diff options
Diffstat (limited to 'src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp')
| -rw-r--r-- | src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp | 786 |
1 files changed, 0 insertions, 786 deletions
diff --git a/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp b/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp deleted file mode 100644 index 0c72912642..0000000000 --- a/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp +++ /dev/null @@ -1,786 +0,0 @@ -/* - * Copyright (c) 2017-2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/KernelDescriptors.h" -#include "arm_compute/core/Log.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Validate.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 "src/core/gpu/cl/kernels/ClCastKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyNativeKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionOutputStageKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmLowpReductionKernel.h" -#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" -#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h" - -#include "utils/TypePrinter.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: - { - 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; -} - -// 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 }; -} - -inline bool is_gemm_reshaped(CLGEMMKernelType kernel_type) -{ - switch(kernel_type) - { - case CLGEMMKernelType::NATIVE: - return false; - case CLGEMMKernelType::RESHAPED_ONLY_RHS: - 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>()), - _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 != nullptr ? c : nullptr, 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); - - GEMMRHSMatrixInfo rhs_info; - GEMMLHSMatrixInfo lhs_info; - - // Arguments used by GEMMReshapeInfo - // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo - // 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); - - // Check if we need to reshape the matrix A and matrix B - _is_gemm_reshaped = is_gemm_reshaped(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(_is_gemm_reshaped) - { - 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); - } - - // 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(_is_gemm_reshaped && 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 - { - _run_output_stage = true; - - if(_is_gemm_reshaped) - { - _mm_reshaped_only_rhs_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(_is_gemm_reshaped) - { - // Configure and tune matrix multiply kernel - _mm_reshaped_only_rhs_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) - { - // 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) - { - 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) - { - 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 }, - }); - } - CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_pack, false); - } - 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 && _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 |