This layer represents a QLstm operation. More...

#include <QLstmLayer.hpp>

Inheritance diagram for QLstmLayer:

Collaboration diagram for QLstmLayer:

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

QLstmLayer *	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 QLstmLayer. 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...

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

Public Member Functions inherited from LayerWithParameters< QLstmDescriptor >
const QLstmDescriptor &	GetParameters () const override
	If the layer has a descriptor return it. More...

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

bool	GetAllowExpandedDims () 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) override
	Set the backend of the IConnectableLayer. More...

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)

void	SetAllowExpandedDims (bool allowExpandedDims)

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

void	SetAdditionalInfoForObject (const AdditionalInfoObjectPtr &additionalInfo)

virtual const BaseDescriptor &	GetParameters () const override
	If the layer has a descriptor return it. More...

Public Attributes
QLstmBasicParameters	m_BasicParameters

QLstmOptCifgParameters	m_CifgParameters

QLstmOptProjectionParameters	m_ProjectionParameters

QLstmOptPeepholeParameters	m_PeepholeParameters

QLstmOptLayerNormParameters	m_LayerNormParameters

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

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

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

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

	~LayerWithParameters ()=default

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

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

Layer::ImmutableConstantTensors	GetConnectedConstantAsInputTensors () const

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 *Layer::CreateWorkload. More...

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

virtual ConstantTensors	GetConstantTensorsByRef () override final

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< QLstmDescriptor >
using	DescriptorType = QLstmDescriptor

Public Types inherited from IConnectableLayer
using	ConstantTensors = std::vector< std::reference_wrapper< std::shared_ptr< ConstTensorHandle > >>

using	ImmutableConstantTensors = std::vector< std::reference_wrapper< const std::shared_ptr< ConstTensorHandle > >>

Protected Attributes inherited from LayerWithParameters< QLstmDescriptor >
QLstmDescriptor	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 QLstm operation.

Definition at line 79 of file QLstmLayer.hpp.

Constructor & Destructor Documentation

◆ QLstmLayer()

QLstmLayer	(	const QLstmDescriptor &	param,
		const char *	name
	)

protected

Constructor to create a QLstmLayer.

Parameters

[in] name Optional name for the layer.

Definition at line 17 of file QLstmLayer.cpp.

         : LayerWithParameters(3, 3, LayerType::QLstm, param, name)
 {
 }

References armnn::QLstm.

◆ ~QLstmLayer()

~QLstmLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

◆ Clone()

QLstmLayer * 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 81 of file QLstmLayer.cpp.

 {
     auto layer = CloneBase<QLstmLayer>(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)
     {
         if (!m_Param.m_CifgEnabled) {
             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 QLstm 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 QLstmLayer.cpp.

 {
     QLstmQueueDescriptor 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.CreateWorkload(LayerType::QLstm, descriptor, PrepInfoAndDesc(descriptor));
 }

◆ ExecuteStrategy()

void ExecuteStrategy ( IStrategy & strategy ) const

overridevirtual

Apply a visitor to this layer.

Reimplemented from Layer.

Definition at line 403 of file QLstmLayer.cpp.

 {
     std::vector<ConstTensor> constTensors;
     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 cifig parameters
     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 (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 (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 (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::ImmutableConstantTensors GetConstantTensorsByRef ( ) const

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 368 of file QLstmLayer.cpp.

 {
     // For API stability DO NOT ALTER order and add new members to the end of vector
     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 153 of file QLstmLayer.cpp.

 {
     if (inputShapes.size() != 3)
     {
         throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
                                "\" - should be \"3\".");
     }
  
     // Get input values for validation
     unsigned int batchSize = inputShapes[0][0];
     unsigned int outputSize = inputShapes[1][1];
     unsigned int numUnits = inputShapes[2][1];
  
     std::vector<TensorShape> outShapes;
     outShapes.push_back(TensorShape({ batchSize, outputSize })); // outputStateOut
     outShapes.push_back(TensorShape({ batchSize, numUnits })); // cellStateOut
     outShapes.push_back(TensorShape({ batchSize, outputSize })); // output
  
     return outShapes;
 }

Referenced by QLstmLayer::ValidateTensorShapesFromInputs().

◆ ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

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

Parameters

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

Implements Layer.

Definition at line 174 of file QLstmLayer.cpp.

