diff options
Diffstat (limited to 'src/armnn')
-rw-r--r-- | src/armnn/InternalTypes.cpp | 1 | ||||
-rw-r--r-- | src/armnn/InternalTypes.hpp | 1 | ||||
-rw-r--r-- | src/armnn/LayerSupport.cpp | 17 | ||||
-rw-r--r-- | src/armnn/LayersFwd.hpp | 2 | ||||
-rw-r--r-- | src/armnn/Network.cpp | 141 | ||||
-rw-r--r-- | src/armnn/Network.hpp | 4 | ||||
-rw-r--r-- | src/armnn/layers/QLstmLayer.cpp | 512 | ||||
-rw-r--r-- | src/armnn/layers/QLstmLayer.hpp | 124 | ||||
-rw-r--r-- | src/armnn/test/ConstTensorLayerVisitor.cpp | 785 | ||||
-rw-r--r-- | src/armnn/test/ConstTensorLayerVisitor.hpp | 32 | ||||
-rw-r--r-- | src/armnn/test/InferOutputTests.cpp | 3 | ||||
-rw-r--r-- | src/armnn/test/InferOutputTests.hpp | 58 |
12 files changed, 1666 insertions, 14 deletions
diff --git a/src/armnn/InternalTypes.cpp b/src/armnn/InternalTypes.cpp index d688257142..2fe38fc963 100644 --- a/src/armnn/InternalTypes.cpp +++ b/src/armnn/InternalTypes.cpp @@ -58,6 +58,7 @@ char const* GetLayerTypeAsCString(LayerType type) case LayerType::PreCompiled: return "PreCompiled"; case LayerType::Prelu: return "Prelu"; case LayerType::Quantize: return "Quantize"; + case LayerType::QLstm: return "QLstm"; case LayerType::QuantizedLstm: return "QuantizedLstm"; case LayerType::Reshape: return "Reshape"; case LayerType::Resize: return "Resize"; diff --git a/src/armnn/InternalTypes.hpp b/src/armnn/InternalTypes.hpp index 8dd9a9eb1c..ee4a710d14 100644 --- a/src/armnn/InternalTypes.hpp +++ b/src/armnn/InternalTypes.hpp @@ -58,6 +58,7 @@ enum class LayerType PreCompiled, Prelu, Quantize, + QLstm, QuantizedLstm, Reshape, Resize, diff --git a/src/armnn/LayerSupport.cpp b/src/armnn/LayerSupport.cpp index 3c244b0454..73e54b3006 100644 --- a/src/armnn/LayerSupport.cpp +++ b/src/armnn/LayerSupport.cpp @@ -483,6 +483,23 @@ bool IsQuantizeSupported(const BackendId& backend, FORWARD_LAYER_SUPPORT_FUNC(backend, IsQuantizeSupported, input, output); } +bool IsQLstmSupported(const BackendId& backend, + const TensorInfo& input, + const TensorInfo& previousOutputIn, + const TensorInfo& previousCellStateIn, + const TensorInfo& outputStateOut, + const TensorInfo& cellStateOut, + const TensorInfo& output, + const QLstmDescriptor& descriptor, + const LstmInputParamsInfo& paramsInfo, + char* reasonIfUnsupported, + size_t reasonIfUnsupportedMaxLength) + +{ + FORWARD_LAYER_SUPPORT_FUNC(backend, IsQLstmSupported, input, previousOutputIn, previousCellStateIn, + outputStateOut, cellStateOut, output, descriptor, paramsInfo); +} + bool IsQuantizedLstmSupported(const BackendId& backend, const TensorInfo& input, const TensorInfo& previousCellStateIn, diff --git a/src/armnn/LayersFwd.hpp b/src/armnn/LayersFwd.hpp index 4159f488c1..20544137b9 100644 --- a/src/armnn/LayersFwd.hpp +++ b/src/armnn/LayersFwd.hpp @@ -50,6 +50,7 @@ #include "layers/PreCompiledLayer.hpp" #include "layers/PreluLayer.hpp" #include "layers/QuantizeLayer.hpp" +#include "layers/QLstmLayer.hpp" #include "layers/QuantizedLstmLayer.hpp" #include "layers/ReshapeLayer.hpp" #include "layers/ResizeLayer.hpp" @@ -137,6 +138,7 @@ DECLARE_LAYER(Pooling2d) DECLARE_LAYER(PreCompiled) DECLARE_LAYER(Prelu) DECLARE_LAYER(Quantize) +DECLARE_LAYER(QLstm) DECLARE_LAYER(QuantizedLstm) DECLARE_LAYER(Reshape) DECLARE_LAYER(Resize) diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 9eef7b2fb6..7a6fa8f78c 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -1670,6 +1670,147 @@ IConnectableLayer* Network::AddQuantizedLstmLayer(const QuantizedLstmInputParams return layer; } +IConnectableLayer* Network::AddQLstmLayer(const QLstmDescriptor& descriptor, + const LstmInputParams& params, + const char* name) +{ + const auto layer = m_Graph->AddLayer<QLstmLayer>(descriptor, name); + + // QLstm Basic Parameters + layer->m_BasicParameters.m_InputToForgetWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToForgetWeights)); + layer->m_BasicParameters.m_InputToCellWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToCellWeights)); + layer->m_BasicParameters.m_InputToOutputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToOutputWeights)); + layer->m_BasicParameters.m_RecurrentToForgetWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToForgetWeights)); + layer->m_BasicParameters.m_RecurrentToCellWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToCellWeights)); + layer->m_BasicParameters.m_RecurrentToOutputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToOutputWeights)); + layer->m_BasicParameters.m_ForgetGateBias = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_ForgetGateBias)); + layer->m_BasicParameters.m_CellBias = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellBias)); + layer->m_BasicParameters.m_OutputGateBias = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_OutputGateBias)); + + // QLstm Cifg parameters + if(!descriptor.m_CifgEnabled) + { + if(params.m_InputToInputWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Input To Input Weights cannot be NULL"); + } + + if(params.m_RecurrentToInputWeights == nullptr) + { + throw InvalidArgumentException( + "AddQLstmLayer: Recurrent To Input Weights cannot be NULL"); + } + + if(params.m_InputGateBias == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Input Gate Bias cannot be NULL"); + } + + layer->m_CifgParameters.m_InputToInputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputToInputWeights)); + layer->m_CifgParameters.m_RecurrentToInputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_RecurrentToInputWeights)); + layer->m_CifgParameters.m_InputGateBias = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputGateBias)); + } + + // QLstm Projection parameters + if(descriptor.m_ProjectionEnabled) + { + if(params.m_ProjectionWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Projection Weights cannot be NULL"); + } + + if(params.m_ProjectionBias == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Projection Biases cannot be NULL"); + } + + layer->m_ProjectionParameters.m_ProjectionWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_ProjectionWeights)); + layer->m_ProjectionParameters.m_ProjectionBias = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_ProjectionBias)); + } + + // QLstm Peephole params + if(descriptor.m_PeepholeEnabled) + { + if(params.m_CellToForgetWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Cell To Forget Weights cannot be NULL"); + } + + if(params.m_CellToOutputWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Cell To Output Weights cannot be NULL"); + } + + if(!descriptor.m_CifgEnabled) + { + if(params.m_CellToInputWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Cell To Input Weights cannot be NULL"); + } + + layer->m_PeepholeParameters.m_CellToInputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToInputWeights)); + } + + layer->m_PeepholeParameters.m_CellToForgetWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToForgetWeights)); + layer->m_PeepholeParameters.m_CellToOutputWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_CellToOutputWeights)); + } + + // QLstm Layer