MLCE-530 Add support for UnidirectionalSequenceLstm to RefWorkload
* Add implementation of IsUnidirectionalSequenceLstmSupported to RefLayerSupport
* Add RefUnidirectionalSequenceLstmWorkload
* Refactor Lstm to be able to use for Lstm and SequenceLstm
* Unit tests
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Ibc066d213213a11b955dfefbe518de643298ba0c
diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp
index 7188a7b..f4a5482 100644
--- a/include/armnn/Descriptors.hpp
+++ b/include/armnn/Descriptors.hpp
@@ -926,7 +926,7 @@
, m_PeepholeEnabled(false)
, m_ProjectionEnabled(false)
, m_LayerNormEnabled(false)
- , m_TimeMajor(true)
+ , m_TimeMajor(false)
{}
bool operator ==(const LstmDescriptor& rhs) const
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index 319cdb1..d87f858 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -3734,9 +3734,7 @@
std::vector<DataType> supportedTypes =
{
- DataType::Float16,
- DataType::Float32,
- DataType::QAsymmS8
+ DataType::Float32
};
// check for supported type of one input and match them with all the other input and output
diff --git a/src/backends/backendsCommon/common.mk b/src/backends/backendsCommon/common.mk
index ff9375d..5d33947 100644
--- a/src/backends/backendsCommon/common.mk
+++ b/src/backends/backendsCommon/common.mk
@@ -93,7 +93,8 @@
test/layerTests/StackTestImpl.cpp \
test/layerTests/StridedSliceTestImpl.cpp \
test/layerTests/SubtractionTestImpl.cpp \
- test/layerTests/TransposeConvolution2dTestImpl.cpp
+ test/layerTests/TransposeConvolution2dTestImpl.cpp \
+ test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
ifeq ($(ARMNN_REF_ENABLED),1)
COMMON_TEST_SOURCES += \
diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt
index 755cd21..4561fd7 100644
--- a/src/backends/backendsCommon/test/CMakeLists.txt
+++ b/src/backends/backendsCommon/test/CMakeLists.txt
@@ -169,6 +169,8 @@
layerTests/SubtractionTestImpl.hpp
layerTests/TransposeConvolution2dTestImpl.cpp
layerTests/TransposeConvolution2dTestImpl.hpp
+ layerTests/UnidirectionalSequenceLstmTestImpl.cpp
+ layerTests/UnidirectionalSequenceLstmTestImpl.hpp
)
if (ARMNNREF)
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp
index 46eb6ee..fcb1f71 100644
--- a/src/backends/backendsCommon/test/LayerTests.hpp
+++ b/src/backends/backendsCommon/test/LayerTests.hpp
@@ -67,3 +67,4 @@
#include <backendsCommon/test/layerTests/SubtractionTestImpl.hpp>
#include <backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp>
#include <backendsCommon/test/layerTests/TransposeTestImpl.hpp>
+#include <backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp>
diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
new file mode 100644
index 0000000..ac22d5d
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
@@ -0,0 +1,1030 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "UnidirectionalSequenceLstmTestImpl.hpp"
+
+#include <armnn/utility/NumericCast.hpp>
+
+#include <backendsCommon/TensorHandle.hpp>
+
+#include <backendsCommon/test/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadTestUtils.hpp>
+
+#include <ResolveType.hpp>
+
+namespace {
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory,
+ const std::vector<T>& input,
+ const std::vector<T>& 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<unsigned int>(inputShape[0]);
+ unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
+ unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
+ unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
+ unsigned numUnits = outputSize;
+
+ armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
+
+ armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
+
+ std::vector<T> inputVector;
+ inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
+
+ std::vector<T> cellStateInVector(batchSize * numUnits, T());
+ std::vector<T> outputStateInVector(batchSize * outputSize, T());
+
+ std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
+
+ std::vector<T> outputVector;
+ outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> 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 tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
+
+ std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
+ -0.117484632f, 0.3298470976f, -0.1179017122f,
+ 0.214305695f, 0.42135173085f, 0.003878414626f,
+ -0.348303917f, -0.1881275477f, 0.0343011027f };
+
+ std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
+ -0.3810434485f, 0.268383264f, -0.009807467424f,
+ -0.3522925403f, -0.24275735512f, -0.28344226125f,
+ 0.13512269116f, -0.4932442977f, -0.10039821991f };
+
+ std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
+ 0.386399507f, -0.259465157985f, -0.16545993089f,
+ -0.4230232555f, 0.341664791103f, -0.18127849691f,
+ -0.2277662414f, -0.55275535589f, 0.34184026718f };
+
+ std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
+ 0.53969591851f, 0.23393625035f, -0.27140527306f,
+ 0.50009280443f, 0.07511717046f, 0.3998299249f,
+ -0.51717478049f, 0.1889653282f, -0.367323637f };
+
+ std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
+ -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
+ 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
+ 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f };
+
+ std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
+ -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
+ -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
+ -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
+
+ std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
+ -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
+ 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
+ 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
+
+ std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
+ -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
+ 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
+ -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
+
+ std::vector<float> inputGateBias = { 0., 0., 0., 0. };
+
+ std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
+
+ std::vector<float> cellBias = { 0., 0., 0., 0. };
+
+ std::vector<float> outputGateBias = { 0., 0., 0., 0. };
+
+ armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
+ 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(&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_ClippingThresCell = 10;
+ data.m_Parameters.m_ClippingThresProj = 0;
+ 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_TimeMajor = false;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(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<T, 3>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 3>
+UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory,
+ const std::vector<T>& input,
+ const std::vector<T>& 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<unsigned int>(inputShape[1]);
+ unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
+ unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
+ unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
+ unsigned numUnits = outputSize;
+
+ armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
+
+ armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);
+
+ std::vector<T> inputVector;
+ inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
+
+ std::vector<T> cellStateInVector(batchSize * numUnits, T());
+ std::vector<T> outputStateInVector(batchSize * outputSize, T());
+
+ std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
+
+ std::vector<T> outputVector;
+ outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> 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 tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
+ armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
+
+ std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f,
+ 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f,
+ 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f,
+ -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f };
+
+ std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f,
+ -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f,
+ -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f,
+ -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f };
+
+ std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f,
+ 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f,
+ 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f,
+ -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f };
+
+ std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f,
+ -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f,
+ 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f,
+ -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f };
+
+ std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f,
+ -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f,
+ -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f,
+ 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f };
+
+ std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f,
+ 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f,
+ -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f,
+ 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f };
+
+ std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f,
+ -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f,
+ -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f,
+ -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f };
+
+ std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f,
+ -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f,
+ 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f,
+ 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f };
+
+ std::vector<float> inputGateBias = { 0., 0., 0., 0. };
+
+ std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
+
+ std::vector<float> cellBias = { 0., 0., 0., 0. };
+
+ std::vector<float> outputGateBias = { 0., 0., 0., 0. };
+
+ armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
+ 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(&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_ClippingThresCell = 10;
+ data.m_Parameters.m_ClippingThresProj = 0;
+ 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_TimeMajor = true;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(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<T, 3>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
+}
+
+} // anonymous namespace
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory) {
+ armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32);
+ std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
+ 3., 2., 1., 2., 3., 4.,
+ 5., 4., 3., 2., 1., 2. };
+
+ armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32);
+ std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,
+ -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,
+ -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,
+ -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,
+ -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,
+ -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };
+ return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
+}
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory) {
+ armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32);
+ std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
+ 3., 2., 1., 2., 3., 4.,
+ 5., 4., 3., 2., 1., 2. };
+
+ armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32);
+ std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,
+ 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,
+ -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,
+ 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,
+ 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,
+ 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };
+ return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(
+ workloadFactory, memoryManager, tensorHandleFactory,
+ input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
+}
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory)
+{
+ IgnoreUnused(memoryManager);
+ unsigned int batchSize = 2;
+ unsigned int timeSize = 3;
+ unsigned int outputSize = 5;
+ unsigned int inputSize = 4;
+ unsigned numUnits = 6;
+
+ armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+
+ const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
+ 3., 2., 1., 2., 3., 4.,
+ 5., 4., 3., 2., 1., 2.,
+ 1., 2., 3., 4., 5., 4.};
+
+ std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
+ std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+
+ std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
+
+ const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
+ -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,
+ -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,
+ 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,
+ -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,
+ -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> 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 tensorInfo5({outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32);
+
+ std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,
+ -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,
+ -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,
+ -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,
+ -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,
+ -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };
+
+ std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,
+ 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,
+ 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,
+ -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,
+ -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,
+ 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};
+
+ std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
+ -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
+ -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
+ -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
+ -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
+ 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };
+
+ std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,
+ -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,
+ -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,
+ 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,
+ 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,
+ -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };
+
+ std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,
+ 0.10380666f, 0.053110216f, -0.06928846f };
+
+ std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,
+ 0.23027696f, 0.11098921f, 0.08989442f };
+
+ std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,
+ 0.033463873f, -0.1483596f, 0.029460307f };
+
+ std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,
+ 0.12648113f, 0.027195795f, 0.35373217f };
+
+ std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
+ -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
+ -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
+ -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
+ 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
+ 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
+ -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
+ 0.14283475f, -0.07390571f };
+
+ std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
+ 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
+ 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
+ -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
+ 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
+ 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
+ -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
+ -0.019443132f, -0.030755889f };
+
+ std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
+ 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
+ -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
+ 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
+ 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
+ -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
+ -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
+ 0.061878487f, -0.04729229f };
+
+ std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,
+ -0.045984812f,-0.01255415f, -0.0026479573f,
+ -0.08196161f, -0.054914974f, -0.0046604523f,
+ -0.029587349f, -0.044576716f, -0.07480124f,
+ -0.082868785f, 0.023254942f, 0.027502948f,
+ -0.0039728214f, -0.08683098f, -0.08116779f,
+ -0.014675607f, -0.037924774f, -0.023314456f,
+ -0.007401714f, -0.09255757f, 0.029460307f,
+ -0.08829125f, -0.005139627f, -0.08989442f,
+ -0.0555066f, 0.13596267f, 0.025062224f };
+
+ std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,
+ 0.018586371f, -0.037586458f, -0.15312155f };
+
+ std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,
+ -0.012770197f, 0.041331276f, -0.072311886f };
+
+ std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,
+ 0.002913762f, 0.17764764f, -0.5495371f };
+
+ std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
+ 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
+ -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
+ -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
+ 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
+ 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };
+
+ std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
+
+ armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4);
+ armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4);
+ armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4);
+ armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4);
+ armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5);
+ armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5);
+ armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5);
+ armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5);
+ armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6);
+ armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6);
+ armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6);
+ armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6);
+ armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6);
+ armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6);
+ armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6);
+ armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6);
+ armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5);
+
+ 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());
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.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_CellToInputWeights = &cellToInputWeightsTensor;
+ data.m_InputGateBias = &inputGateBiasTensor;
+ data.m_ForgetGateBias = &forgetGateBiasTensor;
+ data.m_CellBias = &cellBiasTensor;
+ data.m_OutputGateBias = &outputGateBiasTensor;
+ data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
+ data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
+ data.m_ProjectionWeights = &projectionWeightsTensor;
+ data.m_ProjectionBias = &projectionBiasTensor;
+
+ // Flags to set test configuration
+ data.m_Parameters.m_ActivationFunc = 4;
+ data.m_Parameters.m_CifgEnabled = false;
+ data.m_Parameters.m_PeepholeEnabled = true;
+ data.m_Parameters.m_ProjectionEnabled = true;
+ data.m_Parameters.m_LayerNormEnabled = false;
+ data.m_Parameters.m_TimeMajor = false;
+ data.m_Parameters.m_ClippingThresCell = 10.0f;
+
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(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<float, 3>(actualOutput,
+ expectedOutput,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
+}
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory)
+{
+ IgnoreUnused(memoryManager);
+ unsigned int batchSize = 3;
+ unsigned int timeSize = 2;
+ unsigned int outputSize = 4;
+ unsigned int inputSize = 3;
+ unsigned numUnits = 5;
+
+ armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+
+ const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
+ 3., 2., 1., 2., 3., 4.,
+ 5., 4., 3., 2., 1., 2. };
+
+ std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
+ std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+
+ std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
+
+ const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
+ 0.11458f, 0.0407109f, 0.300327f, 0.174301f,
+ 0.0864761f, 0.0362912f, 0.178635f, 0.115689f,
+ 0.108008f, 0.0386623f, 0.273471f, 0.167115f,
+ 0.0859545f, 0.0331481f, 0.186051f, 0.11888f,
+ 0.106649f, 0.0276847f, 0.229863f, 0.166958f };
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> 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({outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32);
+
+ std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
+ -0.117484632f, 0.3298470976f, -0.1179017122f,
+ 0.214305695f, 0.42135173085f, 0.003878414626f,
+ -0.348303917f, -0.1881275477f, 0.0343011027f,
+ -0.38837709614f, -0.05636804124f, 0.4259087456f};
+
+ std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
+ -0.3810434485f, 0.268383264f, -0.009807467424f,
+ -0.3522925403f, -0.24275735512f, -0.28344226125f,
+ 0.13512269116f, -0.4932442977f, -0.10039821991f,
+ 0.2726137042f, 0.09216640889f, -0.06551410215f};
+
+ std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
+ 0.386399507f, -0.259465157985f, -0.16545993089f,
+ -0.4230232555f, 0.341664791103f, -0.18127849691f,
+ -0.2277662414f, -0.55275535589f, 0.34184026718f,
+ 0.3954237699f, -0.19407111404f, 0.30412107706f};
+
+ std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
+ 0.53969591851f, 0.23393625035f, -0.27140527306f,
+ 0.50009280443f, 0.07511717046f, 0.3998299249f,
+ -0.51717478049f, 0.1889653282f, -0.367323637f,
+ -0.12584099173f, -0.12319286912f, 0.2407919466f};
+
+ std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
+ std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
+ std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
+ std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
+
+ std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
+ -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
+ 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
+ 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,
+ 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };
+
+ std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
+ -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
+ -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
+ -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,
+ 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };
+
+ std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
+ -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
+ 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
+ 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,
+ 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };
+
+ std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
+ -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
+ 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
+ -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,
+ 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };
+
+ std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };
+ std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };
+ std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };
+
+ std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,
+ 0.1f, 0.5f, 0.3f, 0.08f,
+ 0.07f, 0.2f, -0.4f, 0.2f,
+ 0.5f, -0.4f, 0.3f, -0.2f,
+ 0.3f, 0.08f, -0.07f, 0.2f};
+
+ std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
