xref: /aosp_15_r20/external/armnn/src/armnnSerializer/test/LstmSerializationTests.cpp (revision 89c4ff92f2867872bb9e2354d150bf0c8c502810)

//
// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "../Serializer.hpp"
#include "SerializerTestUtils.hpp"

#include <armnn/Descriptors.hpp>
#include <armnn/INetwork.hpp>
#include <armnn/IRuntime.hpp>
#include <armnnDeserializer/IDeserializer.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <armnn/LstmParams.hpp>
#include <armnn/QuantizedLstmParams.hpp>

#include <doctest/doctest.h>
#include <fmt/format.h>


TEST_SUITE("SerializerTests")
{
template<typename Descriptor>
armnn::LstmInputParams ConstantVector2LstmInputParams(const std::vector<armnn::ConstTensor>& constants,
                                                      Descriptor& descriptor)
{
    armnn::LstmInputParams lstmInputParams;
    size_t i = 0;

    // Inserting basic paramters
    lstmInputParams.m_InputToForgetWeights     = &constants[i++];
    lstmInputParams.m_InputToCellWeights       = &constants[i++];
    lstmInputParams.m_InputToOutputWeights     = &constants[i++];
    lstmInputParams.m_RecurrentToForgetWeights = &constants[i++];
    lstmInputParams.m_RecurrentToCellWeights   = &constants[i++];
    lstmInputParams.m_RecurrentToOutputWeights = &constants[i++];
    lstmInputParams.m_ForgetGateBias           = &constants[i++];
    lstmInputParams.m_CellBias                 = &constants[i++];
    lstmInputParams.m_OutputGateBias           = &constants[i++];
    if (!descriptor.m_CifgEnabled)
    {
        lstmInputParams.m_InputToInputWeights     = &constants[i++];
        lstmInputParams.m_RecurrentToInputWeights = &constants[i++];
        lstmInputParams.m_InputGateBias           = &constants[i++];
    }

    if (descriptor.m_PeepholeEnabled)
    {
        if (!descriptor.m_CifgEnabled)
        {
            lstmInputParams.m_CellToInputWeights = &constants[i++];
        }
        lstmInputParams.m_CellToForgetWeights = &constants[i++];
        lstmInputParams.m_CellToOutputWeights = &constants[i++];
    }

    if (descriptor.m_ProjectionEnabled)
    {
        lstmInputParams.m_ProjectionWeights = &constants[i++];
        lstmInputParams.m_ProjectionBias    = &constants[i++];
    }

    if (descriptor.m_LayerNormEnabled)
    {
        if (!descriptor.m_CifgEnabled)
        {
            lstmInputParams.m_InputLayerNormWeights = &constants[i++];
        }
        lstmInputParams.m_ForgetLayerNormWeights = &constants[i++];
        lstmInputParams.m_CellLayerNormWeights   = &constants[i++];
        lstmInputParams.m_OutputLayerNormWeights = &constants[i++];
    }

    return lstmInputParams;
}

// Works for Lstm, UnidirectionalSequenceLstm and QLstm (QuantizedLstm uses different parameters)
template<typename Descriptor>
class VerifyLstmLayer : public LayerVerifierBaseWithDescriptor<Descriptor>
{
public:
    VerifyLstmLayer(const std::string& layerName,
                    const std::vector<armnn::TensorInfo>& inputInfos,
                    const std::vector<armnn::TensorInfo>& outputInfos,
                    const Descriptor& descriptor,
                    const armnn::LstmInputParams& inputParams)
        : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
        , m_InputParams(inputParams) {}

    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
                         const armnn::BaseDescriptor& descriptor,
                         const std::vector<armnn::ConstTensor>& constants,
                         const char* name,
                         const armnn::LayerBindingId id = 0) override
    {
        armnn::IgnoreUnused(constants, id);
        switch (layer->GetType())
        {
            case armnn::LayerType::Input: break;
            case armnn::LayerType::Output: break;
            case armnn::LayerType::Lstm:
            case armnn::LayerType::UnidirectionalSequenceLstm:
            {
                this->VerifyNameAndConnections(layer, name);
                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
                this->VerifyDescriptor(internalDescriptor);
                armnn::LstmInputParams lstmParams = ConstantVector2LstmInputParams(constants, internalDescriptor);
                VerifyInputParameters(lstmParams);
                break;
            }
            case armnn::LayerType::QLstm:
            {
                this->VerifyNameAndConnections(layer, name);
                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
                this->VerifyDescriptor(internalDescriptor);
                armnn::LstmInputParams lstmParams = ConstantVector2LstmInputParams(constants, internalDescriptor);
                VerifyInputParameters(lstmParams);
                break;
            }
            default:
            {
                throw armnn::Exception("Unexpected layer type in Lstm test model");
            }
        }
    }

