diff options
Diffstat (limited to 'src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp')
| -rw-r--r-- | src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp | 365 |
1 files changed, 188 insertions, 177 deletions
diff --git a/src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp b/src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp index 8ca128fb07..2ee879b67b 100644 --- a/src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp +++ b/src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp @@ -28,14 +28,14 @@ #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/core/Validate.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/common/utils/Log.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/MemoryHelpers.h" #include "src/cpu/kernels/CpuConvertQuantizedSignednessKernel.h" #include "src/cpu/kernels/CpuGemmInterleave4x4Kernel.h" #include "src/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h" @@ -59,12 +59,12 @@ namespace cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) { cpu::AsmGemmInfo asm_info; - asm_info.method = cpu::AsmConvMethod::Im2Col; - asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); - asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); - asm_info.activation_info = info.activation_info(); - asm_info.output_stage = info.gemmlowp_output_stage(); - asm_info.fast_mode = info.fast_math(); + asm_info.method = cpu::AsmConvMethod::Im2Col; + asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); + asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); + asm_info.activation_info = info.activation_info(); + asm_info.output_stage = info.gemmlowp_output_stage(); + asm_info.fast_mode = info.fast_math(); return asm_info; } @@ -105,7 +105,8 @@ CpuGemmLowpMatrixMultiplyCore::CpuGemmLowpMatrixMultiplyCore() } CpuGemmLowpMatrixMultiplyCore::~CpuGemmLowpMatrixMultiplyCore() = default; -void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *dst, const GEMMInfo &gemm_info) +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)); @@ -122,28 +123,31 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso _reshape_b_only_on_first_run = b->are_values_constant(); _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; + _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) + 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)); + _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)); + _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(); @@ -157,7 +161,7 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso } // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage - if(info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) + if (info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) { _fuse_output_stage = true; _mm_result_s32 = TensorInfo(dst->tensor_shape(), 1, DataType::S32); @@ -166,16 +170,18 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso // Initialize assembly kernel meta-data const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); #ifdef __aarch64__ - if(!(!b->are_values_constant() && b->tensor_shape().z() > 1)) // Disable batch matmul as optimized GeMM handles batching differently. + if (!(!b->are_values_constant() && + b->tensor_shape().z() > 1)) // Disable batch matmul as optimized GeMM handles batching differently. { - switch(a->data_type()) + 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) + 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); @@ -197,13 +203,14 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso } } #endif /* __aarch64__ */ - if(!(_assembly_path || _run_vector_matrix_multiplication)) + 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()); + _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()); @@ -216,13 +223,13 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso _mtx_b_reshape_kernel->configure(b, &_tmp_b); } - if(!_fused_assembly_path) + 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) + if (_a_offset != 0) { _vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32); @@ -232,7 +239,7 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso } // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 - if(_b_offset != 0) + if (_b_offset != 0) { _vector_sum_row = TensorInfo(compute_reductionB_shape(*a_to_use), 1, DataType::S32); @@ -241,24 +248,23 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso _mtx_a_reduction_kernel->configure(a_to_use, &_vector_sum_row, reduction_info); } - if(_fuse_output_stage) + if (_fuse_output_stage) { // Configure matrix multiply kernel - if(!_assembly_path) + 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()); + _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) + if (_flip_signedness) { _convert_from_signed_asymm = std::make_unique<kernels::CpuConvertQuantizedSignednessKernel>(); _convert_from_signed_asymm->configure(&_signed_output, dst); @@ -267,27 +273,29 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso else { // Configure matrix multiply kernel - if(!_assembly_path) + 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), + _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) + _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) + if (_assembly_path) { auto asm_mem_req = _asm_glue->workspace(); _aux_mem[AsmGemmWorkspace] = asm_mem_req[AsmGemmWorkspace]; @@ -295,27 +303,41 @@ void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITenso } // 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()); + _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) +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_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"); @@ -333,28 +355,32 @@ Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITens int32_t b_offset = b->quantization_info().uniform().offset; bool fuse_output_stage = info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE; - if(fuse_output_stage) + 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)); + 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) + 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)); + 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)); + 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(); @@ -374,25 +400,28 @@ Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITens bool run_optimised = false; bool run_optimised_requantized = false; - if(!