IVGCVSW-7579 IVGCVSW-7581 IVGCVSW-7583 Implement Comparison, Concat and Mean in Opaque Delegate

 * Removed input slot check from Connect function as number of TFLite
   and Arm NN inputs can differ.
 * Moved SetupConcatViewOrigin function to DelegateUtils.hpp
 * Simplified validation checks in VistConvolution functions as IsValid
   and IsDynamic were already being called.

Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I858dbe4b643f9d350d9c38ea255ce5effbda4612

1 files changed, 315 insertions, 0 deletions

diff --git a/delegate/opaque/src/Control.hpp b/delegate/opaque/src/Control.hpp
index e16969768e..b3d589756b 100644
--- a/delegate/opaque/src/Control.hpp
+++ b/delegate/opaque/src/Control.hpp
@@ -2,3 +2,318 @@
 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
+
+#pragma once
+
+#include <DelegateUtils.hpp>
+
+#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>
+
+#include <algorithm>
+#include <iterator>
+#include <string>
+#include <vector>
+
+namespace armnnOpaqueDelegate
+{
+
+TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData,
+                                        TfLiteOpaqueContext* tfLiteContext,
+                                        TfLiteOpaqueNode* tfLiteNode,
+                                        int nodeIndex,
+                                        int32_t tfLiteConcatOperatorCode)
+{
+    auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+    if (numInputs < 2)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d",
+                2, numInputs, nodeIndex);
+        return kTfLiteError;
+    }
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Gather input indices and use to get input tensor.
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    std::vector<armnn::TensorInfo> inputTensorInfos;
+    for (int i = 0; i < numInputs; ++i)
+    {
+        const TfLiteOpaqueTensor* inputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[i]);
+        if (!IsValid(tfLiteContext, inputTensor, tfLiteConcatOperatorCode, nodeIndex))
+        {
+            return kTfLiteError;
+        }
+
+        armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(inputTensor);
+        inputTensorInfos.emplace_back(inputTensorInfo);
+    }
+
+    // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC.
+    std::vector<const armnn::TensorInfo*> inputConstTensorInfos;
+    std::transform(inputTensorInfos.begin(),
+                   inputTensorInfos.end(),
+                   std::back_inserter(inputConstTensorInfos),
+                   [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; });
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteConcatOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Setup OriginsDescriptor, axis and view origin
+    auto numConcatView = static_cast<unsigned int>(numInputs);
+    uint32_t inputRank = TfLiteOpaqueTensorNumDims(TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]));
+
+    auto* concatenationParameters =
+            reinterpret_cast<TfLiteConcatenationParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+    if(!concatenationParameters)
+    {
+        throw armnn::Exception(&"TfLiteArmnnDelegate: Concat parameters are null in: " [ nodeIndex ]);
+    }
+
+    const auto concatDimInput = static_cast<unsigned int>(
+            (static_cast<int>(inputRank) + concatenationParameters->axis) % static_cast<int>(inputRank));
+
+    armnn::OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank);
+    concatDescriptor.SetConcatAxis(concatDimInput);
+
+    unsigned int mergeDimOrigin = 0;
+    for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex)
+    {
+        armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(
+                TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[viewIndex]));
+
+        // Sets up concatDescriptor view origin
+        SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin);
+    }
+
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+    // Verify we support the fused activation before attempting to create a layer
+    TfLiteFusedActivation activationType = concatenationParameters->activation;
+
+    TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo,
+                                                                    outputTensorInfo, activationType);
+    if(activationStatus != kTfLiteOk)
+    {
+        return kTfLiteError;
+    }
+
+    // Check if supported
+    bool isSupported = false;
+    armnn::BackendId setBackend;
+    auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CONCATENATION",
+                                          tfLiteContext,
+                                          IsConcatSupported,
+                                          delegateData.m_Backends,
+                                          isSupported,
+                                          setBackend,
+                                          inputConstTensorInfos,
+                                          outputTensorInfo,
+                                          concatDescriptor);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Setup layer and connect.
+    armnn::IConnectableLayer* concatenationLayer = delegateData.m_Network->AddConcatLayer(concatDescriptor);
+    concatenationLayer->SetBackendId(setBackend);
+    ARMNN_ASSERT(concatenationLayer != nullptr);
+
+    // Connect the Constant Inputs
+    auto inputsTensorsProcess = ProcessInputs(concatenationLayer,
+                                              delegateData,
+                                              tfLiteContext,
+                                              tfLiteNode);
+    if (inputsTensorsProcess == kTfLiteError)
+    {
+        return inputsTensorsProcess;
+    }
+
+    armnn::IOutputSlot& outputSlot = concatenationLayer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+    if(Connect(concatenationLayer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk)
+    {
+        return kTfLiteError;
+    }
+
+    if (activationType == kTfLiteActNone)
+    {
+        // No Activation
+        return kTfLiteOk;
+    }
+
+    // Check and Create activation
+    return FusedActivation(tfLiteContext, tfLiteNode, activationType, concatenationLayer, 0, delegateData);
+}
+
+TfLiteStatus VisitMeanOperator(DelegateData& delegateData,
+                               TfLiteOpaqueContext* tfLiteContext,
+                               TfLiteOpaqueNode* tfLiteNode,
+                               int nodeIndex,
+                               int32_t tfLiteMeanOperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Gather input indices and use to get input tensor.
+    int numInputs = 0;
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteMeanOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Use input indices to get axis tensor.
+    const TfLiteOpaqueTensor* tfLiteAxisTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
+    if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteMeanOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteMeanOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo =  GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& axisTensorInfo =   GetTensorInfoForTfLiteOpaqueTensor(tfLiteAxisTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+    auto* axisTensorData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteAxisTensor));
+
+    std::vector<int32_t> axis;
+    // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis.
+    for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i)
+    {
+        axis.emplace_back(axisTensorData[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; });
+
+    // Setup MeanDescriptor and assign axis and keepDims
+    armnn::MeanDescriptor desc;
+    desc.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end());
+    desc.m_KeepDims = inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? true : false;
+
+    // Check if supported
+    bool isSupported = false;
+    armnn::BackendId setBackend;
+    auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("MEAN",
+                                          tfLiteContext,
+                                          IsMeanSupported,
+                                          delegateData.m_Backends,
+                                          isSupported,
+                                          setBackend,
+                                          inputTensorInfo,
+                                          outputTensorInfo,
+                                          desc);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Setup layer and connect.
+    armnn::IConnectableLayer* meanLayer = delegateData.m_Network->AddMeanLayer(desc);
+    meanLayer->SetBackendId(setBackend);
+    ARMNN_ASSERT(meanLayer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // try to connect the Constant Inputs if there are any
+    if(ProcessInputs(meanLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    return Connect(meanLayer, tfLiteContext, tfLiteNode, delegateData);
+}
+
+TfLiteStatus VisitControlOperator(DelegateData& delegateData,
+                                  TfLiteOpaqueContext* tfLiteContext,
+                                  TfLiteOpaqueNode* tfLiteNode,
+                                  int nodeIndex,
+                                  int32_t operatorCode)
+{
+    switch(operatorCode)
+    {
+        case kTfLiteBuiltinConcatenation:
+            return VisitConcatenationOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode);
+        case kTfLiteBuiltinMean:
+            return VisitMeanOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode);
+        default:
+            return kTfLiteError;
+    }
+}
+
+} // namespace armnnDelegate
+