IVGCVSW-3526 Add layer norm support for lstm serialization

* Adds layer norm support for serialization/deserialization
* Adds related unit tests

Change-Id: If80b668accc8b0754a93d18ab3a243284cb383d1
Signed-off-by: Jan Eilers <jan.eilers@arm.com>

5 files changed, 930 insertions, 2 deletions

diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index 29493816a8..6707cc7a26 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -1326,6 +1326,39 @@ IConnectableLayer* Network::AddLstmLayer(const LstmDescriptor&  descriptor,
         layer->m_PeepholeParameters.m_CellToOutputWeights =
             std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToOutputWeights));
     }
+
+    //Lstm Layer Normalization params
+    if(descriptor.m_LayerNormEnabled)
+    {
+        if(!descriptor.m_CifgEnabled)
+        {
+            if(params.m_InputLayerNormWeights == nullptr)
+            {
+                throw InvalidArgumentException("AddLstmLayer: Input layer normalization weights cannot be NULL");
+            }
+            layer->m_LayerNormParameters.m_InputLayerNormWeights =
+                    std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputLayerNormWeights));
+        }
+
+        if(params.m_ForgetLayerNormWeights == nullptr)
+        {
+            throw InvalidArgumentException("AddLstmLayer: Forget layer normalization weights cannot be NULL");
+        }
+        if(params.m_CellLayerNormWeights == nullptr)
+        {
+            throw InvalidArgumentException("AddLstmLayer: Cell layer normalization weights cannot be NULL");
+        }
+        if(params.m_OutputLayerNormWeights == nullptr)
+        {
+            throw InvalidArgumentException("AddLstmLayer: Output layer normalization weights cannot be NULL");
+        }
+        layer->m_LayerNormParameters.m_ForgetLayerNormWeights =
+                std::make_unique<ScopedCpuTensorHandle>(*(params.m_ForgetLayerNormWeights));
+        layer->m_LayerNormParameters.m_CellLayerNormWeights =
+                std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellLayerNormWeights));
+        layer->m_LayerNormParameters.m_OutputLayerNormWeights =
+                std::make_unique<ScopedCpuTensorHandle>(*(params.m_OutputLayerNormWeights));
+    }
     return layer;
 }
 
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 7f1831c989..47ed3a65ed 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -2089,6 +2089,7 @@ armnn::LstmDescriptor Deserializer::GetLstmDescriptor(Deserializer::LstmDescript
     desc.m_CifgEnabled = lstmDescriptor->cifgEnabled();
     desc.m_PeepholeEnabled = lstmDescriptor->peepholeEnabled();
     desc.m_ProjectionEnabled = lstmDescriptor->projectionEnabled();
+    desc.m_LayerNormEnabled = lstmDescriptor->layerNormEnabled();
 
     return desc;
 }
@@ -2171,6 +2172,26 @@ void Deserializer::ParseLstm(GraphPtr graph, unsigned int layerIndex)
         lstmInputParams.m_CellToOutputWeights = &cellToOutputWeights;
     }
 
+    armnn::ConstTensor inputLayerNormWeights;
+    armnn::ConstTensor forgetLayerNormWeights;
+    armnn::ConstTensor cellLayerNormWeights;
+    armnn::ConstTensor outputLayerNormWeights;
+    if (lstmDescriptor.m_LayerNormEnabled)
+    {
+        if (!lstmDescriptor.m_CifgEnabled)
+        {
+            inputLayerNormWeights = ToConstTensor(flatBufferInputParams->inputLayerNormWeights());
+            lstmInputParams.m_InputLayerNormWeights = &inputLayerNormWeights;
+        }
+        forgetLayerNormWeights = ToConstTensor(flatBufferInputParams->forgetLayerNormWeights());
+        cellLayerNormWeights = ToConstTensor(flatBufferInputParams->cellLayerNormWeights());
+        outputLayerNormWeights = ToConstTensor(flatBufferInputParams->outputLayerNormWeights());
+
+        lstmInputParams.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
+        lstmInputParams.m_CellLayerNormWeights = &cellLayerNormWeights;
+        lstmInputParams.m_OutputLayerNormWeights = &outputLayerNormWeights;
+    }
+
     IConnectableLayer* layer = m_Network->AddLstmLayer(lstmDescriptor, lstmInputParams, layerName.c_str());
 
     armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[0]);
diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs
index e3953df83e..0fd8da7e8f 100644
--- a/src/armnnSerializer/ArmnnSchema.fbs
+++ b/src/armnnSerializer/ArmnnSchema.fbs
@@ -537,6 +537,11 @@ table LstmInputParams {
 
