Port NEGEMMLowp Part 2

Details:
Extend NEConvertQuantizedSignednessKernel
Port NEGEMMInterleave4x4Kernel to CpuGemmInterleave4x4Kernel
Port NEGEMMTranspose1xWKernel to CpuGemmTranspose1xWKernel
Port NEGEMMLowpMatrixAReductionKernel to CpuGemmLowpMatrixAReductionKernel
Port NEGEMMLowpMatrixBReductionKernel to CpuGemmLowpMatrixBReductionKernel
Port NEGEMMLowpOffsetContributionOutputStageKernel to CpuGemmLowpOffsetContributionOutputStageKernel
Port NEGEMMLowpOffsetContributionKernel to CpuGemmLowpOffsetContributionKernel

Resolves: COMPMID-4403

Change-Id: I3227f052f25e7b41d073bbea1da8a881fcd78b8e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5875
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

1 files changed, 54 insertions, 610 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 0aba3c03ec..641a2c2b5f 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -23,660 +23,104 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.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/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/runtime/IWeightsManager.h"
+#include "arm_compute/runtime/MemoryGroup.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "arm_compute/runtime/Tensor.h"
-#include "arm_compute/runtime/TensorAllocator.h"
-#include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/MemoryHelpers.h"
 
-#include "arm_compute/core/ITensorPack.h"
-#include "arm_compute/runtime/MemoryGroup.h"
-#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
-#include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
-#include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
-#include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h"
-#include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h"
-#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
+#include "src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h"
+
+using namespace arm_compute::experimental;
 
 namespace arm_compute
 {
-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();
-
-    return asm_info;
-}
-} // namespace
-
 struct NEGEMMLowpMatrixMultiplyCore::Impl
 {
-    MemoryGroup                                                    memory_group{};
-    IWeightsManager                                               *weights_manager{ nullptr };
-    std::unique_ptr<cpu::CpuGemmAssemblyDispatch>                  asm_glue{ nullptr };
-    std::unique_ptr<NEGEMMLowpMatrixMultiplyKernel>                mm_kernel{ nullptr };
-    std::unique_ptr<cpu::kernels::CpuGemmInterleave4x4Kernel>      mtx_a_reshape_kernel{ nullptr };
-    std::unique_ptr<cpu::kernels::CpuGemmTranspose1xWKernel>       mtx_b_reshape_kernel{ nullptr };
-    std::unique_ptr<NEGEMMLowpMatrixAReductionKernel>              mtx_a_reduction_kernel{ nullptr };
-    std::unique_ptr<NEGEMMLowpMatrixBReductionKernel>              mtx_b_reduction_kernel{ nullptr };
-    std::unique_ptr<NEGEMMLowpOffsetContributionKernel>            offset_contribution_kernel{ nullptr };
-    std::unique_ptr<NEGEMMLowpOffsetContributionOutputStageKernel> offset_contribution_output_stage_kernel{ nullptr };
-    std::unique_ptr<NEActivationLayer>                             activation_func{ nullptr };
-    std::unique_ptr<NEConvertQuantizedSignednessKernel>            convert_to_signed_asymm{ nullptr };
-    std::unique_ptr<NEConvertQuantizedSignednessKernel>            convert_from_signed_asymm{ nullptr };
-
-    const ITensor *a_to_use{ nullptr };
-    Tensor         vector_sum_col{};
-    Tensor         vector_sum_row{};
-    Tensor         tmp_a{};
-    Tensor         tmp_b{};
-    Tensor         mm_result_s32{};
-    Tensor         signed_a{};
-    Tensor         signed_output{};
-    const ITensor *original_b{ nullptr };
-    int32_t        a_offset{ 0 };
-    int32_t        b_offset{ 0 };
-
-    bool run_vector_matrix_multiplication{ false };
-    bool assembly_path{ false };
-    bool fused_assembly_path{ false };
-    bool reshape_b_only_on_first_run{ false };
-    bool is_prepared{ false };
-    bool fuse_output_stage{ false };
-    bool run_activation{ false };
-    bool flip_signedness{ false };
-
-    experimental::MemoryRequirements aux_mem_req{};
-    ITensorPack                      asm_glue_run_pack{};
-    ITensorPack                      asm_glue_prep_pack{};
-    WorkspaceData<Tensor>            asm_glue_workspace{};
+    const ITensor                                      *b{ nullptr };
+    std::unique_ptr<cpu::CpuGemmLowpMatrixMultiplyCore> op{ nullptr };
+    ITensorPack                                         run_pack{};
+    ITensorPack                                         prep_pack{};
+    MemoryGroup                                         memory_group{};
+    IWeightsManager                                    *weights_manager{ nullptr };
+    MemoryRequirements                                  aux_mem_req{};
+    WorkspaceData<Tensor>                               workspace_tensors{};
+    bool                                                is_prepared{ false };
 };
 
-using namespace arm_compute::experimental;
-using namespace arm_compute::misc::shape_calculator;
-
-NEGEMMLowpMatrixMultiplyCore::~NEGEMMLowpMatrixMultiplyCore() = default;
-
 NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
-    : _impl(std::make_unique<struct NEGEMMLowpMatrixMultiplyCore::Impl>())
+    : _impl(std::make_unique<Impl>())
 {
-    _impl->memory_group    = MemoryGroup(memory_manager);
     _impl->weights_manager = weights_manager;
+    _impl->memory_group    = MemoryGroup(memory_manager);
 }
+NEGEMMLowpMatrixMultiplyCore::~NEGEMMLowpMatrixMultiplyCore() = default;
 
 void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *output, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
-    ARM_COMPUTE_UNUSED(c);
-    ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), gemm_info));
-
-    const ITensor *matrix_a = a;
-    const ITensor *matrix_b = b;
-    GEMMInfo       info     = gemm_info;
-
-    // Set internal variables
-    _impl->a_offset                         = a->info()->quantization_info().uniform().offset;
-    _impl->b_offset                         = b->info()->quantization_info().uniform().offset;
-    _impl->run_vector_matrix_multiplication = a->info()->dimension(1) < 2;
-    _impl->reshape_b_only_on_first_run      = info.reshape_b_only_on_first_run();
-    _impl->is_prepared                      = false;
-    _impl->fused_assembly_path              = false;
-    _impl->flip_signedness                  = is_data_type_quantized_per_channel(b->info()->data_type()) && (a->info()->data_type() == DataType::QASYMM8) && _impl->reshape_b_only_on_first_run;
-    _impl->original_b                       = b;
-
-    _impl->asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>();
-
-    _impl->a_to_use = a;
-
-    // Convert to QASYMM8 -> QASYMM8_SIGNED and back
-    if(_impl->flip_signedness)
-    {
-        const int32_t                 offset_correction = 128;
-        const DataType                dt                = DataType::QASYMM8_SIGNED;
-        const UniformQuantizationInfo iqinfo            = _impl->a_to_use->info()->quantization_info().uniform();
-
-        _impl->signed_a.allocator()->init(_impl->a_to_use->info()->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction)));
-        _impl->memory_group.manage(&_impl->signed_a);
-        _impl->convert_to_signed_asymm = std::make_unique<NEConvertQuantizedSignednessKernel>();
-        _impl->convert_to_signed_asymm->configure(_impl->a_to_use, &_impl->signed_a);
-        _impl->a_to_use = &_impl->signed_a;
-        _impl->a_offset = _impl->signed_a.info()->quantization_info().uniform().offset;
-
-        const UniformQuantizationInfo oqinfo = output->info()->quantization_info().uniform();
-        _impl->memory_group.manage(&_impl->signed_output);
-        _impl->signed_output.allocator()->init(output->info()->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         = _impl->signed_output.info()->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 = &_impl->signed_a;
-    }
-
-    // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage
-    if(info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
-    {
-        _impl->fuse_output_stage = true;
-        _impl->memory_group.manage(&_impl->mm_result_s32);
-        TensorInfo info_mm_result_s32(output->info()->tensor_shape(), 1, DataType::S32);
-        _impl->mm_result_s32.allocator()->init(info_mm_result_s32);
-    }
-
-    // Initialize assembly kernel meta-data
-    const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info);
-#ifdef __aarch64__
-    switch(a->info()->data_type())
-    {
-        case DataType::QASYMM8:
-        case DataType::QASYMM8_SIGNED:
-        case DataType::U8:
-        case DataType::S8:
-        {
-            if(is_data_type_quantized_asymmetric(_impl->a_to_use->info()->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
-            {
-                auto c_info_to_use = c == nullptr ? nullptr : c->info();
-                _impl->asm_glue->configure(_impl->a_to_use->info(), b->info(), c_info_to_use, output->info(), asm_info);
-                _impl->fused_assembly_path = _impl->asm_glue->is_configured();
-                _impl->asm_glue_run_pack.add_const_tensor(TensorType::ACL_SRC_2, c);
