| /* |
| * Copyright (c) 2021-2024 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/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/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/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" |
| #include "src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h" |
| #include "src/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h" |
| #include "src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h" |
| #include "src/cpu/kernels/CpuGemmTranspose1xWKernel.h" |
| #include "src/cpu/operators/CpuActivation.h" |
| #include "src/cpu/operators/internal/CpuGemmAssemblyDispatch.h" |
| #include "src/cpu/utils/CpuAuxTensorHandler.h" |
| |
| using namespace arm_compute::misc::shape_calculator; |
| using namespace arm_compute::experimental; |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace |
| { |
| cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) |
| { |
| cpu::AsmGemmInfo asm_info; |
| asm_info.method = cpu::AsmConvMethod::Im2Col; |
| asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); |
| asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); |
| asm_info.activation_info = info.activation_info(); |
| asm_info.output_stage = info.gemmlowp_output_stage(); |
| asm_info.fast_mode = info.fast_math(); |
| asm_info.accumulate = info.accumulate(); |
| |
| 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)); |
| ARM_COMPUTE_LOG_PARAMS(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 = 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; |
| |
| _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__ |
| if (!(!b->are_values_constant() && |
| b->tensor_shape().z() > 1)) // Disable batch matmul as optimized GeMM handles batching differently. |
| { |
| 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) |
| { |
| const auto asm_mem_req = _asm_glue->workspace(); |
| for (unsigned int slot = 0; slot < asm_mem_req.size(); ++slot) |
| { |
| _aux_mem[slot] = asm_mem_req[slot]; |
| } |
| } |
| |
| // 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"); |
| |
| // When using accumulation(in place summation), for now, the only supported DataType for output is S32. |
| if (gemm_info.accumulate()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE, |
| "Accumulation is not supported for output QASYMM8/QASYMM8_SIGNED"); |
| } |
| |
| 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 (!(!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) |
| { |
| run_optimised = bool(CpuGemmAssemblyDispatch::validate(a_to_use, b, c, output, asm_info)); |
| run_optimised_requantized = run_optimised; |
| } |
| else |
| { |
| run_optimised = bool(CpuGemmAssemblyDispatch::validate( |
| a_to_use, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, asm_info)); |
| } |
| } |
| |
| if (run_optimised) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0)); |
| if (info.depth_output_gemm3d() != 0) |
| { |
| if (info.reinterpret_input_as_3d()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1)); |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), |
| "NEGEMM cannot reinterpret the input tensor as 3D"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, |
| "NEGEMM cannot reinterpret the output tensor as 3D"); |
| |
| const bool run_vector_matrix_multiplication = a->dimension(1) < 2; |
| if (!run_vector_matrix_multiplication) |
| { |
| matrix_a_info = &tmp_a_info; |
| matrix_b_info = &tmp_b_info; |
| |
| // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ] |
| TensorShape shape_tmp_a = a->tensor_shape(); |
| shape_tmp_a.set(0, a->dimension(0) * 4); |
| shape_tmp_a.set(1, std::ceil(a->dimension(1) / 4.f)); |
| |
| // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] |
| TensorShape shape_tmp_b = b->tensor_shape(); |
| shape_tmp_b.set(0, b->dimension(1) * 16); |
| shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f)); |
| |
| // Validate interleave kernel |
| auto_init_if_empty(tmp_a_info, a_to_use->clone()->set_tensor_shape(shape_tmp_a)); |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(shape_tmp_b)); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmInterleave4x4Kernel::validate(a_to_use, &tmp_a_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmTranspose1xWKernel::validate(b, &tmp_b_info)); |
| } |
| } |
| |
| if (!run_optimised_requantized) |
| { |
| TensorInfo info_vector_sum_col{}; |
| TensorInfo info_vector_sum_row{}; |
| |
