BatchMatMulQueueDescriptor Struct Reference

#include <WorkloadData.hpp>

Inheritance diagram for BatchMatMulQueueDescriptor:

Public Member Functions
void	Validate (const WorkloadInfo &workloadInfo) const
Public Member Functions inherited from QueueDescriptorWithParameters< BatchMatMulDescriptor >
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

Additional Inherited Members
Public Attributes inherited from QueueDescriptorWithParameters< BatchMatMulDescriptor >
BatchMatMulDescriptor	m_Parameters
Public Attributes inherited from QueueDescriptor
std::vector< ITensorHandle * >	m_Inputs
std::vector< ITensorHandle * >	m_Outputs
void *	m_AdditionalInfoObject
bool	m_AllowExpandedDims = false
Protected Member Functions inherited from QueueDescriptorWithParameters< BatchMatMulDescriptor >
	QueueDescriptorWithParameters ()=default
	QueueDescriptorWithParameters (QueueDescriptorWithParameters const &)=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 788 of file WorkloadData.hpp.

Member Function Documentation

Validate()

void Validate

(

const WorkloadInfo &

workloadInfo

)

const

Definition at line 4146 of file WorkloadData.cpp.

References armnn::BFloat16, armnn::Float16, armnn::Float32, BatchMatMulDescriptor::GetAxesNotMul(), BatchMatMulDescriptor::GetAxesToMul(), TensorInfo::GetNumDimensions(), TensorInfo::GetShape(), WorkloadInfo::m_InputTensorInfos, WorkloadInfo::m_OutputTensorInfos, armnn::NCDHW, armnn::NCHW, armnn::NDHWC, armnn::NHWC, armnn::QAsymmS8, armnn::QAsymmU8, and armnn::QSymmS16.

{
    const std::string descriptorName{"BatchMatMulDescriptor"};

    ValidateNumInputs(workloadInfo,  descriptorName, 2);
    ValidateNumOutputs(workloadInfo, descriptorName, 1);

    // Inputs must be: both 2D+
    // For inputs X and Y whose dimensions to be multiplied are (M,N) and (I,J) respectively,
    // axes N and I must be the same size

    const auto& inputTensorXInfo = workloadInfo.m_InputTensorInfos[0];
    const auto& inputTensorYInfo = workloadInfo.m_InputTensorInfos[1];
    const auto& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];

    std::vector<DataType> supportedTypes =
    {
        DataType::BFloat16,
        DataType::Float16,
        DataType::Float32,
        DataType::QAsymmS8,
        DataType::QAsymmU8,
        DataType::QSymmS16
    };

    ValidateDataTypes(inputTensorXInfo, supportedTypes, descriptorName);
    ValidateDataTypes(inputTensorYInfo, supportedTypes, descriptorName);
    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);

    if ((inputTensorXInfo.GetNumDimensions() < 2) ||
        (inputTensorYInfo.GetNumDimensions() < 2))
    {
        throw InvalidArgumentException(descriptorName + ": Input tensors are not 2D or greater.");
    }

    if(m_Parameters.m_DataLayoutX.has_value())
    {
        switch(m_Parameters.m_DataLayoutX.value())
        {
            case DataLayout::NCHW:
            case DataLayout::NHWC:
                if(inputTensorXInfo.GetNumDimensions() != 4)
                {
                    throw InvalidArgumentException(descriptorName +
                        ": Input tensor X does not have the correct "
                        "number of dimensions for the Data Layout that it has been assigned.");
                }
                break;
            case DataLayout::NCDHW:
            case DataLayout::NDHWC:
                if(inputTensorXInfo.GetNumDimensions() != 5)
                {
                    throw InvalidArgumentException(descriptorName +
                        ": Input tensor X does not have the correct "
                        "number of dimensions for the Data Layout that it has been assigned.");
                }
                break;
            default:
                break;
        }
    }

    if(m_Parameters.m_DataLayoutY.has_value())
    {
        switch(m_Parameters.m_DataLayoutY.value())
        {
            case DataLayout::NCHW:
            case DataLayout::NHWC:
                if(inputTensorYInfo.GetNumDimensions() != 4)
                {
                    throw InvalidArgumentException(descriptorName +
                        ": Input tensor Y does not have the correct "
                        "number of dimensions for the Data Layout that it has been assigned.");
                }
                break;
            case DataLayout::NCDHW:
            case DataLayout::NDHWC:
                if(inputTensorYInfo.GetNumDimensions() != 5)
                {
                    throw InvalidArgumentException(descriptorName +
                        ": Input tensor Y does not have the correct "
                        "number of dimensions for the Data Layout that it has been assigned.");
                }
                break;
            default:
                break;
        }
    }

    auto axesToMul = BatchMatMulDescriptor::GetAxesToMul(m_Parameters,
                                                         inputTensorXInfo.GetShape(),
                                                         inputTensorYInfo.GetShape());

    if(inputTensorXInfo.GetShape()[axesToMul.first.second]
       != inputTensorYInfo.GetShape()[axesToMul.second.first])
    {
        throw InvalidArgumentException(descriptorName +
            ": The final axis of input tensor X must be the same size as "
            "the second last axis of input tensor Y.");
    }

    auto axesNotMul = BatchMatMulDescriptor::GetAxesNotMul(m_Parameters,
                                                           inputTensorXInfo.GetShape(),
                                                           inputTensorYInfo.GetShape());

