| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonUnidirectionalSequenceLstmWorkload.hpp" |
| #include "NeonWorkloadUtils.hpp" |
| |
| #include <aclCommon/ArmComputeUtils.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| #include <armnnUtils/Permute.hpp> |
| #include <neon/test/NeonWorkloadFactoryHelper.hpp> |
| #include <backendsCommon/WorkloadUtils.hpp> |
| |
| #include "neon/NeonTensorHandle.hpp" |
| |
| namespace |
| { |
| |
| unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis) |
| { |
| return (numDimensions - axis) - 1; |
| } |
| } //namespace |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| NeonUnidirectionalSequenceLstmWorkload::NeonUnidirectionalSequenceLstmWorkload |
| (const UnidirectionalSequenceLstmQueueDescriptor& descriptor, const WorkloadInfo& info) |
| : NeonBaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonUnidirectionalSequenceLstmWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| GetGuid()); |
| |
| // Input/Output tensors |
| const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& outputStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); |
| const arm_compute::ITensor& cellStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); |
| |
| arm_compute::ITensor& outputStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| arm_compute::ITensor& cellStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor(); |
| arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor(); |
| |
| TensorInfo inputInfo = info.m_InputTensorInfos[0]; |
| TensorInfo outputInfo = info.m_OutputTensorInfos[2]; |
| |
| TensorShape inputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetShape(); |
| TensorShape outputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->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]; |
| |
| 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::NEPermute> layer(new arm_compute::NEPermute()); |
| |
| TensorInfo permuteOutInfo = inputInfo; |
| permuteOutInfo.SetShape(timeMajorShapeInput); |
| BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo); |
| armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut); |
| |
| // Permute to time major format. |
| layer->configure(&input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U)); |
| m_Permute1.reset(layer.release()); |
| } |
| |
| // |
| // Split and Concat Tensors |
| // |
| for (unsigned int i = 0; i < maxTime; ++i) |
| { |
| arm_compute::Tensor splitter_out; |
| arm_compute::Tensor concat_in; |
| |
| auto splitterTensorInfo = inputInfo; |
| auto concatTensorInfo = outputInfo; |
| splitterTensorInfo.SetShape({batchSize, inputSize}); |
| concatTensorInfo.SetShape({batchSize, outputSize}); |
| BuildArmComputeTensor(splitter_out, splitterTensorInfo); |
| BuildArmComputeTensor(concat_in, concatTensorInfo); |
| |
| armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out); |
| armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in); |
| |
| // append to std::vector<arm_compute::Tensor> |
| 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::ITensor*> |
| 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::NESplit> split_layer(new arm_compute::NESplit()); |
| unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), |
| *splitAxis.begin()); |
| if (!m_Data.m_Parameters.m_TimeMajor) |
| { |
| split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit); |
| } else |
| { |
| split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit); |
| } |
| |
| split_layer->prepare(); |
| m_Splitter.reset(split_layer.release()); |
| } |
| |
| // |
| // Lstm |
| // |
| arm_compute::LSTMParams<arm_compute::ITensor> lstm_param; |
| |
| lstm_param.set_cell_clip_params(descriptor.m_Parameters.m_ClippingThresCell); |
| lstm_param.set_projection_clip_params(descriptor.m_Parameters.m_ClippingThresProj); |
| |
| lstm_param.set_matmul_scale_params(descriptor.m_Parameters.m_InputIntermediateScale, |
| descriptor.m_Parameters.m_ForgetIntermediateScale, |
| descriptor.m_Parameters.m_CellIntermediateScale, |
| descriptor.m_Parameters.m_OutputIntermediateScale); |
| |
| lstm_param.set_hidden_state_params(descriptor.m_Parameters.m_HiddenStateZeroPoint, |
| descriptor.m_Parameters.m_HiddenStateScale); |
| |
| m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); |
| |
| m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); |
| |
| m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); |
| |
| m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); |
| |
| m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); |
| |
| m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); |
| |
| m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); |
| |
| m_CellBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); |
| |
| m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| 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::Tensor>(); |
| BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); |
| |
| m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); |
| |
| m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| if (m_Data.m_CellToInputWeights != nullptr) |
| { |
| BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); |
| } |
| |
| m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| 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::Tensor>(); |
| BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); |
| |
| m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>(); |
| 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::Tensor>(); |
| BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); |
| |
| m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| 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::Tensor>(); |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); |