+
+ std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };
+ std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
+ std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };
+ std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };
+
+ armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3);
+ armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3);
+ armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3);
+ armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3);
+ armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4);
+ armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4);
+ armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4);
+ armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4);
+ armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5);
+ armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5);
+ armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5);
+ armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5);
+ armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5);
+ armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4);
+
+ armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5);
+ armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5);
+
+ 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());
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
+ AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
+
+ AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
+ AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.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_CellToInputWeights = &cellToInputWeightsTensor;
+ data.m_InputGateBias = &inputGateBiasTensor;
+ data.m_ForgetGateBias = &forgetGateBiasTensor;
+ data.m_CellBias = &cellBiasTensor;
+ data.m_OutputGateBias = &outputGateBiasTensor;
+ data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
+ data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
+ data.m_ProjectionWeights = &projectionWeightsTensor;
+ data.m_ProjectionBias = &projectionBiasTensor;
+
+ data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
+ data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
+ data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
+ data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
+
+ // Flags to set test configuration
+ data.m_Parameters.m_ActivationFunc = 4;
+ data.m_Parameters.m_CifgEnabled = false;
+ data.m_Parameters.m_PeepholeEnabled = true;
+ data.m_Parameters.m_ProjectionEnabled = true;
+ data.m_Parameters.m_LayerNormEnabled = true;
+ data.m_Parameters.m_TimeMajor = false;
+ data.m_Parameters.m_ClippingThresCell = 10.0f;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(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<float, 3>(actualOutput,
+ expectedOutput,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
+}
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory)
+{
+ IgnoreUnused(memoryManager);
+ unsigned int batchSize = 3;
+ unsigned int timeSize = 2;
+ unsigned int inputSize = 3;
+ unsigned int outputSize = 4;
+ unsigned numUnits = outputSize;
+
+ armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
+
+ armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+
+ std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
+ 3., 2., 1., 2., 3., 4.,
+ 5., 4., 3., 2., 1., 2. };
+
+ std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
+ std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+
+ std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
+
+ std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
+ -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,
+ -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,
+ -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,
+ -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,
+ -0.031675f, 0.125987f, -0.526695f, -0.110093f };
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> 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}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32);
+ armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32);
+
+ std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
+ -0.3810434485f, 0.268383264f, -0.009807467424f,
+ -0.3522925403f, -0.24275735512f, -0.28344226125f,
+ 0.13512269116f, -0.4932442977f, -0.10039821991f };
+
+ std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
+ 0.386399507f, -0.259465157985f, -0.16545993089f,
+ -0.4230232555f, 0.341664791103f, -0.18127849691f,
+ -0.2277662414f, -0.55275535589f, 0.34184026718f };
+
+ std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
+ 0.53969591851f, 0.23393625035f, -0.27140527306f,
+ 0.50009280443f, 0.07511717046f, 0.3998299249f,
+ -0.51717478049f, 0.1889653282f, -0.367323637f };
+
+ std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
+ -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
+ -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
+ -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
+
+ std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
+ -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
+ 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
+ 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
+
+ std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
+ -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
+ 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
+ -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
+
+ std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };
+
+ std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };
+
+ std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
+
+ std::vector<float> cellBias = { 0., 0., 0., 0. };
+
+ std::vector<float> outputGateBias = { 0., 0., 0., 0. };
+
+ armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
+ armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
+ armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4);
+ armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4);
+ armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
+ armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
+ armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
+
+ AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
+ AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
+ AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
+ AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
+ AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
+ AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
+
+ data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
+ data.m_InputToCellWeights = &inputToCellWeightsTensor;
+ data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
+ data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
+ data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
+ data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
+ data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
+ data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
+ data.m_ForgetGateBias = &forgetGateBiasTensor;
+ data.m_CellBias = &cellBiasTensor;
+ data.m_OutputGateBias = &outputGateBiasTensor;
+
+ // Flags to set test configuration
+ data.m_Parameters.m_ClippingThresCell = 10;
+ data.m_Parameters.m_ClippingThresProj = 0;
+ data.m_Parameters.m_ActivationFunc = 4;
+ data.m_Parameters.m_CifgEnabled = true;
+ data.m_Parameters.m_PeepholeEnabled = true;
+ data.m_Parameters.m_ProjectionEnabled = false;
+ data.m_Parameters.m_TimeMajor = false;
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(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<float, 3>(actualOutput,
+ outputVector,
+ outputHandle->GetShape(),
+ outputTensorInfo.GetShape());
+}
diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp
new file mode 100644
index 0000000..7b14065
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.hpp
@@ -0,0 +1,36 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "LayerTestResult.hpp"
+
+#include <armnn/backends/IBackendInternal.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory);
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory);
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory);
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory);
+
+LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::ITensorHandleFactory& tensorHandleFactory);
\ No newline at end of file
diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp
index 1b05c4e..2603371 100644
--- a/src/backends/reference/RefLayerSupport.cpp
+++ b/src/backends/reference/RefLayerSupport.cpp
@@ -1242,6 +1242,7 @@
"Reference Lstm: input and outputStateOut types are mismatched");
supported &= CheckSupportRule(TypesAreEqual(input, cellStateOut), reasonIfUnsupported,
"Reference Lstm: input and cellStateOut types are mismatched");
+
supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported,
"Reference Lstm: input and output types are mismatched");
// check layer parameters
@@ -2288,4 +2289,150 @@
return supported;
}
+bool RefLayerSupport::IsUnidirectionalSequenceLstmSupported(
+ const TensorInfo& input,
+ const TensorInfo& outputStateIn,
+ const TensorInfo& cellStateIn,
+ const TensorInfo& output,
+ const Optional<TensorInfo>& hiddenStateOutput,
+ const Optional<TensorInfo>& cellStateOutput,
+ const UnidirectionalSequenceLstmDescriptor& descriptor,
+ const LstmInputParamsInfo& paramsInfo,
+ Optional<std::string&> reasonIfUnsupported) const
+{
+ IgnoreUnused(descriptor);
+ IgnoreUnused(paramsInfo);
+ IgnoreUnused(outputStateIn);
+ IgnoreUnused(cellStateIn);
+ bool supported = true;
+
+ if (hiddenStateOutput.has_value() || cellStateOutput.has_value())
+ {
+ reasonIfUnsupported.value() += "Reference UnidirectionalSequenceLstm: hidden state output "
+ "and cell state output are not supported at the moment.";
+ }
+
+ std::array<DataType, 1> supportedTypes =
+ {
+ DataType::Float32
+ };
+
+ std::array<DataType, 1> supportedWeightTypes =
+ {
+ DataType::Float32
+ };
+
+ // check inputs and outputs
+ supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input is not a supported type.");
+ supported &= CheckSupportRule(TypesAreEqual(input, outputStateIn), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and outputStateIn types are mismatched");
+ supported &= CheckSupportRule(TypesAreEqual(input, cellStateIn), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and cellStateIn types are mismatched");
+
+ supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and output types are mismatched");
+ // check layer parameters
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToForgetWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputToForgetWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToCellWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputToCellWeights is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToOutputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputToOutputWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetRecurrentToForgetWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: RecurrentToForgetWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetRecurrentToCellWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: RecurrentToCellWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetRecurrentToOutputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: RecurrentToOutputWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetForgetGateBias()), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and ForgetGateBias types "
+ "are mismatched");
+ supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellBias()), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and CellBias types are mismatched");
+ supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetOutputGateBias()), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and OutputGateBias types "
