This layer represents a LSTM operation. More...

#include <UnidirectionalSequenceLstmLayer.hpp>

Inheritance diagram for UnidirectionalSequenceLstmLayer:

Public Member Functions
virtual std::unique_ptr< IWorkload >	CreateWorkload (const IWorkloadFactory &factory) const override
	Makes a workload for the UnidirectionalSequence LSTM type. More...

UnidirectionalSequenceLstmLayer *	Clone (Graph &graph) const override
	Creates a dynamically-allocated copy of this layer. More...

void	ValidateTensorShapesFromInputs () override
	Check if the input tensor shape(s) will lead to a valid configuration of UnidirectionalSequenceLstmLayer. More...

std::vector< TensorShape >	InferOutputShapes (const std::vector< TensorShape > &inputShapes) const override
	By default returns inputShapes if the number of inputs are equal to number of outputs, otherwise infers the output shapes from given input shapes and layer properties. More...

ARMNN_NO_DEPRECATE_WARN_BEGIN void	Accept (ILayerVisitor &visitor) const override

ARMNN_NO_DEPRECATE_WARN_END void	ExecuteStrategy (IStrategy &strategy) const override
	Apply a visitor to this layer. More...

Public Member Functions inherited from LayerWithParameters< LstmDescriptor >
const LstmDescriptor &	GetParameters () const

void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override
	Helper to serialize the layer parameters to string (currently used in DotSerializer and company). More...

Public Member Functions inherited from Layer
	Layer (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const char *name)

	Layer (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, DataLayout layout, const char *name)

const std::string &	GetNameStr () const

const OutputHandler &	GetOutputHandler (unsigned int i=0) const

OutputHandler &	GetOutputHandler (unsigned int i=0)

ShapeInferenceMethod	GetShapeInferenceMethod () const

const std::vector< InputSlot > &	GetInputSlots () const

const std::vector< OutputSlot > &	GetOutputSlots () const

std::vector< InputSlot >::iterator	BeginInputSlots ()

std::vector< InputSlot >::iterator	EndInputSlots ()

std::vector< OutputSlot >::iterator	BeginOutputSlots ()

std::vector< OutputSlot >::iterator	EndOutputSlots ()

bool	IsOutputUnconnected ()

void	ResetPriority () const

LayerPriority	GetPriority () const

LayerType	GetType () const override
	Returns the armnn::LayerType of this layer. More...

DataType	GetDataType () const

const BackendId &	GetBackendId () const

void	SetBackendId (const BackendId &id)

virtual void	CreateTensorHandles (const TensorHandleFactoryRegistry &registry, const IWorkloadFactory &factory, const bool IsMemoryManaged=true)

void	VerifyLayerConnections (unsigned int expectedConnections, const CheckLocation &location) const

virtual void	ReleaseConstantData ()

template<typename Op >
void	OperateOnConstantTensors (Op op)

const char *	GetName () const override
	Returns the name of the layer. More...

unsigned int	GetNumInputSlots () const override
	Returns the number of connectable input slots. More...

unsigned int	GetNumOutputSlots () const override
	Returns the number of connectable output slots. More...

const InputSlot &	GetInputSlot (unsigned int index) const override
	Get a const input slot handle by slot index. More...

InputSlot &	GetInputSlot (unsigned int index) override
	Get the input slot handle by slot index. More...

const OutputSlot &	GetOutputSlot (unsigned int index=0) const override
	Get the const output slot handle by slot index. More...

OutputSlot &	GetOutputSlot (unsigned int index=0) override
	Get the output slot handle by slot index. More...

void	SetGuid (LayerGuid guid)

LayerGuid	GetGuid () const final
	Returns the unique id of the layer. More...

void	AddRelatedLayerName (const std::string layerName)

const std::list< std::string > &	GetRelatedLayerNames ()

virtual void	Reparent (Graph &dest, std::list< Layer *>::const_iterator iterator)=0

void	BackendSelectionHint (Optional< BackendId > backend) final
	Provide a hint for the optimizer as to which backend to prefer for this layer. More...

Optional< BackendId >	GetBackendHint () const

void	SetShapeInferenceMethod (ShapeInferenceMethod shapeInferenceMethod)

template<typename T >
std::shared_ptr< T >	GetAdditionalInformation () const

void	SetAdditionalInfoForObject (const AdditionalInfoObjectPtr &additionalInfo)

Public Member Functions inherited from IConnectableLayer
ARMNN_NO_DEPRECATE_WARN_BEGIN	ARMNN_DEPRECATED_MSG_REMOVAL_DATE ("Accept is deprecated. The ILayerVisitor that works in conjunction with this " "Accept function is deprecated. Use IStrategy in combination with " "ExecuteStrategy instead, which is an ABI/API stable version of the " "visitor pattern.", "22.05") virtual void Accept(ILayerVisitor &visitor) const =0
	Apply a visitor to this layer. More...

Public Attributes
LstmBasicParameters	m_BasicParameters

LstmOptCifgParameters	m_CifgParameters

LstmOptProjectionParameters	m_ProjectionParameters

LstmOptPeepholeParameters	m_PeepholeParameters

LstmOptLayerNormParameters	m_LayerNormParameters

Protected Member Functions
	UnidirectionalSequenceLstmLayer (const LstmDescriptor &param, const char *name)
	Constructor to create a UnidirectionalSequenceLstmLayer. More...

	~UnidirectionalSequenceLstmLayer ()=default
	Default destructor. More...

Layer::ConstantTensors	GetConstantTensorsByRef () override
	Retrieve the handles to the constant values stored by the layer. More...

Protected Member Functions inherited from LayerWithParameters< LstmDescriptor >
	LayerWithParameters (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const LstmDescriptor &param, const char *name)

	~LayerWithParameters ()=default

WorkloadInfo	PrepInfoAndDesc (QueueDescriptor &descriptor) const
	Helper function to reduce duplication in *LayerCreateWorkload. More...

void	ExecuteStrategy (IStrategy &strategy) const override
	Apply a visitor to this layer. More...

Protected Member Functions inherited from Layer
virtual	~Layer ()=default

template<typename QueueDescriptor >
void	CollectQueueDescriptorInputs (QueueDescriptor &descriptor, WorkloadInfo &info) const

template<typename QueueDescriptor >
void	CollectQueueDescriptorOutputs (QueueDescriptor &descriptor, WorkloadInfo &info) const

void	ValidateAndCopyShape (const TensorShape &outputShape, const TensorShape &inferredShape, const ShapeInferenceMethod shapeInferenceMethod, const std::string &layerName, const unsigned int outputSlotIndex=0)

void	VerifyShapeInferenceType (const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)

template<typename QueueDescriptor >
WorkloadInfo	PrepInfoAndDesc (QueueDescriptor &descriptor) const
	Helper function to reduce duplication in *LayerCreateWorkload. More...

template<typename LayerType , typename ... Params>
LayerType *	CloneBase (Graph &graph, Params &&... params) const

void	SetAdditionalInfo (QueueDescriptor &descriptor) const

Protected Member Functions inherited from IConnectableLayer
	~IConnectableLayer ()
	Objects are not deletable via the handle. More...

Additional Inherited Members
Public Types inherited from LayerWithParameters< LstmDescriptor >
using	DescriptorType = LstmDescriptor

Protected Types inherited from Layer
using	ConstantTensors = std::vector< std::reference_wrapper< std::shared_ptr< ConstTensorHandle > >>

Protected Attributes inherited from LayerWithParameters< LstmDescriptor >
LstmDescriptor	m_Param
	The parameters for the layer (not including tensor-valued weights etc.). More...

Protected Attributes inherited from Layer
AdditionalInfoObjectPtr	m_AdditionalInfoObject

std::vector< OutputHandler >	m_OutputHandlers

ShapeInferenceMethod	m_ShapeInferenceMethod

Detailed Description

This layer represents a LSTM operation.

Definition at line 16 of file UnidirectionalSequenceLstmLayer.hpp.

Constructor & Destructor Documentation

◆ UnidirectionalSequenceLstmLayer()

UnidirectionalSequenceLstmLayer	(	const LstmDescriptor &	param,
		const char *	name
	)

protected

Constructor to create a UnidirectionalSequenceLstmLayer.

Parameters

[in]	param	LstmDescriptor to configure the lstm operation.
[in]	name	Optional name for the layer.

Definition at line 17 of file UnidirectionalSequenceLstmLayer.cpp.

References armnn::UnidirectionalSequenceLstm.

         : LayerWithParameters(3, 1, LayerType::UnidirectionalSequenceLstm, param, name)
 {
 }

◆ ~UnidirectionalSequenceLstmLayer()

~UnidirectionalSequenceLstmLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

◆ Accept()

ARMNN_NO_DEPRECATE_WARN_BEGIN void Accept ( ILayerVisitor & visitor ) const

override

Definition at line 311 of file UnidirectionalSequenceLstmLayer.cpp.

References ARMNN_NO_DEPRECATE_WARN_END, and armnn::IgnoreUnused().

 {
     IgnoreUnused(visitor);
     throw armnn::Exception("UnidirectionalSequenceLstmLayer: VisitUnidirectionalSequenceLstmLayer is not implemented");
 }

◆ Clone()

UnidirectionalSequenceLstmLayer * Clone ( Graph & graph ) const

overridevirtual

Creates a dynamically-allocated copy of this layer.

Parameters

[in] graph The graph into which this layer is being cloned.

Implements Layer.

Definition at line 80 of file UnidirectionalSequenceLstmLayer.cpp.

 {
     auto layer = CloneBase<UnidirectionalSequenceLstmLayer>(graph, m_Param, GetName());
 
     layer->m_BasicParameters.m_InputToForgetWeights = m_BasicParameters.m_InputToForgetWeights ?
             m_BasicParameters.m_InputToForgetWeights
                 : nullptr;
     layer->m_BasicParameters.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights ?
             m_BasicParameters.m_InputToCellWeights : nullptr;
     layer->m_BasicParameters.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights ?
             m_BasicParameters.m_InputToOutputWeights : nullptr;
     layer->m_BasicParameters.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights ?
             m_BasicParameters.m_RecurrentToForgetWeights : nullptr;
     layer->m_BasicParameters.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights ?
             m_BasicParameters.m_RecurrentToCellWeights : nullptr;
     layer->m_BasicParameters.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights ?
             m_BasicParameters.m_RecurrentToOutputWeights : nullptr;
     layer->m_BasicParameters.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias ?
             m_BasicParameters.m_ForgetGateBias : nullptr;
     layer->m_BasicParameters.m_CellBias = m_BasicParameters.m_CellBias ?
             m_BasicParameters.m_CellBias : nullptr;
     layer->m_BasicParameters.m_OutputGateBias = m_BasicParameters.m_OutputGateBias ?
             m_BasicParameters.m_OutputGateBias : nullptr;
 
     if (!m_Param.m_CifgEnabled)
     {
         layer->m_CifgParameters.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights ?
                 m_CifgParameters.m_InputToInputWeights : nullptr;
         layer->m_CifgParameters.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights ?
                 m_CifgParameters.m_RecurrentToInputWeights : nullptr;
         layer->m_CifgParameters.m_InputGateBias = m_CifgParameters.m_InputGateBias ?
                 m_CifgParameters.m_InputGateBias : nullptr;
     }
 
     if (m_Param.m_ProjectionEnabled)
     {
         layer->m_ProjectionParameters.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights ?
                m_ProjectionParameters.m_ProjectionWeights : nullptr;
         layer->m_ProjectionParameters.m_ProjectionBias = m_ProjectionParameters.m_ProjectionBias ?
                m_ProjectionParameters.m_ProjectionBias : nullptr;
     }
 
     if (m_Param.m_PeepholeEnabled)
     {
         if (!m_Param.m_CifgEnabled)
         {
             layer->m_PeepholeParameters.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights ?
                 m_PeepholeParameters.m_CellToInputWeights : nullptr;
         }
         layer->m_PeepholeParameters.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights ?
                m_PeepholeParameters.m_CellToForgetWeights : nullptr;
         layer->m_PeepholeParameters.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights ?
                m_PeepholeParameters.m_CellToOutputWeights : nullptr;
     }
 
     if (m_Param.m_LayerNormEnabled)
     {
         layer->m_LayerNormParameters.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights ?
                m_LayerNormParameters.m_InputLayerNormWeights : nullptr;
         layer->m_LayerNormParameters.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights ?
                m_LayerNormParameters.m_ForgetLayerNormWeights : nullptr;
         layer->m_LayerNormParameters.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights ?
                m_LayerNormParameters.m_CellLayerNormWeights : nullptr;
         layer->m_LayerNormParameters.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights ?
                m_LayerNormParameters.m_OutputLayerNormWeights : nullptr;
     }
 
     return std::move(layer);
 }

◆ CreateWorkload()

std::unique_ptr< IWorkload > CreateWorkload ( const IWorkloadFactory & factory ) const

overridevirtual

Makes a workload for the UnidirectionalSequence LSTM type.

Parameters

[in]	graph	The graph where this layer can be found.
[in]	factory	The workload factory which will create the workload.

Returns: A pointer to the created workload, or nullptr if not created.

Implements Layer.

Definition at line 22 of file UnidirectionalSequenceLstmLayer.cpp.

 {
     UnidirectionalSequenceLstmQueueDescriptor descriptor;
 
     // Basic parameters
     descriptor.m_InputToForgetWeights = m_BasicParameters.m_InputToForgetWeights.get();
     descriptor.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights.get();
     descriptor.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights.get();
     descriptor.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights.get();
     descriptor.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights.get();
     descriptor.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights.get();
     descriptor.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias.get();
     descriptor.m_CellBias = m_BasicParameters.m_CellBias.get();
     descriptor.m_OutputGateBias = m_BasicParameters.m_OutputGateBias.get();
 
     // Cifg parameters
     if (!m_Param.m_CifgEnabled)
     {
         descriptor.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights.get();
         descriptor.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights.get();
         descriptor.m_InputGateBias = m_CifgParameters.m_InputGateBias.get();
     }
 
     // Projection parameters
     if (m_Param.m_ProjectionEnabled)
     {
         descriptor.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights.get();
         descriptor.m_ProjectionBias    = m_ProjectionParameters.m_ProjectionBias.get();
     }
 
     // Peephole parameters
     if (m_Param.m_PeepholeEnabled)
     {
         if (!m_Param.m_CifgEnabled)
         {
             descriptor.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights.get();
         }
         descriptor.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights.get();
         descriptor.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights.get();
     }
 
     // Layer normalisation parameters
     if(m_Param.m_LayerNormEnabled)
     {
         if (!m_Param.m_CifgEnabled)
         {
             descriptor.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights.get();
         }
         descriptor.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights.get();
         descriptor.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights.get();
         descriptor.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights.get();
     }
 
     SetAdditionalInfo(descriptor);
 
     return factory.CreateUnidirectionalSequenceLstm(descriptor, PrepInfoAndDesc(descriptor));
 }

◆ ExecuteStrategy()

ARMNN_NO_DEPRECATE_WARN_END void ExecuteStrategy ( IStrategy & strategy ) const

overridevirtual

Apply a visitor to this layer.

Reimplemented from Layer.

Definition at line 318 of file UnidirectionalSequenceLstmLayer.cpp.