protected:
    void VerifyInputParameters(const armnn::LstmInputParams& params)
    {
        this->VerifyConstTensors(
            "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
        this->VerifyConstTensors(
            "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
        this->VerifyConstTensors(
            "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
        this->VerifyConstTensors(
            "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
        this->VerifyConstTensors(
            "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
        this->VerifyConstTensors(
            "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
        this->VerifyConstTensors(
            "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
        this->VerifyConstTensors(
            "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
        this->VerifyConstTensors(
            "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
        this->VerifyConstTensors(
            "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
        this->VerifyConstTensors(
            "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
        this->VerifyConstTensors(
            "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
        this->VerifyConstTensors(
            "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
        this->VerifyConstTensors(
            "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
        this->VerifyConstTensors(
            "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
        this->VerifyConstTensors(
            "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
        this->VerifyConstTensors(
            "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
        this->VerifyConstTensors(
            "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
        this->VerifyConstTensors(
            "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
        this->VerifyConstTensors(
            "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
        this->VerifyConstTensors(
            "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
    }

private:
    armnn::LstmInputParams m_InputParams;
};

TEST_CASE("SerializeDeserializeLstmCifgPeepholeNoProjection")
{
    armnn::LstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = false;
    descriptor.m_PeepholeEnabled = true;

    const uint32_t batchSize = 1;
    const uint32_t inputSize = 2;
    const uint32_t numUnits = 4;
    const uint32_t outputSize = numUnits;

    armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);

    std::vector<float> forgetGateBiasData(numUnits, 1.0f);
    armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);

    std::vector<float> cellBiasData(numUnits, 0.0f);
    armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);

    std::vector<float> outputGateBiasData(numUnits, 0.0f);
    armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("lstm");
    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);

    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);

    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::LstmDescriptor> checker(
        layerName,
        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
        descriptor,
        params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeAndProjection")
{
    armnn::LstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled = true;

    const uint32_t batchSize = 2;
    const uint32_t inputSize = 5;
    const uint32_t numUnits = 20;
    const uint32_t outputSize = 16;

    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);

    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);

    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);

    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);

    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);

    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);

    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);

    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);

    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);

    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);

    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionBiasData(outputSize, 0.f);
    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // additional params because: descriptor.m_CifgEnabled = false
    params.m_InputToInputWeights = &inputToInputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_CellToInputWeights = &cellToInputWeights;
    params.m_InputGateBias = &inputGateBias;

    // additional params because: descriptor.m_ProjectionEnabled = true
    params.m_ProjectionWeights = &projectionWeights;
    params.m_ProjectionBias = &projectionBias;

    // additional params because: descriptor.m_PeepholeEnabled = true
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("lstm");
    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);

    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);

    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::LstmDescriptor> checker(
        layerName,
        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
        descriptor,
        params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm")
{
    armnn::LstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled = true;
    descriptor.m_LayerNormEnabled = true;

    const uint32_t batchSize = 2;
    const uint32_t inputSize = 5;
    const uint32_t numUnits = 20;
    const uint32_t outputSize = 16;

    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);

    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);

    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);

    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);

    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);

    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);

    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);

    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);

    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);

    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);

    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionBiasData(outputSize, 0.f);
    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);

    std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outLayerNormWeights(tensorInfo20, forgetGateBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // additional params because: descriptor.m_CifgEnabled = false
    params.m_InputToInputWeights = &inputToInputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_CellToInputWeights = &cellToInputWeights;
    params.m_InputGateBias = &inputGateBias;

    // additional params because: descriptor.m_ProjectionEnabled = true
    params.m_ProjectionWeights = &projectionWeights;
    params.m_ProjectionBias = &projectionBias;

    // additional params because: descriptor.m_PeepholeEnabled = true
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    // additional params because: despriptor.m_LayerNormEnabled = true
    params.m_InputLayerNormWeights = &inputLayerNormWeights;
    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
    params.m_CellLayerNormWeights = &cellLayerNormWeights;
    params.m_OutputLayerNormWeights = &outLayerNormWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("lstm");
    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);

    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);

    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);

    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::LstmDescriptor> checker(
            layerName,
            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
            descriptor,
            params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("EnsureLstmLayersBackwardCompatibility")
{
    // The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection
    // enabled. That data was obtained before additional layer normalization parameters where added to the
    // lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can
    // still be loaded
    const std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =
    {
        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
        0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,
        0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,
        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,
        0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,
        0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,
        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,
        0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,
        0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,
        0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,
        0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
        0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,
        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
        0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,
        0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,
        0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,
        0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,
        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,
        0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,
        0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,
        0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,
        0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,
        0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,
        0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,
        0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,
        0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,
        0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,
        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,
        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,
        0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,
        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
        0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,
        0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,
        0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,
        0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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        0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
        0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00
    };

    armnn::INetworkPtr deserializedNetwork =
        DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),
                                       lstmNoCifgWithPeepholeAndProjectionModel.end()));

    CHECK(deserializedNetwork);

