since we ignore the output stage, output data type has to be S32 to pass the validation + TensorInfo output_info_copy(*output); + output_info_copy.set_data_type(DataType::S32); + if (!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(a, &tmp_b_info, &output_info_copy, + gemm_kernel_info))) + { + return false; + } + return true; +} + +// Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> +auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, + bool reinterpret_input_as_3d, + int depth_output_gemm3d, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *output) +{ + auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query); + if (config) + { + if (validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, + query.k, reinterpret_input_as_3d, depth_output_gemm3d)) + { + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; + } + } + config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; +} + +// Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs +std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> +auto_select_gemm_config_reshaped_only_rhs_mmul(auto_heuristics::CommonQuery query, + bool reinterpret_input_as_3d, + int depth_output_gemm3d, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *output) +{ + ARM_COMPUTE_UNUSED(a, b, output, reinterpret_input_as_3d, depth_output_gemm3d); + auto config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); + validate_lhs_rhs_info_reshaped_only_rhs_mmul(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, + query.k, reinterpret_input_as_3d, depth_output_gemm3d); + ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE( + "Use reshaped_only_rhs_mmul config from default heuristics: LHS info: %s ; RHS info: %s ", + to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); + return {config.lhs_info, config.rhs_info}; +} + +inline bool is_gemm_reshaped(CLGEMMKernelType kernel_type) +{ + switch (kernel_type) + { + case CLGEMMKernelType::NATIVE: + return false; + case CLGEMMKernelType::RESHAPED_ONLY_RHS: + case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: + return true; + default: + ARM_COMPUTE_ERROR("Not supported gemmlowp kernel!"); + } +} +} // namespace + +ClGemmLowpMatrixMultiplyCore::ClGemmLowpMatrixMultiplyCore() + : _weights_to_qasymm8(std::make_unique<ClCastKernel>()), + _mm_native_kernel(std::make_unique<ClGemmLowpMatrixMultiplyNativeKernel>()), + _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>()), + _mm_reshaped_only_rhs_mmul_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel>()), + _mtx_b_reshape_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()), + _mtx_a_reduction_kernel(std::make_unique<ClGemmLowpMatrixAReductionKernel>()), + _mtx_b_reduction_kernel(std::make_unique<ClGemmLowpMatrixBReductionKernel>()), + _offset_contribution_kernel(std::make_unique<ClGemmLowpOffsetContributionKernel>()), + _offset_contribution_output_stage_kernel(std::make_unique<ClGemmLowpOffsetContributionOutputStageKernel>()), + _aux_mem(AuxTensorIdx::Count) +{ +} + +ClGemmLowpMatrixMultiplyCore::~ClGemmLowpMatrixMultiplyCore() = default; + +void ClGemmLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_context, + ITensorInfo *a, + ITensorInfo *b, + ITensorInfo *c, + ITensorInfo *output, + const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); + ARM_COMPUTE_ERROR_THROW_ON(ClGemmLowpMatrixMultiplyCore::validate(a, b, c, output, gemm_info)); + ARM_COMPUTE_LOG_PARAMS(a, b, c, output, gemm_info); + + _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); + _a_offset = a->quantization_info().uniform().offset; + _convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && + is_data_type_quantized_symmetric(b->data_type()) && a->data_type() == DataType::QASYMM8; + _b_offset = _convert_to_qasymm8 ? -128 : b->quantization_info().uniform().offset; + _gemm_info = gemm_info; + + // Get the GPU target + const GPUTarget gpu_target = CLScheduler::get().target(); + + // Set the target for the kernels + _mm_native_kernel->set_target(gpu_target); + _mm_reshaped_only_rhs_kernel->set_target(gpu_target); + _mm_reshaped_only_rhs_mmul_kernel->set_target(gpu_target); + + GEMMRHSMatrixInfo rhs_info; + GEMMLHSMatrixInfo lhs_info; + + // Arguments used by GEMMReshapeInfo + // in order to know how the matrices have been reshaped + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + + const auto reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d); + + _gemm_kernel_type = auto_select_gemm_kernel( + auto_heuristics::CommonQuery{gpu_target, a->data_type(), m, n, k, batch_size}, _reshape_b_only_on_first_run); + + if (_convert_to_qasymm8) + { + // Set data type for converted weights + _qasymm8_weights = *b; + _qasymm8_weights.set_data_type(DataType::QASYMM8); + _weights_to_qasymm8->configure(compile_context, b, &_qasymm8_weights, ConvertPolicy::WRAP); + } + + ITensorInfo *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : b; + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) + { + matrix_b = &_tmp_b; + + // Pick up the GEMM configuration + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}, reinterpret_input_as_3d, + depth_output_gemm3d, a, _convert_to_qasymm8 ? &_qasymm8_weights : b, output); + + // Configure reshape RHS kernel + _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, + rhs_info); + } + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) + { + matrix_b = &_tmp_b; + + // Pick up the GEMM configuration + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs_mmul( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}, reinterpret_input_as_3d, + depth_output_gemm3d, a, _convert_to_qasymm8 ? &_qasymm8_weights : b, output); + + // Configure reshape RHS kernel + _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, + rhs_info); + } + + // Using default reduction info + const GEMMLowpReductionKernelInfo reduction_info{}; + + // Initialize matrix B reduction kernel only if _a_offset is not equal to 0 + if (_a_offset != 0) + { + _vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32); + + // Configure Matrix B reduction kernel + _mtx_b_reduction_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, + &_vector_sum_col, reduction_info); + } + + // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 + if (_b_offset != 0) + { + _vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); + + // Configure matrix A reduction kernel + _mtx_a_reduction_kernel->configure(compile_context, a, &_vector_sum_row, reduction_info); + } + + GEMMKernelInfo gemm_kernel_info; + gemm_kernel_info.m = m; + gemm_kernel_info.n = n; + gemm_kernel_info.k = k; + gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; + gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + gemm_kernel_info.lhs_info = lhs_info; + gemm_kernel_info.rhs_info = rhs_info; + gemm_kernel_info.a_offset = _a_offset; + gemm_kernel_info.b_offset = _b_offset; + // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage + if (gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) + { + // Configure offset contribution kernel + const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) + ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() + : 1; + + _gemm_output_stage_multipliers = TensorInfo(TensorShape(num_filters), 1, DataType::S32); + _gemm_output_stage_shifts = TensorInfo(TensorShape(num_filters), 1, DataType::S32); + + GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage(); + gemmlowp_output_stage.output_data_type = a->data_type(); + if (num_filters == 1) + { + // Per-channel quantization with OFM == 1 is equivalent to uniform quantization. + // Setting this flag to false prevents the kernel from adding useless padding to the output multipliers and shifts + gemmlowp_output_stage.is_quantized_per_channel = false; + } + + gemm_kernel_info.output_stage = gemmlowp_output_stage; + + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS && + gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + // Configure and tune matrix multiply kernel with fused output stage + _mm_reshaped_only_rhs_kernel->configure( + compile_context, a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col, + _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, + &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); + } + else if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL && + gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + // Configure and tune matrix multiply kernel with fused output stage + _mm_reshaped_only_rhs_mmul_kernel->configure( + compile_context, a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col, + _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, + &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); + } + else + { + _run_output_stage = true; + + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) + { + _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, + gemm_kernel_info); + } + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) + { + _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, + gemm_kernel_info); + } + else + { + // Pick up the GEMM configuration + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}, a, + _convert_to_qasymm8 ? &_qasymm8_weights : matrix_b, reshape_info); + + // Configure matrix multiply kernel + _mm_native_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, lhs_info, rhs_info, + reshape_info); + + _offset_contribution_output_stage_kernel->configure( + compile_context, &_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, + _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, output, a->dimension(0), + _a_offset, _b_offset, gemmlowp_output_stage, &_gemm_output_stage_multipliers, + &_gemm_output_stage_shifts); + } + } + } + else + { + _run_offset_contribution = true; + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) + { + // Configure and tune matrix multiply kernel + _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info); + } + else if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) + { + // Configure and tune matrix multiply kernel + _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info); + } + else + { + // Pick up the GEMM configuration + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}, a, + _convert_to_qasymm8 ? &_qasymm8_weights : b, reshape_info); + + // Configure matrix multiply kernel + _mm_native_kernel->configure(compile_context, a, matrix_b, output, lhs_info, rhs_info, reshape_info); + } + + // Configure offset contribution kernel + _offset_contribution_kernel->configure(compile_context, output, _a_offset == 0 ? nullptr : &_vector_sum_col, + _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, + a->dimension(0), _a_offset, _b_offset); + } + + // Request memory + _aux_mem[RhsQAsymm8] = + MemoryInfo(offset_int_vec(RhsQAsymm8), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, + _qasymm8_weights.total_size()); + if (is_gemm_reshaped(_gemm_kernel_type)) + { + // Overwrite Rhs as prepare if gemm is reshaped as there will be a two-step transformation + _aux_mem[RhsQAsymm8] = + MemoryInfo(offset_int_vec(RhsQAsymm8), + _reshape_b_only_on_first_run ? MemoryLifetime::Prepare : MemoryLifetime::Temporary, + _qasymm8_weights.total_size()); + _aux_mem[RhsReshape] = MemoryInfo( + offset_int_vec(RhsReshape), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); + } + if (_a_offset != 0) + { + _aux_mem[VecSumCol] = + MemoryInfo(offset_int_vec(VecSumCol), + _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, + _vector_sum_col.total_size()); + } + if (_b_offset != 0) + { + _aux_mem[VecSumRow] = + MemoryInfo(offset_int_vec(VecSumRow), MemoryLifetime::Temporary, _vector_sum_row.total_size()); + } + _aux_mem[ResultS32] = MemoryInfo(offset_int_vec(ResultS32), MemoryLifetime::Temporary, _mm_result_s32.total_size()); + _aux_mem[Multipliers] = MemoryInfo(offset_int_vec(Multipliers), MemoryLifetime::Persistent, + _gemm_output_stage_multipliers.total_size()); + _aux_mem[Shifts] = + MemoryInfo(offset_int_vec(Shifts), MemoryLifetime::Persistent, _gemm_output_stage_shifts.total_size()); +} + +Status ClGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, + DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8 && b->data_type() == DataType::QASYMM8_SIGNED); + ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8_SIGNED && b->data_type() == DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); + + int32_t a_offset = a->quantization_info().uniform().offset; + int32_t b_offset = b->quantization_info().uniform().offset; + + const ITensorInfo *matrix_a_info = a; + + TensorInfo tmp_b_info{}; + GEMMRHSMatrixInfo rhs_info; + GEMMLHSMatrixInfo lhs_info; + + // Get the GPU target + const GPUTarget gpu_target = CLScheduler::get().target(); + + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + + bool reshape_matrix_b = is_gemm_reshaped( + auto_select_gemm_kernel(auto_heuristics::CommonQuery{gpu_target, a->data_type(), m, n, k, batch_size}, + gemm_info.reshape_b_only_on_first_run())); + + const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d); + + bool convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && + is_data_type_quantized_symmetric(b->data_type()) && + is_data_type_quantized_asymmetric(a->data_type()); + TensorInfo weights_info(*b); + if (convert_to_qasymm8) + { + b_offset = -128; + weights_info.set_data_type(DataType::QASYMM8); + ARM_COMPUTE_RETURN_ON_ERROR(ClCastKernel::validate(b, &weights_info, ConvertPolicy::WRAP)); + } + const ITensorInfo *matrix_b_info = &weights_info; + if (reshape_matrix_b) + { + matrix_b_info = &tmp_b_info; + + // Pick up the GEMM configuration + // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + const auto res = select_default_gemm_config_reshaped_only_rhs( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}); + lhs_info = res.lhs_info; + rhs_info = res.rhs_info; + + // Validate reshape RHS kernel + auto_init_if_empty(tmp_b_info, + weights_info.clone()->set_tensor_shape(compute_rhs_reshaped_shape(weights_info, rhs_info))); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info)); + } + + TensorInfo info_vector_sum_col{}; + TensorInfo info_vector_sum_row{}; + + const GEMMLowpReductionKernelInfo reduction_info; + // Validate matrix B reduction kernel only if _a_offset is not equal to 0 + if (a_offset != 0) + { + info_vector_sum_col = TensorInfo(compute_reductionA_shape(weights_info), 1, DataType::S32); + + // Configure Matrix B reduction kernel + ARM_COMPUTE_RETURN_ON_ERROR( + ClGemmLowpMatrixBReductionKernel::validate(&weights_info, &info_vector_sum_col, reduction_info)); + } + + // Validate Matrix A reduction kernel only if _b_offset is not equal to 0 + if (b_offset != 0) + { + info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); + + // Configure matrix A reduction kernel + ARM_COMPUTE_RETURN_ON_ERROR( + ClGemmLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row, reduction_info)); + } + + GEMMKernelInfo gemm_kernel_info; + gemm_kernel_info.m = m; + gemm_kernel_info.n = n; + gemm_kernel_info.k = k; + gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; + gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + gemm_kernel_info.lhs_info = lhs_info; + gemm_kernel_info.rhs_info = rhs_info; + gemm_kernel_info.a_offset = a_offset; + gemm_kernel_info.b_offset = b_offset; + if (gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) + { + const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) + ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() + : 1; + + const TensorInfo gemm_output_stage_multipliers_shifts_info( + TensorInfo(TensorShape(num_filters), 1, DataType::S32)); + + GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage(); + gemmlowp_output_stage.output_data_type = a->data_type(); + + gemm_kernel_info.output_stage = gemmlowp_output_stage; + if (reshape_matrix_b && + gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate( + matrix_a_info, matrix_b_info, output, gemm_kernel_info, a_offset == 0 ? nullptr : &info_vector_sum_col, + b_offset == 0 ? nullptr : &info_vector_sum_row, c, &gemm_output_stage_multipliers_shifts_info, + &gemm_output_stage_multipliers_shifts_info)); + } + else + { + TensorInfo mm_result_s32_info{}; + + if (reshape_matrix_b) + { + // Output tensor auto inizialitation if not yet initialized + auto_init_if_empty(mm_result_s32_info, a->clone() + ->set_tensor_shape(compute_mm_shape( + *matrix_a_info, *matrix_b_info, reshape_info)) + .set_data_type(DataType::S32)); + + // Validate matrix multiply + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate( + matrix_a_info, matrix_b_info, &mm_result_s32_info, gemm_kernel_info)); + } + else + { + // Output tensor auto inizialitation if not yet initialized + auto_init_if_empty(mm_result_s32_info, a->clone() + ->set_tensor_shape(compute_mm_shape( + *matrix_a_info, *matrix_b_info, false, reshape_info)) + .set_data_type(DataType::S32)); + + // Pick up the GEMM configuration + // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + const auto res = select_default_gemm_config_native( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}); + lhs_info = res.lhs_info; + rhs_info = res.rhs_info; + + // Validate matrix multiply + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate( + matrix_a_info, matrix_b_info, &mm_result_s32_info, lhs_info, rhs_info, reshape_info)); + } + + // Validate offset contribution kernel + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionOutputStageKernel::validate( + &mm_result_s32_info, a_offset == 0 ? nullptr : &info_vector_sum_col, + b_offset == 0 ? nullptr : &info_vector_sum_row, c, output, a_offset, b_offset, gemmlowp_output_stage, + &gemm_output_stage_multipliers_shifts_info, &gemm_output_stage_multipliers_shifts_info)); + } + } + else + { + if (reshape_matrix_b) + { + // Validate matrix multiply + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate( + matrix_a_info, matrix_b_info, output, gemm_kernel_info)); + } + else + { + // Pick up the GEMM configuration + // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration + const auto res = select_default_gemm_config_native( + auto_heuristics::CommonQuery{gpu_target, DataType::QASYMM8, m, n, k, batch_size}); + lhs_info = res.lhs_info; + rhs_info = res.rhs_info; + + // Validate matrix multiply + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate( + matrix_a_info, matrix_b_info, output, lhs_info, rhs_info, reshape_info)); + } + + if (output->total_size() != 0) + { + // Validate offset contribution kernel + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionKernel::validate( + output, a_offset == 0 ? nullptr : &info_vector_sum_col, b_offset == 0 ? nullptr : &info_vector_sum_row, + c, a_offset, b_offset)); + } + } + + return Status{}; +} + +void ClGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) +{ + const ITensor *a = tensors.get_const_tensor(ACL_SRC_0); + const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); + const ITensor *c = tensors.get_const_tensor(ACL_SRC_2); + ITensor *dst = tensors.get_tensor(ACL_DST); + + ARM_COMPUTE_ERROR_ON_NULLPTR(a, dst); + + CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true); + CLAuxTensorHandler vec_sum_row(offset_int_vec(VecSumRow), _vector_sum_row, tensors, true); + CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, true); + CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true); + CLAuxTensorHandler res32(offset_int_vec(ResultS32), _mm_result_s32, tensors, true); + CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, true); + CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, true); + + // Prepare the consts if needed + prepare(tensors); + + const ITensor *matrix_a = a; + const ITensor *matrix_b = _convert_to_qasymm8 ? rhs_qasymm8.get() : b; + + if (is_gemm_reshaped(_gemm_kernel_type)) + { + matrix_b = tmp_b.get(); + if (!_reshape_b_only_on_first_run) + { + // Run reshape matrix B + ITensorPack mtx_b_reshape_pack = {{TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b}, + {TensorType::ACL_DST, tmp_b.get()}}; + CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_reshape_pack, false); + } + } + + // Run matrix B reduction kernel only if _a_offset is not equal to 0 + if (_a_offset != 0 && !_reshape_b_only_on_first_run) + { + ITensorPack mtx_b_red_pack = {{TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b}, + {TensorType::ACL_DST, vec_sum_col.get()}}; + CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false); + } + + // Run matrix A reduction kernel only if _b_offset is not equal to 0 + if (_b_offset != 0) + { + ITensorPack mtx_a_red_pack = {{TensorType::ACL_SRC, matrix_a}, {TensorType::ACL_DST, vec_sum_row.get()}}; + CLScheduler::get().enqueue_op(*_mtx_a_reduction_kernel, mtx_a_red_pack, false); + } + + // Run matrix multiply + if (is_gemm_reshaped(_gemm_kernel_type)) + { + ITensorPack gemm_reshaped_pack; + if (_run_offset_contribution) + { + gemm_reshaped_pack = ITensorPack({{TensorType::ACL_SRC_0, matrix_a}, + {TensorType::ACL_SRC_1, matrix_b}, + {TensorType::ACL_DST, _run_output_stage ? res32.get() : dst}}); + } + else + { + gemm_reshaped_pack = ITensorPack({ + {TensorType::ACL_SRC, matrix_a}, + {TensorType::ACL_SRC_1, matrix_b}, + {TensorType::ACL_BIAS, c}, + {TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get()}, + {TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get()}, + {TensorType::ACL_SHIFTS, shifts.get()}, + {TensorType::ACL_MULTIPLIERS, multipliers.get()}, + {TensorType::ACL_DST, dst}, + }); + } + if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) + { + CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_pack, false); + } + else