Normalization params + if(descriptor.m_LayerNormEnabled) + { + if(params.m_ForgetLayerNormWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Forget layer normalization weights cannot be NULL"); + } + + if(params.m_CellLayerNormWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Cell layer normalization weights cannot be NULL"); + } + + if(params.m_OutputLayerNormWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Output layer normalization weights cannot be NULL"); + } + + if(!descriptor.m_CifgEnabled) + { + if(params.m_InputLayerNormWeights == nullptr) + { + throw InvalidArgumentException("AddQLstmLayer: Input layer normalization weights cannot be NULL"); + } + + layer->m_LayerNormParameters.m_InputLayerNormWeights = + std::make_unique<ScopedCpuTensorHandle>(*(params.m_InputLayerNormWeights)); + } + + 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; +} + void Network::Accept(ILayerVisitor& visitor) const { for (auto layer : GetGraph()) diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp index 17eacba48a..df4a35fff3 100644 --- a/src/armnn/Network.hpp +++ b/src/armnn/Network.hpp @@ -234,6 +234,10 @@ public: IConnectableLayer* AddStandInLayer(const StandInDescriptor& descriptor, const char* name = nullptr) override; + IConnectableLayer* AddQLstmLayer(const QLstmDescriptor& descriptor, + const LstmInputParams& params, + const char* name = nullptr) override; + IConnectableLayer* AddQuantizedLstmLayer(const QuantizedLstmInputParams& params, const char* name = nullptr) override; diff --git a/src/armnn/layers/QLstmLayer.cpp b/src/armnn/layers/QLstmLayer.cpp new file mode 100644 index 0000000000..393a7029aa --- /dev/null +++ b/src/armnn/layers/QLstmLayer.cpp @@ -0,0 +1,512 @@ +// +// Copyright © 2020 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#include "QLstmLayer.hpp" + +#include "LayerCloneBase.hpp" + +#include <armnn/LstmParams.hpp> +#include <armnn/TypesUtils.hpp> +#include <backendsCommon/CpuTensorHandle.hpp> +#include <backendsCommon/WorkloadFactory.hpp> + +namespace armnn +{ + +QLstmLayer::QLstmLayer(const QLstmDescriptor& param, const char* name) + : LayerWithParameters(3, 3, LayerType::QLstm, param, name) +{ +} + +std::unique_ptr<IWorkload> QLstmLayer::CreateWorkload(const IWorkloadFactory& factory) const +{ + QLstmQueueDescriptor descriptor; + + // Basic parameters + descriptor.m_InputToForgetWeights = m_BasicParameters.m_InputToForgetWeights.get(); + descriptor.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights.get(); + descriptor.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights.get(); + descriptor.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights.get(); + descriptor.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights.get(); + descriptor.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights.get(); + descriptor.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias.get(); + descriptor.m_CellBias = m_BasicParameters.m_CellBias.get(); + descriptor.m_OutputGateBias = m_BasicParameters.m_OutputGateBias.get(); + + // CIFG parameters + if (!m_Param.m_CifgEnabled) + { + descriptor.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights.get(); + descriptor.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights.get(); + descriptor.m_InputGateBias = m_CifgParameters.m_InputGateBias.get(); + } + + // Projection parameters + if (m_Param.m_ProjectionEnabled) + { + descriptor.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights.get(); + descriptor.m_ProjectionBias = m_ProjectionParameters.m_ProjectionBias.get(); + } + + // Peephole parameters + if (m_Param.m_PeepholeEnabled) + { + if (!m_Param.m_CifgEnabled) + { + descriptor.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights.get(); + } + + descriptor.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights.get(); + descriptor.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights.get(); + } + + // Layer normalisation parameters + if(m_Param.m_LayerNormEnabled) + { + if (!m_Param.m_CifgEnabled) + { + descriptor.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights.get(); + } + descriptor.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights.get(); + descriptor.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights.get(); + descriptor.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights.get(); + } + + return factory.CreateQLstm(descriptor, PrepInfoAndDesc(descriptor)); +} + +QLstmLayer* QLstmLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase<QLstmLayer>(graph, m_Param, GetName()); + + layer->m_BasicParameters.m_InputToForgetWeights = m_BasicParameters.m_InputToForgetWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToForgetWeights) : nullptr; + layer->m_BasicParameters.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToCellWeights) : nullptr; + layer->m_BasicParameters.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToOutputWeights) : nullptr; + layer->m_BasicParameters.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToForgetWeights) : nullptr; + layer->m_BasicParameters.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToCellWeights) : nullptr; + layer->m_BasicParameters.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToOutputWeights) : nullptr; + layer->m_BasicParameters.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_ForgetGateBias) : nullptr; + layer->m_BasicParameters.m_CellBias = m_BasicParameters.m_CellBias ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_CellBias) : nullptr; + layer->m_BasicParameters.m_OutputGateBias = m_BasicParameters.m_OutputGateBias ? + std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_OutputGateBias) : nullptr; + + if (!m_Param.m_CifgEnabled) + { + layer->m_CifgParameters.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_InputToInputWeights) : nullptr; + layer->m_CifgParameters.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_RecurrentToInputWeights) : nullptr; + layer->m_CifgParameters.m_InputGateBias = m_CifgParameters.m_InputGateBias ? + std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_InputGateBias) : nullptr; + } + + if (m_Param.m_ProjectionEnabled) + { + layer->m_ProjectionParameters.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_ProjectionParameters.m_ProjectionWeights) : nullptr; + layer->m_ProjectionParameters.m_ProjectionBias = m_ProjectionParameters.m_ProjectionBias ? + std::make_unique<ScopedCpuTensorHandle>(*m_ProjectionParameters.m_ProjectionBias) : nullptr; + } + + if (m_Param.m_PeepholeEnabled) + { + if (!m_Param.m_CifgEnabled) { + layer->m_PeepholeParameters.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToInputWeights) : nullptr; + } + + layer->m_PeepholeParameters.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToForgetWeights) : nullptr; + layer->m_PeepholeParameters.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToOutputWeights) : nullptr; + } + + if (m_Param.m_LayerNormEnabled) + { + if (!m_Param.m_CifgEnabled) { + layer->m_LayerNormParameters.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_InputLayerNormWeights) : nullptr; + } + + layer->m_LayerNormParameters.