    // generating the same model parameters which where used to serialize the model (Layer norm is not specified)
    armnn::LstmDescriptor descriptor;
    descriptor.m_ActivationFunc    = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled       = false;
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled   = true;

    const uint32_t batchSize  = 2u;
    const uint32_t inputSize  = 5u;
    const uint32_t numUnits   = 20u;
    const uint32_t outputSize = 16u;

    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);

    std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);

    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);

    std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);

    std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);

    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);

    std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);

    std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);

    std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);

    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);

    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionBiasData(outputSize, 0.0f);
    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights     = &inputToForgetWeights;
    params.m_InputToCellWeights       = &inputToCellWeights;
    params.m_InputToOutputWeights     = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias           = &forgetGateBias;
    params.m_CellBias                 = &cellBias;
    params.m_OutputGateBias           = &outputGateBias;

    // additional params because: descriptor.m_CifgEnabled = false
    params.m_InputToInputWeights      = &inputToInputWeights;
    params.m_RecurrentToInputWeights  = &recurrentToInputWeights;
    params.m_CellToInputWeights       = &cellToInputWeights;
    params.m_InputGateBias            = &inputGateBias;

    // additional params because: descriptor.m_ProjectionEnabled = true
    params.m_ProjectionWeights        = &projectionWeights;
    params.m_ProjectionBias           = &projectionBias;

    // additional params because: descriptor.m_PeepholeEnabled = true
    params.m_CellToForgetWeights      = &cellToForgetWeights;
    params.m_CellToOutputWeights      = &cellToOutputWeights;

    const std::string layerName("lstm");
    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32, 0.0f , 0);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32, 0.0f , 0);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32, 0.0f , 0);
    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32, 0.0f , 0);

    VerifyLstmLayer<armnn::LstmDescriptor> checker(
            layerName,
            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
            descriptor,
            params);
    deserializedNetwork->ExecuteStrategy(checker);
}

armnn::QuantizedLstmInputParams ConstantsVector2QuantizedLstmInputParams(
        const std::vector<armnn::ConstTensor>& constants)
{
    armnn::QuantizedLstmInputParams params;

    // index for constants vector
    size_t i = 0;

    // Get input parameters
    params.m_InputToInputWeights  = &constants[i++];
    params.m_InputToForgetWeights = &constants[i++];
    params.m_InputToCellWeights   = &constants[i++];
    params.m_InputToOutputWeights = &constants[i++];

    params.m_RecurrentToInputWeights  = &constants[i++];
    params.m_RecurrentToForgetWeights = &constants[i++];
    params.m_RecurrentToCellWeights   = &constants[i++];
    params.m_RecurrentToOutputWeights = &constants[i++];

    params.m_InputGateBias  = &constants[i++];
    params.m_ForgetGateBias = &constants[i++];
    params.m_CellBias       = &constants[i++];
    params.m_OutputGateBias = &constants[i++];

    return params;
}

class VerifyQuantizedLstmLayer : public LayerVerifierBase
{

public:
    VerifyQuantizedLstmLayer(const std::string& layerName,
                             const std::vector<armnn::TensorInfo>& inputInfos,
                             const std::vector<armnn::TensorInfo>& outputInfos,
                             const armnn::QuantizedLstmInputParams& inputParams)
        : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}

    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
                         const armnn::BaseDescriptor& descriptor,
                         const std::vector<armnn::ConstTensor>& constants,
                         const char* name,
                         const armnn::LayerBindingId id = 0) override
    {
        armnn::IgnoreUnused(descriptor, constants, id);
        switch (layer->GetType())
        {
            case armnn::LayerType::Input: break;
            case armnn::LayerType::Output: break;
            case armnn::LayerType::QuantizedLstm:
            {
                VerifyNameAndConnections(layer, name);
                armnn::QuantizedLstmInputParams params = ConstantsVector2QuantizedLstmInputParams(constants);
                VerifyInputParameters(params);
                break;
            }
            default:
            {
                throw armnn::Exception(fmt::format("Unexpected layer type in QuantizedLstm test model:",
                                                           layer->GetName()));
            }
        }
    }

protected:
    void VerifyInputParameters(const armnn::QuantizedLstmInputParams& params)
    {
        VerifyConstTensors("m_InputToInputWeights",
                           m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
        VerifyConstTensors("m_InputToForgetWeights",
                           m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
        VerifyConstTensors("m_InputToCellWeights",
                           m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
        VerifyConstTensors("m_InputToOutputWeights",
                           m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
        VerifyConstTensors("m_RecurrentToInputWeights",
                           m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
        VerifyConstTensors("m_RecurrentToForgetWeights",
                           m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
        VerifyConstTensors("m_RecurrentToCellWeights",
                           m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
        VerifyConstTensors("m_RecurrentToOutputWeights",
                           m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
        VerifyConstTensors("m_InputGateBias",
                           m_InputParams.m_InputGateBias, params.m_InputGateBias);
        VerifyConstTensors("m_ForgetGateBias",
                           m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
        VerifyConstTensors("m_CellBias",
                           m_InputParams.m_CellBias, params.m_CellBias);
        VerifyConstTensors("m_OutputGateBias",
                           m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
    }

private:
    armnn::QuantizedLstmInputParams m_InputParams;
};

