IVGCVSW-7109: Add Batch MatMul front end support - Reference
* Descriptors added for BatchMatMul
* Layer definition added
* Input validation added (will likely change when opt. param support comes in)
* Ref workload implementation for BatchMatMul added (will also change with opt. param support)
* Ref layer tests made for BatchMatMul
* CMake and other build files updated
Signed-off-by: Samuel Yap <samuel.yap@arm.com>
Change-Id: Ic885301da543ee0fbe7922b85e7f9658c4efc617
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index 606821b..9a4c60f 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -4143,5 +4143,232 @@
}
}
+void BatchMatMulQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const
+{
+ 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.
+}
+
} // namespace armnn
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