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_ForgetLayerNormWeights) : nullptr; + layer->m_LayerNormParameters.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_CellLayerNormWeights) : nullptr; + layer->m_LayerNormParameters.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights ? + std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_OutputLayerNormWeights) : nullptr; + } + + return std::move(layer); +} + +std::vector<TensorShape> QLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const +{ + BOOST_ASSERT(inputShapes.size() == 3); + + // Get input values for validation + unsigned int batchSize = inputShapes[0][0]; + unsigned int outputSize = inputShapes[1][1]; + unsigned int numUnits = inputShapes[2][1]; + + std::vector<TensorShape> outShapes; + outShapes.push_back(TensorShape({ batchSize, outputSize })); // outputStateOut + outShapes.push_back(TensorShape({ batchSize, numUnits })); // cellStateOut + outShapes.push_back(TensorShape({ batchSize, outputSize })); // output + + return outShapes; +} + +void QLstmLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(3, CHECK_LOCATION()); + + auto inferredShapes = InferOutputShapes( + { + GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), // input + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), // previousOutputIn + GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape() // previousCellStateIn + }); + + BOOST_ASSERT(inferredShapes.size() == 3); + + // Check if the weights are nullptr for basic params + BOOST_ASSERT_MSG(m_BasicParameters.m_InputToForgetWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_InputToCellWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_InputToCellWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_InputToOutputWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToForgetWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToCellWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_RecurrentToOutputWeights != nullptr, + "QLstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_ForgetGateBias != nullptr, + "QLstmLayer: m_BasicParameters.m_ForgetGateBias should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_CellBias != nullptr, + "QLstmLayer: m_BasicParameters.m_CellBias should not be null."); + BOOST_ASSERT_MSG(m_BasicParameters.m_OutputGateBias != nullptr, + "QLstmLayer: m_BasicParameters.m_OutputGateBias should not be null."); + + if (!m_Param.m_CifgEnabled) + { + BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights != nullptr, + "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not be null."); + BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights != nullptr, + "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null."); + BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias != nullptr, + "QLstmLayer: m_CifgParameters.m_InputGateBias should not be null."); + + ConditionalThrowIfNotEqual<LayerValidationException>( + "QLstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", + GetOutputSlot(0).GetTensorInfo().GetShape(), + inferredShapes[0]); + } + else + { + BOOST_ASSERT_MSG(m_CifgParameters.m_InputToInputWeights == nullptr, + "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled."); + BOOST_ASSERT_MSG(m_CifgParameters.m_RecurrentToInputWeights == nullptr, + "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should " + "not have a value when CIFG is enabled."); + BOOST_ASSERT_MSG(m_CifgParameters.m_InputGateBias == nullptr, + "QLstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled."); + + ConditionalThrowIfNotEqual<LayerValidationException>( + "QLstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", + GetOutputSlot(0).GetTensorInfo().GetShape(), + inferredShapes[0]); + } + + if (m_Param.m_ProjectionEnabled) + { + BOOST_ASSERT_MSG(m_ProjectionParameters.m_ProjectionWeights != nullptr, + "QLstmLayer: m_ProjectionParameters.m_ProjectionWeights should not be null."); + BOOST_ASSERT_MSG(m_ProjectionParameters.m_ProjectionBias != nullptr, + "QLstmLayer: m_ProjectionParameters.m_ProjectionBias should not be null."); + } + + if (m_Param.m_PeepholeEnabled) + { + if (!m_Param.m_CifgEnabled) { + BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToInputWeights != nullptr, + "QLstmLayer: m_PeepholeParameters.m_CellToInputWeights should not be null " + "when Peephole is enabled and CIFG is disabled."); + } + + BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToForgetWeights != nullptr, + "QLstmLayer: m_PeepholeParameters.m_CellToForgetWeights should not be null."); + BOOST_ASSERT_MSG(m_PeepholeParameters.m_CellToOutputWeights != nullptr, + "QLstmLayer: m_PeepholeParameters.m_CellToOutputWeights should not be null."); + } + + ConditionalThrowIfNotEqual<LayerValidationException>( + "QLstmLayer: TensorShape set on OutputSlot[1] does not match the inferred shape.", + GetOutputSlot(1).GetTensorInfo().GetShape(), + inferredShapes[1]); + ConditionalThrowIfNotEqual<LayerValidationException>( + "QLstmLayer: TensorShape set on OutputSlot[2] does not match the inferred shape.", + GetOutputSlot(2).GetTensorInfo().GetShape(), + inferredShapes[2]); + + if (m_Param.m_LayerNormEnabled) + { + if(!m_Param.m_CifgEnabled) + { + BOOST_ASSERT_MSG(m_LayerNormParameters.m_InputLayerNormWeights != nullptr, + "QLstmLayer: m_LayerNormParameters.m_InputLayerNormWeights should not be null."); + } + BOOST_ASSERT_MSG(m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr, + "QLstmLayer: m_LayerNormParameters.m_ForgetLayerNormWeights should not be null."); + BOOST_ASSERT_MSG(m_LayerNormParameters.m_CellLayerNormWeights != nullptr, + "QLstmLayer: m_LayerNormParameters.m_CellLayerNormWeights should not be null."); + BOOST_ASSERT_MSG(m_LayerNormParameters.m_OutputLayerNormWeights != nullptr, + "QLstmLayer: m_LayerNormParameters.m_UutputLayerNormWeights should not be null."); + } +} + +Layer::ConstantTensors QLstmLayer::GetConstantTensorsByRef() +{ + return {m_BasicParameters.m_InputToForgetWeights, + m_BasicParameters.m_InputToCellWeights, + m_BasicParameters.m_InputToOutputWeights, + m_BasicParameters.m_RecurrentToForgetWeights, + m_BasicParameters.m_RecurrentToCellWeights, + m_BasicParameters.m_RecurrentToOutputWeights, + m_BasicParameters.m_ForgetGateBias, + m_BasicParameters.m_CellBias, + m_BasicParameters.m_OutputGateBias, + + // Cifg parameters + m_CifgParameters.m_InputToInputWeights, + m_CifgParameters.m_RecurrentToInputWeights, + m_CifgParameters.m_InputGateBias, + + // Projection parameters + m_ProjectionParameters.m_ProjectionWeights, + m_ProjectionParameters.m_ProjectionBias, + + // Peephole parameters + m_PeepholeParameters.m_CellToInputWeights, + m_PeepholeParameters.m_CellToForgetWeights, + m_PeepholeParameters.m_CellToOutputWeights, + + // Layer normalisation parameters + m_LayerNormParameters.m_InputLayerNormWeights, + m_LayerNormParameters.m_ForgetLayerNormWeights, + m_LayerNormParameters.m_CellLayerNormWeights, + m_LayerNormParameters.m_OutputLayerNormWeights}; +} + +void QLstmLayer::Accept(ILayerVisitor& visitor) const +{ + LstmInputParams inputParams; + + ConstTensor inputToInputWeightsTensor; + if (m_CifgParameters.m_InputToInputWeights != nullptr) + { + ConstTensor inputToInputWeightsTensorCopy(m_CifgParameters.m_InputToInputWeights->GetTensorInfo(), + m_CifgParameters.m_InputToInputWeights->Map(true)); + inputToInputWeightsTensor = inputToInputWeightsTensorCopy; + inputParams.m_InputToInputWeights = &inputToInputWeightsTensor; + } + + ConstTensor inputToForgetWeightsTensor; + if (m_BasicParameters.m_InputToForgetWeights != nullptr) + { + ConstTensor inputToForgetWeightsTensorCopy(m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(), + m_BasicParameters.m_InputToForgetWeights->Map(true)); + inputToForgetWeightsTensor = inputToForgetWeightsTensorCopy; + inputParams.m_InputToForgetWeights = &inputToForgetWeightsTensor; + } + + ConstTensor inputToCellWeightsTensor; + if (m_BasicParameters.m_InputToCellWeights != nullptr) + { + ConstTensor inputToCellWeightsTensorCopy(m_BasicParameters.m_InputToCellWeights->GetTensorInfo(), + m_BasicParameters.m_InputToCellWeights->Map(true)); + inputToCellWeightsTensor = inputToCellWeightsTensorCopy; + inputParams.m_InputToCellWeights = &inputToCellWeightsTensor; + } + + ConstTensor inputToOutputWeightsTensor; + if (m_BasicParameters.m_InputToOutputWeights != nullptr) + { + ConstTensor inputToOutputWeightsTensorCopy(m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(), + m_BasicParameters.m_InputToOutputWeights->Map(true)); + inputToOutputWeightsTensor = inputToOutputWeightsTensorCopy; + inputParams.m_InputToOutputWeights = &inputToOutputWeightsTensor; + } + + ConstTensor recurrentToInputWeightsTensor; + if (m_CifgParameters.m_RecurrentToInputWeights != nullptr) + { + ConstTensor recurrentToInputWeightsTensorCopy( + m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo(), + m_CifgParameters.m_RecurrentToInputWeights->Map(true)); + recurrentToInputWeightsTensor = recurrentToInputWeightsTensorCopy; + inputParams.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + } + + ConstTensor recurrentToForgetWeightsTensor; + if (m_BasicParameters.m_RecurrentToForgetWeights != nullptr) + { + ConstTensor recurrentToForgetWeightsTensorCopy( + m_BasicParameters.m_RecurrentToForgetWeights->GetTensorInfo(), + m_BasicParameters.m_RecurrentToForgetWeights->Map(true)); + recurrentToForgetWeightsTensor = recurrentToForgetWeightsTensorCopy; + inputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + } + + ConstTensor recurrentToCellWeightsTensor; + if (m_BasicParameters.m_RecurrentToCellWeights != nullptr) + { + ConstTensor recurrentToCellWeightsTensorCopy( + m_BasicParameters.m_RecurrentToCellWeights->GetTensorInfo(), + m_BasicParameters.m_RecurrentToCellWeights->Map(true)); + recurrentToCellWeightsTensor = recurrentToCellWeightsTensorCopy; + inputParams.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + } + + ConstTensor recurrentToOutputWeightsTensor; + if (m_BasicParameters.m_RecurrentToOutputWeights != nullptr) + { + ConstTensor recurrentToOutputWeightsTensorCopy( + m_BasicParameters.m_RecurrentToOutputWeights->GetTensorInfo(), + m_BasicParameters.m_RecurrentToOutputWeights->Map(true)); + recurrentToOutputWeightsTensor = recurrentToOutputWeightsTensorCopy; + inputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + } + + ConstTensor cellToInputWeightsTensor; + if (m_PeepholeParameters.m_CellToInputWeights != nullptr) + { + ConstTensor cellToInputWeightsTensorCopy(m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo(), + m_PeepholeParameters.m_CellToInputWeights->Map(true)); + cellToInputWeightsTensor = cellToInputWeightsTensorCopy; + inputParams.m_CellToInputWeights = &cellToInputWeightsTensor; + } + + ConstTensor cellToForgetWeightsTensor; + if (m_PeepholeParameters.m_CellToForgetWeights != nullptr) + { + ConstTensor cellToForgetWeightsTensorCopy(m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(), + m_PeepholeParameters.m_CellToForgetWeights->Map(true)); + cellToForgetWeightsTensor = cellToForgetWeightsTensorCopy; + inputParams.m_CellToForgetWeights = &cellToForgetWeightsTensor; + } + + ConstTensor cellToOutputWeightsTensor; + if (m_PeepholeParameters.m_CellToOutputWeights != nullptr) + { + ConstTensor cellToOutputWeightsTensorCopy(m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(), + m_PeepholeParameters.m_CellToOutputWeights->Map(true)); + cellToOutputWeightsTensor = cellToOutputWeightsTensorCopy; + inputParams.m_CellToOutputWeights = &cellToOutputWeightsTensor; + } + + ConstTensor inputGateBiasTensor; + if (m_CifgParameters.m_InputGateBias != nullptr) + { + ConstTensor inputGateBiasTensorCopy(m_CifgParameters.m_InputGateBias->GetTensorInfo(), + m_CifgParameters.m_InputGateBias->Map(true)); + inputGateBiasTensor = inputGateBiasTensorCopy; + inputParams.m_InputGateBias = &inputGateBiasTensor; + } + + ConstTensor forgetGateBiasTensor; + if (m_BasicParameters.m_ForgetGateBias != nullptr) + { + ConstTensor forgetGateBiasTensorCopy(m_BasicParameters.m_ForgetGateBias->GetTensorInfo(), + m_BasicParameters.m_ForgetGateBias->Map(true)); + forgetGateBiasTensor = forgetGateBiasTensorCopy; + inputParams.m_ForgetGateBias = &forgetGateBiasTensor; + } + + ConstTensor cellBiasTensor; + if (m_BasicParameters.m_CellBias != nullptr) + { + ConstTensor cellBiasTensorCopy(m_BasicParameters.m_CellBias->GetTensorInfo(), + m_BasicParameters.m_CellBias->Map(true)); + cellBiasTensor = cellBiasTensorCopy; + inputParams.m_CellBias = &cellBiasTensor; + } + + ConstTensor outputGateBias; + if (m_BasicParameters.m_OutputGateBias != nullptr) + { + ConstTensor outputGateBiasCopy(m_BasicParameters.m_OutputGateBias->GetTensorInfo(), + m_BasicParameters.m_OutputGateBias->Map(true)); + outputGateBias = outputGateBiasCopy; + inputParams.m_OutputGateBias = &outputGateBias; + } + + ConstTensor projectionWeightsTensor; + if (m_ProjectionParameters.m_ProjectionWeights != nullptr) + { + ConstTensor projectionWeightsTensorCopy(m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(), + m_ProjectionParameters.m_ProjectionWeights->Map(true)); + projectionWeightsTensor = projectionWeightsTensorCopy; + inputParams.m_ProjectionWeights = &projectionWeightsTensor; + } + + ConstTensor projectionBiasTensor; + if (m_ProjectionParameters.m_ProjectionBias != nullptr) + { + ConstTensor projectionBiasTensorCopy(m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(), + m_ProjectionParameters.m_ProjectionBias->Map(true)); + projectionBiasTensor = projectionBiasTensorCopy; + inputParams.m_ProjectionBias = &projectionBiasTensor; + } + + ConstTensor inputLayerNormTensor; + if (m_LayerNormParameters.m_InputLayerNormWeights != nullptr) + { + ConstTensor inputLayerNormTensorCopy(m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(), + m_LayerNormParameters.m_InputLayerNormWeights->Map(true)); + inputLayerNormTensor = inputLayerNormTensorCopy; + inputParams.m_InputLayerNormWeights = &inputLayerNormTensor; + } + + ConstTensor forgetLayerNormTensor; + if (m_LayerNormParameters.m_ForgetLayerNormWeights != nullptr) + { + ConstTensor forgetLayerNormTensorCopy(m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(), + m_LayerNormParameters.m_ForgetLayerNormWeights->Map(true)); + forgetLayerNormTensor = forgetLayerNormTensorCopy; + inputParams.m_ForgetLayerNormWeights = &forgetLayerNormTensor; + } + + ConstTensor cellLayerNormTensor; + if (m_LayerNormParameters.m_CellLayerNormWeights != nullptr) + { + ConstTensor cellLayerNormTensorCopy(m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(), + m_LayerNormParameters.m_CellLayerNormWeights->Map(true)); + cellLayerNormTensor = cellLayerNormTensorCopy; + inputParams.m_CellLayerNormWeights = &cellLayerNormTensor; + } + + ConstTensor outputLayerNormTensor; + if (m_LayerNormParameters.m_OutputLayerNormWeights != nullptr) + { + ConstTensor outputLayerNormTensorCopy(m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(), + m_LayerNormParameters.m_OutputLayerNormWeights->Map(true)); + outputLayerNormTensor = outputLayerNormTensorCopy; + inputParams.m_OutputLayerNormWeights = &outputLayerNormTensor; + } + + + visitor.VisitQLstmLayer(this, GetParameters(), inputParams, GetName()); +} + +} // namespace armnn diff --git a/src/armnn/layers/QLstmLayer.hpp b/src/armnn/layers/QLstmLayer.hpp new file mode 100644 index 0000000000..2d40b7e29e --- /dev/null +++ b/src/armnn/layers/QLstmLayer.hpp @@ -0,0 +1,124 @@ +// +// Copyright © 2020 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "LayerWithParameters.hpp" + +namespace armnn +{ + +class ScopedCpuTensorHandle; + +struct QLstmBasicParameters +{ + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_InputToForgetWeights; + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_InputToCellWeights; + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_InputToOutputWeights; + + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_RecurrentToForgetWeights; + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_RecurrentToCellWeights; + /// A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_RecurrentToOutputWeights; + + /// A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). + std::unique_ptr<ScopedCpuTensorHandle> m_ForgetGateBias; + /// A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). + std::unique_ptr<ScopedCpuTensorHandle> m_CellBias; + /// A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). + std::unique_ptr<ScopedCpuTensorHandle> m_OutputGateBias; +}; + +struct QLstmOptProjectionParameters +{ + /// A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_ProjectionWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [output_size] (int32). + std::unique_ptr<ScopedCpuTensorHandle> m_ProjectionBias; +}; + +struct QLstmOptPeepholeParameters +{ + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_CellToInputWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_CellToForgetWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_CellToOutputWeights; +}; + +struct QLstmOptCifgParameters +{ + /// A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_InputToInputWeights; + /// A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8). + std::unique_ptr<ScopedCpuTensorHandle> m_RecurrentToInputWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (int32). + std::unique_ptr<ScopedCpuTensorHandle> m_InputGateBias; +}; + +struct QLstmOptLayerNormParameters +{ + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_InputLayerNormWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_ForgetLayerNormWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_CellLayerNormWeights; + /// A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16). + std::unique_ptr<ScopedCpuTensorHandle> m_OutputLayerNormWeights; +}; + +/// This layer represents a QLstm operation. +class QLstmLayer : public LayerWithParameters<QLstmDescriptor> +{ +public: + + QLstmBasicParameters m_BasicParameters; + QLstmOptCifgParameters m_CifgParameters; + QLstmOptProjectionParameters m_ProjectionParameters; + QLstmOptPeepholeParameters m_PeepholeParameters; + QLstmOptLayerNormParameters m_LayerNormParameters; + + /// Makes a workload for the QLstm type. + /// @param [in] graph The graph where this layer can be found. + /// @param [in] factory The workload factory which will create the workload. + /// @return A pointer to the created workload, or nullptr if not created. + virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override; + + /// Creates a dynamically-allocated copy of this layer. + /// @param [in] graph The graph into which this layer is being cloned. + QLstmLayer* Clone(Graph& graph) const override; + + /// Check if the input tensor shape(s) + /// will lead to a valid configuration of @ref QLstmLayer. + void ValidateTensorShapesFromInputs() override; + + /// By default returns inputShapes if the number of inputs are equal to number of outputs, + /// otherwise infers the output shapes from given input shapes and layer properties. + /// @param [in] inputShapes The input shapes layer has. + /// @return A vector to the inferred output shape. + std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override; + + void Accept(ILayerVisitor& visitor) const override; + +protected: + /// Constructor to create a QLstmLayer. + /// @param [in] name Optional name for the layer. + QLstmLayer(const QLstmDescriptor& param, const char* name); + + /// Default destructor + ~QLstmLayer() = default; + + /// Retrieve the handles to the constant values stored by the layer. + /// @return A vector of the constant tensors stored by this layer. + Layer::ConstantTensors GetConstantTensorsByRef() override; +}; + +} // namespace armnn diff --git a/src/armnn/test/ConstTensorLayerVisitor.cpp b/src/armnn/test/ConstTensorLayerVisitor.cpp index ada665e4e9..7ef3dd2920 100644 --- a/src/armnn/test/ConstTensorLayerVisitor.cpp +++ b/src/armnn/test/ConstTensorLayerVisitor.cpp @@ -107,6 +107,104 @@ void TestLstmLayerVisitor::CheckInputParameters(const LstmInputParams& inputPara CheckConstTensorPtrs("CellBias", m_InputParams.m_CellBias, inputParams.m_CellBias); } +void TestQLstmLayerVisitor::CheckConstTensorPtrs(const std::string& name, + const ConstTensor* expected, + const ConstTensor* actual) +{ + if (expected == nullptr) + { + BOOST_CHECK_MESSAGE(actual == nullptr, name + " actual should have been a nullptr"); + } + else + { + BOOST_CHECK_MESSAGE(actual != nullptr, name + " actual should have been set"); + if (actual != nullptr) + { + CheckConstTensors(*expected, *actual); + } + } +} + +void TestQLstmLayerVisitor::CheckDescriptor(const QLstmDescriptor& descriptor) +{ + BOOST_CHECK(m_Descriptor.m_CellClip == descriptor.m_CellClip); + BOOST_CHECK(m_Descriptor.m_ProjectionClip == descriptor.m_ProjectionClip); + BOOST_CHECK(m_Descriptor.m_CifgEnabled == descriptor.m_CifgEnabled); + BOOST_CHECK(m_Descriptor.m_PeepholeEnabled == descriptor.m_PeepholeEnabled); + BOOST_CHECK(m_Descriptor.m_ProjectionEnabled == descriptor.m_ProjectionEnabled); +} + +void TestQLstmLayerVisitor::CheckInputParameters(const LstmInputParams& inputParams) +{ + CheckConstTensorPtrs("InputToInputWeights", + m_InputParams.m_InputToInputWeights, + inputParams.m_InputToInputWeights); + + CheckConstTensorPtrs("InputToForgetWeights", + m_InputParams.m_InputToForgetWeights, + inputParams.m_InputToForgetWeights); + + CheckConstTensorPtrs("InputToCellWeights", + m_InputParams.m_InputToCellWeights, + inputParams.m_InputToCellWeights); + + CheckConstTensorPtrs("InputToOutputWeights", + m_InputParams.m_InputToOutputWeights, + inputParams.m_InputToOutputWeights); + + CheckConstTensorPtrs("RecurrentToInputWeights", + m_InputParams.m_RecurrentToInputWeights, + inputParams.m_RecurrentToInputWeights); + + CheckConstTensorPtrs("RecurrentToForgetWeights", + m_InputParams.m_RecurrentToForgetWeights, + inputParams.m_RecurrentToForgetWeights); + + CheckConstTensorPtrs("RecurrentToCellWeights", + m_InputParams.m_RecurrentToCellWeights, + inputParams.m_RecurrentToCellWeights); + + CheckConstTensorPtrs("RecurrentToOutputWeights", + m_InputParams.m_RecurrentToOutputWeights, + inputParams.m_RecurrentToOutputWeights); + + CheckConstTensorPtrs("CellToInputWeights", + m_InputParams.m_CellToInputWeights, + inputParams.m_CellToInputWeights); + + CheckConstTensorPtrs("CellToForgetWeights", + m_InputParams.m_CellToForgetWeights, + inputParams.m_CellToForgetWeights); + + CheckConstTensorPtrs("CellToOutputWeights", + m_InputParams.m_CellToOutputWeights, + inputParams.m_CellToOutputWeights); + + CheckConstTensorPtrs("ProjectionWeights", m_InputParams.m_ProjectionWeights, inputParams.m_ProjectionWeights); + CheckConstTensorPtrs("ProjectionBias", m_InputParams.m_ProjectionBias, inputParams.m_ProjectionBias); + + CheckConstTensorPtrs("InputGateBias", m_InputParams.m_InputGateBias, inputParams.m_InputGateBias); + CheckConstTensorPtrs("ForgetGateBias", m_InputParams.m_ForgetGateBias, inputParams.m_ForgetGateBias); + CheckConstTensorPtrs("CellBias", m_InputParams.m_CellBias, inputParams.m_CellBias); + CheckConstTensorPtrs("OutputGateBias", m_InputParams.m_OutputGateBias, inputParams.m_OutputGateBias); + + CheckConstTensorPtrs("InputLayerNormWeights", + m_InputParams.m_InputLayerNormWeights, + inputParams.m_InputLayerNormWeights); + + CheckConstTensorPtrs("ForgetLayerNormWeights", + m_InputParams.m_ForgetLayerNormWeights, + inputParams.m_ForgetLayerNormWeights); + + CheckConstTensorPtrs("CellLayerNormWeights", + m_InputParams.m_CellLayerNormWeights, + inputParams.m_CellLayerNormWeights); + + CheckConstTensorPtrs("OutputLayerNormWeights", + m_InputParams.m_OutputLayerNormWeights, + inputParams.m_OutputLayerNormWeights); +} + void TestQuantizedLstmLayerVisitor::CheckConstTensorPtrs(const std::string& name, const ConstTensor* expected, const ConstTensor* actual) @@ -891,7 +989,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerCifgDisabled) Network net; - IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName); + IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params, layerName); layer->Accept(visitor); } @@ -978,7 +1076,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerPeephole) Network net; - IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params); + IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params); layer->Accept(visitor); } @@ -1065,7 +1163,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerPeephole) Network net; - IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName); + IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params, layerName); layer->Accept(visitor); } @@ -1152,7 +1250,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerProjection) Network net; - IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params); + IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params); layer->Accept(visitor); } @@ -1239,52 +1337,713 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerProjection) Network net; - IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName); + IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params, layerName); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckQLstmLayerBasic) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = true; + + // Basic params ONLY + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + 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; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckNamedQLstmLayerBasic) +{ + const char* layerName = "QLstmLayer"; + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = true; + + // Basic params ONLY + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + 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; + + TestQLstmLayerVisitor visitor(descriptor, params, layerName); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params, layerName); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckQLstmLayerCifgDisabled) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = false; + + // Basic params + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + // CIFG disabled params + std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToInputWeights( + TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::QSymmS8), inputToInputWeightsData); + + std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToInputWeights(TensorInfo( + 4, recurrentToInputWeightsDimensions.data(), DataType::QSymmS8), recurrentToInputWeightsData); + + std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor inputGateBias( + TensorInfo(4, inputGateBiasDimensions.data(), DataType::Signed32), inputGateBiasData); + + LstmInputParams params; + + // Basic 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 disabled params + params.m_InputToInputWeights = &inputToInputWeights; + params.m_RecurrentToInputWeights = &recurrentToInputWeights; + params.m_InputGateBias = &inputGateBias; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckQLstmLayerCifgDisabledPeepholeEnabled) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = false; + descriptor.m_PeepholeEnabled = true; + + // Basic params + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + // CIFG disabled params + std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToInputWeights( + TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::QSymmS8), inputToInputWeightsData); + + std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToInputWeights(TensorInfo( + 4, recurrentToInputWeightsDimensions.data(), DataType::QSymmS8), recurrentToInputWeightsData); + + std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor inputGateBias( + TensorInfo(4, inputGateBiasDimensions.data(), DataType::Signed32), inputGateBiasData); + + // Peephole enabled, CIFG disabled params + std::vector<int16_t> cellToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellToInputWeights( + TensorInfo(4, cellToInputWeightsDimensions.data(), DataType::QSymmS16), cellToInputWeightsData); + + std::vector<int16_t> cellToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellToForgetWeights( + TensorInfo(4, cellToForgetWeightsDimensions.data(), DataType::QSymmS16), cellToForgetWeightsData); + + std::vector<int16_t> cellToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellToOutputWeights( + TensorInfo(4, cellToOutputWeightsDimensions.data(), DataType::QSymmS16), cellToOutputWeightsData); + + LstmInputParams params; + + // Basic 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 disabled params + params.m_InputToInputWeights = &inputToInputWeights; + params.m_RecurrentToInputWeights = &recurrentToInputWeights; + params.m_InputGateBias = &inputGateBias; + + // Peephole enabled, CIFG disabled params + params.m_CellToInputWeights = &cellToInputWeights; + params.m_CellToForgetWeights = &cellToForgetWeights; + params.m_CellToOutputWeights = &cellToOutputWeights; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckQLstmLayerCifgEnabledPeepholeEnabled) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = true; + descriptor.m_PeepholeEnabled = true; + + // Basic params + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + // Peephole enabled and CIFG enabled params + std::vector<int16_t> cellToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellToForgetWeights( + TensorInfo(4, cellToForgetWeightsDimensions.data(), DataType::QSymmS16), cellToForgetWeightsData); + + std::vector<int16_t> cellToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellToOutputWeights( + TensorInfo(4, cellToOutputWeightsDimensions.data(), DataType::QSymmS16), cellToOutputWeightsData); + + LstmInputParams params; + + // Basic 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; + + // Peephole enabled and CIFG enabled params + params.m_CellToForgetWeights = &cellToForgetWeights; + params.m_CellToOutputWeights = &cellToOutputWeights; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); layer->Accept(visitor); } +BOOST_AUTO_TEST_CASE(CheckQLstmLayerProjectionEnabled) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = true; + descriptor.m_ProjectionEnabled = true; + + // Basic params ONLY + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + // Projection enabled params + std::vector<uint8_t> projectionWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> projectionWeightsDimensions = {1, 1, 3, 3}; + ConstTensor projectionWeights(TensorInfo( + 4, projectionWeightsDimensions.data(), DataType::QSymmS8), projectionWeightsData); + + std::vector<int32_t> projectionBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> projectionBiasDimensions = {1, 1, 3, 3}; + ConstTensor projectionBias(TensorInfo( + 4, projectionBiasDimensions.data(), DataType::Signed32), projectionBiasData); + + LstmInputParams params; + + // Basic 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; + + // Projection enabled params + params.m_ProjectionWeights = &projectionWeights; + params.m_ProjectionBias = &projectionBias; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); + layer->Accept(visitor); +} + +BOOST_AUTO_TEST_CASE(CheckQLstmLayerCifgDisabledLayerNormEnabled) +{ + QLstmDescriptor descriptor; + descriptor.m_ProjectionClip = 0.5f; + descriptor.m_CellClip = 0.3f; + descriptor.m_CifgEnabled = false; + descriptor.m_LayerNormEnabled = true; + + // Basic params + std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToForgetWeights( + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); + + std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToCellWeights( + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); + + std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToOutputWeights( + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); + + std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToForgetWeights(TensorInfo( + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); + + std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToCellWeights(TensorInfo( + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); + + std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToOutputWeights(TensorInfo( + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); + + std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor forgetGateBias(TensorInfo( + 4, forgetGateBiasDimensions.data(), DataType::Signed32), forgetGateBiasData); + + std::vector<int32_t> cellBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3}; + ConstTensor cellBias(TensorInfo( + 4, cellBiasDimensions.data(), DataType::Signed32), cellBiasData); + + std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor outputGateBias(TensorInfo( + 4, outputGateBiasDimensions.data(), DataType::Signed32), outputGateBiasData); + + // CIFG disabled params + std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputToInputWeights( + TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::QSymmS8), inputToInputWeightsData); + + std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3}; + ConstTensor recurrentToInputWeights(TensorInfo( + 4, recurrentToInputWeightsDimensions.data(), DataType::QSymmS8), recurrentToInputWeightsData); + + std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputGateBiasDimensions = {1, 1, 3, 3}; + ConstTensor inputGateBias( + TensorInfo(4, inputGateBiasDimensions.data(), DataType::Signed32), inputGateBiasData); + + // Layer Norm enabled, CIFG disabled params + std::vector<int16_t> inputLayerNormWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> inputLayerNormWeightsDimensions = {1, 1, 3, 3}; + ConstTensor inputLayerNormWeights( + TensorInfo(4, inputLayerNormWeightsDimensions.data(), DataType::QSymmS16), inputLayerNormWeightsData); + + std::vector<int16_t> forgetLayerNormWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> forgetLayerNormWeightsDimensions = {1, 1, 3, 3}; + ConstTensor forgetLayerNormWeights( + TensorInfo(4, forgetLayerNormWeightsDimensions.data(), DataType::QSymmS16), forgetLayerNormWeightsData); + + std::vector<int16_t> cellLayerNormWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> cellLayerNormWeightsDimensions = {1, 1, 3, 3}; + ConstTensor cellLayerNormWeights( + TensorInfo(4, cellLayerNormWeightsDimensions.data(), DataType::QSymmS16), cellLayerNormWeightsData); + + std::vector<int16_t> outputLayerNormWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + std::vector<unsigned int> outputLayerNormWeightsDimensions = {1, 1, 3, 3}; + ConstTensor outputLayerNormWeights( + TensorInfo(4, outputLayerNormWeightsDimensions.data(), DataType::QSymmS16), outputLayerNormWeightsData); + + LstmInputParams params; + + // Basic 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 disabled params + params.m_InputToInputWeights = &inputToInputWeights; + params.m_RecurrentToInputWeights = &recurrentToInputWeights; + params.m_InputGateBias = &inputGateBias; + + // Layer Norm enabled, CIFG disabled params + params.m_InputLayerNormWeights = &inputLayerNormWeights; + params.m_ForgetLayerNormWeights = &forgetLayerNormWeights; + params.m_CellLayerNormWeights = &cellLayerNormWeights; + params.m_OutputLayerNormWeights = &outputLayerNormWeights; + + TestQLstmLayerVisitor visitor(descriptor, params); + + Network net; + + IConnectableLayer* const layer = net.AddQLstmLayer(descriptor, params); + layer->Accept(visitor); +} + + BOOST_AUTO_TEST_CASE(CheckQuantizedLstmLayer) { std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3}; ConstTensor