TEST_CASE("SerializeDeserializeQuantizedLstm")
{
    const uint32_t batchSize = 1;
    const uint32_t inputSize = 2;
    const uint32_t numUnits = 4;
    const uint32_t outputSize = numUnits;

    // Scale/Offset for input/output, cellState In/Out, weights, bias
    float inputOutputScale = 0.0078125f;
    int32_t inputOutputOffset = 128;

    float cellStateScale = 0.00048828125f;
    int32_t cellStateOffset = 0;

    float weightsScale = 0.00408021f;
    int32_t weightsOffset = 100;

    float biasScale = 3.1876640625e-05f;
    int32_t biasOffset = 0;

    // The shape of weight data is {outputSize, inputSize} = {4, 2}
    armnn::TensorShape inputToInputWeightsShape = {4, 2};
    std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
    armnn::TensorInfo inputToInputWeightsInfo(inputToInputWeightsShape,
                                              armnn::DataType::QAsymmU8,
                                              weightsScale,
                                              weightsOffset,
                                              true);
    armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);

    armnn::TensorShape inputToForgetWeightsShape = {4, 2};
    std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
    armnn::TensorInfo inputToForgetWeightsInfo(inputToForgetWeightsShape,
                                               armnn::DataType::QAsymmU8,
                                               weightsScale,
                                               weightsOffset,
                                               true);
    armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);

    armnn::TensorShape inputToCellWeightsShape = {4, 2};
    std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
    armnn::TensorInfo inputToCellWeightsInfo(inputToCellWeightsShape,
                                             armnn::DataType::QAsymmU8,
                                             weightsScale,
                                             weightsOffset,
                                             true);
    armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);

    armnn::TensorShape inputToOutputWeightsShape = {4, 2};
    std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
    armnn::TensorInfo inputToOutputWeightsInfo(inputToOutputWeightsShape,
                                               armnn::DataType::QAsymmU8,
                                               weightsScale,
                                               weightsOffset,
                                               true);
    armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);

    // The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}
    armnn::TensorShape recurrentToInputWeightsShape = {4, 4};
    std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
    armnn::TensorInfo recurrentToInputWeightsInfo(recurrentToInputWeightsShape,
                                                  armnn::DataType::QAsymmU8,
                                                  weightsScale,
                                                  weightsOffset,
                                                  true);
    armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);

    armnn::TensorShape recurrentToForgetWeightsShape = {4, 4};
    std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
    armnn::TensorInfo recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,
                                                   armnn::DataType::QAsymmU8,
                                                   weightsScale,
                                                   weightsOffset,
                                                   true);
    armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);

    armnn::TensorShape recurrentToCellWeightsShape = {4, 4};
    std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
    armnn::TensorInfo recurrentToCellWeightsInfo(recurrentToCellWeightsShape,
                                                 armnn::DataType::QAsymmU8,
                                                 weightsScale,
                                                 weightsOffset,
                                                 true);
    armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);

    armnn::TensorShape recurrentToOutputWeightsShape = {4, 4};
    std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
    armnn::TensorInfo recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,
                                                   armnn::DataType::QAsymmU8,
                                                   weightsScale,
                                                   weightsOffset,
                                                   true);
    armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);

    // The shape of bias data is {outputSize} = {4}
    armnn::TensorShape inputGateBiasShape = {4};
    std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};
    armnn::TensorInfo inputGateBiasInfo(inputGateBiasShape,
                                        armnn::DataType::Signed32,
                                        biasScale,
                                        biasOffset,
                                        true);
    armnn::ConstTensor inputGateBias(inputGateBiasInfo, inputGateBiasData);

    armnn::TensorShape forgetGateBiasShape = {4};
    std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};
    armnn::TensorInfo forgetGateBiasInfo(forgetGateBiasShape,
                                         armnn::DataType::Signed32,
                                         biasScale,
                                         biasOffset,
                                         true);
    armnn::ConstTensor forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);

    armnn::TensorShape cellBiasShape = {4};
    std::vector<int32_t> cellBiasData = {1, 2, 3, 4};
    armnn::TensorInfo cellBiasInfo(cellBiasShape,
                                   armnn::DataType::Signed32,
                                   biasScale,
                                   biasOffset,
                                   true);
    armnn::ConstTensor cellBias(cellBiasInfo, cellBiasData);

    armnn::TensorShape outputGateBiasShape = {4};
    std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};
    armnn::TensorInfo outputGateBiasInfo(outputGateBiasShape,
                                         armnn::DataType::Signed32,
                                         biasScale,
                                         biasOffset,
                                         true);
    armnn::ConstTensor outputGateBias(outputGateBiasInfo, outputGateBiasData);

    armnn::QuantizedLstmInputParams params;
    params.m_InputToInputWeights = &inputToInputWeights;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_InputGateBias = &inputGateBias;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("QuantizedLstm");
    armnn::IConnectableLayer* const quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());
    armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(1);

    // Connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize },
                                      armnn::DataType::QAsymmU8,
                                      inputOutputScale,
                                      inputOutputOffset);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits },
                                          armnn::DataType::QSymmS16,
                                          cellStateScale,
                                          cellStateOffset);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize },
                                            armnn::DataType::QAsymmU8,
                                            inputOutputScale,
                                            inputOutputOffset);

    inputLayer->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(1));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(2));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    quantizedLstmLayer->GetOutputSlot(0).Connect(cellStateOut->GetInputSlot(0));
    quantizedLstmLayer->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    quantizedLstmLayer->GetOutputSlot(1).Connect(outputLayer->GetInputSlot(0));
    quantizedLstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyQuantizedLstmLayer checker(layerName,
                                     {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
                                     {cellStateTensorInfo, outputStateTensorInfo},
                                     params);

    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeQLstmBasic")
{
    armnn::QLstmDescriptor descriptor;

    descriptor.m_CifgEnabled       = true;
    descriptor.m_ProjectionEnabled = false;
    descriptor.m_PeepholeEnabled   = false;
    descriptor.m_LayerNormEnabled  = false;

    descriptor.m_CellClip       = 0.0f;
    descriptor.m_ProjectionClip = 0.0f;

    descriptor.m_InputIntermediateScale  = 0.00001f;
    descriptor.m_ForgetIntermediateScale = 0.00001f;
    descriptor.m_CellIntermediateScale   = 0.00001f;
    descriptor.m_OutputIntermediateScale = 0.00001f;

    descriptor.m_HiddenStateScale     = 0.07f;
    descriptor.m_HiddenStateZeroPoint = 0;

    const unsigned int numBatches = 2;
    const unsigned int inputSize  = 5;
    const unsigned int outputSize = 4;
    const unsigned int numUnits   = 4;

    // Scale/Offset quantization info
    float inputScale    = 0.0078f;
    int32_t inputOffset = 0;

    float outputScale    = 0.0078f;
    int32_t outputOffset = 0;

    float cellStateScale    = 3.5002e-05f;
    int32_t cellStateOffset = 0;

    float weightsScale    = 0.007f;
    int32_t weightsOffset = 0;

    float biasScale    = 3.5002e-05f / 1024;
    int32_t biasOffset = 0;

    // Weights and bias tensor and quantization info
    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
                                       armnn::DataType::QSymmS8,
                                       weightsScale,
                                       weightsOffset,
                                       true);

    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
                                           armnn::DataType::QSymmS8,
                                           weightsScale,
                                           weightsOffset,
                                           true);

    armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset, true);

    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());

    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);

    std::vector<int8_t> recurrentToForgetWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToCellWeightsData   =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToOutputWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());

    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);

    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
    std::vector<int32_t> cellBiasData(numUnits, 0);
    std::vector<int32_t> outputGateBiasData(numUnits, 0);

    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);

    // Set up params
    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights   = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;

    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;

    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias       = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // Create network
    armnn::INetworkPtr network = armnn::INetwork::Create();
    const std::string layerName("qLstm");

    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);

    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());

    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);

    // Input/Output tensor info
    armnn::TensorInfo inputInfo({numBatches , inputSize},
                                armnn::DataType::QAsymmS8,
                                inputScale,
                                inputOffset);

    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
                                    armnn::DataType::QSymmS16,
                                    cellStateScale,
                                    cellStateOffset);

    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
                                      armnn::DataType::QAsymmS8,
                                      outputScale,
                                      outputOffset);

    // Connect input/output slots
    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
    input->GetOutputSlot(0).SetTensorInfo(inputInfo);

    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);

    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);

    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::QLstmDescriptor> checker(
            layerName,
            {inputInfo, cellStateInfo, outputStateInfo},
            {outputStateInfo, cellStateInfo, outputStateInfo},
            descriptor,
            params);

    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeQLstmCifgLayerNorm")
{
    armnn::QLstmDescriptor descriptor;

    // CIFG params are used when CIFG is disabled
    descriptor.m_CifgEnabled       = true;
    descriptor.m_ProjectionEnabled = false;
    descriptor.m_PeepholeEnabled   = false;
    descriptor.m_LayerNormEnabled  = true;

    descriptor.m_CellClip       = 0.0f;
    descriptor.m_ProjectionClip = 0.0f;

    descriptor.m_InputIntermediateScale  = 0.00001f;
    descriptor.m_ForgetIntermediateScale = 0.00001f;
    descriptor.m_CellIntermediateScale   = 0.00001f;
    descriptor.m_OutputIntermediateScale = 0.00001f;

    descriptor.m_HiddenStateScale     = 0.07f;
    descriptor.m_HiddenStateZeroPoint = 0;

    const unsigned int numBatches = 2;
    const unsigned int inputSize  = 5;
    const unsigned int outputSize = 4;
    const unsigned int numUnits   = 4;

