Revert "Revert "IVGCVSW-6267 Add support of Unidirectional Sequence Lstm fp32/fp16 to Cl""
This reverts commit 79cef69b1ec58f9ce010461eaaad04c896a4fe15.
Reason for revert: 22.05 release.
Change-Id: Id2ecbf563e8808694fb8605604e8c3c39c29cec2
diff --git a/src/backends/cl/workloads/CMakeLists.txt b/src/backends/cl/workloads/CMakeLists.txt
index 6e7dd36..423a4a6 100644
--- a/src/backends/cl/workloads/CMakeLists.txt
+++ b/src/backends/cl/workloads/CMakeLists.txt
@@ -125,6 +125,8 @@
ClTransposeConvolution2dWorkload.hpp
ClTransposeWorkload.cpp
ClTransposeWorkload.hpp
+ ClUnidirectionalSequenceLstmFloatWorkload.cpp
+ ClUnidirectionalSequenceLstmFloatWorkload.hpp
ClWorkloads.hpp
ClWorkloadUtils.hpp
)
diff --git a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
new file mode 100644
index 0000000..cc9aea8
--- /dev/null
+++ b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
@@ -0,0 +1,903 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ClUnidirectionalSequenceLstmFloatWorkload.hpp"
+#include "ClWorkloadUtils.hpp"
+
+#include <aclCommon/ArmComputeUtils.hpp>
+#include <aclCommon/ArmComputeTensorUtils.hpp>
+
+#include <armnn/utility/NumericCast.hpp>
+#include <armnnUtils/Permute.hpp>
+#include <cl/test/ClWorkloadFactoryHelper.hpp>
+#include <backendsCommon/WorkloadUtils.hpp>
+
+#include "cl/ClTensorHandle.hpp"
+
+namespace
+{
+unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis)
+{
+ return (numDimensions - axis) - 1;
+}
+} //namespace
+
+namespace armnn
+{
+using namespace armcomputetensorutils;
+
+ClUnidirectionalSequenceLstmFloatWorkload::ClUnidirectionalSequenceLstmFloatWorkload
+ (const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info,
+ const arm_compute::CLCompileContext& clCompileContext)
+ : FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)
+{
+ // Report Profiling Details
+ ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClUnidirectionalSequenceLstmFloatWorkload_Construct",
+ descriptor.m_Parameters,
+ info,
+ GetGuid());
+
+ const arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+ arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+ TensorInfo inputInfo = info.m_InputTensorInfos[0];
+ TensorInfo outputInfo = info.m_OutputTensorInfos[0];
+
+ arm_compute::DataType armComputeDataType = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetDataType();
+ armnn::DataType armnnDataType = GetArmNNDataType(armComputeDataType);
+
+ TensorShape inputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetShape();
+ TensorShape cellStateLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetShape();
+ TensorShape outputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetShape();
+
+ unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
+ unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
+ unsigned int inputSize = inputLayerShape[2];
+ unsigned int outputSize = outputLayerShape[2];
+ unsigned int numUnits = cellStateLayerShape[1];
+
+ const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
+ const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
+
+ //
+ // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.
+ //
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute());
+
+ TensorInfo permuteOutInfo = inputInfo;
+ permuteOutInfo.SetShape(timeMajorShapeInput);
+ BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);
+
+ // Permute to time major format.
+ layer->configure(clCompileContext, &input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));
+ m_Permute1.reset(layer.release());
+ }
+
+ //
+ // Split and Concat Tensors
+ //
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ arm_compute::CLTensor splitter_out;
+ arm_compute::CLTensor concat_in;
+
+ auto splitterTensorInfo = inputInfo;
+ auto concatTensorInfo = outputInfo;
+ splitterTensorInfo.SetShape({batchSize, inputSize});
+ concatTensorInfo.SetShape({batchSize, outputSize});
+ BuildArmComputeTensor(splitter_out, splitterTensorInfo);
+ BuildArmComputeTensor(concat_in, concatTensorInfo);
+
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);
+
+ // append to std::vector<arm_compute::CLTensor>
+ m_SplitterOutputsTensors.push_back(std::move(splitter_out));
+ m_ConcatInputsTensors.push_back(std::move(concat_in));
+ }
+
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ // append to std::vector<arm_compute::ICLTensor*>
+ m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]);
+ m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]);
+ }
+
+ //
+ // Split
+ //
+ unsigned int numberDimensions = 3;
+ unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension)
+
+ if (maxTime != 1) // ACL split does not work with only one element to split.
