Automatic Differentiation

Background

Historically, the most common question on the MOOSE mailing list has been "why is my solve not converging?" The equivalent question is also posted on the Computational Science StackExchange under the title Why is Newton's method not converging? The leading bullet in the accepted answer is that the Jacobian is wrong. Coding Jacobians can be a difficult and tedious task, especially for physics that require complex material models. Instead of spending their time running simulations and generating results, physicists and engineers may sink days or weeks into constructing accurate Jacobians. Oftentimes, the developer will just throw up their hands and elect an approximate Jacobian method like Preconditioned Jacobian Free Newton Krylov (PJFNK) where the Jacobian is never explicitly formed but instead its action on vectors is approximated using finite differences and an approximate preconditioning matrix is hand coded for constructing an effective preconditioner. While effective in many cases, the quality of the matrix-free approximation is closely tied to selection of a differencing parameter whose size should vary based on the noisiness of the nonlinear system of equations. If the nonlinear functions are noisy and too small of a differencing parameter is chosen, truncation will lead to a Jacobian approximation that is actually a non-linear operator. If the differencing parameter is too large, then the approximated derivatives will be inaccurate if the differenced function is nonlinear. For multi-physics problems in which the magnitudes of solution and residual components may vary wildly, an arbitrary choice of differencing parameter may lead to an accurate approximation of the Jacobian action for one physics but lead to the aforementioned truncation error in another. The presence of truncation error and hence a non-linear operator is evident in a linear solve derived from porous flow equations coupled with heat transport:

 0 KSP unpreconditioned resid norm 5.645573426379e+16 true resid norm 5.645573426379e+16 ||r(i)||/||b|| 1.000000000000e+00
 1 KSP unpreconditioned resid norm 2.965618482980e+12 true resid norm 2.965618483296e+12 ||r(i)||/||b|| 5.252997807874e-05
 2 KSP unpreconditioned resid norm 2.724648158452e+12 true resid norm 2.890172312561e+14 ||r(i)||/||b|| 5.119360061914e-03
 3 KSP unpreconditioned resid norm 8.335608026448e+11 true resid norm 5.178511494633e+15 ||r(i)||/||b|| 9.172693548605e-02
 4 KSP unpreconditioned resid norm 8.335450917197e+11 true resid norm 8.675908349302e+17 ||r(i)||/||b|| 1.536762998912e+01
 5 KSP unpreconditioned resid norm 6.022626374152e+11 true resid norm 1.028328406646e+22 ||r(i)||/||b|| 1.821477339823e+05
 6 KSP unpreconditioned resid norm 6.020981842850e+11 true resid norm 1.028926824260e+22 ||r(i)||/||b|| 1.822537316498e+05
 7 KSP unpreconditioned resid norm 2.079735227207e+11 true resid norm 6.050540886054e+21 ||r(i)||/||b|| 1.071731855932e+05

While the unpreconditioned residual norm, produced through Generalized Minimum Residual Method (GMRES) iterations, drops by five orders of magnitude during the solve, the true residual norm computed via actually increases by five orders of magnitude. In MOOSE, a right preconditioned GMRES is chosen by default, where the unpreconditioned residual mathematically equal to the actual residual if the preconditioning matrix is well-conditioned. The inconsistencies generated here are, partially, because of the inaccuracy of the preconditioning matrix. The net result of such bad linear solves is a diverging Newton's method since the computed Newton update is "wrong":

0 Nonlinear |R| = 1.138921e+06
1 Nonlinear |R| = 1.392349e+11
2 Nonlinear |R| = 2.881060e+10
3 Nonlinear |R| = 8.473409e+09
4 Nonlinear |R| = 2.017729e+09
5 Nonlinear |R| = 7.634503e+08
6 Nonlinear |R| = 5.645573e+16
Nonlinear solve did not converge due to DIVERGED_FNORM_NAN iterations 6

If we instead form an explicit Jacobian and eschew finite differencing, Newton's method converges, albeit sub-quadratically since the hand-coded Jacobian is inaccurate:

 0 Nonlinear |R| = 1.138921e+06
 1 Nonlinear |R| = 2.171654e+05
 2 Nonlinear |R| = 4.550729e+04
 3 Nonlinear |R| = 1.026123e+04
 4 Nonlinear |R| = 1.267614e+03
 5 Nonlinear |R| = 6.531982e+01
 6 Nonlinear |R| = 4.427446e-01
 7 Nonlinear |R| = 8.025639e-05
 8 Nonlinear |R| = 3.195008e-06
 9 Nonlinear |R| = 1.669770e-07
10 Nonlinear |R| = 1.201906e-08
Nonlinear solve converged due to CONVERGED_FNORM_ABS iterations 10

Given the multi-physics aim of MOOSE and the clear pitfalls associated with differencing approximations, there is clear motivation to form accurate explicit representations of the matrix. Note, even if a perfect Jacobian action can be achieved via the finite difference scheme, a suitable preconditioning matrix is required to construct a robust and efficient solver.