     cellToForgetWeights:ConstTensor;
     cellToOutputWeights:ConstTensor;
+
+    inputLayerNormWeights:ConstTensor;
+    forgetLayerNormWeights:ConstTensor;
+    cellLayerNormWeights:ConstTensor;
+    outputLayerNormWeights:ConstTensor;
 }
 
 table LstmDescriptor {
@@ -546,6 +551,7 @@ table LstmDescriptor {
     cifgEnabled:bool = true;
     peepholeEnabled:bool = false;
     projectionEnabled:bool = false;
+    layerNormEnabled:bool = false;
 }
 
 table LstmLayer {
diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp
index b59bac6041..05df2c942a 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -402,7 +402,8 @@ void SerializerVisitor::VisitLstmLayer(const armnn::IConnectableLayer* layer, co
         descriptor.m_ClippingThresProj,
         descriptor.m_CifgEnabled,
         descriptor.m_PeepholeEnabled,
-        descriptor.m_ProjectionEnabled);
+        descriptor.m_ProjectionEnabled,
+        descriptor.m_LayerNormEnabled);
 
     // Get mandatory input parameters
     auto inputToForgetWeights = CreateConstTensorInfo(*params.m_InputToForgetWeights);
@@ -424,6 +425,10 @@ void SerializerVisitor::VisitLstmLayer(const armnn::IConnectableLayer* layer, co
     flatbuffers::Offset<serializer::ConstTensor> projectionBias;
     flatbuffers::Offset<serializer::ConstTensor> cellToForgetWeights;
     flatbuffers::Offset<serializer::ConstTensor> cellToOutputWeights;
+    flatbuffers::Offset<serializer::ConstTensor> inputLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> forgetLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> cellLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> outputLayerNormWeights;
 
     if (!descriptor.m_CifgEnabled)
     {
@@ -445,6 +450,17 @@ void SerializerVisitor::VisitLstmLayer(const armnn::IConnectableLayer* layer, co
         cellToOutputWeights = CreateConstTensorInfo(*params.m_CellToOutputWeights);
     }
 
+    if (descriptor.m_LayerNormEnabled)
+    {
+        if (!descriptor.m_CifgEnabled)
+        {
+            inputLayerNormWeights = CreateConstTensorInfo((*params.m_InputLayerNormWeights));
+        }
+        forgetLayerNormWeights = CreateConstTensorInfo(*params.m_ForgetLayerNormWeights);
+        cellLayerNormWeights   = CreateConstTensorInfo(*params.m_CellLayerNormWeights);
+        outputLayerNormWeights = CreateConstTensorInfo(*params.m_OutputLayerNormWeights);
+    }
+
     auto fbLstmParams = serializer::CreateLstmInputParams(
         m_flatBufferBuilder,
         inputToForgetWeights,
@@ -463,7 +479,11 @@ void SerializerVisitor::VisitLstmLayer(const armnn::IConnectableLayer* layer, co
         projectionWeights,
         projectionBias,
         cellToForgetWeights,
-        cellToOutputWeights);
+        cellToOutputWeights,
+        inputLayerNormWeights,
+        forgetLayerNormWeights,
+        cellLayerNormWeights,
+        outputLayerNormWeights);
 
     auto fbLstmLayer = serializer::CreateLstmLayer(
         m_flatBufferBuilder,
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 3d74d88e30..74ad2db4e5 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -2778,6 +2778,7 @@ protected:
         BOOST_TEST(m_Descriptor.m_CifgEnabled == descriptor.m_CifgEnabled);
         BOOST_TEST(m_Descriptor.m_PeepholeEnabled = descriptor.m_PeepholeEnabled);
         BOOST_TEST(m_Descriptor.m_ProjectionEnabled == descriptor.m_ProjectionEnabled);
+        BOOST_TEST(m_Descriptor.m_LayerNormEnabled == descriptor.m_LayerNormEnabled);
     }
     void VerifyInputParameters(const armnn::LstmInputParams& params)
     {
@@ -2815,6 +2816,14 @@ protected:
             "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
         VerifyConstTensors(
             "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
+        VerifyConstTensors(
+            "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
+        VerifyConstTensors(
+            "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
+        VerifyConstTensors(
+            "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
+        VerifyConstTensors(
+            "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
     }
     void VerifyConstTensors(const std::string& tensorName,
                             const armnn::ConstTensor* expectedPtr,
@@ -3124,4 +3133,843 @@ BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)
     deserializedNetwork->Accept(checker);
 }
 
