From f87b90e4dbb906436cf205a2a19e199bfe9224ed Mon Sep 17 00:00:00 2001 From: Cathal Corbett Date: Tue, 22 Feb 2022 14:43:32 +0000 Subject: Revert "IVGCVSW-6268 Add support of Unidirectional Sequence Lstm fp32/fp16 to Neon" This reverts commit b0baff73b1574a198e57d46fcd704cedc43cea16. Reason for revert: cannot update ACL pin until 22.02 release. Change-Id: I049a125ba3b6a9b1cd6514ef9dd14d807773ed00 --- docs/02_operator_list.dox | 14 - src/backends/aclCommon/ArmComputeTensorUtils.cpp | 32 - src/backends/aclCommon/ArmComputeTensorUtils.hpp | 3 - src/backends/aclCommon/ArmComputeUtils.hpp | 24 - .../UnidirectionalSequenceLstmTestImpl.cpp | 218 ----- .../UnidirectionalSequenceLstmTestImpl.hpp | 10 - src/backends/cl/workloads/ClLstmFloatWorkload.cpp | 71 +- src/backends/neon/NeonLayerSupport.cpp | 34 - src/backends/neon/NeonLayerSupport.hpp | 10 - src/backends/neon/NeonWorkloadFactory.cpp | 5 - src/backends/neon/backend.mk | 3 +- src/backends/neon/test/NeonLayerTests.cpp | 16 - src/backends/neon/workloads/CMakeLists.txt | 2 - .../neon/workloads/NeonLstmFloatWorkload.cpp | 68 +- ...NeonUnidirectionalSequenceLstmFloatWorkload.cpp | 911 --------------------- ...NeonUnidirectionalSequenceLstmFloatWorkload.hpp | 92 --- src/backends/neon/workloads/NeonWorkloads.hpp | 1 - src/backends/reference/test/RefLayerTests.cpp | 4 - 18 files changed, 122 insertions(+), 1396 deletions(-) delete mode 100644 src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp delete mode 100644 src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp diff --git a/docs/02_operator_list.dox b/docs/02_operator_list.dox index b51ba5775e..e1eec58e4e 100644 --- a/docs/02_operator_list.dox +++ b/docs/02_operator_list.dox @@ -3323,20 +3323,6 @@ where N = batches, C = channels, H = height, W = width FLOAT32 QASYMMS8 - CpuAcc - - - - -
Input Types -
FLOAT32 -
- -
Weight Types -
FLOAT32 -
UnmapLayer Layer to perform unmap operation on tensor. diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.cpp b/src/backends/aclCommon/ArmComputeTensorUtils.cpp index 2dc6d2a2b2..9ed7b7b437 100644 --- a/src/backends/aclCommon/ArmComputeTensorUtils.cpp +++ b/src/backends/aclCommon/ArmComputeTensorUtils.cpp @@ -45,38 +45,6 @@ arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType, bool multi } } -armnn::DataType GetArmNNDataType(arm_compute::DataType dataType) -{ - switch(dataType) - { - case arm_compute::DataType::BFLOAT16: - return armnn::DataType::BFloat16; - case arm_compute::DataType::U8: - return armnn::DataType::Boolean; - case arm_compute::DataType::F16: - return armnn::DataType::Float16; - case arm_compute::DataType::F32: - return armnn::DataType::Float32; - case arm_compute::DataType::QASYMM8_SIGNED: - return armnn::DataType::QAsymmS8; - case arm_compute::DataType::QASYMM8: - return armnn::DataType::QAsymmU8; - case arm_compute::DataType::QSYMM16: - return armnn::DataType::QSymmS16; - case arm_compute::DataType::S64: - return armnn::DataType::Signed64; - case arm_compute::DataType::QSYMM8_PER_CHANNEL: - return armnn::DataType::QSymmS8; - case arm_compute::DataType::QSYMM8: - return armnn::DataType::QSymmS8; - case arm_compute::DataType::S32: - return armnn::DataType::Signed32; - default: - ARMNN_ASSERT_MSG(false, "Unknown data type"); - return armnn::DataType::Float32; - } -} - arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, unsigned int originalInputRank, const std::vector& armnnAxes) diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.hpp b/src/backends/aclCommon/ArmComputeTensorUtils.hpp index ba6ef6a3fe..30df31b79d 100644 --- a/src/backends/aclCommon/ArmComputeTensorUtils.hpp +++ b/src/backends/aclCommon/ArmComputeTensorUtils.hpp @@ -25,9 +25,6 @@ namespace armcomputetensorutils /// Utility function to map an armnn::DataType to corresponding arm_compute::DataType. arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType, bool multiScales); -/// Utility function to map an arm_compute::DataType to corresponding armnn::DataType. -armnn::DataType GetArmNNDataType(arm_compute::DataType datatype); - /// Utility function used to set up an arm_compute::Coordinates from a vector of ArmNN Axes for reduction functions arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, unsigned int originalInputRank, diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp index fab52ffb0f..e76af02765 100644 --- a/src/backends/aclCommon/ArmComputeUtils.hpp +++ b/src/backends/aclCommon/ArmComputeUtils.hpp @@ -112,30 +112,6 @@ ConvertAdditionalInfoToAclActivationLayerInfo(const QueueDescriptor& queueDescri return arm_compute::ActivationLayerInfo(); } -inline arm_compute::ActivationLayerInfo -ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction) -{ - // For preparing the object for the class ActivationLayerInfo, we need to consider 5 situations. - switch (activationFunction) - { - case 0: - return arm_compute::ActivationLayerInfo(); // no activation, do nothing - case 1: - return arm_compute::ActivationLayerInfo(arm_compute::ActivationLayerInfo::ActivationFunction::RELU); - case 3: - return arm_compute::ActivationLayerInfo( - arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0); - case 4: - return arm_compute::ActivationLayerInfo( - arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0); - case 6: - return arm_compute::ActivationLayerInfo( - arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC); - default: - throw armnn::Exception("Wrong Type of Activation Function!"); - } -} - inline arm_compute::ComparisonOperation ConvertComparisonOperationToAcl(const ComparisonDescriptor& descriptor) { switch (descriptor.m_Operation) diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp index c719472711..66a26cc41d 100644 --- a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp @@ -16,190 +16,6 @@ namespace { -template> -LayerTestResult -UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory, - const std::vector& input, - const std::vector& outputExpected, - const armnn::TensorShape& inputShape, - const armnn::TensorShape& outputExpectedShape, - float qScale = 0.0f, - int32_t qOffset = 0, - armnn::DataType constantDataType = armnn::DataType::Float32) -{ - IgnoreUnused(memoryManager); - unsigned int batchSize = armnn::numeric_cast(inputShape[1]); - unsigned int inputSize = armnn::numeric_cast(inputShape[2]); - unsigned int outputSize = armnn::numeric_cast(outputExpectedShape[2]); - unsigned numUnits = outputSize; - - armnn::TensorInfo inputTensorInfo({1, batchSize , inputSize}, ArmnnType, qScale, qOffset ); - armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); - armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); - - armnn::TensorInfo outputTensorInfo({1, batchSize, outputSize}, ArmnnType, qScale, qOffset); - - std::vector inputVector; - inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); - - std::vector cellStateInVector(batchSize * numUnits, T()); - std::vector outputStateInVector(batchSize * outputSize, T()); - - std::vector actualOutput(outputTensorInfo.GetNumElements()); - - std::vector outputVector; - outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); - - std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr cellStateInHandle = - tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); - std::unique_ptr outputStateInHandle = - tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); - - std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); - - armnn::UnidirectionalSequenceLstmQueueDescriptor data; - armnn::WorkloadInfo info; - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); - AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); - - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset); - armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset); - armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); - - std::vector inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f, - -0.34550029f, 0.04266912f, -0.15680569f, - -0.34856534f, 0.43890524f}; - - std::vector inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f, - -0.31343272f, -0.40032279f, 0.44781327f, - 0.01387155f, -0.35593212f}; - - std::vector inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, - -0.20583314f, 0.44344562f, 0.22077113f, - -0.29909778f}; - - std::vector inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f, - 0.40525138f, 0.44272184f, 0.03897077f, - -0.1556896f, 0.19487578f}; - - std::vector recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f, - -0.35746509f, 0.28902304f, 0.08183324f, - -0.16555229f, 0.02286911f, -0.13566875f, - 0.03034258f, 0.48091322f, -0.12528998f, - 0.24077177f, -0.51332325f, -0.33502164f, - 0.10629296f}; - - std::vector recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f, - 0.2112639f, 0.27654213f, 0.20864892f, - -0.07646349f, 0.45877004f, 0.00141793f, - -0.14609534f, 0.36447752f, 0.09196436f, - 0.28053468f, 0.01560611f, -0.20127171f, - -0.01140004f}; - - std::vector recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f, - 0.26320225f, 0.05695659f, -0.00123841f, - -0.4744786f, -0.35869038f, -0.06418842f, - -0.13502428f, -0.501764f, 0.22830659f, - -0.46367589f, 0.26016325f, -0.03894562f, - -0.16368064f}; - - std::vector recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f, - 0.09215671f, 0.24107647f, -0.39835793f, - 0.18212086f, 0.01301402f, 0.48572797f, - -0.50656658f, 0.20047462f, -0.20607421f, - -0.51818722f, -0.15390486f, 0.0468148f, - 0.39922136f}; - - std::vector cellToInputWeights = {0., 0., 0., 0.}; - - std::vector inputGateBias = {0., 0., 0., 0.}; - - std::vector forgetGateBias = {1., 1., 1., 1.}; - - std::vector cellBias = {0., 0., 0., 0.}; - - std::vector outputGateBias = {0., 0., 0., 0.}; - - armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo8); - armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo8); - armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo8); - armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo8); - armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); - armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); - armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); - armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); - armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo4); - armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); - armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); - armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); - armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); - - AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); - AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); - AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); - AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); - AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); - AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); - AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); - AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); - AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); - AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); - AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); - AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); - AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); - - data.m_InputToInputWeights = &inputToInputWeightsTensor; - data.m_InputToForgetWeights = &inputToForgetWeightsTensor; - data.m_InputToCellWeights = &inputToCellWeightsTensor; - data.m_InputToOutputWeights = &inputToOutputWeightsTensor; - data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; - data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; - data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; - data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; - data.m_InputGateBias = &inputGateBiasTensor; - data.m_ForgetGateBias = &forgetGateBiasTensor; - data.m_CellBias = &cellBiasTensor; - data.m_OutputGateBias = &outputGateBiasTensor; - - // Flags to set test configuration - data.m_Parameters.m_ActivationFunc = 4; - data.m_Parameters.m_CifgEnabled = false; - data.m_Parameters.m_PeepholeEnabled = false; - data.m_Parameters.m_ProjectionEnabled = false; - data.m_Parameters.m_ClippingThresCell = 10; - data.m_Parameters.m_ClippingThresProj = 0; - data.m_Parameters.m_TimeMajor = true; - - std::unique_ptr workload - = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info); - inputHandle->Allocate(); - outputStateInHandle->Allocate(); - cellStateInHandle->Allocate(); - - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); - CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); - CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); - - workload->Execute(); - - CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - - return LayerTestResult(actualOutput, - outputVector, - outputHandle->GetShape(), - outputTensorInfo.GetShape()); -} - template> LayerTestResult UnidirectionalSequenceLstmLayerFloat32TestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -553,40 +369,6 @@ UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl( } // anonymous namespace -LayerTestResult UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) -{ - armnn::TensorInfo inputDesc({1, 2, 2}, armnn::DataType::Float32); - std::vector input = {2., 3., 3., 4.}; - - armnn::TensorInfo outputDesc({1, 2, 4}, armnn::DataType::Float32); - std::vector expectedOutput = - {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, - -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}; - - return UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl( - workloadFactory, memoryManager, tensorHandleFactory, - input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape()); -} - -LayerTestResult UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) { - armnn::TensorInfo inputInfo({3, 1, 3}, armnn::DataType::Float32); - std::vector input = { 1., 2., 3., 4., 5., 4., 3., 2., 1. }; - - armnn::TensorInfo outputInfo({3, 1, 4}, armnn::DataType::Float32); - std::vector expectedOutput = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f, - -0.0424661f, -0.231802f, -0.513374f, -0.00680323f, - -0.0668735f, 0.204078f, -0.42765f, -0.0312321f }; - return UnidirectionalSequenceLstmLayerFloat32TestImpl( - workloadFactory, memoryManager, tensorHandleFactory, - input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); -} - LayerTestResult UnidirectionalSequenceLstmLayerFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp index f303b28c10..3a1d178ccb 100644 --- a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp @@ -10,16 +10,6 @@ #include #include -LayerTestResult UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory); - -LayerTestResult UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory); - LayerTestResult UnidirectionalSequenceLstmLayerFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, diff --git a/src/backends/cl/workloads/ClLstmFloatWorkload.cpp b/src/backends/cl/workloads/ClLstmFloatWorkload.cpp index e190f33bbc..37dfab6a5f 100644 --- a/src/backends/cl/workloads/ClLstmFloatWorkload.cpp +++ b/src/backends/cl/workloads/ClLstmFloatWorkload.cpp @@ -7,7 +7,6 @@ #include #include #include -#include #include #include @@ -20,8 +19,8 @@ namespace armnn { using namespace armcomputetensorutils; -ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor& descriptor, - const WorkloadInfo& info, +ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor &descriptor, + const WorkloadInfo &info, const arm_compute::CLCompileContext& clCompileContext) : FloatWorkload(descriptor, info) { @@ -29,7 +28,7 @@ ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor& descriptor, ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClLstmFloatWorkload_Construct", descriptor.m_Parameters, info, - GetGuid()); + this->GetGuid()); arm_compute::LSTMParams lstm_param; @@ -164,8 +163,35 @@ ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor& descriptor, float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); + arm_compute::ActivationLayerInfo activationLayerInfo; + if (m_Data.m_Parameters.m_ActivationFunc == 0) + { + // no activation, do nothing + } + else if (m_Data.m_Parameters.m_ActivationFunc == 1) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::RELU); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 3) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 4) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 6) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC); + } + else + { + throw armnn::Exception("Wrong Type of Activation Function!"); + } { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClLstmFloatWorkload_configure"); @@ -237,7 +263,7 @@ ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor& descriptor, void ClLstmFloatWorkload::Execute() const { - ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClLstmFloatWorkload_Execute", GetGuid()); + ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClLstmFloatWorkload_Execute", this->GetGuid()); RunClFunction(m_LstmLayer, CHECK_LOCATION()); } @@ -328,8 +354,35 @@ arm_compute::Status ClLstmFloatWorkloadValidate(const TensorInfo& input, const T float projection_threshold = descriptor.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); + arm_compute::ActivationLayerInfo activationLayerInfo; + if (descriptor.m_ActivationFunc == 0) + { + // no activation, do nothing + } + else if (descriptor.m_ActivationFunc == 1) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::RELU); + } + else if (descriptor.m_ActivationFunc == 3) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0); + } + else if (descriptor.m_ActivationFunc == 4) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0); + } + else if (descriptor.m_ActivationFunc == 6) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC); + } + else + { + throw armnn::Exception("Wrong Type of Activation Function!"); + } if (descriptor.m_LayerNormEnabled) { diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp index 8901e47a0a..2b2229a4de 100644 --- a/src/backends/neon/NeonLayerSupport.cpp +++ b/src/backends/neon/NeonLayerSupport.cpp @@ -76,7 +76,6 @@ #include "workloads/NeonSubtractionWorkload.hpp" #include "workloads/NeonTransposeConvolution2dWorkload.hpp" #include "workloads/NeonTransposeWorkload.hpp" -#include "workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp" #endif namespace armnn @@ -345,17 +344,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type, *(PolymorphicDowncast(&descriptor)), lstmParamsInfo.value(), reasonIfUnsupported); - case LayerType::UnidirectionalSequenceLstm: - return IsUnidirectionalSequenceLstmSupported(infos[0], - infos[1], - infos[2], - infos[3], - infos[4], - infos[5], - *(PolymorphicDowncast(&descriptor)), - lstmParamsInfo.value(), - reasonIfUnsupported); case LayerType::Maximum: return IsMaximumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); case LayerType::Mean: @@ -1433,26 +1421,4 @@ bool NeonLayerSupport::IsTransposeSupported(const TensorInfo& input, FORWARD_WORKLOAD_VALIDATE_FUNC(NeonTransposeWorkloadValidate, reasonIfUnsupported, input, output, descriptor); } -bool NeonLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input, - const TensorInfo& outputStateIn, - const TensorInfo& cellStateIn, - const TensorInfo& output, - const Optional& hiddenStateOutput, - const Optional& cellStateOutput, - const UnidirectionalSequenceLstmDescriptor& descriptor, - const LstmInputParamsInfo& paramsInfo, - Optional reasonIfUnsupported) const -{ - FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmFloatWorkloadValidate, - reasonIfUnsupported, - input, - outputStateIn, - cellStateIn, - output, - hiddenStateOutput, - cellStateOutput, - descriptor, - paramsInfo); -} - } // namespace armnn diff --git a/src/backends/neon/NeonLayerSupport.hpp b/src/backends/neon/NeonLayerSupport.hpp index 1eef41fda5..afa9b419e6 100644 --- a/src/backends/neon/NeonLayerSupport.hpp +++ b/src/backends/neon/NeonLayerSupport.hpp @@ -336,16 +336,6 @@ public: const TransposeDescriptor& descriptor, Optional reasonIfUnsupported = EmptyOptional()) const override; - bool IsUnidirectionalSequenceLstmSupported(const TensorInfo& input, - const TensorInfo& outputStateIn, - const TensorInfo& cellStateIn, - const TensorInfo& output, - const Optional& hiddenStateOutput, - const Optional& cellStateOutput, - const UnidirectionalSequenceLstmDescriptor& descriptor, - const LstmInputParamsInfo& paramsInfo, - Optional reasonIfUnsupported) const override; - private: const IBackendInternal::IBackendSpecificModelContextPtr m_ModelContextPtr; diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp index 7d94dafc9a..19d322b75d 100644 --- a/src/backends/neon/NeonWorkloadFactory.cpp +++ b/src/backends/neon/NeonWorkloadFactory.cpp @@ -555,11 +555,6 @@ std::unique_ptr NeonWorkloadFactory::CreateWorkload(LayerType type, info, m_MemoryManager->GetIntraLayerManager()); } - case LayerType::UnidirectionalSequenceLstm : - { - auto desc = PolymorphicDowncast(&descriptor); - return MakeWorkloadHelper(*desc, info); - } default: return nullptr; } diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk index d43426f7f4..8ae50ac7e0 100644 --- a/src/backends/neon/backend.mk +++ b/src/backends/neon/backend.mk @@ -84,8 +84,7 @@ BACKEND_SOURCES := \ workloads/NeonStridedSliceWorkload.cpp \ workloads/NeonSubtractionWorkload.cpp \ workloads/NeonTransposeConvolution2dWorkload.cpp \ - workloads/NeonTransposeWorkload.cpp \ - workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp + workloads/NeonTransposeWorkload.cpp else diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp index 231e2b0e7a..9648c1626a 100644 --- a/src/backends/neon/test/NeonLayerTests.cpp +++ b/src/backends/neon/test/NeonLayerTests.cpp @@ -907,22 +907,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(QLstm2, QLstmTest2) // QuantizedLstm ARMNN_AUTO_TEST_CASE_WITH_THF(QuantizedLstm, QuantizedLstmTest) -// Unidirectional Sequence Lstm -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatch, - UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatch, - UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32, - UnidirectionalSequenceLstmLayerFloat32Test) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajor, - UnidirectionalSequenceLstmLayerFloat32TimeMajorTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjection, - UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNorm, - UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjection, - UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest) - // Mean ARMNN_AUTO_TEST_CASE_WITH_THF(MeanSimpleFloat32, MeanSimpleTest) ARMNN_AUTO_TEST_CASE_WITH_THF(MeanSimpleAxisFloat32, MeanSimpleAxisTest) diff --git a/src/backends/neon/workloads/CMakeLists.txt b/src/backends/neon/workloads/CMakeLists.txt index bae51b9c79..0c64a19bf9 100644 --- a/src/backends/neon/workloads/CMakeLists.txt +++ b/src/backends/neon/workloads/CMakeLists.txt @@ -131,8 +131,6 @@ list(APPEND armnnNeonBackendWorkloads_sources NeonTransposeConvolution2dWorkload.hpp NeonTransposeWorkload.cpp NeonTransposeWorkload.hpp - NeonUnidirectionalSequenceLstmFloatWorkload.cpp - NeonUnidirectionalSequenceLstmFloatWorkload.hpp NeonWorkloads.hpp NeonWorkloadUtils.hpp ) diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp index 19c85f7f33..2f14ab9022 100644 --- a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp +++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp @@ -6,8 +6,7 @@ #include "NeonLstmFloatWorkload.hpp" #include "NeonWorkloadUtils.hpp" -#include -#include +#include "aclCommon/ArmComputeTensorUtils.hpp" #include @@ -17,14 +16,14 @@ namespace armnn { using namespace armcomputetensorutils; -NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info) +NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info) : FloatWorkload(descriptor, info) { // Report Profiling Details ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct", descriptor.m_Parameters, info, - GetGuid()); + this->GetGuid()); arm_compute::LSTMParams lstm_param; @@ -161,8 +160,36 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descript float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); + arm_compute::ActivationLayerInfo activationLayerInfo; + if (m_Data.m_Parameters.m_ActivationFunc == 0) + { + // no activation, do nothing + } + else if (m_Data.m_Parameters.m_ActivationFunc == 1) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::RELU); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 3) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 4) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0); + } + else if (m_Data.m_Parameters.m_ActivationFunc == 6) + { + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC); + } + else + { + throw armnn::Exception("Wrong Type of Activation Function!"); + } + m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(), m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(), @@ -246,7 +273,7 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descript void NeonLstmFloatWorkload::Execute() const { - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid()); + ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", this->GetGuid()); m_LstmLayer.run(); } @@ -363,8 +390,31 @@ arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input, float projection_threshold = descriptor.m_ClippingThresProj; // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); + arm_compute::ActivationLayerInfo activationLayerInfo; + switch (descriptor.m_ActivationFunc) + { + case 0: + // no activation, do nothing + break; + case 1: + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::RELU); + break; + case 3: + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0); + break; + case 4: + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0); + break; + case 6: + activationLayerInfo = arm_compute::ActivationLayerInfo( + arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC); + break; + default: + throw armnn::Exception("Wrong Type of Activation Function!"); + } return arm_compute::NELSTMLayer::validate(&aclInputInfo, &aclInputToForgetWeightsInfo, diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp deleted file mode 100644 index c911afb237..0000000000 --- a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp +++ /dev/null @@ -1,911 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "NeonUnidirectionalSequenceLstmFloatWorkload.hpp" -#include "NeonWorkloadUtils.hpp" - -#include -#include - -#include -#include -#include -#include - -#include "neon/NeonTensorHandle.hpp" - -namespace -{ -unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis) -{ - return (numDimensions - axis) - 1; -} -} //namespace - -namespace armnn -{ -using namespace armcomputetensorutils; - -NeonUnidirectionalSequenceLstmFloatWorkload::NeonUnidirectionalSequenceLstmFloatWorkload - (const UnidirectionalSequenceLstmQueueDescriptor& descriptor, const WorkloadInfo& info) - : FloatWorkload(descriptor, info) -{ - // Report Profiling Details - ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonUnidirectionalSequenceLstmFloatWorkload_Construct", - descriptor.m_Parameters, - info, - GetGuid()); - - const arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); - arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); - - TensorInfo inputInfo = info.m_InputTensorInfos[0]; - TensorInfo outputInfo = info.m_OutputTensorInfos[0]; - - arm_compute::DataType armComputeDataType = static_cast(m_Data.m_Inputs[0])->GetDataType(); - armnn::DataType armnnDataType = GetArmNNDataType(armComputeDataType); - - TensorShape inputLayerShape = static_cast(m_Data.m_Inputs[0])->GetShape(); - TensorShape cellStateLayerShape = static_cast(m_Data.m_Inputs[2])->GetShape(); - TensorShape outputLayerShape = static_cast(m_Data.m_Outputs[0])->GetShape(); - - unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; - unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; - unsigned int inputSize = inputLayerShape[2]; - unsigned int outputSize = outputLayerShape[2]; - unsigned int numUnits = cellStateLayerShape[1]; - - const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); - const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); - - // - // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format. - // - if (!m_Data.m_Parameters.m_TimeMajor) - { - std::unique_ptr layer(new arm_compute::NEPermute()); - - TensorInfo permuteOutInfo = inputInfo; - permuteOutInfo.SetShape(timeMajorShapeInput); - BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo); - armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut); - - // Permute to time major format. - layer->configure(&input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U)); - m_Permute1.reset(layer.release()); - } - - // - // Split and Concat Tensors - // - for (unsigned int i = 0; i < maxTime; ++i) - { - arm_compute::Tensor splitter_out; - arm_compute::Tensor concat_in; - - auto splitterTensorInfo = inputInfo; - auto concatTensorInfo = outputInfo; - splitterTensorInfo.SetShape({batchSize, inputSize}); - concatTensorInfo.SetShape({batchSize, outputSize}); - BuildArmComputeTensor(splitter_out, splitterTensorInfo); - BuildArmComputeTensor(concat_in, concatTensorInfo); - - armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out); - armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in); - - // append to std::vector - m_SplitterOutputsTensors.push_back(std::move(splitter_out)); - m_ConcatInputsTensors.push_back(std::move(concat_in)); - } - - for (unsigned int i = 0; i < maxTime; ++i) - { - // append to std::vector - m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]); - m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]); - } - - // - // Split - // - unsigned int numberDimensions = 3; - unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) - - if (maxTime != 1) // ACL split does not work with only one element to split. - { - ViewsDescriptor splitterDesc(maxTime, numberDimensions); - unsigned int splitterDimSizes[3] = {1, batchSize, inputSize}; - for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx) - { - splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx); - for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx) - { - splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]); - } - } - - std::set splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput); - - std::unique_ptr split_layer(new arm_compute::NESplit()); - unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), - *splitAxis.begin()); - if (!m_Data.m_Parameters.m_TimeMajor) - { - split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit); - } else - { - split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit); - } - - split_layer->prepare(); - m_Splitter.reset(split_layer.release()); - } - - // - // Lstm - // - arm_compute::LSTMParams lstm_param; - - m_InputToForgetWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); - - m_InputToCellWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); - - m_InputToOutputWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); - - m_RecurrentToForgetWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); - - m_RecurrentToCellWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); - - m_RecurrentToOutputWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); - - m_ForgetGateBiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); - - m_CellBiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); - - m_OutputGateBiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo()); - - // for future reference: check the AndroidNN API for the logic here - if (!m_Data.m_Parameters.m_CifgEnabled) - { - m_InputToInputWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); - - m_RecurrentToInputWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); - - m_CellToInputWeightsTensor = std::make_unique(); - if (m_Data.m_CellToInputWeights != nullptr) - { - BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); - } - - m_InputGateBiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo()); - - lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(), - m_RecurrentToInputWeightsTensor.get(), - m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : nullptr, - m_InputGateBiasTensor.get()); - } - - if (m_Data.m_Parameters.m_ProjectionEnabled) - { - m_ProjectionWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); - - m_ProjectionBiasTensor = std::make_unique(); - if (m_Data.m_ProjectionBias != nullptr) - { - BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo()); - } - - lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(), - m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : nullptr); - } - - if (m_Data.m_Parameters.m_PeepholeEnabled) - { - m_CellToForgetWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); - - m_CellToOutputWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo()); - - lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get()); - } - - if (m_Data.m_Parameters.m_LayerNormEnabled) - { - m_InputLayerNormWeightsTensor = std::make_unique(); - if (!m_Data.m_Parameters.m_CifgEnabled) - { - BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); - } - - m_ForgetLayerNormWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); - - m_CellLayerNormWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); - - m_OutputLayerNormWeightsTensor = std::make_unique(); - BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo()); - - auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get(); - lstm_param.set_layer_normalization_params(inputNormWeightTensor, - m_ForgetLayerNormWeightsTensor.get(), - m_CellLayerNormWeightsTensor.get(), - m_OutputLayerNormWeightsTensor.get()); - } - - arm_compute::ITensor& output_state_in = static_cast(m_Data.m_Inputs[1])->GetTensor(); - arm_compute::ITensor& cell_state_in = static_cast(m_Data.m_Inputs[2])->GetTensor(); - - arm_compute::ITensor& output_state_out = static_cast(m_Data.m_Inputs[1])->GetTensor(); - arm_compute::ITensor& cell_state_out = static_cast(m_Data.m_Inputs[2])->GetTensor(); - - m_ScratchBuffer = std::make_unique(); - if (m_Data.m_Parameters.m_CifgEnabled) - { - // scratch_buffer [num_units * 3, batch_size] with CIFG - BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType)); - } - else - { - // scratch_buffer [num_units * 4, batch_size] without CIFG - BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType)); - } - - // Need to be set at negative threshold to be compatible for ACL - float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell; - float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; - - // For preparing the object for the class ActivationLayerInfo, consider 5 situations - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); - - for (unsigned int i = 0; i != maxTime; ++i) - { - // Set LSTM input and output ITensors depending on: - // input format (timeMajor) & number of LSTM batches (maxTime). - arm_compute::ITensor* outputLSTM; - arm_compute::ITensor* inputLSTM; - - // If there is only one LSTM time major batch, we will not concat OR permute. - // Set input of LSTM to be first input ITensor. - // Set output of LSTM to be final output ITensor. - // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. - if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor) - { - TensorShape inputShape = GetTensorShape((&input)->info()->tensor_shape(), 1U); - TensorShape outputShape = GetTensorShape((&output)->info()->tensor_shape(), 1U); - - TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); - TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); - - auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); - auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); - - (&input)->info()->set_tensor_shape(acl_input_shape_shrink); - inputLSTM = const_cast(&input); - - (&output)->info()->set_tensor_shape(acl_output_shape_shrink); - outputLSTM = &output; - } - // If there is only one LSTM batch major batch, we will not concat, only permute. - // Set input of LSTM to be output of initial permute. - // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. - // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. - else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor) - { - TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U); - TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); - auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); - m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink); - inputLSTM = &m_PermuteFirstOut; - - outputLSTM = const_cast(m_ConcatInputs[i]); - } - // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. - else - { - inputLSTM = m_SplitterOutputs[i]; - outputLSTM = const_cast(m_ConcatInputs[i]); - } - - std::unique_ptr lstm_layer(new arm_compute::NELSTMLayer()); - lstm_layer->configure(inputLSTM, - m_InputToForgetWeightsTensor.get(), - m_InputToCellWeightsTensor.get(), - m_InputToOutputWeightsTensor.get(), - m_RecurrentToForgetWeightsTensor.get(), - m_RecurrentToCellWeightsTensor.get(), - m_RecurrentToOutputWeightsTensor.get(), - m_ForgetGateBiasTensor.get(), - m_CellBiasTensor.get(), - m_OutputGateBiasTensor.get(), - &output_state_in, - &cell_state_in, - m_ScratchBuffer.get(), - &output_state_out, - &cell_state_out, - outputLSTM, - lstm_param, - activationLayerInfo, - cell_threshold, - projection_threshold); - - m_Layers.emplace_back(std::move(lstm_layer)); - } - - armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer); - - InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights); - InitializeArmComputeTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights); - InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights); - InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights); - InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights); - InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights); - InitializeArmComputeTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias); - InitializeArmComputeTensorData(*m_CellBiasTensor, m_Data.m_CellBias); - InitializeArmComputeTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias); - - if (!m_Data.m_Parameters.m_CifgEnabled) - { - InitializeArmComputeTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights); - InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights); - if (m_Data.m_CellToInputWeights != nullptr) - { - InitializeArmComputeTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights); - } - InitializeArmComputeTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias); - } - - if (m_Data.m_Parameters.m_ProjectionEnabled) - { - InitializeArmComputeTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights); - if (m_Data.m_ProjectionBias != nullptr) - { - InitializeArmComputeTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias); - } - } - - if (m_Data.m_Parameters.m_PeepholeEnabled) - { - InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights); - InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights); - } - - if (m_Data.m_Parameters.m_LayerNormEnabled) - { - if (!m_Data.m_Parameters.m_CifgEnabled) - { - InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights); - } - InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights); - InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights); - InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights); - } - - // Force Compute Library to perform the necessary copying and reshaping. - // After which delete all the input tensors that will no longer be needed. - for (uint32_t i = 0; i < m_Layers.size(); ++i) - { - m_Layers[i]->prepare(); - } - - // - // Concat - // - - // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. - TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U); - TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); - TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); - - if (maxTime != 1) // ACL concat does not work with only one element to concatenate. - { - for (unsigned int i = 0; i < maxTime; ++i) - { - m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); - } - - ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views). - for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx) - { - concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx); - concatDescriptor.SetConcatAxis(dimension); - } - - m_Concat.reset(new arm_compute::NEConcatenateLayer()); - unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis()); - if (!m_Data.m_Parameters.m_TimeMajor) - { - TensorInfo concatOuputTensorInfo = outputInfo; - concatOuputTensorInfo.SetShape(timeMajorShapeOutput); - BuildArmComputeTensor(concat_out, concatOuputTensorInfo); - armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out); - - m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat); - } - else - { - m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat); - } - - m_Concat->prepare(); - } - // If only one LSTM batch, we do not concat and/or permute. - // Must ensure final output info is expanded to correct batch major dimensions. - else - { - if (!m_Data.m_Parameters.m_TimeMajor) - { - (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor)); - } - else - { - (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); - } - } - - // - // Permute: only done if input/output are in batch major format. - // - if (!m_Data.m_Parameters.m_TimeMajor) - { - // Output now time major. Permute output back to batch major. - std::unique_ptr layer(new arm_compute::NEPermute()); - if (maxTime != 1) - { - layer->configure(&concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U)); - } - else - { - layer->configure(m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U)); - } - m_Permute2.reset(layer.release()); - } - - FreeUnusedTensors(); -} - -void NeonUnidirectionalSequenceLstmFloatWorkload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonUnidirectionalSequenceLstmFloatWorkload_Execute", GetGuid()); - if (m_Permute1) - { - m_Permute1->run(); - } - if (m_Splitter) - { - m_Splitter->run(); - } - for (uint32_t i = 0; i < m_Layers.size(); ++i) - { - m_Layers[i]->run(); - } - if (m_Concat) - { - m_Concat->run(); - } - if (m_Permute2) - { - m_Permute2->run(); - } -} - -arm_compute::Status -NeonUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input, - const TensorInfo& outputStateIn, - const TensorInfo& cellStateIn, - const TensorInfo& output, - const Optional& hiddenStateOutput, - const Optional& cellStateOutput, - const UnidirectionalSequenceLstmDescriptor& descriptor, - const LstmInputParamsInfo& paramsInfo) -{ - IgnoreUnused(hiddenStateOutput, cellStateOutput); - - TensorShape inputLayerShape = input.GetShape(); - TensorShape outputLayerShape = outputStateIn.GetShape(); - - unsigned int maxTime = descriptor.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; - unsigned int batchSize = descriptor.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; - unsigned int inputSize = inputLayerShape[2]; - unsigned int outputSize = outputLayerShape[2]; - - const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); - const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); - - arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK, - "Permute1 status"); - arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK, - "Split status"); - arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK, - "LSTM status"); - arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK, - "Concat status"); - arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK, - "Permute2 status"); - - const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); - const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); - - // - // Permute validate - // - TensorInfo permuteOutInfo = TensorInfo(input); - arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo); - if (!descriptor.m_TimeMajor) - { - statusPermute1 = arm_compute::NEPermute::validate(&aclInputInfo, - &aclPermuteOutInfo, - arm_compute::PermutationVector(0U, 2U, 1U)); - } - - // - // Split and Concat Tensors validate - // - std::vector splitterOutputsTensorInfos; - std::vector concatInputsTensorInfos; - std::vector splitterOutputsTensorInfosPtr; - std::vector concatInputsTensorInfosPtr; - splitterOutputsTensorInfos.reserve(maxTime); - concatInputsTensorInfos.reserve(maxTime); - for (unsigned int i = 0; i < maxTime; ++i) - { - arm_compute::TensorInfo splitter_out; - arm_compute::TensorInfo concat_in; - - auto splitterTensorInfo = TensorInfo(input); - auto concatTensorInfo = TensorInfo(output); - splitterTensorInfo.SetShape({batchSize, inputSize}); - concatTensorInfo.SetShape({batchSize, outputSize}); - - arm_compute::TensorInfo aclSplitterTensorInfo - = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo); - arm_compute::TensorInfo aclConcatTensorInfo - = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo); - - splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo); - concatInputsTensorInfos.emplace_back(aclConcatTensorInfo); - splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]); - concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]); - } - - // - // Split validate - // - unsigned int numberDimensions = 3; - unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) - unsigned int aclAxisSplit = CalcAclAxis(numberDimensions, dimension); - - if (maxTime != 1) // ACL split does not work with only one element to split. - { - if (!descriptor.m_TimeMajor) - { - statusSplit = arm_compute::NESplit::validate(&aclPermuteOutInfo, - splitterOutputsTensorInfosPtr, - aclAxisSplit); - } else - { - statusSplit = arm_compute::NESplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit); - } - } - - // - // LSTM validate - // - - arm_compute::LSTMParams lstm_params_info; - - const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); - const TensorInfo& outputStateOut = TensorInfo(outputStateIn.GetShape(), input.GetDataType()); - const TensorInfo& cellStateOut = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); - - // The inputs and outputs - const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn); - const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn); - const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer); - const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut); - const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut); - - // Basic parameters - const arm_compute::TensorInfo aclInputToForgetWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights()); - const arm_compute::TensorInfo aclInputToCellWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights()); - const arm_compute::TensorInfo aclInputToOutputWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights()); - const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights()); - const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights()); - const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo - = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights()); - const arm_compute::TensorInfo aclForgetGateBiasInfo - = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias()); - const arm_compute::TensorInfo aclCellBiasInfo - = BuildArmComputeTensorInfo(paramsInfo.GetCellBias()); - const arm_compute::TensorInfo aclOutputGateBiasInfo - = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias()); - - arm_compute::TensorInfo aclInputToInputWeightsInfo; - arm_compute::TensorInfo aclRecurrentToInputWeightsInfo; - arm_compute::TensorInfo aclCellToInputWeightsInfo; - arm_compute::TensorInfo aclInputGateBiasInfo; - arm_compute::TensorInfo aclProjectionWeightsInfo; - arm_compute::TensorInfo aclProjectionBiasInfo; - arm_compute::TensorInfo aclCellToForgetWeightsInfo; - arm_compute::TensorInfo aclCellToOutputWeightsInfo; - - arm_compute::TensorInfo aclInputLayerNormWeightsInfo; - arm_compute::TensorInfo aclForgetLayerNormWeightsInfo; - arm_compute::TensorInfo aclCellLayerNormWeightsInfo; - arm_compute::TensorInfo aclOutputLayerNormWeightsInfo; - - - if (!descriptor.m_CifgEnabled) - { - if (descriptor.m_PeepholeEnabled) - { - aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights()); - } - aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights()); - aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights()); - aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias()); - - lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, - &aclRecurrentToInputWeightsInfo, - descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr, - &aclInputGateBiasInfo); - } - - if (descriptor.m_ProjectionEnabled) - { - if (paramsInfo.m_ProjectionBias != nullptr) - { - aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias()); - } - aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights()); - - lstm_params_info.set_projection_params(&aclProjectionWeightsInfo, - paramsInfo.m_ProjectionBias ? &aclProjectionBiasInfo : nullptr); - } - - if (descriptor.m_PeepholeEnabled) - { - aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights()); - aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights()); - - lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo); - } - - if (descriptor.m_LayerNormEnabled) - { - if (!descriptor.m_CifgEnabled) - { - aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights()); - } - aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights()); - aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights()); - aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights()); - - lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr : - &aclInputLayerNormWeightsInfo, - &aclForgetLayerNormWeightsInfo, - &aclCellLayerNormWeightsInfo, - &aclOutputLayerNormWeightsInfo); - } - - // Need to be set at negative threshold to be compatible for ACL - float cell_threshold = descriptor.m_ClippingThresCell; - float projection_threshold = descriptor.m_ClippingThresProj; - - arm_compute::ActivationLayerInfo activationLayerInfo = - ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); - - for (unsigned int i = 0; i != maxTime; ++i) - { - - // Set LSTM input and output ITensors depending on: - // input format (timeMajor) & number of LSTM batches (maxTime). - arm_compute::ITensorInfo* outputLSTM; - arm_compute::ITensorInfo* inputLSTM; - - // If there is only one LSTM time major batch, we will not concat OR permute. - // Set input of LSTM to be first input ITensor. - // Set output of LSTM to be final output ITensor. - // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. - if (maxTime == 1 && !descriptor.m_TimeMajor) - { - TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U); - TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U); - - TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); - TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); - - auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); - auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); - - const_cast(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink); - inputLSTM = const_cast(&aclInputInfo); - - const_cast(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink); - outputLSTM = const_cast(&aclOutputInfo); - } - // If there is only one LSTM batch major batch, we will not concat, only permute. - // Set input of LSTM to be output of initial permute. - // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. - // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. - else if (maxTime == 1 && !descriptor.m_TimeMajor) - { - TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U); - TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); - auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); - aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink); - inputLSTM = &aclPermuteOutInfo; - - outputLSTM = const_cast(concatInputsTensorInfosPtr[i]); - } - // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. - else - { - inputLSTM = splitterOutputsTensorInfosPtr[i]; - outputLSTM = const_cast(concatInputsTensorInfosPtr[i]); - } - - statusLSTM = arm_compute::NELSTMLayer::validate(inputLSTM, - &aclInputToForgetWeightsInfo, - &aclInputToCellWeightsInfo, - &aclInputToOutputWeightsInfo, - &aclRecurrentToForgetWeightsInfo, - &aclRecurrentToCellWeightsInfo, - &aclRecurrentToOutputWeightsInfo, - &aclForgetGateBiasInfo, - &aclCellBiasInfo, - &aclOutputGateBiasInfo, - &aclOutputStateInInfo, - &aclCellStateInInfo, - &aclScratchBufferInfo, - &aclOutputStateOutInfo, - &aclCellStateOutInfo, - outputLSTM, - lstm_params_info, - activationLayerInfo, - cell_threshold, - projection_threshold); - - if (statusLSTM.error_code() != arm_compute::ErrorCode::OK) - { - break; - } - } - - // - // Concat validate - // - - // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. - TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U); - TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); - TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); - - TensorInfo