| /* |
| * Copyright (c) 2017-2020 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 "arm_compute/runtime/CL/functions/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/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/CL/gemm/native/CLGEMMNativeKernelConfiguration.h" |
| #include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h" |
| #include "src/core/CL/kernels/CLDepthConvertLayerKernel.h" |
| #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" |
| #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" |
| #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" |
| #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h" |
| #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h" |
| #include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/runtime/CL/gemm/CLGEMMKernelSelection.h" |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| using namespace arm_compute::cl_gemm; |
| |
| namespace |
| { |
| inline bool is_gemm_reshaped(unsigned int m, unsigned int n, unsigned int k, DataType data_type, bool reshape_b_only_on_first_run) |
| { |
| std::unique_ptr<ICLGEMMKernelSelection> gemm_kernel = CLGEMMKernelSelectionFactory::create(CLScheduler::get().target()); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_kernel.get()); |
| |
| CLGEMMKernelSelectionParams params; |
| params.m = m; |
| params.n = n; |
| params.k = k; |
| params.is_rhs_constant = reshape_b_only_on_first_run; |
| params.data_type = data_type; |
| |
| switch(gemm_kernel->select_kernel(params)) |
| { |
| case CLGEMMKernelType::NATIVE: |
| return false; |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
| return true; |
| default: |
| ARM_COMPUTE_ERROR("Not supported gemmlowp kernel!"); |
| } |
| } |
| } // namespace |
| |
| CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), |
| _weights_to_qasymm8(std::make_unique<CLDepthConvertLayerKernel>()), |
| _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>()), |
| _qasymm8_weights(), |
| _vector_sum_col(), |
| _vector_sum_row(), |
| _tmp_b(), |
| _mm_result_s32(), |
| _gemm_output_stage_multipliers(), |
| _gemm_output_stage_shifts(), |
| _matrix_a(nullptr), |
| _original_b(nullptr), |
| _output(nullptr), |
| _a_offset(0), |
| _b_offset(0), |
| _is_gemm_reshaped(true), |
| _reshape_b_only_on_first_run(false), |
| _is_prepared(false), |
| _run_output_stage(false), |
| _convert_to_qasymm8(false), |
| _run_offset_contribution(false) |
| { |
| } |
| |
| CLGEMMLowpMatrixMultiplyCore::~CLGEMMLowpMatrixMultiplyCore() = default; |
| |
| void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, gemm_info); |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), gemm_info)); |
| |
| _is_prepared = false; |
| _original_b = b; |
| _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| _a_offset = a->info()->quantization_info().uniform().offset; |
| _matrix_a = a; |
| _output = output; |
| |
| _convert_to_qasymm8 = is_data_type_quantized_per_channel(b->info()->data_type()) && is_data_type_quantized_symmetric(b->info()->data_type()) |
| && a->info()->data_type() == DataType::QASYMM8; |
| _b_offset = _convert_to_qasymm8 ? -128 : b->info()->quantization_info().uniform().offset; |
| |
| // 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->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| |
| // Check if we need to reshape the matrix A and matrix B |
| _is_gemm_reshaped = is_gemm_reshaped(m, n, k, a->info()->data_type(), _reshape_b_only_on_first_run); |
| |
| if(_convert_to_qasymm8) |
| { |
| // Set data type for converted weights |
| TensorInfo weights_info(*b->info()); |
| weights_info.set_data_type(DataType::QASYMM8); |
| _qasymm8_weights.allocator()->init(weights_info); |
| _weights_to_qasymm8->configure(compile_context, b, &_qasymm8_weights, ConvertPolicy::WRAP, 0); |
| } |
| |
| const ICLTensor *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : b; |
| if(_is_gemm_reshaped) |
| { |
| matrix_b = &_tmp_b; |
| |
| if(!_reshape_b_only_on_first_run) |
| { |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| // Pick up the GEMM configuration |
| // Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED doesn't matter, since it only affect the shape configuration |
| std::tie(lhs_info, rhs_info) = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // 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) |
| { |
| TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32); |
| _vector_sum_col.allocator()->init(info_vector_sum_col); |
| if(!_reshape_b_only_on_first_run) |
| { |
| _memory_group.manage(&_vector_sum_col); |
| } |
| |
| // 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) |
| { |
| TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 1, DataType::S32); |
| _vector_sum_row.allocator()->init(info_vector_sum_row); |
| _memory_group.manage(&_vector_sum_row); |
| |
| // 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.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32)); |
| _gemm_output_stage_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32)); |
| |
| GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage(); |
| gemmlowp_output_stage.output_data_type = _matrix_a->info()->data_type(); |
| |
| 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, _matrix_a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col, |
| _b_offset == 0 ? nullptr : &_vector_sum_row, c, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); |
