JacobianCGTest.h

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/**********************************************************************************************************************
This file is part of the Control Toolbox (https://github.com/ethz-adrl/control-toolbox), copyright by ETH Zurich.
Licensed under the BSD-2 license (see LICENSE file in main directory)
**********************************************************************************************************************/
#pragma once

// define the input and output sizes of the function
const size_t inDim = 3;   
const size_t outDim = 2;  
const bool verbose = false;

typedef DerivativesCppadJIT<inDim, outDim> derivativesCppadJIT;
typedef DerivativesCppadCG<inDim, outDim> derivativesCppadCG;
typedef DerivativesCppad<inDim, outDim> derivativesCppad;

template <typename SCALAR>
Eigen::Matrix<SCALAR, outDim, 1> testFunction(const Eigen::Matrix<SCALAR, inDim, 1>& x)
{
    Eigen::Matrix<SCALAR, outDim, 1> y;

    y(0) = 3 * x(0) + 2 * x(0) * x(0) - x(1) * x(2);
    y(1) = x(2) + x(1) + 3;

    return y;
}

template <typename SCALAR>
Eigen::Matrix<SCALAR, outDim, inDim> jacobianCheck(const Eigen::Matrix<SCALAR, inDim, 1>& x)
{
    Eigen::Matrix<SCALAR, outDim, inDim> jac;

    jac << 3 + 4 * x(0), -x(2), -x(1), 0, 1, 1;

    return jac;
}

template <typename SCALAR>
Eigen::Matrix<SCALAR, inDim, inDim> hessianCheck(const Eigen::Matrix<SCALAR, inDim, 1>& x,
    const Eigen::Matrix<SCALAR, outDim, 1>& w)
{
    Eigen::Matrix<SCALAR, inDim, inDim> hes;

    hes << 4, 0, 0, 0, 0, -1, 0, -1, 0;

    return w(0) * hes;
}


void executeForwardZeroTest(const bool useDynamicLib)
{
    // create a function handle (also works for class methods, lambdas, function pointers, ...)
    typename derivativesCppadJIT::FUN_TYPE_CG f_cg = testFunction<derivativesCppadJIT::CG_SCALAR>;
    typename derivativesCppad::FUN_TYPE_AD f_ad = testFunction<derivativesCppad::AD_SCALAR>;

    // initialize the Auto-Diff Codegen Jacobian
    derivativesCppadJIT jacCG(f_cg);
    derivativesCppad jacAd(f_ad);

    DerivativesCppadSettings settings;
    settings.createForwardZero_ = true;
    settings.createJacobian_ = true;
    settings.useDynamicLibrary_ = useDynamicLib;

    // create a random double vector
    Eigen::VectorXd someVec(inDim);
    someVec.setRandom();

    // test evaluation of forward zero before compilation
    Eigen::VectorXd vecOut = jacAd.forwardZero(someVec);

    // compile the Jacobian
    jacCG.compileJIT(settings, "forwardZeroTestLib", verbose);

    // test evaluation of forward zero after compilation
    Eigen::VectorXd vecOut2 = jacCG.forwardZero(someVec);

    // verify the outputs
    ASSERT_LT((vecOut - vecOut2).array().abs().maxCoeff(), 1e-10);
}


void executeJITCompilationTest(bool useDynamicLib)
{
    // create a function handle (also works for class methods, lambdas, function pointers, ...)
    typename derivativesCppadJIT::FUN_TYPE_CG f = testFunction<derivativesCppadJIT::CG_SCALAR>;
    typename derivativesCppad::FUN_TYPE_AD f_ad = testFunction<derivativesCppad::AD_SCALAR>;

    // initialize the Auto-Diff Codegen Jacobian
    derivativesCppadJIT jacCG(f);
    derivativesCppad jacAd(f_ad);

    DerivativesCppadSettings settings;
    settings.createJacobian_ = true;
    settings.useDynamicLibrary_ = useDynamicLib;

    // compile the Jacobian
    jacCG.compileJIT(settings, "jacobianCGLib", verbose);

    // create an input vector
    Eigen::Matrix<double, inDim, 1> x;

    for (size_t i = 0; i < 1000; i++)
    {
        // create a random input
        x.setRandom();

        // verify agains the analytical Jacobian
        ASSERT_LT((jacCG.jacobian(x) - jacobianCheck(x)).array().abs().maxCoeff(), 1e-10);
        ASSERT_LT((jacAd.jacobian(x) - jacobianCheck(x)).array().abs().maxCoeff(), 1e-10);
        ASSERT_LT((jacCG.jacobian(x) - jacAd.jacobian(x)).array().abs().maxCoeff(), 1e-10);
    }
}


void executeJitHessianTest(bool useDynamicLib)
{
    typename derivativesCppadJIT::FUN_TYPE_CG f = testFunction<derivativesCppadJIT::CG_SCALAR>;
    typename derivativesCppad::FUN_TYPE_AD f_ad = testFunction<derivativesCppad::AD_SCALAR>;

    derivativesCppadJIT hessianCg(f);
    derivativesCppad hessianAd(f_ad);

    DerivativesCppadSettings settings;
    settings.createHessian_ = true;
    settings.useDynamicLibrary_ = useDynamicLib;

    hessianCg.compileJIT(settings, "hessianCGLib", verbose);

    Eigen::Matrix<double, inDim, 1> x;
    Eigen::Matrix<double, outDim, 1> w;

    for (size_t i = 0; i < 1000; ++i)
    {
        x.setRandom();
        w.setRandom();

        ASSERT_LT((hessianCg.hessian(x, w) - hessianCheck(x, w)).array().abs().maxCoeff(), 1e-10);
        ASSERT_LT((hessianAd.hessian(x, w) - hessianCheck(x, w)).array().abs().maxCoeff(), 1e-10);
        ASSERT_LT((hessianCg.hessian(x, w) - hessianAd.hessian(x, w)).array().abs().maxCoeff(), 1e-10);
    }
}


void executeJITCloneTest(bool useDynamicLib)
{
    typename derivativesCppadJIT::FUN_TYPE_CG f = testFunction<derivativesCppadJIT::CG_SCALAR>;
    typename derivativesCppad::FUN_TYPE_AD f_ad = testFunction<derivativesCppad::AD_SCALAR>;

    // initialize the Auto-Diff Codegen Jacobian
    std::shared_ptr<derivativesCppadJIT> jacCG(new derivativesCppadJIT(f));
    std::shared_ptr<derivativesCppad> jacAd(new derivativesCppad(f_ad));

    DerivativesCppadSettings settings;
    settings.createJacobian_ = true;
    settings.useDynamicLibrary_ = useDynamicLib;

    // compile the Jacobian
    jacCG->compileJIT(settings, "jacobianCGLib", verbose);

    // create an input vector
    Eigen::Matrix<double, inDim, 1> x;

    std::shared_ptr<derivativesCppadJIT> jacCG_cloned(jacCG->clone());

    // make sure the underlying dynamic libraries are not identical
    if (useDynamicLib && (jacCG_cloned->getDynamicLib() == jacCG->getDynamicLib()))
    {
        std::cout << "FATAL ERROR: dynamic library not cloned correctly in JIT." << std::endl;
        ASSERT_TRUE(false);
    }
#ifdef LLVM
    if (!useDynamicLib && (jacCG_cloned->getLlvmLib() == jacCG->getLlvmLib()))
    {
        std::cout << "FATAL ERROR: Llvm library not cloned correctly in JIT." << std::endl;
        ASSERT_TRUE(false);
    }
#endif

    for (size_t i = 0; i < 100; i++)
    {
        // create a random input
        x.setRandom();

        // verify agains the analytical Jacobian
        ASSERT_LT((jacCG_cloned->jacobian(x) - jacobianCheck(x)).array().abs().maxCoeff(), 1e-10);
        ASSERT_LT((jacCG_cloned->jacobian(x) - jacAd->jacobian(x)).array().abs().maxCoeff(), 1e-10);
    }
}

TEST(JacobianCGTest, ForwardZeroTest)
{
    try
    {
        executeForwardZeroTest(true);  // using dynamic library
#ifdef LLVM
        executeForwardZeroTest(false);  //using llvm jit
#endif

    } catch (std::exception& e)
    {
        std::cout << "Exception thrown: " << e.what() << std::endl;
        ASSERT_TRUE(false);
    }
}

TEST(JacobianCGTest, JITCompilationTest)
{
    try
    {
        executeJITCompilationTest(true);  // using dynamic library
#ifdef LLVM
        executeJITCompilationTest(false);  //using llvm jit
#endif

    } catch (std::exception& e)
    {
        std::cout << "Exception thrown: " << e.what() << std::endl;
        ASSERT_TRUE(false);
    }
}

TEST(HessianCGTest, JITHessianTest)
{
    try
    {
        executeJitHessianTest(true);  // using dynamic library
#ifdef LLVM
        executeJitHessianTest(false);  //using llvm jit
#endif
    } catch (std::exception& e)
    {
        std::cout << "Exception thrown: " << e.what() << std::endl;
        ASSERT_TRUE(false);
    }
}

TEST(JacobianCGTest, DISABLED_LlvmCloneTest)
{
    try
    {
        executeJITCloneTest(false);  // Jit using llvm in-memory library
    } catch (std::exception& e)
    {
        std::cout << "Exception thrown: " << e.what() << std::endl;
        ASSERT_TRUE(false);
    }
}

TEST(JacobianCGTest, JitCloneTest)
{
    try
    {
        executeJITCloneTest(true);  // Jit using dynamic library
    } catch (std::exception& e)
    {
        std::cout << "Exception thrown: " << e.what() << std::endl;
        ASSERT_TRUE(false);
    }
}


// /*!
//  * Test for writing the codegenerated Jacobian to file
//  */
TEST(JacobianCGTest, CodegenTest)
{
    // create a function handle (also works for class methods, lambdas, function pointers, ...)
    typename derivativesCppadCG::FUN_TYPE_CG f = testFunction<derivativesCppadCG::CG_SCALAR>;

    // initialize the Auto-Diff Codegen Jacobian
    derivativesCppadCG jacCG(f);

    // generate code for the Jacobian, similar to jacobianCheck()
    jacCG.generateJacobianSource("TestJacobian");

    // generate code for the actual function, will evaluate to the same as testFunction()
    jacCG.generateForwardZeroSource("TestForwardZero");

    jacCG.generateHessianSource("TestHessian");
}