#include <WorkloadData.hpp>

Inheritance diagram for LstmQueueDescriptor:

Collaboration diagram for LstmQueueDescriptor:

Public Member Functions
	LstmQueueDescriptor ()
void	Validate (const WorkloadInfo &workloadInfo) const
Public Member Functions inherited from QueueDescriptorWithParameters< LstmDescriptor >
virtual	~QueueDescriptorWithParameters ()=default
Public Member Functions inherited from QueueDescriptor
virtual	~QueueDescriptor ()=default
void	ValidateTensorNumDimensions (const TensorInfo &tensor, std::string const &descName, unsigned int numDimensions, std::string const &tensorName) const
void	ValidateTensorNumDimNumElem (const TensorInfo &tensorInfo, unsigned int numDimension, unsigned int numElements, std::string const &tensorName) const
void	ValidateInputsOutputs (const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
template<typename T>
const T *	GetAdditionalInformation () const

Public Attributes
const ConstTensorHandle *	m_InputToInputWeights
const ConstTensorHandle *	m_InputToForgetWeights
const ConstTensorHandle *	m_InputToCellWeights
const ConstTensorHandle *	m_InputToOutputWeights
const ConstTensorHandle *	m_RecurrentToInputWeights
const ConstTensorHandle *	m_RecurrentToForgetWeights
const ConstTensorHandle *	m_RecurrentToCellWeights
const ConstTensorHandle *	m_RecurrentToOutputWeights
const ConstTensorHandle *	m_CellToInputWeights
const ConstTensorHandle *	m_CellToForgetWeights
const ConstTensorHandle *	m_CellToOutputWeights
const ConstTensorHandle *	m_InputGateBias
const ConstTensorHandle *	m_ForgetGateBias
const ConstTensorHandle *	m_CellBias
const ConstTensorHandle *	m_OutputGateBias
const ConstTensorHandle *	m_ProjectionWeights
const ConstTensorHandle *	m_ProjectionBias
const ConstTensorHandle *	m_InputLayerNormWeights
const ConstTensorHandle *	m_ForgetLayerNormWeights
const ConstTensorHandle *	m_CellLayerNormWeights
const ConstTensorHandle *	m_OutputLayerNormWeights
Public Attributes inherited from QueueDescriptorWithParameters< LstmDescriptor >
LstmDescriptor	m_Parameters
Public Attributes inherited from QueueDescriptor
std::vector< ITensorHandle * >	m_Inputs
std::vector< ITensorHandle * >	m_Outputs
void *	m_AdditionalInfoObject
bool	m_AllowExpandedDims = false

Additional Inherited Members
Protected Member Functions inherited from QueueDescriptorWithParameters< LstmDescriptor >
	QueueDescriptorWithParameters ()=default
QueueDescriptorWithParameters &	operator= (QueueDescriptorWithParameters const &)=default
Protected Member Functions inherited from QueueDescriptor
	QueueDescriptor ()
	QueueDescriptor (QueueDescriptor const &)=default
QueueDescriptor &	operator= (QueueDescriptor const &)=default

Detailed Description

Definition at line 400 of file WorkloadData.hpp.

Constructor & Destructor Documentation

◆ LstmQueueDescriptor()

LstmQueueDescriptor ( )

inline

Definition at line 402 of file WorkloadData.hpp.

        : m_InputToInputWeights(nullptr)
        , m_InputToForgetWeights(nullptr)
        , m_InputToCellWeights(nullptr)
        , m_InputToOutputWeights(nullptr)
        , m_RecurrentToInputWeights(nullptr)
        , m_RecurrentToForgetWeights(nullptr)
        , m_RecurrentToCellWeights(nullptr)
        , m_RecurrentToOutputWeights(nullptr)
        , m_CellToInputWeights(nullptr)
        , m_CellToForgetWeights(nullptr)
        , m_CellToOutputWeights(nullptr)
        , m_InputGateBias(nullptr)
        , m_ForgetGateBias(nullptr)
        , m_CellBias(nullptr)
        , m_OutputGateBias(nullptr)
        , m_ProjectionWeights(nullptr)
        , m_ProjectionBias(nullptr)
        , m_InputLayerNormWeights(nullptr)
        , m_ForgetLayerNormWeights(nullptr)
        , m_CellLayerNormWeights(nullptr)
        , m_OutputLayerNormWeights(nullptr)
    {
    }

References m_CellBias, m_CellLayerNormWeights, m_CellToForgetWeights, m_CellToInputWeights, m_CellToOutputWeights, m_ForgetGateBias, m_ForgetLayerNormWeights, m_InputGateBias, m_InputLayerNormWeights, m_InputToCellWeights, m_InputToForgetWeights, m_InputToInputWeights, m_InputToOutputWeights, m_OutputGateBias, m_OutputLayerNormWeights, m_ProjectionBias, m_ProjectionWeights, m_RecurrentToCellWeights, m_RecurrentToForgetWeights, m_RecurrentToInputWeights, and m_RecurrentToOutputWeights.