concatOuputTensorInfo = TensorInfo(output); - concatOuputTensorInfo.SetShape(timeMajorShapeOutput); - arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo); - - if (maxTime != 1) // ACL concat does not work with only one element to concatenate. - { - for (unsigned int i = 0; i < maxTime; ++i) - { - auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor); - concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand); - } - - unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension); - if (!descriptor.m_TimeMajor) - { - statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, - &aclConcatOuputTensorInfo, - aclAxisConcat); - } - else - { - statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, - &aclOutputInfo, - aclAxisConcat); - } - } - // If only one LSTM batch, we do not concat and/or permute. - // Must ensure final output info is expanded to correct batch major dimensions. - else - { - if (!descriptor.m_TimeMajor) - { - const_cast(&aclInputInfo)->set_tensor_shape( - BuildArmComputeTensorShape(shapeExpandBatchMajor)); - } - else - { - const_cast(&aclInputInfo)->set_tensor_shape( - BuildArmComputeTensorShape(shapeExpandTimeMajor)); - } - } - - // - // Permute validate - // - if (!descriptor.m_TimeMajor) - { - // Output now time major. Permute output back to batch major. - if (maxTime != 1) - { - statusPermute2 = arm_compute::NEPermute::validate(&aclConcatOuputTensorInfo, - &aclOutputInfo, - arm_compute::PermutationVector(0U, 2U, 1U)); - } - else - { - statusPermute2 = arm_compute::NEPermute::validate(concatInputsTensorInfosPtr[0], - &aclOutputInfo, - arm_compute::PermutationVector(0U, 2U, 1U)); - } - } - - auto okCode = arm_compute::ErrorCode::OK; - if (statusPermute1.error_code() == okCode && - statusSplit.error_code() == okCode && - statusLSTM .error_code() == okCode && - statusConcat.error_code() == okCode && - statusPermute2.error_code() == okCode) - { - return arm_compute::Status(arm_compute::ErrorCode::OK, - "All Unidirectional Sequence LSTM layer validate status OK."); - } - else - { - return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, - "Unidirectional Sequence LSTM layer validate status failed."); - } -} - -void NeonUnidirectionalSequenceLstmFloatWorkload::FreeUnusedTensors() -{ - FreeTensorIfUnused(m_InputToInputWeightsTensor); - FreeTensorIfUnused(m_InputToForgetWeightsTensor); - FreeTensorIfUnused(m_InputToCellWeightsTensor); - FreeTensorIfUnused(m_InputToOutputWeightsTensor); - FreeTensorIfUnused(m_RecurrentToInputWeightsTensor); - FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor); - FreeTensorIfUnused(m_RecurrentToCellWeightsTensor); - FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor); - FreeTensorIfUnused(m_CellToInputWeightsTensor); - FreeTensorIfUnused(m_CellToForgetWeightsTensor); - FreeTensorIfUnused(m_CellToOutputWeightsTensor); - FreeTensorIfUnused(m_InputGateBiasTensor); - FreeTensorIfUnused(m_ForgetGateBiasTensor); - FreeTensorIfUnused(m_CellBiasTensor); - FreeTensorIfUnused(m_OutputGateBiasTensor); - FreeTensorIfUnused(m_ProjectionWeightsTensor); - FreeTensorIfUnused(m_ProjectionBiasTensor); - FreeTensorIfUnused(m_InputLayerNormWeightsTensor); - FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor); - FreeTensorIfUnused(m_CellLayerNormWeightsTensor); - FreeTensorIfUnused(m_OutputLayerNormWeightsTensor); - FreeTensorIfUnused(m_ScratchBuffer); -} - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp deleted file mode 100644 index 10c2ecbd19..0000000000 --- a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp +++ /dev/null @@ -1,92 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include -#include -#include -#include - -#include "arm_compute/graph/Tensor.h" -#include "arm_compute/runtime/NEON/functions/NELSTMLayer.h" -#include "arm_compute/runtime/NEON/functions/NEPermute.h" -#include "arm_compute/runtime/NEON/functions/NESplit.h" -#include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h" - -namespace armnn -{ - -class NeonUnidirectionalSequenceLstmFloatWorkload : public FloatWorkload -{ -public: - NeonUnidirectionalSequenceLstmFloatWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - - // - // ACL layers required to fully form a Unidirectional Sequence LSTM layer. - // - mutable std::unique_ptr m_Permute1; - mutable std::unique_ptr m_Splitter; - mutable std::vector> m_Layers; - mutable std::unique_ptr m_Concat; - mutable std::unique_ptr m_Permute2; - - // - // ACL LSTM arm_compute::Tensors. - // - std::unique_ptr m_InputToInputWeightsTensor; - std::unique_ptr m_InputToForgetWeightsTensor; - std::unique_ptr m_InputToCellWeightsTensor; - std::unique_ptr m_InputToOutputWeightsTensor; - std::unique_ptr m_RecurrentToInputWeightsTensor; - std::unique_ptr m_RecurrentToForgetWeightsTensor; - std::unique_ptr m_RecurrentToCellWeightsTensor; - std::unique_ptr m_RecurrentToOutputWeightsTensor; - std::unique_ptr m_CellToInputWeightsTensor; - std::unique_ptr m_CellToForgetWeightsTensor; - std::unique_ptr m_CellToOutputWeightsTensor; - std::unique_ptr m_InputGateBiasTensor; - std::unique_ptr m_ForgetGateBiasTensor; - std::unique_ptr m_CellBiasTensor; - std::unique_ptr m_OutputGateBiasTensor; - std::unique_ptr m_ProjectionWeightsTensor; - std::unique_ptr m_ProjectionBiasTensor; - - std::unique_ptr m_ScratchBuffer; - - std::unique_ptr m_InputLayerNormWeightsTensor; - std::unique_ptr m_ForgetLayerNormWeightsTensor; - std::unique_ptr m_CellLayerNormWeightsTensor; - std::unique_ptr m_OutputLayerNormWeightsTensor; - - // - // Additional ACL arm_compute::Tensors and std::vector. - // Required to perform splitting, concatenation and permutations. - // - arm_compute::Tensor m_PermuteFirstOut; - std::vector m_SplitterOutputsTensors; - std::vector m_ConcatInputsTensors; - std::vector m_SplitterOutputs; - std::vector m_ConcatInputs; - arm_compute::Tensor concat_out; - - void FreeUnusedTensors(); -}; - -arm_compute::Status -NeonUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input, - const TensorInfo& outputStateIn, - const TensorInfo& cellStateIn, - const TensorInfo& output, - const Optional& hiddenStateOutput, - const Optional& cellStateOutput, - const UnidirectionalSequenceLstmDescriptor& descriptor, - const LstmInputParamsInfo& paramsInfo); - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonWorkloads.hpp b/src/backends/neon/workloads/NeonWorkloads.hpp index 4f5ba2d708..a8134a130b 100644 --- a/src/backends/neon/workloads/NeonWorkloads.hpp +++ b/src/backends/neon/workloads/NeonWorkloads.hpp @@ -68,4 +68,3 @@ #include "NeonSubtractionWorkload.hpp" #include "NeonTransposeConvolution2dWorkload.hpp" #include "NeonTransposeWorkload.hpp" -#include "NeonUnidirectionalSequenceLstmFloatWorkload.hpp" diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp index b3df088c39..69694e0275 100644 --- a/src/backends/reference/test/RefLayerTests.cpp +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -2554,10 +2554,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ReduceMinFloat32, ReduceMinSimpleTest) // Unidirectional Sequence Lstm -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatch, - UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatch, - UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest) ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32, UnidirectionalSequenceLstmLayerFloat32Test) ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajor, -- cgit v1.2.1