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>
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 0aba3c0..641a2c2 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)
+ _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 =
{
- 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)
+ { TensorType::ACL_SRC_0, a },
+ { TensorType::ACL_SRC_1, b },
+ { TensorType::ACL_SRC_2, c },
+ { TensorType::ACL_DST, output }
+ };
+ _impl->prep_pack =
{
- _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();
- }
+ { 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->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; });
+
+ if(has_reshape != std::end(_impl->aux_mem_req))
{
- _impl->asm_glue->prepare(_impl->asm_glue_prep_pack);
+ _impl->b->mark_as_unused();
+ }
- 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))
+ // Release temporary tensors that are only used in prepare stage
+ for(auto &ws : _impl->workspace_tensors)
+ {
+ const int slot = ws.first;
+ for(auto &m : _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);
+ if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare)
+ {
+ auto tensor = ws.second.get();
+ tensor->allocator()->free();
+ break;
+ }
}
}
- // Run non-assembly reshape
- else if(_impl->reshape_b_only_on_first_run && !_impl->run_vector_matrix_multiplication && !_impl->asm_glue->is_configured())
- {
- // 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);
- }
-
- // 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)
- {
- _impl->vector_sum_col.allocator()->allocate();
- NEScheduler::get().schedule(_impl->mtx_b_reduction_kernel.get(), Window::DimX);
- }
-
_impl->is_prepared = true;
}
}
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
index f3a3d23..946791a 100644
--- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
@@ -23,6 +23,7 @@
*/
#include "arm_compute/runtime/NEON/functions/NEQLSTMLayer.h"
+#include "arm_compute/core/ITensorPack.h"
#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/QuantizationInfo.h"
#include "arm_compute/core/Utils.h"
@@ -30,12 +31,8 @@
#include "arm_compute/core/utils/misc/InfoHelpers.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/NEON/NEScheduler.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/NEON/kernels/NEQLSTMLayerNormalizationKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h"
#include "src/core/helpers/WindowHelpers.h"
namespace arm_compute
@@ -223,29 +220,29 @@
_input_to_input_weights = lstm_params.input_to_input_weights();
_recurrent_to_input_weights = lstm_params.recurrent_to_input_weights();
- _input_to_input_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _recurrent_to_input_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _input_to_input_reduction->configure(_input_to_input_weights, &_input_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_input_reduction->configure(_recurrent_to_input_weights, &_recurrent_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _recurrent_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _input_to_input_reduction->configure(_input_to_input_weights->info(), _input_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_input_reduction->configure(_recurrent_to_input_weights->info(), _recurrent_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
}
- _input_to_forget_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _recurrent_to_forget_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _input_to_cell_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _recurrent_to_cell_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _input_to_output_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _recurrent_to_output_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
+ _input_to_forget_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _recurrent_to_forget_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _input_to_cell_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _recurrent_to_cell_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _input_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _recurrent_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
- _recurrent_to_cell_reduction->configure(input_to_forget_weights, &_input_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_forget_reduction->configure(recurrent_to_forget_weights, &_recurrent_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_cell_reduction->configure(input_to_cell_weights, &_input_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_cell_reduction->configure(recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
- _input_to_output_reduction->configure(input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
- _recurrent_to_output_reduction->configure(recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_forget_reduction->configure(input_to_forget_weights->info(), _input_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_forget_reduction->configure(recurrent_to_forget_weights->info(), _recurrent_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_cell_reduction->configure(input_to_cell_weights->info(), _input_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_cell_reduction->configure(recurrent_to_cell_weights->info(), _recurrent_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+ _input_to_output_reduction->configure(input_to_output_weights->info(), _input_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+ _recurrent_to_output_reduction->configure(recurrent_to_output_weights->info(), _recurrent_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