 {
     std::vector<ConstTensor> constTensors;
 
     LstmDescriptor descriptor = GetParameters();
 
     ManagedConstTensorHandle managedInputToForgetWeights(m_BasicParameters.m_InputToForgetWeights);
     ManagedConstTensorHandle managedInputToCellWeights(m_BasicParameters.m_InputToCellWeights);
     ManagedConstTensorHandle managedInputToOutputWeights(m_BasicParameters.m_InputToOutputWeights);
     ManagedConstTensorHandle managedRecurrentToForgetWeights(m_BasicParameters.m_RecurrentToForgetWeights);
     ManagedConstTensorHandle managedRecurrentToCellWeights(m_BasicParameters.m_RecurrentToCellWeights);
     ManagedConstTensorHandle managedRecurrentToOutputWeights(m_BasicParameters.m_RecurrentToOutputWeights);
     ManagedConstTensorHandle managedForgetGateBias(m_BasicParameters.m_ForgetGateBias);
     ManagedConstTensorHandle managedCellBias(m_BasicParameters.m_CellBias);
     ManagedConstTensorHandle managedOutputGateBias(m_BasicParameters.m_OutputGateBias);
 
     // Cifg parameters
     ManagedConstTensorHandle managedInputToInputWeights(m_CifgParameters.m_InputToInputWeights);
     ManagedConstTensorHandle managedRecurrentToInputWeights(m_CifgParameters.m_RecurrentToInputWeights);
     ManagedConstTensorHandle managedInputGateBias(m_CifgParameters.m_InputGateBias);
 
     // Projection parameters
     ManagedConstTensorHandle managedProjectionWeights(m_ProjectionParameters.m_ProjectionWeights);
     ManagedConstTensorHandle managedProjectionBias(m_ProjectionParameters.m_ProjectionBias);
 
     // Peephole parameters
     ManagedConstTensorHandle managedCellToInputWeights(m_PeepholeParameters.m_CellToInputWeights);
     ManagedConstTensorHandle managedCellToForgetWeights(m_PeepholeParameters.m_CellToForgetWeights);
     ManagedConstTensorHandle managedCellToOutputWeights(m_PeepholeParameters.m_CellToOutputWeights);
 
     // Layer normalisation parameters
     ManagedConstTensorHandle managedInputLayerNormWeights(m_LayerNormParameters.m_InputLayerNormWeights);
     ManagedConstTensorHandle managedForgetLayerNormWeights(m_LayerNormParameters.m_ForgetLayerNormWeights);
     ManagedConstTensorHandle managedCellLayerNormWeights(m_LayerNormParameters.m_CellLayerNormWeights);
     ManagedConstTensorHandle managedOutputLayerNormWeights(m_LayerNormParameters.m_OutputLayerNormWeights);
 
     // First add mandatory/basic parameters
     if (m_BasicParameters.m_InputToForgetWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedInputToForgetWeights.GetTensorInfo(),
                                               managedInputToForgetWeights.Map()));
     }
     if (m_BasicParameters.m_InputToCellWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedInputToCellWeights.GetTensorInfo(),
                                               managedInputToCellWeights.Map()));
     }
     if (m_BasicParameters.m_InputToOutputWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedInputToOutputWeights.GetTensorInfo(),
                                               managedInputToOutputWeights.Map()));
     }
     if (m_BasicParameters.m_RecurrentToForgetWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(
                 managedRecurrentToForgetWeights.GetTensorInfo(),
                 managedRecurrentToForgetWeights.Map()));
     }
     if (m_BasicParameters.m_RecurrentToCellWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(
                 managedRecurrentToCellWeights.GetTensorInfo(),
                 managedRecurrentToCellWeights.Map()));
     }
     if (m_BasicParameters.m_RecurrentToOutputWeights != nullptr)
     {
         constTensors.emplace_back(ConstTensor(
                 managedRecurrentToOutputWeights.GetTensorInfo(),
                 managedRecurrentToOutputWeights.Map()));
     }
     if (m_BasicParameters.m_ForgetGateBias != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedForgetGateBias.GetTensorInfo(),
                                               managedForgetGateBias.Map()));
     }
     if (m_BasicParameters.m_CellBias != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedCellBias.GetTensorInfo(),
                                               managedCellBias.Map()));
     }
     if (m_BasicParameters.m_OutputGateBias != nullptr)
     {
         constTensors.emplace_back(ConstTensor(managedOutputGateBias.GetTensorInfo(),
                                               managedOutputGateBias.Map()));
     }
 
