IVGCVSW-7555 Restructure Delegate

* New folders created:
  * common is for common code where TfLite API is not used
  * classic is for existing delegate implementations
  * opaque is for new opaque delegate implementation,
  * tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed

1 files changed, 201 insertions, 0 deletions

diff --git a/delegate/test/LogicalTestHelper.hpp b/delegate/test/LogicalTestHelper.hpp
new file mode 100644
index 0000000000..2f2ae7bf40
--- /dev/null
+++ b/delegate/test/LogicalTestHelper.hpp
@@ -0,0 +1,201 @@
+//
+// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+std::vector<char> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode,
+                                                 tflite::TensorType tensorType,
+                                                 const std::vector <int32_t>& input0TensorShape,
+                                                 const std::vector <int32_t>& input1TensorShape,
+                                                 const std::vector <int32_t>& outputTensorShape,
+                                                 float quantScale = 1.0f,
+                                                 int quantOffset  = 0)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+    buffers.push_back(CreateBuffer(flatBufferBuilder));
+    buffers.push_back(CreateBuffer(flatBufferBuilder));
+    buffers.push_back(CreateBuffer(flatBufferBuilder));
+    buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+    auto quantizationParameters =
+        CreateQuantizationParameters(flatBufferBuilder,
+                                     0,
+                                     0,
+                                     flatBufferBuilder.CreateVector<float>({ quantScale }),
+                                     flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+
+    std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
+                                                                      input0TensorShape.size()),
+                              tensorType,
+                              1,
+                              flatBufferBuilder.CreateString("input_0"),
+                              quantizationParameters);
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
+                                                                      input1TensorShape.size()),
+                              tensorType,
+                              2,
+                              flatBufferBuilder.CreateString("input_1"),
+                              quantizationParameters);
+    tensors[2] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                      outputTensorShape.size()),
+                              tensorType,
+                              3,
+                              flatBufferBuilder.CreateString("output"),
+                              quantizationParameters);
+
+    // create operator
+    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+    flatbuffers::Offset<void> operatorBuiltinOptions = 0;
+    switch (logicalOperatorCode)
+    {
+        case BuiltinOperator_LOGICAL_AND:
+        {
+            operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions;
+            operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union();
+            break;
+        }
+        case BuiltinOperator_LOGICAL_OR:
+        {
+            operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions;
+            operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union();
+            break;
+        }
+        default:
+            break;
+    }
+    const std::vector<int32_t> operatorInputs{ {0, 1} };
+    const std::vector<int32_t> operatorOutputs{ 2 };
+    flatbuffers::Offset <Operator> logicalBinaryOperator =
+        CreateOperator(flatBufferBuilder,
+                       0,
+                       flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+                       operatorBuiltinOptionsType,
+                       operatorBuiltinOptions);
+
+    const std::vector<int> subgraphInputs{ {0, 1} };
+    const std::vector<int> subgraphOutputs{ 2 };
+    flatbuffers::Offset <SubGraph> subgraph =
+        CreateSubGraph(flatBufferBuilder,
+                       flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+                       flatBufferBuilder.CreateVector(&logicalBinaryOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: Logical Binary Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, logicalOperatorCode);
+
+    flatbuffers::Offset <Model> flatbufferModel =
+        CreateModel(flatBufferBuilder,
+                    TFLITE_SCHEMA_VERSION,
+                    flatBufferBuilder.CreateVector(&operatorCode, 1),
+                    flatBufferBuilder.CreateVector(&subgraph, 1),
+                    modelDescription,
+                    flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+    flatBufferBuilder.Finish(flatbufferModel);
+
+    return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+                             flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template <typename T>
+void LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode,
+                       tflite::TensorType tensorType,
+                       std::vector<armnn::BackendId>& backends,
+                       std::vector<int32_t>& input0Shape,
+                       std::vector<int32_t>& input1Shape,
+                       std::vector<int32_t>& expectedOutputShape,
+                       std::vector<T>& input0Values,
+                       std::vector<T>& input1Values,
+                       std::vector<T>& expectedOutputValues,
+                       float quantScale = 1.0f,
+                       int quantOffset  = 0)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode,
+                                                                   tensorType,
+                                                                   input0Shape,
+                                                                   input1Shape,
+                                                                   expectedOutputShape,
+                                                                   quantScale,
+                                                                   quantOffset);
+
+    const Model* tfLiteModel = GetModel(modelBuffer.data());
+    // Create TfLite Interpreters
+    std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&armnnDelegateInterpreter) == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter != nullptr);
+    CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+    std::unique_ptr<Interpreter> tfLiteInterpreter;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&tfLiteInterpreter) == kTfLiteOk);
+    CHECK(tfLiteInterpreter != nullptr);
+    CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+    // Create the ArmNN Delegate
+    armnnDelegate::DelegateOptions delegateOptions(backends);
+    std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+        theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+                         armnnDelegate::TfLiteArmnnDelegateDelete);
+    CHECK(theArmnnDelegate != nullptr);
+    // Modify armnnDelegateInterpreter to use armnnDelegate
+    CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+    // Set input data for the armnn interpreter
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values);
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values);
+
+    // Set input data for the tflite interpreter
+    armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values);
+    armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values);
+
+    // Run EnqueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function
+    // directly. This is because Boolean types get converted to a bit representation in a vector.
+    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+    auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId);
+    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId);
+
+    armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+
+    armnnDelegateInterpreter.reset(nullptr);
+    tfLiteInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
\ No newline at end of file