    {   // Separate scope so we don't pollute the rest of the scope with our temp variables
        // e.g. NHWC isnt compatible with NCHW as of now
        DataLayout xLayout;
        DataLayout yLayout;

        if(m_Parameters.m_DataLayoutX == EmptyOptional())
        {
            xLayout = DataLayout::NCHW; // Not equivalent - I'm just concerned with the last 2 axes
        }
        else
        {
            xLayout = m_Parameters.m_DataLayoutX.value();
        }

        if(m_Parameters.m_DataLayoutY == EmptyOptional())
        {
            yLayout = DataLayout::NCHW;
        }
        else
        {
            yLayout = m_Parameters.m_DataLayoutY.value();
        }

        if(xLayout == DataLayout::NCHW || xLayout == DataLayout::NCDHW)
        {
            if(yLayout == DataLayout::NHWC || yLayout == DataLayout::NDHWC)
            {
                throw InvalidArgumentException(descriptorName +
                    ": Invalid input tensor data layout combination.");
            }
        }
        if(yLayout == DataLayout::NCHW || yLayout == DataLayout::NCDHW)
        {
            if(xLayout == DataLayout::NHWC || xLayout == DataLayout::NDHWC)
            {
                throw InvalidArgumentException(descriptorName +
                    ": Invalid input tensor data layout combination.");
            }
        }
    }

    // Simulate aligning the ends of the matrix dims and prepending 1's to the beginning of the shorter one
    unsigned int outputTensorDimSize = std::max(inputTensorXInfo.GetNumDimensions(),
                                                inputTensorYInfo.GetNumDimensions());
    if(outputTensorDimSize-2 > 0)
    {
        TensorInfo tiXNotMul = TensorInfo(TensorShape(outputTensorDimSize-2),
                                          DataType::Float32);
        TensorInfo tiYNotMul = TensorInfo(TensorShape(outputTensorDimSize-2),
                                          DataType::Float32);
        TensorInfo tiOutNotMul = TensorInfo(TensorShape(outputTensorDimSize-2),
                                            DataType::Float32);

        auto doAxisExtension = [&](std::vector<unsigned int> axisIndices, TensorInfo& ti)
        {
            auto sizeDiff = (outputTensorDimSize-2) - axisIndices.size();

            for(unsigned int i = 0; i < sizeDiff; i++)
            {
                axisIndices.insert(axisIndices.begin(), 1);
            }

            for(unsigned int i = 0; i < ti.GetNumDimensions(); i++)
            {
                ti.GetShape()[i] = inputTensorXInfo.GetShape()[i];
            }
        };

        doAxisExtension(axesNotMul.first, tiXNotMul);
        doAxisExtension(axesNotMul.second, tiYNotMul);

        for(unsigned int i = 0; i < tiOutNotMul.GetNumDimensions(); i++)
        {
            tiOutNotMul.GetShape()[i] = std::max(tiXNotMul.GetShape()[i],
                                                 tiYNotMul.GetShape()[i]);
        }

        ValidateBroadcastTensorShapesMatch(tiXNotMul,
                                           tiYNotMul,
                                           tiOutNotMul,
                                           descriptorName,
                                           "input_X",
                                           "input_Y");
    }

    // Also check descriptor parameter validity
    // This will eventually be moved to the start of the function as explained below
    if ((!m_Parameters.m_TransposeX.empty() && !m_Parameters.m_AdjointX.empty()) ||
        (!m_Parameters.m_TransposeY.empty() && !m_Parameters.m_AdjointY.empty()))
    {
        throw InvalidArgumentException(descriptorName +
            ": Invalid descriptor parameters - Transpose and Adjoint "
            "vectors cannot both be true for a given input tensor.");
    }

    if(m_Parameters.m_TransposeX.size() != 0 && m_Parameters.m_TransposeX.size() != inputTensorXInfo.GetNumDimensions())
    {
        throw InvalidArgumentException(descriptorName +
            ": Invalid descriptor parameter - Transpose X vector must be "
            "the same size as tensor input X's dimensionality.");
    }
    if(m_Parameters.m_AdjointX.size() != 0 && m_Parameters.m_AdjointX.size() != inputTensorXInfo.GetNumDimensions())
    {
        throw InvalidArgumentException(descriptorName +
            ": Invalid descriptor parameter - Adjoint X vector must be "
            "the same size as tensor input X's dimensionality.");
    }
    if(m_Parameters.m_TransposeY.size() != 0 && m_Parameters.m_TransposeY.size() != inputTensorYInfo.GetNumDimensions())
    {
        throw InvalidArgumentException(descriptorName +
            ": Invalid descriptor parameter - Transpose Y vector must be "
            "the same size as tensor input Y's dimensionality.");
    }
    if(m_Parameters.m_AdjointY.size() != 0 && m_Parameters.m_AdjointY.size() != inputTensorXInfo.GetNumDimensions())
    {
        throw InvalidArgumentException(descriptorName +
            ": Invalid descriptor parameter - Adjoint Y vector must be "
            "the same size as tensor input Y's dimensionality.");
    }
    // Note: for adjoint/transpose, you'll need to do the validation atop the resultant permutation.
}

---

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

• include/armnn/backends/WorkloadData.hpp
• src/backends/backendsCommon/WorkloadData.cpp