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Diffstat (limited to 'delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp')
-rw-r--r-- | delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp | 722 |
1 files changed, 722 insertions, 0 deletions
diff --git a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp new file mode 100644 index 0000000000..9d6ef87e3f --- /dev/null +++ b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp @@ -0,0 +1,722 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/interpreter.h> +#include <tensorflow/lite/kernels/register.h> +#include <tensorflow/lite/model.h> +#include <tensorflow/lite/schema/schema_generated.h> +#include <tensorflow/lite/version.h> +#include <tensorflow/lite/c/common.h> + +#include <doctest/doctest.h> + + +#include <armnn/utility/IgnoreUnused.hpp> +#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, flatBufferBuilder.CreateVector({}))); + 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(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>(tensorInfoInputSize.data(), + tensorInfoInputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("inputToInputWeights"), + weightQuantizationParameters)); + 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>(tensorInfoInputSize.data(), + tensorInfoInputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("inputToForgetWeights"), + weightQuantizationParameters)); + 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>(tensorInfoInputSize.data(), + tensorInfoInputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("inputToCellWeights"), + weightQuantizationParameters)); + 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>(tensorInfoInputSize.data(), + tensorInfoInputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("inputToOutputWeights"), + weightQuantizationParameters)); + 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>(tensorInfoOutputSize.data(), + tensorInfoOutputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("recurrentToInputWeights"), + weightQuantizationParameters)); + 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>(tensorInfoOutputSize.data(), + tensorInfoOutputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("recurrentToForgetWeights"), + weightQuantizationParameters)); + 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>(tensorInfoOutputSize.data(), + tensorInfoOutputSize.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("recurrentToCellWeights"), + weightQuantizationParameters)); + 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>(tensorInfoOutputSize.data(), + tensorInfoOutputSize.size()), + tensorType, + buffers.size() - 1 , + flatBufferBuilder.CreateString("recurrentToOutputWeights"), + weightQuantizationParameters)); + 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>(tensorInfoNumUnits.data(), + tensorInfoNumUnits.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("cellToInputWeights"), + weightQuantizationParameters)); + 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>(tensorInfoNumUnits.data(), + tensorInfoNumUnits.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("cellToForgetWeights"), + weightQuantizationParameters)); + 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>(tensorInfoNumUnits.data(), + tensorInfoNumUnits.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("cellToOutputWeights"), + weightQuantizationParameters)); + 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(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(buffers.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(buffers.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(buffers.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(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>(projectionWeightDimensions.data(), + projectionWeightDimensions.size()), + tensorType, + buffers.size() - 1, + flatBufferBuilder.CreateString("projectionWeights"), + weightQuantizationParameters)); + 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(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(buffers.size() - 1); + } + else + { + operatorInputs.push_back(kTfLiteOptionalTensor); + } + + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + 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(buffers.size() - 1); + + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + 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(buffers.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(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(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(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(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(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(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(buffers.size() - 1); + } + else + { + operatorInputs.push_back(kTfLiteOptionalTensor); + } + int outputBufferId = buffers.size(); + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + tensors.push_back(CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), + outputShape.size()), + ::tflite::TensorType_FLOAT32, + outputBufferId, + flatBufferBuilder.CreateString("output"))); + std::vector<int> operatorOutputs; + operatorOutputs.push_back(buffers.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.data(), buffers.size())); + + flatBufferBuilder.Finish(flatbufferModel); + + return std::vector<char>(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template <typename T> +void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, + 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, + float quantScale = 0.1f) +{ + using namespace tflite; + + 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); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteInterpreter) == kTfLiteOk); + CHECK(tfLiteInterpreter != nullptr); + CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); + + // Create the ArmNN Delegate + armnnDelegate::DelegateOptions delegateOptions(backends); + std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); + CHECK(theArmnnDelegate != nullptr); + // Modify armnnDelegateInterpreter to use armnnDelegate + CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; + auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + armnnDelegateInputData[i] = inputValues[i]; + } + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); + + if (tensorType == ::tflite::TensorType_INT8) + { + // Allow 2% tolerance for Quantized weights + armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, + expectedOutputValues.size(), 2); + armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, + expectedOutputValues.size(), 2); + armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, + expectedOutputValues.size(), 2); + } + else + { + armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); + } +} + +} // anonymous namespace
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