| // |
| // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <schema_generated.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| template <typename T> |
| std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, |
| int32_t batchSize, |
| 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<T>& inputGateBias, |
| const std::vector<T>& forgetGateBias, |
| const std::vector<T>& cellBias, |
| const std::vector<T>& outputGateBias, |
| bool hasProjectionWeights, |
| const std::vector<T>& projectionWeights, |
| bool hasProjectionBias, |
| const std::vector<T>& projectionBias, |
| bool hasInputLayerNormWeights, |
| const std::vector<T>& inputLayerNormWeights, |
| bool hasForgetLayerNormWeights, |
| const std::vector<T>& forgetLayerNormWeights, |
| bool hasCellLayerNormWeights, |
| const std::vector<T>& cellLayerNormWeights, |
| bool hasOutputLayerNormWeights, |
| const std::vector<T>& outputLayerNormWeights, |
| tflite::ActivationFunctionType activationFunction, |
| float clippingThresCell, |
| float clippingThresProj, |
| float quantScale = 1.0f, |
| int quantOffset = 0, |
| float outputQuantScale = 2.0f, |
| int outputQuantOffset = 0) |
| { |
| |
| std::vector <int32_t> tensorInfo0 {}; |
| std::vector <int32_t> tensorInfo4 {numUnits}; |
| std::vector <int32_t> tensorInfo8 {numUnits, static_cast<int32_t>(2)}; |
| std::vector <int32_t> tensorInfo16 {numUnits, static_cast<int32_t>(4)}; |
| |
| std::vector<int32_t> inputShape {batchSize , inputSize}; |
| std::vector<int32_t> outputShape {batchSize , outputSize}; |
| |
| std::vector<int32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<int32_t> cellStateInDimensions{batchSize, numUnits}; |
| |
| 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>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| auto outputQuantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), |
| inputShape.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo8.data(), |
| tensorInfo8.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToInputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo8.data(), |
| tensorInfo8.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToForgetWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo8.data(), |
| tensorInfo8.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToCellWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo8.data(), |
| tensorInfo8.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputToOutputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo16.data(), |
| tensorInfo16.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToInputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo16.data(), |
| tensorInfo16.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToForgetWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo16.data(), |
| tensorInfo16.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("recurrentToCellWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo16.data(), |
| tensorInfo16.size()), |
| tensorType, |
| buffers.size() - 1 , |
| flatBufferBuilder.CreateString("recurrentToOutputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToInputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToForgetWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellToOutputWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasInputGateBias) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputGateBias.data()), |
| sizeof(T) * inputGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputGateBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(forgetGateBias.data()), |
| sizeof(T) * forgetGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("forgetGateBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()), |
| sizeof(T) * cellBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()), |
| sizeof(T) * outputGateBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputGateBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputGateBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasProjectionBias) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionBias.data()), |
| sizeof(T) * projectionBias.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("projectionBias"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.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()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputStateInInfo"), |
| outputQuantizationParameters, |
| true)); |
| operatorInputs.push_back(buffers.size() - 1); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), |
| cellStateInDimensions.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellStateInInfo"), |
| outputQuantizationParameters, |
| true)); |
| operatorInputs.push_back(buffers.size() - 1); |
| |
| if (hasInputLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()), |
| sizeof(T) * inputLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("inputLayerNormWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasForgetLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t *>(forgetLayerNormWeights.data()), |
| sizeof(T) * forgetLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("forgetLayerNormWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasCellLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellLayerNormWeights.data()), |
| sizeof(T) * cellLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("cellLayerNormWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| |
| if (hasOutputLayerNormWeights) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector( |
| reinterpret_cast<const uint8_t *>(outputLayerNormWeights.data()), |
| sizeof(T) * outputLayerNormWeights.size()))); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), |
| tensorInfo4.size()), |
| tensorType, |
| buffers.size() - 1, |
| flatBufferBuilder.CreateString("outputLayerNormWeights"), |
| outputQuantizationParameters)); |
| operatorInputs.push_back(buffers.size() - 1); |
| } |
| else |
| { |
| operatorInputs.push_back(kTfLiteOptionalTensor); |
| } |
| int outputBufferId = buffers.size(); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors.push_back(CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), |
| outputShape.size()), |
| tensorType, |
| outputBufferId, |
| flatBufferBuilder.CreateString("output"), |
| outputQuantizationParameters)); |
| std::vector<int> operatorOutputs; |
| operatorOutputs.push_back(buffers.size() - 1); |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_LSTMOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = |
| CreateLSTMOptions(flatBufferBuilder, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj).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: LSTM Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| tflite::BuiltinOperator_LSTM); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void LstmTestImpl(std::vector<armnn::BackendId>& backends, |
| tflite::TensorType tensorType, |
| int32_t batchSize, |
| 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<T>& inputGateBias, |
| const std::vector<T>& forgetGateBias, |
| const std::vector<T>& cellBias, |
| const std::vector<T>& outputGateBias, |
| bool hasProjectionWeights, |
| const std::vector<T>& projectionWeights, |
| bool hasProjectionBias, |
| const std::vector<T>& projectionBias, |
| bool hasInputLayerNormWeights, |
| const std::vector<T>& inputLayerNormWeights, |
| bool hasForgetLayerNormWeights, |
| const std::vector<T>& forgetLayerNormWeights, |
| bool hasCellLayerNormWeights, |
| const std::vector<T>& cellLayerNormWeights, |
| bool hasOutputLayerNormWeights, |
| const std::vector<T>& outputLayerNormWeights, |
| std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| tflite::ActivationFunctionType activationFunction, |
| float clippingThresCell, |
| float clippingThresProj) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreateLstmTfLiteModel(tensorType, |
| batchSize, |
| 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); |
| |
| std::vector<int32_t> expectedOutputShape {batchSize , outputSize}; |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |