From 7891a73ef36f4ad7b71069b3c57694f85bb79454 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 20 Aug 2021 21:39:25 +0100 Subject: Move CPU/GPU files from Core/Runtime to the respective backend folders Legacy structure contained two libraries core/runtime with two backends in each. We reduce the core/runtime libraries to a single library thus merging the backend files Signed-off-by: Georgios Pinitas Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- .../operators/CpuGemmLowpMatrixMultiplyCore.cpp | 711 --------------------- 1 file changed, 711 deletions(-) delete mode 100644 src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp (limited to 'src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp') diff --git a/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp deleted file mode 100644 index 7affc3f506..0000000000 --- a/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp +++ /dev/null @@ -1,711 +0,0 @@ -/* - * Copyright (c) 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/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/KernelDescriptors.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "arm_compute/runtime/TensorAllocator.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/MemoryHelpers.h" - -#include "src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h" -#include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h" -#include "src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h" -#include "src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h" -#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h" -#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h" -#include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h" -#include "src/runtime/cpu/operators/CpuActivation.h" -#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h" -#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h" - -using namespace arm_compute::misc::shape_calculator; -using namespace arm_compute::experimental; - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) -{ - cpu::AsmGemmInfo asm_info; - asm_info.method = cpu::AsmConvMethod::Im2Col; - asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); - asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); - asm_info.activation_info = info.activation_info(); - asm_info.output_stage = info.gemmlowp_output_stage(); - asm_info.fast_mode = info.fast_math(); - - return asm_info; -} -} // namespace - -CpuGemmLowpMatrixMultiplyCore::CpuGemmLowpMatrixMultiplyCore() - : _asm_glue(std::make_unique()), - _mm_kernel(), - _mtx_a_reshape_kernel(), - _mtx_b_reshape_kernel(), - _mtx_a_reduction_kernel(), - _mtx_b_reduction_kernel(), - _offset_contribution_kernel(), - _offset_contribution_output_stage_kernel(), - _activation_func(), - _convert_to_signed_asymm(), - _convert_from_signed_asymm(), - _vector_sum_col(), - _vector_sum_row(), - _tmp_a(), - _tmp_b(), - _mm_result_s32(), - _signed_a(), - _signed_output(), - _a_offset(0), - _b_offset(0), - _run_vector_matrix_multiplication(false), - _assembly_path(false), - _fused_assembly_path(false), - _reshape_b_only_on_first_run(false), - _is_prepared(false), - _fuse_output_stage(false), - _run_activation(false), - _flip_signedness(false), - _gemm_info(), - _aux_mem(Count) -{ -} -CpuGemmLowpMatrixMultiplyCore::~CpuGemmLowpMatrixMultiplyCore() = default; - -void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *dst, const GEMMInfo &gemm_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, dst); - ARM_COMPUTE_ERROR_THROW_ON(CpuGemmLowpMatrixMultiplyCore::validate(a, b, c, dst, gemm_info)); - - const ITensorInfo *matrix_a = a; - const ITensorInfo *matrix_b = b; - GEMMInfo info = gemm_info; - - // Set internal variables - _a_offset = a->quantization_info().uniform().offset; - _b_offset = b->quantization_info().uniform().offset; - _run_vector_matrix_multiplication = a->dimension(1) < 2; - _reshape_b_only_on_first_run = info.reshape_b_only_on_first_run(); - _is_prepared = false; - _fused_assembly_path = false; - _flip_signedness = is_data_type_quantized_per_channel(b->data_type()) && (a->data_type() == DataType::QASYMM8) && _reshape_b_only_on_first_run; - _gemm_info = gemm_info; - - _asm_glue = std::make_unique(); - - const ITensorInfo *a_to_use = a; - - // Convert to QASYMM8 -> QASYMM8_SIGNED and back - if(_flip_signedness) - { - const int32_t offset_correction = 128; - const DataType dt = DataType::QASYMM8_SIGNED; - const UniformQuantizationInfo iqinfo = a_to_use->quantization_info().uniform(); - - _signed_a = a_to_use->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction)); - _convert_to_signed_asymm = std::make_unique(); - _convert_to_signed_asymm->configure(a_to_use, &_signed_a); - a_to_use = &_signed_a; - _a_offset = _signed_a.quantization_info().uniform().offset; - - const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); - _signed_output = dst->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(oqinfo.scale, oqinfo.offset - offset_correction)); - - // Output stage correction - GEMMLowpOutputStageInfo output_stage_corr = info.gemmlowp_output_stage(); - output_stage_corr.gemmlowp_offset = _signed_output.quantization_info().uniform().offset; - output_stage_corr.gemmlowp_min_bound -= offset_correction; - output_stage_corr.gemmlowp_max_bound -= offset_correction; - info.set_gemmlowp_output_stage(output_stage_corr); - - // Update matrix a - matrix_a = &_signed_a; - } - - // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage - if(info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) - { - _fuse_output_stage = true; - _mm_result_s32 = TensorInfo(dst->tensor_shape(), 1, DataType::S32); - } - - // Initialize assembly kernel meta-data - const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); -#ifdef __aarch64__ - switch(a->data_type()) - { - case DataType::QASYMM8: - case DataType::QASYMM8_SIGNED: - case DataType::U8: - case DataType::S8: - { - if(is_data_type_quantized_asymmetric(a_to_use->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) - { - auto c_info_to_use = c == nullptr ? nullptr : c; - _asm_glue->configure(a_to_use, b, c_info_to_use, dst, asm_info); - _fused_assembly_path = _asm_glue->is_configured(); - } - else - { - auto output_to_use = (_fuse_output_stage ? &_mm_result_s32 : dst); - _asm_glue->configure(a_to_use, b, nullptr, output_to_use, asm_info); - } - _assembly_path = _asm_glue->is_configured(); - break; - } - default: - { - ARM_COMPUTE_ERROR("Datatype not supported"); - break; - } - } -#endif /* __aarch64__ */ - if(!(_assembly_path || _run_vector_matrix_multiplication)) - { - matrix_a = &_tmp_a; - matrix_b = &_tmp_b; - - // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ] - _tmp_a = TensorInfo(compute_interleaved_shape(*a_to_use), 1, a_to_use->data_type(), a_to_use->quantization_info()); - // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] - _tmp_b = TensorInfo(compute_transpose1xW_shape(*b), 1, b->data_type(), b->quantization_info()); - - // Configure interleave kernel - _mtx_a_reshape_kernel = std::make_unique(); - _mtx_a_reshape_kernel->configure(a_to_use, &_tmp_a); - - // Configure transpose kernel - _mtx_b_reshape_kernel = std::make_unique(); - _mtx_b_reshape_kernel->configure(b, &_tmp_b); - } - - if(!_fused_assembly_path) - { - // Build reduction info - const GEMMLowpReductionKernelInfo reduction_info(a_to_use->dimension(0), false, 0, false); - - // 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 = std::make_unique(); - _mtx_b_reduction_kernel->configure(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_to_use), 1, DataType::S32); - - // Configure matrix A reduction kernel - _mtx_a_reduction_kernel = std::make_unique(); - _mtx_a_reduction_kernel->configure(a_to_use, &_vector_sum_row, reduction_info); - } - - if(_fuse_output_stage) - { - // Configure matrix multiply kernel - if(!_assembly_path) - { - _mm_kernel = std::make_unique(); - _mm_kernel->configure(matrix_a, matrix_b, &_mm_result_s32); - } - - _offset_contribution_output_stage_kernel = std::make_unique(); - _offset_contribution_output_stage_kernel->configure(&_mm_result_s32, - _a_offset == 0 ? nullptr : &_vector_sum_col, - _b_offset == 0 ? nullptr : &_vector_sum_row, c, - _flip_signedness ? &_signed_output : dst, - a->dimension(0), - _a_offset, _b_offset, info.gemmlowp_output_stage()); - - if(_flip_signedness) - { - _convert_from_signed_asymm = std::make_unique(); - _convert_from_signed_asymm->configure(&_signed_output, dst); - } - } - else - { - // Configure matrix multiply kernel - if(!_assembly_path) - { - _mm_kernel = std::make_unique(); - _mm_kernel->configure(matrix_a, matrix_b, dst); - } - // Configure offset contribution kernel - _offset_contribution_kernel = std::make_unique(); - _offset_contribution_kernel->configure(dst, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a_to_use->dimension(0), - _a_offset, _b_offset); - } - } - // Configure activation - const ActivationLayerInfo &activation = gemm_info.activation_info(); - _run_activation = activation.enabled() && (!_assembly_path || !cpu::CpuGemmAssemblyDispatch::is_activation_supported(activation)); - if(_run_activation) - { - _activation_func = std::make_unique(); - _activation_func->configure(dst, nullptr, activation); - } - - if(_assembly_path) - { - auto asm_mem_req = _asm_glue->workspace(); - _aux_mem[AsmGemmWorkspace] = asm_mem_req[AsmGemmWorkspace]; - _aux_mem[Pretranspose] = asm_mem_req[Pretranspose]; - } - - // Request memory for LHS and RHS reshape matrix - _aux_mem[VectorSumCol] = MemoryInfo(offset_int_vec(VectorSumCol), !_fused_assembly_path && _a_offset != 0 - && _reshape_b_only_on_first_run ? - MemoryLifetime::Persistent : - MemoryLifetime::Temporary, - _vector_sum_col.total_size()); - _aux_mem[VectorSumRow] = MemoryInfo(offset_int_vec(VectorSumRow), MemoryLifetime::Temporary, _vector_sum_row.total_size()); - _aux_mem[TmpA] = MemoryInfo(offset_int_vec(TmpA), MemoryLifetime::Temporary, _tmp_a.total_size()); - _aux_mem[TmpB] = MemoryInfo(offset_int_vec(TmpB), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); - _aux_mem[MMResultS32] = MemoryInfo(offset_int_vec(MMResultS32), MemoryLifetime::Temporary, _mm_result_s32.total_size()); - _aux_mem[SignedA] = MemoryInfo(offset_int_vec(SignedA), MemoryLifetime::Temporary, _signed_a.total_size()); - _aux_mem[SignedOutput] = MemoryInfo(offset_int_vec(SignedOutput), MemoryLifetime::Temporary, _signed_output.total_size()); -} - -Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info) -{ - 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_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32, DataType::QASYMM8, DataType::QASYMM8_SIGNED); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(c != nullptr && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::NONE, "Bias addition not supported in NEGEMMLowpMatrixMultiplyCore for output S32"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1), - "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); - 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"); - - GEMMInfo info = gemm_info; - const ITensorInfo *matrix_a_info = a; - const ITensorInfo *matrix_b_info = b; - - const ITensorInfo *a_to_use = a; - - TensorInfo tmp_a_info{}; - TensorInfo tmp_b_info{}; - TensorInfo mm_result_s32_info{}; - - int32_t a_offset = a->quantization_info().uniform().offset; - int32_t b_offset = b->quantization_info().uniform().offset; - - bool fuse_output_stage = info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE; - if(fuse_output_stage) - { - auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(output->tensor_shape()).set_data_type(DataType::S32)); - } - - // Convert QASYMM8->QASYMM8_SIGNED - TensorInfo signed_a{}; - TensorInfo signed_output{}; - bool flip_signedness = is_data_type_quantized_per_channel(b->data_type()) && (a->data_type() == DataType::QASYMM8) && info.reshape_b_only_on_first_run(); - if(flip_signedness) - { - const