diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp | 148 |
1 files changed, 118 insertions, 30 deletions
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 617d66cf24..01a99f7aca 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -42,9 +42,9 @@ using namespace arm_compute::misc::shape_calculator; NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(memory_manager), _asm_glue(memory_manager), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), - _offset_contribution_kernel(), _offset_contribution_output_stage_kernel(), _activation_func(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _mm_result_s32(), _original_b(nullptr), - _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) + _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(), _original_b(nullptr), _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) { } @@ -56,6 +56,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, const ITensor *matrix_a = a; const ITensor *matrix_b = b; + GEMMInfo info = gemm_info; // Clear state _mtx_a_reshape_kernel = nullptr; @@ -65,13 +66,41 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, _a_offset = a->info()->quantization_info().uniform().offset; _b_offset = b->info()->quantization_info().uniform().offset; _run_vector_matrix_multiplication = a->info()->dimension(1) < 2; - _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); + _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->info()->data_type()) && (a->info()->data_type() == DataType::QASYMM8) && _reshape_b_only_on_first_run; _original_b = b; + const ITensor *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->info()->quantization_info().uniform(); + + _signed_a.allocator()->init(a_to_use->info()->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction))); + _memory_group.manage(&_signed_a); + _convert_to_signed_asymm.configure(a_to_use, &_signed_a); + a_to_use = &_signed_a; + _a_offset = _signed_a.info()->quantization_info().uniform().offset; + + const UniformQuantizationInfo oqinfo = output->info()->quantization_info().uniform(); + _memory_group.manage(&_signed_output); + _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 = _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); + } + // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage - if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) + if(info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) { _fuse_output_stage = true; _memory_group.manage(&_mm_result_s32); @@ -83,17 +112,18 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, switch(a->info()->data_type()) { case DataType::QASYMM8: + case DataType::QASYMM8_SIGNED: case DataType::U8: case DataType::S8: { - if(a->info()->data_type() == DataType::QASYMM8 && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + if(a_to_use->info()->data_type() == DataType::QASYMM8 && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { - _asm_glue.configure(a, b, c, output, gemm_info); + _asm_glue.configure(a_to_use, b, c, output, gemm_info); _fused_assembly_path = _asm_glue.is_configured(); } else { - _asm_glue.configure(a, b, nullptr, _fuse_output_stage ? &_mm_result_s32 : output, gemm_info); + _asm_glue.configure(a_to_use, b, nullptr, _fuse_output_stage ? &_mm_result_s32 : output, gemm_info); } _assembly_path = _asm_glue.is_configured(); break; @@ -111,7 +141,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, matrix_b = &_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(*a->info()), 1, a->info()->data_type(), a->info()->quantization_info()); + TensorInfo a_info(compute_interleaved_shape(*a_to_use->info()), 1, a_to_use->info()->data_type(), 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()); _tmp_a.allocator()->init(a_info); @@ -125,7 +155,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, // Configure interleave kernel { auto k = arm_compute::support::cpp14::make_unique<NEGEMMInterleave4x4Kernel>(); - k->configure(a, &_tmp_a); + k->configure(a_to_use, &_tmp_a); _mtx_a_reshape_kernel = std::move(k); } @@ -151,19 +181,19 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, } // Configure Matrix B reduction kernel - _mtx_b_reduction_kernel.configure(b, &_vector_sum_col, a->info()->dimension(0), false); + _mtx_b_reduction_kernel.configure(b, &_vector_sum_col, a_to_use->info()->dimension(0), false); } // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 if(_b_offset != 0) { - TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 1, DataType::S32); + TensorInfo info_vector_sum_row(compute_reductionB_shape(*a_to_use->info()), 1, DataType::S32); _vector_sum_row.allocator()->init(info_vector_sum_row); _memory_group.manage(&_vector_sum_row); // Configure matrix A reduction kernel - _mtx_a_reduction_kernel.configure(a, &_vector_sum_row, a->info()->dimension(0), false); + _mtx_a_reduction_kernel.configure(a_to_use, &_vector_sum_row, a_to_use->info()->dimension(0), false); } if(_fuse_output_stage) @@ -176,8 +206,17 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, _mm_kernel = std::move(k); } - _offset_contribution_output_stage_kernel.configure(&_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, c, output, a->info()->dimension(0), - _a_offset, _b_offset, gemm_info.gemmlowp_output_stage()); + _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 : output, + a->info()->dimension(0), + _a_offset, _b_offset, info.gemmlowp_output_stage()); + + if(_flip_signedness) + { + _convert_from_signed_asymm.configure(&_signed_output, output); + } } else { @@ -189,7 +228,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, _mm_kernel = std::move(k); } // Configure offset contribution kernel - _offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a->info()->dimension(0), _a_offset, _b_offset); + _offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a_to_use->info()->dimension(0), _a_offset, _b_offset); } } @@ -228,22 +267,31 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, { _mm_result_s32.allocator()->allocate(); } + + if(_flip_signedness) + { + _signed_a.allocator()->allocate(); + _signed_output.allocator()->allocate(); + } } 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); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::QASYMM8, DataType::QSYMM8_PER_CHANNEL); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b); 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{}; @@ -251,31 +299,57 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso int32_t a_offset = a->quantization_info().uniform().offset; int32_t b_offset = b->quantization_info().uniform().offset; - bool fuse_output_stage = gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE; + 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); + } + // 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->data_type())) + if(a_to_use->data_type() == DataType::QASYMM8 && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { - run_optimised = bool(NEGEMMAssemblyDispatch::validate(a, b, c, output, gemm_info)); + run_optimised = bool(NEGEMMAssemblyDispatch::validate(a_to_use, b, c, output, gemm_info)); run_optimised_requantized = run_optimised; } else { - run_optimised = bool(NEGEMMAssemblyDispatch::validate(a, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, gemm_info)); + run_optimised = bool(NEGEMMAssemblyDispatch::validate(a_to_use, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, gemm_info)); } if(run_optimised) { ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0)); - if(gemm_info.depth_output_gemm3d() != 0) + if(info.depth_output_gemm3d() != 0) { - if(gemm_info.reinterpret_input_as_3d()) + 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)); @@ -292,8 +366,8 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso } else { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D"); + 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) @@ -312,10 +386,10 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f)); // Validate interleave kernel - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(shape_tmp_a)); + 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(NEGEMMInterleave4x4Kernel::validate(a, &tmp_a_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(a_to_use, &tmp_a_info)); ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(b, &tmp_b_info)); } } @@ -340,7 +414,7 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso 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, &info_vector_sum_row, a->dimension(0), false)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(a_to_use, &info_vector_sum_row, a->dimension(0), false)); } if(fuse_output_stage) @@ -354,8 +428,10 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso 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, output, a_offset, b_offset, - gemm_info.gemmlowp_output_stage())); + c, + flip_signedness ? &signed_output : output, + a_offset, b_offset, + info.gemmlowp_output_stage())); } else { @@ -397,6 +473,12 @@ void NEGEMMLowpMatrixMultiplyCore::run() NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY); } + // Convert QASYMM8->QASYMM8_SIGNED + if(_flip_signedness) + { + NEScheduler::get().schedule(&_convert_to_signed_asymm, Window::DimY); + } + // Run GEMM if(_asm_glue.is_configured()) { @@ -433,6 +515,12 @@ void NEGEMMLowpMatrixMultiplyCore::run() } } + // Convert QASYMM8_SIGNED->QASYMM8 + if(_flip_signedness) + { + NEScheduler::get().schedule(&_convert_from_signed_asymm, Window::DimY); + } + // Run fused activation if(_run_activation) { |