delegate/src/test/BatchMatMulTestHelper.hpp

//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include "TestUtils.hpp"

#include <armnn_delegate.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
{

    std::vector<char> CreateBatchMatMulTfLiteModel(
            tflite::BuiltinOperator bmmOperatorCode,
            tflite::TensorType tensorType,
            const std::vector <int32_t>& LHSInputTensorShape,
            const std::vector <int32_t>& RHSInputTensorShape,
            const std::vector <int32_t>& outputTensorShape,
            bool adjX = false,
            bool adjY = false,
            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>, 3> tensors;
        tensors[0] = CreateTensor(flatBufferBuilder,
                                  flatBufferBuilder.CreateVector<int32_t>(LHSInputTensorShape.data(),
                                                                          LHSInputTensorShape.size()),
                                  tensorType,
                                  0,
                                  flatBufferBuilder.CreateString("LHSInput"),
                                  quantizationParameters);

        tensors[1] = CreateTensor(flatBufferBuilder,
                                  flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(),
                                                                          RHSInputTensorShape.size()),
                                  tensorType,
                                  0,
                                  flatBufferBuilder.CreateString("RHSInput"),
                                  quantizationParameters);

        tensors[2] = CreateTensor(flatBufferBuilder,
                                  flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
                                                                          outputTensorShape.size()),
                                  tensorType,
                                  0,
                                  flatBufferBuilder.CreateString("output"),
                                  quantizationParameters);

        // create operator
        tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions;
        flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder,
                                                                                    adjX,
                                                                                    adjY).Union();

        const std::vector<int32_t> operatorInputs{{0, 1}};
        const std::vector<int32_t> operatorOutputs{2};
        flatbuffers::Offset <Operator> bmmOperator =
                CreateOperator(flatBufferBuilder,
                               0,
                               flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
                               flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(),
                                                                       operatorOutputs.size()),
                               operatorBuiltinOptionsType,
                               operatorBuiltinOptions);

        const std::vector<int> subgraphInputs{{0, 1}};
        const std::vector<int> subgraphOutputs{2};
        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(&bmmOperator, 1));

        flatbuffers::Offset <flatbuffers::String> modelDescription =
                flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model");
        flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode);

        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 BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode,
                       tflite::TensorType tensorType,
                       std::vector<armnn::BackendId>& backends,
                       std::vector<int32_t>& LHSInputShape,
                       std::vector<int32_t>& RHSInputShape,
                       std::vector<int32_t>& outputShape,
                       std::vector<T>& LHSInputValues,
                       std::vector<T>& RHSInputValues,
                       std::vector<T>& expectedOutputValues,
                       bool adjX = false,
                       bool adjY = false,
                       float quantScale = 1.0f,
                       int quantOffset  = 0)
    {
        using namespace tflite;
        std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode,
                                                                     tensorType,
                                                                     LHSInputShape,
                                                                     RHSInputShape,
                                                                     outputShape,
                                                                     adjX,
                                                                     adjY,
                                                                     quantScale,
                                                                     quantOffset);

        const Model* tfLiteModel = GetModel(modelBuffer.data());
        CHECK(tfLiteModel != nullptr);
        // 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
        armnnDelegate::DelegateOptions delegateOptions(backends);
        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
        auto tfLiteDelegateLHSInputId = tfLiteInterpreter->inputs()[0];
        auto tfLiteDelegateLHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateLHSInputId);
        auto tfLiteDelegateRHSInputId = tfLiteInterpreter->inputs()[1];
        auto tfLiteDelegateRHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateRHSInputId);
        for (unsigned int i = 0; i < LHSInputValues.size(); ++i)
        {
            tfLiteDelegateLHSInputData[i] = LHSInputValues[i];
        }
        for (unsigned int i = 0; i < RHSInputValues.size(); ++i)
        {
            tfLiteDelegateRHSInputData[i] = RHSInputValues[i];
        }

        auto armnnDelegateLHSInputId = armnnDelegateInterpreter->inputs()[0];
        auto armnnDelegateLHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateLHSInputId);
        auto armnnDelegateRHSInputId = armnnDelegateInterpreter->inputs()[1];
        auto armnnDelegateRHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateRHSInputId);
        for (unsigned int i = 0; i < LHSInputValues.size(); ++i)
        {
            armnnDelegateLHSInputData[i] = LHSInputValues[i];
        }
        for (unsigned int i = 0; i < RHSInputValues.size(); ++i)
        {
            armnnDelegateRHSInputData[i] = RHSInputValues[i];
        }
        // Run EnqueueWorkload
        CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
        CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);

        armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter,
                                         outputShape, expectedOutputValues);
    }

} // anonymous namespace