Some users elect to use symbolic differentiation packages like SymPy or Mathematica; however, for functions of any complexity the resulting gradient expressions can take up to several pages and can be quite difficult to translate from notebook to code. An alternative to numerical and symbolic differentiation is AD, which applies the chain rule to elementary operations at every step of the computer program and which applies at most a small constant factor (estimated to have an upper bound of 5) of additional arithmetic operations. Weighed against the untold number of developer hours sunk into attempting to create accurate hand-coded Jacobians and analyst hours spent waiting for problems with poor hand-coded or approximated Jacobians to converge, the small additional CPU cost imposed by AD seemed well worth the trade. With an accurate Jacobian formed using AD, the overall simulation can be much faster than that utilizing a deficient hand-coded matrix. Having elected to pursue AD, there was a choice between forward and reverse modes. Forward mode AD is best suited for problems with many more outputs than inputs; reverse mode is best suited for many more inputs than outputs. The latter case is more prevalent in deep learning applications and is what is implemented in popular machine learning libraries like PyTorch. In solution of nonlinear systems of equations the number of inputs and outputs are equivalent, so the choice is not clear-cut. However, given the architecture of MOOSE in which the residuals are constructed from finite element solutions, which themselves are naturally constructed from the nonlinear degrees of freedom, forward propagation was the logical choice. Forward-mode AD relies on the concept of DualNumbers which can be implemented either through source code transformation or operator overloading. The latter is more easily implemented in programming languages that support it such as C++, the language that MOOSE is written in. Conveniently, the C++ header-only library MetaPhysicL came ready-made with a DualNumber template class and an operator-overload AD implementation that fit into the MOOSE architecture with minimal disruption to the code-base. The AD capability of MetaPhysicL was merged into the MOOSE code-base in the fall of 2018.

AD implementation - MetaPhysicL

The automatic differentiation classes in MetaPhysicL were originally developed and tested in the Manufactured Analytical Solution Abstraction (MASA) library, used for generating manufactured solution tests for increasingly complicated physics simulations, when it was discovered that multiple symbolic differentiation packages were suffering software failures on sufficiently large problems. Symbolically differentiating manufactured solution fields through e.g. 3-D Navier-Stokes physics caused a combinatorial explosion, leading to corresponding forcing functions that were hundreds of kilobytes in length, or required many man-hours of manual simplification, or failed altogether on some Computer Algebra System software. Automatic differentiation allowed for the generation of manufactured solution forcing functions using code that was hardly more complex than the physics equations themselves. The classes used for this effort were eventually published as an independent library, MetaPhysicL, for wider use and further development.

DualNumber is the centerpiece class of the automatic differentiation capability. It stores value and derivatives members which correspond to and respectively. value and derivatives types are determined by T and D template parameters, where T is some floating point type, and D is equivalent to T for single-argument functions or equal to some container type for a generic vector of arguments (see the next paragraph). MetaPhysicL overloads binary arithmetic operators (+,-,*,/), unary functions (std::sin, std::cos, std::exp etc.), and binary functions (std::pow, std::max, std::min etc.), ensuring that any calculation involving a DualNumber propagates both the function value and its derivatives.

MOOSE used to leverage one of two MetaPhysicL container class templates depending on user configuration. The default MOOSE configuration used to be the NumberArray class template which accepts std::size_t N and typename T template arguments where N denotes the length of an underlying C-array that holds the NumberArray data and T is the floating-point type held by the C-array. NumberArray is an ideal derivative container choice when there is dense coupling between physics variables; this is because operator and function overloads for NumberArray operate on the entire underlying C-array. The second MetaPhysicL container class, and the only one that is currently leveraged by MOOSE, is SemiDynamicSparseNumberArray, which is a more ideal choice for problems in which variable coupling is sparse or when a user wishes to solve a variety of problems with a single library configuration. In contrast to NumberArray which only holds a single C-array of floating-type data, SemiDynamicSparseNumberArray additionally holds an array (as a std::array) of integers corresponding to dof indices. The existence of this additional data member enables sparse operations that may involve only a subset of the elements in the underlying floating-point data. As an explicit example of when these sparse operations are useful, consider a user who may configure MOOSE with an underlying derivative storage container size of 81 for solid mechanics simulations on 3D second-order hexahedral finite elements (3 displacement variables 27 degrees of freedom per variable per finite element = 81 local dofs). When running 3D, second-order cases, the non-sparse NumberArray container would have been 100% efficient. However, if the user later wished to run a 2D, second-order case (2 displacement variables 9 degrees of freedom per variable per finite element = 18 local dofs) with the same MOOSE configuration, they would have been performing times more work than is necessary if using NumberArray. Because SemiDynamicSparseNumberArray tracks the sparsity pattern, it will only initialize and operate on the floating-point array elements that are required, i.e. the "sparse size" (stored as a _dynamic_N data member) of its data containers will never exceed what is required for the run-time problem, e.g. 18 for the 2D second-order solid mechanics example.

AD in MOOSE

As mentioned in above, MetaPhysicL is a forward-mode AD package. For a finite element framework like MOOSE derivative seeding begins when constructing local finite element solutions. The finite element solution approximation is given by where the represent the degrees of freedom and the are shape functions associated with the dofs. For a Lagrange basis, shape functions and dofs are tied to mesh nodes. To illustrate initiation of the AD process, we will consider construction of a local finite element solution on a QUAD4 element, that is to say a quadrilateral with a number of nodes equal to the number of vertices. This element type when combined with a Lagrange basis has four dofs which contribute to the local solution, one for each element node. In MOOSE we assign these local degree of freedom solution values (the local ) to a variable class data member called _ad_dof_values where the ad prefix denotes automatic differentiation. We then seed a derivative value of (recognizing that when ) at a corresponding local dof index determined through a somewhat arbitrary numbering scheme. We choose a variable major numbering scheme such that the local degrees of freedom are in a contiguous block for each variable, e.g. if we have two variables in the system, and , then the numbering scheme for a QUAD4 element with Lagrange basis would look like with subscripts corresponding to the local node number. We can examine the dependence of the local finite element solution on each degree of freedom for an arbitrary point in the domain; we know analytically the expected derivatives: . For a chosen Gaussian integration point , we know the corresponding Lagrange values: , and we can check and verify whether our automatically differentiated solution _ad_u matches:

(lldb) p _ad_u[0]
(MetaPhysicL::DualNumber<double, MetaPhysicL::SemiDynamicSparseNumberArray<double, unsigned int, MetaPhysicL::NWrapper<50> >, false>) $10 = {
  _val = 0
  _deriv = {
    MetaPhysicL::DynamicSparseNumberBase<MetaPhysicL::DynamicStdArrayWrapper<double, MetaPhysicL::NWrapper<50> >, MetaPhysicL::DynamicStdArrayWrapper<unsigned int, MetaPhysicL::NWrapper<50> >, MetaPhysicL::SemiDynamicSparseNumberArray, double, unsigned int, MetaPhysicL::NWrapper<50> > = {
      _data = {
        _data = {
          _M_elems = {
            [0] = 0.62200846792814624
            [1] = 0.16666666666666669
            [2] = 0.044658198738520456
            [3] = 0.16666666666666669
            [4] = 0
            [5] = 4.82252338622924E-317
            [6] = 4.8224680508769058E-317
            [7] = 3.9525251667299724E-323

Note that some of the unused values in indices 4–7 appear to contain garbage. This is actually desirable; it indicates that for the SemiDynamicSparseNumberArray container, unnecessary components of the derivative vector are left uninitialized.

We can also check variable coupling. Let us assume a reaction-type problem in which the variable disappears at a rate directly proportional to its concentration and appears at a rate directly proportional to the concentration of the variable . The strong form of this residual is then simply . Examining the derivatives of this term produced by automatic differentiation

(lldb) p strong_residual
(MetaPhysicL::DualNumber<double, MetaPhysicL::SemiDynamicSparseNumberArray<double, unsigned int, MetaPhysicL::NWrapper<50> >, false>) $1 = {
  _val = 0
  _deriv = {
    MetaPhysicL::DynamicSparseNumberBase<MetaPhysicL::DynamicStdArrayWrapper<double, MetaPhysicL::NWrapper<50> >, MetaPhysicL::DynamicStdArrayWrapper<unsigned int, MetaPhysicL::NWrapper<50> >, MetaPhysicL::SemiDynamicSparseNumberArray, double, unsigned int, MetaPhysicL::NWrapper<50> > = {
      _data = {
        _data = {
          _M_elems = {
            [0] = 0.62200846792814624
            [1] = 0.16666666666666669
            [2] = 0.044658198738520456
            [3] = 0.16666666666666669
            [4] = -0.62200846792814624
            [5] = -0.16666666666666669
            [6] = -0.044658198738520456
            [7] = -0.16666666666666669

we see exactly what we would expect: values 0–3 corresponding to the indices are equivalent to that shown in the previous lldb output whereas the values in 4–7, corresponding to the indices, are equal to the negative of that shown in the previous lldb output. In general, the quality of automatic differentiation results are verified with unit testing in MetaPhysicL and using a PetscJacobianTester in MOOSE which compares the Jacobian produced by automatic differentiation against that generated using finite differencing of the residuals. The latter test relies on using well-scaled problems; for poorly-scaled problems floating point errors can result in a loss in accuracy of the finite-differenced Jacobian.

./configure --with-derivative-size=n