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
diff --git a/Android.mk b/Android.mk
index 0e4e6f9..884624e 100644
--- a/Android.mk
+++ b/Android.mk
@@ -1,5 +1,5 @@
#
-# Copyright © 2017 ARM Ltd. All rights reserved.
+# Copyright © 2017-2023 ARM Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
#
@@ -227,6 +227,7 @@
src/armnn/layers/DetectionPostProcessLayer.cpp \
src/armnn/layers/DivisionLayer.cpp \
src/armnn/layers/ElementwiseBaseLayer.cpp \
+ src/armnn/layers/ElementwiseBinaryLayer.cpp \
src/armnn/layers/ElementwiseUnaryLayer.cpp \
src/armnn/layers/FakeQuantizationLayer.cpp \
src/armnn/layers/FillLayer.cpp \
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3b788cd..be6c4c6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,6 +1,6 @@
#
-# Copyright © 2018-2023 Arm Ltd and Contributors. All rights reserved.
-# Copyright 2020 NXP
+# Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2020 NXP
# SPDX-License-Identifier: MIT
#
cmake_minimum_required (VERSION 3.0.2) # 3.0.2 required for return() statement used in AddDllCopyCommands.cmake
@@ -222,6 +222,8 @@
src/armnn/layers/DetectionPostProcessLayer.cpp
src/armnn/layers/ElementwiseBaseLayer.hpp
src/armnn/layers/ElementwiseBaseLayer.cpp
+ src/armnn/layers/ElementwiseBinaryLayer.hpp
+ src/armnn/layers/ElementwiseBinaryLayer.cpp
src/armnn/layers/ElementwiseUnaryLayer.hpp
src/armnn/layers/ElementwiseUnaryLayer.cpp
src/armnn/layers/FakeQuantizationLayer.hpp
diff --git a/delegate/src/ElementwiseBinary.hpp b/delegate/src/ElementwiseBinary.hpp
index 52c6b24..fa9021b 100644
--- a/delegate/src/ElementwiseBinary.hpp
+++ b/delegate/src/ElementwiseBinary.hpp
@@ -27,15 +27,17 @@
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
+ std::vector<armnn::TensorInfo> infos { inputInfo1, inputInfo2, outputInfo };
FORWARD_LAYER_SUPPORT_FUNC("ADD",
tfLiteContext,
- IsAdditionSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputInfo,
+ armnn::BinaryOperation::Add);
};
validateFunc(outputInfo, isSupported);
@@ -54,13 +56,14 @@
{
FORWARD_LAYER_SUPPORT_FUNC("DIV",
tfLiteContext,
- IsDivisionSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputTensorInfo,
+ armnn::BinaryOperation::Div);
};
validateFunc(outputInfo, isSupported);
@@ -107,13 +110,14 @@
{
FORWARD_LAYER_SUPPORT_FUNC("MAXIMUM",
tfLiteContext,
- IsMaximumSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputTensorInfo,
+ armnn::BinaryOperation::Maximum);
};
validateFunc(outputInfo, isSupported);
@@ -131,13 +135,14 @@
{
FORWARD_LAYER_SUPPORT_FUNC("MINIMUM",
tfLiteContext,
- IsMinimumSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputTensorInfo,
+ armnn::BinaryOperation::Minimum);
};
validateFunc(outputInfo, isSupported);
@@ -155,13 +160,14 @@
{
FORWARD_LAYER_SUPPORT_FUNC("MUL",
tfLiteContext,
- IsMultiplicationSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputTensorInfo,
+ armnn::BinaryOperation::Mul);
};
validateFunc(outputInfo, isSupported);
@@ -179,13 +185,14 @@
{
FORWARD_LAYER_SUPPORT_FUNC("SUB",
tfLiteContext,
- IsSubtractionSupported,
+ IsElementwiseBinarySupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo1,
inputInfo2,
- outputTensorInfo);
+ outputTensorInfo,
+ armnn::BinaryOperation::Sub);
};
validateFunc(outputInfo, isSupported);
@@ -196,7 +203,8 @@
DelegateData& delegateData,
const armnn::TensorInfo& outputTensorInfo)
{
- armnn::IConnectableLayer* divisionLayer = delegateData.m_Network->AddDivisionLayer();
+ armnn::IConnectableLayer* divisionLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Div);
// if the output of the div is Signed32 the Floor layer is not required
if (armnn::DataType::Signed32 == outputTensorInfo.GetDataType())
{
@@ -330,10 +338,12 @@
switch(elementwiseBinaryOperatorCode)
{
case kTfLiteBuiltinAdd:
- elementwiseBinaryLayer = delegateData.m_Network->AddAdditionLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Add);
break;
case kTfLiteBuiltinDiv:
- elementwiseBinaryLayer = delegateData.m_Network->AddDivisionLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Div);
break;
case kTfLiteBuiltinFloorDiv:
{
@@ -343,16 +353,20 @@
}
break;
case kTfLiteBuiltinMaximum:
- elementwiseBinaryLayer = delegateData.m_Network->AddMaximumLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Maximum);
break;
case kTfLiteBuiltinMinimum:
- elementwiseBinaryLayer = delegateData.m_Network->AddMinimumLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Minimum);
break;
case kTfLiteBuiltinMul:
- elementwiseBinaryLayer = delegateData.m_Network->AddMultiplicationLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Mul);
break;
case kTfLiteBuiltinSub:
- elementwiseBinaryLayer = delegateData.m_Network->AddSubtractionLayer();
+ elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer(
+ armnn::BinaryOperation::Sub);
break;
default:
return kTfLiteError;
diff --git a/include/armnn/BackendHelper.hpp b/include/armnn/BackendHelper.hpp
index cf60b01..85aabe0 100644
--- a/include/armnn/BackendHelper.hpp
+++ b/include/armnn/BackendHelper.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2019,2021-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -161,6 +161,12 @@
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported = EmptyOptional());
+ bool IsElementwiseBinarySupported(const TensorInfo& input0,
+ const TensorInfo& input1,
+ const TensorInfo& output,
+ const ElementwiseBinaryDescriptor& descriptor,
+ Optional<std::string&> reasonIfUnsupported = EmptyOptional());
+
bool IsElementwiseUnarySupported(const TensorInfo& input,
const TensorInfo& output,
const ElementwiseUnaryDescriptor& descriptor,
diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp
index 7c6942c..9ff894f 100644
--- a/include/armnn/Descriptors.hpp
+++ b/include/armnn/Descriptors.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -105,6 +105,26 @@
ComparisonOperation m_Operation;
};
+/// A ElementwiseBinaryDescriptor for the ElementwiseBinaryLayer
+struct ElementwiseBinaryDescriptor : BaseDescriptor
+{
+ ElementwiseBinaryDescriptor()
+ : ElementwiseBinaryDescriptor(BinaryOperation::Add)
+ {}
+
+ ElementwiseBinaryDescriptor(BinaryOperation operation)
+ : m_Operation(operation)
+ {}
+
+ bool operator ==(const ElementwiseBinaryDescriptor &rhs) const
+ {
+ return m_Operation == rhs.m_Operation;
+ }
+
+ /// Specifies the elementwiseBinary operation to execute
+ BinaryOperation m_Operation;
+};
+
/// A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer
struct ElementwiseUnaryDescriptor : BaseDescriptor
{
diff --git a/include/armnn/DescriptorsFwd.hpp b/include/armnn/DescriptorsFwd.hpp
index 0883350..2c25a49 100644
--- a/include/armnn/DescriptorsFwd.hpp
+++ b/include/armnn/DescriptorsFwd.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -20,6 +20,7 @@
struct Convolution3dDescriptor;
struct DepthwiseConvolution2dDescriptor;
struct DetectionPostProcessDescriptor;
+struct ElementwiseBinaryDescriptor;
struct ElementwiseUnaryDescriptor;
struct FakeQuantizationDescriptor;
struct FillDescriptor;
diff --git a/include/armnn/INetwork.hpp b/include/armnn/INetwork.hpp
index c944d09..4eac0cf 100644
--- a/include/armnn/INetwork.hpp
+++ b/include/armnn/INetwork.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -349,9 +349,16 @@
const ConstTensor& anchors,
const char* name = nullptr);
+ /// Add an ElementwiseBinary layer to the network.
+ /// @param name - Optional name for the layer.
+ /// @param desc - Descriptor for the elementwiseBinary operations.
+ /// @return - Interface for configuring the layer.
+ IConnectableLayer* AddElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& elementwiseUnaryDescriptor,
+ const char* name = nullptr);
+
/// Add an ElementwiseUnary layer to the network.
/// @param name - Optional name for the layer.
- /// @param desc - Descriptor for the elementwiseUnary operation.
+ /// @param desc - Descriptor for the elementwiseUnary operations.
/// @return - Interface for configuring the layer.
IConnectableLayer* AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor& elementwiseUnaryDescriptor,
const char* name = nullptr);
diff --git a/include/armnn/Types.hpp b/include/armnn/Types.hpp
index 4dc613b..3c64e82 100644
--- a/include/armnn/Types.hpp
+++ b/include/armnn/Types.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2018-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -133,6 +133,16 @@
Sin = 7
};
+enum class BinaryOperation
+{
+ Add = 0,
+ Div = 1,
+ Maximum = 2,
+ Minimum = 3,
+ Mul = 4,
+ Sub = 5
+};
+
enum class PoolingAlgorithm
{
Max = 0,
@@ -385,6 +395,7 @@
/// This list uses X macro technique.
/// See https://en.wikipedia.org/wiki/X_Macro for more info
+// New layers should be added at last position to minimize instability.
#define LIST_OF_LAYER_TYPE \
X(Activation) \
X(Addition) \
@@ -458,8 +469,9 @@
X(Pooling3d) \
X(GatherNd) \
X(BatchMatMul) \
+ X(ElementwiseBinary) \
-// New layers should be added at last to minimize instability.
+// New layers should be added at last position to minimize instability.
/// When adding a new layer, adapt also the LastLayer enum value in the
/// enum class LayerType below
@@ -469,7 +481,7 @@
LIST_OF_LAYER_TYPE
#undef X
FirstLayer = Activation,
- LastLayer = BatchMatMul
+ LastLayer = ElementwiseBinary
};
const char* GetLayerTypeAsCString(LayerType type);
diff --git a/include/armnn/TypesUtils.hpp b/include/armnn/TypesUtils.hpp
index 4dd5360..fbe9e8f 100644
--- a/include/armnn/TypesUtils.hpp
+++ b/include/armnn/TypesUtils.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2018-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -72,6 +72,20 @@
}
}
+constexpr char const* GetBinaryOperationAsCString(BinaryOperation operation)
+{
+ switch (operation)
+ {
+ case BinaryOperation::Add: return "Add";
+ case BinaryOperation::Div: return "Div";
+ case BinaryOperation::Maximum: return "Maximum";
+ case BinaryOperation::Minimum: return "Minimum";
+ case BinaryOperation::Mul: return "Mul";
+ case BinaryOperation::Sub: return "Sub";
+ default: return "Unknown";
+ }
+}
+
constexpr char const* GetUnaryOperationAsCString(UnaryOperation operation)
{
switch (operation)
diff --git a/include/armnn/backends/WorkloadData.hpp b/include/armnn/backends/WorkloadData.hpp
index 7a0a765..2abd267 100644
--- a/include/armnn/backends/WorkloadData.hpp
+++ b/include/armnn/backends/WorkloadData.hpp
@@ -663,6 +663,11 @@
void Validate(const WorkloadInfo& workloadInfo) const;
};
+struct ElementwiseBinaryQueueDescriptor : QueueDescriptorWithParameters<ElementwiseBinaryDescriptor>
+{
+ void Validate(const WorkloadInfo& workloadInfo) const;
+};
+
struct ElementwiseUnaryQueueDescriptor : QueueDescriptorWithParameters<ElementwiseUnaryDescriptor>
{
void Validate(const WorkloadInfo& workloadInfo) const;
diff --git a/include/armnnTestUtils/MockBackend.hpp b/include/armnnTestUtils/MockBackend.hpp
index e5378bf..05df1b4 100644
--- a/include/armnnTestUtils/MockBackend.hpp
+++ b/include/armnnTestUtils/MockBackend.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -320,6 +320,11 @@
reasonIfUnsupported);
}
}
+ case LayerType::ElementwiseBinary:
+ {
+ auto elementwiseDesc = *(PolymorphicDowncast<const ElementwiseBinaryDescriptor*>(&descriptor));
+ return (elementwiseDesc.m_Operation == BinaryOperation::Add);
+ }
default:
return false;
}
diff --git a/src/armnn/BackendHelper.cpp b/src/armnn/BackendHelper.cpp
index af38ce8..580c52c 100644
--- a/src/armnn/BackendHelper.cpp
+++ b/src/armnn/BackendHelper.cpp
@@ -600,6 +600,22 @@
reasonIfUnsupported);
}
+bool LayerSupportHandle::IsElementwiseBinarySupported(const TensorInfo &input0,
+ const TensorInfo &input1,
+ const TensorInfo &output,
+ const ElementwiseBinaryDescriptor &descriptor,
+ Optional<std::string &> reasonIfUnsupported)
+{
+ TensorInfos infos{input0, input1, output};
+
+ return m_LayerSupport->IsLayerSupported(LayerType::ElementwiseBinary,
+ infos,
+ descriptor,
+ EmptyOptional(),
+ EmptyOptional(),
+ reasonIfUnsupported);
+}
+
bool LayerSupportHandle::IsElementwiseUnarySupported(const TensorInfo& input,
const TensorInfo& output,
const ElementwiseUnaryDescriptor& descriptor,
diff --git a/src/armnn/LayersFwd.hpp b/src/armnn/LayersFwd.hpp
index 43862d5..f634272 100644
--- a/src/armnn/LayersFwd.hpp
+++ b/src/armnn/LayersFwd.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -27,6 +27,7 @@
#include "layers/DequantizeLayer.hpp"
#include "layers/DetectionPostProcessLayer.hpp"
#include "layers/DivisionLayer.hpp"
+#include "layers/ElementwiseBinaryLayer.hpp"
#include "layers/ElementwiseUnaryLayer.hpp"
#include "layers/FakeQuantizationLayer.hpp"
#include "layers/FillLayer.hpp"
@@ -127,6 +128,7 @@
DECLARE_LAYER(Dequantize)
DECLARE_LAYER(DetectionPostProcess)
DECLARE_LAYER(Division)
+DECLARE_LAYER(ElementwiseBinary)
DECLARE_LAYER(ElementwiseUnary)
DECLARE_LAYER(FakeQuantization)
DECLARE_LAYER(Fill)
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index 08d3280..9ebb67b 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017, 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -20,7 +20,6 @@
#include <backendsCommon/TensorHandleFactoryRegistry.hpp>
#include <armnn/Exceptions.hpp>
-#include <armnn/Utils.hpp>
#include <armnn/TypesUtils.hpp>
#include <armnn/BackendRegistry.hpp>
#include <armnn/Logging.hpp>
@@ -36,10 +35,8 @@
#include <fcntl.h>
#include <algorithm>
-#include <fstream>
#include <memory>
#include <vector>
-#include <algorithm>
namespace armnn
{
@@ -58,7 +55,6 @@
return pNetworkImpl->AddInputLayer(id, name);
}
-
IConnectableLayer* INetwork::AddArgMinMaxLayer(const ArgMinMaxDescriptor& desc,
const char* name)
{
@@ -126,6 +122,11 @@
return pNetworkImpl->AddDetectionPostProcessLayer(descriptor, anchors, name);
}
+IConnectableLayer* INetwork::AddElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& elementwiseBinaryDescriptor,
+ const char* name)
+{
+ return pNetworkImpl->AddElementwiseBinaryLayer(elementwiseBinaryDescriptor, name);
+}
IConnectableLayer* INetwork::AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor& elementwiseUnaryDescriptor,
const char* name)
@@ -133,7 +134,6 @@
return pNetworkImpl->AddElementwiseUnaryLayer(elementwiseUnaryDescriptor, name);
}
-
IConnectableLayer* INetwork::AddFillLayer(const FillDescriptor& fillDescriptor,
const char* name)
{
@@ -1853,6 +1853,12 @@
return m_Graph->AddLayer<ComparisonLayer>(comparisonDescriptor, name);
}
+IConnectableLayer* NetworkImpl::AddElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& elementwiseBinaryDesc,
+ const char* name)
+{
+ return m_Graph->AddLayer<ElementwiseBinaryLayer>(elementwiseBinaryDesc, name);
+}
+
IConnectableLayer* NetworkImpl::AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor& elementwiseUnaryDescriptor,
const char* name)
{
diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp
index a37a4be..03642ce 100644
--- a/src/armnn/Network.hpp
+++ b/src/armnn/Network.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -95,6 +95,9 @@
IConnectableLayer* AddDivisionLayer(const char* name = nullptr);
+ IConnectableLayer* AddElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& elementwiseBinaryDescriptor,
+ const char* name = nullptr);
+
IConnectableLayer* AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor& elementwiseUnaryDescriptor,
const char* name = nullptr);
diff --git a/src/armnn/layers/ElementwiseBinaryLayer.cpp b/src/armnn/layers/ElementwiseBinaryLayer.cpp
new file mode 100644
index 0000000..ae1813f
--- /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 0000000..78e3f41
--- /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
diff --git a/src/armnn/optimizations/AddBroadcastReshapeLayer.hpp b/src/armnn/optimizations/AddBroadcastReshapeLayer.hpp
index b9e8584..dbde72b 100644
--- a/src/armnn/optimizations/AddBroadcastReshapeLayer.hpp
+++ b/src/armnn/optimizations/AddBroadcastReshapeLayer.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -18,7 +18,7 @@
static const std::set<armnn::LayerType> broadcastOps{ LayerType::Addition, LayerType::Division,
LayerType::Maximum, LayerType::Minimum,
LayerType::Multiplication, LayerType::Prelu,
- LayerType::Subtraction };
+ LayerType::Subtraction, LayerType::ElementwiseBinary };
class AddBroadcastReshapeLayerImpl
{
diff --git a/src/armnn/optimizations/MovePermuteUp.hpp b/src/armnn/optimizations/MovePermuteUp.hpp
index ae8a28c..19078b3 100644
--- a/src/armnn/optimizations/MovePermuteUp.hpp
+++ b/src/armnn/optimizations/MovePermuteUp.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017-2018,2020,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -68,6 +68,12 @@
case LayerType::MemCopy:
case LayerType::Multiplication:
return true;
+ case LayerType::ElementwiseBinary:
+ {
+ auto descriptor = PolymorphicDowncast<const ElementwiseBinaryDescriptor*>(&base.GetParameters());
+ return (descriptor->m_Operation == BinaryOperation::Add ||
+ descriptor->m_Operation == BinaryOperation::Mul);
+ }
default:
return false;
}
diff --git a/src/armnn/optimizations/MoveTransposeUp.hpp b/src/armnn/optimizations/MoveTransposeUp.hpp
index 999a4eb..40f6b9c 100644
--- a/src/armnn/optimizations/MoveTransposeUp.hpp
+++ b/src/armnn/optimizations/MoveTransposeUp.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd. All rights reserved.
+// Copyright © 2020,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -68,6 +68,12 @@
case LayerType::MemCopy:
case LayerType::Multiplication:
return true;
+ case LayerType::ElementwiseBinary:
+ {
+ auto descriptor = PolymorphicDowncast<const ElementwiseBinaryDescriptor*>(&base.GetParameters());
+ return (descriptor->m_Operation == BinaryOperation::Add ||
+ descriptor->m_Operation == BinaryOperation::Mul);
+ }
default:
return false;
}
diff --git a/src/armnn/test/GraphTests.cpp b/src/armnn/test/GraphTests.cpp
index eea7ae8..b1b1a84 100644
--- a/src/armnn/test/GraphTests.cpp
+++ b/src/armnn/test/GraphTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <GraphUtils.hpp>
@@ -36,7 +36,7 @@
CHECK_NOTHROW(graph.AddLayer<armnn::InputLayer>(0, "layerA"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerB"));
- CHECK_NOTHROW(graph.AddLayer<armnn::AdditionLayer>("layerC"));
+ CHECK_NOTHROW(graph.AddLayer<armnn::ElementwiseBinaryLayer>(armnn::BinaryOperation::Add, "layerC"));
CHECK_NOTHROW(graph.AddLayer<armnn::OutputLayer>(0, "output"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerD"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerE"));
@@ -82,7 +82,7 @@
CHECK_NOTHROW(graph.AddLayer<armnn::InputLayer>(0, "layerA"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerB"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerC"));
- CHECK_NOTHROW(graph.AddLayer<armnn::AdditionLayer>("layerD"));
+ CHECK_NOTHROW(graph.AddLayer<armnn::ElementwiseBinaryLayer>(armnn::BinaryOperation::Add, "layerD"));
CHECK_NOTHROW(graph.AddLayer<armnn::OutputLayer>(0, "output"));
armnn::Layer* const layerA = GetFirstLayerWithName(graph, "layerA");
@@ -168,7 +168,7 @@
CHECK_NOTHROW(graph.AddLayer<armnn::InputLayer>(0, "layerA"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerB"));
CHECK_NOTHROW(graph.AddLayer<armnn::ActivationLayer>(activationDefaults, "layerC"));
- CHECK_NOTHROW(graph.AddLayer<armnn::AdditionLayer>("layerD"));
+ CHECK_NOTHROW(graph.AddLayer<armnn::ElementwiseBinaryLayer>(armnn::BinaryOperation::Add, "layerD"));
CHECK_NOTHROW(graph.AddLayer<armnn::OutputLayer>(0, "output"));
armnn::Layer* const layerA = GetFirstLayerWithName(graph, "layerA");
@@ -548,7 +548,7 @@
armnn::SplitterLayer* const splitterLayer = graph.AddLayer<armnn::SplitterLayer>(splitterDesc, "splitter");
splitterLayer->SetBackendId(armnn::Compute::GpuAcc);
- armnn::AdditionLayer* const additionLayer = graph.AddLayer<armnn::AdditionLayer>("addition");
+ auto* const additionLayer = graph.AddLayer<armnn::ElementwiseBinaryLayer>(armnn::BinaryOperation::Add, "addition");
additionLayer->SetBackendId(armnn::Compute::CpuRef);
armnn::OutputLayer* const outputLayer = graph.AddLayer<armnn::OutputLayer>(0, "output");
diff --git a/src/armnn/test/NetworkTests.cpp b/src/armnn/test/NetworkTests.cpp
index 058f079..0bfad4d 100644
--- a/src/armnn/test/NetworkTests.cpp
+++ b/src/armnn/test/NetworkTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -32,7 +32,7 @@
{
armnn::NetworkImpl net;
LayerGuid inputId = net.AddInputLayer(0)->GetGuid();
- LayerGuid addId = net.AddAdditionLayer()->GetGuid();
+ LayerGuid addId = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Add)->GetGuid();
LayerGuid outputId = net.AddOutputLayer(0)->GetGuid();
CHECK(inputId != addId);
@@ -50,7 +50,7 @@
{
armnn::INetworkPtr inet(armnn::INetwork::Create());
inet->AddInputLayer(0);
- inet->AddAdditionLayer();
+ inet->AddElementwiseBinaryLayer(armnn::BinaryOperation::Add);
inet->AddActivationLayer(armnn::ActivationDescriptor());
inet->AddOutputLayer(0);
}
@@ -59,7 +59,7 @@
{
armnn::NetworkImpl net;
net.AddInputLayer(0);
- net.AddAdditionLayer();
+ net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Add);
net.AddActivationLayer(armnn::ActivationDescriptor());
net.AddOutputLayer(0);
}
@@ -136,13 +136,15 @@
softmaxLayer->GetOutputSlot(0).Connect(batchNormalizationLayer->GetInputSlot(0));
- armnn::IConnectableLayer* const additionLayer = net.AddAdditionLayer("addition");
+ armnn::IConnectableLayer* const additionLayer = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Add,
+ "addition");
CHECK(additionLayer);
batchNormalizationLayer->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(0));
batchNormalizationLayer->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(1));
- armnn::IConnectableLayer* const multiplicationLayer = net.AddMultiplicationLayer("multiplication");
+ armnn::IConnectableLayer* const multiplicationLayer = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Mul,
+ "multiplication");
CHECK(multiplicationLayer);
additionLayer->GetOutputSlot(0).Connect(multiplicationLayer->GetInputSlot(0));
@@ -338,7 +340,7 @@
splitterLayer->GetOutputSlot(1).Connect(softmax2Layer->GetInputSlot(0));
// Adds addition layer.
- layer = net.AddAdditionLayer("add layer");
+ layer = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Add, "add layer");
CHECK(layer);
softmax1Layer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
@@ -382,7 +384,7 @@
splitterLayer->GetOutputSlot(1).Connect(softmax2Layer->GetInputSlot(0));
// Adds multiplication layer.
- layer = net.AddMultiplicationLayer("multiplication layer");
+ layer = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Mul, "multiplication layer");
CHECK(layer);
softmax1Layer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
@@ -634,15 +636,27 @@
CHECK(originalDescriptor.m_DataLayout == armnn::DataLayout::NCHW);
}
-TEST_CASE("CheckNullDescriptor")
+TEST_CASE("CheckNotNullDescriptor")
{
armnn::NetworkImpl net;
- armnn::IConnectableLayer* const addLayer = net.AddAdditionLayer();
+ armnn::IConnectableLayer* const addLayer = net.AddElementwiseBinaryLayer(armnn::BinaryOperation::Add);
CHECK(addLayer);
const armnn::BaseDescriptor& descriptor = addLayer->GetParameters();
// additional layer has no descriptor so a NullDescriptor will be returned
+ CHECK(descriptor.IsNull() == false);
+}
+
+TEST_CASE("CheckNullDescriptor")
+{
+ armnn::NetworkImpl net;
+ armnn::IConnectableLayer* const addLayer = net.AddPreluLayer();
+
+ CHECK(addLayer);
+
+ const armnn::BaseDescriptor& descriptor = addLayer->GetParameters();
+ // Prelu has no descriptor so a NullDescriptor will be returned
CHECK(descriptor.IsNull() == true);
}
diff --git a/src/armnn/test/TestNameAndDescriptorLayerVisitor.cpp b/src/armnn/test/TestNameAndDescriptorLayerVisitor.cpp
index cfdaaf5..b8607d1 100644
--- a/src/armnn/test/TestNameAndDescriptorLayerVisitor.cpp
+++ b/src/armnn/test/TestNameAndDescriptorLayerVisitor.cpp
@@ -1,12 +1,10 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "TestNameAndDescriptorLayerVisitor.hpp"
#include "Network.hpp"
-#include <armnn/Exceptions.hpp>
-
#include <doctest/doctest.h>
namespace
@@ -84,6 +82,12 @@
}
template<>
+armnn::ElementwiseBinaryDescriptor GetDescriptor<armnn::ElementwiseBinaryDescriptor>()
+{
+ return armnn::ElementwiseBinaryDescriptor(armnn::BinaryOperation::Add);
+}
+
+template<>
armnn::ElementwiseUnaryDescriptor GetDescriptor<armnn::ElementwiseUnaryDescriptor>()
{
return armnn::ElementwiseUnaryDescriptor(armnn::UnaryOperation::Abs);
@@ -273,12 +277,14 @@
TEST_SUITE("TestNameAndDescriptorLayerVisitor")
{
-TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Activation, CheckAdditionLayerVisitorNameAndDescriptor)
+TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Activation, CheckActivationLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(ArgMinMax, CheckArgMinMaxLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(DepthToSpace, CheckDepthToSpaceLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(BatchToSpaceNd, CheckBatchToSpaceNdLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Comparison, CheckComparisonLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Concat, CheckConcatLayerVisitorNameAndDescriptor)
+TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(ElementwiseBinary,
+ CheckElementwiseBinaryLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(ElementwiseUnary, CheckElementwiseUnaryLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Fill, CheckFillLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Gather, CheckGatherLayerVisitorNameAndDescriptor)
@@ -304,7 +310,7 @@
TEST_CASE_CHECK_LAYER_VISITOR_NAME_AND_DESCRIPTOR(Transpose, CheckTransposeLayerVisitorNameAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(Activation,
- CheckAdditionLayerVisitorNameNullptrAndDescriptor)
+ CheckActivationLayerVisitorNameNullptrAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(ArgMinMax,
CheckArgMinMaxLayerVisitorNameNullptrAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(DepthToSpace,
@@ -315,6 +321,8 @@
CheckComparisonLayerVisitorNameNullptrAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(Concat,
CheckConcatLayerVisitorNameNullptrAndDescriptor)
+TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(ElementwiseBinary,
+ CheckElementwiseBinaryLayerVisitorNameNullptrAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(ElementwiseUnary,
CheckElementwiseUnaryLayerVisitorNameNullptrAndDescriptor)
TEST_CASE_CHECK_LAYER_VISITOR_NAME_NULLPTR_AND_DESCRIPTOR(Fill,
diff --git a/src/armnn/test/TestNameAndDescriptorLayerVisitor.hpp b/src/armnn/test/TestNameAndDescriptorLayerVisitor.hpp
index b1f9512..9889035 100644
--- a/src/armnn/test/TestNameAndDescriptorLayerVisitor.hpp
+++ b/src/armnn/test/TestNameAndDescriptorLayerVisitor.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -63,6 +63,7 @@
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(Comparison)
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(Concat)
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(DepthToSpace)
+DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(ElementwiseBinary)
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(ElementwiseUnary)
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(Fill)
DECLARE_TEST_NAME_AND_DESCRIPTOR_LAYER_VISITOR_CLASS(Gather)
diff --git a/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp b/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp
index 0636a00..59dfb86 100644
--- a/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp
+++ b/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -27,7 +27,7 @@
auto input0 = graph.AddLayer<InputLayer>(0, "input0");
auto input1 = graph.AddLayer<InputLayer>(1, "input1");
- auto add = graph.AddLayer<AdditionLayer>("add");
+ auto add = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Add, "add");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input0->GetOutputSlot().SetTensorInfo(info0);
input1->GetOutputSlot().SetTensorInfo(info1);
@@ -40,7 +40,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<AdditionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -48,19 +48,19 @@
// Broadcast reshape layer has been added to the graph correctly
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
- &IsLayerOfType<InputLayer>,
- &IsLayerOfType<InputLayer>,
- &IsLayerOfType<ReshapeLayer>,
- &IsLayerOfType<AdditionLayer>,
- &IsLayerOfType<OutputLayer>));
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ReshapeLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
+ &IsLayerOfType<OutputLayer>));
Layer* const reshapeLayer = GetFirstLayerWithName(graph, reshapeLayerName);
CHECK(reshapeLayer);
auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo();
// Tensorshape and the data type are correct
- CHECK((addedReshapeTensorInfo.GetShape() == expectedReshapeShape));
- CHECK((addedReshapeTensorInfo.GetDataType() == expectedDataType));
+ CHECK_EQ(addedReshapeTensorInfo.GetShape(), expectedReshapeShape);
+ CHECK_EQ(addedReshapeTensorInfo.GetDataType(), expectedDataType);
}
TEST_CASE("AddBroadcastReshapeLayerSimpleTest")
@@ -121,7 +121,7 @@
auto input0 = graph.AddLayer<InputLayer>(0, "input0");
auto input1 = graph.AddLayer<InputLayer>(1, "input1");
- auto sub = graph.AddLayer<SubtractionLayer>("sub");
+ auto sub = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Sub, "sub");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input0->GetOutputSlot().SetTensorInfo(info0);
input1->GetOutputSlot().SetTensorInfo(info1);
@@ -134,7 +134,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<SubtractionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -145,7 +145,7 @@
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ReshapeLayer>,
- &IsLayerOfType<SubtractionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:sub-0");
@@ -153,8 +153,8 @@
auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo();
// Tensorshape and the data type are correct
- CHECK((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 1, 5 })));
- CHECK((addedReshapeTensorInfo.GetDataType() == DataType::Float32));
+ CHECK_EQ(addedReshapeTensorInfo.GetShape(), TensorShape({ 1, 1, 1, 5 }));
+ CHECK_EQ(addedReshapeTensorInfo.GetDataType(), DataType::Float32);
}
TEST_CASE("AddBroadcastReshapeLayerDivisionTest")
@@ -166,7 +166,7 @@
auto input0 = graph.AddLayer<InputLayer>(0, "input0");
auto input1 = graph.AddLayer<InputLayer>(1, "input1");
- auto div = graph.AddLayer<DivisionLayer>("div");
+ auto div = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Div, "div");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input0->GetOutputSlot().SetTensorInfo(info0);
input1->GetOutputSlot().SetTensorInfo(info1);
@@ -179,7 +179,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<DivisionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -190,7 +190,7 @@
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ReshapeLayer>,
- &IsLayerOfType<DivisionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:div-0");
@@ -198,8 +198,8 @@
auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo();
// Tensorshape and the data type are correct
- CHECK((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 4, 5 })));
- CHECK((addedReshapeTensorInfo.GetDataType() == DataType::QAsymmS8));
+ CHECK_EQ(addedReshapeTensorInfo.GetShape(), TensorShape({ 1, 1, 4, 5 }));
+ CHECK_EQ(addedReshapeTensorInfo.GetDataType(), DataType::QAsymmS8);
}
TEST_CASE("AddBroadcastReshapeLayerMultiplicationTest")
@@ -211,7 +211,7 @@
auto input0 = graph.AddLayer<InputLayer>(0, "input0");
auto input1 = graph.AddLayer<InputLayer>(1, "input1");
- auto mul = graph.AddLayer<MultiplicationLayer>("mul");
+ auto mul = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Mul, "mul");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input0->GetOutputSlot().SetTensorInfo(info0);
input1->GetOutputSlot().SetTensorInfo(info1);
@@ -224,7 +224,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -235,7 +235,7 @@
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ReshapeLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:mul-0");
@@ -243,8 +243,8 @@
auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo();
// Tensorshape and the data type are correct
- CHECK((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 3, 5 })));
- CHECK((addedReshapeTensorInfo.GetDataType() == DataType::QAsymmU8));
+ CHECK_EQ(addedReshapeTensorInfo.GetShape(), TensorShape({ 1, 1, 3, 5 }));
+ CHECK_EQ(addedReshapeTensorInfo.GetDataType(), DataType::QAsymmU8);
}
TEST_CASE("AddNoBroadcastReshapeLayerTest")
@@ -256,7 +256,7 @@
auto input0 = graph.AddLayer<InputLayer>(0, "input0");
auto input1 = graph.AddLayer<InputLayer>(1, "input1");
- auto mul = graph.AddLayer<MultiplicationLayer>("mul");
+ auto mul = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Mul, "mul");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input0->GetOutputSlot().SetTensorInfo(info0);
input1->GetOutputSlot().SetTensorInfo(info1);
@@ -269,7 +269,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -279,7 +279,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:mul-0");
@@ -295,7 +295,7 @@
auto input = graph.AddLayer<InputLayer>(0, "input");
auto constant = graph.AddLayer<ConstantLayer>("constant");
- auto mul = graph.AddLayer<MultiplicationLayer>("mul");
+ auto mul = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Mul, "mul");
auto output = graph.AddLayer<OutputLayer>(0, "output");
uint8_t tensor[] = { 1, 1, 1, 1, 1 };
@@ -313,7 +313,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -323,7 +323,7 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
- &IsLayerOfType<MultiplicationLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
TensorShape expectedShape = TensorShape{ 1, 1, 1, 5 };
@@ -351,8 +351,8 @@
auto input = graph.AddLayer<InputLayer>(0, "input");
auto constant = graph.AddLayer<ConstantLayer>("constant");
- auto add1 = graph.AddLayer<AdditionLayer>("add1");
- auto add2 = graph.AddLayer<AdditionLayer>("add2");
+ auto add1 = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Add, "add1");
+ auto add2 = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Add, "add2");
auto output = graph.AddLayer<OutputLayer>(0, "output");
input->GetOutputSlot().SetTensorInfo(inputInfo);
@@ -371,8 +371,8 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
- &IsLayerOfType<AdditionLayer>,
- &IsLayerOfType<AdditionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Run optimizer
@@ -384,8 +384,8 @@
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ReshapeLayer>,
&IsLayerOfType<ReshapeLayer>,
- &IsLayerOfType<AdditionLayer>,
- &IsLayerOfType<AdditionLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
+ &IsLayerOfType<ElementwiseBinaryLayer>,
&IsLayerOfType<OutputLayer>));
// Ensure the output shape of the constant hasn't changed.
diff --git a/src/armnn/test/optimizations/MovePermuteUpTests.cpp b/src/armnn/test/optimizations/MovePermuteUpTests.cpp
index 152e799..018286c 100644
--- a/src/armnn/test/optimizations/MovePermuteUpTests.cpp
+++ b/src/armnn/test/optimizations/MovePermuteUpTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -36,7 +36,7 @@
head = graph.InsertNewLayer<armnn::ActivationLayer>(head->GetInputSlot(0), armnn::ActivationDescriptor{}, "");
head->GetOutputHandler().SetTensorInfo(info);
- head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), "");
+ head = graph.InsertNewLayer<armnn::ElementwiseBinaryLayer>(head->GetInputSlot(0), armnn::BinaryOperation::Add, "");
head->GetOutputHandler().SetTensorInfo(info);
// Inserts input for 2nd input of Addition.
@@ -54,7 +54,7 @@
head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), "");
head->GetOutputHandler().SetTensorInfo(info);
- head = graph.InsertNewLayer<armnn::MultiplicationLayer>(head->GetInputSlot(0), "");
+ head = graph.InsertNewLayer<armnn::ElementwiseBinaryLayer>(head->GetInputSlot(0), armnn::BinaryOperation::Mul, "");
head->GetOutputHandler().SetTensorInfo(info);
// Inserts input for 2nd input of Multiplication.
@@ -69,9 +69,9 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::InputLayer>, &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::MultiplicationLayer>, &IsLayerOfType<armnn::MemCopyLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::MemCopyLayer>,
&IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::FakeQuantizationLayer>,
- &IsLayerOfType<armnn::AdditionLayer>, &IsLayerOfType<armnn::ActivationLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::ActivationLayer>,
&IsLayerOfType<armnn::PermuteLayer>, &IsLayerOfType<armnn::OutputLayer>));
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(MovePermuteUp()));
@@ -80,10 +80,11 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::InputLayer>, &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::PermuteLayer>, &IsLayerOfType<armnn::PermuteLayer>,
- &IsLayerOfType<armnn::PermuteLayer>, &IsLayerOfType<armnn::MultiplicationLayer>,
+ &IsLayerOfType<armnn::PermuteLayer>, &IsLayerOfType<armnn::ElementwiseBinaryLayer>,
&IsLayerOfType<armnn::MemCopyLayer>, &IsLayerOfType<armnn::FloorLayer>,
- &IsLayerOfType<armnn::FakeQuantizationLayer>, &IsLayerOfType<armnn::AdditionLayer>,
- &IsLayerOfType<armnn::ActivationLayer>, &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::FakeQuantizationLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::ActivationLayer>,
+ &IsLayerOfType<armnn::OutputLayer>));
std::list<std::string> testRelatedLayers = { permuteLayerName };
diff --git a/src/armnn/test/optimizations/MoveTransposeUpTests.cpp b/src/armnn/test/optimizations/MoveTransposeUpTests.cpp
index 09bf9ae..6a6010c 100644
--- a/src/armnn/test/optimizations/MoveTransposeUpTests.cpp
+++ b/src/armnn/test/optimizations/MoveTransposeUpTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd. All rights reserved.
+// Copyright © 2020-2021,2023 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -37,7 +37,7 @@
head = graph.InsertNewLayer<armnn::ActivationLayer>(head->GetInputSlot(0), armnn::ActivationDescriptor{}, "");
head->GetOutputHandler().SetTensorInfo(info);
- head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), "");
+ head = graph.InsertNewLayer<armnn::ElementwiseBinaryLayer>(head->GetInputSlot(0), armnn::BinaryOperation::Add, "");
head->GetOutputHandler().SetTensorInfo(info);
// Inserts input for 2nd input of Addition.