-                _impl->asm_glue_run_pack.add_tensor(TensorType::ACL_DST, output);
-            }
-            else
-            {
-                auto output_to_use = (_impl->fuse_output_stage ? &_impl->mm_result_s32 : output);
-                _impl->asm_glue->configure(_impl->a_to_use->info(), b->info(), nullptr, output_to_use->info(), asm_info);
-                _impl->asm_glue_run_pack.add_tensor(TensorType::ACL_DST, output_to_use);
-            }
-            _impl->assembly_path = _impl->asm_glue->is_configured();
-
-            if(_impl->assembly_path)
-            {
-                _impl->asm_glue_run_pack.add_const_tensor(TensorType::ACL_SRC_0, _impl->a_to_use);
-
-                _impl->aux_mem_req        = _impl->asm_glue->workspace();
-                _impl->asm_glue_prep_pack = { { TensorType::ACL_SRC_1, b }, { TensorType::ACL_SRC_2, c } };
-
-                _impl->asm_glue_workspace = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->asm_glue_run_pack, _impl->asm_glue_prep_pack);
-            }
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("Datatype not supported");
-            break;
-        }
-    }
-#endif /* __aarch64__ */
-    if(!(_impl->assembly_path || _impl->run_vector_matrix_multiplication))
-    {
-        matrix_a = &_impl->tmp_a;
-        matrix_b = &_impl->tmp_b;
-
-        // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
-        TensorInfo a_info(compute_interleaved_shape(*_impl->a_to_use->info()), 1, _impl->a_to_use->info()->data_type(), _impl->a_to_use->info()->quantization_info());
-        // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
-        TensorInfo b_info(compute_transpose1xW_shape(*b->info()), 1, b->info()->data_type(), b->info()->quantization_info());
-        _impl->tmp_a.allocator()->init(a_info);
-        _impl->tmp_b.allocator()->init(b_info);
-        _impl->memory_group.manage(&_impl->tmp_a);
-        if(!_impl->reshape_b_only_on_first_run)
-        {
-            _impl->memory_group.manage(&_impl->tmp_b);
-        }
-
-        // Configure interleave kernel
-        _impl->mtx_a_reshape_kernel = std::make_unique<cpu::kernels::CpuGemmInterleave4x4Kernel>();
-        _impl->mtx_a_reshape_kernel->configure(_impl->a_to_use->info(), _impl->tmp_a.info());
-
-        // Configure transpose kernel
-        _impl->mtx_b_reshape_kernel = std::make_unique<cpu::kernels::CpuGemmTranspose1xWKernel>();
-        _impl->mtx_b_reshape_kernel->configure(b->info(), _impl->tmp_b.info());
-    }
-
-    if(!_impl->fused_assembly_path)
-    {
-        // Build reduction info
-        const GEMMLowpReductionKernelInfo reduction_info(_impl->a_to_use->info()->dimension(0), false, 0, false);
-
-        // Initialize matrix B reduction kernel only if _impl->a_offset is not equal to 0
-        if(_impl->a_offset != 0)
-        {
-            TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32);
-
-            _impl->vector_sum_col.allocator()->init(info_vector_sum_col);
-            if(!_impl->reshape_b_only_on_first_run)
-            {
-                _impl->memory_group.manage(&_impl->vector_sum_col);
-            }
-
-            // Configure Matrix B reduction kernel
-            _impl->mtx_b_reduction_kernel = std::make_unique<NEGEMMLowpMatrixBReductionKernel>();
-            _impl->mtx_b_reduction_kernel->configure(b, &_impl->vector_sum_col, reduction_info);
-        }
-
-        // Initialize Matrix A reduction kernel only if _impl->b_offset is not equal to 0
-        if(_impl->b_offset != 0)
-        {
-            TensorInfo info_vector_sum_row(compute_reductionB_shape(*_impl->a_to_use->info()), 1, DataType::S32);
-
-            _impl->vector_sum_row.allocator()->init(info_vector_sum_row);
-            _impl->memory_group.manage(&_impl->vector_sum_row);
-
-            // Configure matrix A reduction kernel
-            _impl->mtx_a_reduction_kernel = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
-            _impl->mtx_a_reduction_kernel->configure(_impl->a_to_use, &_impl->vector_sum_row, reduction_info);
-        }
-
-        if(_impl->fuse_output_stage)
-        {
-            // Configure matrix multiply kernel