+ "are mismatched");
+ if (!descriptor.m_CifgEnabled)
+ {
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToInputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputToInputWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetRecurrentToInputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: RecurrentToInputWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputGateBias()), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and InputGateBias types "
+ "are mismatched");
+ if (descriptor.m_PeepholeEnabled)
+ {
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellToInputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: CellToInputWeights "
+ "is not a supported type.");
+ }
+ }
+ if (descriptor.m_PeepholeEnabled)
+ {
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellToForgetWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: CellToForgetWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellToOutputWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: CellToOutputWeights "
+ "is not a supported type.");
+ }
+ if (descriptor.m_ProjectionEnabled)
+ {
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetProjectionWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: ProjectionWeights "
+ "is not a supported type.");
+ if (paramsInfo.m_ProjectionBias != nullptr)
+ {
+ supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetProjectionBias()), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: input and ProjectionBias types "
+ "are mismatched");
+ }
+ }
+ if (descriptor.m_LayerNormEnabled)
+ {
+ if (!descriptor.m_CifgEnabled)
+ {
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputLayerNormWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputLayerNormWeights "
+ "is not a supported type.");
+ }
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetForgetLayerNormWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: ForgetLayerNormWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellLayerNormWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: CellLayerNormWeights "
+ "is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetOutputLayerNormWeights(), supportedWeightTypes),
+ reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: OutputLayerNormWeights "
+ "is not a supported type.");
+ }
+
+ return supported;
+}
+
} // namespace armnn
diff --git a/src/backends/reference/RefLayerSupport.hpp b/src/backends/reference/RefLayerSupport.hpp
index c060f79..a1b4dc7 100644
--- a/src/backends/reference/RefLayerSupport.hpp
+++ b/src/backends/reference/RefLayerSupport.hpp
@@ -370,6 +370,17 @@
const TensorInfo& output,
const TransposeDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
+
+ bool IsUnidirectionalSequenceLstmSupported(
+ const TensorInfo& input,
+ const TensorInfo& outputStateIn,
+ const TensorInfo& cellStateIn,
+ const TensorInfo& output,
+ const Optional<TensorInfo>& hiddenStateOutput,
+ const Optional<TensorInfo>& cellStateOutput,
+ const UnidirectionalSequenceLstmDescriptor& descriptor,
+ const LstmInputParamsInfo& paramsInfo,
+ Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
};
} // namespace armnn
diff --git a/src/backends/reference/RefWorkloadFactory.cpp b/src/backends/reference/RefWorkloadFactory.cpp
index 606f531..16cf17c 100644
--- a/src/backends/reference/RefWorkloadFactory.cpp
+++ b/src/backends/reference/RefWorkloadFactory.cpp
@@ -712,4 +712,11 @@
return std::make_unique<RefTransposeConvolution2dWorkload>(descriptor, info);
}
+std::unique_ptr<IWorkload> RefWorkloadFactory::CreateUnidirectionalSequenceLstm(
+ const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info) const
+{
+ return std::make_unique<RefUnidirectionalSequenceLstmWorkload>(descriptor, info);;
+}
+
} // namespace armnn
diff --git a/src/backends/reference/RefWorkloadFactory.hpp b/src/backends/reference/RefWorkloadFactory.hpp
index 2beffa7..113aca7 100644
--- a/src/backends/reference/RefWorkloadFactory.hpp
+++ b/src/backends/reference/RefWorkloadFactory.hpp
@@ -276,6 +276,10 @@
std::unique_ptr<IWorkload> CreateTransposeConvolution2d(const TransposeConvolution2dQueueDescriptor& descriptor,
const WorkloadInfo& info) const override;
+ std::unique_ptr<IWorkload> CreateUnidirectionalSequenceLstm(
+ const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info) const override;
+
private:
template <typename F32Workload, typename U8Workload, typename QueueDescriptorType>
std::unique_ptr<IWorkload> MakeWorkload(const QueueDescriptorType& descriptor, const WorkloadInfo& info) const;
diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk
index bf18284..17ddbe0 100644
--- a/src/backends/reference/backend.mk
+++ b/src/backends/reference/backend.mk
@@ -37,6 +37,7 @@
workloads/Gather.cpp \
workloads/InstanceNorm.cpp \
workloads/LogSoftmax.cpp \
+ workloads/Lstm.cpp \
workloads/LstmUtils.cpp \
workloads/Concatenate.cpp \
workloads/Pad.cpp \
@@ -95,6 +96,7 @@
workloads/RefSplitterWorkload.cpp \
workloads/RefTransposeConvolution2dWorkload.cpp \
workloads/RefTransposeWorkload.cpp \
+ workloads/RefUnidirectionalSequenceLstmWorkload.cpp \
workloads/Resize.cpp \
workloads/Slice.cpp \
workloads/SpaceToBatchNd.cpp \
diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp
index 45e3717..0cf36f2 100644
--- a/src/backends/reference/test/RefLayerTests.cpp
+++ b/src/backends/reference/test/RefLayerTests.cpp
@@ -2330,4 +2330,16 @@
ARMNN_AUTO_TEST_CASE_WITH_THF(ReduceMinFloat32, ReduceMinSimpleTest<DataType::Float32>)
ARMNN_AUTO_TEST_CASE_WITH_THF(ReduceMinNegativeAxisFloat32, ReduceMinNegativeAxisTest<DataType::Float32>)
+// Unidirectional Sequence Lstm
+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)
+
}
\ No newline at end of file
diff --git a/src/backends/reference/workloads/CMakeLists.txt b/src/backends/reference/workloads/CMakeLists.txt
index 7a769e5..b9f477c 100644
--- a/src/backends/reference/workloads/CMakeLists.txt
+++ b/src/backends/reference/workloads/CMakeLists.txt
@@ -42,6 +42,8 @@
Log.hpp
LogSoftmax.cpp
LogSoftmax.hpp
+ Lstm.cpp
+ Lstm.hpp
LstmUtils.hpp
LstmUtils.cpp
Maximum.hpp
@@ -162,6 +164,8 @@
RefTransposeConvolution2dWorkload.hpp
RefTransposeWorkload.cpp
RefTransposeWorkload.hpp
+ RefUnidirectionalSequenceLstmWorkload.cpp
+ RefUnidirectionalSequenceLstmWorkload.hpp
RefWorkloads.hpp
RefWorkloadUtils.hpp
Resize.cpp
diff --git a/src/backends/reference/workloads/Lstm.cpp b/src/backends/reference/workloads/Lstm.cpp
new file mode 100644
index 0000000..c1fb2bf
--- /dev/null
+++ b/src/backends/reference/workloads/Lstm.cpp
@@ -0,0 +1,259 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Activation.hpp"
+#include "Lstm.hpp"
+#include "LstmUtils.hpp"
+
+namespace armnn
+{
+
+void LstmImpl(const LstmDescriptor& descriptor,
+ const TensorInfo& inputInfo,
+ const TensorInfo& outputInfo,
+ const TensorShape& inputToOutputWeightsShape,
+ const TensorShape& recurrentToOutputWeightsShape,
+ std::unique_ptr<Decoder<float>>& inputData,
+ std::unique_ptr<Decoder<float>>& outputStateIn,
+ std::unique_ptr<Decoder<float>>& cellStateIn,
+ std::unique_ptr<Encoder<float>>& outputStateOut,
+ std::unique_ptr<Encoder<float>>& cellStateOut,
+ std::unique_ptr<Encoder<float>>& output,
+ std::unique_ptr<Decoder<float>>& cellStateOutDecoder,
+ std::unique_ptr<Decoder<float>>& outputDecoder,
+ std::unique_ptr<Decoder<float>>& inputToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToCellWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToCellWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& forgetGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& cellBiasTensor,
+ std::unique_ptr<Decoder<float>>& outputGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& projectionWeightsTensor,
+ std::unique_ptr<Decoder<float>>& projectionBiasTensor,
+ std::unique_ptr<Decoder<float>>& inputLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& forgetLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& cellLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& outputLayerNormWeights,
+ std::unique_ptr<Encoder<float>>& inputGateScratch,
+ std::unique_ptr<Encoder<float>>& cellScratch,
+ std::unique_ptr<Encoder<float>>& forgetGateScratch,
+ std::unique_ptr<Encoder<float>>& outputGateScratch,
+ std::unique_ptr<Decoder<float>>& inputGateScratchDecoder,
+ std::unique_ptr<Decoder<float>>& cellScratchDecoder,
+ std::unique_ptr<Decoder<float>>& forgetGateScratchDecoder,
+ std::unique_ptr<Decoder<float>>& outputGateScratchDecoder,
+ float layerNormEpsilon)
+{
+ // This is a porting of the LSTM::Eval() method in the Android code base
+ // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp
+
+ const TensorShape& inputShape = inputInfo.GetShape();
+ const DataType& outputType = outputInfo.GetDataType();
+
+ const uint32_t nBatch = inputShape[0];
+ const uint32_t nInput = inputShape[1];
+
+ const uint32_t nCell = inputToOutputWeightsShape[0];
+ const uint32_t nOutput = recurrentToOutputWeightsShape[1];
+
+ const bool useCifg = descriptor.m_CifgEnabled;
+ const bool usePeephole = descriptor.m_PeepholeEnabled;
+ const bool useLayerNorm = descriptor.m_LayerNormEnabled;
+
+ if (!useLayerNorm)
+ {
+ // Initialize scratch buffers with bias.
+ if (!useCifg)
+ {
+ VectorBatchVectorAssign(*inputGateBiasTensor,
+ nCell, nBatch, *inputGateScratch);
+ }
+ VectorBatchVectorAssign(*forgetGateBiasTensor,
+ nCell, nBatch, *forgetGateScratch);
+ VectorBatchVectorAssign(*cellBiasTensor,
+ nCell, nBatch, *cellScratch);
+ VectorBatchVectorAssign(*outputGateBiasTensor,
+ nCell, nBatch, *outputGateScratch);
+ }
+ else
+ {
+ // Initialize scratch buffers with zeroes.
+ if (!useCifg)
+ {
+ ZeroVector(*inputGateScratch, nCell * nBatch);
+ }
+ ZeroVector(*forgetGateScratch, nCell * nBatch);
+ ZeroVector(*cellScratch , nCell * nBatch);
+ ZeroVector(*outputGateScratch, nCell * nBatch);
+ }
+
+ // For each batch and cell: compute input_weight * input.
+ if (!useCifg)
+ {
+ MatrixBatchVectorMultiplyAccumulate(*inputToInputWeightsTensor,
+ nCell, nInput, *inputData, nBatch, *inputGateScratch);
+ }
+ MatrixBatchVectorMultiplyAccumulate(*inputToForgetWeightsTensor,
+ nCell, nInput, *inputData, nBatch, *forgetGateScratch);
+ MatrixBatchVectorMultiplyAccumulate(*inputToCellWeightsTensor,
+ nCell, nInput, *inputData, nBatch, *cellScratch);
+ MatrixBatchVectorMultiplyAccumulate(*inputToOutputWeightsTensor,
+ nCell, nInput, *inputData, nBatch, *outputGateScratch);
+
+ // For each batch and cell: compute recurrent_weight * output_state.
+ if (!useCifg)
+ {
+ MatrixBatchVectorMultiplyAccumulate(*recurrentToInputWeightsTensor,
+ nCell, nOutput, *outputStateIn, nBatch, *inputGateScratch);
+ }
+ MatrixBatchVectorMultiplyAccumulate(*recurrentToForgetWeightsTensor,
+ nCell, nOutput, *outputStateIn, nBatch, *forgetGateScratch);
+ MatrixBatchVectorMultiplyAccumulate(*recurrentToCellWeightsTensor,
+ nCell, nOutput, *outputStateIn, nBatch, *cellScratch);
+ MatrixBatchVectorMultiplyAccumulate(*recurrentToOutputWeightsTensor,
+ nCell, nOutput, *outputStateIn, nBatch, *outputGateScratch);
+
+ // For each batch and cell: update input gate.
+ if (!useCifg)
+ {
+ if (usePeephole)
+ {
+ VectorBatchVectorCwiseProductAccumulate(*cellToInputWeightsTensor,
+ nCell, *cellStateIn, nBatch, *inputGateScratch);
+ }
+ if (useLayerNorm)
+ {
+ MeanStddevNormalization(*inputGateScratchDecoder,
+ *inputGateScratch, nCell, nBatch, layerNormEpsilon);
+ VectorBatchVectorCwiseProduct(*inputLayerNormWeights,
+ nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
+ VectorBatchVectorAdd(*inputGateBiasTensor,
+ nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
+ }
+ Activation(*inputGateScratchDecoder, *inputGateScratch,
+ TensorInfo({nCell, nBatch}, outputType),
+ ActivationFunction::Sigmoid, 0, 0);
+ }
+
+ // For each batch and cell: update forget gate.