    // Scale/Offset quantization info
    float inputScale    = 0.0078f;
    int32_t inputOffset = 0;

    float outputScale    = 0.0078f;
    int32_t outputOffset = 0;

    float cellStateScale    = 3.5002e-05f;
    int32_t cellStateOffset = 0;

    float weightsScale    = 0.007f;
    int32_t weightsOffset = 0;

    float layerNormScale    = 3.5002e-05f;
    int32_t layerNormOffset = 0;

    float biasScale    = layerNormScale / 1024;
    int32_t biasOffset = 0;

    // Weights and bias tensor and quantization info
    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
                                       armnn::DataType::QSymmS8,
                                       weightsScale,
                                       weightsOffset,
                                       true);

    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
                                           armnn::DataType::QSymmS8,
                                           weightsScale,
                                           weightsOffset,
                                           true);

    armnn::TensorInfo biasInfo({numUnits},
                               armnn::DataType::Signed32,
                               biasScale,
                               biasOffset,
                               true);

    armnn::TensorInfo layerNormWeightsInfo({numUnits},
                                           armnn::DataType::QSymmS16,
                                           layerNormScale,
                                           layerNormOffset,
                                           true);

    // Mandatory params
    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());

    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);

    std::vector<int8_t> recurrentToForgetWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToCellWeightsData   =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToOutputWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());

    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);

    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
    std::vector<int32_t> cellBiasData(numUnits, 0);
    std::vector<int32_t> outputGateBiasData(numUnits, 0);

    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);

    // Layer Norm
    std::vector<int16_t> forgetLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
    std::vector<int16_t> cellLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
    std::vector<int16_t> outputLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());

    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);

    // Set up params
    armnn::LstmInputParams params;

    // Mandatory params
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights   = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;

    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;

    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias       = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // Layer Norm
    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
    params.m_OutputLayerNormWeights = &outputLayerNormWeights;

    // Create network
    armnn::INetworkPtr network = armnn::INetwork::Create();
    const std::string layerName("qLstm");

    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);

    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());

    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(2);

    // Input/Output tensor info
    armnn::TensorInfo inputInfo({numBatches , inputSize},
                                armnn::DataType::QAsymmS8,
                                inputScale,
                                inputOffset);

    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
                                    armnn::DataType::QSymmS16,
                                    cellStateScale,
                                    cellStateOffset);

    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
                                      armnn::DataType::QAsymmS8,
                                      outputScale,
                                      outputOffset);

    // Connect input/output slots
    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
    input->GetOutputSlot(0).SetTensorInfo(inputInfo);

    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);

    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);

    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,
                                                    {inputInfo, cellStateInfo, outputStateInfo},
                                                    {outputStateInfo, cellStateInfo, outputStateInfo},
                                                    descriptor,
                                                    params);

    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeQLstmAdvanced")
{
    armnn::QLstmDescriptor descriptor;

    descriptor.m_CifgEnabled       = false;
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled   = true;
    descriptor.m_LayerNormEnabled  = true;

    descriptor.m_CellClip       = 0.1f;
    descriptor.m_ProjectionClip = 0.1f;

    descriptor.m_InputIntermediateScale  = 0.00001f;
    descriptor.m_ForgetIntermediateScale = 0.00001f;
    descriptor.m_CellIntermediateScale   = 0.00001f;
    descriptor.m_OutputIntermediateScale = 0.00001f;

    descriptor.m_HiddenStateScale     = 0.07f;
    descriptor.m_HiddenStateZeroPoint = 0;

    const unsigned int numBatches = 2;
    const unsigned int inputSize  = 5;
    const unsigned int outputSize = 4;
    const unsigned int numUnits   = 4;

    // Scale/Offset quantization info
    float inputScale    = 0.0078f;
    int32_t inputOffset = 0;

    float outputScale    = 0.0078f;
    int32_t outputOffset = 0;

    float cellStateScale    = 3.5002e-05f;
    int32_t cellStateOffset = 0;

    float weightsScale    = 0.007f;
    int32_t weightsOffset = 0;

    float layerNormScale    = 3.5002e-05f;
    int32_t layerNormOffset = 0;

    float biasScale    = layerNormScale / 1024;
    int32_t biasOffset = 0;

    // Weights and bias tensor and quantization info
    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
                                       armnn::DataType::QSymmS8,
                                       weightsScale,
                                       weightsOffset,
                                       true);

    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
                                           armnn::DataType::QSymmS8,
                                           weightsScale,
                                           weightsOffset,
                                           true);

    armnn::TensorInfo biasInfo({numUnits},
                               armnn::DataType::Signed32,
                               biasScale,
                               biasOffset,
                               true);

    armnn::TensorInfo peepholeWeightsInfo({numUnits},
                                          armnn::DataType::QSymmS16,
                                          weightsScale,
                                          weightsOffset,
                                          true);

    armnn::TensorInfo layerNormWeightsInfo({numUnits},
                                           armnn::DataType::QSymmS16,
                                           layerNormScale,
                                           layerNormOffset,
                                           true);

    armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits},
                                             armnn::DataType::QSymmS8,
                                             weightsScale,
                                             weightsOffset,
                                             true);

