| // |
| // Copyright © 2021, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <tensorflow/lite/version.h> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnn/Types.hpp> |
| |
| #include <initializer_list> |
| #include <iterator> |
| #include <vector> |
| |
| namespace |
| { |
| |
| template<typename T> |
| std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType tensorType, |
| int32_t batchSize, |
| int32_t timeSize, |
| int32_t inputSize, |
| int32_t outputSize, |
| int32_t numUnits, |
| bool hasInputToInputWeights, |
| const std::vector<T>& inputToInputWeights, |
| const std::vector<T>& inputToForgetWeights, |
| const std::vector<T>& inputToCellWeights, |
| const std::vector<T>& inputToOutputWeights, |
| bool hasRecurrentToInputWeights, |
| const std::vector<T>& recurrentToInputWeights, |
| const std::vector<T>& recurrentToForgetWeights, |
| const std::vector<T>& recurrentToCellWeights, |
| const std::vector<T>& recurrentToOutputWeights, |
| bool hasCellToInputWeights, |
| const std::vector<T>& cellToInputWeights, |
| bool hasCellToForgetWeights, |
| const std::vector<T>& cellToForgetWeights, |
| bool hasCellToOutputWeights, |
| const std::vector<T>& cellToOutputWeights, |
| bool hasInputGateBias, |
| const std::vector<float>& inputGateBias, |
| const std::vector<float>& forgetGateBias, |
| const std::vector<float>& cellBias, |
| const std::vector<float>& outputGateBias, |
| bool hasProjectionWeights, |
| const std::vector<T>& projectionWeights, |
| bool hasProjectionBias, |
| const std::vector<float>& projectionBias, |
| bool hasInputLayerNormWeights, |
| const std::vector<float>& inputLayerNormWeights, |
| bool hasForgetLayerNormWeights, |
| const std::vector<float>& forgetLayerNormWeights, |
| bool hasCellLayerNormWeights, |
| const std::vector<float>& cellLayerNormWeights, |
| bool hasOutputLayerNormWeights, |
| const std::vector<float>& outputLayerNormWeights, |
| tflite::ActivationFunctionType activationFunction, |
| float clippingThresCell, |
| float clippingThresProj, |
| bool isTimeMajor, |
| float quantScale, |
| int quantOffset = 0) |
| { |
| |
| std::vector<int32_t> tensorInfo0{}; |
| std::vector<int32_t> tensorInfoNumUnits{numUnits}; |
| std::vector<int32_t> tensorInfoInputSize{numUnits, inputSize}; |
| std::vector<int32_t> tensorInfoOutputSize{numUnits, outputSize}; |
| |
| std::vector<int32_t> inputShape; |
| std::vector<int32_t> outputShape; |
| if (isTimeMajor) |
| { |
| inputShape = {timeSize, batchSize, inputSize}; |
| outputShape = {timeSize, batchSize, outputSize}; |
| } |
| else |
| { |
| inputShape = {batchSize, timeSize, inputSize}; |
| outputShape = {batchSize, timeSize, outputSize}; |
| } |
| std::vector<int32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<int32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<int32_t> projectionWeightDimensions{outputSize, numUnits}; |
| std::vector<int32_t> projectionBiasDimensions{outputSize}; |
| |
| std::vector<int> operatorInputs; |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| std::vector<flatbuffers::Offset<Tensor>> tensors; |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({1.0f}), |
| flatBufferBuilder.CreateVector<int64_t>({0})); |
| |
| auto weightQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({quantScale}), |
| flatBufferBuilder.CreateVector<int64_t>({quantOffset})); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), |
| inputShape.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("input_0"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| if (hasInputToInputWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(inputToInputWeights.data()), |
| sizeof(T) * inputToInputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), |
| tensorInfoInputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToInputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(inputToForgetWeights.data()), |
| sizeof(T) * inputToForgetWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), |
| tensorInfoInputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToForgetWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(inputToCellWeights.data()), |
| sizeof(T) * inputToCellWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), |
| tensorInfoInputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToCellWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(inputToOutputWeights.data()), |
| sizeof(T) * inputToOutputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), |
| tensorInfoInputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToOutputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| if (hasRecurrentToInputWeights) |
| { |
| buffers.push_back(CreateBuffer( |
| flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(recurrentToInputWeights.data()), |
| sizeof(T) * recurrentToInputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), |
| tensorInfoOutputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToInputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| recurrentToForgetWeights.data()), |
| sizeof(T) * recurrentToForgetWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), |
| tensorInfoOutputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToForgetWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| recurrentToCellWeights.data()), |
| sizeof(T) * recurrentToCellWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), |
| tensorInfoOutputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToCellWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| recurrentToOutputWeights.data()), |
| sizeof(T) * recurrentToOutputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), |
| tensorInfoOutputSize.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToOutputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| if (hasCellToInputWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| cellToInputWeights.data()), |
| sizeof(T) * cellToInputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToInputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasCellToForgetWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| cellToForgetWeights.data()), |
| sizeof(T) * cellToForgetWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToForgetWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasCellToOutputWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| cellToOutputWeights.data()), |
| sizeof(T) * cellToOutputWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToOutputWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasInputGateBias) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputGateBias.data()), |
| sizeof(float) * inputGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputGateBias"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(forgetGateBias.data()), |