-            if(!_impl->assembly_path)
-            {
-                _impl->mm_kernel = std::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
-                _impl->mm_kernel->configure(matrix_a, matrix_b, &_impl->mm_result_s32);
-            }
-
-            _impl->offset_contribution_output_stage_kernel = std::make_unique<NEGEMMLowpOffsetContributionOutputStageKernel>();
-            _impl->offset_contribution_output_stage_kernel->configure(&_impl->mm_result_s32,
-                                                                      _impl->a_offset == 0 ? nullptr : &_impl->vector_sum_col,
-                                                                      _impl->b_offset == 0 ? nullptr : &_impl->vector_sum_row, c,
-                                                                      _impl->flip_signedness ? &_impl->signed_output : output,
-                                                                      a->info()->dimension(0),
-                                                                      _impl->a_offset, _impl->b_offset, info.gemmlowp_output_stage());
-
-            if(_impl->flip_signedness)
-            {
-                _impl->convert_from_signed_asymm = std::make_unique<NEConvertQuantizedSignednessKernel>();
-                _impl->convert_from_signed_asymm->configure(&_impl->signed_output, output);
-            }
-        }
-        else
-        {
-            // Configure matrix multiply kernel
-            if(!_impl->assembly_path)
-            {
-                _impl->mm_kernel = std::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
-                _impl->mm_kernel->configure(matrix_a, matrix_b, output);
-            }
-            // Configure offset contribution kernel
-            _impl->offset_contribution_kernel = std::make_unique<NEGEMMLowpOffsetContributionKernel>();
-            _impl->offset_contribution_kernel->configure(output, _impl->a_offset == 0 ? nullptr : &_impl->vector_sum_col, _impl->b_offset == 0 ? nullptr : &_impl->vector_sum_row,
-                                                         _impl->a_to_use->info()->dimension(0),
-                                                         _impl->a_offset, _impl->b_offset);
-        }
-    }
-    // Configure activation
-    const ActivationLayerInfo &activation = gemm_info.activation_info();
-    _impl->run_activation                 = activation.enabled() && (!_impl->assembly_path || !cpu::CpuGemmAssemblyDispatch::is_activation_supported(activation));
-    if(_impl->run_activation)
-    {
-        _impl->activation_func = std::make_unique<NEActivationLayer>();
-        _impl->activation_func->configure(output, nullptr, activation);
-    }
-
-    // Allocate tensors
-    if(!_impl->assembly_path && !_impl->run_vector_matrix_multiplication)
-    {
-        _impl->tmp_a.allocator()->allocate();
-        if(!_impl->reshape_b_only_on_first_run)
-        {
-            _impl->tmp_b.allocator()->allocate();
-        }
-    }
-
-    if(!_impl->fused_assembly_path)
-    {
-        if(_impl->a_offset != 0 && !_impl->reshape_b_only_on_first_run)
-        {
-            _impl->vector_sum_col.allocator()->allocate();
-        }
-
-        if(_impl->b_offset != 0)
-        {
-            _impl->vector_sum_row.allocator()->allocate();
-        }
-    }
-
-    if(_impl->fuse_output_stage)
-    {
-        _impl->mm_result_s32.allocator()->allocate();
-    }
-
-    if(_impl->flip_signedness)
-    {
-        _impl->signed_a.allocator()->allocate();
-        _impl->signed_output.allocator()->allocate();
-    }
+    _impl->b  = b;
+    _impl->op = std::make_unique<cpu::CpuGemmLowpMatrixMultiplyCore>();
+    _impl->op->configure(a->info(), b->info(), (c != nullptr ? c->info() : nullptr), output->info(), gemm_info);
+    _impl->run_pack =
+    {
+        { TensorType::ACL_SRC_0, a },
+        { TensorType::ACL_SRC_1, b },
+        { TensorType::ACL_SRC_2, c },
+        { TensorType::ACL_DST, output }
+    };
+    _impl->prep_pack =
+    {
+        { TensorType::ACL_SRC_1, b },
+        { TensorType::ACL_SRC_2, c }
+    };
+    _impl->aux_mem_req       = _impl->op->workspace();
+    _impl->workspace_tensors = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
 }
 
