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

1 files changed, 107 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
new file mode 100644
index 0000000000..6546a6ae9e
--- /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