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/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"