 Status NEGEMMLowpMatrixMultiplyCore::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(NEConvertQuantizedSignednessKernel::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 cpu::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(cpu::CpuGemmAssemblyDispatch::validate(a_to_use, b, c, output, asm_info));
-        run_optimised_requantized = run_optimised;
-    }
-    else
-    {
-        run_optimised = bool(cpu::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(cpu::kernels::CpuGemmInterleave4x4Kernel::validate(a_to_use, &tmp_a_info));
-            ARM_COMPUTE_RETURN_ON_ERROR(cpu::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(NEGEMMLowpMatrixBReductionKernel::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(NEGEMMLowpMatrixAReductionKernel::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(), "NEGEMMLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D");
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "NEGEMMLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D");
-
-                ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info));
-            }
-
-            // Validate offset contribution kernel
-            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOffsetContributionOutputStageKernel::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(), "NEGEMMLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D");
-                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "NEGEMMLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D");
-
-                ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output));
-            }
-            // Validate offset contribution kernel
-            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOffsetContributionKernel::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(NEActivationLayer::validate(output, nullptr, activation));
-    }
-
-    return Status{};
+    return cpu::CpuGemmLowpMatrixMultiplyCore::validate(a, b, c, output, gemm_info);
 }
 
 void NEGEMMLowpMatrixMultiplyCore::run()
 {
     prepare();
-
     MemoryGroupResourceScope scope_mg(_impl->memory_group);
-
-    // Convert QASYMM8->QASYMM8_SIGNED
-    if(_impl->flip_signedness)
-    {
-        NEScheduler::get().schedule(_impl->convert_to_signed_asymm.get(), Window::DimY);
-    }
-
-    // Run GEMM
-    if(_impl->asm_glue->is_configured())
-    {
-        _impl->asm_glue->run(_impl->asm_glue_run_pack);
-    }
-    else
-    {
-        if(!_impl->run_vector_matrix_multiplication)
-        {
-            // Run interleave kernel
-            ITensorPack interleave_pack{ { ACL_SRC, _impl->a_to_use }, { ACL_DST, &_impl->tmp_a } };
-            NEScheduler::get().schedule_op(_impl->mtx_a_reshape_kernel.get(), Window::DimY, _impl->mtx_a_reshape_kernel->window(), interleave_pack);
-
-            if(!_impl->reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                ITensorPack reshape_b_pack{ { ACL_SRC, _impl->original_b }, { ACL_DST, &_impl->tmp_b } };
-                NEScheduler::get().schedule_op(_impl->mtx_b_reshape_kernel.get(), Window::DimY, _impl->mtx_b_reshape_kernel->window(), reshape_b_pack);
-            }
-        }
-        NEScheduler::get().schedule(_impl->mm_kernel.get(), Window::DimY);
-    }
-
-    if(!_impl->fused_assembly_path)
-    {
-        // Run matrix A reduction kernel only if _impl->b_offset is not equal to 0
-        if(_impl->b_offset != 0)
-        {
-            NEScheduler::get().schedule(_impl->mtx_a_reduction_kernel.get(), Window::DimX);
-        }
-
-        // Run matrix B reduction kernel only if _impl->a_offset is not equal to 0
-        if(_impl->a_offset != 0 && !_impl->reshape_b_only_on_first_run)
-        {
-            NEScheduler::get().schedule(_impl->mtx_b_reduction_kernel.get(), Window::DimX);
-        }
-
-        if(_impl->fuse_output_stage)
-        {
-            // Run offset contribution kernel
-            NEScheduler::get().schedule(_impl->offset_contribution_output_stage_kernel.get(), Window::DimY);
-        }
-        else
-        {
-            // Run offset contribution kernel
-            NEScheduler::get().schedule(_impl->offset_contribution_kernel.get(), Window::DimY);
-        }
-    }
-
-    // Convert QASYMM8_SIGNED->QASYMM8
-    if(!_impl->fused_assembly_path && _impl->fuse_output_stage && _impl->flip_signedness)
-    {
-        NEScheduler::get().schedule(_impl->convert_from_signed_asymm.get(), Window::DimY);
-    }
-
-    // Run fused activation unless already run in the fused assembly
-    if(_impl->run_activation)
-    {
-        _impl->activation_func->run();
-    }
+    _impl->op->run(_impl->run_pack);
 }
 
