NeonUnidirectionalSequenceLstmWorkload Class Reference

#include <NeonUnidirectionalSequenceLstmWorkload.hpp>

Inheritance diagram for NeonUnidirectionalSequenceLstmWorkload:

Collaboration diagram for NeonUnidirectionalSequenceLstmWorkload:

Public Member Functions
	NeonUnidirectionalSequenceLstmWorkload (const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info)
virtual void	Execute () const override
Public Member Functions inherited from NeonBaseWorkload< UnidirectionalSequenceLstmQueueDescriptor >
	NeonBaseWorkload (const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info)
void	ReplaceInputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
void	ReplaceOutputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
Public Member Functions inherited from BaseWorkload< UnidirectionalSequenceLstmQueueDescriptor >
	BaseWorkload (const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info)
virtual const std::string &	GetName () const override
void	ExecuteAsync (ExecutionData &executionData) override
void	PostAllocationConfigure () override
const UnidirectionalSequenceLstmQueueDescriptor &	GetData () const
arm::pipe::ProfilingGuid	GetGuid () const final
virtual bool	SupportsTensorHandleReplacement () const override
Public Member Functions inherited from IWorkload
virtual	~IWorkload ()
virtual arm::pipe::ProfilingGuid	GetGuid () const =0
virtual bool	SupportsTensorHandleReplacement () const =0
virtual const std::string &	GetName () const =0
virtual void	RegisterDebugCallback (const DebugCallbackFunction &)
virtual armnn::Optional< armnn::MemoryRequirements >	GetMemoryRequirements ()

Additional Inherited Members
Protected Member Functions inherited from NeonBaseWorkload< UnidirectionalSequenceLstmQueueDescriptor >
virtual void	Reconfigure ()
Protected Attributes inherited from BaseWorkload< UnidirectionalSequenceLstmQueueDescriptor >
UnidirectionalSequenceLstmQueueDescriptor	m_Data
const arm::pipe::ProfilingGuid	m_Guid
const std::string	m_Name

Detailed Description

Definition at line 22 of file NeonUnidirectionalSequenceLstmWorkload.hpp.

Constructor & Destructor Documentation

NeonUnidirectionalSequenceLstmWorkload()

NeonUnidirectionalSequenceLstmWorkload	(	const UnidirectionalSequenceLstmQueueDescriptor &	descriptor,
		const WorkloadInfo &	info
	)

Definition at line 33 of file NeonUnidirectionalSequenceLstmWorkload.cpp.

    : 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();
}

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, armnn::info, and QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters.

Member Function Documentation

Execute()

void Execute ( ) const

overridevirtual

Implements IWorkload.

Definition at line 465 of file NeonUnidirectionalSequenceLstmWorkload.cpp.

{
    ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonUnidirectionalSequenceLstmWorkload_Execute");
    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();
    }
}

References ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID.

---

The documentation for this class was generated from the following files:

• src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.hpp
• src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp