| // | |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. | |
| // SPDX-License-Identifier: MIT | |
| // | |
| #pragma once | |
| #include <armnn_delegate.hpp> | |
| #include "ConvolutionTestHelper.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 <tensorflow/lite/schema/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, flatBufferBuilder.CreateVector({}))); | |
| 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, | |
| 0, | |
| flatBufferBuilder.CreateString("input_0"), | |
| quantizationParameters); | |
| tensors[1] = CreateTensor(flatBufferBuilder, | |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), | |
| tensorShape.size()), | |
| tensorType, | |
| 0, | |
| flatBufferBuilder.CreateString("input_1"), | |
| quantizationParameters); | |
| tensors[2] = CreateTensor(flatBufferBuilder, | |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), | |
| tensorShape.size()), | |
| tensorType, | |
| 0, | |
| flatBufferBuilder.CreateString("input_2"), | |
| quantizationParameters); | |
| tensors[3] = CreateTensor(flatBufferBuilder, | |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), | |
| tensorShape.size()), | |
| tensorType, | |
| 0, | |
| flatBufferBuilder.CreateString("add"), | |
| quantizationParameters); | |
| tensors[4] = CreateTensor(flatBufferBuilder, | |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), | |
| tensorShape.size()), | |
| tensorType, | |
| 0, | |
| 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()); | |
| } | |
| void ReduceFp32ToBf16TestImpl() | |
| { | |
| using namespace tflite; | |
| // Set input data | |
| std::vector<int32_t> inputShape{ 1, 5, 5, 1 }; | |
| std::vector<int32_t> filterShape{ 1, 3, 3, 1 }; | |
| std::vector<int32_t> biasShape{ 1 }; | |
| std::vector<int32_t> outputShape{ 1, 3, 3, 1 }; | |
| std::vector<float> inputValues = | |
| { | |
| 1, 5, 2, 3, 5, | |
| 8, 7, 3, 6, 3, | |
| 3, 3, 9, 1, 9, | |
| 4, 1, 8, 1, 3, | |
| 6, 8, 1, 9, 2 | |
| }; | |
| std::vector<float> filterValues = | |
| { | |
| 4, 5, 6, | |
| 0, 0, 0, | |
| 3, 2, 1 | |
| }; | |
| std::vector<float> biasValues = { 5 }; | |
| std::vector<float> expectedResult = | |
| { | |
| 28, 38, 29, | |
| 96, 104, 53, | |
| 31, 55, 24 | |
| }; | |
| tflite::Padding padding = Padding_SAME; | |
| std::vector<char> modelBuffer; | |
| modelBuffer = CreateConv2dTfLiteModel<float>(BuiltinOperator_CONV_2D, | |
| ::tflite::TensorType_FLOAT32, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| padding, | |
| ActivationFunctionType_NONE, | |
| inputShape, | |
| filterShape, | |
| biasShape, | |
| outputShape, | |
| filterValues, | |
| biasValues); | |
| 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); | |
| // Create the Armnn Delegate | |
| std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; | |
| std::vector<armnn::BackendOptions> backendOptions; | |
| // Enable debug with BF16 enabled | |
| armnn::OptimizerOptions optimizerOptions(false, true, true, false); | |
| armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); | |
| 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(armnnDelegateInterpreter, 0, inputValues); | |
| // Run EnqueueWorkload | |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); | |
| // Compare output data | |
| auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; | |
| auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); | |
| armnnDelegate::CompareData(expectedResult.data(), armnnDelegateOutputData, expectedResult.size()); | |
| armnnDelegateInterpreter.reset(nullptr); | |
| } | |
| 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); | |
| } | |
| } // anonymous namespace |