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Diffstat (limited to 'src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp')
| -rw-r--r-- | src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp | 879 |
1 files changed, 879 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp new file mode 100644 index 0000000000..dfbbb3c879 --- /dev/null +++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp @@ -0,0 +1,879 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonUnidirectionalSequenceLstmWorkload.hpp" +#include "NeonWorkloadUtils.hpp" + +#include <aclCommon/ArmComputeUtils.hpp> +#include <aclCommon/ArmComputeTensorUtils.hpp> + +#include <armnn/utility/NumericCast.hpp> +#include <armnnUtils/Permute.hpp> +#include <neon/test/NeonWorkloadFactoryHelper.hpp> +#include <backendsCommon/WorkloadUtils.hpp> + +#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<UnidirectionalSequenceLstmQueueDescriptor>(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<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& outputStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); + const arm_compute::ITensor& cellStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); + + arm_compute::ITensor& outputStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + arm_compute::ITensor& cellStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor(); + arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor(); + + TensorInfo inputInfo = info.m_InputTensorInfos[0]; + TensorInfo outputInfo = info.m_OutputTensorInfos[2]; + + TensorShape inputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetShape(); + TensorShape outputLayerShape = static_cast<IAclTensorHandle*>(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<arm_compute::NEPermute> 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<arm_compute::Tensor> + 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<arm_compute::ITensor*> + 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<unsigned int> splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput); + + std::unique_ptr<arm_compute::NESplit> 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<arm_compute::ITensor> 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<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); + + m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); + + m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); + + m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); + + m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); + + m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); + + m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); + + m_CellBiasTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); + + m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); + 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<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); + + m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); + + m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); + if (m_Data.m_CellToInputWeights != nullptr) + { + BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); + } + + m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); + 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<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); + + m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>(); + 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<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); + + m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); + 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<arm_compute::Tensor>(); + if (!m_Data.m_Parameters.m_CifgEnabled) + { + BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); + } + + m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); + + m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); + + m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); + 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<arm_compute::ITensor*>(&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<arm_compute::ITensor*>(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<arm_compute::ITensor*>(m_ConcatInputs[i]); + } + + std::unique_ptr<arm_compute::NEQLSTMLayer> 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<arm_compute::NEPermute> 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<arm_compute::TensorInfo> splitterOutputsTensorInfos; + std::vector<arm_compute::TensorInfo> concatInputsTensorInfos; + std::vector<arm_compute::ITensorInfo*> splitterOutputsTensorInfosPtr; + std::vector<const arm_compute::ITensorInfo*> 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<arm_compute::ITensorInfo> 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<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink); + inputLSTM = const_cast<arm_compute::TensorInfo*>(&aclInputInfo); + + const_cast<arm_compute::TensorInfo*>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink); + outputLSTM = const_cast<arm_compute::TensorInfo*>(&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<arm_compute::ITensorInfo*>(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<arm_compute::ITensorInfo*>(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<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape( + BuildArmComputeTensorShape(shapeExpandBatchMajor)); + } + else + { + const_cast<arm_compute::TensorInfo*>(&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 |