| const GEMMLowpReductionKernelInfo reduction_info(a_to_use->dimension(0), false, 0, false); |
| |
| // Validate matrix B reduction kernel only if _a_offset is not equal to 0 |
| if (a_offset != 0) |
| { |
| info_vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32); |
| |
| // Configure Matrix B reduction kernel |
| ARM_COMPUTE_RETURN_ON_ERROR( |
| kernels::CpuGemmLowpMatrixBReductionKernel::validate(b, &info_vector_sum_col, reduction_info)); |
| } |
| |
| // Validate Matrix A reduction kernel only if _b_offset is not equal to 0 |
| if (b_offset != 0) |
| { |
| info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); |
| |
| // Configure matrix A reduction kernel |
| ARM_COMPUTE_RETURN_ON_ERROR( |
| kernels::CpuGemmLowpMatrixAReductionKernel::validate(a_to_use, &info_vector_sum_row, reduction_info)); |
| } |
| |
| if (fuse_output_stage) |
| { |
| if (!run_optimised) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| info.reinterpret_input_as_3d(), |
| "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| info.depth_output_gemm3d() != 0, |
| "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D"); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixMultiplyKernel::validate( |
| matrix_a_info, matrix_b_info, &mm_result_s32_info)); |
| } |
| |
| // Validate offset contribution kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionOutputStageKernel::validate( |
| &mm_result_s32_info, a_offset == 0 ? nullptr : &info_vector_sum_col, |
| b_offset == 0 ? nullptr : &info_vector_sum_row, c, flip_signedness ? &signed_output : output, a_offset, |
| b_offset, info.gemmlowp_output_stage())); |
| } |
| else |
| { |
| if (!run_optimised) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| info.reinterpret_input_as_3d(), |
| "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| info.depth_output_gemm3d() != 0, |
| "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D"); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR( |
| kernels::CpuGemmLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output)); |
| } |
| // Validate offset contribution kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionKernel::validate( |
| output, a_offset == 0 ? nullptr : &info_vector_sum_col, b_offset == 0 ? nullptr : &info_vector_sum_row, |
| a_offset, b_offset)); |
| } |
| } |
| |
| // Validate activation |
| const ActivationLayerInfo &activation = gemm_info.activation_info(); |
| if (activation.enabled()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(output, nullptr, activation)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) |
| { |
| prepare(tensors); |
| |
| auto a = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| auto b = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| auto c = tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| auto a_to_use = a; |
| auto matrix_a = a; |
| auto matrix_b = b; |
| |
| CpuAuxTensorHandler vector_sum_col(offset_int_vec(VectorSumCol), _vector_sum_col, tensors, false); |
| CpuAuxTensorHandler vector_sum_row(offset_int_vec(VectorSumRow), _vector_sum_row, tensors, false); |
| CpuAuxTensorHandler tmp_a(offset_int_vec(TmpA), _tmp_a, tensors, false); |
| CpuAuxTensorHandler tmp_b(offset_int_vec(TmpB), _tmp_b, tensors, true); |
| CpuAuxTensorHandler mm_result_s32(offset_int_vec(MMResultS32), _mm_result_s32, tensors, false); |
| CpuAuxTensorHandler signed_a(offset_int_vec(SignedA), _signed_a, tensors, false); |
| CpuAuxTensorHandler signed_output(offset_int_vec(SignedOutput), _signed_output, tensors, false); |
| |
| // Convert QASYMM8->QASYMM8_SIGNED |
| if (_flip_signedness) |
| { |
| ITensorPack pack = {{TensorType::ACL_SRC, a}, {TensorType::ACL_DST, signed_a.get()}}; |
| NEScheduler::get().schedule_op(_convert_to_signed_asymm.get(), Window::DimY, _convert_to_signed_asymm->window(), |
| pack); |
| a_to_use = signed_a.get(); |
| matrix_a = signed_a.get(); |
| } |
| |
| // Run GEMM |
| if (_asm_glue->is_configured()) |
| { |
| ITensorPack asm_glue_tensors = tensors; |
| auto output_to_use = (_fuse_output_stage ? mm_result_s32.get() : dst); |
| if (is_data_type_quantized_asymmetric(a_to_use->info()->data_type()) && |
| _gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) |
| { |
| asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use); |
| asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b); |
| asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_2, c); |