inputToInputWeights( - TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::QAsymmU8), inputToInputWeightsData); + TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::QSymmS8), inputToInputWeightsData); std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3}; ConstTensor inputToForgetWeights( - TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QAsymmU8), inputToForgetWeightsData); + TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::QSymmS8), inputToForgetWeightsData); std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3}; ConstTensor inputToCellWeights( - TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QAsymmU8), inputToCellWeightsData); + TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::QSymmS8), inputToCellWeightsData); std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3}; ConstTensor inputToOutputWeights( - TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QAsymmU8), inputToOutputWeightsData); + TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::QSymmS8), inputToOutputWeightsData); std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3}; ConstTensor recurrentToInputWeights(TensorInfo( - 4, recurrentToInputWeightsDimensions.data(), DataType::QAsymmU8), recurrentToInputWeightsData); + 4, recurrentToInputWeightsDimensions.data(), DataType::QSymmS8), recurrentToInputWeightsData); std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3}; ConstTensor recurrentToForgetWeights(TensorInfo( - 4, recurrentToForgetWeightsDimensions.data(), DataType::QAsymmU8), recurrentToForgetWeightsData); + 4, recurrentToForgetWeightsDimensions.data(), DataType::QSymmS8), recurrentToForgetWeightsData); std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3}; ConstTensor recurrentToCellWeights(TensorInfo( - 4, recurrentToCellWeightsDimensions.data(), DataType::QAsymmU8), recurrentToCellWeightsData); + 4, recurrentToCellWeightsDimensions.data(), DataType::QSymmS8), recurrentToCellWeightsData); std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3}; ConstTensor recurrentToOutputWeights(TensorInfo( - 4, recurrentToOutputWeightsDimensions.data(), DataType::QAsymmU8), recurrentToOutputWeightsData); + 4, recurrentToOutputWeightsDimensions.data(), DataType::QSymmS8), recurrentToOutputWeightsData); std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4, 5, 6, 7, 8, 9}; diff --git a/src/armnn/test/ConstTensorLayerVisitor.hpp b/src/armnn/test/ConstTensorLayerVisitor.hpp index 203c5fd91b..e423e0f6e3 100644 --- a/src/armnn/test/ConstTensorLayerVisitor.hpp +++ b/src/armnn/test/ConstTensorLayerVisitor.hpp @@ -221,6 +221,38 @@ private: LstmInputParams m_InputParams; }; +class TestQLstmLayerVisitor : public TestLayerVisitor +{ +public: + explicit TestQLstmLayerVisitor(const QLstmDescriptor& descriptor, + const LstmInputParams& params, + const char* name = nullptr) + : TestLayerVisitor(name) + , m_Descriptor(descriptor) + , m_InputParams(params) + {} + + void VisitQLstmLayer(const IConnectableLayer* layer, + const QLstmDescriptor& descriptor, + const LstmInputParams& params, + const char* name = nullptr) + { + CheckLayerPointer(layer); + CheckLayerName(name); + CheckDescriptor(descriptor); + CheckInputParameters(params); + } + +protected: + void CheckDescriptor(const QLstmDescriptor& descriptor); + void CheckInputParameters(const LstmInputParams& inputParams); + void CheckConstTensorPtrs(const std::string& name, const ConstTensor* expected, const ConstTensor* actual); + +private: + QLstmDescriptor m_Descriptor; + LstmInputParams m_InputParams; +}; + class TestQuantizedLstmLayerVisitor : public TestLayerVisitor { diff --git a/src/armnn/test/InferOutputTests.cpp b/src/armnn/test/InferOutputTests.cpp index 3293cef0f7..015ab67079 100644 --- a/src/armnn/test/InferOutputTests.cpp +++ b/src/armnn/test/InferOutputTests.cpp @@ -46,6 +46,9 @@ ARMNN_SIMPLE_TEST_CASE(DepthwiseConvolution2dInferOutputShape, DepthwiseConvolut // TransposeConvolution2D ARMNN_SIMPLE_TEST_CASE(TransposeConvolution2dInferOutputShape, TransposeConvolution2dInferOutputShapeTest) +// QLstm +ARMNN_SIMPLE_TEST_CASE(QLstmInferOutputShape, QLstmInferOutputShapeTest) + // QuantizedLstm ARMNN_SIMPLE_TEST_CASE(QuantizedLstmInferOutputShape, QuantizedLstmInferOutputShapeTest) diff --git a/src/armnn/test/InferOutputTests.hpp b/src/armnn/test/InferOutputTests.hpp index b03449b568..70afbc9b3f 100644 --- a/src/armnn/test/InferOutputTests.hpp +++ b/src/armnn/test/InferOutputTests.hpp @@ -7,7 +7,6 @@ #include "TestUtils.hpp" - #include <Graph.hpp> #include <layers/ArgMinMaxLayer.hpp> #include <layers/BatchToSpaceNdLayer.hpp> @@ -530,6 +529,63 @@ void DepthwiseConvolution2dInferOutputShapeTest() BOOST_CHECK(expectedOutputShape == depthwiseConvolution2dLayer->InferOutputShapes(shapes).at(0)); } +// QLstm +void QLstmInferOutputShapeImpl(const armnn::QLstmDescriptor descriptor, + const std::vector<armnn::TensorShape>& inputShapes, + std::vector<armnn::TensorShape>& outputShapes) +{ + armnn::Graph graph; + armnn::QLstmLayer* const qLstmLayer = graph.AddLayer<armnn::QLstmLayer>(descriptor, "qLstm"); + outputShapes = qLstmLayer->InferOutputShapes(inputShapes); +} + +void QLstmInferOutputShapeTest() +{ + armnn::QLstmDescriptor descriptor; + descriptor.m_PeepholeEnabled = true; + descriptor.m_CifgEnabled = false; + descriptor.m_ProjectionEnabled = false; + + // Input shapes + const std::vector<unsigned int> inputShape{ 2, 5 }; + const std::vector<unsigned int> previousOutputInShape{ 2, 4 }; + const std::vector<unsigned int> previousCellStateInShape{ 2, 4 }; + + armnn::TensorShape inputTensorShape(2, inputShape.data()); + armnn::TensorShape previousOutputInTensorShape(2, previousOutputInShape.data()); + armnn::TensorShape previousCellStateInTensorShape(2, previousCellStateInShape.data()); + + std::vector<armnn::TensorShape> inShapes + { + inputTensorShape, + previousOutputInTensorShape, + previousCellStateInTensorShape + }; + + // Output shapes + const std::vector<unsigned int> outputStateOutShape{ 2, 4 }; + const std::vector<unsigned int> cellStateOutShape{ 2, 4 }; + const std::vector<unsigned int> outputShape{ 2, 4 }; + armnn::TensorShape outputStateOutTensorShape(2, outputShape.data()); + armnn::TensorShape cellStateOutTensorShape(2, cellStateOutShape.data()); + armnn::TensorShape outputTensorShape(2, outputShape.data()); + + std::vector<armnn::TensorShape> expectedOutShapes + { + outputStateOutTensorShape, + cellStateOutTensorShape, + outputTensorShape + }; + + std::vector<armnn::TensorShape> actualOutShapes; + BOOST_CHECK_NO_THROW(QLstmInferOutputShapeImpl(descriptor, inShapes, actualOutShapes)); + + BOOST_CHECK(actualOutShapes.size() == 3); + BOOST_CHECK(expectedOutShapes[0] == actualOutShapes[0]); + BOOST_CHECK(expectedOutShapes[1] == actualOutShapes[1]); + BOOST_CHECK(expectedOutShapes[2] == actualOutShapes[2]); +} + // QuantizedLstm void QuantizedLstmInferOutputShapeImpl(const std::vector<armnn::TensorShape>& inputShapes, std::vector<armnn::TensorShape>& outputShapes) |