| sizeof(float) * forgetGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("forgetGateBias"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellBias.data()), |
| sizeof(float) * cellBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellBias"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(outputGateBias.data()), |
| sizeof(float) * outputGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputGateBias"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| if (hasProjectionWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(projectionWeights.data()), |
| sizeof(T) * projectionWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(projectionWeightDimensions.data(), |
| projectionWeightDimensions.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("projectionWeights"), |
| weightQuantizationParameters)); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasProjectionBias) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(projectionBias.data()), |
| sizeof(float) * projectionBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(projectionBiasDimensions.data(), |
| projectionBiasDimensions.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("projectionBias"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), |
| outputStateInDimensions.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputStateInInfo"), |
| quantizationParameters, |
| true)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), |
| cellStateInDimensions.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellStateInInfo"), |
| quantizationParameters, |
| true)); |
| operatorInputs.push_back(tensors.size() - 1); |
| |
| if (hasInputLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(inputLayerNormWeights.data()), |
| sizeof(float) * inputLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputLayerNormWeights"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasForgetLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(forgetLayerNormWeights.data()), |
| sizeof(float) * forgetLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("forgetLayerNormWeights"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasCellLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( |
| cellLayerNormWeights.data()), |
| sizeof(float) * cellLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellLayerNormWeights"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasOutputLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t*>(outputLayerNormWeights.data()), |
| sizeof(float) * outputLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), |
| tensorInfoNumUnits.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputLayerNormWeights"))); |
| operatorInputs.push_back(tensors.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), |
| outputShape.size()), |
| ::tflite::TensorType_FLOAT32, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("output"))); |
| std::vector<int> operatorOutputs; |
| operatorOutputs.push_back(tensors.size() - 1); |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = |
| CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor).Union(); |
| |
| flatbuffers::Offset<Operator> lstmOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), |
| operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), |
| operatorOutputs.size()), |
| operatorBuiltinOptionsType, operatorBuiltinOptions); |
| |
| flatbuffers::Offset<SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), |
| operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), |
| operatorOutputs.size()), |
| flatBufferBuilder.CreateVector(&lstmOperator, 1)); |
| |
| flatbuffers::Offset<flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString( |
| "ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); |
| flatbuffers::Offset<OperatorCode> operatorCode = |
| CreateOperatorCode(flatBufferBuilder, |
| tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); |
| |
| flatbuffers::Offset<Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers)); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template<typename T> |
| void UnidirectionalSequenceLstmTestImpl(tflite::TensorType tensorType, |
| int32_t batchSize, |
| int32_t timeSize, |
| int32_t inputSize, |
| int32_t outputSize, |
| int32_t numUnits, |
| bool hasInputToInputWeights, |
| const std::vector<T>& inputToInputWeights, |
| const std::vector<T>& inputToForgetWeights, |
| const std::vector<T>& inputToCellWeights, |
| const std::vector<T>& inputToOutputWeights, |
| bool hasRecurrentToInputWeights, |
| const std::vector<T>& recurrentToInputWeights, |
| const std::vector<T>& recurrentToForgetWeights, |
| const std::vector<T>& recurrentToCellWeights, |
| const std::vector<T>& recurrentToOutputWeights, |
| bool hasCellToInputWeights, |
| const std::vector<T>& cellToInputWeights, |
| bool hasCellToForgetWeights, |
| const std::vector<T>& cellToForgetWeights, |
| bool hasCellToOutputWeights, |
| const std::vector<T>& cellToOutputWeights, |
| bool hasInputGateBias, |
| const std::vector<float>& inputGateBias, |
| const std::vector<float>& forgetGateBias, |
| const std::vector<float>& cellBias, |
| const std::vector<float>& outputGateBias, |
| bool hasProjectionWeights, |
| const std::vector<T>& projectionWeights, |
| bool hasProjectionBias, |
| const std::vector<float>& projectionBias, |
| bool hasInputLayerNormWeights, |
| const std::vector<float>& inputLayerNormWeights, |
| bool hasForgetLayerNormWeights, |
| const std::vector<float>& forgetLayerNormWeights, |
| bool hasCellLayerNormWeights, |
| const std::vector<float>& cellLayerNormWeights, |
| bool hasOutputLayerNormWeights, |
| const std::vector<float>& outputLayerNormWeights, |
| std::vector<float>& inputValues, |
| std::vector<float>& expectedOutputValues, |
| tflite::ActivationFunctionType activationFunction, |
| float clippingThresCell, |
| float clippingThresProj, |
| bool isTimeMajor, |
| const std::vector<armnn::BackendId>& backends = {}, |
| float quantScale = 0.1f) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| quantScale); |
| |
| std::vector<int32_t> outputShape; |
| if (isTimeMajor) |
| { |
| outputShape = {timeSize, batchSize, outputSize}; |
| } |
| else |
| { |
| outputShape = {batchSize, timeSize, outputSize}; |
| } |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| |
| if (tensorType == ::tflite::TensorType_INT8) |
| { |
| // Allow 2% tolerance for Quantized weights |
| armnnDelegate::CompareData(expectedOutputValues.data(), armnnOutputValues.data(), |
| expectedOutputValues.size(), 2); |
| armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteOutputValues.data(), |
| expectedOutputValues.size(), 2); |
| armnnDelegate::CompareData(tfLiteOutputValues.data(), armnnOutputValues.data(), |
| expectedOutputValues.size(), 2); |
| } |
| else |
| { |
| armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| } |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |