android13/packages/modules/NeuralNetworks/runtime/test/fuzzing/RandomGraphGenerator.h

/*
 * Copyright (C) 2019 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#ifndef ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_TEST_FUZZING_RANDOM_GRAPH_GENERATOR_H
#define ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_TEST_FUZZING_RANDOM_GRAPH_GENERATOR_H

#include <memory>
#include <string>
#include <vector>

#include "TestHarness.h"
#include "TestNeuralNetworksWrapper.h"
#include "fuzzing/RandomVariable.h"

namespace android {
namespace nn {
namespace fuzzing_test {

using OperandBuffer = std::vector<int32_t>;

struct OperandSignature;
struct OperationSignature;
class OperationManager;

enum class RandomOperandType { INPUT = 0, OUTPUT = 1, INTERNAL = 2, CONST = 3, NO_VALUE = 4 };

struct RandomOperand {
    // Describes the properties of the values of an operand. For operation inputs, this specifies
    // what is required; for outputs, this specifies what is guaranteed.
    // The graph generation algorithm will use this information to decide whether to wire an output
    // to an input or not.
    enum ValueProperty : int {
        NON_ZERO = 1 << 0,
        NON_NEGATIVE = 1 << 1,
    };

    RandomOperandType type;
    int valueProperties = 0;
    test_helper::TestOperandType dataType;
    float scale = 0.0f;
    int32_t zeroPoint = 0;
    std::vector<RandomVariable> dimensions;
    OperandBuffer buffer;
    std::vector<RandomVariable> randomBuffer;

    // The finalizer will be invoked after RandomVariableNetwork::freeze().
    // Operand buffer will be set during this step (if not set before).
    std::function<void(RandomOperand*)> finalizer = nullptr;

    // The index of the operand in the model as returned from model->addOperand(...).
    int32_t opIndex = -1;
    // The index of the input/output as specified in model->identifyInputsAndOutputs(...).
    int32_t ioIndex = -1;

    // If set true, this operand will be ignored during the accuracy checking step.
    bool doNotCheckAccuracy = false;

    // If set true, this operand will not be connected to another operation, e.g. if this operand is
    // an operation output, then it will not be used as an input to another operation, and will
    // eventually end up being a model output.
    bool doNotConnect = false;

    RandomOperand(const OperandSignature& op, test_helper::TestOperandType dataType, uint32_t rank);

    // Resize the underlying operand buffer.
    template <typename T>
    void resizeBuffer(uint32_t len) {
        constexpr size_t valueSize = sizeof(OperandBuffer::value_type);
        uint32_t bufferSize = (sizeof(T) * len + valueSize - 1) / valueSize;
        buffer.resize(bufferSize);
    }

    // Get the operand value as the specified type. The caller is reponsible for making sure that
    // the index is not out of range.
    template <typename T>
    T& value(uint32_t index = 0) {
        return reinterpret_cast<T*>(buffer.data())[index];
    }
    template <>
    RandomVariable& value<RandomVariable>(uint32_t index) {
        return randomBuffer[index];
    }

    // The caller is reponsible for making sure that the operand is indeed a scalar.
    template <typename T>
    void setScalarValue(const T& val) {
        resizeBuffer<T>(/*len=*/1);
        value<T>() = val;
    }

    // Check if a directed edge between [other -> this] is valid. If yes, add the edge.
    // Where "this" must be of type INPUT and "other" must be of type OUTPUT.
    bool createEdgeIfValid(const RandomOperand& other) const;

    // The followings are only intended to be used after RandomVariableNetwork::freeze().
    std::vector<uint32_t> getDimensions() const;
    uint32_t getNumberOfElements() const;
    size_t getBufferSize() const;
};

struct RandomOperation {
    test_helper::TestOperationType opType;
    std::vector<std::shared_ptr<RandomOperand>> inputs;
    std::vector<std::shared_ptr<RandomOperand>> outputs;
    std::function<void(RandomOperation*)> finalizer = nullptr;
    RandomOperation(const OperationSignature& operation);
};

// The main interface of the random graph generator.
class RandomGraph {
   public:
    RandomGraph() = default;

    // Generate a random graph with numOperations and dimensionRange from a seed.
    bool generate(uint32_t seed, uint32_t numOperations, uint32_t dimensionRange);

    // Create a test model of the generated graph. The operands will always have fully-specified
    // dimensions. The output buffers are only allocated but not initialized.
    test_helper::TestModel createTestModel();

    const std::vector<RandomOperation>& getOperations() const { return mOperations; }

   private:
    // Generate the graph structure.
    bool generateGraph(uint32_t numOperations);

    // Fill in random values for dimensions, constants, and inputs.
    bool generateValue();

    std::vector<RandomOperation> mOperations;
    std::vector<std::shared_ptr<RandomOperand>> mOperands;
};

}  // namespace fuzzing_test
}  // namespace nn
}  // namespace android

#endif  // ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_TEST_FUZZING_RANDOM_GRAPH_GENERATOR_H