+ {
+ ViewsDescriptor splitterDesc(maxTime, numberDimensions);
+ unsigned int splitterDimSizes[3] = {1, batchSize, inputSize};
+ for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx)
+ {
+ splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);
+ for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx)
+ {
+ splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);
+ }
+ }
+
+ std::set<unsigned int> splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput);
+
+ std::unique_ptr<arm_compute::CLSplit> split_layer(new arm_compute::CLSplit());
+ unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), *splitAxis.begin());
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);
+ }
+ else
+ {
+ split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit);
+ }
+
+ split_layer->prepare();
+ m_Splitter.reset(split_layer.release());
+ }
+
+ //
+ // Lstm
+ //
+ arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;
+
+ m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
+
+ m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
+
+ m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
+
+ m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
+
+ m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
+
+ m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
+
+ m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
+
+ m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
+
+ m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
+
+ // for future reference: check the AndroidNN API for the logic here
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
+
+ m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
+
+ m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ if (m_Data.m_CellToInputWeights != nullptr)
+ {
+ BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
+ }
+
+ m_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
+
+ lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
+ m_RecurrentToInputWeightsTensor.get(),
+ m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : nullptr,
+ m_InputGateBiasTensor.get());
+ }
+
+ if (m_Data.m_Parameters.m_ProjectionEnabled)
+ {
+ m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
+
+ m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();
+ if (m_Data.m_ProjectionBias != nullptr)
+ {
+ BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
+ }
+
+ lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
+ m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : nullptr);
+ }
+
+ if (m_Data.m_Parameters.m_PeepholeEnabled)
+ {
+ m_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
+
+ m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
+
+ lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
+ }
+
+ if (m_Data.m_Parameters.m_LayerNormEnabled)
+ {
+ m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());
+ }
+
+ m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());
+
+ m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());
+
+ m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
+ BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());
+
+ auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();
+ lstm_param.set_layer_normalization_params(inputNormWeightTensor,
+ m_ForgetLayerNormWeightsTensor.get(),
+ m_CellLayerNormWeightsTensor.get(),
+ m_OutputLayerNormWeightsTensor.get());
+ }
+
+ arm_compute::ICLTensor& output_state_in = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+ arm_compute::ICLTensor& cell_state_in = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
+
+ arm_compute::ICLTensor& output_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+ arm_compute::ICLTensor& cell_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
+
+ m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();
+ if (m_Data.m_Parameters.m_CifgEnabled)
+ {
+ // scratch_buffer [num_units * 3, batch_size] with CIFG
+ BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType));
+ }
+ else
+ {
+ // scratch_buffer [num_units * 4, batch_size] without CIFG
+ BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType));
+ }
+
+ // Need to be set at negative threshold to be compatible for ACL
+ float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell;
+ float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
+
+ // For preparing the object for the class ActivationLayerInfo, consider 5 situations
+ arm_compute::ActivationLayerInfo activationLayerInfo =
+ ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
+
+ for (unsigned int i = 0; i != maxTime; ++i)
+ {
+ // Set LSTM input and output ITensors depending on:
+ // input format (timeMajor) & number of LSTM batches (maxTime).
+ arm_compute::ICLTensor* outputLSTM;
+ arm_compute::ICLTensor* inputLSTM;
+ // If there is only one LSTM time major batch, we will not concat OR permute.
+ // Set input of LSTM to be first input ITensor.
+ // Set output of LSTM to be final output ITensor.
+ // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
+ if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape((&input)->info()->tensor_shape(), 1U);
+ TensorShape outputShape = GetTensorShape((&output)->info()->tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
+ (&input)->info()->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = const_cast<arm_compute::ICLTensor*>(&input);
+ (&output)->info()->set_tensor_shape(acl_output_shape_shrink);
+ outputLSTM = &output;
+ }
+ // If there is only one LSTM batch major batch, we will not concat, only permute.