if(_has_projection)
{
- _projection_reduction = std::make_unique<NEGEMMLowpMatrixAReductionKernel>();
- _projection_reduction->configure(_projection_weights, &_projection_eff_bias, GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
+ _projection_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _projection_reduction->configure(_projection_weights->info(), _projection_eff_bias.info(), GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
if(_projection_bias != nullptr)
{
_projection_bias_add.configure(_projection_bias, &_projection_eff_bias, &_projection_eff_bias, ConvertPolicy::SATURATE);
@@ -658,21 +655,26 @@
const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32);
if(!lstm_params.has_cifg_opt())
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.recurrent_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset,
- true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
+ -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.recurrent_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
+ -qoutput_state_in.offset,
+ true)));
}
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
+ -qoutput_state_in.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset,
+ true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false,
+ -qoutput_state_in.offset, true)));
if(lstm_params.has_projection())
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false,
- lstm_params.hidden_state_zero(),
- true)));
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false,
+ lstm_params.hidden_state_zero(),
+ true)));
if(lstm_params.projection_bias() != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.projection_bias(), 1, DataType::S32);
@@ -1107,8 +1109,20 @@
{
_input_to_input_eff_bias.allocator()->allocate();
_recurrent_to_input_eff_bias.allocator()->allocate();
- NEScheduler::get().schedule(_input_to_input_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_recurrent_to_input_reduction.get(), Window::DimY);
+
+ ITensorPack packII =
+ {
+ { TensorType::ACL_SRC, _input_to_input_weights },
+ { TensorType::ACL_DST, &_input_to_input_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_input_to_input_reduction.get(), Window::DimY, _input_to_input_reduction->window(), packII);
+
+ ITensorPack packRI =
+ {
+ { TensorType::ACL_SRC, _recurrent_to_input_weights },
+ { TensorType::ACL_DST, &_recurrent_to_input_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_recurrent_to_input_reduction.get(), Window::DimY, _recurrent_to_input_reduction->window(), packRI);
_input_to_input_weights_transposed.allocator()->allocate();
_recurrent_to_input_weights_transposed.allocator()->allocate();
@@ -1123,17 +1137,58 @@
_recurrent_to_cell_eff_bias.allocator()->allocate();
_input_to_output_eff_bias.allocator()->allocate();
_recurrent_to_output_eff_bias.allocator()->allocate();
- NEScheduler::get().schedule(_input_to_forget_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_recurrent_to_forget_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_input_to_cell_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_recurrent_to_cell_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_input_to_output_reduction.get(), Window::DimY);
- NEScheduler::get().schedule(_recurrent_to_output_reduction.get(), Window::DimY);
+
+ ITensorPack packIF =
+ {
+ { TensorType::ACL_SRC, _input_to_forget_weights },
+ { TensorType::ACL_DST, &_input_to_forget_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_input_to_forget_reduction.get(), Window::DimY, _input_to_forget_reduction->window(), packIF);
+
+ ITensorPack packRF =
+ {
+ { TensorType::ACL_SRC, _recurrent_to_forget_weights },
+ { TensorType::ACL_DST, &_recurrent_to_forget_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_recurrent_to_forget_reduction.get(), Window::DimY, _recurrent_to_forget_reduction->window(), packRF);
+
+ ITensorPack packIC =
+ {
+ { TensorType::ACL_SRC, _input_to_cell_weights },
+ { TensorType::ACL_DST, &_input_to_cell_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_input_to_cell_reduction.get(), Window::DimY, _input_to_cell_reduction->window(), packIC);
+
+ ITensorPack packRC =
+ {
+ { TensorType::ACL_SRC, _recurrent_to_cell_weights },
+ { TensorType::ACL_DST, &_recurrent_to_cell_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_recurrent_to_cell_reduction.get(), Window::DimY, _recurrent_to_cell_reduction->window(), packRC);
+
+ ITensorPack packIO =
+ {
+ { TensorType::ACL_SRC, _input_to_output_weights },
+ { TensorType::ACL_DST, &_input_to_output_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_input_to_output_reduction.get(), Window::DimY, _input_to_output_reduction->window(), packIO);
+
+ ITensorPack packRO =
+ {
+ { TensorType::ACL_SRC, _recurrent_to_output_weights },
+ { TensorType::ACL_DST, &_recurrent_to_output_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_recurrent_to_output_reduction.get(), Window::DimY, _recurrent_to_output_reduction->window(), packRO);
if(_has_projection)
{
_projection_eff_bias.allocator()->allocate();
- NEScheduler::get().schedule(_projection_reduction.get(), Window::DimY);
+ ITensorPack pack =
+ {
+ { TensorType::ACL_SRC, _projection_weights },
+ { TensorType::ACL_DST, &_projection_eff_bias }
+ };
+ NEScheduler::get().schedule_op(_projection_reduction.get(), Window::DimY, _projection_reduction->window(), pack);
