// // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "NeonUnidirectionalSequenceLstmWorkload.hpp" #include "NeonWorkloadUtils.hpp" #include #include #include #include #include #include #include "neon/NeonTensorHandle.hpp" namespace { unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis) { return (numDimensions - axis) - 1; } } //namespace namespace armnn { using namespace armcomputetensorutils; NeonUnidirectionalSequenceLstmWorkload::NeonUnidirectionalSequenceLstmWorkload (const UnidirectionalSequenceLstmQueueDescriptor& descriptor, const WorkloadInfo& info) : NeonBaseWorkload(descriptor, info) { // Report Profiling Details ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonUnidirectionalSequenceLstmWorkload_Construct", descriptor.m_Parameters, info, GetGuid()); // Input/Output tensors const arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ITensor& outputStateIn = static_cast(m_Data.m_Inputs[1])->GetTensor(); const arm_compute::ITensor& cellStateIn = static_cast(m_Data.m_Inputs[2])->GetTensor(); arm_compute::ITensor& outputStateOut = static_cast(m_Data.m_Outputs[0])->GetTensor(); arm_compute::ITensor& cellStateOut = static_cast(m_Data.m_Outputs[1])->GetTensor(); arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[2])->GetTensor(); TensorInfo inputInfo = info.m_InputTensorInfos[0]; TensorInfo outputInfo = info.m_OutputTensorInfos[2]; TensorShape inputLayerShape = static_cast(m_Data.m_Inputs[0])->GetShape(); TensorShape outputLayerShape = static_cast(m_Data.m_Outputs[2])->GetShape(); unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; unsigned int inputSize = inputLayerShape[2]; unsigned int outputSize = outputLayerShape[2]; const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); // // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format. // if (!m_Data.m_Parameters.m_TimeMajor) { std::unique_ptr layer(new arm_compute::NEPermute()); TensorInfo permuteOutInfo = inputInfo; permuteOutInfo.SetShape(timeMajorShapeInput); BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo); armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut); // Permute to time major format. layer->configure(&input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U)); m_Permute1.reset(layer.release()); } // // Split and Concat Tensors // for (unsigned int i = 0; i < maxTime; ++i) { arm_compute::Tensor splitter_out; arm_compute::Tensor concat_in; auto splitterTensorInfo = inputInfo; auto concatTensorInfo = outputInfo; splitterTensorInfo.SetShape({batchSize, inputSize}); concatTensorInfo.SetShape({batchSize, outputSize}); BuildArmComputeTensor(splitter_out, splitterTensorInfo); BuildArmComputeTensor(concat_in, concatTensorInfo); armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out); armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in); // append to std::vector m_SplitterOutputsTensors.push_back(std::move(splitter_out)); m_ConcatInputsTensors.push_back(std::move(concat_in)); } for (unsigned int i = 0; i < maxTime; ++i) { // append to std::vector m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]); m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]); } // // Split // unsigned int numberDimensions = 3; unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) if (maxTime != 1) // ACL split does not work with only one element to split. { ViewsDescriptor splitterDesc(maxTime, numberDimensions); unsigned int splitterDimSizes[3] = {1, batchSize, inputSize}; for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx) { splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx); for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx) { splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]); } } std::set splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput); std::unique_ptr split_layer(new arm_compute::NESplit()); unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), *splitAxis.begin()); if (!m_Data.m_Parameters.m_TimeMajor) { split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit); } else { split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit); } split_layer->prepare(); m_Splitter.reset(split_layer.release()); } // // Lstm // arm_compute::LSTMParams lstm_param; lstm_param.set_cell_clip_params(descriptor.m_Parameters.m_ClippingThresCell); lstm_param.set_projection_clip_params(descriptor.m_Parameters.m_ClippingThresProj); lstm_param.set_matmul_scale_params(descriptor.m_Parameters.m_InputIntermediateScale, descriptor.m_Parameters.m_ForgetIntermediateScale, descriptor.m_Parameters.m_CellIntermediateScale, descriptor.m_Parameters.m_OutputIntermediateScale); lstm_param.set_hidden_state_params(descriptor.m_Parameters.m_HiddenStateZeroPoint, descriptor.m_Parameters.m_HiddenStateScale); 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 ? 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 ? 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()); auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get(); lstm_param.set_layer_normalization_params(inputNormWeightTensor, m_ForgetLayerNormWeightsTensor.get(), m_CellLayerNormWeightsTensor.get(), m_OutputLayerNormWeightsTensor.get()); } for (unsigned int i = 0; i != maxTime; ++i) { // Set LSTM input and output ITensors depending on: // input format (timeMajor) & number of LSTM batches (maxTime). arm_compute::ITensor* outputLSTM; arm_compute::ITensor* inputLSTM; // If there is only one LSTM time major batch, we will not concat OR permute. // Set input of LSTM to be first input ITensor. // Set output of LSTM to be final output ITensor. // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor) { TensorShape inputShape = GetTensorShape(input.info()->tensor_shape(), 1U); TensorShape outputShape = GetTensorShape(output.info()->tensor_shape(), 1U); TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); input.info()->set_tensor_shape(acl_input_shape_shrink); inputLSTM = const_cast(&input); output.info()->set_tensor_shape(acl_output_shape_shrink); outputLSTM = &output; } // If there is only one LSTM batch major batch, we will not concat, only permute. // Set input of LSTM to be output of initial permute. // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor) { TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U); TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink); inputLSTM = &m_PermuteFirstOut; outputLSTM = const_cast(m_ConcatInputs[i]); } // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. else { inputLSTM = m_SplitterOutputs[i]; outputLSTM = const_cast(m_ConcatInputs[i]); } std::unique_ptr lstm_layer(new arm_compute::NEQLSTMLayer()); lstm_layer->configure(inputLSTM, 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(), &cellStateIn, &outputStateIn, &cellStateOut, &outputStateOut, outputLSTM, lstm_param); m_Layers.emplace_back(std::move(lstm_layer)); } 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. for (uint32_t i = 0; i < m_Layers.size(); ++i) { m_Layers[i]->prepare(); } // // Concat // // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U); TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); if (maxTime != 1) // ACL concat does not work with only one element to concatenate. { for (unsigned int i = 0; i < maxTime; ++i) { m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); } ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views). for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx) { concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx); concatDescriptor.SetConcatAxis(dimension); } m_Concat.reset(new arm_compute::NEConcatenateLayer()); unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis()); if (!m_Data.m_Parameters.m_TimeMajor) { TensorInfo concatOutputTensorInfo = outputInfo; concatOutputTensorInfo.SetShape(timeMajorShapeOutput); BuildArmComputeTensor(concat_out, concatOutputTensorInfo); armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out); m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat); } else { m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat); } m_Concat->prepare(); } // If only one LSTM batch, we do not concat and/or permute. // Must ensure final output info is expanded to correct batch major dimensions. else { if (!m_Data.m_Parameters.m_TimeMajor) { output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor)); } else { output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); } } // // Permute: only done if input/output are in batch major format. // if (!m_Data.m_Parameters.m_TimeMajor) { // Output now time major. Permute output back to batch major. std::unique_ptr layer(new arm_compute::NEPermute()); if (maxTime != 1) { layer->configure(&concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U)); } else { layer->configure(m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U)); } m_Permute2.reset(layer.release()); } FreeUnusedTensors(); } void NeonUnidirectionalSequenceLstmWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonUnidirectionalSequenceLstmWorkload_Execute", GetGuid()); if (m_Permute1) { m_Permute1->run(); } if (m_Splitter) { m_Splitter->run(); } for (uint32_t i = 0; i < m_Layers.size(); ++i) { m_Layers[i]->run(); } if (m_Concat) { m_Concat->run(); } if (m_Permute2) { m_Permute2->run(); } } arm_compute::Status NeonUnidirectionalSequenceLstmWorkloadValidate(const TensorInfo& input, const TensorInfo& outputStateIn, const TensorInfo& cellStateIn, const TensorInfo& outputStateOut, const TensorInfo& cellStateOut, const TensorInfo& output, const UnidirectionalSequenceLstmDescriptor& descriptor, const LstmInputParamsInfo& paramsInfo) { TensorShape inputLayerShape = input.GetShape(); TensorShape outputLayerShape = output.GetShape(); unsigned int maxTime = descriptor.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; unsigned int batchSize = descriptor.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; unsigned int inputSize = inputLayerShape[2]; unsigned int outputSize = outputLayerShape[2]; const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK, "Permute1 status"); arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK, "Split status"); arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK, "LSTM status"); arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK, "Concat status"); arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK, "Permute2 status"); const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); // // Permute validate // TensorInfo permuteOutInfo = TensorInfo(input); arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo); if (!descriptor.m_TimeMajor) { statusPermute1 = arm_compute::NEPermute::validate(&aclInputInfo, &aclPermuteOutInfo, arm_compute::PermutationVector(0U, 2U, 1U)); } // // Split and Concat Tensors validate // std::vector splitterOutputsTensorInfos; std::vector concatInputsTensorInfos; std::vector splitterOutputsTensorInfosPtr; std::vector concatInputsTensorInfosPtr; splitterOutputsTensorInfos.reserve(maxTime); concatInputsTensorInfos.reserve(maxTime); for (unsigned int i = 0; i < maxTime; ++i) { arm_compute::TensorInfo splitter_out; arm_compute::TensorInfo concat_in; auto splitterTensorInfo = TensorInfo(input); auto concatTensorInfo = TensorInfo(output); splitterTensorInfo.SetShape({batchSize, inputSize}); concatTensorInfo.SetShape({batchSize, outputSize}); arm_compute::TensorInfo aclSplitterTensorInfo = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo); arm_compute::TensorInfo aclConcatTensorInfo = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo); splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo); concatInputsTensorInfos.emplace_back(aclConcatTensorInfo); splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]); concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]); } // // Split