Landau is a language for dynamical systems with automatic differentiation.
Landau is a Turing incomplete statically typed domain-specific differentiable language. The Turing incompleteness provides the ability of sophisticated source code analysis and, as a result, a highly optimized compiled code. Among other things, the language syntax supports functions, compile-time ranged for loops, if/else branching constructions, real variables and arrays, and the ability to manually discard calculation where the automatic derivatives values are expected to be negligibly small. In spite of reasonable restrictions, the language is rich enough to express and differentiate any cumbersome paper-equation with practically no effort.
The automatic differentiation (forward mode) technique can be built on the
concept of dual numbers. Dual numbers can be thought as a pair
However, the manual usage of dual numbers makes the implementation of the equations more tedious and harder to maintain.
The task is to calculate the (automatic) derivatives of functions
Describing this problem in Landau:
parameter[k] x0 # Declare parameters
# w.r.t which derivatives should be taken
real[k * k] func(real[k] x, real[k * k] dxdx0) {
x[:] ' x0[:] = dxdx0[:] # Tell the compiler
# about dx/dx0 jacobian
real[k] f = 2 * sin(x[:]) + x[:] # Compose the function's body
func[:] = f[:] ' x0[:] # Write the numerical value
# of df/dx0 jacobian
# to the return array
}Landau uses a hybrid approach of derivatives calculation: symbolic differentiation at the expression level and automatic (forward mode) at the assignation level. Consider the following piece of Landau code:
real[k] g, f
g[:] = 2 * sin(x[:]) + x[:]
for i = [0 : k]
f[i] = 2 * g[i] + g[i] * x[i]The right-hand side of the assignation is differentiated symbolically. The
result of computation is stored in the corresponding array (dgdx0). Whenever
the result of
dgdx0[..] = 2 * cos(x[..]) * dxdx0[..] + dxdx0[..]
g[:] = 2 * sin(x[:]) + x[:]
for i = [0 : k]
dfdx0[..] = 2 * dgdx0[..]
+ dgdx0[..] * x[i] + g[i] * dxdx0[..]
f[i] = 2 * g[i] + g[i] * x[i]The .. is not a valid syntax construction, but rather a placeholder for a
slice index expression not mentioned in the listing for the simplicity purpose.
Indexing mechanics is revealed later in the Implementation details section.
The Landau transpiler is built on top of the Racket platform and consists of two main parts: the syntax analyzer and the code generator. The job of the syntax analyzer is:
- unrolling all loops into an assignation sequence
- tracing dependencies of variables and their derivatives
- checking the program for correctness, e.g. catching out-of-range errors.
For example, this piece of Landau code will cause the compile-time error:
real[4] x
for i = [0 : 5]
x[i] = 1. # Compile error
~~~~^ index 4 out of range [0, 3]The code generator is capable to emit Racket and ANSI-C code. It is possible to use 80-bits floating-point numbers (extended precision) without changing the Landau source.
Landau facilitates the development of dynamical models used in celestial mechanics and possibly other areas. Landau:
- generates highly efficient C code (or not-so-efficient Racket syntax objects that are useful if you are programming in Racket);
- exempts user of C pointer managing trouble and low-level debugging;
- allows easy notation of mathematical equations, while also allowing loops and functions;
- and, of course, provides first-class automatic derivatives (while you can use Landau without them, too).
The Landau-generated C code is portable on different compilers on Windows, Linux or MacOS.
The model development consists of three stages:
- Manual model description in Landau language (
model.dau) - Transpiling to ANSI-C (code generation
model.c) - Compiling to a shared library for a specific platform
(
model.dll,model.so,model.dylib)
As an example, consider the Landau program for modeling spacecraft movement around a planet -- spacecraft.dau. Spacecraft's initial position and velocity, as well as the gravitational parameter of the planet, are supposed to be determined by an external implementation of the least-squares method. Derivatives of the right part of the movement equation w.r.t. the initial state vector and the planet mass are handled by Landau.
The work is published in papers:
- I. Dolgakov, D. Pavlov. "Landau: language for dynamical systems with automatic differentiation". Zap. Nauch. Sem. POMI 485 (2019) (Full text).