     // Add cifg parameters
     if (!descriptor.m_CifgEnabled)
     {
         if (m_CifgParameters.m_InputToInputWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedInputToInputWeights.GetTensorInfo(),
                                                   managedInputToInputWeights.Map()));
         }
         if (m_CifgParameters.m_RecurrentToInputWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(
                     managedRecurrentToInputWeights.GetTensorInfo(),
                     managedRecurrentToInputWeights.Map()));
         }
         if (m_CifgParameters.m_InputGateBias != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedInputGateBias.GetTensorInfo(),
                                                   managedInputGateBias.Map()));
         }
     }
 
     // Add peephole parameters
     if (descriptor.m_PeepholeEnabled)
     {
         if (!descriptor.m_CifgEnabled)
         {
             if (m_PeepholeParameters.m_CellToInputWeights != nullptr)
             {
                 constTensors.emplace_back(ConstTensor(managedCellToInputWeights.GetTensorInfo(),
                                                       managedCellToInputWeights.Map()));
             }
         }
         if (m_PeepholeParameters.m_CellToForgetWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedCellToForgetWeights.GetTensorInfo(),
                                                   managedCellToForgetWeights.Map()));
         }
         if (m_PeepholeParameters.m_CellToOutputWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedCellToOutputWeights.GetTensorInfo(),
                                                   managedCellToOutputWeights.Map()));
         }
     }
 
     // Add projection parameters
     if (descriptor.m_ProjectionEnabled)
     {
         if (m_ProjectionParameters.m_ProjectionWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedProjectionWeights.GetTensorInfo(),
                                                   managedProjectionWeights.Map()));
         }
         if (m_ProjectionParameters.m_ProjectionBias != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedProjectionBias.GetTensorInfo(),
                                                   managedProjectionBias.Map()));
         }
     }
 
     // Add norm parameters
     if (descriptor.m_LayerNormEnabled)
     {
         if (!descriptor.m_CifgEnabled)
         {
             if (m_LayerNormParameters.m_InputLayerNormWeights != nullptr)
             {
                 constTensors.emplace_back(ConstTensor(managedInputLayerNormWeights.GetTensorInfo(),
                                                       managedInputLayerNormWeights.Map()));
             }
         }
         if (m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedForgetLayerNormWeights.GetTensorInfo(),
                                                   managedForgetLayerNormWeights.Map()));
         }
         if (m_LayerNormParameters.m_CellLayerNormWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedCellLayerNormWeights.GetTensorInfo(),
                                                   managedCellLayerNormWeights.Map()));
         }
         if (m_LayerNormParameters.m_OutputLayerNormWeights != nullptr)
         {
             constTensors.emplace_back(ConstTensor(managedOutputLayerNormWeights.GetTensorInfo(),
                                                   managedOutputLayerNormWeights.Map()));
         }
     }
 
     strategy.ExecuteStrategy(this, GetParameters(), constTensors, GetName());
 }

◆ GetConstantTensorsByRef()

Layer::ConstantTensors GetConstantTensorsByRef ( )

overrideprotectedvirtual

Retrieve the handles to the constant values stored by the layer.

Returns: A vector of the constant tensors stored by this layer.

Reimplemented from Layer.

Definition at line 277 of file UnidirectionalSequenceLstmLayer.cpp.