Member Function Documentation

◆ Validate()

void Validate ( const WorkloadInfo & workloadInfo ) const

Definition at line 2021 of file WorkloadData.cpp.

{
    // ported from android/ml/nn/common/operations/LSTM.cpp CheckInputTensorDimensions()
 
    const std::string descriptorName{"LstmQueueDescriptor"};
 
    // check dimensions of all inputs and outputs
    if (workloadInfo.m_InputTensorInfos.size() != 3)
    {
        throw InvalidArgumentException(descriptorName + ": Invalid number of inputs.");
    }
    if (workloadInfo.m_OutputTensorInfos.size() != 4)
    {
        throw InvalidArgumentException(descriptorName + ": Invalid number of outputs.");
    }
 
    std::vector<DataType> supportedTypes =
    {
        DataType::BFloat16,
        DataType::Float16,
        DataType::Float32,
        DataType::QSymmS16
    };
 
    // check for supported type of one input and match them with all the other input and output
    ValidateDataTypes(workloadInfo.m_InputTensorInfos[0], supportedTypes, descriptorName);
 
    // type matches all other inputs
    for (uint32_t i = 1u; i < workloadInfo.m_InputTensorInfos.size(); ++i)
    {
        ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
                                     workloadInfo.m_InputTensorInfos[i],
                                     descriptorName,
                                     "input_0",
                                     "input_" + std::to_string(i));
    }
    // type matches all other outputs
    for (uint32_t i = 0u; i < workloadInfo.m_OutputTensorInfos.size(); ++i)
    {
        ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
                                     workloadInfo.m_OutputTensorInfos[i],
                                     "LstmQueueDescriptor",
                                     "input_0",
                                     "output_" + std::to_string(i));
    }
 
    // Making sure clipping parameters have valid values.
    // == 0 means no clipping
    //  > 0 means clipping
    if (m_Parameters.m_ClippingThresCell < 0.0f)
    {
        throw InvalidArgumentException(descriptorName + ": negative cell clipping threshold is invalid");
    }
    if (m_Parameters.m_ClippingThresProj < 0.0f)
    {
        throw InvalidArgumentException(descriptorName + ": negative projection clipping threshold is invalid");
    }
 
    // Inferring batch size, number of outputs and number of cells from the inputs.
    const uint32_t n_input = workloadInfo.m_InputTensorInfos[0].GetShape()[1];
    const uint32_t n_batch = workloadInfo.m_InputTensorInfos[0].GetShape()[0];
    ValidatePointer(m_InputToOutputWeights, "Null pointer check", "InputToOutputWeights");
    const uint32_t n_cell = m_InputToOutputWeights->GetShape()[0];
    ValidatePointer(m_RecurrentToOutputWeights, "Null pointer check", "RecurrentToOutputWeights");
    const uint32_t n_output = m_RecurrentToOutputWeights->GetShape()[1];
 
    // input tensor
    ValidateTensorNumDimNumElem(workloadInfo.m_InputTensorInfos[0], 2, (n_batch * n_input),
                                descriptorName + " input_0");
    // outputStateInTensor
    ValidateTensorNumDimNumElem(workloadInfo.m_InputTensorInfos[1], 2, (n_batch * n_output),
                                descriptorName + " input_1");
    // outputStateInTensor
    ValidateTensorNumDimNumElem(workloadInfo.m_InputTensorInfos[2], 2, (n_batch * n_cell),
                                descriptorName + " input_2");
    // scratchBufferTensor
    unsigned int scratchBufferSize = m_Parameters.m_CifgEnabled ? n_cell * 3 : n_cell * 4;
    ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[0], 2, (n_batch * scratchBufferSize),
                                descriptorName + " output_0");
    // outputStateOutTensor
    ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[1], 2, (n_batch * n_output),
                                descriptorName + " output_1");
    // cellStateOutTensor
    ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[2], 2, (n_batch * n_cell),
                                descriptorName + " output_2");
    // outputTensor
    ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[3], 2, (n_batch * n_output),
                                descriptorName + " output_3");
 
    // check that dimensions of inputs/outputs and QueueDescriptor data match with each other
    if ( m_InputToInputWeights )
    {
        ValidateTensorNumDimNumElem(m_InputToInputWeights->GetTensorInfo(), 2,
                                      (n_cell * n_input), "InputLayerNormWeights");
    }
 
    ValidatePointer(m_InputToForgetWeights, "Null pointer check", "InputToForgetWeights");
    ValidateTensorNumDimNumElem(m_InputToForgetWeights->GetTensorInfo(), 2,
                                  (n_cell * n_input), "InputToForgetWeights");
 
    ValidatePointer(m_InputToCellWeights, "Null pointer check", "InputToCellWeights");
    ValidateTensorNumDimNumElem(m_InputToCellWeights->GetTensorInfo(), 2,
                                  (n_cell * n_input), "InputToCellWeights");
 
    if ( m_RecurrentToInputWeights )
    {
        ValidateTensorNumDimNumElem(m_RecurrentToInputWeights->GetTensorInfo(), 2,
                                      (n_cell * n_output), "RecurrentToInputWeights");
    }
 
    ValidatePointer(m_RecurrentToForgetWeights, "Null pointer check", "RecurrentToForgetWeights");
    ValidateTensorNumDimNumElem(m_RecurrentToForgetWeights->GetTensorInfo(), 2,
                                  (n_cell * n_output), "RecurrentToForgetWeights");
 
    ValidatePointer(m_RecurrentToCellWeights, "Null pointer check", "RecurrentToCellWeights");
    ValidateTensorNumDimNumElem(m_RecurrentToCellWeights->GetTensorInfo(), 2,
                                  (n_cell * n_output), "RecurrentToCellWeights");
 
    // Make sure the input-gate's parameters are either both present (regular
    // LSTM) or not at all (CIFG-LSTM). And CifgEnable is set accordingly.
    bool cifg_weights_all_or_none = ((m_InputToInputWeights && m_RecurrentToInputWeights &&
                                     !m_Parameters.m_CifgEnabled) ||
                                     (!m_InputToInputWeights && !m_RecurrentToInputWeights &&
                                     m_Parameters.m_CifgEnabled));
    if (!cifg_weights_all_or_none)
    {
        throw InvalidArgumentException(descriptorName + ": Input-Gate's parameters InputToInputWeights and "
                                       "RecurrentToInputWeights must either both be present (regular LSTM) "
                                       "or both not present (CIFG-LSTM). In addition CifgEnable must be set "
                                       "accordingly.");
    }
 
    if ( m_CellToInputWeights )
    {
        ValidateTensorNumDimNumElem(m_CellToInputWeights->GetTensorInfo(), 1,
                                      n_cell, "CellToInputWeights");
    }
    if ( m_CellToForgetWeights )
    {
        ValidateTensorNumDimNumElem(m_CellToForgetWeights->GetTensorInfo(), 1,
                                      n_cell, "CellToForgetWeights");
    }
    if ( m_CellToOutputWeights )
    {
        ValidateTensorNumDimNumElem(m_CellToOutputWeights->GetTensorInfo(), 1,
                                      n_cell, "CellToOutputWeights");
    }
 
    // Making sure the peephole weights are there all or none. And PeepholeEnable is set accordingly.
    bool peephole_weights_all_or_none =
            (((m_CellToInputWeights || m_Parameters.m_CifgEnabled) &&  m_CellToForgetWeights
            && m_CellToOutputWeights && m_Parameters.m_PeepholeEnabled)
            || ( !m_CellToInputWeights && !m_CellToForgetWeights
            && !m_CellToOutputWeights && !m_Parameters.m_PeepholeEnabled));
    if (!peephole_weights_all_or_none)
    {
        throw InvalidArgumentException(descriptorName + ": Invalid combination of peephole parameters.");
    }
 
    // Make sure the input gate bias is present only when not a CIFG-LSTM.
    if (m_Parameters.m_CifgEnabled)
    {
        if (m_InputGateBias)
        {
            throw InvalidArgumentException(descriptorName + ": InputGateBias is present and CIFG-LSTM is enabled.");
        }
    }
    else
    {
        if (!m_InputGateBias)
        {
            throw InvalidArgumentException(descriptorName + ": If CIFG-LSTM is disabled InputGateBias "
                                           "must be present.");
        }
        ValidateTensorNumDimNumElem(m_InputGateBias->GetTensorInfo(), 1,
                                      n_cell, "InputGateBias");
    }
 
    ValidatePointer(m_ForgetGateBias, "Null pointer check", "ForgetGateBias");
    ValidateTensorNumDimNumElem(m_ForgetGateBias->GetTensorInfo(), 1, n_cell, "ForgetGateBias");
 
