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Diffstat (limited to 'delegate/test/DelegateOptionsTestHelper.hpp')
| -rw-r--r-- | delegate/test/DelegateOptionsTestHelper.hpp | 343 |
1 files changed, 343 insertions, 0 deletions
diff --git a/delegate/test/DelegateOptionsTestHelper.hpp b/delegate/test/DelegateOptionsTestHelper.hpp new file mode 100644 index 0000000000..fb5403c7de --- /dev/null +++ b/delegate/test/DelegateOptionsTestHelper.hpp @@ -0,0 +1,343 @@ +// +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <armnn_delegate.hpp> + +#include "TestUtils.hpp" + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/interpreter.h> +#include <tensorflow/lite/kernels/register.h> +#include <tensorflow/lite/model.h> +#include <schema_generated.h> +#include <tensorflow/lite/version.h> + +#include <doctest/doctest.h> + +namespace +{ + +struct StreamRedirector +{ +public: + StreamRedirector(std::ostream &stream, std::streambuf *newStreamBuffer) + : m_Stream(stream), m_BackupBuffer(m_Stream.rdbuf(newStreamBuffer)) {} + + ~StreamRedirector() { m_Stream.rdbuf(m_BackupBuffer); } + +private: + std::ostream &m_Stream; + std::streambuf *m_BackupBuffer; +}; + +std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType, + const std::vector<int32_t>& tensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + + std::array<flatbuffers::Offset<Tensor>, 5> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("input_0"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 2, + flatBufferBuilder.CreateString("input_1"), + quantizationParameters); + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 3, + flatBufferBuilder.CreateString("input_2"), + quantizationParameters); + tensors[3] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 4, + flatBufferBuilder.CreateString("add"), + quantizationParameters); + tensors[4] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 5, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions addBuiltinOptionsType = tflite::BuiltinOptions_AddOptions; + flatbuffers::Offset<void> addBuiltinOptions = + CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); + + tflite::BuiltinOptions divBuiltinOptionsType = tflite::BuiltinOptions_DivOptions; + flatbuffers::Offset<void> divBuiltinOptions = + CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); + + std::array<flatbuffers::Offset<Operator>, 2> operators; + const std::vector<int32_t> addInputs{0, 1}; + const std::vector<int32_t> addOutputs{3}; + operators[0] = CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(addInputs.data(), addInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(addOutputs.data(), addOutputs.size()), + addBuiltinOptionsType, + addBuiltinOptions); + const std::vector<int32_t> divInputs{3, 2}; + const std::vector<int32_t> divOutputs{4}; + operators[1] = CreateOperator(flatBufferBuilder, + 1, + flatBufferBuilder.CreateVector<int32_t>(divInputs.data(), divInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(divOutputs.data(), divOutputs.size()), + divBuiltinOptionsType, + divBuiltinOptions); + + const std::vector<int> subgraphInputs{0, 1, 2}; + const std::vector<int> subgraphOutputs{4}; + flatbuffers::Offset<SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(operators.data(), operators.size())); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Add and Div Operator Model"); + + std::array<flatbuffers::Offset<OperatorCode>, 2> codes; + codes[0] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_ADD); + codes[1] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_DIV); + + flatbuffers::Offset<Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(codes.data(), codes.size()), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + + flatBufferBuilder.Finish(flatbufferModel); + + return std::vector<char>(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +std::vector<char> CreateCeilTfLiteModel(tflite::TensorType tensorType, + const std::vector <int32_t>& tensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder)); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({quantScale}), + flatBufferBuilder.CreateVector<int64_t>({quantOffset})); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + const std::vector<int32_t> operatorInputs({0}); + const std::vector<int32_t> operatorOutputs({1}); + + flatbuffers::Offset<Operator> ceilOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_NONE); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: CEIL Operator Model"); + flatbuffers::Offset<OperatorCode> operatorCode = + CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_CEIL); + + const std::vector<int32_t> subgraphInputs({0}); + const std::vector<int32_t> subgraphOutputs({1}); + flatbuffers::Offset<SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&ceilOperator, 1)); + + 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 DelegateOptionTest(tflite::TensorType tensorType, + const std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& tensorShape, + std::vector<T>& input0Values, + std::vector<T>& input1Values, + std::vector<T>& input2Values, + std::vector<T>& expectedOutputValues, + const armnnDelegate::DelegateOptions& delegateOptions, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateAddDivTfLiteModel(tensorType, + tensorShape, + quantScale, + quantOffset); + + 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 + 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 + armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values); + armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values); + armnnDelegate::FillInput(tfLiteInterpreter, 2, input2Values); + + armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values); + armnnDelegate::FillInput(armnnDelegateInterpreter, 2, input2Values); + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +template <typename T> +void DelegateOptionNoFallbackTest(tflite::TensorType tensorType, + const std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& tensorShape, + std::vector<T>& inputValues, + std::vector<T>& expectedOutputValues, + const armnnDelegate::DelegateOptions& delegateOptions, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateCeilTfLiteModel(tensorType, + tensorShape, + quantScale, + quantOffset); + + 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 + std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); + CHECK(theArmnnDelegate != nullptr); + // Modify armnnDelegateInterpreter to use armnnDelegate + try + { + armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()); + } + catch (const armnn::Exception& e) + { + // Forward the exception message to std::cout + std::cout << e.what() << std::endl; + } + + // Set input data + armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues); + armnnDelegate::FillInput(armnnDelegateInterpreter, 0, inputValues); + + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); + + armnnDelegateInterpreter.reset(nullptr); +} + +} // anonymous namespace
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