| } |
| |
| m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); |
| |
| m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); |
| |
| m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| 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()); |
| } |
| |
| for (unsigned int i = 0; i != maxTime; ++i) |
| { |
| // Set LSTM input and output ITensors depending on: |
| // input format (timeMajor) & number of LSTM batches (maxTime). |
| arm_compute::ITensor* outputLSTM; |
| arm_compute::ITensor* inputLSTM; |
| |
| // If there is only one LSTM time major batch, we will not concat OR permute. |
| // Set input of LSTM to be first input ITensor. |
| // Set output of LSTM to be final output ITensor. |
| // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. |
| if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor) |
| { |
| TensorShape inputShape = GetTensorShape(input.info()->tensor_shape(), 1U); |
| TensorShape outputShape = GetTensorShape(output.info()->tensor_shape(), 1U); |
| |
| TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); |
| TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); |
| |
| auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); |
| auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); |
| |
| input.info()->set_tensor_shape(acl_input_shape_shrink); |
| inputLSTM = const_cast<arm_compute::ITensor*>(&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::ITensor*>(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::ITensor*>(m_ConcatInputs[i]); |
| } |
| |
| std::unique_ptr<arm_compute::NEQLSTMLayer> lstm_layer(new arm_compute::NEQLSTMLayer()); |
| |
| lstm_layer->configure(inputLSTM, |
| m_InputToForgetWeightsTensor.get(), |
| m_InputToCellWeightsTensor.get(), |
| m_InputToOutputWeightsTensor.get(), |
| m_RecurrentToForgetWeightsTensor.get(), |
| m_RecurrentToCellWeightsTensor.get(), |
| m_RecurrentToOutputWeightsTensor.get(), |
| m_ForgetGateBiasTensor.get(), |
| m_CellBiasTensor.get(), |
| m_OutputGateBiasTensor.get(), |
| &cellStateIn, |
| &outputStateIn, |
| &cellStateOut, |
| &outputStateOut, |
| outputLSTM, |
| lstm_param); |
| |
| m_Layers.emplace_back(std::move(lstm_layer)); |
| } |
| |
| InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights); |
| InitializeArmComputeTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights); |
| InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights); |
| InitializeArmComputeTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias); |
| InitializeArmComputeTensorData(*m_CellBiasTensor, m_Data.m_CellBias); |
| InitializeArmComputeTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias); |
| |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| InitializeArmComputeTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights); |
| InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights); |
| if (m_Data.m_CellToInputWeights != nullptr) |
| { |
| InitializeArmComputeTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights); |
| } |
| InitializeArmComputeTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias); |
| } |
| |
| if (m_Data.m_Parameters.m_ProjectionEnabled) |
| { |
| InitializeArmComputeTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights); |
| if (m_Data.m_ProjectionBias != nullptr) |
| { |
| InitializeArmComputeTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias); |
| } |
| } |
| |
| if (m_Data.m_Parameters.m_PeepholeEnabled) |
| { |
| InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights); |
| InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights); |
| } |
| |
| if (m_Data.m_Parameters.m_LayerNormEnabled) |
| { |
| if (!m_Data.m_Parameters.m_CifgEnabled) |
| { |
| InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights); |
| } |
| InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights); |
| InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights); |
| InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights); |
| } |
| |
| // Force Compute Library to perform the necessary copying and reshaping. |
| // After which delete all the input tensors that will no longer be needed. |
| for (uint32_t i = 0; i < m_Layers.size(); ++i) |
| { |
| m_Layers[i]->prepare(); |
| } |
| |
| // |
| // Concat |
| // |
| |
| // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. |
| TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U); |
| TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); |
| TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); |
| |
| if (maxTime != 1) // ACL concat does not work with only one element to concatenate. |
| { |
| for (unsigned int i = 0; i < maxTime; ++i) |
| { |
| m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); |
| } |
| ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views). |
| |
| for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx) |
| { |
| concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx); |
| concatDescriptor.SetConcatAxis(dimension); |
| } |
| m_Concat.reset(new arm_compute::NEConcatenateLayer()); |
| |
| unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis()); |
| if (!m_Data.m_Parameters.m_TimeMajor) |
| { |
| TensorInfo concatOutputTensorInfo = outputInfo; |
| concatOutputTensorInfo.SetShape(timeMajorShapeOutput); |
| BuildArmComputeTensor(concat_out, concatOutputTensorInfo); |
| 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::NEPermute> layer(new arm_compute::NEPermute()); |
| if (maxTime != 1) |
| { |
| layer->configure(&concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U)); |
| } |
| else |
| { |
| layer->configure(m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U)); |