| } |
| else |
| { |
| _run_output_stage = true; |
| |
| _memory_group.manage(&_mm_result_s32); |
| |
| if(_is_gemm_reshaped) |
| { |
| _mm_reshaped_only_rhs_kernel->configure(compile_context, _matrix_a, matrix_b, &_mm_result_s32, gemm_kernel_info); |
| } |
| else |
| { |
| // Pick up the GEMM configuration |
| std::tie(lhs_info, rhs_info) = CLGEMMNativeKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // Configure matrix multiply kernel |
| _mm_native_kernel->configure(compile_context, _matrix_a, matrix_b, &_mm_result_s32, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d)); |
| |
| _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, output, |
| a->info()->dimension(0), |
| _a_offset, _b_offset, gemmlowp_output_stage, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); |
| _mm_result_s32.allocator()->allocate(); |
| } |
| } |
| |
| _gemm_output_stage_multipliers.allocator()->allocate(); |
| _gemm_output_stage_shifts.allocator()->allocate(); |
| // Compute GEMM output multipliers and shifts for output stage |
| _gemm_output_stage_multipliers.map(); |
| _gemm_output_stage_shifts.map(); |
| std::memcpy(_gemm_output_stage_multipliers.ptr_to_element(Coordinates(0)), gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.data(), num_filters * sizeof(int32_t)); |
| std::memcpy(_gemm_output_stage_shifts.ptr_to_element(Coordinates(0)), gemm_info.gemmlowp_output_stage().gemmlowp_shifts.data(), num_filters * sizeof(int32_t)); |
| _gemm_output_stage_multipliers.unmap(); |
| _gemm_output_stage_shifts.unmap(); |
| } |
| else |
| { |
| _run_offset_contribution = true; |
| if(_is_gemm_reshaped) |
| { |
| // Configure and tune matrix multiply kernel |
| _mm_reshaped_only_rhs_kernel->configure(compile_context, _matrix_a, matrix_b, output, gemm_kernel_info); |
| } |
| else |
| { |
| // Pick up the GEMM configuration |
| std::tie(lhs_info, rhs_info) = CLGEMMNativeKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // Configure matrix multiply kernel |
| _mm_native_kernel->configure(compile_context, _matrix_a, matrix_b, output, lhs_info, rhs_info, GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d)); |
| } |
| |
| // 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, a->info()->dimension(0), _a_offset, |
| _b_offset); |
| } |
| |
| // Allocate tensors |
| if(_is_gemm_reshaped) |
| { |
| if(!_reshape_b_only_on_first_run) |
| { |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| if(_a_offset != 0 && !_reshape_b_only_on_first_run) |
| { |
| _vector_sum_col.allocator()->allocate(); |
| } |
| |
| if(_b_offset != 0) |
| { |
| _vector_sum_row.allocator()->allocate(); |
| } |
| } |
| |
| 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(m, n, k, a->data_type(), 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(CLDepthConvertLayerKernel::validate(b, &weights_info, ConvertPolicy::WRAP, 0)); |
| } |
| const ITensorInfo *matrix_b_info = &weights_info; |
| if(reshape_matrix_b) |
| { |
| matrix_b_info = &tmp_b_info; |
| |
| // Pick up the GEMM configuration |
| std::tie(lhs_info, rhs_info) = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // 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 |
| std::tie(lhs_info, rhs_info) = CLGEMMNativeKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // 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 |
| std::tie(lhs_info, rhs_info) = CLGEMMNativeKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); |
| |
| // 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() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_is_gemm_reshaped) |
| { |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run reshape matrix B |
| CLScheduler::get().enqueue(*_mtx_b_reshape_kernel, 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) |
| { |
| CLScheduler::get().enqueue(*_mtx_b_reduction_kernel, false); |
| } |
| |
| // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| CLScheduler::get().enqueue(*_mtx_a_reduction_kernel, false); |
| } |
| |
| // Run matrix multiply |
| if(_is_gemm_reshaped) |
| { |
| CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_kernel, false); |
| } |
| else |
| { |
| CLScheduler::get().enqueue(*_mm_native_kernel, false); |
| } |
| if(_run_output_stage) |
| { |
| // Run offset contribution/output stage kernel |
| CLScheduler::get().enqueue(*_offset_contribution_output_stage_kernel, true); |
| } |
| if(_run_offset_contribution) |
| { |
| // Run offset contribution kernel |
| CLScheduler::get().enqueue(*_offset_contribution_kernel, true); |
| } |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::prepare() |
| { |
| if(!_is_prepared) |
| { |
| if(_convert_to_qasymm8) |
| { |
| _qasymm8_weights.allocator()->allocate(); |
| CLScheduler::get().enqueue(*_weights_to_qasymm8, false); |
| } |
| |
| if(_is_gemm_reshaped && _reshape_b_only_on_first_run) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_b->is_used()); |
| |
| // Run reshape kernel and mark original weights tensor as unused |
| _tmp_b.allocator()->allocate(); |
| CLScheduler::get().enqueue(*_mtx_b_reshape_kernel, false); |
| _original_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) |
| { |
| _vector_sum_col.allocator()->allocate(); |
| CLScheduler::get().enqueue(*_mtx_b_reduction_kernel, false); |
| } |
| |
| CLScheduler::get().queue().finish(); |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |