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

3 files changed, 55 insertions, 53 deletions

diff --git a/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp b/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp
index 0636a00234..59dfb862a0 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 @@ void AddBroadcastReshapeLayerOptimizerTest(const TensorInfo& info0,
 
     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 @@ void AddBroadcastReshapeLayerOptimizerTest(const TensorInfo& info0,
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<InputLayer>,
-                             &IsLayerOfType<AdditionLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -48,19 +48,19 @@ void AddBroadcastReshapeLayerOptimizerTest(const TensorInfo& info0,
 
     // 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 @@ TEST_CASE("AddBroadcastReshapeLayerSubtractionTest")
 
     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 @@ TEST_CASE("AddBroadcastReshapeLayerSubtractionTest")
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<InputLayer>,
-                             &IsLayerOfType<SubtractionLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -145,7 +145,7 @@ TEST_CASE("AddBroadcastReshapeLayerSubtractionTest")
                              &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 @@ TEST_CASE("AddBroadcastReshapeLayerSubtractionTest")
     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 @@ TEST_CASE("AddBroadcastReshapeLayerDivisionTest")
 
     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 @@ TEST_CASE("AddBroadcastReshapeLayerDivisionTest")
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<InputLayer>,
-                             &IsLayerOfType<DivisionLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -190,7 +190,7 @@ TEST_CASE("AddBroadcastReshapeLayerDivisionTest")
                              &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 @@ TEST_CASE("AddBroadcastReshapeLayerDivisionTest")
     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 @@ TEST_CASE("AddBroadcastReshapeLayerMultiplicationTest")
 
     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 @@ TEST_CASE("AddBroadcastReshapeLayerMultiplicationTest")
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<InputLayer>,
-                             &IsLayerOfType<MultiplicationLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -235,7 +235,7 @@ TEST_CASE("AddBroadcastReshapeLayerMultiplicationTest")
                              &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 @@ TEST_CASE("AddBroadcastReshapeLayerMultiplicationTest")
     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 @@ TEST_CASE("AddNoBroadcastReshapeLayerTest")
 
     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 @@ TEST_CASE("AddNoBroadcastReshapeLayerTest")
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<InputLayer>,
-                             &IsLayerOfType<MultiplicationLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -279,7 +279,7 @@ TEST_CASE("AddNoBroadcastReshapeLayerTest")
     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 @@ TEST_CASE("ReshapeParentConstLayerTest")
 
     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 @@ TEST_CASE("ReshapeParentConstLayerTest")
     CHECK(CheckSequence(graph.cbegin(), graph.cend(),
                              &IsLayerOfType<InputLayer>,
                              &IsLayerOfType<ConstantLayer>,
-                             &IsLayerOfType<MultiplicationLayer>,
+                             &IsLayerOfType<ElementwiseBinaryLayer>,
                              &IsLayerOfType<OutputLayer>));
 
     // Run optimizer
@@ -323,7 +323,7 @@ TEST_CASE("ReshapeParentConstLayerTest")
     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 @@ TEST_CASE("ReshapeParentConstAddLayerMultipleConnectionsTest")
 
     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 @@ TEST_CASE("ReshapeParentConstAddLayerMultipleConnectionsTest")
     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 @@ TEST_CASE("ReshapeParentConstAddLayerMultipleConnectionsTest")
                              &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 152e79925b..018286c70d 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 @@ TEST_CASE("MovePermuteUpTest")
     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 @@ TEST_CASE("MovePermuteUpTest")
     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 @@ TEST_CASE("MovePermuteUpTest")
 
     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 @@ TEST_CASE("MovePermuteUpTest")
     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 09bf9ae7d9..6a6010cb4d 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 @@ TEST_CASE("MoveTransposeUpTest")
     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 @@ TEST_CASE("MoveTransposeUpTest")
     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 @@ TEST_CASE("MoveTransposeUpTest")
 
     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 @@ TEST_CASE("MoveTransposeUpTest")
     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 };