| // |
| // 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 |