+ // Set input of LSTM to be output of initial permute.
+ // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
+ // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
+ else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = &m_PermuteFirstOut;
+ outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]);
+ }
+ // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
+ else
+ {
+ inputLSTM = m_SplitterOutputs[i];
+ outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]);
+ }
+
+ std::unique_ptr<arm_compute::CLLSTMLayer> lstm_layer(new arm_compute::CLLSTMLayer());
+ lstm_layer->configure(clCompileContext,
+ inputLSTM,
+ m_InputToForgetWeightsTensor.get(),
+ m_InputToCellWeightsTensor.get(),
+ m_InputToOutputWeightsTensor.get(),
+ m_RecurrentToForgetWeightsTensor.get(),
+ m_RecurrentToCellWeightsTensor.get(),
+ m_RecurrentToOutputWeightsTensor.get(),
+ m_ForgetGateBiasTensor.get(),
+ m_CellBiasTensor.get(),
+ m_OutputGateBiasTensor.get(),
+ &output_state_in,
+ &cell_state_in,
+ m_ScratchBuffer.get(),
+ &output_state_out,
+ &cell_state_out,
+ outputLSTM,
+ lstm_param,
+ activationLayerInfo,
+ cell_threshold,
+ projection_threshold);
+
+ m_Layers.emplace_back(std::move(lstm_layer));
+ }
+
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);
+
+ InitializeArmComputeClTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights);
+ InitializeArmComputeClTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights);
+ InitializeArmComputeClTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights);
+ InitializeArmComputeClTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights);
+ InitializeArmComputeClTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights);
+ InitializeArmComputeClTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights);
+ InitializeArmComputeClTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias);
+ InitializeArmComputeClTensorData(*m_CellBiasTensor, m_Data.m_CellBias);
+ InitializeArmComputeClTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias);
+
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ InitializeArmComputeClTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights);
+ InitializeArmComputeClTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights);
+ if (m_Data.m_CellToInputWeights != nullptr)
+ {
+ InitializeArmComputeClTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights);
+ }
+ InitializeArmComputeClTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias);
+ }
+
+ if (m_Data.m_Parameters.m_ProjectionEnabled)
+ {
+ InitializeArmComputeClTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights);
+ if (m_Data.m_ProjectionBias != nullptr)
+ {
+ InitializeArmComputeClTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias);
+ }
+ }
+
+ if (m_Data.m_Parameters.m_PeepholeEnabled)
+ {
+ InitializeArmComputeClTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights);
+ InitializeArmComputeClTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights);
+ }
+
+ if (m_Data.m_Parameters.m_LayerNormEnabled)
+ {
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ InitializeArmComputeClTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);
+ }
+ InitializeArmComputeClTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);
+ InitializeArmComputeClTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);
+ InitializeArmComputeClTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);
+ }
+
+ // Force Compute Library to perform the necessary copying and reshaping.
+ // After which delete all the input tensors that will no longer be needed.
+ for (uint32_t i = 0; i < m_Layers.size(); ++i)
+ {
+ m_Layers[i]->prepare();
+ }
+
+ //
+ // Concat
+ //
+
+ // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
+ TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U);
+ TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
+ TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
+
+ if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
+ {
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+
+ ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views).
+ for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx)
+ {
+ concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx);
+ concatDescriptor.SetConcatAxis(dimension);
+ }
+
+ m_Concat.reset(new arm_compute::CLConcatenateLayer());
+ unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(),
+ concatDescriptor.GetConcatAxis());
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorInfo concatOuputTensorInfo = outputInfo;
+ concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
+ BuildArmComputeTensor(concat_out, concatOuputTensorInfo);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);
+
+ m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);
+ }
+ else
+ {
+ m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat);
+ }
+
+ m_Concat->prepare();
+ }
+ // If only one LSTM batch, we do not concat and/or permute.
+ // Must ensure final output info is expanded to correct batch major dimensions.
+ else
+ {
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));
+ }
+ else
+ {
+ (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+ }
+
+ //
+ // Permute: only done if input/output are in batch major format.
+ //
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ // Output now time major. Permute output back to batch major.
+ std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute());
+ if (maxTime != 1)
+ {
+ layer->configure(clCompileContext, &concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ else
+ {
+ layer->configure(clCompileContext, m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ m_Permute2.reset(layer.release());
+ }
+
+ FreeUnusedTensors();
+}
+
+void ClUnidirectionalSequenceLstmFloatWorkload::Execute() const
+{
+ ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClUnidirectionalSequenceLstmFloatWorkload_Execute", GetGuid());
+ if (m_Permute1)
+ {
+ m_Permute1->run();
+ }
+ if (m_Splitter)
+ {
+ m_Splitter->run();
+ }
+ for (uint32_t i = 0; i < m_Layers.size(); ++i)
+ {
+ m_Layers[i]->run();
+ }
+ if (m_Concat)
+ {
+ m_Concat->run();
+ }
+ if (m_Permute2)
+ {
+ m_Permute2->run();
+ }
+}
+
+arm_compute::Status
+ClUnidirectionalSequenceLstmFloatWorkloadValidate(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)
+{
+ IgnoreUnused(hiddenStateOutput, cellStateOutput);
+
+ TensorShape inputLayerShape = input.GetShape();
+ TensorShape outputLayerShape = outputStateIn.GetShape();
+
+ unsigned int maxTime = descriptor.m_TimeMajor?inputLayerShape[0]:inputLayerShape[1];
+ unsigned int batchSize = descriptor.m_TimeMajor?inputLayerShape[1]:inputLayerShape[0];
+ unsigned int inputSize = inputLayerShape[2];
+ unsigned int outputSize = outputLayerShape[2];
+
+ const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
+ const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
+
+ arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Permute1 status");
+ arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Split status");
+ arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "LSTM status");
+ arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Concat status");
+ arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Permute2 status");
+
+ const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+ const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+
+ //
+ // Permute validate
+ //
+ TensorInfo permuteOutInfo = TensorInfo(input);
+ arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo);
+ if (!descriptor.m_TimeMajor)
+ {
+ statusPermute1 = arm_compute::CLPermute::validate(&aclInputInfo,
+ &aclPermuteOutInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+
+ //
+ // Split and Concat Tensors validate
+ //
+ std::vector<arm_compute::TensorInfo> splitterOutputsTensorInfos;
+ std::vector<arm_compute::TensorInfo> concatInputsTensorInfos;
+ std::vector<arm_compute::ITensorInfo*> splitterOutputsTensorInfosPtr;
+ std::vector<const arm_compute::ITensorInfo*> concatInputsTensorInfosPtr;
+ splitterOutputsTensorInfos.reserve(maxTime);
+ concatInputsTensorInfos.reserve(maxTime);
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ arm_compute::TensorInfo splitter_out;
+ arm_compute::TensorInfo concat_in;
+
+ auto splitterTensorInfo = TensorInfo(input);
+ auto concatTensorInfo = TensorInfo(output);
+ splitterTensorInfo.SetShape({batchSize, inputSize});
+ concatTensorInfo.SetShape({batchSize, outputSize});
+
+ arm_compute::TensorInfo aclSplitterTensorInfo
+ = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo);
+ arm_compute::TensorInfo aclConcatTensorInfo
+ = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo);
+
+ splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo);
+ concatInputsTensorInfos.emplace_back(aclConcatTensorInfo);
+ splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]);
+ concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]);
+ }
+
+ //
+ // Split validate
+ //
+ unsigned int numberDimensions = 3;
+ unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension)
+ unsigned int aclAxisSplit = CalcAclAxis(numberDimensions, dimension);
+
+ if (maxTime != 1) // ACL split does not work with only one element to split.