if(_projection_bias != nullptr)
{
_projection_bias_add.run();
diff --git a/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp
new file mode 100644
index 0000000..651ce43
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp
@@ -0,0 +1,717 @@
+/*
+ * 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();
+
+ return asm_info;
+}
+} // namespace
+
+CpuGemmLowpMatrixMultiplyCore::CpuGemmLowpMatrixMultiplyCore()
+ : _asm_glue(std::make_unique<CpuGemmAssemblyDispatch>()),
+ _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<cpu::CpuGemmAssemblyDispatch>();
+
+ 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<kernels::CpuConvertQuantizedSignednessKernel>();
+ _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<kernels::CpuGemmInterleave4x4Kernel>();
+ _mtx_a_reshape_kernel->configure(a_to_use, &_tmp_a);
+
+ // Configure transpose kernel
+ _mtx_b_reshape_kernel = std::make_unique<kernels::CpuGemmTranspose1xWKernel>();
+ _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<kernels::CpuGemmLowpMatrixBReductionKernel>();
+ _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<kernels::CpuGemmLowpMatrixAReductionKernel>();
+ _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<kernels::CpuGemmLowpMatrixMultiplyKernel>();
+ _mm_kernel->configure(matrix_a, matrix_b, &_mm_result_s32);
+ }
+
+ _offset_contribution_output_stage_kernel = std::make_unique<kernels::CpuGemmLowpOffsetContributionOutputStageKernel>();
+ _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<kernels::CpuConvertQuantizedSignednessKernel>();
+ _convert_from_signed_asymm->configure(&_signed_output, dst);
+ }
+ }
+ else
+ {
+ // Configure matrix multiply kernel
+ if(!_assembly_path)
+ {
+ _mm_kernel = std::make_unique<kernels::CpuGemmLowpMatrixMultiplyKernel>();
+ _mm_kernel->configure(matrix_a, matrix_b, dst);
+ }
+ // Configure offset contribution kernel
+ _offset_contribution_kernel = std::make_unique<kernels::CpuGemmLowpOffsetContributionKernel>();
+ _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<CpuActivation>();
+ _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();
+ }
+
+ // 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);
+
+ auto has_reshape = std::find_if(_aux_mem.begin(),
+ _aux_mem.end(),
+ [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+
+ if(has_reshape != std::end(_aux_mem))
+ {
+ original_b->mark_as_unused();
+ }
+ }
+ // 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<ITensor *>(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<ITensor *>(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
diff --git a/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h
new file mode 100644
index 0000000..1d0e470
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h
@@ -0,0 +1,174 @@
+/*
+ * 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.
+ */
+#ifndef ARM_COMPUTE_CPU_GEMMLOWP_MATRIXMULTIPLY_CORE_H
+#define ARM_COMPUTE_CPU_GEMMLOWP_MATRIXMULTIPLY_CORE_H
+
+#include "arm_compute/core/TensorInfo.h"
+#include "src/core/common/Macros.h"
+#include "src/runtime/cpu/ICpuOperator.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+class CpuGemmInterleave4x4Kernel;
+class CpuGemmLowpMatrixMultiplyKernel;
+class CpuGemmLowpOffsetContributionKernel;
+class CpuGemmLowpOffsetContributionOutputStageKernel;
+class CpuGemmLowpMatrixAReductionKernel;
+class CpuGemmLowpMatrixBReductionKernel;
+class CpuGemmTranspose1xWKernel;
+class CpuConvertQuantizedSignednessKernel;
+} // namespace kernels
+class CpuGemmAssemblyDispatch;
+class CpuActivation;
+
+/** Basic function to execute GEMMLowpMatrixMultiplyCore. This function calls the following kernels if the DOT product instruction is not available:
+ *
+ * -# @ref kernels::CpuGemmInterleave4x4Kernel
+ * -# @ref kernels::CpuGemmTranspose1xWKernel
+ * -# @ref kernels::CpuGemmLowpMatrixMultiplyKernel
+ * -# @ref kernels::CpuGemmLowpOffsetContributionKernel
+ * -# @ref CpuActivation
+ *
+ * otherwise if the DOT product instruction is available:
+ *
+ * -# @ref kernels::CpuGemmLowpOffsetContributionKernel
+ *
+*/
+class CpuGemmLowpMatrixMultiplyCore : public ICpuOperator
+{
+public:
+ /** Constructor */
+ CpuGemmLowpMatrixMultiplyCore();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpMatrixMultiplyCore);
+ /** Destructor */
+ ~CpuGemmLowpMatrixMultiplyCore();
+ /** Initialise the kernel's inputs, output
+ *
+ * Valid data layouts:
+ * - NHWC
+ * - NCHW
+ *
+ * Valid data type configurations:
+ * |src0 |src1 |src2 |dst |
+ * |:--------------|:------------------|:--------|:--------------|
+ * |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
+ * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 |
+ * |QASYMM8 |QSYMM8 |S32 |QASYMM8 |
+ * |QASYMM8 |QASYMM8 |S32 |S32 |
+ * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |S32 |
+ * |QASYMM8 |QSYMM8 |S32 |S32 |
+ * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
+ * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED |
+ * |QASYMM8_SIGNED |QSYMM8 |S32 |QASYMM8_SIGNED |
+ * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |S32 |
+ * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |S32 |
+ * |QASYMM8_SIGNED |QSYMM8 |S32 |S32 |
+ *
+ * @note GEMM_LOWP: low precision GEMM kernel
+ * This kernel performs the following computations:
+ *
+ * -# Convert a values from QASYMM8 to int32 and add a_offset to each of them.
