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>
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index 2949381..6707cc7 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -1326,6 +1326,39 @@
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 7f1831c..47ed3a6 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -2089,6 +2089,7 @@
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 @@
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 e3953df..0fd8da7 100644
--- a/src/armnnSerializer/ArmnnSchema.fbs
+++ b/src/armnnSerializer/ArmnnSchema.fbs
@@ -537,6 +537,11 @@
cellToForgetWeights:ConstTensor;
cellToOutputWeights:ConstTensor;
+
+ inputLayerNormWeights:ConstTensor;
+ forgetLayerNormWeights:ConstTensor;
+ cellLayerNormWeights:ConstTensor;
+ outputLayerNormWeights:ConstTensor;
}
table LstmDescriptor {
@@ -546,6 +551,7 @@
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 b59bac60..05df2c9 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -402,7 +402,8 @@
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 @@
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 @@
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 @@
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 3d74d88..74ad2db 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -2778,6 +2778,7 @@
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 @@
"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 @@
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,
+ 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,
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+ 0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+ 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()