+ {
+ if (!descriptor.m_TimeMajor)
+ {
+ statusSplit = arm_compute::CLSplit::validate(&aclPermuteOutInfo,
+ splitterOutputsTensorInfosPtr,
+ aclAxisSplit);
+ }
+ else
+ {
+ statusSplit = arm_compute::CLSplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit);
+ }
+ }
+
+ //
+ // LSTM validate
+ //
+
+ arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
+
+ const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
+ const TensorInfo& outputStateOut = TensorInfo(outputStateIn.GetShape(), input.GetDataType());
+ const TensorInfo& cellStateOut = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
+
+ // The inputs and outputs
+ const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
+ const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);
+ const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);
+ const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);
+ const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);
+
+ // Basic parameters
+ const arm_compute::TensorInfo aclInputToForgetWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights());
+ const arm_compute::TensorInfo aclInputToCellWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights());
+ const arm_compute::TensorInfo aclInputToOutputWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights());
+ const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights());
+ const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights());
+ const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights());
+ const arm_compute::TensorInfo aclForgetGateBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias());
+ const arm_compute::TensorInfo aclCellBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetCellBias());
+ const arm_compute::TensorInfo aclOutputGateBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias());
+
+ arm_compute::TensorInfo aclInputToInputWeightsInfo;
+ arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;
+ arm_compute::TensorInfo aclCellToInputWeightsInfo;
+ arm_compute::TensorInfo aclInputGateBiasInfo;
+ arm_compute::TensorInfo aclProjectionWeightsInfo;
+ arm_compute::TensorInfo aclProjectionBiasInfo;
+ arm_compute::TensorInfo aclCellToForgetWeightsInfo;
+ arm_compute::TensorInfo aclCellToOutputWeightsInfo;
+
+ arm_compute::TensorInfo aclInputLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclCellLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;
+
+
+ if (!descriptor.m_CifgEnabled)
+ {
+ if (descriptor.m_PeepholeEnabled)
+ {
+ aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights());
+ }
+ aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights());
+ aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights());
+ aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias());
+
+ lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo,
+ &aclRecurrentToInputWeightsInfo,
+ descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr,
+ &aclInputGateBiasInfo);
+ }
+
+ if (descriptor.m_ProjectionEnabled)
+ {
+ if (paramsInfo.m_ProjectionBias != nullptr)
+ {
+ aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias());
+ }
+ aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights());
+
+ lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,
+ paramsInfo.m_ProjectionBias ? &aclProjectionBiasInfo : nullptr);
+ }
+
+ if (descriptor.m_PeepholeEnabled)
+ {
+ aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights());
+ aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights());
+
+ lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);
+ }
+
+ if (descriptor.m_LayerNormEnabled)
+ {
+ if (!descriptor.m_CifgEnabled)
+ {
+ aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights());
+ }
+ aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights());
+ aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights());
+ aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights());
+
+ lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr :
+ &aclInputLayerNormWeightsInfo,
+ &aclForgetLayerNormWeightsInfo,
+ &aclCellLayerNormWeightsInfo,
+ &aclOutputLayerNormWeightsInfo);
+ }
+
+ // Need to be set at negative threshold to be compatible for ACL
+ float cell_threshold = descriptor.m_ClippingThresCell;
+ float projection_threshold = descriptor.m_ClippingThresProj;
+
+ arm_compute::ActivationLayerInfo activationLayerInfo =
+ ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc);
+
+ for (unsigned int i = 0; i != maxTime; ++i)
+ {
+
+ // Set LSTM input and output ITensors depending on:
+ // input format (timeMajor) & number of LSTM batches (maxTime).
+ arm_compute::ITensorInfo* outputLSTM;
+ arm_compute::ITensorInfo* inputLSTM;
+ // If there is only one LSTM time major batch, we will not concat OR permute.
+ // Set input of LSTM to be first input ITensor.
+ // Set output of LSTM to be final output ITensor.
+ // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
+ if (maxTime == 1 && !descriptor.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U);
+ TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = const_cast<arm_compute::TensorInfo*>(&aclInputInfo);
+ const_cast<arm_compute::TensorInfo*>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink);
+ outputLSTM = const_cast<arm_compute::TensorInfo*>(&aclOutputInfo);
+ }
+ // If there is only one LSTM batch major batch, we will not concat, only permute.
+ // Set input of LSTM to be output of initial permute.
+ // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
+ // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
+ else if (maxTime == 1 && !descriptor.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = &aclPermuteOutInfo;
+ outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
+ }
+ // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
+ else
+ {
+ inputLSTM = splitterOutputsTensorInfosPtr[i];
+ outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
+ }
+
+ statusLSTM = arm_compute::CLLSTMLayer::validate(inputLSTM,
+ &aclInputToForgetWeightsInfo,
+ &aclInputToCellWeightsInfo,
+ &aclInputToOutputWeightsInfo,
+ &aclRecurrentToForgetWeightsInfo,
+ &aclRecurrentToCellWeightsInfo,
+ &aclRecurrentToOutputWeightsInfo,
+ &aclForgetGateBiasInfo,
+ &aclCellBiasInfo,
+ &aclOutputGateBiasInfo,
+ &aclOutputStateInInfo,
+ &aclCellStateInInfo,
+ &aclScratchBufferInfo,
+ &aclOutputStateOutInfo,
+ &aclCellStateOutInfo,
+ outputLSTM,
+ lstm_params_info,
+ activationLayerInfo,
+ cell_threshold,
+ projection_threshold);
+
+ if (statusLSTM.error_code() != arm_compute::ErrorCode::OK)
+ {
+ break;
+ }
+ }
+
+ //
+ // Concat validate
+ //
+
+ // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
+ TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U);
+ TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
+ TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
+
+ TensorInfo concatOuputTensorInfo = TensorInfo(output);
+ concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
+ arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo);
+
+ if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
+ {
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor);
+ concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand);
+ }
+
+ unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension);
+ if (!descriptor.m_TimeMajor)
+ {
+ statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,
+ &aclConcatOuputTensorInfo,
+ aclAxisConcat);
+ }
+ else
+ {
+ statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,
+ &aclOutputInfo,
+ aclAxisConcat);
+ }
+ }
+ // If only one LSTM batch, we do not concat and/or permute.
+ // Must ensure final output info is expanded to correct batch major dimensions.
+ else
+ {
+ if (!descriptor.m_TimeMajor)
+ {
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
+ BuildArmComputeTensorShape(shapeExpandBatchMajor));
+ }
+ else
+ {
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
+ BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+ }
+ //
+ // Permute validate
+ //
+ if (!descriptor.m_TimeMajor)
+ {
+ // Output now time major. Permute output back to batch major.
+ if (maxTime != 1)
+ {
+ statusPermute2 = arm_compute::CLPermute::validate(&aclConcatOuputTensorInfo,
+ &aclOutputInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ else
+ {
+ statusPermute2 = arm_compute::CLPermute::validate(concatInputsTensorInfosPtr[0],
+ &aclOutputInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ }
+
+ auto okCode = arm_compute::ErrorCode::OK;
+ if (statusPermute1.error_code() == okCode &&
+ statusSplit.error_code() == okCode &&
+ statusLSTM .error_code() == okCode &&
+ statusConcat.error_code() == okCode &&
+ statusPermute2.error_code() == okCode)
+ {
+ return arm_compute::Status(arm_compute::ErrorCode::OK,
+ "All Unidirectional Sequence LSTM layer validate status OK.");
+ }
+ else
+ {
+ return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unidirectional Sequence LSTM layer validate status failed.");
+ }
+}
+
+void ClUnidirectionalSequenceLstmFloatWorkload::FreeUnusedTensors()
+{
+ FreeTensorIfUnused(m_InputToInputWeightsTensor);
+ FreeTensorIfUnused(m_InputToForgetWeightsTensor);
+ FreeTensorIfUnused(m_InputToCellWeightsTensor);
+ FreeTensorIfUnused(m_InputToOutputWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);
+ FreeTensorIfUnused(m_CellToInputWeightsTensor);
+ FreeTensorIfUnused(m_CellToForgetWeightsTensor);
+ FreeTensorIfUnused(m_CellToOutputWeightsTensor);
+ FreeTensorIfUnused(m_InputGateBiasTensor);
+ FreeTensorIfUnused(m_ForgetGateBiasTensor);
+ FreeTensorIfUnused(m_CellBiasTensor);
+ FreeTensorIfUnused(m_OutputGateBiasTensor);
+ FreeTensorIfUnused(m_ProjectionWeightsTensor);
+ FreeTensorIfUnused(m_ProjectionBiasTensor);