+ if (usePeephole)
+ {
+ VectorBatchVectorCwiseProductAccumulate(*cellToForgetWeightsTensor, nCell,
+ *cellStateIn, nBatch, *forgetGateScratch);
+ }
+ if (useLayerNorm)
+ {
+ MeanStddevNormalization(*forgetGateScratchDecoder,
+ *forgetGateScratch, nCell, nBatch, layerNormEpsilon);
+ VectorBatchVectorCwiseProduct(*forgetLayerNormWeights,
+ nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
+ VectorBatchVectorAdd(*forgetGateBiasTensor,
+ nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
+ }
+ Activation(*forgetGateScratchDecoder, *forgetGateScratch,
+ TensorInfo({nCell, nBatch}, outputType),
+ ActivationFunction::Sigmoid, 0, 0);
+
+ // For each batch and cell: update the cell.
+ if (useLayerNorm)
+ {
+ MeanStddevNormalization(*cellScratchDecoder,
+ *cellScratch, nCell, nBatch, layerNormEpsilon);
+ VectorBatchVectorCwiseProduct(*cellLayerNormWeights,
+ nCell, *cellScratchDecoder, nBatch, *cellScratch);
+ VectorBatchVectorAdd(*cellBiasTensor,
+ nCell, *cellScratchDecoder, nBatch, *cellScratch);
+ }
+
+ VectorVectorCwiseProduct(*forgetGateScratchDecoder, *cellStateIn, nBatch * nCell, *cellStateOut);
+
+ ActivationFunction armnnActivationFunc = ActivationFunction::Sigmoid;
+ float a = 0;
+ float b = 0;
+ SetActivationParameters(descriptor.m_ActivationFunc, armnnActivationFunc, a, b);
+
+ if (descriptor.m_ActivationFunc > 0)
+ {
+ Activation(*cellScratchDecoder, *cellScratch,
+ TensorInfo({nCell, nBatch}, outputType),
+ armnnActivationFunc, a, b);
+ }
+ if (useCifg)
+ {
+ Sub1Vector(*forgetGateScratchDecoder, nBatch * nCell, *forgetGateScratch);
+ VectorVectorCwiseProductAccumulate(
+ *cellScratchDecoder, *forgetGateScratchDecoder, nBatch * nCell, *cellStateOut);
+ }
+ else
+ {
+ VectorVectorCwiseProductAccumulate(
+ *cellScratchDecoder, *inputGateScratchDecoder, nBatch * nCell, *cellStateOut);
+ }
+ if (descriptor.m_ClippingThresCell > 0.0)
+ {
+ ClipVector(*cellStateOutDecoder, nBatch * nCell, descriptor.m_ClippingThresCell, *cellStateOut);
+ }
+
+ // For each batch and cell: update the output gate.
+ if (usePeephole)
+ {
+ VectorBatchVectorCwiseProductAccumulate(*cellToOutputWeightsTensor,
+ nCell, *cellStateOutDecoder, nBatch, *outputGateScratch);
+ }
+ if (useLayerNorm)
+ {
+ MeanStddevNormalization(*outputGateScratchDecoder,
+ *outputGateScratch, nCell, nBatch, layerNormEpsilon);
+ VectorBatchVectorCwiseProduct(*outputLayerNormWeights,
+ nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
+ VectorBatchVectorAdd(*outputGateBiasTensor,
+ nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
+ }
+ Activation(*outputGateScratchDecoder, *outputGateScratch,
+ TensorInfo({nCell, nBatch}, outputType),
+ ActivationFunction::Sigmoid, 0, 0);
+
+ if (descriptor.m_ActivationFunc > 0)
+ {
+ Activation(*cellStateOutDecoder, *cellScratch,
+ TensorInfo({nCell, nBatch}, outputType),
+ armnnActivationFunc, a, b);
+ }
+
+ VectorVectorCwiseProduct(*outputGateScratchDecoder, *cellScratchDecoder, nBatch * nCell, *outputGateScratch);
+
+ // For each batch: update the projection and output_state.
+ if (descriptor.m_ProjectionEnabled)
+ {
+ if (projectionBiasTensor)
+ {
+ VectorBatchVectorAssign(*projectionBiasTensor,
+ nOutput, nBatch, *output);
+ }
+ MatrixBatchVectorMultiplyAccumulate(*projectionWeightsTensor,
+ nOutput, nCell, *outputGateScratchDecoder, nBatch, *output);
+
+ if (descriptor.m_ClippingThresProj > 0.0)
+ {
+ ClipVector(*outputDecoder, nBatch * nOutput, descriptor.m_ClippingThresProj, *output);
+ }
+ }
+ else
+ {
+ CopyVector(*outputGateScratchDecoder, nBatch * nOutput, *output);
+ }
+
+ CopyVector(*outputDecoder, nBatch * nOutput, *outputStateOut);
+}
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/Lstm.hpp b/src/backends/reference/workloads/Lstm.hpp
new file mode 100644
index 0000000..7d0a1d4
--- /dev/null
+++ b/src/backends/reference/workloads/Lstm.hpp
@@ -0,0 +1,61 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/TypesUtils.hpp>
+#include <backendsCommon/WorkloadData.hpp>
+
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+
+namespace armnn
+{
+
+void LstmImpl(const LstmDescriptor& descriptor,
+ const TensorInfo& inputInfo,
+ const TensorInfo& outputInfo,
+ const TensorShape& inputToOutputWeightsShape,
+ const TensorShape& recurrentToOutputWeightsShape,
+ std::unique_ptr<Decoder<float>>& inputData,
+ std::unique_ptr<Decoder<float>>& outputStateIn,
+ std::unique_ptr<Decoder<float>>& cellStateIn,
+ std::unique_ptr<Encoder<float>>& outputStateOut,
+ std::unique_ptr<Encoder<float>>& cellStateOut,
+ std::unique_ptr<Encoder<float>>& output,
+ std::unique_ptr<Decoder<float>>& cellStateOutDecoder,
+ std::unique_ptr<Decoder<float>>& outputDecoder,
+ std::unique_ptr<Decoder<float>>& inputToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToCellWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToCellWeightsTensor,
+ std::unique_ptr<Decoder<float>>& recurrentToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToInputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToForgetWeightsTensor,
+ std::unique_ptr<Decoder<float>>& cellToOutputWeightsTensor,
+ std::unique_ptr<Decoder<float>>& inputGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& forgetGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& cellBiasTensor,
+ std::unique_ptr<Decoder<float>>& outputGateBiasTensor,
+ std::unique_ptr<Decoder<float>>& projectionWeightsTensor,
+ std::unique_ptr<Decoder<float>>& projectionBiasTensor,
+ std::unique_ptr<Decoder<float>>& inputLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& forgetLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& cellLayerNormWeights,
+ std::unique_ptr<Decoder<float>>& outputLayerNormWeights,
+ std::unique_ptr<Encoder<float>>& inputGateScratch,
+ std::unique_ptr<Encoder<float>>& cellScratch,
+ std::unique_ptr<Encoder<float>>& forgetGateScratch,
+ std::unique_ptr<Encoder<float>>& outputGateScratch,
+ std::unique_ptr<Decoder<float>>& inputGateScratchDecoder,
+ std::unique_ptr<Decoder<float>>& cellScratchDecoder,
+ std::unique_ptr<Decoder<float>>& forgetGateScratchDecoder,
+ std::unique_ptr<Decoder<float>>& outputGateScratchDecoder,
+ float layerNormEpsilon);
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefLstmWorkload.cpp b/src/backends/reference/workloads/RefLstmWorkload.cpp
index 3ddfd33..1ff6f50 100644
--- a/src/backends/reference/workloads/RefLstmWorkload.cpp
+++ b/src/backends/reference/workloads/RefLstmWorkload.cpp
@@ -7,6 +7,7 @@
#include "Activation.hpp"
#include "Encoders.hpp"
#include "Decoders.hpp"
+#include "Lstm.hpp"
#include "LstmUtils.hpp"
#include "RefWorkloadUtils.hpp"
@@ -57,7 +58,6 @@
const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
const TensorShape& inputShape = inputInfo.GetShape();
- const DataType& outputType = outputInfo.GetDataType();
std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputInfo, outputs[1]->Map());
std::unique_ptr<Encoder<float>> cellStateOut = MakeEncoder<float>(outputInfo, outputs[2]->Map());
@@ -71,10 +71,7 @@
std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(inputInfo, inputs[2]->Map());
const uint32_t nBatch = inputShape[0];
- const uint32_t nInput = inputShape[1];
-
const uint32_t nCell = m_InputToOutputWeightsTensor->GetShape()[0];
- const uint32_t nOutput = m_RecurrentToOutputWeightsTensor->GetShape()[1];
const bool useCifg = m_Data.m_Parameters.m_CifgEnabled;
const bool usePeephole = m_Data.m_Parameters.m_PeepholeEnabled;
@@ -154,6 +151,9 @@
std::unique_ptr<Decoder<float>> cellLayerNormWeights;
std::unique_ptr<Decoder<float>> outputLayerNormWeights;
+ const TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();
+ const TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();
+
if (useLayerNorm)
{
if (!useCifg)
@@ -204,190 +204,49 @@
}
}
- if (!useLayerNorm)
- {
- // Initialize scratch buffers with bias.
- if (!useCifg)
- {
- VectorBatchVectorAssign(*inputGateBiasTensor,
- nCell, nBatch, *inputGateScratch);
- }
- VectorBatchVectorAssign(*forgetGateBiasTensor,
- nCell, nBatch, *forgetGateScratch);
- VectorBatchVectorAssign(*cellBiasTensor,
- nCell, nBatch, *cellScratch);
- VectorBatchVectorAssign(*outputGateBiasTensor,
- nCell, nBatch, *outputGateScratch);
- }
- else
- {
- // Initialize scratch buffers with zeroes.
- if (!useCifg)
- {
- ZeroVector(*inputGateScratch, nCell * nBatch);
- }
- ZeroVector(*forgetGateScratch, nCell * nBatch);
- ZeroVector(*cellScratch , nCell * nBatch);
- ZeroVector(*outputGateScratch, nCell * nBatch);
- }
-
- // For each batch and cell: compute input_weight * input.
- if (!useCifg)
- {
- MatrixBatchVectorMultiplyAccumulate(*inputToInputWeightsTensor,
- nCell, nInput, *inputData, nBatch, *inputGateScratch);
- }
- MatrixBatchVectorMultiplyAccumulate(*inputToForgetWeightsTensor,
- nCell, nInput, *inputData, nBatch, *forgetGateScratch);
- MatrixBatchVectorMultiplyAccumulate(*inputToCellWeightsTensor,
- nCell, nInput, *inputData, nBatch, *cellScratch);
- MatrixBatchVectorMultiplyAccumulate(*inputToOutputWeightsTensor,
- nCell, nInput, *inputData, nBatch, *outputGateScratch);
-
- // For each batch and cell: compute recurrent_weight * output_state.
- if (!useCifg)
- {
- MatrixBatchVectorMultiplyAccumulate(*recurrentToInputWeightsTensor,
- nCell, nOutput, *outputStateIn, nBatch, *inputGateScratch);
- }
- MatrixBatchVectorMultiplyAccumulate(*recurrentToForgetWeightsTensor,
- nCell, nOutput, *outputStateIn, nBatch, *forgetGateScratch);
- MatrixBatchVectorMultiplyAccumulate(*recurrentToCellWeightsTensor,
- nCell, nOutput, *outputStateIn, nBatch, *cellScratch);
- MatrixBatchVectorMultiplyAccumulate(*recurrentToOutputWeightsTensor,
- nCell, nOutput, *outputStateIn, nBatch, *outputGateScratch);
-
- // For each batch and cell: update input gate.
- if (!useCifg)
- {
- if (usePeephole)
- {
- VectorBatchVectorCwiseProductAccumulate(*cellToInputWeightsTensor,
- nCell, *cellStateIn, nBatch, *inputGateScratch);
- }
- if (useLayerNorm)
- {
- MeanStddevNormalization(*inputGateScratchDecoder,
- *inputGateScratch, nCell, nBatch, m_LayerNormEpsilon);
- VectorBatchVectorCwiseProduct(*inputLayerNormWeights,
- nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
- VectorBatchVectorAdd(*inputGateBiasTensor,
- nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
- }
- Activation(*inputGateScratchDecoder, *inputGateScratch,
- TensorInfo({nCell, nBatch}, outputType),
- ActivationFunction::Sigmoid, 0, 0);
- }
-
- // For each batch and cell: update forget gate.
- if (usePeephole)
- {
- VectorBatchVectorCwiseProductAccumulate(*cellToForgetWeightsTensor, nCell,
- *cellStateIn, nBatch, *forgetGateScratch);
- }
- if (useLayerNorm)
- {
- MeanStddevNormalization(*forgetGateScratchDecoder,
- *forgetGateScratch, nCell, nBatch, m_LayerNormEpsilon);
- VectorBatchVectorCwiseProduct(*forgetLayerNormWeights,
- nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
- VectorBatchVectorAdd(*forgetGateBiasTensor,
- nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
- }
- Activation(*forgetGateScratchDecoder, *forgetGateScratch,
- TensorInfo({nCell, nBatch}, outputType),
- ActivationFunction::Sigmoid, 0, 0);
-
- // For each batch and cell: update the cell.
- if (useLayerNorm)
- {
- MeanStddevNormalization(*cellScratchDecoder,
- *cellScratch, nCell, nBatch, m_LayerNormEpsilon);
- VectorBatchVectorCwiseProduct(*cellLayerNormWeights,
- nCell, *cellScratchDecoder, nBatch, *cellScratch);
- VectorBatchVectorAdd(*cellBiasTensor,
- nCell, *cellScratchDecoder, nBatch, *cellScratch);
- }
-
- VectorVectorCwiseProduct(*forgetGateScratchDecoder, *cellStateIn, nBatch * nCell, *cellStateOut);
-
- ActivationFunction armnnActivationFunc = ActivationFunction::Sigmoid;
- float a = 0;
- float b = 0;
- SetActivationParameters(m_Data.m_Parameters.m_ActivationFunc, armnnActivationFunc, a, b);
-
- if (m_Data.m_Parameters.m_ActivationFunc > 0)
- {
- Activation(*cellScratchDecoder, *cellScratch,
- TensorInfo({nCell, nBatch}, outputType),
- armnnActivationFunc, a, b);
- }
- if (useCifg)
- {
- Sub1Vector(*forgetGateScratchDecoder, nBatch * nCell, *forgetGateScratch);
- VectorVectorCwiseProductAccumulate(
- *cellScratchDecoder, *forgetGateScratchDecoder, nBatch * nCell, *cellStateOut);
- }
- else
- {
- VectorVectorCwiseProductAccumulate(
- *cellScratchDecoder, *inputGateScratchDecoder, nBatch * nCell, *cellStateOut);
- }
- if (m_Data.m_Parameters.m_ClippingThresCell > 0.0)
- {
- ClipVector(*cellStateOutDecoder, nBatch * nCell, m_Data.m_Parameters.m_ClippingThresCell, *cellStateOut);
- }
-
- // For each batch and cell: update the output gate.
- if (usePeephole)
- {
- VectorBatchVectorCwiseProductAccumulate(*cellToOutputWeightsTensor,
- nCell, *cellStateOutDecoder, nBatch, *outputGateScratch);
- }
- if (useLayerNorm)
- {
- MeanStddevNormalization(*outputGateScratchDecoder,
- *outputGateScratch, nCell, nBatch, m_LayerNormEpsilon);
- VectorBatchVectorCwiseProduct(*outputLayerNormWeights,
- nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
- VectorBatchVectorAdd(*outputGateBiasTensor,
- nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
- }
- Activation(*outputGateScratchDecoder, *outputGateScratch,
- TensorInfo({nCell, nBatch}, outputType),
- ActivationFunction::Sigmoid, 0, 0);
-
- if (m_Data.m_Parameters.m_ActivationFunc > 0)
- {
- Activation(*cellStateOutDecoder, *cellScratch,
- TensorInfo({nCell, nBatch}, outputType),
- armnnActivationFunc, a, b);
- }
-
- VectorVectorCwiseProduct(*outputGateScratchDecoder, *cellScratchDecoder, nBatch * nCell, *outputGateScratch);
-
- // For each batch: update the projection and output_state.