 {
     return {m_BasicParameters.m_InputToForgetWeights,
             m_BasicParameters.m_InputToCellWeights,
             m_BasicParameters.m_InputToOutputWeights,
             m_BasicParameters.m_RecurrentToForgetWeights,
             m_BasicParameters.m_RecurrentToCellWeights,
             m_BasicParameters.m_RecurrentToOutputWeights,
             m_BasicParameters.m_ForgetGateBias,
             m_BasicParameters.m_CellBias,
             m_BasicParameters.m_OutputGateBias,
 
             // Cifg parameters
             m_CifgParameters.m_InputToInputWeights,
             m_CifgParameters.m_RecurrentToInputWeights,
             m_CifgParameters.m_InputGateBias,
 
             // Projection parameters
             m_ProjectionParameters.m_ProjectionWeights,
             m_ProjectionParameters.m_ProjectionBias,
 
             // Peephole parameters
             m_PeepholeParameters.m_CellToInputWeights,
             m_PeepholeParameters.m_CellToForgetWeights,
             m_PeepholeParameters.m_CellToOutputWeights,
 
             // Layer normalisation parameters
             m_LayerNormParameters.m_InputLayerNormWeights,
             m_LayerNormParameters.m_ForgetLayerNormWeights,
             m_LayerNormParameters.m_CellLayerNormWeights,
             m_LayerNormParameters.m_OutputLayerNormWeights};
 }

◆ InferOutputShapes()

std::vector< TensorShape > InferOutputShapes ( const std::vector< TensorShape > & inputShapes ) const

overridevirtual

By default returns inputShapes if the number of inputs are equal to number of outputs, otherwise infers the output shapes from given input shapes and layer properties.

Parameters

[in] inputShapes The input shapes layer has.

Returns: A vector to the inferred output shape.

Reimplemented from Layer.

Definition at line 150 of file UnidirectionalSequenceLstmLayer.cpp.

References ARMNN_ASSERT, LayerWithParameters< LstmDescriptor >::m_Param, and LstmDescriptor::m_TimeMajor.

Referenced by UnidirectionalSequenceLstmLayer::ValidateTensorShapesFromInputs().

 {
     ARMNN_ASSERT(inputShapes.size() == 3);
 
     // Get input values for validation
     unsigned int outputSize = inputShapes[1][1];
 
     std::vector<TensorShape> outShapes;
     if (m_Param.m_TimeMajor)
     {
         outShapes.push_back(TensorShape({inputShapes[0][0], inputShapes[0][1], outputSize}));
     }
     else
     {
         outShapes.push_back(TensorShape({inputShapes[0][0], inputShapes[0][1], outputSize}));
     }
     return outShapes;
 }

◆ ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

Check if the input tensor shape(s) will lead to a valid configuration of UnidirectionalSequenceLstmLayer.

Parameters

[in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.

Implements Layer.

Definition at line 170 of file UnidirectionalSequenceLstmLayer.cpp.