+BOOST_AUTO_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);
+    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);
+    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);
+    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);
+    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
+    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
+
+    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
+    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));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer checker(
+            layerName,
+            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+            descriptor,
+            params);
+    deserializedNetwork->Accept(checker);
+}
+
+BOOST_AUTO_TEST_CASE(EnsureLstmLayersBackwardCompatibility)
+{
+    // The hex array below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection
+    // enabled. That array is created 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
+    unsigned int size = 10900;
+    const unsigned char LstmNoCifgWithPeepholeAndProjection_Model[] = {
+            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,
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+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x1A,0xFD,0xFF,0xFF,0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x14,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x10,0x00,0x0C,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x05,0x00,
+            0x06,0x00,0x07,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x01,0x01,0x04,0x00,0x00,0x00,0x2E,0xFE,0xFF,0xFF,
+            0x03,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+            0x04,0x00,0x00,0x00,0x6C,0x73,0x74,0x6D,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xEC,0x00,0x00,0x00,
+            0xD0,0x00,0x00,0x00,0xB4,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x5C,0x00,0x00,0x00,
+            0x30,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x14,0xFF,0xFF,0xFF,0x03,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+            0xA6,0xFD,0xFF,0xFF,0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x02,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3C,0xFF,0xFF,0xFF,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+            0xCE,0xFD,0xFF,0xFF,0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x02,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x64,0xFF,0xFF,0xFF,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+            0xF6,0xFD,0xFF,0xFF,0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x02,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0xB4,0xFE,0xFF,0xFF,0x04,0x00,0x00,0x00,0x1A,0xFE,0xFF,0xFF,
+            0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x50,0x00,0x00,0x00,0xF0,0xFF,0xFF,0xFF,0x02,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x08,0x00,0x10,0x00,0x04,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x00,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0xE8,0xFE,0xFF,0xFF,0x00,0x00,0x00,0x09,0x04,0x00,0x00,0x00,0x7E,0xFF,0xFF,0xFF,
+            0x0C,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x04,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+            0x02,0x00,0x00,0x00,0x76,0xFF,0xFF,0xFF,0x02,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+            0x10,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+            0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x68,0xFF,0xFF,0xFF,0x04,0x00,0x00,0x00,0xCE,0xFE,0xFF,0xFF,
+            0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x10,0x00,0x00,0x00,0x08,0x00,0x0E,0x00,0x07,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x09,
+            0x0C,0x00,0x00,0x00,0x00,0x00,0x06,0x00,0x08,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0C,0x00,0x00,0x00,
+            0x08,0x00,0x0E,0x00,0x04,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+            0x00,0x00,0x0E,0x00,0x18,0x00,0x04,0x00,0x08,0x00,0x0C,0x00,0x10,0x00,0x14,0x00,0x0E,0x00,0x00,0x00,
+            0x01,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+            0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0C,0x00,0x00,0x00,
+            0x08,0x00,0x08,0x00,0x00,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6E,0xFF,0xFF,0xFF,
+            0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+            0x14,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x07,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x09,
+            0x04,0x00,0x00,0x00,0xF6,0xFF,0xFF,0xFF,0x0C,0x00,0x00,0x00,0x00,0x00,0x06,0x00,0x0A,0x00,0x04,0x00,
+            0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x0E,0x00,0x14,0x00,0x00,0x00,0x04,0x00,0x08,0x00,
+            0x0C,0x00,0x10,0x00,0x0E,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+            0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+            0x0C,0x00,0x00,0x00,0x08,0x00,0x0A,0x00,0x00,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+            0x00,0x00,0x0A,0x00,0x10,0x00,0x08,0x00,0x07,0x00,0x0C,0x00,0x0A,0x00,0x00,0x00,0x00,0x00,0x00,0x01,
+            0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0 };
+
+    std::stringstream ss;
+    for (unsigned int i = 0; i < size; ++i)
+    {
+        ss << LstmNoCifgWithPeepholeAndProjection_Model[i];
+    }
+    std::string lstmLayerNetwork = ss.str();
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(lstmLayerNetwork);
+    BOOST_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 = 2;
+    const uint32_t inputSize = 5;
+    const uint32_t numUnits = 20;
+    const uint32_t outputSize = 16;
+
+    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
+    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);
+    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);
+    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);
+    std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
+    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
+
+    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
+    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);
+    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);
+
+    // lets verify that the deserialized model without the new layer normalization parameters still works
+    VerifyLstmLayer checker(
+            layerName,
+            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+            descriptor,
+            params);
+    deserializedNetwork->Accept(checker);
+}
+
 BOOST_AUTO_TEST_SUITE_END()