@@ -55,7 +55,7 @@
head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), "");
head->GetOutputHandler().SetTensorInfo(info);
- head = graph.InsertNewLayer<armnn::MultiplicationLayer>(head->GetInputSlot(0), "");
+ head = graph.InsertNewLayer<armnn::ElementwiseBinaryLayer>(head->GetInputSlot(0), armnn::BinaryOperation::Mul, "");
head->GetOutputHandler().SetTensorInfo(info);
// Inserts input for 2nd input of Multiplication.
@@ -70,9 +70,9 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::InputLayer>, &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::MultiplicationLayer>, &IsLayerOfType<armnn::MemCopyLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::MemCopyLayer>,
&IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::FakeQuantizationLayer>,
- &IsLayerOfType<armnn::AdditionLayer>, &IsLayerOfType<armnn::ActivationLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::ActivationLayer>,
&IsLayerOfType<armnn::TransposeLayer>, &IsLayerOfType<armnn::OutputLayer>));
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(MoveTransposeUp()));
@@ -81,10 +81,11 @@
CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::InputLayer>, &IsLayerOfType<armnn::InputLayer>,
&IsLayerOfType<armnn::TransposeLayer>, &IsLayerOfType<armnn::TransposeLayer>,
- &IsLayerOfType<armnn::TransposeLayer>, &IsLayerOfType<armnn::MultiplicationLayer>,
+ &IsLayerOfType<armnn::TransposeLayer>, &IsLayerOfType<armnn::ElementwiseBinaryLayer>,
&IsLayerOfType<armnn::MemCopyLayer>, &IsLayerOfType<armnn::FloorLayer>,
- &IsLayerOfType<armnn::FakeQuantizationLayer>, &IsLayerOfType<armnn::AdditionLayer>,
- &IsLayerOfType<armnn::ActivationLayer>, &IsLayerOfType<armnn::OutputLayer>));
+ &IsLayerOfType<armnn::FakeQuantizationLayer>,
+ &IsLayerOfType<armnn::ElementwiseBinaryLayer>, &IsLayerOfType<armnn::ActivationLayer>,
+ &IsLayerOfType<armnn::OutputLayer>));
std::list<std::string> testRelatedLayers = { transposeLayerName };
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 702b060..ed92188 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -229,6 +229,7 @@
m_ParserFunctions[Layer_DequantizeLayer] = &DeserializerImpl::ParseDequantize;
m_ParserFunctions[Layer_DetectionPostProcessLayer] = &DeserializerImpl::ParseDetectionPostProcess;
m_ParserFunctions[Layer_DivisionLayer] = &DeserializerImpl::ParseDivision;
+ m_ParserFunctions[Layer_ElementwiseBinaryLayer] = &DeserializerImpl::ParseElementwiseBinary;
m_ParserFunctions[Layer_ElementwiseUnaryLayer] = &DeserializerImpl::ParseElementwiseUnary;
m_ParserFunctions[Layer_EqualLayer] = &DeserializerImpl::ParseEqual;
m_ParserFunctions[Layer_FullyConnectedLayer] = &DeserializerImpl::ParseFullyConnected;
@@ -325,6 +326,8 @@
return graphPtr->layers()->Get(layerIndex)->layer_as_DivisionLayer()->base();
case Layer::Layer_EqualLayer:
return graphPtr->layers()->Get(layerIndex)->layer_as_EqualLayer()->base();
+ case Layer::Layer_ElementwiseBinaryLayer:
+ return graphPtr->layers()->Get(layerIndex)->layer_as_ElementwiseBinaryLayer()->base();
case Layer::Layer_ElementwiseUnaryLayer:
return graphPtr->layers()->Get(layerIndex)->layer_as_ElementwiseUnaryLayer()->base();
case Layer::Layer_FullyConnectedLayer:
@@ -562,7 +565,28 @@
}
}
-armnn::UnaryOperation ToUnaryOperation(armnnSerializer::UnaryOperation operation)
+armnn::BinaryOperation ToElementwiseBinaryOperation(armnnSerializer::BinaryOperation operation)
+{
+ switch (operation)
+ {
+ case armnnSerializer::BinaryOperation::BinaryOperation_Add:
+ return armnn::BinaryOperation::Add;
+ case armnnSerializer::BinaryOperation::BinaryOperation_Div:
+ return armnn::BinaryOperation::Div;
+ case armnnSerializer::BinaryOperation::BinaryOperation_Maximum:
+ return armnn::BinaryOperation::Maximum;
+ case armnnSerializer::BinaryOperation::BinaryOperation_Minimum:
+ return armnn::BinaryOperation::Minimum;
+ case armnnSerializer::BinaryOperation::BinaryOperation_Mul:
+ return armnn::BinaryOperation::Mul;
+ case armnnSerializer::BinaryOperation::BinaryOperation_Sub:
+ return armnn::BinaryOperation::Sub;
+ default:
+ throw armnn::InvalidArgumentException("Binary operation unknown");
+ }
+}
+
+armnn::UnaryOperation ToElementwiseUnaryOperation(armnnSerializer::UnaryOperation operation)
{
switch (operation)
{
@@ -1226,7 +1250,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Add);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -1745,7 +1770,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Div);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -1935,7 +1961,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Minimum);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -1955,7 +1982,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Maximum);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -2030,6 +2058,33 @@
RegisterOutputSlots(graph, layerIndex, layer);
}
+void IDeserializer::DeserializerImpl::ParseElementwiseBinary(GraphPtr graph, unsigned int layerIndex)
+{
+ CHECK_LAYERS(graph, 0, layerIndex);
+ CHECK_LOCATION();
+
+ auto inputs = GetInputs(graph, layerIndex);
+ CHECK_VALID_SIZE(inputs.size(), 2);
+
+ auto outputs = GetOutputs(graph, layerIndex);
+ CHECK_VALID_SIZE(outputs.size(), 1);
+
+ auto fbLayer = graph->layers()->Get(layerIndex)->layer_as_ElementwiseBinaryLayer();
+ auto fbDescriptor = fbLayer->descriptor();
+
+ armnn::ElementwiseBinaryDescriptor descriptor;
+ descriptor.m_Operation = ToElementwiseBinaryOperation(fbDescriptor->operation());
+
+ const std::string& layerName = GetLayerName(graph, layerIndex);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
+
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ RegisterInputSlots(graph, layerIndex, layer);
+ RegisterOutputSlots(graph, layerIndex, layer);
+}
+
void IDeserializer::DeserializerImpl::ParseElementwiseUnary(GraphPtr graph, unsigned int layerIndex)
{
CHECK_LAYERS(graph, 0, layerIndex);
@@ -2045,7 +2100,7 @@
auto fbDescriptor = fbLayer->descriptor();
armnn::ElementwiseUnaryDescriptor descriptor;
- descriptor.m_Operation = ToUnaryOperation(fbDescriptor->operation());
+ descriptor.m_Operation = ToElementwiseUnaryOperation(fbDescriptor->operation());
const std::string& layerName = GetLayerName(graph, layerIndex);
IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());
@@ -2106,7 +2161,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Mul);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
@@ -3023,7 +3079,8 @@
CHECK_VALID_SIZE(outputs.size(), 1);
auto layerName = GetLayerName(graph, layerIndex);
- IConnectableLayer* layer = m_Network->AddSubtractionLayer(layerName.c_str());
+ armnn::ElementwiseBinaryDescriptor descriptor(armnn::BinaryOperation::Sub);
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(descriptor, layerName.c_str());
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
diff --git a/src/armnnDeserializer/Deserializer.hpp b/src/armnnDeserializer/Deserializer.hpp
index bd01a35..b75c68d 100644
--- a/src/armnnDeserializer/Deserializer.hpp
+++ b/src/armnnDeserializer/Deserializer.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -103,6 +103,7 @@
void ParseDequantize(GraphPtr graph, unsigned int layerIndex);
void ParseDetectionPostProcess(GraphPtr graph, unsigned int layerIndex);
void ParseDivision(GraphPtr graph, unsigned int layerIndex);
+ void ParseElementwiseBinary(GraphPtr graph, unsigned int layerIndex);
void ParseElementwiseUnary(GraphPtr graph, unsigned int layerIndex);
void ParseEqual(GraphPtr graph, unsigned int layerIndex);
void ParseFill(GraphPtr graph, unsigned int layerIndex);
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp
index 936216f..26e2cee 100644
--- a/src/armnnOnnxParser/OnnxParser.cpp
+++ b/src/armnnOnnxParser/OnnxParser.cpp
@@ -1558,7 +1558,7 @@
}
- IConnectableLayer* layer = m_Network->AddAdditionLayer(node.name().c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Add, node.name().c_str());
ARMNN_ASSERT(layer != nullptr);
auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer,
diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs
index 2dbfd85..fb3bc01 100644
--- a/src/armnnSerializer/ArmnnSchema.fbs
+++ b/src/armnnSerializer/ArmnnSchema.fbs
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -183,6 +183,7 @@
Pooling3d = 66,
GatherNd = 67,
BatchMatMul = 68,
+ ElementwiseBinary = 69,
}
// Base layer table to be used as part of other layers
@@ -324,6 +325,24 @@
base:LayerBase;
}
+enum BinaryOperation : byte {
+ Add = 0,
+ Div = 1,
+ Maximum = 2,
+ Minimum = 3,
+ Mul = 4,
+ Sub = 5
+}
+
+table ElementwiseBinaryDescriptor {
+ operation:BinaryOperation;
+}
+
+table ElementwiseBinaryLayer {
+ base:LayerBase;
+ descriptor:ElementwiseBinaryDescriptor;
+}
+
enum UnaryOperation : byte {
Abs = 0,
Rsqrt = 1,
@@ -1094,6 +1113,7 @@
Pooling3dLayer,
GatherNdLayer,
BatchMatMulLayer,
+ ElementwiseBinaryLayer,
}
table AnyLayer {
diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp
index c9a3022..5a095d9 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Serializer.hpp"
@@ -533,6 +533,21 @@
CreateAnyLayer(fbDivisionLayer.o, serializer::Layer::Layer_DivisionLayer);
}
+void SerializerStrategy::SerializeElementwiseBinaryLayer(const armnn::IConnectableLayer* layer,
+ const armnn::ElementwiseBinaryDescriptor& descriptor,
+ const char* name)
+{
+ IgnoreUnused(name);
+
+ auto fbBaseLayer = CreateLayerBase(layer, serializer::LayerType::LayerType_ElementwiseBinary);
+ auto fbDescriptor = serializer::CreateElementwiseBinaryDescriptor(
+ m_flatBufferBuilder,
+ GetFlatBufferBinaryOperation(descriptor.m_Operation));
+
+ auto fbLayer = serializer::CreateElementwiseBinaryLayer(m_flatBufferBuilder, fbBaseLayer, fbDescriptor);
+ CreateAnyLayer(fbLayer.o, serializer::Layer::Layer_ElementwiseBinaryLayer);
+}
+
void SerializerStrategy::SerializeElementwiseUnaryLayer(const armnn::IConnectableLayer* layer,
const armnn::ElementwiseUnaryDescriptor& descriptor,
const char* name)
@@ -2119,6 +2134,13 @@
SerializeDivisionLayer(layer, name);
break;
}
+ case armnn::LayerType::ElementwiseBinary :
+ {
+ const armnn::ElementwiseBinaryDescriptor& layerDescriptor =
+ static_cast<const armnn::ElementwiseBinaryDescriptor&>(descriptor);
+ SerializeElementwiseBinaryLayer(layer, layerDescriptor, name);
+ break;
+ }
case armnn::LayerType::ElementwiseUnary :
{
const armnn::ElementwiseUnaryDescriptor& layerDescriptor =
diff --git a/src/armnnSerializer/Serializer.hpp b/src/armnnSerializer/Serializer.hpp
index 60fed4f..4d5e806 100644
--- a/src/armnnSerializer/Serializer.hpp
+++ b/src/armnnSerializer/Serializer.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -172,6 +172,10 @@
void SerializeDivisionLayer(const armnn::IConnectableLayer* layer,
const char* name = nullptr);
+ void SerializeElementwiseBinaryLayer(const armnn::IConnectableLayer* layer,
+ const armnn::ElementwiseBinaryDescriptor& descriptor,
+ const char* name = nullptr);
+
void SerializeElementwiseUnaryLayer(const armnn::IConnectableLayer* layer,
const armnn::ElementwiseUnaryDescriptor& descriptor,
const char* name = nullptr);
diff --git a/src/armnnSerializer/SerializerUtils.cpp b/src/armnnSerializer/SerializerUtils.cpp
index 49ce721..703f56f 100644
--- a/src/armnnSerializer/SerializerUtils.cpp
+++ b/src/armnnSerializer/SerializerUtils.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2019-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -107,9 +107,30 @@
}
}
-armnnSerializer::UnaryOperation GetFlatBufferUnaryOperation(armnn::UnaryOperation comparisonOperation)
+armnnSerializer::BinaryOperation GetFlatBufferBinaryOperation(armnn::BinaryOperation binaryOperation)
{
- switch (comparisonOperation)
+ switch (binaryOperation)
+ {
+ case armnn::BinaryOperation::Add:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Add;
+ case armnn::BinaryOperation::Div:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Div;
+ case armnn::BinaryOperation::Maximum:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Maximum;
+ case armnn::BinaryOperation::Minimum:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Minimum;
+ case armnn::BinaryOperation::Mul:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Mul;
+ case armnn::BinaryOperation::Sub:
+ return armnnSerializer::BinaryOperation::BinaryOperation_Sub;
+ default:
+ throw armnn::InvalidArgumentException("Elementwise Binary operation unknown");
+ }
+}
+
+armnnSerializer::UnaryOperation GetFlatBufferUnaryOperation(armnn::UnaryOperation unaryOperation)
+{
+ switch (unaryOperation)
{
case armnn::UnaryOperation::Abs:
return armnnSerializer::UnaryOperation::UnaryOperation_Abs;
@@ -128,7 +149,7 @@
case armnn::UnaryOperation::Sin:
return armnnSerializer::UnaryOperation::UnaryOperation_Sin;
default:
- throw armnn::InvalidArgumentException("Unary operation unknown");
+ throw armnn::InvalidArgumentException("Elementwise Unary operation unknown");
}
}
diff --git a/src/armnnSerializer/SerializerUtils.hpp b/src/armnnSerializer/SerializerUtils.hpp
index 07cdc2a..628385e 100644
--- a/src/armnnSerializer/SerializerUtils.hpp
+++ b/src/armnnSerializer/SerializerUtils.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017,2019-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -18,6 +18,8 @@
armnnSerializer::DataLayout GetFlatBufferDataLayout(armnn::DataLayout dataLayout);
+armnnSerializer::BinaryOperation GetFlatBufferBinaryOperation(armnn::BinaryOperation binaryOperation);
+
armnnSerializer::UnaryOperation GetFlatBufferUnaryOperation(armnn::UnaryOperation unaryOperation);
armnnSerializer::PoolingAlgorithm GetFlatBufferPoolingAlgorithm(armnn::PoolingAlgorithm poolingAlgorithm);
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 3573a81..6ddc971 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -331,6 +331,7 @@
case armnn::LayerType::Input: break;
case armnn::LayerType::Output: break;
case armnn::LayerType::Addition: break;
+ case armnn::LayerType::ElementwiseBinary: break;
default:
{
this->VerifyNameAndConnections(layer, name);
@@ -972,6 +973,47 @@
deserializedNetwork->ExecuteStrategy(verifier);
}
+void SerializeElementwiseBinaryTest(armnn::BinaryOperation binaryOperation)
+{
+ auto layerName = GetBinaryOperationAsCString(binaryOperation);
+ const armnn::TensorInfo tensorInfo({ 1, 5, 2, 3 }, armnn::DataType::Float32);
+ armnn::ElementwiseBinaryDescriptor descriptor(binaryOperation);
+
+ armnn::INetworkPtr network = armnn::INetwork::Create();
+ armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
+ armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1);
+ armnn::IConnectableLayer* const elementwiseBinaryLayer = network->AddElementwiseBinaryLayer(descriptor,
+ layerName);
+ armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
+
+ inputLayer0->GetOutputSlot(0).Connect(elementwiseBinaryLayer->GetInputSlot(0));
+ inputLayer1->GetOutputSlot(0).Connect(elementwiseBinaryLayer->GetInputSlot(1));
+ elementwiseBinaryLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+
+ inputLayer0->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ inputLayer1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ elementwiseBinaryLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+ std::string serializedNetwork = SerializeNetwork(*network);
+ armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(serializedNetwork);
+ CHECK(deserializedNetwork);
+
+ LayerVerifierBaseWithDescriptor<armnn::ElementwiseBinaryDescriptor>
+ verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo }, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
+}
+
+TEST_CASE("SerializeElementwiseBinary")
+{
+ using op = armnn::BinaryOperation;
+ std::initializer_list<op> allBinaryOperations = {op::Add, op::Div, op::Maximum, op::Minimum, op::Mul, op::Sub};
+
+ for (auto binaryOperation : allBinaryOperations)
+ {
+ SerializeElementwiseBinaryTest(binaryOperation);
+ }
+}
+
void SerializeElementwiseUnaryTest(armnn::UnaryOperation unaryOperation)
{
auto layerName = GetUnaryOperationAsCString(unaryOperation);
@@ -2883,6 +2925,7 @@
CompareConstTensor(constants.at(0), m_LayerInput);
break;
}
+ case armnn::LayerType::ElementwiseBinary: break;
default:
{
throw armnn::Exception("Unexpected layer type in test model");
diff --git a/src/armnnTestUtils/CreateWorkload.hpp b/src/armnnTestUtils/CreateWorkload.hpp
index 0846d21..691adbf 100644
--- a/src/armnnTestUtils/CreateWorkload.hpp
+++ b/src/armnnTestUtils/CreateWorkload.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2021-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -125,7 +125,41 @@
// Makes the workload and checks it.
auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
- DescriptorType queueDescriptor = workload->GetData();
+ auto queueDescriptor = workload->GetData();
+ CHECK(queueDescriptor.m_Inputs.size() == 2);
+ CHECK(queueDescriptor.m_Outputs.size() == 1);
+
+ // Returns so we can do extra, backend-specific tests.
+ return workload;
+}
+
+template <typename WorkloadType, armnn::DataType DataType>
+std::unique_ptr<WorkloadType> CreateElementwiseBinaryWorkloadTest(armnn::IWorkloadFactory & factory,
+ armnn::Graph & graph,
+ armnn::BinaryOperation binaryOperation)
+{
+ // Creates the layer we're testing.
+ ElementwiseBinaryDescriptor descriptor(binaryOperation);
+ //ElementwiseBinaryDescriptor descriptor = ElementwiseBinaryDescriptor(binaryOperation);
+
+ Layer* const layer = graph.AddLayer<ElementwiseBinaryLayer>(descriptor, "layer");
+
+ // Creates extra layers.
+ Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
+ Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
+ Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
+
+ // Connects up.
+ armnn::TensorInfo tensorInfo({2, 3}, DataType);
+ Connect(input1, layer, tensorInfo, 0, 0);
+ Connect(input2, layer, tensorInfo, 0, 1);
+ Connect(layer, output, tensorInfo);
+ CreateTensorHandles(graph, factory);
+
+ // Makes the workload and checks it.
+ auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
+
+ auto queueDescriptor = workload->GetData();
CHECK(queueDescriptor.m_Inputs.size() == 2);
CHECK(queueDescriptor.m_Outputs.size() == 1);
@@ -191,6 +225,7 @@
return workload;
}
+
template<typename WorkloadType,
typename DescriptorType,
armnn::DataType DataType>
diff --git a/src/armnnTestUtils/MockBackend.cpp b/src/armnnTestUtils/MockBackend.cpp
index 5dfe9a3..7441d0c 100644
--- a/src/armnnTestUtils/MockBackend.cpp
+++ b/src/armnnTestUtils/MockBackend.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -69,6 +69,7 @@
case armnn::LayerType::Constant:
case armnn::LayerType::Addition:
case armnn::LayerType::Convolution2d:
+ case armnn::LayerType::ElementwiseBinary:
// Layer supported
return true;
default:
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index b7e2762..dc5afca 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -1838,7 +1838,7 @@
TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, "Input 0", "Input 1");
- IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Maximum, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -1868,7 +1868,7 @@
TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, "Input 0", "Input 1");
- IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Minimum, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -2384,7 +2384,7 @@
armnn::TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
auto layerName = fmt::format("Sub:{}:{}", subgraphIndex, operatorIndex);
- IConnectableLayer* layer = m_Network->AddSubtractionLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Sub, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -2416,7 +2416,7 @@
armnn::TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
auto layerName = fmt::format("Div:{}:{}", subgraphIndex, operatorIndex);
- IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Div, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -2444,7 +2444,7 @@
armnn::TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
auto layerName = fmt::format("Div:{}:{}", subgraphIndex, operatorIndex);
- IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Div, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -2475,7 +2475,7 @@
armnn::TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
auto layerName = fmt::format("Add:{}:{}", subgraphIndex, operatorIndex);
- IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Add, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
@@ -2506,7 +2506,7 @@
armnn::TensorInfo input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);
auto layerName = fmt::format("Mul:{}:{}", subgraphIndex, operatorIndex);
- IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str());
+ IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Mul, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});
diff --git a/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp b/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
index c9d6c71..599d353 100644
--- a/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeSubgraphUtils.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -147,6 +147,27 @@
}
template<typename LayerType>
+LayerType* FuseElementwiseBinaryLayer(OptimizationViews& optimizationViews,
+ LayerType* baseLayer,
+ ActivationLayer* activationLayer,
+ ActivationDescriptor& activationDesc,
+ BinaryOperation operation,
+ std::string name)
+{
+ IConnectableLayer* replacement = optimizationViews.GetINetwork()->AddElementwiseBinaryLayer(operation,
+ name.c_str());
+ LayerType* replacementLayer = PolymorphicDowncast<LayerType*>(replacement);
+
+ FuseLayer(optimizationViews,
+ baseLayer,
+ replacementLayer,
+ activationLayer,
+ activationDesc);
+
+ return replacementLayer;
+}
+
+template<typename LayerType>
LayerType* FuseBatchNormalizationLayer(OptimizationViews& optimizationViews,
LayerType* baseLayer,
ActivationLayer* activationLayer,
diff --git a/src/backends/backendsCommon/CMakeLists.txt b/src/backends/backendsCommon/CMakeLists.txt
index 8d7e114..28ff205 100644
--- a/src/backends/backendsCommon/CMakeLists.txt
+++ b/src/backends/backendsCommon/CMakeLists.txt
@@ -1,5 +1,5 @@
#
-# Copyright © 2017, 2023 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
#
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index 62dfc6a..6a5963d 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -35,11 +35,8 @@
case DataType::Float32:
return DataType::Float32;
case DataType::QAsymmS8:
- return DataType::Signed32;
case DataType::QAsymmU8:
- return DataType::Signed32;
case DataType::QSymmS8:
- return DataType::Signed32;
case DataType::QSymmS16:
return DataType::Signed32;
default:
@@ -3668,6 +3665,35 @@
}
}
+void ElementwiseBinaryQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const
+{
+ const std::string descriptorName{"ElementwiseBinaryQueueDescriptor"};
+
+ ValidateNumInputs(workloadInfo, descriptorName, 2);
+ ValidateNumOutputs(workloadInfo, descriptorName, 1);
+
+ const TensorInfo& inputTensorInfo0 = workloadInfo.m_InputTensorInfos[0];
+ const TensorInfo& inputTensorInfo1 = workloadInfo.m_InputTensorInfos[1];
+ const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];
+
+ std::vector<DataType> supportedTypes =
+ {
+ DataType::BFloat16,
+ DataType::Float16,
+ DataType::Float32,
+ DataType::QAsymmS8,
+ DataType::QAsymmU8,
+ DataType::QSymmS16,
+ DataType::Signed32
+ };
+
+ ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);
+ ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);
+
+ ValidateTensorDataTypesMatch(inputTensorInfo0, outputTensorInfo, descriptorName, "input", "output");
+ ValidateTensorDataTypesMatch(inputTensorInfo1, outputTensorInfo, descriptorName, "input", "output");
+}
+
void ElementwiseUnaryQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const
{
const std::string descriptorName{"ElementwiseUnaryQueueDescriptor"};
diff --git a/src/backends/backendsCommon/WorkloadFactory.cpp b/src/backends/backendsCommon/WorkloadFactory.cpp
index 1283f67..51bc3e6 100644
--- a/src/backends/backendsCommon/WorkloadFactory.cpp
+++ b/src/backends/backendsCommon/WorkloadFactory.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -15,7 +15,6 @@
#include <armnn/utility/TransformIterator.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
-#include <armnn/backends/TensorHandle.hpp>
#include <sstream>
@@ -91,7 +90,8 @@
auto backendFactory = backendRegistry.GetFactory(backendId);
auto backendObject = backendFactory();
- auto layerSupportObject = LayerSupportHandle(backendObject->GetLayerSupport(modelOptions), backendId);
+ auto layerSupport = backendObject->GetLayerSupport(modelOptions);
+ auto layerSupportObject = LayerSupportHandle(layerSupport, backendId);
switch(layer.GetType())
{
@@ -109,6 +109,7 @@
}
case LayerType::Addition:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -117,6 +118,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::ArgMinMax:
@@ -392,6 +394,24 @@
reason);
break;
}
+ case LayerType::ElementwiseBinary:
+ {
+ auto cLayer = PolymorphicDowncast<const ElementwiseBinaryLayer*>(&layer);
+
+ const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
+ const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
+ const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
+ std::vector<TensorInfo> infos = { OverrideDataType(input0, dataType),
+ OverrideDataType(input1, dataType),
+ OverrideDataType(output, dataType) };
+ result = layerSupport->IsLayerSupported(LayerType::ElementwiseBinary,
+ infos,
+ cLayer->GetParameters(),
+ EmptyOptional(),
+ EmptyOptional(),
+ reason);
+ break;
+ }
case LayerType::ElementwiseUnary:
{
auto cLayer = PolymorphicDowncast<const ElementwiseUnaryLayer*>(&layer);
@@ -740,6 +760,7 @@
}
case LayerType::Maximum:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -748,6 +769,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::MemCopy:
@@ -814,6 +836,7 @@
}
case LayerType::Multiplication:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -822,6 +845,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::Normalization:
@@ -1052,6 +1076,7 @@
}
case LayerType::Division:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -1060,6 +1085,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::Rank:
@@ -1254,6 +1280,7 @@
}
case LayerType::Subtraction:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -1262,6 +1289,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::Switch:
@@ -1291,6 +1319,7 @@
}
case LayerType::Minimum:
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();
const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();
const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();
@@ -1298,6 +1327,7 @@
OverrideDataType(input1, dataType),
OverrideDataType(output, dataType),
reason);
+ ARMNN_NO_DEPRECATE_WARN_END
break;
}
case LayerType::Prelu:
@@ -1670,6 +1700,11 @@
auto divisionQueueDescriptor = PolymorphicDowncast<const DivisionQueueDescriptor*>(&descriptor);
return CreateDivision(*divisionQueueDescriptor, info);
}
+ case LayerType::ElementwiseBinary:
+ {
+ auto queueDescriptor = PolymorphicDowncast<const ElementwiseBinaryQueueDescriptor*>(&descriptor);
+ return CreateWorkload(LayerType::ElementwiseBinary, *queueDescriptor, info);
+ }
case LayerType::ElementwiseUnary:
{
auto elementwiseUnaryQueueDescriptor
diff --git a/src/backends/backendsCommon/common.mk b/src/backends/backendsCommon/common.mk
index 3545331..986d253 100644
--- a/src/backends/backendsCommon/common.mk
+++ b/src/backends/backendsCommon/common.mk
@@ -1,5 +1,5 @@
#
-# Copyright © 2017 ARM Ltd. All rights reserved.
+# Copyright © 2017-2023 ARM Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
#
diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt
index 509157a..77335d5 100644
--- a/src/backends/backendsCommon/test/CMakeLists.txt
+++ b/src/backends/backendsCommon/test/CMakeLists.txt
@@ -24,6 +24,7 @@
DetectionPostProcessEndToEndTestImpl.hpp
DynamicBackendTests.cpp
DynamicBackendTests.hpp
+ ElementwiseBinaryEndToEndTestImpl.hpp
ElementwiseUnaryEndToEndTestImpl.hpp
EndToEndTestImpl.hpp
FillEndToEndTestImpl.hpp
diff --git a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
new file mode 100644
index 0000000..6546a6a
--- /dev/null
+++ b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
@@ -0,0 +1,107 @@
+//
+// Copyright © 2023 Arm Ltd and contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "CommonTestUtils.hpp"
+
+#include <ResolveType.hpp>
+
+#include <armnn/INetwork.hpp>
+#include <armnn/utility/NumericCast.hpp>
+
+#include <doctest/doctest.h>
+
+#include <vector>
+
+namespace
+{
+
+template<armnn::DataType ArmnnTypeInput>
+INetworkPtr CreateElementwiseBinaryNetwork(const TensorShape& input1Shape,
+ const TensorShape& input2Shape,
+ const TensorShape& outputShape,
+ BinaryOperation operation,
+ const float qScale = 1.0f,
+ const int32_t qOffset = 0)
+{
+ using namespace armnn;
+
+ INetworkPtr net(INetwork::Create());
+
+ TensorInfo input1TensorInfo(input1Shape, ArmnnTypeInput, qScale, qOffset, true);
+ TensorInfo input2TensorInfo(input2Shape, ArmnnTypeInput, qScale, qOffset, true);
+ TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
+
+ IConnectableLayer* input1 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(0));
+ IConnectableLayer* input2 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(1));
+ IConnectableLayer* elementwiseBinaryLayer = net->AddElementwiseBinaryLayer(operation, "elementwiseUnary");
+ IConnectableLayer* output = net->AddOutputLayer(0, "output");
+
+ Connect(input1, elementwiseBinaryLayer, input1TensorInfo, 0, 0);
+ Connect(input2, elementwiseBinaryLayer, input2TensorInfo, 0, 1);
+ Connect(elementwiseBinaryLayer, output, outputTensorInfo, 0, 0);
+
+ return net;
+}
+
+template<armnn::DataType ArmnnInType,
+ typename TInput = armnn::ResolveType<ArmnnInType>>
+void ElementwiseBinarySimpleEndToEnd(const std::vector<BackendId>& backends,
+ BinaryOperation operation)
+{
+ using namespace armnn;
+
+ const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
+ const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
+
+ const TensorShape& input1Shape = { 2, 2, 2, 2 };
+ const TensorShape& input2Shape = { 1 };
+ const TensorShape& outputShape = { 2, 2, 2, 2 };
+
+ // Builds up the structure of the network
+ INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
+ operation, qScale, qOffset);
+
+ CHECK(net);
+
+ const std::vector<float> input1({ 1, -1, 1, 1, 5, -5, 5, 5, -3, 3, 3, 3, 4, 4, -4, 4 });
+
+ const std::vector<float> input2({ 2 });
+ std::vector<float> expectedOutput;
+ switch (operation) {
+ case armnn::BinaryOperation::Add:
+ expectedOutput = { 3, 1, 3, 3, 7, -3, 7, 7, -1, 5, 5, 5, 6, 6, -2, 6 };
+ break;
+ case armnn::BinaryOperation::Div:
+ expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f, 2.5f, -2.5f, 2.5f, 2.5f, -1.5f, 1.5f, 1.5f, 1.5f, 2, 2, -2, 2};
+ break;
+ case armnn::BinaryOperation::Maximum:
+ expectedOutput = { 2, 2, 2, 2, 5, 2, 5, 5, 2, 3, 3, 3, 4, 4, 2, 4 };
+ break;
+ case armnn::BinaryOperation::Minimum:
+ expectedOutput = { 1, -1, 1, 1, 2, -5, 2, 2, -3, 2, 2, 2, 2, 2, -4, 2 };
+ break;
+ case armnn::BinaryOperation::Mul:
+ expectedOutput = { 2, -2, 2, 2, 10, -10, 10, 10, -6, 6, 6, 6, 8, 8, -8, 8 };
+ break;
+ case armnn::BinaryOperation::Sub:
+ expectedOutput = { -1, -3, -1, -1, 3, -7, 3, 3, -5, 1, 1, 1, 2, 2, -6, 2 };
+ break;
+ default:
+ throw("Invalid Elementwise Binary operation");
+ }
+ const std::vector<float> expectedOutput_const = expectedOutput;
+ // quantize data
+ std::vector<TInput> qInput1Data = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
+ std::vector<TInput> qInput2Data = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
+ std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
+
+ std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInput1Data }, { 1, qInput2Data }};
+ std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
+
+ EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
+}
+
+} // anonymous namespace
diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
index fb7a027..5b95d3c 100644
--- a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
+++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -664,6 +664,8 @@
DECLARE_LAYER_POLICY_2_PARAM(DetectionPostProcess)
+DECLARE_LAYER_POLICY_2_PARAM(ElementwiseBinary)
+
DECLARE_LAYER_POLICY_2_PARAM(ElementwiseUnary)
DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization)
diff --git a/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp
index cd865de..5e619df 100644
--- a/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp
+++ b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -21,7 +21,7 @@
//Defines layers.
auto input = net->AddInputLayer(0);
- auto add = net->AddAdditionLayer();
+ auto add = net->AddElementwiseBinaryLayer(armnn::BinaryOperation::Add);
auto output = net->AddOutputLayer(0);
// Connects layers.
@@ -54,7 +54,7 @@
" edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
" " << inputId << " [label=\"{Input|Guid : " << inputId << "\\lLayerType : Input\\l"
"BackendID : CpuRef\\l}\"];\n"
- " " << addId << " [label=\"{Addition|Guid : " << addId << "\\lLayerType : Addition\\l"
+ " " << addId << " [label=\"{ElementwiseBinary|Guid : " << addId << "\\lLayerType : ElementwiseBinary\\l"
"BackendID : CpuRef\\l}\"];\n"
" " << outputId << " [label=\"{Output|Guid : " << outputId << "\\lLayerType : Output\\l"
"BackendID : CpuRef\\l}\"];\n"
@@ -187,7 +187,7 @@
layer->GetOutputSlot(0).SetTensorInfo(desc);
armnn::IConnectableLayer* prevLayer = layer;
- layer = net->AddMultiplicationLayer("ml");
+ layer = net->AddElementwiseBinaryLayer(armnn::BinaryOperation::Mul, "ml");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
@@ -258,7 +258,7 @@
layer->GetOutputSlot(0).SetTensorInfo(desc);
armnn::IConnectableLayer* prevLayer = layer;
- layer = net->AddMultiplicationLayer("ml");
+ layer = net->AddElementwiseBinaryLayer(armnn::BinaryOperation::Mul, "ml");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
diff --git a/src/backends/backendsCommon/test/mockBackend/MockImportLayerSupport.hpp b/src/backends/backendsCommon/test/mockBackend/MockImportLayerSupport.hpp
index 380ce4a..da4b7ab 100644
--- a/src/backends/backendsCommon/test/mockBackend/MockImportLayerSupport.hpp
+++ b/src/backends/backendsCommon/test/mockBackend/MockImportLayerSupport.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -16,7 +16,7 @@
public:
bool IsLayerSupported(const LayerType& type,
const std::vector<TensorInfo>& infos,
- const BaseDescriptor& /*descriptor*/,
+ const BaseDescriptor& descriptor,
const Optional<LstmInputParamsInfo>& /*lstmParamsInfo*/,
const Optional<QuantizedLstmInputParamsInfo>& /*quantizedLstmParamsInfo*/,
Optional<std::string&> reasonIfUnsupported) const override
@@ -25,6 +25,11 @@
{
case LayerType::Addition:
return IsAdditionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
+ case LayerType::ElementwiseBinary:
+ {
+ auto elementwiseDesc = *(PolymorphicDowncast<const ElementwiseBinaryDescriptor*>(&descriptor));
+ return (elementwiseDesc.m_Operation == BinaryOperation::Add);
+ }
case LayerType::Input:
return IsInputSupported(infos[0], reasonIfUnsupported);
case LayerType::Output:
diff --git a/src/backends/cl/ClBackend.cpp b/src/backends/cl/ClBackend.cpp
index d2e8fbf..a10b6fb 100644
--- a/src/backends/cl/ClBackend.cpp
+++ b/src/backends/cl/ClBackend.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -311,7 +311,8 @@
if ((base.GetType() == LayerType::DepthwiseConvolution2d || base.GetType() == LayerType::Convolution2d
|| base.GetType() == LayerType::BatchNormalization || base.GetType() == LayerType::FullyConnected
|| base.GetType() == LayerType::Addition || base.GetType() == LayerType::Multiplication
- || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division)
+ || base.GetType() == LayerType::Subtraction || base.GetType() == LayerType::Division
+ || base.GetType() == LayerType::ElementwiseBinary)
&& (base.GetAdditionalInformation<ActivationDescriptor>() == nullptr))
{
for (auto output = base.BeginOutputSlots(); output != base.EndOutputSlots(); ++output)
@@ -542,6 +543,90 @@
untouched.erase(activationLayer->GetGuid());
}
}
+ else if (base.GetType() == LayerType::ElementwiseBinary)
+ {
+ ElementwiseBinaryLayer* baseLayer = PolymorphicDowncast<ElementwiseBinaryLayer*>(&base);
+
+ if (baseLayer->GetParameters().m_Operation == BinaryOperation::Add)
+ {
+ arm_compute::Status status = ClAdditionValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Add,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Div)
+ {
+ arm_compute::Status status = ClDivisionWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Div,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Mul)
+ {
+ arm_compute::Status status = ClMultiplicationWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Mul,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Sub)
+ {
+ arm_compute::Status status = ClSubtractionValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Sub,
+ name);
+ }
+ }
+ // No fusion available for other BinaryOperations
+ }
}
}
}
diff --git a/src/backends/cl/ClLayerSupport.cpp b/src/backends/cl/ClLayerSupport.cpp
index cb2d756..89bcf9b 100644
--- a/src/backends/cl/ClLayerSupport.cpp
+++ b/src/backends/cl/ClLayerSupport.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -347,6 +347,56 @@
return IsDequantizeSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::Division:
return IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
+ case LayerType::ElementwiseBinary:
+ {
+ auto desc = *(PolymorphicDowncast<const ElementwiseBinaryDescriptor *>(&descriptor));
+
+ switch (desc.m_Operation)
+ {
+ case BinaryOperation::Add:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClAdditionValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Div:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClDivisionWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Minimum:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClMinimumWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2]);
+ case BinaryOperation::Maximum:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClMaximumWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2]);
+ case BinaryOperation::Mul:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClMultiplicationWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Sub:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(ClSubtractionValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ default:
+ return false;
+ }
+ }
case LayerType::ElementwiseUnary:
return IsElementwiseUnarySupported(infos[0],
infos[1],
diff --git a/src/backends/cl/ClWorkloadFactory.cpp b/src/backends/cl/ClWorkloadFactory.cpp
index 6bf510a..0228677 100644
--- a/src/backends/cl/ClWorkloadFactory.cpp
+++ b/src/backends/cl/ClWorkloadFactory.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ClWorkloadFactory.hpp"
@@ -405,6 +405,75 @@
auto divisionQueueDescriptor = PolymorphicDowncast<const DivisionQueueDescriptor*>(&descriptor);
return std::make_unique<ClDivisionWorkload>(*divisionQueueDescriptor, info, m_CLCompileContext);
}
+ case LayerType::ElementwiseBinary :
+ {
+ auto elementwiseBinaryQueueDescriptor
+ = PolymorphicDowncast<const ElementwiseBinaryQueueDescriptor*>(&descriptor);
+
+ switch (elementwiseBinaryQueueDescriptor->m_Parameters.m_Operation)
+ {
+ case BinaryOperation::Add:
+ {
+ AdditionQueueDescriptor additionQueueDescriptor;
+ additionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ additionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ additionQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClAdditionWorkload>(additionQueueDescriptor, info, m_CLCompileContext);
+ }
+ case BinaryOperation::Div:
+ {
+ DivisionQueueDescriptor divisionQueueDescriptor;
+ divisionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ divisionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ divisionQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClDivisionWorkload>(divisionQueueDescriptor, info, m_CLCompileContext);
+ }
+ case BinaryOperation::Maximum:
+ {
+ MaximumQueueDescriptor maximumQueueDescriptor;
+ maximumQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ maximumQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ maximumQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClMaximumWorkload>(maximumQueueDescriptor, info, m_CLCompileContext);
+ }
+ case BinaryOperation::Minimum:
+ {
+ MinimumQueueDescriptor minimumQueueDescriptor;
+ minimumQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ minimumQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ minimumQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClMinimumWorkload>(minimumQueueDescriptor, info, m_CLCompileContext);
+ }
+ case BinaryOperation::Mul:
+ {
+ MultiplicationQueueDescriptor multiplicationQueueDescriptor;
+ multiplicationQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ multiplicationQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ multiplicationQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClMultiplicationWorkload>(multiplicationQueueDescriptor,
+ info,
+ m_CLCompileContext);
+ }
+ case BinaryOperation::Sub:
+ {
+ SubtractionQueueDescriptor subtractionQueueDescriptor;
+ subtractionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ subtractionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ subtractionQueueDescriptor.m_AdditionalInfoObject =
+ elementwiseBinaryQueueDescriptor->m_AdditionalInfoObject;
+ return std::make_unique<ClSubtractionWorkload>(subtractionQueueDescriptor,
+ info,
+ m_CLCompileContext);
+ }
+ default:
+ return nullptr;
+ }
+ }
case LayerType::ElementwiseUnary :
{
auto elementwiseUnaryQueueDescriptor
diff --git a/src/backends/cl/test/ClCreateWorkloadTests.cpp b/src/backends/cl/test/ClCreateWorkloadTests.cpp
index adea733..c49ca23 100644
--- a/src/backends/cl/test/ClCreateWorkloadTests.cpp
+++ b/src/backends/cl/test/ClCreateWorkloadTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -66,19 +66,17 @@
}
template <typename WorkloadType,
- typename DescriptorType,
- typename LayerType,
armnn::DataType DataType>
-static void ClCreateElementwiseWorkloadTest()
+static void ClCreateElementwiseWorkloadTest(BinaryOperation binaryOperator)
{
Graph graph;
ClWorkloadFactory factory =
ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager());
- auto workload = CreateElementwiseWorkloadTest<WorkloadType, DescriptorType, LayerType, DataType>(factory, graph);
+ auto workload = CreateElementwiseBinaryWorkloadTest<WorkloadType, DataType>(factory, graph, binaryOperator);
// Checks that inputs/outputs are as we expect them (see definition of CreateElementwiseWorkloadTest).