(!b->are_values_constant() && b->tensor_shape().z() > 1)) // Disable batch matmul as optimized GeMM handles batching differently. + if (!(!b->are_values_constant() && + b->tensor_shape().z() > 1)) // Disable batch matmul as optimized GeMM handles batching differently. { - if(is_data_type_quantized_asymmetric(a_to_use->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + 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)); + run_optimised = bool(CpuGemmAssemblyDispatch::validate( + a_to_use, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, asm_info)); } } - if(run_optimised) + if (run_optimised) { ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0)); - if(info.depth_output_gemm3d() != 0) + if (info.depth_output_gemm3d() != 0) { - if(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)); @@ -409,11 +438,13 @@ Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITens } 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"); + 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) + if (!run_vector_matrix_multiplication) { matrix_a_info = &tmp_a_info; matrix_b_info = &tmp_b_info; @@ -437,7 +468,7 @@ Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITens } } - if(!run_optimised_requantized) + if (!run_optimised_requantized) { TensorInfo info_vector_sum_col{}; TensorInfo info_vector_sum_row{}; @@ -445,62 +476,70 @@ Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITens 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) + 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)); + 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) + 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)); + ARM_COMPUTE_RETURN_ON_ERROR( + kernels::CpuGemmLowpMatrixAReductionKernel::validate(a_to_use, &info_vector_sum_row, reduction_info)); } - if(fuse_output_stage) + if (fuse_output_stage) { - if(!run_optimised) + 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)); + 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())); + 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) + 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)); + 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)); + 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()) + if (activation.enabled()) { ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(output, nullptr, activation)); } @@ -529,24 +568,22 @@ void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) CpuAuxTensorHandler signed_output(offset_int_vec(SignedOutput), _signed_output, tensors, false); // Convert QASYMM8->QASYMM8_SIGNED - if(_flip_signedness) + 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); + ITensorPack pack = {{TensorType::ACL_SRC, a}, {TensorType::ACL_DST, signed_a.get()}}; + NEScheduler::get().schedule_op(_convert_to_signed_asymm.get(), Window::DimY, _convert_to_signed_asymm->window(), + pack); a_to_use = signed_a.get(); matrix_a = signed_a.get(); } // Run GEMM - if(_asm_glue->is_configured()) + 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) + 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); @@ -563,35 +600,25 @@ void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) } else { - if(!_run_vector_matrix_multiplication) + 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); + 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) + if (!_reshape_b_only_on_first_run) { - ITensorPack pack_b = - { - { TensorType::ACL_SRC, b }, - { TensorType::ACL_DST, tmp_b.get() } - }; + 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); + 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) + 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()); } @@ -602,31 +629,25 @@ void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) NEScheduler::get().schedule_op(_mm_kernel.get(), Window::DimY, _mm_kernel->window(), pack_mm); } - if(!_fused_assembly_path) + if (!_fused_assembly_path) { // Run matrix A reduction kernel only if _b_offset is not equal to 0 - if(_b_offset != 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); + 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) + 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); + 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) + if (_fuse_output_stage) { ITensorPack pack; pack.add_tensor(TensorType::ACL_SRC_0, mm_result_s32.get()); @@ -636,7 +657,8 @@ void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) 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); + NEScheduler::get().schedule_op(_offset_contribution_output_stage_kernel.get(), Window::DimY, + _offset_contribution_output_stage_kernel->window(), pack); } else { @@ -646,68 +668,57 @@ void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) 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); + 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) + 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); + 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) + if (_run_activation) { - ITensorPack pack = - { - { TensorType::ACL_SRC, dst }, - { TensorType::ACL_DST, dst } - }; + ITensorPack pack = {{TensorType::ACL_SRC, dst}, {TensorType::ACL_DST, dst}}; _activation_func->run(pack); } } void CpuGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors) { - if(!_is_prepared) + if (!_is_prepared) { auto original_b = tensors.get_const_tensor(TensorType::ACL_SRC_1); // Run assembly reshape - if(_asm_glue->is_configured()) + if (_asm_glue->is_configured()) { _asm_glue->prepare(tensors); } // Run non-assembly reshape - else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication && !_asm_glue->is_configured()) + 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))); + 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); + 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) + 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))); + 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); + 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; } |