KLUJAX

version: 0.5.0.post4

A sparse linear solver for JAX based on the efficient KLU algorithm.

This is a fork of the original klujax package, meant for use in splineax. The aim is to eventually merge the changes here into the upstream package and remove this fork. In the meantime, the version number will stay as 0.5.0.postN, and N will be incremented sequentially on each new published version.

CPU & float64

This library is a wrapper around the SuiteSparse KLU algorithms. This means the algorithm is only implemented for C-arrays and hence is only available for CPU arrays with double precision, i.e. float64 or complex128.

Note that float32/complex64 arrays will be cast to float64/complex128!

Basic Usage

The klujax library provides a basic function solve(Ai, Aj, Ax, b), which solves for x in the sparse linear system Ax=b, where A is explicitly given in COO-format (Ai, Aj, Ax).

NOTE: the sparse matrix represented by (Ai, Aj, Ax) needs to be coalesced! KLUJAX provides a coalesce function (which unfortunately is not jax-jittable).

Supported shapes (? suffix means optional):

Ai: (n_nz,)
Aj: (n_nz,)
Ax: (n_lhs?, n_nz)
b: (n_lhs?, n_col, n_rhs?)
A (represented by (Ai, Aj, Ax)): (n_lhs?, n_col, n_col)

KLUJAX will automatically select a sensible way to act on underdefined dimensions of Ax and b:

dim(Ax)	dim(b)	assumed shape(Ax)	assumed shape(b)
1D	1D	n_nz	n_col
1D	2D	n_nz	n_col x n_rhs
1D	3D	n_nz	n_lhs x n_col x n_rhs
2D	1D	n_lhs x n_nz	n_col
2D	2D	n_lhs x n_nz	n_lhs x n_col
2D	3D	n_lhs x n_nz	n_lhs x n_col x n_rhs

Where the A is always acting on the n_col dimension of b. The n_lhs dim is a shared batch dimension between A and b.

Additional dimensions can be added with jax.vmap (alternatively any higher dimensional problem can be reduced to the one above by properly transposing and reshaping Ax and b).

NOTE: JAX now has an experimental sparse library (jax.experimental.sparse). Using this natively in KLUJAX is not yet supported (but converting from BCOO or COO to Ai, Aj, Ax is trivial).

Basic Example

Script:

import klujax
import jax.numpy as jnp

b = jnp.array([8, 45, -3, 3, 19])
A_dense = jnp.array(
    [
        [2, 3, 0, 0, 0],
        [3, 0, 4, 0, 6],
        [0, -1, -3, 2, 0],
        [0, 0, 1, 0, 0],
        [0, 4, 2, 0, 1],
    ]
)
Ai, Aj = jnp.where(jnp.abs(A_dense) > 0)
Ax = A_dense[Ai, Aj]

result_ref = jnp.linalg.inv(A_dense) @ b
result = klujax.solve(Ai, Aj, Ax, b)

print(jnp.abs(result - result_ref) < 1e-12)
print(result)

Output:

[ True True True True True]
[1. 2. 3. 4. 5.]

Advanced Usage

For high-performance applications like transient simulations or iterative solvers, you should avoid using the high-level klujax.solve function. The klujax.solve is in fact a wrapper around three distinct parts of the KLU algorithm:

Analyze (Symbolic): Inspects the sparsity pattern ($A_i, A_j$) to find optimal permutations and block triangular forms. This depends only on the structure of the matrix.
Factorize (Numeric): Performs the actual LU decomposition. This depends on the values ($A_x$) and requires a symbolic handle.
Solve (Numeric): Executes forward and backward substitution to find $x$. This depends on the right-hand side ($b$) and requires a numeric handle.

Significant performance gains are achieved by hoisting the "Analysis" or "Factorization" steps out of your inner loops.

1. High-Performance Transient Pattern (Reusing Symbolic)

In a simulation where the sparsity pattern is constant but the values ($A_x$) and right-hand side ($b$) change, you should perform the expensive analyze step exactly once outside your JIT loop.

import jax
import klujax

# 1. Analyze once in Python (CPU)
# Returns a SymbolToken: a cache handle that is freed automatically
symbolic = klujax.analyze(Ai, Aj, n_col)

@jax.jit
def simulation_step(Ax_t, b_t, sym):
    # 2. Use the symbolic handle inside JIT
    # The solver will perform numeric factorization and solve
    return klujax.solve_with_symbol(Ai, Aj, Ax_t, b_t, sym)

for t in range(steps):
    x_t = simulation_step(Ax[t], b[t], symbolic)

Fine-Grained Control (Numeric Factorization)

If you need to solve the same system with many different $b$ vectors while the matrix $A$ remains constant, you can further split the numeric factorization. This is often performed in a modified Newton-Raphson loop where the computationally expensive jacobian+factorization is only evaluated once and the solve stage is deemed "cheap" in comparison

# Factorize the matrix once
numeric = klujax.factor(Ai, Aj, Ax, symbolic)

@jax.jit
def fast_solve(b_t, num, sym):
    # This call is extremely fast as it skips factorization entirely
    return klujax.solve_with_numeric(num, b_t, sym)

for i in range(100):
    x_i = fast_solve(b_batch[i], numeric, symbolic)