+ * -# Convert b values from QASYMM8 to int32 add b_offset to each of them.
+ * -# Compute the matrix product of the resulting a * b in int32.
+ *
+ * @note The @p output type is S32 if @p gemm_info.type == GEMMLowpOutputStageType::NONE. It is QASYMM8/QASYMM8_SIGNED otherwise
+ *
+ * @param[in] a First input tensor info (Matrix A). Data type supported: QASYMM8/QASYMM8_SIGNED.
+ * @param[in] b Second input tensor info (Matrix B). Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL.
+ * @param[in] c Third input tensor info (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[out] dst Output tensor info. Data type supported: Data type supported: S32/QASYMM8/QASYMM8_SIGNED
+ * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
+ * if the reshape of matrix B should be executed only for the first run
+ */
+ void configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *dst, const GEMMInfo &gemm_info = GEMMInfo());
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to CpuGemmLowpMatrixMultiplyCore::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *dst, const GEMMInfo &gemm_info = GEMMInfo());
+
+ // Inherited methods overridden:
+ void run(ITensorPack &tensors) override;
+ void prepare(ITensorPack &tensors) override;
+ experimental::MemoryRequirements workspace() const override;
+
+private:
+ enum AuxTensorIdx
+ {
+ AsmGemmWorkspace = 0,
+ Pretranspose,
+ VectorSumCol,
+ VectorSumRow,
+ TmpA,
+ TmpB,
+ MMResultS32,
+ SignedA,
+ SignedOutput,
+ Count
+ };
+
+ std::unique_ptr<CpuGemmAssemblyDispatch> _asm_glue;
+ std::unique_ptr<kernels::CpuGemmLowpMatrixMultiplyKernel> _mm_kernel;
+ std::unique_ptr<kernels::CpuGemmInterleave4x4Kernel> _mtx_a_reshape_kernel;
+ std::unique_ptr<kernels::CpuGemmTranspose1xWKernel> _mtx_b_reshape_kernel;
+ std::unique_ptr<kernels::CpuGemmLowpMatrixAReductionKernel> _mtx_a_reduction_kernel;
+ std::unique_ptr<kernels::CpuGemmLowpMatrixBReductionKernel> _mtx_b_reduction_kernel;
+ std::unique_ptr<kernels::CpuGemmLowpOffsetContributionKernel> _offset_contribution_kernel;
+ std::unique_ptr<kernels::CpuGemmLowpOffsetContributionOutputStageKernel> _offset_contribution_output_stage_kernel;
+ std::unique_ptr<CpuActivation> _activation_func;
+ std::unique_ptr<kernels::CpuConvertQuantizedSignednessKernel> _convert_to_signed_asymm;
+ std::unique_ptr<kernels::CpuConvertQuantizedSignednessKernel> _convert_from_signed_asymm;
+
+ TensorInfo _vector_sum_col;
+ TensorInfo _vector_sum_row;
+ TensorInfo _tmp_a;
+ TensorInfo _tmp_b;
+ TensorInfo _mm_result_s32;
+ TensorInfo _signed_a;
+ TensorInfo _signed_output;
+ int32_t _a_offset;
+ int32_t _b_offset;
+
+ bool _run_vector_matrix_multiplication;
+ bool _assembly_path;
+ bool _fused_assembly_path;
+ bool _reshape_b_only_on_first_run;
+ bool _is_prepared;
+ bool _fuse_output_stage;
+ bool _run_activation;
+ bool _flip_signedness;
+ GEMMInfo _gemm_info;
+ experimental::MemoryRequirements _aux_mem{};
+};
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_GEMMLOWP_MATRIXMULTIPLY_CORE_H */
diff --git a/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h b/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h
index 152e3c6..1cf717c 100644
--- a/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h
+++ b/src/runtime/gpu/cl/utils/ClAuxTensorHandler.h
@@ -41,6 +41,10 @@
CLAuxTensorHandler(int slot_id, TensorInfo &info, ITensorPack &pack, bool pack_inject = false)
: _tensor()
{
+ if(info.total_size() == 0)
+ {
+ return;
+ }
_tensor.allocator()->soft_init(info);
ICLTensor *packed_tensor = utils::cast::polymorphic_downcast<ICLTensor *>(pack.get_tensor(slot_id));