| asm_glue_tensors.add_tensor(TensorType::ACL_DST, dst); |
| } |
| else |
| { |
| asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use); |
| asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b); |
| asm_glue_tensors.add_tensor(TensorType::ACL_DST, output_to_use); |
| } |
| _asm_glue->run(asm_glue_tensors); |
| } |
| else |
| { |
| if (!_run_vector_matrix_multiplication) |
| { |
| matrix_a = tmp_a.get(); |
| matrix_b = tmp_b.get(); |
| // Run interleave kernel |
| ITensorPack pack_a = {{TensorType::ACL_SRC, a_to_use}, {TensorType::ACL_DST, tmp_a.get()}}; |
| NEScheduler::get().schedule_op(_mtx_a_reshape_kernel.get(), Window::DimY, _mtx_a_reshape_kernel->window(), |
| pack_a); |
| |
| if (!_reshape_b_only_on_first_run) |
| { |
| ITensorPack pack_b = {{TensorType::ACL_SRC, b}, {TensorType::ACL_DST, tmp_b.get()}}; |
| // Run transpose kernel |
| NEScheduler::get().schedule_op(_mtx_b_reshape_kernel.get(), Window::DimY, |
| _mtx_b_reshape_kernel->window(), pack_b); |
| } |
| } |
| ITensorPack pack_mm = {{TensorType::ACL_SRC_0, matrix_a}, {TensorType::ACL_SRC_1, matrix_b}}; |
| if (_fuse_output_stage) |
| { |
| pack_mm.add_tensor(TensorType::ACL_DST, mm_result_s32.get()); |
| } |
| else |
| { |
| pack_mm.add_tensor(TensorType::ACL_DST, dst); |
| } |
| NEScheduler::get().schedule_op(_mm_kernel.get(), Window::DimY, _mm_kernel->window(), pack_mm); |
| } |
| |
| if (!_fused_assembly_path) |
| { |
| // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| if (_b_offset != 0) |
| { |
| ITensorPack pack = {{TensorType::ACL_SRC, a_to_use}, {TensorType::ACL_DST, vector_sum_row.get()}}; |
| NEScheduler::get().schedule_op(_mtx_a_reduction_kernel.get(), Window::DimX, |
| _mtx_a_reduction_kernel->window(), pack); |
| } |
| |
| // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| if (_a_offset != 0 && !_reshape_b_only_on_first_run) |
| { |
| ITensorPack pack = {{TensorType::ACL_SRC, b}, {TensorType::ACL_DST, vector_sum_col.get()}}; |
| NEScheduler::get().schedule_op(_mtx_b_reduction_kernel.get(), Window::DimX, |
| _mtx_b_reduction_kernel->window(), pack); |
| } |
| |
| if (_fuse_output_stage) |
| { |
| ITensorPack pack; |
| pack.add_tensor(TensorType::ACL_SRC_0, mm_result_s32.get()); |
| pack.add_tensor(TensorType::ACL_SRC_1, _a_offset == 0 ? nullptr : vector_sum_col.get()); |
| pack.add_tensor(TensorType::ACL_SRC_2, _b_offset == 0 ? nullptr : vector_sum_row.get()); |
| pack.add_tensor(TensorType::ACL_SRC_3, c); |
| pack.add_tensor(TensorType::ACL_DST, _flip_signedness ? signed_output.get() : dst); |
| |
| // Run offset contribution kernel |
| NEScheduler::get().schedule_op(_offset_contribution_output_stage_kernel.get(), Window::DimY, |
| _offset_contribution_output_stage_kernel->window(), pack); |
| } |
| else |
| { |
| ITensorPack pack; |
| pack.add_tensor(TensorType::ACL_SRC_0, _a_offset == 0 ? nullptr : vector_sum_col.get()); |
| pack.add_tensor(TensorType::ACL_SRC_1, _b_offset == 0 ? nullptr : vector_sum_row.get()); |
| pack.add_tensor(TensorType::ACL_DST, dst); |
| |
| // Run offset contribution kernel |
| NEScheduler::get().schedule_op(_offset_contribution_kernel.get(), Window::DimY, |
| _offset_contribution_kernel->window(), pack); |
| } |
| } |
| |
| // Convert QASYMM8_SIGNED->QASYMM8 |
| if (!_fused_assembly_path && _fuse_output_stage && _flip_signedness) |
| { |
| ITensorPack pack = {{TensorType::ACL_SRC, signed_output.get()}, {TensorType::ACL_DST, dst}}; |
| NEScheduler::get().schedule_op(_convert_from_signed_asymm.get(), Window::DimY, |
| _convert_from_signed_asymm->window(), pack); |
| } |
| |
| // Run fused activation unless already run in the fused assembly |
| if (_run_activation) |
| { |
| ITensorPack pack = {{TensorType::ACL_SRC, dst}, {TensorType::ACL_DST, dst}}; |
| _activation_func->run(pack); |
| } |
| } |
| |
| void CpuGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors) |
| { |
| if (!_is_prepared) |
| { |
| auto original_b = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| // Run assembly reshape |
| if (_asm_glue->is_configured()) |
| { |
| _asm_glue->prepare(tensors); |
| } |
| // Run non-assembly reshape |
| else if (_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication && !_asm_glue->is_configured()) |
| { |
| // Run reshape kernel and mark original weights tensor as unused |
| ITensor *tmp_b_p = utils::cast::polymorphic_downcast<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 |