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Diffstat (limited to 'delegate/src/test/DelegateOptionsTestHelper.hpp')
-rw-r--r-- | delegate/src/test/DelegateOptionsTestHelper.hpp | 298 |
1 files changed, 298 insertions, 0 deletions
diff --git a/delegate/src/test/DelegateOptionsTestHelper.hpp b/delegate/src/test/DelegateOptionsTestHelper.hpp new file mode 100644 index 0000000000..6e0cc3154c --- /dev/null +++ b/delegate/src/test/DelegateOptionsTestHelper.hpp @@ -0,0 +1,298 @@ +// +// 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
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