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, 146 insertions, 0 deletions

diff --git a/delegate/classic/src/Reduce.hpp b/delegate/classic/src/Reduce.hpp
new file mode 100644
index 0000000000..2d8b462cd2
--- /dev/null
+++ b/delegate/classic/src/Reduce.hpp
@@ -0,0 +1,146 @@
+//
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <tensorflow/lite/builtin_ops.h>
+#include <tensorflow/lite/c/builtin_op_data.h>
+#include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/kernels/internal/tensor_ctypes.h>
+#include <tensorflow/lite/minimal_logging.h>
+
+namespace armnnDelegate
+{
+
+TfLiteStatus VisitReduceOperator(DelegateData& delegateData,
+                                 TfLiteContext* tfLiteContext,
+                                 TfLiteNode* tfLiteNode,
+                                 int nodeIndex,
+                                 int32_t reduceOperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, reduceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, reduceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
+
+    // Get const axis value from model and set it to descriptor.
+    const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+    if (!IsValid(tfLiteContext, tfLiteAxisTensor, reduceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& axisTensorInfo =   GetTensorInfoForTfLiteTensor(tfLiteAxisTensor);
+    auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor);
+
+    std::vector<int32_t> axis;
+    // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis.
+    if (axisTensorData != nullptr)
+    {
+        for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i)
+        {
+            axis.emplace_back(axisTensorData[i]);
+        }
+    }
+    else
+    {
+        for (unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); ++i)
+        {
+            axis.push_back(i);
+        }
+    }
+
+    // Convert the axis to unsigned int and remove duplicates.
+    unsigned int rank = inputTensorInfo.GetNumDimensions();
+    std::set<unsigned int> uniqueAxis;
+    std::transform(axis.begin(),
+                   axis.end(),
+                   std::inserter(uniqueAxis, uniqueAxis.begin()),
+                   [rank](int i)->unsigned int{ return (i + rank) % rank; });
+
+    armnn::ReduceDescriptor desc;
+    desc.m_vAxis.assign(uniqueAxis.begin(), uniqueAxis.end());
+
+    auto* reducerParameters = reinterpret_cast<TfLiteReducerParams*>(tfLiteNode->builtin_data);
+    desc.m_KeepDims = reducerParameters->keep_dims;
+    if (reduceOperatorCode == kTfLiteBuiltinReduceMax)
+    {
+        desc.m_ReduceOperation = armnn::ReduceOperation::Max;
+    }
+    else if (reduceOperatorCode == kTfLiteBuiltinReduceMin)
+    {
+        desc.m_ReduceOperation = armnn::ReduceOperation::Min;
+    }
+    else if (reduceOperatorCode == kTfLiteBuiltinSum)
+    {
+        desc.m_ReduceOperation = armnn::ReduceOperation::Sum;
+    }
+    else if (reduceOperatorCode == kTfLiteBuiltinReduceProd)
+    {
+        desc.m_ReduceOperation = armnn::ReduceOperation::Prod;
+    }
+    else
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Unsupported Reduction Operator #%d node #%d: ",
+            reduceOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    bool isSupported = false;
+    armnn::BackendId setBackend;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC("REDUCE",
+                                   tfLiteContext,
+                                   IsReduceSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   setBackend,
+                                   inputTensorInfo,
+                                   outInfo,
+                                   desc);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add an Reduce layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddReduceLayer(desc);
+    layer->SetBackendId(setBackend);
+    ARMNN_ASSERT(layer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // try to connect the Constant Inputs if there are any
+    if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    // Connect
+    return Connect(layer, tfLiteNode, delegateData);
+}
+
+} // namespace armnnDelegate