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-rw-r--r--delegate/src/test/DelegateOptionsTestHelper.hpp298
1 files changed, 298 insertions, 0 deletions
diff --git a/delegate/src/test/DelegateOptionsTestHelper.hpp b/delegate/src/test/DelegateOptionsTestHelper.hpp
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+++ b/delegate/src/test/DelegateOptionsTestHelper.hpp
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+//
+// 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 \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-16 19:17:22 +0000