 void NEGEMMLowpMatrixMultiplyCore::prepare()
 {
     if(!_impl->is_prepared)
     {
-        // Run assembly reshape
-        if(_impl->asm_glue->is_configured())
-        {
-            _impl->asm_glue->prepare(_impl->asm_glue_prep_pack);
+        _impl->op->prepare(_impl->prep_pack);
 
-            auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
-                                            _impl->aux_mem_req.end(),
-                                            [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+        auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
+                                        _impl->aux_mem_req.end(),
+                                        [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
 
-            if(has_reshape != std::end(_impl->aux_mem_req))
-            {
-                _impl->original_b->mark_as_unused();
-            }
-            else
-            {
-                _impl->asm_glue_run_pack.add_const_tensor(ACL_SRC_1, _impl->original_b);
-            }
-        }
-        // Run non-assembly reshape
-        else if(_impl->reshape_b_only_on_first_run && !_impl->run_vector_matrix_multiplication && !_impl->asm_glue->is_configured())
+        if(has_reshape != std::end(_impl->aux_mem_req))
         {
-            // Run reshape kernel and mark original weights tensor as unused
-            _impl->tmp_b.allocator()->allocate();
-            ITensorPack reshape_b_pack{ { ACL_SRC, _impl->original_b }, { ACL_DST, &_impl->tmp_b } };
-            NEScheduler::get().schedule_op(_impl->mtx_b_reshape_kernel.get(), Window::DimY, _impl->mtx_b_reshape_kernel->window(), reshape_b_pack);
+            _impl->b->mark_as_unused();
         }
 
-        // Run matrix B reduction kernel only if _impl->a_offset is not equal to 0
-        if(!_impl->fused_assembly_path && _impl->a_offset != 0 && _impl->reshape_b_only_on_first_run)
+        // Release temporary tensors that are only used in prepare stage
+        for(auto &ws : _impl->workspace_tensors)
         {
-            _impl->vector_sum_col.allocator()->allocate();
-            NEScheduler::get().schedule(_impl->mtx_b_reduction_kernel.get(), Window::DimX);
+            const int slot = ws.first;
+            for(auto &m : _impl->aux_mem_req)
+            {
+                if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare)
+                {
+                    auto tensor = ws.second.get();
+                    tensor->allocator()->free();
+                    break;
+                }
+            }
         }
-
         _impl->is_prepared = true;
     }
 }