- DescriptorType queueDescriptor = workload->GetData();
+ auto queueDescriptor = workload->GetData();
auto inputHandle1 = PolymorphicDowncast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]);
auto inputHandle2 = PolymorphicDowncast<IClTensorHandle*>(queueDescriptor.m_Inputs[1]);
auto outputHandle = PolymorphicDowncast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]);
@@ -93,73 +91,55 @@
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateAdditionFloatWorkload")
{
ClCreateElementwiseWorkloadTest<ClAdditionWorkload,
- AdditionQueueDescriptor,
- AdditionLayer,
- armnn::DataType::Float32>();
+ armnn::DataType::Float32>(BinaryOperation::Add);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateAdditionFloat16Workload")
{
ClCreateElementwiseWorkloadTest<ClAdditionWorkload,
- AdditionQueueDescriptor,
- AdditionLayer,
- armnn::DataType::Float16>();
+ armnn::DataType::Float16>(BinaryOperation::Add);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateSubtractionFloatWorkload")
{
ClCreateElementwiseWorkloadTest<ClSubtractionWorkload,
- SubtractionQueueDescriptor,
- SubtractionLayer,
- armnn::DataType::Float32>();
+ armnn::DataType::Float32>(BinaryOperation::Sub);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateSubtractionFloat16Workload")
{
ClCreateElementwiseWorkloadTest<ClSubtractionWorkload,
- SubtractionQueueDescriptor,
- SubtractionLayer,
- armnn::DataType::Float16>();
+ armnn::DataType::Float16>(BinaryOperation::Sub);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateMultiplicationFloatWorkloadTest")
{
ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload,
- MultiplicationQueueDescriptor,
- MultiplicationLayer,
- armnn::DataType::Float32>();
+ armnn::DataType::Float32>(BinaryOperation::Mul);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateMultiplicationFloat16WorkloadTest")
{
ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload,
- MultiplicationQueueDescriptor,
- MultiplicationLayer,
- armnn::DataType::Float16>();
+ armnn::DataType::Float16>(BinaryOperation::Mul);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateMultiplicationUint8WorkloadTest")
{
ClCreateElementwiseWorkloadTest<ClMultiplicationWorkload,
- MultiplicationQueueDescriptor,
- MultiplicationLayer,
- armnn::DataType::QAsymmU8>();
+ armnn::DataType::QAsymmU8>(BinaryOperation::Mul);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateDivisionFloatWorkloadTest")
{
ClCreateElementwiseWorkloadTest<ClDivisionWorkload,
- DivisionQueueDescriptor,
- DivisionLayer,
- armnn::DataType::Float32>();
+ armnn::DataType::Float32>(BinaryOperation::Div);
}
TEST_CASE_FIXTURE(ClContextControlFixture, "CreateDivisionFloat16WorkloadTest")
{
ClCreateElementwiseWorkloadTest<ClDivisionWorkload,
- DivisionQueueDescriptor,
- DivisionLayer,
- armnn::DataType::Float16>();
+ armnn::DataType::Float16>(BinaryOperation::Div);
}
template <typename WorkloadType,
diff --git a/src/backends/cl/test/ClFallbackTests.cpp b/src/backends/cl/test/ClFallbackTests.cpp
index 51a983a..9443116 100644
--- a/src/backends/cl/test/ClFallbackTests.cpp
+++ b/src/backends/cl/test/ClFallbackTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -24,8 +24,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -172,8 +172,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -301,8 +301,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
@@ -460,8 +460,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
diff --git a/src/backends/cl/test/Fp16SupportTest.cpp b/src/backends/cl/test/Fp16SupportTest.cpp
index da6ea10..28ae479 100644
--- a/src/backends/cl/test/Fp16SupportTest.cpp
+++ b/src/backends/cl/test/Fp16SupportTest.cpp
@@ -28,7 +28,7 @@
Layer* const inputLayer1 = graph.AddLayer<InputLayer>(1, "input1");
Layer* const inputLayer2 = graph.AddLayer<InputLayer>(2, "input2");
- Layer* const additionLayer = graph.AddLayer<AdditionLayer>("addition");
+ Layer* const additionLayer = graph.AddLayer<ElementwiseBinaryLayer>(BinaryOperation::Add, "addition");
Layer* const outputLayer = graph.AddLayer<armnn::OutputLayer>(0, "output");
TensorInfo fp16TensorInfo({1, 2, 3, 5}, armnn::DataType::Float16);
@@ -57,7 +57,7 @@
IConnectableLayer* inputLayer1 = net->AddInputLayer(0);
IConnectableLayer* inputLayer2 = net->AddInputLayer(1);
- IConnectableLayer* additionLayer = net->AddAdditionLayer();
+ IConnectableLayer* additionLayer = net->AddElementwiseBinaryLayer(BinaryOperation::Add);
IConnectableLayer* outputLayer = net->AddOutputLayer(0);
inputLayer1->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(0));
diff --git a/src/backends/neon/NeonBackend.cpp b/src/backends/neon/NeonBackend.cpp
index 968bce4..cea2aa3 100644
--- a/src/backends/neon/NeonBackend.cpp
+++ b/src/backends/neon/NeonBackend.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -395,6 +395,92 @@
untouched.erase(activationLayer->GetGuid());
}
}
+ else if (base.GetType() == LayerType::ElementwiseBinary)
+ {
+ ElementwiseBinaryLayer* baseLayer = PolymorphicDowncast<ElementwiseBinaryLayer*>(&base);
+
+ if (baseLayer->GetParameters().m_Operation == BinaryOperation::Add)
+ {
+ arm_compute::Status status = NeonAdditionWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Add,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Div)
+ {
+ arm_compute::Status status = NeonDivisionWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Div,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Mul)
+ {
+ arm_compute::Status status = NeonMultiplicationWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Mul,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ else if (baseLayer->GetParameters().m_Operation == BinaryOperation::Sub)
+ {
+ arm_compute::Status status = NeonSubtractionWorkloadValidate(
+ baseLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ baseLayer->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(),
+ activationLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(),
+ &activationDesc);
+
+ if (status)
+ {
+ FuseElementwiseBinaryLayer<ElementwiseBinaryLayer>(optimizationViews,
+ baseLayer,
+ activationLayer,
+ activationDesc,
+ BinaryOperation::Sub,
+ name);
+ untouched.erase(baseLayer->GetGuid());
+ untouched.erase(activationLayer->GetGuid());
+ }
+ }
+ // No fusion available for other BinaryOperations
+ }
}
}
}
diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp
index ee155a2..672b2f3 100644
--- a/src/backends/neon/NeonLayerSupport.cpp
+++ b/src/backends/neon/NeonLayerSupport.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -346,6 +346,56 @@
}
case LayerType::Division:
return support.IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
+ case LayerType::ElementwiseBinary:
+ {
+ auto desc = *(PolymorphicDowncast<const ElementwiseBinaryDescriptor *>(&descriptor));
+
+ switch (desc.m_Operation)
+ {
+ case BinaryOperation::Add:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonAdditionWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Div:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDivisionWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Maximum:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMaximumWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2]);
+ case BinaryOperation::Minimum:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMinimumWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2]);
+ case BinaryOperation::Mul:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMultiplicationWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ case BinaryOperation::Sub:
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSubtractionWorkloadValidate,
+ reasonIfUnsupported,
+ infos[0],
+ infos[1],
+ infos[2],
+ nullptr);
+ default:
+ return false;
+ }
+ }
case LayerType::ElementwiseUnary:
return support.IsElementwiseUnarySupported(infos[0],
infos[1],
diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp
index dccd4a3..08168ec 100644
--- a/src/backends/neon/NeonWorkloadFactory.cpp
+++ b/src/backends/neon/NeonWorkloadFactory.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -280,6 +280,59 @@
auto divisionQueueDescriptor = PolymorphicDowncast<const DivisionQueueDescriptor*>(&descriptor);
return std::make_unique<NeonDivisionWorkload>(*divisionQueueDescriptor, info);
}
+ case LayerType::ElementwiseBinary :
+ {
+ auto elementwiseBinaryQueueDescriptor
+ = PolymorphicDowncast<const ElementwiseBinaryQueueDescriptor*>(&descriptor);
+
+ switch (elementwiseBinaryQueueDescriptor->m_Parameters.m_Operation)
+ {
+ case BinaryOperation::Add:
+ {
+ AdditionQueueDescriptor additionQueueDescriptor;
+ additionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ additionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonAdditionWorkload>(additionQueueDescriptor, info);
+ }
+ case BinaryOperation::Div:
+ {
+ DivisionQueueDescriptor divisionQueueDescriptor;
+ divisionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ divisionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonDivisionWorkload>(divisionQueueDescriptor, info);
+ }
+ case BinaryOperation::Maximum:
+ {
+ MaximumQueueDescriptor maximumQueueDescriptor;
+ maximumQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ maximumQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonMaximumWorkload>(maximumQueueDescriptor, info);
+ }
+ case BinaryOperation::Minimum:
+ {
+ MinimumQueueDescriptor minimumQueueDescriptor;
+ minimumQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ minimumQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonMinimumWorkload>(minimumQueueDescriptor, info);
+ }
+ case BinaryOperation::Mul:
+ {
+ MultiplicationQueueDescriptor multiplicationQueueDescriptor;
+ multiplicationQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ multiplicationQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonMultiplicationWorkload>(multiplicationQueueDescriptor, info);
+ }
+ case BinaryOperation::Sub:
+ {
+ SubtractionQueueDescriptor subtractionQueueDescriptor;
+ subtractionQueueDescriptor.m_Inputs = descriptor.m_Inputs;
+ subtractionQueueDescriptor.m_Outputs = descriptor.m_Outputs;
+ return std::make_unique<NeonSubtractionWorkload>(subtractionQueueDescriptor, info);
+ }
+ default:
+ return nullptr;
+ }
+ }
case LayerType::ElementwiseUnary :
{
auto elementwiseUnaryQueueDescriptor
diff --git a/src/backends/neon/test/NeonFallbackTests.cpp b/src/backends/neon/test/NeonFallbackTests.cpp
index 8e0e0ab..40df2dc 100644
--- a/src/backends/neon/test/NeonFallbackTests.cpp
+++ b/src/backends/neon/test/NeonFallbackTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -38,8 +38,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -183,7 +183,7 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
- IConnectableLayer* add = net->AddAdditionLayer("add");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
@@ -318,8 +318,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
- IConnectableLayer* add = net->AddAdditionLayer("add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0));
@@ -465,7 +465,7 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
- IConnectableLayer* add = net->AddAdditionLayer("add");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
@@ -599,8 +599,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
- IConnectableLayer* add = net->AddAdditionLayer("add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0));
@@ -725,8 +725,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -878,8 +878,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -1013,8 +1013,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
@@ -1177,8 +1177,8 @@
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
IConnectableLayer* input2 = net->AddInputLayer(2, "input2");
- IConnectableLayer* add = net->AddAdditionLayer("add");
- IConnectableLayer* sub = net->AddSubtractionLayer("sub");
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(BinaryOperation::Add, "add");
+ IConnectableLayer* sub = net->AddElementwiseBinaryLayer(BinaryOperation::Sub, "sub");
IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp
index a5015a7..cbc6723 100644
--- a/src/backends/reference/RefLayerSupport.cpp