    ValidatePointer(m_CellBias, "Null pointer check", "CellBias");
    ValidateTensorNumDimNumElem(m_CellBias->GetTensorInfo(), 1, n_cell, "CellBias");
 
    ValidatePointer(m_OutputGateBias, "Null pointer check", "OutputGateBias");
    ValidateTensorNumDimNumElem(m_OutputGateBias->GetTensorInfo(), 1, n_cell, "OutputGateBias");
 
    if (m_ProjectionWeights)
    {
        ValidateTensorNumDimNumElem(m_ProjectionWeights->GetTensorInfo(), 2,
                                      (n_cell * n_output), "ProjectionWeights");
    }
    if (m_ProjectionBias)
    {
        ValidateTensorNumDimNumElem(m_ProjectionBias->GetTensorInfo(), 1, n_output, "ProjectionBias");
    }
 
    // Making sure the projection tensors are consistent:
    // 1) If projection weight is not present, then projection bias should not be
    // present.
    // 2) If projection weight is present, then projection bias is optional.
    bool projecton_tensors_consistent = ((!m_ProjectionWeights && !m_ProjectionBias &&
                                        !m_Parameters.m_ProjectionEnabled)
                                        || (m_ProjectionWeights && !m_ProjectionBias &&
                                        m_Parameters.m_ProjectionEnabled)
                                        || (m_ProjectionWeights && m_ProjectionBias &&
                                        m_Parameters.m_ProjectionEnabled));
    if (!projecton_tensors_consistent)
    {
        throw InvalidArgumentException(descriptorName + ": Projection tensors are inconsistent.");
    }
 
    // The four layer normalization weights either all have values or none of them have values. Additionally, if
    // CIFG is used, input layer normalization weights tensor is omitted and the other layer normalization weights
    // either all have values or none of them have values. Layer normalization is used when the values of all the
    // layer normalization weights are present
    if (m_InputLayerNormWeights)
    {
        ValidateTensorNumDimNumElem(m_InputLayerNormWeights->GetTensorInfo(), 1, n_cell, "InputLayerNormWeights");
    }
    if (m_ForgetLayerNormWeights)
    {
        ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights->GetTensorInfo(), 1, n_cell, "ForgetLayerNormWeights");
    }
    if (m_CellLayerNormWeights)
    {
        ValidateTensorNumDimNumElem(m_CellLayerNormWeights->GetTensorInfo(), 1, n_cell, "CellLayerNormWeights");
    }
    if (m_OutputLayerNormWeights)
    {
        ValidateTensorNumDimNumElem(m_OutputLayerNormWeights->GetTensorInfo(), 1, n_cell, "OutputLayerNormWeights");
    }
 
    if (m_Parameters.m_LayerNormEnabled)
    {
        if (!m_Parameters.m_CifgEnabled)
        {
            if (!m_InputLayerNormWeights)
            {
                throw InvalidArgumentException(descriptorName + ": Layer normalisation is enabled and CIFG-LSTM is "
                                               "disabled but InputLayerNormWeights are not present");
            }
            ValidateTensorNumDimNumElem(m_InputLayerNormWeights->GetTensorInfo(),
                                          1, n_cell, "InputLayerNormWeights");
        }
        else if (m_InputLayerNormWeights)
        {
            throw InvalidArgumentException(descriptorName + ":InputLayerNormWeights are present while CIFG is "
                                           "enabled");
        }
 
        ValidatePointer(m_ForgetLayerNormWeights, "Null pointer check layer normalisation enabled",
                        "ForgetLayerNormWeights");
        ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights->GetTensorInfo(), 1, n_cell, "ForgetLayerNormWeights");
 
        ValidatePointer(m_OutputLayerNormWeights, "Null pointer check layer normalisation enabled",
                        "OutputLayerNormWeights");
        ValidateTensorNumDimNumElem(m_OutputLayerNormWeights->GetTensorInfo(), 1, n_cell, "OutputLayerNormWeights");
 
        ValidatePointer(m_CellLayerNormWeights, "Null pointer check layer normalisation enabled",
                        "CellLayerNormWeights");
        ValidateTensorNumDimNumElem(m_CellLayerNormWeights->GetTensorInfo(), 1, n_cell, "CellLayerNormWeights");
    }
    else if (m_InputLayerNormWeights || m_ForgetLayerNormWeights || m_OutputLayerNormWeights || m_CellLayerNormWeights)
    {
        throw InvalidArgumentException(descriptorName + ": Layer normalisation is disabled but one or more layer "
                                       "normalisation weights are present.");
    }
}