int32_t offset_correction = 128; - const DataType dt = DataType::QASYMM8_SIGNED; - const UniformQuantizationInfo iqinfo = a_to_use->quantization_info().uniform(); - - signed_a = a_to_use->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction)); - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuConvertQuantizedSignednessKernel::validate(a_to_use, &signed_a)); - a_to_use = &signed_a; - a_offset = signed_a.quantization_info().uniform().offset; - - const UniformQuantizationInfo oqinfo = output->quantization_info().uniform(); - signed_output = output->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(oqinfo.scale, oqinfo.offset - offset_correction)); - - // Output stage correction - GEMMLowpOutputStageInfo output_stage_corr = info.gemmlowp_output_stage(); - output_stage_corr.gemmlowp_offset = signed_output.quantization_info().uniform().offset; - output_stage_corr.gemmlowp_min_bound -= offset_correction; - output_stage_corr.gemmlowp_max_bound -= offset_correction; - info.set_gemmlowp_output_stage(output_stage_corr); - - // Update matrix a - matrix_a_info = &signed_a; - } - - // Initialize assembly kernel meta-data - const AsmGemmInfo asm_info = init_assembly_metadata(info); - - // Check if we need to run the optimized assembly kernel - bool run_optimised = false; - bool run_optimised_requantized = false; - if(is_data_type_quantized_asymmetric(a_to_use->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) - { - run_optimised = bool(CpuGemmAssemblyDispatch::validate(a_to_use, b, c, output, asm_info)); - run_optimised_requantized = run_optimised; - } - else - { - run_optimised = bool(CpuGemmAssemblyDispatch::validate(a_to_use, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, asm_info)); - } - - if(run_optimised) - { - ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0)); - if(info.depth_output_gemm3d() != 0) - { - if(info.reinterpret_input_as_3d()) - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1)); - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2)); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D"); - - const bool run_vector_matrix_multiplication = a->dimension(1) < 2; - if(!run_vector_matrix_multiplication) - { - matrix_a_info = &tmp_a_info; - matrix_b_info = &tmp_b_info; - - // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ] - TensorShape shape_tmp_a = a->tensor_shape(); - shape_tmp_a.set(0, a->dimension(0) * 4); - shape_tmp_a.set(1, std::ceil(a->dimension(1) / 4.f)); - - // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] - TensorShape shape_tmp_b = b->tensor_shape(); - shape_tmp_b.set(0, b->dimension(1) * 16); - shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f)); - - // Validate interleave kernel - auto_init_if_empty(tmp_a_info, a_to_use->clone()->set_tensor_shape(shape_tmp_a)); - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(shape_tmp_b)); - - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmInterleave4x4Kernel::validate(a_to_use, &tmp_a_info)); - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmTranspose1xWKernel::validate(b, &tmp_b_info)); - } - } - - if(!run_optimised_requantized) - { - TensorInfo info_vector_sum_col{}; - TensorInfo info_vector_sum_row{}; - - const GEMMLowpReductionKernelInfo reduction_info(a_to_use->dimension(0), false, 0, false); - - // 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(*b), 1, DataType::S32); - - // Configure Matrix B reduction kernel - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixBReductionKernel::validate(b, &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(kernels::CpuGemmLowpMatrixAReductionKernel::validate(a_to_use, &info_vector_sum_row, reduction_info)); - } - - if(fuse_output_stage) - { - if(!run_optimised) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D"); - - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info)); - } - - // Validate offset contribution kernel - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info, - a_offset == 0 ? nullptr : &info_vector_sum_col, - b_offset == 0 ? nullptr : &info_vector_sum_row, - c, - flip_signedness ? &signed_output : output, - a_offset, b_offset, - info.gemmlowp_output_stage())); - } - else - { - if(!run_optimised) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D"); - - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output)); - } - // Validate offset contribution kernel - ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionKernel::validate(output, - a_offset == 0 ? nullptr : &info_vector_sum_col, - b_offset == 0 ? nullptr : &info_vector_sum_row, - a_offset, b_offset)); - } - } - - // Validate activation - const ActivationLayerInfo &activation = gemm_info.activation_info(); - if(activation.enabled()) - { - ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(output, nullptr, activation)); - } - - return Status{}; -} - -void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto a = tensors.get_const_tensor(TensorType::ACL_SRC_0); - auto b = tensors.get_const_tensor(TensorType::ACL_SRC_1); - auto c = tensors.get_const_tensor(TensorType::ACL_SRC_2); - auto dst = tensors.get_tensor(TensorType::ACL_DST); - auto a_to_use = a; - auto matrix_a = a; - auto matrix_b = b; - - CpuAuxTensorHandler vector_sum_col(offset_int_vec(VectorSumCol), _vector_sum_col, tensors, false); - CpuAuxTensorHandler vector_sum_row(offset_int_vec(VectorSumRow), _vector_sum_row, tensors, false); - CpuAuxTensorHandler tmp_a(offset_int_vec(TmpA), _tmp_a, tensors, false); - CpuAuxTensorHandler tmp_b(offset_int_vec(TmpB), _tmp_b, tensors, true); - CpuAuxTensorHandler mm_result_s32(offset_int_vec(MMResultS32), _mm_result_s32, tensors, false); - CpuAuxTensorHandler signed_a(offset_int_vec(SignedA), _signed_a, tensors, false); - CpuAuxTensorHandler signed_output(offset_int_vec(SignedOutput), _signed_output, tensors, false); - - // Convert QASYMM8->QASYMM8_SIGNED - if(_flip_signedness) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, a }, - { TensorType::ACL_DST, signed_a.get() } - }; - NEScheduler::get().schedule_op(_convert_to_signed_asymm.get(), Window::DimY, _convert_to_signed_asymm->window(), pack); - a_to_use = signed_a.get(); - matrix_a = signed_a.get(); - } - - // Run GEMM - if(_asm_glue->is_configured()) - { - ITensorPack asm_glue_tensors = tensors; - auto output_to_use = (_fuse_output_stage ? mm_result_s32.get() : dst); - if(is_data_type_quantized_asymmetric(a_to_use->info()->data_type()) && _gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) - { - asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use); - asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b); - asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_2, c); - asm_glue_tensors.add_tensor(TensorType::ACL_DST, dst); - } - else - { - asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use); - asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b); - asm_glue_tensors.add_tensor(TensorType::ACL_DST, output_to_use); - } - _asm_glue->run(asm_glue_tensors); - } - else - { - if(!_run_vector_matrix_multiplication) - { - matrix_a = tmp_a.get(); - matrix_b = tmp_b.get(); - // Run interleave kernel - ITensorPack pack_a = - { - { TensorType::ACL_SRC, a_to_use }, - { TensorType::ACL_DST, tmp_a.get() } - }; - NEScheduler::get().schedule_op(_mtx_a_reshape_kernel.get(), Window::DimY, _mtx_a_reshape_kernel->window(), pack_a); - - if(!_reshape_b_only_on_first_run) - { - ITensorPack pack_b = - { - { TensorType::ACL_SRC, b }, - { TensorType::ACL_DST, tmp_b.get() } - }; - // Run transpose kernel - NEScheduler::get().schedule_op(_mtx_b_reshape_kernel.get(), Window::DimY, _mtx_b_reshape_kernel->window(), pack_b); - } - } - ITensorPack pack_mm = - { - { TensorType::ACL_SRC_0, matrix_a }, - { TensorType::ACL_SRC_1, matrix_b } - }; - if(_fuse_output_stage) - { - pack_mm.add_tensor(TensorType::ACL_DST, mm_result_s32.get()); - } - else - { - pack_mm.add_tensor(TensorType::ACL_DST, dst); - } - NEScheduler::get().schedule_op(_mm_kernel.get(), Window::DimY, _mm_kernel->window(), pack_mm); - } - - if(!