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Diffstat (limited to 'src/armnnSerializer/test/LstmSerializationTests.cpp')
| -rw-r--r-- | src/armnnSerializer/test/LstmSerializationTests.cpp | 2199 |
1 files changed, 2199 insertions, 0 deletions
diff --git a/src/armnnSerializer/test/LstmSerializationTests.cpp b/src/armnnSerializer/test/LstmSerializationTests.cpp new file mode 100644 index 0000000000..4705c0bd28 --- /dev/null +++ b/src/armnnSerializer/test/LstmSerializationTests.cpp @@ -0,0 +1,2199 @@ +// +// Copyright © 2021 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 <boost/test/unit_test.hpp> + +#include <fmt/format.h> + + +BOOST_AUTO_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 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: + { + 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; +}; + +BOOST_AUTO_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); + 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); + 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); + 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyLstmLayer<armnn::LstmDescriptor> checker( + layerName, + {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo}, + {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo}, + descriptor, + params); + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_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); + 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); + + 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyLstmLayer<armnn::LstmDescriptor> checker( + layerName, + {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo}, + {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo}, + descriptor, + params); + deserializedNetwork->ExecuteStrategy(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<armnn::LstmDescriptor> checker( + layerName, + {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo}, + {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo}, + descriptor, + params); + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_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, 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0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00 + }; + + armnn::INetworkPtr deserializedNetwork = + DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(), + lstmNoCifgWithPeepholeAndProjectionModel.end())); + + 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 = 2u; + const uint32_t inputSize = 5u; + const uint32_t numUnits = 20u; + const uint32_t outputSize = 16u; + + 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); + + 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; +}; + +BOOST_AUTO_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); + 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); + 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); + 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); + 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); + 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); + 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); + 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); + 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); + 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); + 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); + 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); + 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyQuantizedLstmLayer checker(layerName, + {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo}, + {cellStateTensorInfo, outputStateTensorInfo}, + params); + + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_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); + + armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset); + + 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyLstmLayer<armnn::QLstmDescriptor> checker( + layerName, + {inputInfo, cellStateInfo, outputStateInfo}, + {outputStateInfo, cellStateInfo, outputStateInfo}, + descriptor, + params); + + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_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); + + armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({numUnits}, + armnn::DataType::Signed32, + biasScale, + biasOffset); + + armnn::TensorInfo layerNormWeightsInfo({numUnits}, + armnn::DataType::QSymmS16, + layerNormScale, + layerNormOffset); + + // 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName, + {inputInfo, cellStateInfo, outputStateInfo}, + {outputStateInfo, cellStateInfo, outputStateInfo}, + descriptor, + params); + + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_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); + + armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({numUnits}, + armnn::DataType::Signed32, + biasScale, + biasOffset); + + armnn::TensorInfo peepholeWeightsInfo({numUnits}, + armnn::DataType::QSymmS16, + weightsScale, + weightsOffset); + + armnn::TensorInfo layerNormWeightsInfo({numUnits}, + armnn::DataType::QSymmS16, + layerNormScale, + layerNormOffset); + + armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + // 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)); + BOOST_CHECK(deserializedNetwork); + + VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName, + {inputInfo, cellStateInfo, outputStateInfo}, + {outputStateInfo, cellStateInfo, outputStateInfo}, + descriptor, + params); + + deserializedNetwork->ExecuteStrategy(checker); +} + +BOOST_AUTO_TEST_SUITE_END() |