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, 141 insertions, 0 deletions

diff --git a/delegate/classic/src/Slice.hpp b/delegate/classic/src/Slice.hpp
new file mode 100644
index 0000000000..f19e3327e4
--- /dev/null
+++ b/delegate/classic/src/Slice.hpp
@@ -0,0 +1,141 @@
+//
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/utility/IgnoreUnused.hpp>
+
+#include <tensorflow/lite/builtin_ops.h>
+#include <tensorflow/lite/c/builtin_op_data.h>
+#include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/minimal_logging.h>
+
+namespace armnnDelegate
+{
+
+TfLiteStatus VisitSliceOperator(DelegateData& delegateData,
+                                TfLiteContext* tfLiteContext,
+                                TfLiteNode* tfLiteNode,
+                                int nodeIndex,
+                                int32_t sliceOperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Read inputs [input, begin, size]
+    int numInputs = tfLiteNode->inputs->size;
+    std::vector<const TfLiteTensor*> tfLiteInputs;
+    tfLiteInputs.reserve(numInputs);
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    for (int i = 0; i < numInputs; i++)
+    {
+        const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]];
+        tfLiteInputs.push_back(inputTensor);
+        if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex))
+        {
+            return kTfLiteError;
+        }
+    }
+
+    // We save the begin and size tensors in our descriptor. Therefore we have to read those values from inputs
+    int inputRank = tfLiteInputs[0]->dims->size;
+    auto ReadInt32Input = [&](int inputIndex, std::vector<uint32_t>& outputData) ->  TfLiteStatus
+    {
+        if (tfLiteInputs[inputIndex]->type != kTfLiteInt32)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to "
+                    "be of type int32. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int rank = tfLiteInputs[inputIndex]->dims->size;
+        if (rank != 1)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to "
+                    "be a 1D-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int numValues = tfLiteInputs[inputIndex]->dims->data[0];
+        if (numValues != inputRank)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The number of values in the Begin- and Size-Tensors of the "
+                    "Slice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        // return tensor data
+        auto* tensorDataPtr = tflite::GetTensorData<uint32_t>(tfLiteInputs[inputIndex]);
+        outputData.assign(tensorDataPtr, tensorDataPtr+numValues);
+        return kTfLiteOk;
+    };
+
+    std::vector<uint32_t> begin;
+    if (ReadInt32Input(1, begin) != kTfLiteOk)
+        return kTfLiteError;
+    std::vector<uint32_t> size;
+    if (ReadInt32Input(2, size) != kTfLiteOk)
+        return kTfLiteError;
+
+    // Write all data to the descriptor
+    armnn::SliceDescriptor descriptor(begin, size);
+
+    // Validate output
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
+
+    bool isSupported = false;
+    armnn::BackendId setBackend;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC("SLICE",
+                                   tfLiteContext,
+                                   IsSliceSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   setBackend,
+                                   inputTensorInfo,
+                                   outInfo,
+                                   descriptor);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add a Slice layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddSliceLayer(descriptor);
+    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
+