_fused_assembly_path) - { - // Run matrix A reduction kernel only if _b_offset is not equal to 0 - if(_b_offset != 0) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, a_to_use }, - { TensorType::ACL_DST, vector_sum_row.get() } - }; - NEScheduler::get().schedule_op(_mtx_a_reduction_kernel.get(), Window::DimX, _mtx_a_reduction_kernel->window(), pack); - } - - // 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 pack = - { - { TensorType::ACL_SRC, b }, - { TensorType::ACL_DST, vector_sum_col.get() } - }; - NEScheduler::get().schedule_op(_mtx_b_reduction_kernel.get(), Window::DimX, _mtx_b_reduction_kernel->window(), pack); - } - - if(_fuse_output_stage) - { - ITensorPack pack; - pack.add_tensor(TensorType::ACL_SRC_0, mm_result_s32.get()); - pack.add_tensor(TensorType::ACL_SRC_1, _a_offset == 0 ? nullptr : vector_sum_col.get()); - pack.add_tensor(TensorType::ACL_SRC_2, _b_offset == 0 ? nullptr : vector_sum_row.get()); - pack.add_tensor(TensorType::ACL_SRC_3, c); - pack.add_tensor(TensorType::ACL_DST, _flip_signedness ? signed_output.get() : dst); - - // Run offset contribution kernel - NEScheduler::get().schedule_op(_offset_contribution_output_stage_kernel.get(), Window::DimY, _offset_contribution_output_stage_kernel->window(), pack); - } - else - { - ITensorPack pack; - pack.add_tensor(TensorType::ACL_SRC_0, _a_offset == 0 ? nullptr : vector_sum_col.get()); - pack.add_tensor(TensorType::ACL_SRC_1, _b_offset == 0 ? nullptr : vector_sum_row.get()); - pack.add_tensor(TensorType::ACL_DST, dst); - - // Run offset contribution kernel - NEScheduler::get().schedule_op(_offset_contribution_kernel.get(), Window::DimY, _offset_contribution_kernel->window(), pack); - } - } - - // Convert QASYMM8_SIGNED->QASYMM8 - if(!_fused_assembly_path && _fuse_output_stage && _flip_signedness) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, signed_output.get() }, - { TensorType::ACL_DST, dst } - }; - NEScheduler::get().schedule_op(_convert_from_signed_asymm.get(), Window::DimY, _convert_from_signed_asymm->window(), pack); - } - - // Run fused activation unless already run in the fused assembly - if(_run_activation) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, dst }, - { TensorType::ACL_DST, dst } - }; - _activation_func->run(pack); - } -} - -void CpuGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - auto original_b = tensors.get_const_tensor(TensorType::ACL_SRC_1); - // Run assembly reshape - if(_asm_glue->is_configured()) - { - _asm_glue->prepare(tensors); - } - // Run non-assembly reshape - else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication && !_asm_glue->is_configured()) - { - // Run reshape kernel and mark original weights tensor as unused - ITensor *tmp_b_p = utils::cast::polymorphic_downcast(tensors.get_tensor(offset_int_vec(TmpB))); - CpuAuxTensorHandler tmp_b(_tmp_b, *tmp_b_p); - ITensorPack pack = - { - { TensorType::ACL_SRC, original_b }, - { TensorType::ACL_DST, tmp_b.get() } - }; - NEScheduler::get().schedule_op(_mtx_b_reshape_kernel.get(), Window::DimY, _mtx_b_reshape_kernel->window(), pack); - } - - // Run matrix B reduction kernel only if _a_offset is not equal to 0 - if(!_fused_assembly_path && _a_offset != 0 && _reshape_b_only_on_first_run) - { - ITensor *vector_sum_col_p = utils::cast::polymorphic_downcast(tensors.get_tensor(offset_int_vec(VectorSumCol))); - CpuAuxTensorHandler vector_sum_col(_vector_sum_col, *vector_sum_col_p); - ITensorPack pack = - { - { TensorType::ACL_SRC, original_b }, - { TensorType::ACL_DST, vector_sum_col.get() } - }; - NEScheduler::get().schedule_op(_mtx_b_reduction_kernel.get(), Window::DimX, _mtx_b_reduction_kernel->window(), pack); - } - _is_prepared = true; - } -} -experimental::MemoryRequirements CpuGemmLowpMatrixMultiplyCore::workspace() const -{ - return _aux_mem; -} -} // namespace cpu -} // namespace arm_compute -- cgit v1.2.1