- I. Dolgakov, D. Pavlov. "Using capabilities of Racket to implement a domain-specific language". Computer Assisted Mathematics 1 (2019) (Full text).
- Download and install Racket.
- Clone repository:
$ git clone https://gitlab.iaaras.ru/iaaras/landau.git. - Link Landau as a package:
$ cd Landau/ && raco link landau.
config.json:
{
// Language to compile .dau file to
"target_language": "racket" | "ansi-c",
// Whether to use 80-bits floating point
"use_extfloat": true | false,
// Where to write output .c file,
// in case the `target_language` is "ansi-c"
"output_directory": DIRECTORY_PATH
}
$ cd tests
$ racket spacecraft.dau -c -extfl
Success.
The output is written to DIRECTORY_PATH/spacecraft.c
There exist a large number of forward-mode AD software tools for differentiating functions that are written in general-purpose programming languages, like Fortran (ADIFOR) [1], C (ADIC) [2] or C++ (ADOL-C) [3]. Rich features of the ``host'' languages, like arrays, loops, conditions, and recursion, often make it difficult to implement a practically usable AD system without imposing limitations on the language and/or extra technical work when specifying the function, especially in presence of multi-dimensional functions with many independent variables.
On the other hand, there exist a number of languages developed specially for AD tasks, like Taylor [4] and VLAD [5, 6]. Taylor syntax, while very simple and natural, is very limited (no conditionals, loops, arrays, or subprocedures). VLAD, a functional Scheme-like language, has conditionals, loops, recursion, and subprocedures, but does not have arrays or mutability.
Finally, there are tools for differentiating functions specified as mathematical expressions in mathematical computing systems, like MATLAB (ADMAT) [7] or Mathematica (TIDES) [8, 9]. Such tools often require a bigger effort (as compared to a general-purpose languages) to input a practical dynamical system of large dimension with a lot of free variables.
Like VLAD, Landau is a domain-specific language designed with automatic differentiation in mind. Like TIDES and Taylor, Landau offers C code generation. Like general-purpose languages, Landau has common control flow constructs, arrays, and mutability; but unlike general-purpose languages, Landau embraces Turing incompleteness to perform static source analysis and generate efficient code.
There are AD approaches where an initial program is unrolled to a sequence of simple assignments (Wengert list) and AD problem is solved in linear algebra formalism. The initial problem is reduced to a sparse linear equations system. Even though there are known lots of sparse matrix algorithms it is not obvious if it is a good idea to unroll all loops to a giant sparse matrix.
We decided to take the code generation approach preserving the initial program's loops, e.g. Landau loop is transformed into the C or Racket loop where the loop-body is augmented with a derivative calculation code if needed.
Unlike the other AD tools, Landau uses derivative annotations inside a program to be able to predict a number of nonzero derivatives and reduce a machine resources used for jacobian computation and storage. Other AD tools often do not use annotations and hence, make it impossible to perform radical compile-time optimizations of array's derivatives computations.
To illustrate our jacobian storage approach let us take a look at the listing
from the Differentiation method section. One of the simplest possible storage
scheme is to keep all jacobian components in array row by row, e.g. to put
df_i/dx0_j in dfdx0[i * length(x0) + j].
The .. dots, hence, should be substituted as follows:
dfdx0[i:] = 2 * dgdx0[i:]
+ dgdx0[i:] * x[i] + g[i] * dxdx0[i:]
This approach performs well in case of dense jacobians but causes memory and computation time waste in sparse cases.
There is a widespread jacobian compression method based on grouping so-called orthogonal jacobian columns. Two columns are called orthogonal if they don't have nonzero value in the same row. Compression is made by multiplication of the initial sparse jacobian by the specific permutation matrix (so-called seed matrix).
Example: Sparse jacobian where columns 1 and 3 are orthogonal
Seed matrix:
Multiplying J and S we get the compressed jacobian B:
The major shortcoming of this approach is a poor compression in the absence of orthogonal columns which is the case if, say, jacobian has a single row and a column full of nonzero elements.