Safe Refactorization (Status Codes & Conditioning)

klujax.refactor reuses the pivot order picked for the original matrix. That is what makes it fast, but it also means the factorization can fail, or silently lose accuracy, once the values have drifted far enough. Two additions make that recoverable.

klujax.refactor_with_status reports a failure through a status code rather than raising, so it can be branched on under jax.jit where an error would abort everything. klujax.refactor_and_solve_with_status does the same for the fused path.

numeric, status = klujax.refactor_with_status(Ai, Aj, Ax_new, numeric, symbolic)
if status[0] != klujax.KLUStatus.OK:
    # the numeric object is unusable for a solve, but the symbolic one is fine
    klujax.free_numeric(numeric)
    numeric = klujax.factor(Ai, Aj, Ax_new, symbolic)

When status != OK the numeric object may be partially overwritten and must not be used for a solve. It remains valid to pass to free_numeric, and the symbolic object is unaffected and may be reused for a fresh factor.

Degradation short of outright failure is caught by klujax.rcond, the reciprocal pivot growth estimate min|Uii| / max|Uii|. It costs O(n), far less than solving with a probe vector and measuring the residual. klujax.condest gives a proper 1-norm condition number estimate and is the usual follow-up when rcond is borderline.

if klujax.rcond(symbolic, numeric)[0] < 1e-10:
    ...  # pivots have degraded, re-factor from scratch

Lifecycle & Memory Safety

klujax.analyze and klujax.factor return handle tokens (SymbolToken, NumericToken). A token is a small cache id bundled with the arrays it can rebuild from, not a raw pointer, so handles are memory-safe by construction.

Forgetting to free leaks only a bounded amount. Handles live in a process-wide cache that holds a fixed number of KLU objects (eight by default, set by KLUJAX_FACTOR_CACHE). When it overflows, the least recently used object is evicted.
Using a freed or evicted handle is safe. Every call carries the matrix the handle needs, so a call that lands on a missing handle rebuilds it on the spot and continues. This is why freeing is optional and there is no ghost-pointer hazard: there is no pointer to dereference.

Because of this, creating a token inside jax.jit needs no special care. The id threads through the trace as data. A bare free is unordered against the solve, so to actually free inside the trace, order it after the solve by tracking the solution (or passing it as dependency):

@jax.jit
def dynamic_solve(Ai, Aj, Ax, b):
    sym = klujax.analyze(Ai, Aj, 5)
    x = klujax.solve_with_symbol(Ai, Aj, Ax, b, sym)
    klujax.free_symbolic(sym.track(x))   # ordered after the solve
    return x

Summary of Best Practices

Hoist Creations: for best performance, call analyze or factor once outside JIT loops and reuse the handle.
Freeing is optional: use free_symbolic / free_numeric (or the token's close(), or a with block) only to release memory sooner. The token stays usable afterwards.
Watch rebuild_count(): a count that climbs during steady-state solving means the working set is larger than KLUJAX_FACTOR_CACHE. Set KLUJAX_STRICT_CACHE to turn a rebuild into an error while debugging. See the Memory Management guide.

Installation

The library is statically linked to the SuiteSparse C++ library. It can be installed on most platforms as follows:

pip install splineax-klujax

There exist pre-built wheels for Linux and Windows (python 3.8+). If no compatible wheel is found, however, pip will attempt to install the library from source... make sure you have the necessary build dependencies installed (see Installing from Source)

Installing from Source

NOTE: Installing from source should only be necessary when developing the library. If you as the user experience an install from source please create an issue.

Before installing, clone the build dependencies:

git clone --depth 1 --branch v7.2.0 https://github.com/DrTimothyAldenDavis/SuiteSparse suitesparse
git clone --depth 1 --branch main https://github.com/openxla/xla xla
git clone --depth 1 --branch stable https://github.com/pybind/pybind11 pybind11

Linux

On linux, you'll need gcc and g++, then inside the repo:

pip install .

MacOs

On MacOS, you'll need clang, then inside the repo:

pip install .

Windows

On Windows, installing from source is a bit more involved as typically the build dependencies are not installed. To install those, download Visual Studio Community 2017 from here. During installation, go to Workloads and select the following workloads:

Desktop development with C++
Python development

Then go to Individual Components and select the following additional items:

C++/CLI support
VC++ 2015.3 v14.00 (v140) toolset for desktop

Then, download and install Microsoft Visual C++ Redistributable from here.

After these installation steps, run the following commands inside a x64 Native Tools Command Prompt for VS 2017:

set DISTUTILS_USE_SDK=1
pip install .

License & Credits

This library was partly based on:

torch_sparse_solve, LGPL-2.1
SuiteSparse, LGPL-2.1
kagami-c/PyKLU, LGPL-2.1
scipy.sparse, BSD-3

This library vendors an unmodified version of the SuiteSparse libraries in its source (.tar.gz) distribution to allow for static linking. This is in accordance with their LGPL licence.

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splineax-klujax 0.5.0.post4

KLUJAX

CPU & float64

Basic Usage

Basic Example

Advanced Usage

1. High-Performance Transient Pattern (Reusing Symbolic)

Fine-Grained Control (Numeric Factorization)

Safe Refactorization (Status Codes & Conditioning)

Lifecycle & Memory Safety

Summary of Best Practices

Installation

Installing from Source

Linux

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1 maintainer

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