validate // unsigned int numberDimensions = 3; unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) unsigned int aclAxisSplit = CalcAclAxis(numberDimensions, dimension); if (maxTime != 1) // ACL split does not work with only one element to split. { if (!descriptor.m_TimeMajor) { statusSplit = arm_compute::NESplit::validate(&aclPermuteOutInfo, splitterOutputsTensorInfosPtr, aclAxisSplit); } else { statusSplit = arm_compute::NESplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit); } } // // LSTM validate // arm_compute::LSTMParams lstm_params_info; const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); lstm_params_info.set_cell_clip_params(descriptor.m_ClippingThresCell); lstm_params_info.set_projection_clip_params(descriptor.m_ClippingThresProj); // The inputs and outputs 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); // 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 ? &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); } lstm_params_info.set_matmul_scale_params(descriptor.m_InputIntermediateScale, descriptor.m_ForgetIntermediateScale, descriptor.m_CellIntermediateScale, descriptor.m_OutputIntermediateScale); lstm_params_info.set_hidden_state_params(descriptor.m_HiddenStateZeroPoint, descriptor.m_HiddenStateScale); for (unsigned int i = 0; i != maxTime; ++i) { // Set LSTM input and output ITensors depending on: // input format (timeMajor) & number of LSTM batches (maxTime). arm_compute::ITensorInfo* outputLSTM; arm_compute::ITensorInfo* inputLSTM; // If there is only one LSTM time major batch, we will not concat OR permute. // Set input of LSTM to be first input ITensor. // Set output of LSTM to be final output ITensor. // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. if (maxTime == 1 && !descriptor.m_TimeMajor) { TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U); TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U); TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); const_cast(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink); inputLSTM = const_cast(&aclInputInfo); const_cast(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink); outputLSTM = const_cast(&aclOutputInfo); } // If there is only one LSTM batch major batch, we will not concat, only permute. // Set input of LSTM to be output of initial permute. // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. else if (maxTime == 1 && !descriptor.m_TimeMajor) { TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U); TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink); inputLSTM = &aclPermuteOutInfo; outputLSTM = const_cast(concatInputsTensorInfosPtr[i]); } // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. else { inputLSTM = splitterOutputsTensorInfosPtr[i]; outputLSTM = const_cast(concatInputsTensorInfosPtr[i]); } statusLSTM = arm_compute::NEQLSTMLayer::validate(inputLSTM, &aclInputToForgetWeightsInfo, &aclInputToCellWeightsInfo, &aclInputToOutputWeightsInfo, &aclRecurrentToForgetWeightsInfo, &aclRecurrentToCellWeightsInfo, &aclRecurrentToOutputWeightsInfo, &aclForgetGateBiasInfo, &aclCellBiasInfo, &aclOutputGateBiasInfo, &aclCellStateInInfo, &aclOutputStateInInfo, &aclCellStateOutInfo, &aclOutputStateOutInfo, outputLSTM, lstm_params_info); } // // Concat validate // // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U); TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); TensorInfo concatOutputTensorInfo = TensorInfo(output); concatOutputTensorInfo.SetShape(timeMajorShapeOutput); arm_compute::TensorInfo aclConcatOutputTensorInfo= BuildArmComputeTensorInfo(concatOutputTensorInfo); if (maxTime != 1) // ACL concat does not work with only one element to concatenate. { for (unsigned int i = 0; i < maxTime; ++i) { auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor); concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand); } unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension); if (!descriptor.m_TimeMajor) { statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, &aclConcatOutputTensorInfo, aclAxisConcat); } else { statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, &aclOutputInfo, aclAxisConcat); } } // If only one LSTM batch, we do not concat and/or permute. // Must ensure final output info is expanded to correct batch major dimensions. else { if (!descriptor.m_TimeMajor) { const_cast(&aclInputInfo)->set_tensor_shape( BuildArmComputeTensorShape(shapeExpandBatchMajor)); } else { const_cast(&aclInputInfo)->set_tensor_shape( BuildArmComputeTensorShape(shapeExpandTimeMajor)); } } // // Permute validate // if (!descriptor.m_TimeMajor) { // Output now time major. Permute output back to batch major. if (maxTime != 1) { statusPermute2 = arm_compute::NEPermute::validate(&aclConcatOutputTensorInfo, &aclOutputInfo, arm_compute::PermutationVector(0U, 2U, 1U)); } else { statusPermute2 = arm_compute::NEPermute::validate(concatInputsTensorInfosPtr[0], &aclOutputInfo, arm_compute::PermutationVector(0U, 2U, 1U)); } } auto okCode = arm_compute::ErrorCode::OK; if (statusPermute1.error_code() == okCode && statusSplit.error_code() == okCode && statusLSTM .error_code() == okCode && statusConcat.error_code() == okCode && statusPermute2.error_code() == okCode) { return arm_compute::Status(arm_compute::ErrorCode::OK, "All Unidirectional Sequence LSTM layer validate status OK."); } else { return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, "Unidirectional Sequence LSTM layer validate status failed."); } } void NeonUnidirectionalSequenceLstmWorkload::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_InputLayerNormWeightsTensor); FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor); FreeTensorIfUnused(m_CellLayerNormWeightsTensor); FreeTensorIfUnused(m_OutputLayerNormWeightsTensor); } } //namespace armnn