 {
     VerifyLayerConnections(3, CHECK_LOCATION());
  
     const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
  
     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
  
     auto inferredShapes = InferOutputShapes(
     {
         GetInputSlot(0).GetTensorInfo().GetShape(), // input
         GetInputSlot(1).GetTensorInfo().GetShape(), // previousOutputIn
         GetInputSlot(2).GetTensorInfo().GetShape()  // previousCellStateIn
     });
  
     if (inferredShapes.size() != 3)
     {
         throw armnn::LayerValidationException("inferredShapes has "
                                               + std::to_string(inferredShapes.size()) +
                                               " element(s) - should only have 3.");
     }
  
     // Check if the weights are nullptr for basic params
     if (!m_BasicParameters.m_InputToForgetWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_InputToForgetWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_InputToCellWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_InputToCellWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_InputToOutputWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_InputToOutputWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_RecurrentToForgetWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_RecurrentToCellWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_RecurrentToCellWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_RecurrentToOutputWeights)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
     }
  
     if (!m_BasicParameters.m_ForgetGateBias)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_ForgetGateBias should not be null.");
     }
  
     if (!m_BasicParameters.m_CellBias)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_CellBias should not be null.");
     }
  
     if (!m_BasicParameters.m_OutputGateBias)
     {
         throw armnn::LayerValidationException("QLstmLayer: "
                                               "m_BasicParameters.m_OutputGateBias should not be null.");
     }
  
     if (!m_Param.m_CifgEnabled)
     {
         if (!m_CifgParameters.m_InputToInputWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_CifgParameters.m_InputToInputWeights should not be null.");
         }
  
         if (!m_CifgParameters.m_RecurrentToInputWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_CifgParameters.m_RecurrentToInputWeights should not be null.");
         }
  
         if (!m_CifgParameters.m_InputGateBias)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_CifgParameters.m_InputGateBias should not be null.");
         }
  
         ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QLstmLayer");
     }
     else
     {
         if (m_CifgParameters.m_InputToInputWeights)
         {
                 throw armnn::LayerValidationException("QLstmLayer: "
                                                       "m_CifgParameters.m_InputToInputWeights "
                                                       "should not have a value when CIFG is enabled.");
         }
  
         if (m_CifgParameters.m_RecurrentToInputWeights)
         {
                 throw armnn::LayerValidationException("QLstmLayer: "
                                                       "m_CifgParameters.m_RecurrentToInputWeights "
                                                       "should not have a value when CIFG is enabled.");
         }
  
         if (m_CifgParameters.m_InputGateBias)
         {
                 throw armnn::LayerValidationException("QLstmLayer: "
                                                       "m_CifgParameters.m_InputGateBias "
                                                       "should not have a value when CIFG is enabled.");
         }
  
         ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QLstmLayer");
     }
  
     if (m_Param.m_ProjectionEnabled)
     {
         if (!m_ProjectionParameters.m_ProjectionWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_ProjectionParameters.m_ProjectionWeights should not be null.");
         }
     }
  
     if (m_Param.m_PeepholeEnabled)
     {
         if (!m_Param.m_CifgEnabled) {
             if (!m_PeepholeParameters.m_CellToInputWeights)
             {
                 throw armnn::LayerValidationException("QLstmLayer: "
                                                       "m_PeepholeParameters.m_CellToInputWeights should not be null "
                                                       "when Peephole is enabled and CIFG is disabled.");
             }
         }
  
         if (!m_PeepholeParameters.m_CellToForgetWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_PeepholeParameters.m_CellToForgetWeights should not be null.");
         }
  
         if (!m_PeepholeParameters.m_CellToOutputWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_PeepholeParameters.m_CellToOutputWeights should not be null.");
         }
     }
  
     ValidateAndCopyShape(
             GetOutputSlot(1).GetTensorInfo().GetShape(), inferredShapes[1], m_ShapeInferenceMethod, "QLstmLayer", 1);
     ValidateAndCopyShape(
             GetOutputSlot(2).GetTensorInfo().GetShape(), inferredShapes[2], m_ShapeInferenceMethod, "QLstmLayer", 2);
  
     if (m_Param.m_LayerNormEnabled)
     {
         if (!m_Param.m_CifgEnabled)
         {
             if (!m_LayerNormParameters.m_InputLayerNormWeights)
             {
                 throw armnn::LayerValidationException("QLstmLayer: m_LayerNormParameters.m_InputLayerNormWeights "
                                                       "should not be null.");
             }
         }
  
         if (!m_LayerNormParameters.m_ForgetLayerNormWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_LayerNormParameters.m_ForgetLayerNormWeights should not be null.");
         }
  
         if (!m_LayerNormParameters.m_CellLayerNormWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_LayerNormParameters.m_CellLayerNormWeights should not be null.");
         }
  
         if (!m_LayerNormParameters.m_OutputLayerNormWeights)
         {
             throw armnn::LayerValidationException("QLstmLayer: "
                                                   "m_LayerNormParameters.m_UutputLayerNormWeights should not be null.");
         }
     }
 }