    // Mandatory params
    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());

    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);

    std::vector<int8_t> recurrentToForgetWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToCellWeightsData   =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToOutputWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());

    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);

    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
    std::vector<int32_t> cellBiasData(numUnits, 0);
    std::vector<int32_t> outputGateBiasData(numUnits, 0);

    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);

    // CIFG
    std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
    std::vector<int8_t> recurrentToInputWeightsData =
            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
    std::vector<int32_t> inputGateBiasData(numUnits, 1);

    armnn::ConstTensor inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);
    armnn::ConstTensor recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);
    armnn::ConstTensor inputGateBias(biasInfo, inputGateBiasData);

    // Peephole
    std::vector<int16_t> cellToInputWeightsData  = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
    std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
    std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());

    armnn::ConstTensor cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);
    armnn::ConstTensor cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);
    armnn::ConstTensor cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);

    // Projection
    std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());
    std::vector<int32_t> projectionBiasData(outputSize, 1);

    armnn::ConstTensor projectionWeights(projectionWeightsInfo, projectionWeightsData);
    armnn::ConstTensor projectionBias(biasInfo, projectionBiasData);

    // Layer Norm
    std::vector<int16_t> inputLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
    std::vector<int16_t> forgetLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
    std::vector<int16_t> cellLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
    std::vector<int16_t> outputLayerNormWeightsData =
            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());

    armnn::ConstTensor inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);
    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);

    // Set up params
    armnn::LstmInputParams params;

    // Mandatory params
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights   = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;

    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;

    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias       = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // CIFG
    params.m_InputToInputWeights     = &inputToInputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_InputGateBias           = &inputGateBias;

    // Peephole
    params.m_CellToInputWeights  = &cellToInputWeights;
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    // Projection
    params.m_ProjectionWeights = &projectionWeights;
    params.m_ProjectionBias    = &projectionBias;

    // Layer Norm
    params.m_InputLayerNormWeights  = &inputLayerNormWeights;
    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
    params.m_OutputLayerNormWeights = &outputLayerNormWeights;

    // Create network
    armnn::INetworkPtr network = armnn::INetwork::Create();
    const std::string layerName("qLstm");

    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);

    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());

    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);

    // Input/Output tensor info
    armnn::TensorInfo inputInfo({numBatches , inputSize},
                                armnn::DataType::QAsymmS8,
                                inputScale,
                                inputOffset);

    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
                                    armnn::DataType::QSymmS16,
                                    cellStateScale,
                                    cellStateOffset);

    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
                                      armnn::DataType::QAsymmS8,
                                      outputScale,
                                      outputOffset);

    // Connect input/output slots
    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
    input->GetOutputSlot(0).SetTensorInfo(inputInfo);

    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);

    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);

    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);

    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,
                                                    {inputInfo, cellStateInfo, outputStateInfo},
                                                    {outputStateInfo, cellStateInfo, outputStateInfo},
                                                    descriptor,
                                                    params);

    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjection")
{
    armnn::UnidirectionalSequenceLstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = false;
    descriptor.m_PeepholeEnabled = true;
    descriptor.m_TimeMajor = false;

    const uint32_t batchSize = 1;
    const uint32_t timeSize = 2;
    const uint32_t inputSize = 2;
    const uint32_t numUnits = 4;
    const uint32_t outputSize = numUnits;

    armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);

    std::vector<float> forgetGateBiasData(numUnits, 1.0f);
    armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);

    std::vector<float> cellBiasData(numUnits, 0.0f);
    armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);

    std::vector<float> outputGateBiasData(numUnits, 0.0f);
    armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("UnidirectionalSequenceLstm");
    armnn::IConnectableLayer* const unidirectionalSequenceLstmLayer =
        network->AddUnidirectionalSequenceLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(0);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputStateOutTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateOutTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
        layerName,
        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
        {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
        descriptor,
        params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeAndProjection")
{
    armnn::UnidirectionalSequenceLstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled = true;
    descriptor.m_TimeMajor = false;

    const uint32_t batchSize = 2;
    const uint32_t timeSize = 2;
    const uint32_t inputSize = 5;
    const uint32_t numUnits = 20;
    const uint32_t outputSize = 16;

    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);

    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);

    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);

    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);

    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);

    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);

    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);

    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);

    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);

    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);

    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionBiasData(outputSize, 0.f);
    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // additional params because: descriptor.m_CifgEnabled = false
    params.m_InputToInputWeights = &inputToInputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_CellToInputWeights = &cellToInputWeights;
    params.m_InputGateBias = &inputGateBias;

