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/armnn/layers/BatchMatMulLayer.cpp b/src/armnn/layers/BatchMatMulLayer.cpp
new file mode 100644
index 0000000..501de2d
--- /dev/null
+++ b/src/armnn/layers/BatchMatMulLayer.cpp
@@ -0,0 +1,97 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "BatchMatMulLayer.hpp"
+
+#include <armnn/backends/WorkloadFactory.hpp>
+#include "layers/LayerCloneBase.hpp"
+
+namespace armnn
+{
+
+BatchMatMulLayer::BatchMatMulLayer(const BatchMatMulDescriptor& param, const char* name)
+ : LayerWithParameters(2, 1, LayerType::BatchMatMul, param, name)
+{}
+
+std::unique_ptr<IWorkload> BatchMatMulLayer::CreateWorkload(const IWorkloadFactory& factory) const
+{
+ BatchMatMulQueueDescriptor descriptor;
+ SetAdditionalInfo(descriptor);
+
+ return factory.CreateWorkload(LayerType::BatchMatMul, descriptor, PrepInfoAndDesc(descriptor));
+}
+
+BatchMatMulLayer* BatchMatMulLayer::Clone(Graph& graph) const
+{
+ auto layer = CloneBase<BatchMatMulLayer>(graph, m_Param, GetName());
+
+ return std::move(layer);
+}
+
+std::vector<TensorShape> BatchMatMulLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+ ARMNN_ASSERT(inputShapes.size() == 2);
+
+ TensorShape inputXShape = inputShapes[0];
+ TensorShape inputYShape = inputShapes[1];
+
+ // Note: Take into account what pre-adjoint or pre-transposing will do to the inferred output shape
+
+ TensorShape& longerInput = inputXShape.GetNumDimensions() >= inputYShape.GetNumDimensions()?
+ inputXShape:inputYShape;
+ TensorShape& shorterInput = inputXShape.GetNumDimensions() >= inputYShape.GetNumDimensions()?
+ inputYShape:inputXShape;
+
+ unsigned int inputNumDimsOffset = longerInput.GetNumDimensions() - shorterInput.GetNumDimensions();
+
+ unsigned int outputNumDimensions = longerInput.GetNumDimensions();
+
+ std::vector<unsigned int> tensorDimensions(outputNumDimensions, 0);
+
+ auto axesToMul = BatchMatMulDescriptor::GetAxesToMul(m_Param, inputXShape, inputYShape);
+ const auto& longerAxesToMul = (axesToMul.first.first >= axesToMul.second.first &&
+ axesToMul.first.second >= axesToMul.second.second) ?
+ axesToMul.first : axesToMul.second;
+
+ for (unsigned int i = 0; i < outputNumDimensions; ++i)
+ {
+ if (i == longerAxesToMul.first)
+ {
+ tensorDimensions[i] = &shorterInput == &inputXShape ? inputXShape[i - inputNumDimsOffset] : inputXShape[i];
+ }
+ else if(i == longerAxesToMul.second)
+ {
+ tensorDimensions[i] = &shorterInput == &inputYShape ? inputYShape[i - inputNumDimsOffset] : inputYShape[i];
+ }
+ else // The other dimensions not to be multiplied (but may be broadcasted)
+ {
+ // Does NOT validate whether it's a valid broadcast - that's done in the validate func in WorkloadData.cpp
+ tensorDimensions[i] = static_cast<int>(i) - static_cast<int>(inputNumDimsOffset) < 0 ?
+ longerInput[i] :
+ std::max(longerInput[i], shorterInput[i - inputNumDimsOffset]);
+ }
+ }
+
+ auto outputShape = TensorShape(outputNumDimensions, tensorDimensions.data());
+ return std::vector<TensorShape>({ outputShape });
+}
+
+void BatchMatMulLayer::ValidateTensorShapesFromInputs()
+{
+ VerifyLayerConnections(2, 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() });
+
+ ARMNN_ASSERT(inferredShapes.size() == 1);
+
+ ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "BatchMatMulLayer");
+}
+
+} // namespace armnn
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