| } |
| m_Permute2.reset(layer.release()); |
| } |
| |
| FreeUnusedTensors(); |
| } |
| |
| void NeonUnidirectionalSequenceLstmWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonUnidirectionalSequenceLstmWorkload_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 |
| NeonUnidirectionalSequenceLstmWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const UnidirectionalSequenceLstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo) |
| { |
| TensorShape inputLayerShape = input.GetShape(); |
| TensorShape outputLayerShape = output.GetShape(); |
| |
| unsigned int maxTime = descriptor.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; |
| unsigned int batchSize = descriptor.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; |
| unsigned int inputSize = inputLayerShape[2]; |
| unsigned int outputSize = outputLayerShape[2]; |
| |
| const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); |
| const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); |
| |
| arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK, |
| "Permute1 status"); |
| arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK, |
| "Split status"); |
| arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK, |
| "LSTM status"); |
| arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK, |
| "Concat status"); |
| arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK, |
| "Permute2 status"); |
| |
| const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| |
| // |
| // Permute validate |
| // |
| TensorInfo permuteOutInfo = TensorInfo(input); |
| arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo); |
| if (!descriptor.m_TimeMajor) |
| { |
| statusPermute1 = arm_compute::NEPermute::validate(&aclInputInfo, |
| &aclPermuteOutInfo, |
| arm_compute::PermutationVector(0U, 2U, 1U)); |
| } |
| |
| // |
| // Split and Concat Tensors validate |
| // |
| std::vector<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::NESplit::validate(&aclPermuteOutInfo, |
| splitterOutputsTensorInfosPtr, |
| aclAxisSplit); |
| } else |
| { |
| statusSplit = arm_compute::NESplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit); |
| } |
| } |
| |
| // |
| // LSTM validate |
| // |
| |
| arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info; |
| |
| const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); |
| |
| lstm_params_info.set_cell_clip_params(descriptor.m_ClippingThresCell); |
| lstm_params_info.set_projection_clip_params(descriptor.m_ClippingThresProj); |
| // 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); |
| } |
| |
| lstm_params_info.set_matmul_scale_params(descriptor.m_InputIntermediateScale, |
| descriptor.m_ForgetIntermediateScale, |
| descriptor.m_CellIntermediateScale, |
| descriptor.m_OutputIntermediateScale); |
| |
| lstm_params_info.set_hidden_state_params(descriptor.m_HiddenStateZeroPoint, descriptor.m_HiddenStateScale); |
| |
| 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::NEQLSTMLayer::validate(inputLSTM, |
| &aclInputToForgetWeightsInfo, |
| &aclInputToCellWeightsInfo, |
| &aclInputToOutputWeightsInfo, |
| &aclRecurrentToForgetWeightsInfo, |
| &aclRecurrentToCellWeightsInfo, |
| &aclRecurrentToOutputWeightsInfo, |
| &aclForgetGateBiasInfo, |
| &aclCellBiasInfo, |
| &aclOutputGateBiasInfo, |
| &aclCellStateInInfo, |
| &aclOutputStateInInfo, |
| &aclCellStateOutInfo, |
| &aclOutputStateOutInfo, |
| outputLSTM, |
| lstm_params_info); |
| } |
| |
| // |
| // 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 concatOutputTensorInfo = TensorInfo(output); |
| concatOutputTensorInfo.SetShape(timeMajorShapeOutput); |
| arm_compute::TensorInfo aclConcatOutputTensorInfo= BuildArmComputeTensorInfo(concatOutputTensorInfo); |
| |
| if (maxTime != 1) // ACL concat does not work with only one element to concatenate. |
| { |
| for (unsigned int i = 0; i < maxTime; ++i) |
| { |
| auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor); |
| concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand); |
| } |
| |
| unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension); |
| if (!descriptor.m_TimeMajor) |
| { |
| statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, |
| &aclConcatOutputTensorInfo, |
| aclAxisConcat); |
| } |
| else |
| { |
| statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr, |
| &aclOutputInfo, |
| aclAxisConcat); |
| } |
| } |
| // If only one LSTM batch, we do not concat and/or permute. |
| // Must ensure final output info is expanded to correct batch major dimensions. |
| else |
| { |
| if (!descriptor.m_TimeMajor) |
| { |
| const_cast<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::NEPermute::validate(&aclConcatOutputTensorInfo, |
| &aclOutputInfo, |
| arm_compute::PermutationVector(0U, 2U, 1U)); |
| } |
| else |
| { |
| statusPermute2 = arm_compute::NEPermute::validate(concatInputsTensorInfosPtr[0], |
| &aclOutputInfo, |
| arm_compute::PermutationVector(0U, 2U, 1U)); |
| } |
| } |
| |
| auto okCode = arm_compute::ErrorCode::OK; |
| if (statusPermute1.error_code() == okCode && |
| statusSplit.error_code() == okCode && |
| statusLSTM .error_code() == okCode && |
| statusConcat.error_code() == okCode && |
| statusPermute2.error_code() == okCode) |
| { |
| return arm_compute::Status(arm_compute::ErrorCode::OK, |
| "All Unidirectional Sequence LSTM layer validate status OK."); |
| } |
| else |
| { |
| return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, |
| "Unidirectional Sequence LSTM layer validate status failed."); |
| } |
| } |
| |
| void NeonUnidirectionalSequenceLstmWorkload::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); |
| } |
| |
| } //namespace armnn |