References ARMNN_ASSERT, ARMNN_ASSERT_MSG, CHECK_LOCATION, InputSlot::GetConnection(), Layer::GetInputSlot(), Layer::GetOutputSlot(), TensorInfo::GetShape(), IOutputSlot::GetTensorInfo(), OutputSlot::GetTensorInfo(), UnidirectionalSequenceLstmLayer::InferOutputShapes(), UnidirectionalSequenceLstmLayer::m_BasicParameters, LstmBasicParameters::m_CellBias, LstmOptLayerNormParameters::m_CellLayerNormWeights, LstmOptPeepholeParameters::m_CellToForgetWeights, LstmOptPeepholeParameters::m_CellToInputWeights, LstmOptPeepholeParameters::m_CellToOutputWeights, LstmDescriptor::m_CifgEnabled, UnidirectionalSequenceLstmLayer::m_CifgParameters, LstmBasicParameters::m_ForgetGateBias, LstmOptLayerNormParameters::m_ForgetLayerNormWeights, LstmOptCifgParameters::m_InputGateBias, LstmOptLayerNormParameters::m_InputLayerNormWeights, LstmBasicParameters::m_InputToCellWeights, LstmBasicParameters::m_InputToForgetWeights, LstmOptCifgParameters::m_InputToInputWeights, LstmBasicParameters::m_InputToOutputWeights, LstmDescriptor::m_LayerNormEnabled, UnidirectionalSequenceLstmLayer::m_LayerNormParameters, LstmBasicParameters::m_OutputGateBias, LstmOptLayerNormParameters::m_OutputLayerNormWeights, LayerWithParameters< LstmDescriptor >::m_Param, LstmDescriptor::m_PeepholeEnabled, UnidirectionalSequenceLstmLayer::m_PeepholeParameters, LstmDescriptor::m_ProjectionEnabled, UnidirectionalSequenceLstmLayer::m_ProjectionParameters, LstmOptProjectionParameters::m_ProjectionWeights, LstmBasicParameters::m_RecurrentToCellWeights, LstmBasicParameters::m_RecurrentToForgetWeights, LstmOptCifgParameters::m_RecurrentToInputWeights, LstmBasicParameters::m_RecurrentToOutputWeights, Layer::m_ShapeInferenceMethod, Layer::ValidateAndCopyShape(), Layer::VerifyLayerConnections(), and Layer::VerifyShapeInferenceType().

 {
     VerifyLayerConnections(3, CHECK_LOCATION());
 
     const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
     auto inferredShapes = InferOutputShapes( {
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(),
         GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape()
     });
 
     ARMNN_ASSERT(inferredShapes.size() == 1);
 
     // Check if the weights are nullptr
     ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_RecurrentToForgetWeights "
                      "should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_RecurrentToOutputWeights "
                      "should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_CellBias should not be null.");
     ARMNN_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr,
                      "UnidirectionalSequenceLstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
 
     if (!m_Param.m_CifgEnabled)
     {
         ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
         ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_RecurrentToInputWeights "
                          "should not be null.");
         ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
     }
     else
     {
         ARMNN_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr,
             "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value "
             "when CIFG is enabled.");
         ARMNN_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr,
             "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not have a value "
             "when CIFG is enabled.");
         ARMNN_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr,
             "UnidirectionalSequenceLstmLayer: m_CifgParameters.m_InputGateBias should not have a value "
             "when CIFG is enabled.");
     }
 
     if (m_Param.m_ProjectionEnabled)
     {
         ARMNN_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_ProjectionParameters.m_ProjectionWeights "
                          "should not be null.");
     }
 
     if (m_Param.m_PeepholeEnabled)
     {
         if (!m_Param.m_CifgEnabled)
         {
             ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr,
                              "UnidirectionalSequenceLstmLayer: m_PeepholeParameters.m_CellToInputWeights "
                              "should not be null "
                              "when Peephole is enabled and CIFG is disabled.");
         }
         ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_PeepholeParameters.m_CellToForgetWeights "
                          "should not be null.");
         ARMNN_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_PeepholeParameters.m_CellToOutputWeights "
                          "should not be null.");
     }
 
     if (m_Param.m_LayerNormEnabled)
     {
         if(!m_Param.m_CifgEnabled)
         {
             ARMNN_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr,
                              "UnidirectionalSequenceLstmLayer: m_LayerNormParameters.m_inputLayerNormWeights "
                              "should not be null.");
         }
         ARMNN_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_LayerNormParameters.m_forgetLayerNormWeights "
                          "should not be null.");
         ARMNN_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_LayerNormParameters.m_cellLayerNormWeights "
                          "should not be null.");
         ARMNN_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr,
                          "UnidirectionalSequenceLstmLayer: m_LayerNormParameters.m_outputLayerNormWeights "
                          "should not be null.");
     }
 
     ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "UnidirectionalSequenceLstmLayer");
 }