- if (m_Data.m_Parameters.m_ProjectionEnabled)
- {
- if (m_ProjectionBiasTensor)
- {
- VectorBatchVectorAssign(*projectionBiasTensor,
- nOutput, nBatch, *output);
- }
- MatrixBatchVectorMultiplyAccumulate(*projectionWeightsTensor,
- nOutput, nCell, *outputGateScratchDecoder, nBatch, *output);
-
- if (m_Data.m_Parameters.m_ClippingThresProj > 0.0)
- {
- ClipVector(*outputDecoder, nBatch * nOutput, m_Data.m_Parameters.m_ClippingThresProj, *output);
- }
- }
- else
- {
- CopyVector(*outputGateScratchDecoder, nBatch * nOutput, *output);
- }
-
- CopyVector(*outputDecoder, nBatch * nOutput, *outputStateOut);
+ LstmImpl(m_Data.m_Parameters,
+ inputInfo,
+ outputInfo,
+ inputToOutputWeightsShape,
+ recurrentToOutputWeightsShape,
+ inputData,
+ outputStateIn,
+ cellStateIn,
+ outputStateOut,
+ cellStateOut,
+ output,
+ cellStateOutDecoder,
+ outputDecoder,
+ inputToInputWeightsTensor,
+ inputToForgetWeightsTensor,
+ inputToCellWeightsTensor,
+ inputToOutputWeightsTensor,
+ recurrentToInputWeightsTensor,
+ recurrentToForgetWeightsTensor,
+ recurrentToCellWeightsTensor,
+ recurrentToOutputWeightsTensor,
+ cellToInputWeightsTensor,
+ cellToForgetWeightsTensor,
+ cellToOutputWeightsTensor,
+ inputGateBiasTensor,
+ forgetGateBiasTensor,
+ cellBiasTensor,
+ outputGateBiasTensor,
+ projectionWeightsTensor,
+ projectionBiasTensor,
+ inputLayerNormWeights,
+ forgetLayerNormWeights,
+ cellLayerNormWeights,
+ outputLayerNormWeights,
+ inputGateScratch,
+ cellScratch,
+ forgetGateScratch,
+ outputGateScratch,
+ inputGateScratchDecoder,
+ cellScratchDecoder,
+ forgetGateScratchDecoder,
+ outputGateScratchDecoder,
+ m_LayerNormEpsilon);
}
} //namespace armnn
diff --git a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
new file mode 100644
index 0000000..311fa18
--- /dev/null
+++ b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
@@ -0,0 +1,307 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefUnidirectionalSequenceLstmWorkload.hpp"
+#include "Activation.hpp"
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+#include "Lstm.hpp"
+#include "LstmUtils.hpp"
+#include "RefWorkloadUtils.hpp"
+
+#include <armnnUtils/Permute.hpp>
+
+namespace armnn
+{
+
+RefUnidirectionalSequenceLstmWorkload::RefUnidirectionalSequenceLstmWorkload(
+ const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info)
+ : BaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)
+ , m_InputToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToInputWeights))
+ , m_InputToForgetWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToForgetWeights))
+ , m_InputToCellWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToCellWeights))
+ , m_InputToOutputWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToOutputWeights))
+ , m_RecurrentToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_RecurrentToInputWeights))
+ , m_RecurrentToForgetWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToForgetWeights))
+ , m_RecurrentToCellWeightsTensor (AssignScopedTensorHandle(descriptor.m_RecurrentToCellWeights))
+ , m_RecurrentToOutputWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToOutputWeights))
+ , m_CellToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToInputWeights))
+ , m_CellToForgetWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToForgetWeights))
+ , m_CellToOutputWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToOutputWeights))
+ , m_InputGateBiasTensor (AssignScopedTensorHandle(descriptor.m_InputGateBias))
+ , m_ForgetGateBiasTensor (AssignScopedTensorHandle(descriptor.m_ForgetGateBias))
+ , m_CellBiasTensor (AssignScopedTensorHandle(descriptor.m_CellBias))
+ , m_OutputGateBiasTensor (AssignScopedTensorHandle(descriptor.m_OutputGateBias))
+ , m_ProjectionWeightsTensor (AssignScopedTensorHandle(descriptor.m_ProjectionWeights))
+ , m_ProjectionBiasTensor (AssignScopedTensorHandle(descriptor.m_ProjectionBias))
+ , m_InputLayerNormWeights (AssignScopedTensorHandle(descriptor.m_InputLayerNormWeights))
+ , m_ForgetLayerNormWeights (AssignScopedTensorHandle(descriptor.m_ForgetLayerNormWeights))
+ , m_CellLayerNormWeights (AssignScopedTensorHandle(descriptor.m_CellLayerNormWeights))
+ , m_OutputLayerNormWeights (AssignScopedTensorHandle(descriptor.m_OutputLayerNormWeights))
+{}
+
+void RefUnidirectionalSequenceLstmWorkload::Execute() const
+{
+ Execute(m_Data.m_Inputs, m_Data.m_Outputs);
+}
+
+void RefUnidirectionalSequenceLstmWorkload::ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor)
+{
+ Execute(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs);
+}
+
+void RefUnidirectionalSequenceLstmWorkload::Execute(std::vector<ITensorHandle*> inputs,
+ std::vector<ITensorHandle*> outputs) const
+{
+ TensorInfo inputInfo = GetTensorInfo(inputs[0]);
+ const TensorInfo& outputStateInfo = GetTensorInfo(inputs[1]);
+ const TensorInfo& cellStateInfo = GetTensorInfo(inputs[2]);
+ TensorInfo outputInfo = GetTensorInfo(outputs[0]);
+ TensorShape& inputShape = inputInfo.GetShape();
+ TensorShape& outputShape= outputInfo.GetShape();
+ auto inputTensor = reinterpret_cast<float*>(inputs[0]->Map());
+
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ // Permute to time major
+ const PermutationVector& mappings = {1U, 0U, 2U};
+ std::vector<float> inputValue(inputTensor, inputTensor + inputInfo.GetNumElements());
+ inputShape = armnnUtils::Permuted(inputInfo.GetShape(), mappings);
+ inputInfo.SetShape(inputShape);
+ armnnUtils::Permute(inputShape, mappings, inputValue.data(), inputTensor, sizeof(float));
+
+ outputShape = armnnUtils::Permuted(outputInfo.GetShape(), mappings);
+ outputInfo.SetShape(outputShape);
+ }
+ unsigned int maxTime = inputShape[0];
+ unsigned int batchSize = inputShape[1];
+ unsigned int outputSize = outputShape[2];
+ unsigned int inputSize = inputShape[2];
+
+ TensorInfo scratchInfo = outputInfo;
+ scratchInfo.SetShape({batchSize, cellStateInfo.GetShape()[1]});
+
+ std::vector<float> inputGateScratchBuffer;
+ std::vector<float> cellScratchBuffer(scratchInfo.GetNumElements(), 0.);
+ std::vector<float> forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);
+ std::vector<float> outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);
+
+ std::vector<float> outputStateOutBuffer(outputStateInfo.GetNumElements(), 0.);
+ std::vector<float> cellStateOutBuffer(cellStateInfo.GetNumElements(), 0.);
+
+ void* outputStateOutData = outputStateOutBuffer.data();
+ void* cellStateOutData = cellStateOutBuffer.data();
+
+ std::unique_ptr<Encoder<float>> inputGateScratch;
+ std::unique_ptr<Encoder<float>> cellScratch = MakeEncoder<float>(scratchInfo, cellScratchBuffer.data());
+ std::unique_ptr<Encoder<float>> forgetGateScratch = MakeEncoder<float>(scratchInfo, forgetGateScratchBuffer.data());
+ std::unique_ptr<Encoder<float>> outputGateScratch = MakeEncoder<float>(scratchInfo, outputGateScratchBuffer.data());
+
+ std::unique_ptr<Decoder<float>> inputGateScratchDecoder;
+ std::unique_ptr<Decoder<float>> cellScratchDecoder = MakeDecoder<float>(scratchInfo, cellScratchBuffer.data());
+ std::unique_ptr<Decoder<float>> forgetGateScratchDecoder = MakeDecoder<float>(scratchInfo,
+ forgetGateScratchBuffer.data());
+ std::unique_ptr<Decoder<float>> outputGateScratchDecoder = MakeDecoder<float>(scratchInfo,
+ outputGateScratchBuffer.data());
+
+ const bool useCifg = m_Data.m_Parameters.m_CifgEnabled;
+ const bool usePeephole = m_Data.m_Parameters.m_PeepholeEnabled;
+ const bool useLayerNorm = m_Data.m_Parameters.m_LayerNormEnabled;
+
+ if (!useCifg)
+ {
+ inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);
+ inputGateScratch = MakeEncoder<float>(scratchInfo, inputGateScratchBuffer.data());
+ inputGateScratchDecoder = MakeDecoder<float>(scratchInfo, inputGateScratchBuffer.data());
+ }
+
+ std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputStateInfo, outputStateOutData);
+ std::unique_ptr<Encoder<float>> cellStateOut = MakeEncoder<float>(cellStateInfo, cellStateOutData);
+ std::unique_ptr<Decoder<float>> cellStateOutDecoder = MakeDecoder<float>(cellStateInfo, cellStateOutData);
+
+ TensorInfo lstmInputInfo = inputInfo;
+ TensorShape batchInputShape = TensorShape({batchSize, inputSize});
+ lstmInputInfo.SetShape(batchInputShape);
+
+ TensorInfo lstmOutputInfo = outputInfo;
+ lstmOutputInfo.SetShape({batchSize, outputSize});
+
+ const TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();
+ const TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();
+ unsigned int nOutput = recurrentToOutputWeightsShape[1];
+ auto outputStateInData = inputs[1]->Map();
+ std::unique_ptr<Decoder<float>> outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateInData);
+
+ auto cellStateInData = inputs[2]->Map();
+ std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateInData);
+
+ auto currentInputData = reinterpret_cast<float*>(inputs[0]->Map());
+ std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);
+ auto currentOutputData = reinterpret_cast<float*>(outputs[0]->Map());