if (_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) + { + CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_mmul_kernel, gemm_reshaped_pack, false); + } + else + { + ARM_COMPUTE_ERROR("Invalid reshaped kernel"); + } + } + else + { + ITensorPack gemm_native_pack = {{TensorType::ACL_SRC_0, matrix_a}, + {TensorType::ACL_SRC_1, matrix_b}, + {TensorType::ACL_DST, _run_offset_contribution ? dst : res32.get()}}; + CLScheduler::get().enqueue_op(*_mm_native_kernel, gemm_native_pack, false); + } + if (_run_output_stage) + { + // Run offset contribution/output stage kernel + ITensorPack output_stage_pack = { + {TensorType::ACL_SRC, res32.get()}, + {TensorType::ACL_BIAS, c}, + {TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get()}, + {TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get()}, + {TensorType::ACL_SHIFTS, shifts.get()}, + {TensorType::ACL_MULTIPLIERS, multipliers.get()}, + {TensorType::ACL_DST, dst}, + }; + CLScheduler::get().enqueue_op(*_offset_contribution_output_stage_kernel, output_stage_pack, true); + } + if (_run_offset_contribution) + { + // Run offset contribution kernel + ITensorPack offset_contrib_pack = {{TensorType::ACL_SRC_DST, dst}, + {TensorType::ACL_BIAS, c}, + {TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get()}, + {TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get()}}; + CLScheduler::get().enqueue_op(*_offset_contribution_kernel, offset_contrib_pack, true); + } +} + +void ClGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors) +{ + if (!_is_prepared) + { + auto b = tensors.get_const_tensor(TensorType::ACL_SRC_1); + CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true); + CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true); + CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, false); + + ARM_COMPUTE_ERROR_ON_NULLPTR(b); + + if (_convert_to_qasymm8) + { + ITensorPack convert_to_qs8_pack = {{ACL_SRC, b}, {ACL_DST, rhs_qasymm8.get()}}; + CLScheduler::get().enqueue_op(*_weights_to_qasymm8, convert_to_qs8_pack, false); + b->mark_as_unused(); + } + + if (is_gemm_reshaped(_gemm_kernel_type) && _reshape_b_only_on_first_run) + { + // Run reshape kernel and mark original weights tensor as unused + ITensorPack mtx_b_pack = {{TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b}, + {TensorType::ACL_DST, tmp_b.get()}}; + CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_pack, false); + b->mark_as_unused(); + } + + // Run matrix B reduction kernel only if _a_offset is not equal to 0 + if (_a_offset != 0 && _reshape_b_only_on_first_run) + { + ITensorPack mtx_b_red_pack = {{TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b}, + {TensorType::ACL_DST, vec_sum_col.get()}}; + CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false); + } + + // Compute GEMM output multipliers and shifts for output stage + { + const size_t num_filters = (_gemm_info.gemmlowp_output_stage().is_quantized_per_channel) + ? _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() + : 1; + + CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, false); + CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, false); + + ICLTensor *multiplier_tensor = multipliers.get(); + if (multiplier_tensor != nullptr && multiplier_tensor->info()->total_size() > 0) + { + multiplier_tensor->map(CLScheduler::get().queue(), true); + std::memcpy(multiplier_tensor->ptr_to_element(Coordinates(0)), + _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.data(), + num_filters * sizeof(int32_t)); + multiplier_tensor->unmap(CLScheduler::get().queue()); + } + + ICLTensor *shifts_tensor = shifts.get(); + if (shifts.get() != nullptr && shifts_tensor->info()->total_size() > 0) + { + shifts_tensor->map(CLScheduler::get().queue(), true); + std::memcpy(shifts_tensor->ptr_to_element(Coordinates(0)), + _gemm_info.gemmlowp_output_stage().gemmlowp_shifts.data(), num_filters * sizeof(int32_t)); + shifts_tensor->unmap(CLScheduler::get().queue()); + } + } + CLScheduler::get().queue().finish(); + _is_prepared = true; + } +} + +experimental::MemoryRequirements ClGemmLowpMatrixMultiplyCore::workspace() const +{ + return _aux_mem; +} +} // namespace opencl +} // namespace arm_compute |