We chose to use another compression technique storing only nonzero jacobian
values row by row. The sparsity is handled by packing useful jacobian elements
into smaller arrays and generating mappings from the packed derivative indexes to
the original ones and inverse mappings, which map the original indexes to the
packed ones. Here is a brief demonstration of discussed approach
applied to the dfdx0[..] assignation from the listing:
for dfdx0_compressed_idx in length(dfdx0_mappings[i]):
dfdx0_true_idx = dfdx0_mappings[i][dfdx0_compressed_idx]
dfdx0[i * length(dfdx0_mappings[i]) + dfdx0_compressed_idx] =
2 * dgdx0[i * length(dgdx0_mappings[i]) + dgdx0_inverse_mapping[i][dfdx0_true_idx]]
+ dgdx0[i * length(dgdx0_mappings[i]) + dgdx0_inverse_mapping[i][dfdx0_true_idx]] * x[i]
+ g[i] * dxdx0[i * length(dxdx0_mappings[i]) + dxdx0_inverse_mapping[i][dfdx0_true_idx]]
Where dfdx0, dgdx0, dxdx0 are compressed jacobians, dfdx0_mappings is
the mappings array for dfdx0 and dgdx0_inverse_mapping,
dxdx0_inverse_mapping are inverse mappings for dgdx0 and dxdx0.
While being close to optimal in terms of computation time, the chosen AD algorithm is not very memory efficient. The main problem is that inverse mapping of a single array cell takes O(maxidx) in space, where maxidx is a maximal index of nonzero derivative for the array element. To illustrate the problem, say, the mapping is equal to:
value [0 5 6 7 99 100]
index 0 1 2 3 4 5
The inverse mapping is then
value [0 ... 1 2 3 ... 4 5]
index 0 ... 5 6 7 ... 99 100
To store this 5 elements inverse mapping should be an array of 100 elements, which is an absolute waste of memory. Since a set of inverse mapping values is static (read-only) and known at the compile-time the ideal solution of inverse mapping storage problem would be an open addressed hash table with a minimal perfect hash function used for a key construction. But as we know there is no simple way to construct one in case of open addressed hash table. So a better jacobian compression algorithm it is still under consideration.
- FFI (calling C functions inside Landau program).
- Complex numbers.
- Syntactic sugar for array operations.
- Multidimensional arrays.
Better Jacobian compression algorithm.
Unrolling all loops and evaluating all compile-time computable variables is a simple but inefficient approach. We are looking for better ways of performing the static syntax analysis.
- Generate AVX intrinsic functions for the C backend.
- Generate unsafe vector operations for the Racket backend.
[1] Ch. Bischof, A. Carle, G. Corliss, A. Griewank, P. Hovland, “ADIFOR–generating derivative codes from Fortran programs”, Scientific Programming, 1:1 (1992), 11–29
[2] Ch. Bischof, L. Roh, A. J. Mauer-Oats, “ADIC: an extensible automatic differentiation tool for ANSI-C”, Software: Practice and Experience, 27:12 (1997), 1427–1456
[3] A. Griewank, D. Juedes, J. Utke, “Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++”, ACM Transactions on Mathematical Software (TOMS), 22:2 (1996), 131–167
[4] À. Jorba, M. Zou, “A Software Package for the Numerical Integration of ODEs by Means of High-Order Taylor Methods”, Experimental Mathematics, 14:1 (2005), 99–117
[5] J. M. Siskind, B. A. Pearlmutter, “Nesting forward-mode AD in a functional framework”, Higher-Order and Symbolic Computation, 21:4 (2008), 361–376
[6] J. M. Siskind, B, A. Pearlmutter, “Efficient Implementation of a Higher-Order Language with Built-In AD”, AD2016 – 7th International Conference on Algorithmic Differentiation (Oxford, UK, 2016)
[7] T. F. Coleman, A. Verma, “ADMAT: An automatic differentiation toolbox for MATLAB”, Proceedings of the SIAM Workshop on Object Oriented Methods for Inter-Operable Scientific and Engineering Computing, v. 2, SIAM, Philadelphia, PA, 1998
[8] A. Abad, R. Barrio, M. Marco-Buzunariz, M. Rodríguez, “Automatic implementation of the numerical Taylor series method”, Appl. Math. Comput., 268 (2015), 227–245
[9] “Automatic implementation of the numerical Taylor series method: A Mathematica and Sage approach”, Appl. Math. Comput., 268 (2015), 227–245
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