+ std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);
+ std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);
+
+ std::unique_ptr<Decoder<float>> inputToInputWeightsTensor;
+ std::unique_ptr<Decoder<float>> inputToForgetWeightsTensor = MakeDecoder<float>(
+ m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> inputToCellWeightsTensor = MakeDecoder<float>(
+ m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> inputToOutputWeightsTensor = MakeDecoder<float>(
+ m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetConstTensor<void>());
+
+ std::unique_ptr<Decoder<float>> recurrentToInputWeightsTensor;
+ std::unique_ptr<Decoder<float>> recurrentToForgetWeightsTensor = MakeDecoder<float>(
+ m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> recurrentToCellWeightsTensor = MakeDecoder<float>(
+ m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> recurrentToOutputWeightsTensor = MakeDecoder<float>(
+ m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetConstTensor<void>());
+
+ std::unique_ptr<Decoder<float>> inputGateBiasTensor;
+ std::unique_ptr<Decoder<float>> forgetGateBiasTensor = MakeDecoder<float>(
+ m_ForgetGateBiasTensor->GetTensorInfo(), m_ForgetGateBiasTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> cellBiasTensor = MakeDecoder<float>(
+ m_CellBiasTensor->GetTensorInfo(), m_CellBiasTensor->GetConstTensor<void>());
+ std::unique_ptr<Decoder<float>> outputGateBiasTensor = MakeDecoder<float>(
+ m_OutputGateBiasTensor->GetTensorInfo(), m_OutputGateBiasTensor->GetConstTensor<void>());
+
+ std::unique_ptr<Decoder<float>> cellToInputWeightsTensor;
+ std::unique_ptr<Decoder<float>> cellToForgetWeightsTensor;
+ std::unique_ptr<Decoder<float>> cellToOutputWeightsTensor;
+
+ std::unique_ptr<Decoder<float>> projectionWeightsTensor;
+ std::unique_ptr<Decoder<float>> projectionBiasTensor;
+
+ std::unique_ptr<Decoder<float>> inputLayerNormWeights;
+ std::unique_ptr<Decoder<float>> forgetLayerNormWeights;
+ std::unique_ptr<Decoder<float>> cellLayerNormWeights;
+ std::unique_ptr<Decoder<float>> outputLayerNormWeights;
+
+ if (useLayerNorm)
+ {
+ if (!useCifg)
+ {
+ inputLayerNormWeights = MakeDecoder<float>(
+ m_InputLayerNormWeights->GetTensorInfo(), m_InputLayerNormWeights->GetConstTensor<void>());
+ }
+ forgetLayerNormWeights = MakeDecoder<float>(
+ m_ForgetLayerNormWeights->GetTensorInfo(), m_ForgetLayerNormWeights->GetConstTensor<void>());
+ cellLayerNormWeights = MakeDecoder<float>(
+ m_CellLayerNormWeights->GetTensorInfo(), m_CellLayerNormWeights->GetConstTensor<void>());
+ outputLayerNormWeights = MakeDecoder<float>(
+ m_OutputLayerNormWeights->GetTensorInfo(), m_OutputLayerNormWeights->GetConstTensor<void>());
+ }
+
+ if (!useCifg)
+ {
+ inputToInputWeightsTensor = MakeDecoder<float>(
+ m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetConstTensor<void>());
+ inputGateBiasTensor = MakeDecoder<float>(
+ m_InputGateBiasTensor->GetTensorInfo(), m_InputGateBiasTensor->GetConstTensor<void>());
+ recurrentToInputWeightsTensor = MakeDecoder<float>(
+ m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetConstTensor<void>());
+ }
+
+ if (usePeephole)
+ {
+ cellToForgetWeightsTensor = MakeDecoder<float>(
+ m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetConstTensor<void>());
+ cellToOutputWeightsTensor = MakeDecoder<float>(
+ m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetConstTensor<void>());
+ }
+
+ if (!useCifg && usePeephole)
+ {
+ cellToInputWeightsTensor = MakeDecoder<float>(
+ m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetConstTensor<void>());
+ }
+
+ if (m_Data.m_Parameters.m_ProjectionEnabled)
+ {
+ projectionWeightsTensor = MakeDecoder<float>(
+ m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetConstTensor<void>());
+ if (m_ProjectionBiasTensor)
+ {
+ projectionBiasTensor = MakeDecoder<float>(
+ m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetConstTensor<void>());
+ }
+ }
+
+ unsigned int batchInputSize = batchSize * inputSize;
+ unsigned int batchOutputSize = batchSize * nOutput;
+
+ for (unsigned int t = 0; t < maxTime; ++t)
+ {
+ LstmImpl(m_Data.m_Parameters,
+ lstmInputInfo,
+ lstmOutputInfo,
+ inputToOutputWeightsShape,
+ recurrentToOutputWeightsShape,
+ inputData,
+ outputStateIn,
+ cellStateIn,
+ outputStateOut,
+ cellStateOut,
+ output,
+ cellStateOutDecoder,
+ outputDecoder,
+ inputToInputWeightsTensor,
+ inputToForgetWeightsTensor,
+ inputToCellWeightsTensor,
+ inputToOutputWeightsTensor,
+ recurrentToInputWeightsTensor,
+ recurrentToForgetWeightsTensor,
+ recurrentToCellWeightsTensor,
+ recurrentToOutputWeightsTensor,
+ cellToInputWeightsTensor,
+ cellToForgetWeightsTensor,
+ cellToOutputWeightsTensor,
+ inputGateBiasTensor,
+ forgetGateBiasTensor,
+ cellBiasTensor,
+ outputGateBiasTensor,
+ projectionWeightsTensor,
+ projectionBiasTensor,
+ inputLayerNormWeights,
+ forgetLayerNormWeights,
+ cellLayerNormWeights,
+ outputLayerNormWeights,
+ inputGateScratch,
+ cellScratch,
+ forgetGateScratch,
+ outputGateScratch,
+ inputGateScratchDecoder,
+ cellScratchDecoder,
+ forgetGateScratchDecoder,
+ outputGateScratchDecoder,
+ m_LayerNormEpsilon);
+
+ currentInputData += batchInputSize;
+ inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);
+ currentOutputData += batchOutputSize;
+ output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);
+ outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);
+
+ // Assign output state out to the next output state in
+ outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateOutData);
+
+ // Assign cell state out to the next cell state in
+ cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateOutData);
+ }
+
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ // Permute Output back to batch major
+ const PermutationVector& mappings = {1U, 0U, 2U};
+ auto outputData = reinterpret_cast<float*>(outputs[0]->Map());
+ std::vector<float> outputValue(outputData, outputData + outputInfo.GetNumElements());
+ outputShape = armnnUtils::Permuted(outputInfo.GetShape(), mappings);
+ outputInfo.SetShape(outputShape);
+ armnnUtils::Permute(outputShape, mappings, outputValue.data(), outputData, sizeof(float));
+ }
+}
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp
new file mode 100644
index 0000000..8ba7bdc
--- /dev/null
+++ b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp
@@ -0,0 +1,56 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/Workload.hpp>
+#include <backendsCommon/WorkloadData.hpp>
+
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+
+namespace armnn
+{
+
+class RefUnidirectionalSequenceLstmWorkload : public BaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>
+{
+public:
+ explicit RefUnidirectionalSequenceLstmWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info);
+
+ void Execute() const override;
+ void ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor) override;
+
+
+private:
+ void Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const;
+ std::unique_ptr<ScopedTensorHandle> m_InputToInputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_InputToForgetWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_InputToCellWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_InputToOutputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_RecurrentToInputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_RecurrentToForgetWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_RecurrentToCellWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_RecurrentToOutputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_CellToInputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_CellToForgetWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_CellToOutputWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_InputGateBiasTensor;
+ std::unique_ptr<ScopedTensorHandle> m_ForgetGateBiasTensor;
+ std::unique_ptr<ScopedTensorHandle> m_CellBiasTensor;
+ std::unique_ptr<ScopedTensorHandle> m_OutputGateBiasTensor;
+ std::unique_ptr<ScopedTensorHandle> m_ProjectionWeightsTensor;
+ std::unique_ptr<ScopedTensorHandle> m_ProjectionBiasTensor;
+ std::unique_ptr<ScopedTensorHandle> m_InputLayerNormWeights;
+ std::unique_ptr<ScopedTensorHandle> m_ForgetLayerNormWeights;
+ std::unique_ptr<ScopedTensorHandle> m_CellLayerNormWeights;
+ std::unique_ptr<ScopedTensorHandle> m_OutputLayerNormWeights;
+
+ float m_LayerNormEpsilon = static_cast<float>(1e-8);
+};
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefWorkloads.hpp b/src/backends/reference/workloads/RefWorkloads.hpp
index afe63d1..d3ae58e 100644
--- a/src/backends/reference/workloads/RefWorkloads.hpp
+++ b/src/backends/reference/workloads/RefWorkloads.hpp
@@ -69,6 +69,7 @@
#include "RefSpaceToDepthWorkload.hpp"
#include "RefTransposeConvolution2dWorkload.hpp"
#include "RefTransposeWorkload.hpp"
+#include "RefUnidirectionalSequenceLstmWorkload.hpp"
#include "RefWorkloadUtils.hpp"
#include "Resize.hpp"
#include "Softmax.hpp"