xref: /aosp_15_r20/external/armnn/delegate/test/GatherNdTestHelper.hpp (revision 89c4ff92f2867872bb9e2354d150bf0c8c502810)

//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include "TestUtils.hpp"

#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>

#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/version.h>

#include <schema_generated.h>

#include <doctest/doctest.h>

namespace
{

std::vector<char> CreateGatherNdTfLiteModel(tflite::TensorType tensorType,
                                          std::vector<int32_t>& paramsShape,
                                          std::vector<int32_t>& indicesShape,
                                          const std::vector<int32_t>& expectedOutputShape,
                                          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));
    buffers.push_back(CreateBuffer(flatBufferBuilder));
    buffers.push_back(CreateBuffer(flatBufferBuilder));
    buffers.push_back(CreateBuffer(flatBufferBuilder));

    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>(paramsShape.data(),
                                                                      paramsShape.size()),
                              tensorType,
                              1,
                              flatBufferBuilder.CreateString("params"),
                              quantizationParameters);
    tensors[1] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(),
                                                                      indicesShape.size()),
                              ::tflite::TensorType_INT32,
                              2,
                              flatBufferBuilder.CreateString("indices"),
                              quantizationParameters);
    tensors[2] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(),
                                                                      expectedOutputShape.size()),
                              tensorType,
                              3,
                              flatBufferBuilder.CreateString("output"),
                              quantizationParameters);


    // create operator
    tflite::BuiltinOptions    operatorBuiltinOptionsType = tflite::BuiltinOptions_GatherNdOptions;
    flatbuffers::Offset<void> operatorBuiltinOptions     = CreateGatherNdOptions(flatBufferBuilder).Union();

    const std::vector<int>        operatorInputs{{0, 1}};
    const std::vector<int>        operatorOutputs{2};
    flatbuffers::Offset<Operator> controlOperator        =
                                      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(&controlOperator, 1));

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

    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, armnnDelegate::FILE_IDENTIFIER);

    return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
                             flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}

template<typename T>
void GatherNdTest(tflite::TensorType tensorType,
                std::vector<armnn::BackendId>& backends,
                std::vector<int32_t>& paramsShape,
                std::vector<int32_t>& indicesShape,
                std::vector<int32_t>& expectedOutputShape,
                std::vector<T>& paramsValues,
                std::vector<int32_t>& indicesValues,
                std::vector<T>& expectedOutputValues,
                float quantScale = 1.0f,
                int quantOffset = 0)
{
    using namespace delegateTestInterpreter;
    std::vector<char> modelBuffer = CreateGatherNdTfLiteModel(tensorType,
                                                            paramsShape,
                                                            indicesShape,
                                                            expectedOutputShape,
                                                            quantScale,
                                                            quantOffset);
    // Setup interpreter with just TFLite Runtime.
    auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
    CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor<T>(paramsValues, 0) == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(indicesValues, 1) == kTfLiteOk);
    CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
    std::vector<T>       tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
    std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);

    // Setup interpreter with Arm NN Delegate applied.
    auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
    CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor<T>(paramsValues, 0) == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor<int32_t>(indicesValues, 1) == kTfLiteOk);
    CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
    std::vector<T>       armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
    std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);

    armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
    armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);

    tfLiteInterpreter.Cleanup();
    armnnInterpreter.Cleanup();
}
} // anonymous namespace