delegate/opaque/src/Normalization.hpp

//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include <OpaqueDelegateUtils.hpp>

namespace armnnOpaqueDelegate
{

TfLiteStatus VisitL2NormalizationOperator(DelegateData& delegateData,
                                          TfLiteOpaqueContext* tfLiteContext,
                                          TfLiteOpaqueNode* tfLiteNode,
                                          int nodeIndex,
                                          int32_t tfLiteL2NormalizationOperatorCode)
{
    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, 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;
    }
    // Use input indices to get input tensor.
    const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
    if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex))
    {
        return kTfLiteError;
    }
    // Gather output indices and use to get output tensor.
    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;
    }
    // Use output indices to get output tensor.
    const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
    if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex))
    {
        return kTfLiteError;
    }

    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

    armnn::L2NormalizationDescriptor descriptor;
    descriptor.m_DataLayout = armnn::DataLayout::NHWC;

    bool isSupported = false;
    armnn::BackendId setBackend;
    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
    {
        FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("L2_NORMALIZATION",
                                          tfLiteContext,
                                          IsL2NormalizationSupported,
                                          delegateData.m_Backends,
                                          isSupported,
                                          setBackend,
                                          inputTensorInfo,
                                          outInfo,
                                          descriptor);
    };

    if (!delegateData.m_Network)
    {
        validateFunc(outputTensorInfo, isSupported);
        return isSupported ? kTfLiteOk : kTfLiteError;
    }

    // Add a L2Normalization layer
    auto layerName = GetName(armnn::LayerType::L2Normalization, nodeIndex);
    armnn::IConnectableLayer* layer = delegateData.m_Network->AddL2NormalizationLayer(descriptor, layerName.c_str());
    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, nodeIndex) != kTfLiteOk)
    {
        return kTfLiteError;
    }

    // Connect
    return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}


TfLiteStatus VisitLocalResponseNormalizationOperator(DelegateData& delegateData,
                                                     TfLiteOpaqueContext* tfLiteContext,
                                                     TfLiteOpaqueNode* tfLiteNode,
                                                     int nodeIndex,
                                                     int32_t tfLiteNormalizationOperatorCode)
{
    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, 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;
    }
    // Use input indices to get input tensor.
    const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
    if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteNormalizationOperatorCode, nodeIndex))
    {
        return kTfLiteError;
    }
    // Gather output indices and use to get output tensor.
    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;
    }
    // Use output indices to get output tensor.
    const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
    if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteNormalizationOperatorCode, nodeIndex))
    {
        return kTfLiteError;
    }

    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

    armnn::NormalizationDescriptor descriptor;
    descriptor.m_DataLayout = armnn::DataLayout::NHWC;
    descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
    descriptor.m_NormMethodType  = armnn::NormalizationAlgorithmMethod::LocalBrightness;

    auto* nodeParams = reinterpret_cast<TfLiteLocalResponseNormParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
    descriptor.m_NormSize = nodeParams->radius;
    descriptor.m_K        = nodeParams->bias;
    descriptor.m_Alpha    = nodeParams->alpha;
    descriptor.m_Beta     = nodeParams->beta;

    // ArmNN expects normSize to be the full size of the normalization window
    descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);

    bool isSupported = false;
    armnn::BackendId setBackend;
    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
    {
        FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("NORMALIZATION",
                                          tfLiteContext,
                                          IsNormalizationSupported,
                                          delegateData.m_Backends,
                                          isSupported,
                                          setBackend,
                                          inputTensorInfo,
                                          outInfo,
                                          descriptor);
    };

    if (!delegateData.m_Network)
    {
        validateFunc(outputTensorInfo, isSupported);
        return isSupported ? kTfLiteOk : kTfLiteError;
    }

    // Add a Normalization layer
    auto layerName = GetName(armnn::LayerType::Normalization, nodeIndex);
    armnn::IConnectableLayer* layer = delegateData.m_Network->AddNormalizationLayer(descriptor, layerName.c_str());
    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, nodeIndex) != kTfLiteOk)
    {
        return kTfLiteError;
    }

    // Connect
    return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}

} // namespace armnnOpaqueDelegate