IVGCVSW-3808 Add ElementwiseBinaryLayer

!android-nn-driver:9329

 * Added ElementwiseBinaryLayer that can represent all ElementwiseBinary
   operations including Add, Div, Sub, Maximum, Mul and Minimum.
 * Updated Delegate to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated Deserializer to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated OnnxParser to use ElementwiseBinaryLayer instead of the Add
   layer.
 * Updated TfLiteParser to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated CL and Neon tests to use ElementwiseBinaryLayer.
 * Updated CL and Neon Backend Specific Optimizations to accept
   ElementBinaryLayers as well as Add, Div, Mul, Sub, Maximum and Minimum
   layers.

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I7cbb96b60eb01f0e2b57b0541016d48a08b86c75

2 files changed, 137 insertions, 0 deletions

diff --git a/src/armnn/layers/ElementwiseBinaryLayer.cpp b/src/armnn/layers/ElementwiseBinaryLayer.cpp
new file mode 100644
index 0000000000..ae1813f33a
--- /dev/null
+++ b/src/armnn/layers/ElementwiseBinaryLayer.cpp
@@ -0,0 +1,89 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ElementwiseBinaryLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+namespace armnn
+{
+
+ElementwiseBinaryLayer::ElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& param, const char* name)
+    : LayerWithParameters(2, 1, LayerType::ElementwiseBinary, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> ElementwiseBinaryLayer::CreateWorkload(const IWorkloadFactory& factory) const
+{
+    ElementwiseBinaryQueueDescriptor descriptor;
+    SetAdditionalInfo(descriptor);
+
+    return factory.CreateWorkload(LayerType::ElementwiseBinary, descriptor, PrepInfoAndDesc(descriptor));
+}
+
+ElementwiseBinaryLayer* ElementwiseBinaryLayer::Clone(Graph& graph) const
+{
+    return CloneBase<ElementwiseBinaryLayer>(graph, m_Param, GetName());
+}
+
+std::vector<TensorShape> ElementwiseBinaryLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+    ARMNN_ASSERT(inputShapes.size() == 2);
+    TensorShape input0 = inputShapes[0];
+    TensorShape input1 = inputShapes[1];
+
+    if (inputShapes[0].GetNumDimensions() < inputShapes[1].GetNumDimensions())
+    {
+        input1 = inputShapes[0];
+        input0 = inputShapes[1];
+    }
+
+    unsigned int numDims     = input0.GetNumDimensions();
+    unsigned int shiftedDims = input0.GetNumDimensions() - input1.GetNumDimensions();
+
+    // Get the max of the inputs.
+    std::vector<unsigned int> dims(numDims);
+    for (unsigned int i = shiftedDims; i < numDims; i++)
+    {
+        unsigned int dim0 = input0[i];
+        unsigned int dim1 = input1[i - shiftedDims];
+
+        // Validate inputs are broadcast compatible.
+        ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
+                         "Dimensions should either match or one should be of size 1.");
+
+        dims[i] = std::max(dim0, dim1);
+    }
+
+    // Fill in the rest of the shifted dimensions.
+    for (unsigned int i = 0; i < shiftedDims; i++)
+    {
+        dims[i] = input0[i];
+    }
+
+    return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) });
+}
+
+void ElementwiseBinaryLayer::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, GetLayerTypeAsCString(GetType()));
+}
+
+void ElementwiseBinaryLayer::ExecuteStrategy(IStrategy& strategy) const
+{
+    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
+}
+} // namespace armnn
diff --git a/src/armnn/layers/ElementwiseBinaryLayer.hpp b/src/armnn/layers/ElementwiseBinaryLayer.hpp
new file mode 100644
index 0000000000..78e3f41f9e
--- /dev/null
+++ b/src/armnn/layers/ElementwiseBinaryLayer.hpp
@@ -0,0 +1,48 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "LayerWithParameters.hpp"
+
+namespace armnn
+{
+
+/// This layer represents a elementwiseBinary operation.
+class ElementwiseBinaryLayer : public LayerWithParameters<ElementwiseBinaryDescriptor>
+{
+public:
+    /// Makes a workload for the elementwiseBinary type
+    /// @param [in] graph The graph where this layer can be found
+    /// @param [in] factory The workload factory which will create the workload
+    /// @return A pointer to the created workload, or nullptr if not created
+    virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override;
+
+    /// Creates a dynamically-allocated copy of this layer
+    /// @param [in] graph The graph into which this layer is being cloned
+    ElementwiseBinaryLayer* Clone(Graph& graph) const override;
+
+    /// Returns inputShapes by default.
+    /// @param [in] inputShapes The input shapes layer has.
+    /// @return A vector to the inferred output shape.
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
+    /// Check if the input tensor shape(s) will lead to a valid configuration
+    /// of @ref ElementwiseBinaryLayer
+    void ValidateTensorShapesFromInputs() override;
+
+    void ExecuteStrategy(IStrategy& strategy) const override;
+
+protected:
+    /// Constructor to create a ElementwiseBinaryLayer
+    /// @param [in] param ElementwiseBinaryDescriptor to configure the ElementwiseBinaryLayer
+    /// @param [in] name Optional name for the layer
+    ElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& param, const char* name);
+
+    /// Default destructor
+    ~ElementwiseBinaryLayer() = default;
+};
+
+} // namespace armnn