+ FreeTensorIfUnused(m_InputLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_CellLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_ScratchBuffer);
+}
+
+} //namespace armnn
diff --git a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp
new file mode 100644
index 0000000..f50e0a9
--- /dev/null
+++ b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp
@@ -0,0 +1,96 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/LstmParams.hpp>
+#include <armnn/backends/Workload.hpp>
+#include <armnn/backends/WorkloadData.hpp>
+
+#include <arm_compute/graph/Tensor.h>
+#include <arm_compute/runtime/CL/functions/CLLSTMLayer.h>
+#include <arm_compute/runtime/CL/functions/CLPermute.h>
+#include <arm_compute/runtime/CL/functions/CLSplit.h>
+#include <arm_compute/runtime/CL/functions/CLConcatenateLayer.h>
+
+namespace armnn
+{
+
+class ClUnidirectionalSequenceLstmFloatWorkload : public FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>
+{
+public:
+ ClUnidirectionalSequenceLstmFloatWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info,
+ const arm_compute::CLCompileContext& clCompileContext);
+ virtual void Execute() const override;
+
+private:
+
+ //
+ // ACL layers required to fully form a Unidirectional Sequence LSTM layer.
+ //
+
+ // permutation for input (only used when input is batch major)
+ mutable std::unique_ptr<arm_compute::CLPermute> m_Permute1;
+ mutable std::unique_ptr<arm_compute::IFunction> m_Splitter;
+ mutable std::vector<std::unique_ptr<arm_compute::CLLSTMLayer>> m_Layers;
+ mutable std::unique_ptr<arm_compute::CLConcatenateLayer> m_Concat;
+ // permutation for output (only used when input is batch major)
+ mutable std::unique_ptr<arm_compute::CLPermute> m_Permute2;
+
+ //
+ // ACL LSTM arm_compute::CLTensors.
+ //
+ std::unique_ptr<arm_compute::CLTensor> m_InputToInputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_InputToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_InputToCellWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_InputToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_RecurrentToInputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_RecurrentToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_RecurrentToCellWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_RecurrentToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_CellToInputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_CellToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_CellToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_InputGateBiasTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_ForgetGateBiasTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_CellBiasTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_OutputGateBiasTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_ProjectionWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_ProjectionBiasTensor;
+
+ std::unique_ptr<arm_compute::CLTensor> m_ScratchBuffer;
+
+ std::unique_ptr<arm_compute::CLTensor> m_InputLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_ForgetLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_CellLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::CLTensor> m_OutputLayerNormWeightsTensor;
+
+ //
+ // Additional ACL arm_compute::CLTensors and std::vector<arm_compute::CLTensor>.
+ // Required to perform splitting, concatenation and permutations.
+ //
+ arm_compute::CLTensor m_PermuteFirstOut;
+ std::vector<arm_compute::CLTensor> m_SplitterOutputsTensors;
+ std::vector<arm_compute::CLTensor> m_ConcatInputsTensors;
+ std::vector<arm_compute::ICLTensor*> m_SplitterOutputs;
+ std::vector<const arm_compute::ICLTensor*> m_ConcatInputs;
+ arm_compute::CLTensor concat_out;
+
+ void FreeUnusedTensors();
+};
+
+arm_compute::Status
+ClUnidirectionalSequenceLstmFloatWorkloadValidate(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);
+
+} //namespace armnn
diff --git a/src/backends/cl/workloads/ClWorkloads.hpp b/src/backends/cl/workloads/ClWorkloads.hpp
index bb04b17..3558da3 100644
--- a/src/backends/cl/workloads/ClWorkloads.hpp
+++ b/src/backends/cl/workloads/ClWorkloads.hpp
@@ -65,3 +65,4 @@
#include "ClConvertFp32ToFp16Workload.hpp"
#include "ClTransposeConvolution2dWorkload.hpp"
#include "ClTransposeWorkload.hpp"
+#include "ClUnidirectionalSequenceLstmFloatWorkload.hpp"