    // additional params because: descriptor.m_ProjectionEnabled = true
    params.m_ProjectionWeights = &projectionWeights;
    params.m_ProjectionBias = &projectionBias;

    // additional params because: descriptor.m_PeepholeEnabled = true
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("unidirectionalSequenceLstm");
    armnn::IConnectableLayer* const unidirectionalSequenceLstmLayer =
        network->AddUnidirectionalSequenceLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(0);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputStateOutTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateOutTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
        layerName,
        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
        {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
        descriptor,
        params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNorm")
{
    armnn::UnidirectionalSequenceLstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = true;
    descriptor.m_PeepholeEnabled = true;
    descriptor.m_LayerNormEnabled = true;
    descriptor.m_TimeMajor = false;

    const uint32_t batchSize = 2;
    const uint32_t timeSize = 2;
    const uint32_t inputSize = 5;
    const uint32_t numUnits = 20;
    const uint32_t outputSize = 16;

    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);

    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);

    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);

    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);

    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);

    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);

    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);

    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);

    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);

    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);

    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> projectionBiasData(outputSize, 0.f);
    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);

    std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor inputLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor cellLayerNormWeights(tensorInfo20, forgetGateBiasData);

    std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
    armnn::ConstTensor outLayerNormWeights(tensorInfo20, forgetGateBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;

    // additional params because: descriptor.m_CifgEnabled = false
    params.m_InputToInputWeights = &inputToInputWeights;
    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
    params.m_CellToInputWeights = &cellToInputWeights;
    params.m_InputGateBias = &inputGateBias;

    // additional params because: descriptor.m_ProjectionEnabled = true
    params.m_ProjectionWeights = &projectionWeights;
    params.m_ProjectionBias = &projectionBias;

    // additional params because: descriptor.m_PeepholeEnabled = true
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    // additional params because: despriptor.m_LayerNormEnabled = true
    params.m_InputLayerNormWeights = &inputLayerNormWeights;
    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
    params.m_CellLayerNormWeights = &cellLayerNormWeights;
    params.m_OutputLayerNormWeights = &outLayerNormWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("unidirectionalSequenceLstm");
    armnn::IConnectableLayer* const unidirectionalSequenceLstmLayer =
        network->AddUnidirectionalSequenceLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(0);

    // connect up
    armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
    armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
    armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
            layerName,
            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
            {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
            descriptor,
            params);
    deserializedNetwork->ExecuteStrategy(checker);
}

TEST_CASE("SerializeDeserializeUnidirectionalSequenceLstmCifgPeepholeNoProjectionTimeMajor")
{
    armnn::UnidirectionalSequenceLstmDescriptor descriptor;
    descriptor.m_ActivationFunc = 4;
    descriptor.m_ClippingThresProj = 0.0f;
    descriptor.m_ClippingThresCell = 0.0f;
    descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
    descriptor.m_ProjectionEnabled = false;
    descriptor.m_PeepholeEnabled = true;
    descriptor.m_TimeMajor = true;

    const uint32_t batchSize = 1;
    const uint32_t timeSize = 2;
    const uint32_t inputSize = 2;
    const uint32_t numUnits = 4;
    const uint32_t outputSize = numUnits;

    armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);

    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);

    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);

    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);

    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
    armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);

    armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32, 0.0f, 0, true);
    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);

    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
    armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);

    std::vector<float> forgetGateBiasData(numUnits, 1.0f);
    armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);

    std::vector<float> cellBiasData(numUnits, 0.0f);
    armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);

    std::vector<float> outputGateBiasData(numUnits, 0.0f);
    armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);

    armnn::LstmInputParams params;
    params.m_InputToForgetWeights = &inputToForgetWeights;
    params.m_InputToCellWeights = &inputToCellWeights;
    params.m_InputToOutputWeights = &inputToOutputWeights;
    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    params.m_ForgetGateBias = &forgetGateBias;
    params.m_CellBias = &cellBias;
    params.m_OutputGateBias = &outputGateBias;
    params.m_CellToForgetWeights = &cellToForgetWeights;
    params.m_CellToOutputWeights = &cellToOutputWeights;

    armnn::INetworkPtr network = armnn::INetwork::Create();
    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
    const std::string layerName("UnidirectionalSequenceLstm");
    armnn::IConnectableLayer* const unidirectionalSequenceLstmLayer =
        network->AddUnidirectionalSequenceLstmLayer(descriptor, params, layerName.c_str());
    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(0);

    // connect up
    armnn::TensorInfo inputTensorInfo({ timeSize, batchSize, inputSize }, armnn::DataType::Float32);
    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
    armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
    armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
    armnn::TensorInfo outputTensorInfo({ timeSize, batchSize, outputSize }, armnn::DataType::Float32);

    inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);

    outputStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(1));
    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);

    cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);

    unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
    unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);

    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
    CHECK(deserializedNetwork);

    VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
        layerName,
        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
        {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
        descriptor,
        params);
    deserializedNetwork->ExecuteStrategy(checker);
}

}