// // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "NeonLstmFloatWorkload.hpp" #include "NeonWorkloadUtils.hpp" #include #include #include #include "neon/NeonTensorHandle.hpp" namespace armnn { using namespace armcomputetensorutils; NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info) : FloatWorkload(descriptor, info) { // Report Profiling Details ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct", descriptor.m_Parameters, info, GetGuid()); arm_compute::LSTMParams lstm_param; // Basic parameters m_InputToForgetWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); m_InputToCellWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); m_InputToOutputWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); m_RecurrentToForgetWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); m_RecurrentToCellWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); m_RecurrentToOutputWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); m_ForgetGateBiasTensor = std::make_unique(); BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); m_CellBiasTensor = std::make_unique(); BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); m_OutputGateBiasTensor = std::make_unique(); BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo()); // for future reference: check the AndroidNN API for the logic here if (!m_Data.m_Parameters.m_CifgEnabled) { m_InputToInputWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); m_RecurrentToInputWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); m_CellToInputWeightsTensor = std::make_unique(); if (m_Data.m_CellToInputWeights != nullptr) { BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); } m_InputGateBiasTensor = std::make_unique(); BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo()); lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(), m_RecurrentToInputWeightsTensor.get(), m_Data.m_CellToInputWeights != nullptr ? m_CellToInputWeightsTensor.get() : nullptr, m_InputGateBiasTensor.get()); } if (m_Data.m_Parameters.m_ProjectionEnabled) { m_ProjectionWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); m_ProjectionBiasTensor = std::make_unique(); if (m_Data.m_ProjectionBias != nullptr) { BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo()); } lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(), m_Data.m_ProjectionBias != nullptr ? m_ProjectionBiasTensor.get() : nullptr); } if (m_Data.m_Parameters.m_PeepholeEnabled) { m_CellToForgetWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); m_CellToOutputWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo()); lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get()); } if (m_Data.m_Parameters.m_LayerNormEnabled) { m_InputLayerNormWeightsTensor = std::make_unique(); if (!m_Data.m_Parameters.m_CifgEnabled) { BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); } m_ForgetLayerNormWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); m_CellLayerNormWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); m_OutputLayerNormWeightsTensor = std::make_unique(); BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo()); lstm_param.set_layer_normalization_params(m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get(), m_ForgetLayerNormWeightsTensor.get(), m_CellLayerNormWeightsTensor.get(), m_OutputLayerNormWeightsTensor.get()); } const arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); const arm_compute::ITensor& output_state_in = static_cast(m_Data.m_Inputs[1])->GetTensor(); const arm_compute::ITensor& cell_state_in = static_cast(m_Data.m_Inputs[2])->GetTensor(); arm_compute::ITensor& output_state_out = static_cast(m_Data.m_Outputs[1])->GetTensor(); arm_compute::ITensor& cell_state_out = static_cast(m_Data.m_Outputs[2])->GetTensor(); arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[3])->GetTensor(); // Get the batch_size and the num_units from the cellStateIn dimensions const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2]; const unsigned int batch_size = armnn::numeric_cast(inputTensorInfo.GetShape()[0]); const unsigned int num_units = armnn::numeric_cast(inputTensorInfo.GetShape()[1]); m_ScratchBuffer = std::make_unique(); if (m_Data.m_Parameters.m_CifgEnabled) { // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG armnn::TensorInfo scratchBuffer1({ batch_size, num_units * 3 }, DataType::Float32); BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1); } else { // scratch_buffer [num_units * 4, batch_size] without CIFG armnn::TensorInfo scratchBuffer2({ batch_size, num_units * 4 }, DataType::Float32); BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2); } float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell; float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations arm_compute::ActivationLayerInfo activationLayerInfo = ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(), m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(), m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(), m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(), &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out, &cell_state_out, &output, lstm_param, activationLayerInfo, cell_threshold, projection_threshold); armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer); InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights); InitializeArmComputeTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights); InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights); InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights); InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights); InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights); InitializeArmComputeTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias); InitializeArmComputeTensorData(*m_CellBiasTensor, m_Data.m_CellBias); InitializeArmComputeTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias); if (!m_Data.m_Parameters.m_CifgEnabled) { InitializeArmComputeTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights); InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights); if (m_Data.m_CellToInputWeights != nullptr) { InitializeArmComputeTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights); } InitializeArmComputeTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias); } if (m_Data.m_Parameters.m_ProjectionEnabled) { InitializeArmComputeTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights); if (m_Data.m_ProjectionBias != nullptr) { InitializeArmComputeTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias); } } if (m_Data.m_Parameters.m_PeepholeEnabled) { InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights); InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights); } if (m_Data.m_Parameters.m_LayerNormEnabled) { if (!m_Data.m_Parameters.m_CifgEnabled) { InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights); } InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights); InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights); InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights); } // Force