+++ b/src/backends/reference/RefLayerSupport.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -189,6 +189,36 @@
return IsDequantizeSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::Division:
return IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
+ case LayerType::ElementwiseBinary:
+ {
+ std::array<DataType, 7> supportedTypes =
+ {
+ DataType::Float32,
+ DataType::Float16,
+ DataType::QAsymmS8,
+ DataType::QAsymmU8,
+ DataType::QSymmS16,
+ DataType::Signed32
+ };
+
+ bool supported = true;
+ supported &= CheckSupportRule(TypeAnyOf(infos[0], supportedTypes), reasonIfUnsupported,
+ "Reference elementwise unary: input type not supported");
+
+ supported &= CheckSupportRule(TypeAnyOf(infos[1], supportedTypes), reasonIfUnsupported,
+ "Reference elementwise unary: input type not supported");
+
+ supported &= CheckSupportRule(TypeAnyOf(infos[2], supportedTypes), reasonIfUnsupported,
+ "Reference elementwise unary: output type not supported");
+
+ supported &= CheckSupportRule(TypesAreEqual(infos[0], infos[1]), reasonIfUnsupported,
+ "Reference elementwise unary: input types not matching");
+
+ supported &= CheckSupportRule(TypesAreEqual(infos[0], infos[2]), reasonIfUnsupported,
+ "Reference elementwise unary: input and output types not matching");
+
+ return supported;
+ }
case LayerType::ElementwiseUnary:
return IsElementwiseUnarySupported(infos[0],
infos[1],
diff --git a/src/backends/reference/RefLayerSupport.hpp b/src/backends/reference/RefLayerSupport.hpp
index f0e9e35..8e1f68e 100644
--- a/src/backends/reference/RefLayerSupport.hpp
+++ b/src/backends/reference/RefLayerSupport.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
diff --git a/src/backends/reference/RefWorkloadFactory.cpp b/src/backends/reference/RefWorkloadFactory.cpp
index bfe37d7..10f623e 100644
--- a/src/backends/reference/RefWorkloadFactory.cpp
+++ b/src/backends/reference/RefWorkloadFactory.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <Layer.hpp>
@@ -302,6 +302,12 @@
return std::make_unique<RefDivisionWorkload<float>>(*divisionQueueDescriptor, info);
}
}
+ case LayerType::ElementwiseBinary:
+ {
+ auto elementwiseBinaryQueueDescriptor
+ = PolymorphicDowncast<const ElementwiseBinaryQueueDescriptor*>(&descriptor);
+ return std::make_unique<RefElementwiseBinaryWorkload>(*elementwiseBinaryQueueDescriptor, info);
+ }
case LayerType::ElementwiseUnary:
{
auto elementwiseUnaryQueueDescriptor
diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk
index eb2ec2d..c23984c 100644
--- a/src/backends/reference/backend.mk
+++ b/src/backends/reference/backend.mk
@@ -67,6 +67,7 @@
workloads/RefDepthwiseConvolution2dWorkload.cpp \
workloads/RefDequantizeWorkload.cpp \
workloads/RefDetectionPostProcessWorkload.cpp \
+ workloads/RefElementwiseBinaryWorkload.cpp \
workloads/RefElementwiseWorkload.cpp \
workloads/RefElementwiseUnaryWorkload.cpp \
workloads/RefFakeQuantizationFloat32Workload.cpp \
diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp
index 6ff5771..8bf414f 100644
--- a/src/backends/reference/test/RefEndToEndTests.cpp
+++ b/src/backends/reference/test/RefEndToEndTests.cpp
@@ -18,6 +18,7 @@
#include <backendsCommon/test/DepthToSpaceEndToEndTestImpl.hpp>
#include <backendsCommon/test/DequantizeEndToEndTestImpl.hpp>
#include <backendsCommon/test/DetectionPostProcessEndToEndTestImpl.hpp>
+#include <backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp>
#include <backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp>
#include <backendsCommon/test/FillEndToEndTestImpl.hpp>
#include <backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp>
@@ -185,7 +186,7 @@
IConnectableLayer* input1 = net->AddInputLayer(0);
IConnectableLayer* input2 = net->AddInputLayer(1);
- IConnectableLayer* add = net->AddAdditionLayer();
+ IConnectableLayer* add = net->AddElementwiseBinaryLayer(ElementwiseBinaryDescriptor(BinaryOperation::Add));
IConnectableLayer* output = net->AddOutputLayer(0);
input1->GetOutputSlot(0).Connect(add->GetInputSlot(0));
@@ -347,7 +348,7 @@
IConnectableLayer* input1 = net->AddInputLayer(0);
IConnectableLayer* input2 = net->AddInputLayer(1);
- IConnectableLayer* min = net->AddMinimumLayer();
+ IConnectableLayer* min = net->AddElementwiseBinaryLayer(ElementwiseBinaryDescriptor(BinaryOperation::Minimum));
IConnectableLayer* output = net->AddOutputLayer(0);
input1->GetOutputSlot(0).Connect(min->GetInputSlot(0));
@@ -1547,6 +1548,55 @@
{
armnn::experimental::StridedSlicedEndToEndTest<armnn::DataType::Float32>(defaultBackends, 3);
}
+
+TEST_CASE("RefAddEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Add);
+}
+TEST_CASE("RefAddEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Add);
+}
+TEST_CASE("RefDivEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Div);
+}
+TEST_CASE("RefDivEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Div);
+}
+TEST_CASE("RefMulEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Mul);
+}
+TEST_CASE("RefMulEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Mul);
+}
+TEST_CASE("RefSubEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Sub);
+}
+TEST_CASE("RefSubEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Sub);
+}
+TEST_CASE("RefMaximumEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Maximum);
+}
+TEST_CASE("RefMaximumEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Maximum);
+}
+TEST_CASE("RefMinimumEndToEndTestFloat32")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::Float32>(defaultBackends, BinaryOperation::Minimum);
+}
+TEST_CASE("RefMinimumEndToEndTestUint8")
+{
+ ElementwiseBinarySimpleEndToEnd<armnn::DataType::QAsymmU8>(defaultBackends, BinaryOperation::Minimum);
+}
#endif
}
diff --git a/src/backends/reference/test/RefOptimizedNetworkTests.cpp b/src/backends/reference/test/RefOptimizedNetworkTests.cpp
index 7ca1e0e..7e8064f 100644
--- a/src/backends/reference/test/RefOptimizedNetworkTests.cpp
+++ b/src/backends/reference/test/RefOptimizedNetworkTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -53,7 +53,7 @@
layer->GetOutputSlot(0).SetTensorInfo(desc);
armnn::IConnectableLayer* prevLayer = layer;
- layer = net->AddMultiplicationLayer("ml");
+ layer = net->AddElementwiseBinaryLayer(armnn::BinaryOperation::Mul, "ml");
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
diff --git a/src/backends/reference/workloads/CMakeLists.txt b/src/backends/reference/workloads/CMakeLists.txt
index de6c042..3592f22 100644
--- a/src/backends/reference/workloads/CMakeLists.txt
+++ b/src/backends/reference/workloads/CMakeLists.txt
@@ -1,5 +1,5 @@
#
-# Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
#
@@ -108,6 +108,8 @@
RefDequantizeWorkload.hpp
RefDetectionPostProcessWorkload.cpp
RefDetectionPostProcessWorkload.hpp
+ RefElementwiseBinaryWorkload.cpp
+ RefElementwiseBinaryWorkload.hpp
RefElementwiseUnaryWorkload.cpp
RefElementwiseUnaryWorkload.hpp
RefFakeQuantizationFloat32Workload.cpp
diff --git a/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp
new file mode 100644
index 0000000..5dc77f8
--- /dev/null
+++ b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp
@@ -0,0 +1,120 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefElementwiseBinaryWorkload.hpp"
+
+#include "Decoders.hpp"
+#include "ElementwiseFunction.hpp"
+#include "Encoders.hpp"
+#include "RefWorkloadUtils.hpp"
+#include "Maximum.hpp"
+#include "Minimum.hpp"
+
+#include <Profiling.hpp>
+
+#include <armnn/TypesUtils.hpp>
+
+#include <functional>
+
+namespace armnn
+{
+
+template<typename DataType>
+void ExecuteFunction(std::vector<ITensorHandle*> inputs,
+ std::vector<ITensorHandle*> outputs,
+ BinaryOperation operation)
+{
+ const TensorInfo& inputInfo0 = GetTensorInfo(inputs[0]);
+ const TensorInfo& inputInfo1 = GetTensorInfo(inputs[1]);
+ const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
+
+ const TensorShape& inShape0 = inputInfo0.GetShape();
+ const TensorShape& inShape1 = inputInfo1.GetShape();
+ const TensorShape& outShape = outputInfo.GetShape();
+
+ std::unique_ptr<Decoder<DataType>> input0 = MakeDecoder<DataType>(inputInfo0, inputs[0]->Map());
+ std::unique_ptr<Decoder<DataType>> input1 = MakeDecoder<DataType>(inputInfo1, inputs[1]->Map());
+ std::unique_ptr<Encoder<DataType>> output = MakeEncoder<DataType>(outputInfo, outputs[0]->Map());
+
+ using AddFunction = ElementwiseBinaryFunction<std::plus<DataType>>;
+ using DivFunction = ElementwiseBinaryFunction<std::divides<DataType>>;
+ using MaximumFunction = ElementwiseBinaryFunction<armnn::maximum<DataType>>;
+ using MinimumFunction = ElementwiseBinaryFunction<armnn::minimum<DataType>>;
+ using MulFunction = ElementwiseBinaryFunction<std::multiplies<DataType>>;
+ using SubFunction = ElementwiseBinaryFunction<std::minus<DataType>>;
+
+ switch (operation)
+ {
+ case BinaryOperation::Add:
+ {
+ AddFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ case BinaryOperation::Div:
+ {
+ DivFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ case BinaryOperation::Maximum:
+ {
+ MaximumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ case BinaryOperation::Minimum:
+ {
+ MinimumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ case BinaryOperation::Mul:
+ {
+ MulFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ case BinaryOperation::Sub:
+ {
+ SubFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+ break;
+ }
+ default:
+ {
+ throw InvalidArgumentException(std::string("Unsupported binary operation ") +
+ GetBinaryOperationAsCString(operation), CHECK_LOCATION());
+ }
+ }
+}
+
+RefElementwiseBinaryWorkload::RefElementwiseBinaryWorkload(const ElementwiseBinaryQueueDescriptor& desc,
+ const WorkloadInfo& info)
+ : RefBaseWorkload<ElementwiseBinaryQueueDescriptor>(desc, info)
+{}
+
+void RefElementwiseBinaryWorkload::Execute() const
+{
+ Execute(m_Data.m_Inputs, m_Data.m_Outputs);
+}
+
+void RefElementwiseBinaryWorkload::ExecuteAsync(ExecutionData& executionData)
+{
+
+ WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
+ Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
+}
+
+void RefElementwiseBinaryWorkload::Execute(std::vector<ITensorHandle*> inputs,
+ std::vector<ITensorHandle*> outputs) const
+{
+ ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefElementwiseBinaryWorkload_Execute");
+
+ if (GetTensorInfo(inputs[0]).GetDataType() == DataType::Signed32)
+ {
+ ExecuteFunction<int32_t>(inputs, outputs, m_Data.m_Parameters.m_Operation);
+ }
+ else
+ {
+ ExecuteFunction<float>(inputs, outputs, m_Data.m_Parameters.m_Operation);
+ }
+}
+
+} // namespace armnn
diff --git a/src/backends/reference/workloads/RefElementwiseBinaryWorkload.hpp b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.hpp
new file mode 100644
index 0000000..37458a1
--- /dev/null
+++ b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.hpp
@@ -0,0 +1,29 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "BaseIterator.hpp"
+
+#include "RefBaseWorkload.hpp"
+#include <armnn/backends/WorkloadData.hpp>
+
+namespace armnn
+{
+
+class RefElementwiseBinaryWorkload : public RefBaseWorkload<ElementwiseBinaryQueueDescriptor>
+{
+public:
+ using RefBaseWorkload<ElementwiseBinaryQueueDescriptor>::m_Data;
+
+ RefElementwiseBinaryWorkload(const ElementwiseBinaryQueueDescriptor& descriptor, const WorkloadInfo& info);
+ void Execute() const override;
+ void ExecuteAsync(ExecutionData& executionData) override;
+
+private:
+ void Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const;
+};
+
+} // namespace armnn
diff --git a/src/backends/reference/workloads/RefWorkloads.hpp b/src/backends/reference/workloads/RefWorkloads.hpp
index afed71b..dba880b 100644
--- a/src/backends/reference/workloads/RefWorkloads.hpp
+++ b/src/backends/reference/workloads/RefWorkloads.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -26,6 +26,7 @@
#include "RefDetectionPostProcessWorkload.hpp"
#include "RefDequantizeWorkload.hpp"
#include "RefElementwiseWorkload.hpp"
+#include "RefElementwiseBinaryWorkload.hpp"
#include "RefElementwiseUnaryWorkload.hpp"
#include "RefFakeQuantizationFloat32Workload.hpp"
#include "RefFillWorkload.hpp"