Compute Library to perform the necessary copying and reshaping, after which // delete all the input tensors that will no longer be needed m_LstmLayer.prepare(); FreeUnusedTensors(); } void NeonLstmFloatWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid()); m_LstmLayer.run(); } arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input, const TensorInfo& outputStateIn, const TensorInfo& cellStateIn, const TensorInfo& scratchBuffer, const TensorInfo& outputStateOut, const TensorInfo& cellStateOut, const TensorInfo& output, const LstmDescriptor& descriptor, const LstmInputParamsInfo& paramsInfo) { arm_compute::LSTMParams lstm_params_info; // The inputs and outputs const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input); const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn); const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn); const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer); const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut); const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut); const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output); // Basic parameters const arm_compute::TensorInfo aclInputToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights()); const arm_compute::TensorInfo aclInputToCellWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights()); const arm_compute::TensorInfo aclInputToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights()); const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights()); const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights()); const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights()); const arm_compute::TensorInfo aclForgetGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias()); const arm_compute::TensorInfo aclCellBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellBias()); const arm_compute::TensorInfo aclOutputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias()); arm_compute::TensorInfo aclInputToInputWeightsInfo; arm_compute::TensorInfo aclRecurrentToInputWeightsInfo; arm_compute::TensorInfo aclCellToInputWeightsInfo; arm_compute::TensorInfo aclInputGateBiasInfo; arm_compute::TensorInfo aclProjectionWeightsInfo; arm_compute::TensorInfo aclProjectionBiasInfo; arm_compute::TensorInfo aclCellToForgetWeightsInfo; arm_compute::TensorInfo aclCellToOutputWeightsInfo; arm_compute::TensorInfo aclInputLayerNormWeightsInfo; arm_compute::TensorInfo aclForgetLayerNormWeightsInfo; arm_compute::TensorInfo aclCellLayerNormWeightsInfo; arm_compute::TensorInfo aclOutputLayerNormWeightsInfo; if (!descriptor.m_CifgEnabled) { if (descriptor.m_PeepholeEnabled) { aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights()); } aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights()); aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights()); aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias()); lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, &aclRecurrentToInputWeightsInfo, descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr, &aclInputGateBiasInfo); } if (descriptor.m_ProjectionEnabled) { if (paramsInfo.m_ProjectionBias != nullptr) { aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias()); } aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights()); lstm_params_info.set_projection_params(&aclProjectionWeightsInfo, paramsInfo.m_ProjectionBias != nullptr ? &aclProjectionBiasInfo : nullptr); } if (descriptor.m_PeepholeEnabled) { aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights()); aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights()); lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo); } if (descriptor.m_LayerNormEnabled) { if (!descriptor.m_CifgEnabled) { aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights()); } aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights()); aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights()); aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights()); lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr : &aclInputLayerNormWeightsInfo, &aclForgetLayerNormWeightsInfo, &aclCellLayerNormWeightsInfo, &aclOutputLayerNormWeightsInfo); } float cell_threshold = descriptor.m_ClippingThresCell; float projection_threshold = descriptor.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations arm_compute::ActivationLayerInfo activationLayerInfo = ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); return arm_compute::NELSTMLayer::validate(&aclInputInfo, &aclInputToForgetWeightsInfo, &aclInputToCellWeightsInfo, &aclInputToOutputWeightsInfo, &aclRecurrentToForgetWeightsInfo, &aclRecurrentToCellWeightsInfo, &aclRecurrentToOutputWeightsInfo, &aclForgetGateBiasInfo, &aclCellBiasInfo, &aclOutputGateBiasInfo, &aclOutputStateInInfo, &aclCellStateInInfo, &aclScratchBufferInfo, &aclOutputStateOutInfo, &aclCellStateOutInfo, &aclOutputInfo, lstm_params_info, activationLayerInfo, cell_threshold, projection_threshold); } void NeonLstmFloatWorkload::FreeUnusedTensors() { FreeTensorIfUnused(m_InputToInputWeightsTensor); FreeTensorIfUnused(m_InputToForgetWeightsTensor); FreeTensorIfUnused(m_InputToCellWeightsTensor); FreeTensorIfUnused(m_InputToOutputWeightsTensor); FreeTensorIfUnused(m_RecurrentToInputWeightsTensor); FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor); FreeTensorIfUnused(m_RecurrentToCellWeightsTensor); FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor); FreeTensorIfUnused(m_CellToInputWeightsTensor); FreeTensorIfUnused(m_CellToForgetWeightsTensor); FreeTensorIfUnused(m_CellToOutputWeightsTensor); FreeTensorIfUnused(m_InputGateBiasTensor); FreeTensorIfUnused(m_ForgetGateBiasTensor); FreeTensorIfUnused(m_CellBiasTensor); FreeTensorIfUnused(m_OutputGateBiasTensor); FreeTensorIfUnused(m_ProjectionWeightsTensor); FreeTensorIfUnused(m_ProjectionBiasTensor); FreeTensorIfUnused(m_ScratchBuffer); FreeTensorIfUnused(m_InputLayerNormWeightsTensor); FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor); FreeTensorIfUnused(m_CellLayerNormWeightsTensor); FreeTensorIfUnused(m_OutputLayerNormWeightsTensor); } void NeonLstmFloatWorkload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) { ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; this->m_Data.m_Inputs[slot] = tensorHandle; try { Reconfigure(); } catch(armnn::UnimplementedException& e) { // Cannot reconfigure, revert the slot back and throw the exception. this->m_Data.m_Inputs[slot] = backupHandle; throw e; } } // Replace output tensor handle with the given TensorHandle void NeonLstmFloatWorkload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) { ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; this->m_Data.m_Inputs[slot] = tensorHandle; try { Reconfigure(); } catch(armnn::UnimplementedException& e) { // Cannot reconfigure, revert the slot back and throw the exception. this->m_Data.m_Inputs[slot] = backupHandle; throw e; } } void NeonLstmFloatWorkload::Reconfigure() { throw armnn::UnimplementedException("Reconfigure not implemented for this workload"); } } //namespace armnn