vmec-jax 0.0.5

End-to-end differentiable JAX implementation of VMEC2000 for fixed and free-boundary equilibria.

vmec-jax

End-to-end differentiable JAX implementation of VMEC2000 for fixed-boundary and free-boundary ideal-MHD equilibria.

Install

pip install vmec-jax

QI optimization uses booz_xform_jax for the differentiable Boozer transform:

pip install "vmec-jax[qi]"

Developer (editable) install:

git clone https://github.com/uwplasma/vmec_jax
pip install -e "vmec_jax[qi]"

Usage

Run the solver (VMEC2000-style CLI):

vmec_jax input.nfp4_QH_warm_start        # → wout_nfp4_QH_warm_start.nc

Generate diagnostic plots from any wout_*.nc (four-panel output, replicates vmecPlot2.py):

vmec_jax --plot wout_nfp4_QH_warm_start.nc           # saves in same directory
vmec_jax --plot wout_nfp4_QH_warm_start.nc --outdir figures/

From Python:

import vmec_jax as vj

# Run a fixed-boundary solve
run = vj.run_fixed_boundary("input.nfp4_QH_warm_start")

# Run a free-boundary solve
freeb = vj.run_free_boundary("input.cth_like_free_bdy_lasym_small")

# Plot any wout file (produces *_VMECparams.pdf, *_poloidal_plot.png, *_VMECsurfaces.pdf, *_VMEC_3Dplot.png)
vj.plot_wout("wout_nfp4_QH_warm_start.nc", outdir="figures/")

Choosing CPU or GPU

vmec_jax follows the JAX backend you select. If you installed CPU-only JAX, runs use CPU. If you installed GPU-enabled JAX and select a GPU backend, runs use GPU; vmec_jax does not silently force those runs back to CPU.

# Check what JAX will use.
python -c "import jax; print(jax.default_backend()); print(jax.devices())"

# Force CPU for one command.
JAX_PLATFORMS=cpu vmec_jax input.nfp4_QH_warm_start

# Force an accelerator backend after installing GPU-enabled JAX.
JAX_PLATFORM_NAME=gpu vmec_jax input.nfp4_QH_warm_start

# For NVIDIA CUDA specifically, this is also valid.
JAX_PLATFORMS=cuda vmec_jax input.nfp4_QH_warm_start

From Python, leave solver_device unset to inherit JAX's default backend, or pass solver_device="cpu" / solver_device="gpu" explicitly:

import vmec_jax as vj

run_gpu = vj.run_fixed_boundary("input.nfp4_QH_warm_start", solver_device="gpu")
run_cpu = vj.run_fixed_boundary("input.nfp4_QH_warm_start", solver_device="cpu")

For GPU runs, vmec_jax defaults XLA_PYTHON_CLIENT_PREALLOCATE=false before JAX import so the allocator grows on demand. This avoids GPU memory contention between optimization workers and was faster in the exact-Jacobian GPU profile. Set XLA_PYTHON_CLIENT_PREALLOCATE=true before import if you explicitly want JAX's default preallocation behavior.

vmec_jax enables JAX's persistent compilation cache by default, but its default cache path is machine/CPU-feature scoped to avoid reusing CPU AOT executables compiled on a different host. Set VMEC_JAX_COMPILATION_CACHE=0 to disable the persistent cache or VMEC_JAX_COMPILATION_CACHE_DIR=/path/to/cache to choose a custom location.

Showcase (single-grid)

All figures below use the same single-grid run settings: NS_ARRAY=151, NITER_ARRAY=5000, FTOL_ARRAY=1e-14, NSTEP=500.

ITERModel cross-section (VMEC2000 vs vmec_jax)	LandremanPaul2021_QA_lowres cross-section (VMEC2000 vs vmec_jax)
ITERModel iota (VMEC2000 vs vmec_jax)	LandremanPaul2021_QA_lowres iota (VMEC2000 vs vmec_jax)
ITERModel 3D LCFS	LandremanPaul2021_QA_lowres 3D LCFS
ITERModel |B| on LCFS	LandremanPaul2021_QA_lowres |B| on LCFS

Cold vs warm runtime: the cold bar includes XLA JIT compilation on the first call (one-time cost per process); the warm bar is the steady-state solve time for subsequent calls in the same process. VMEC2000 has no compilation overhead, so it is always effectively cold. vmec_jax enables JAX's persistent compilation cache by default under ~/.cache/vmec_jax/jax_cache/<machine-fingerprint> so repeated cold-process runs on the same host can reuse compiled kernels without sharing CPU AOT executables across incompatible machines; set VMEC_JAX_COMPILATION_CACHE=0 to disable it or VMEC_JAX_COMPILATION_CACHE_DIR=/path/to/cache to choose a different location.

Best Stellarator-Symmetric Optimizations

The fixed-boundary optimization examples solve VMEC equilibria and differentiate the objective with the exact discrete-adjoint/tape path. The README only shows one current best LASYM = F result for each target; the full CPU/GPU policy matrix, LASYM panels, finite-beta examples, QI constraint sweep, and all tables live in the optimization guide and optimization sweep results.

Each row below shows the original deck LCFS before any max_mode=1 optimization work, the final LCFS, per-stage objective history, and the final outer-surface |B| in Boozer coordinates computed with booz_xform_jax. This sweep uses NFP=2 seeds for QA/QP/QI and the standard bundled NFP=4 warm start for QH. The current objective priority is primary symmetry/QI quality and rotational-transform control. QA follows the reference omnigenity QA deck with aspect ratio near 5 and signed mean iota target 0.42; QH/QP/QI also use aspect ratio near 5 and abs(mean_iota) >= 0.41. LgradB remains available as an optional script-level term, but it is not active in the default README examples or best-row selection.

The QP and QI rows both start from the bundled NFP=2 QI seed. QP is a quasi-poloidal-symmetry target using that same input deck; the current best QI row uses repeated same-mode continuation at max_mode=3 without a QP preseed. The bundled NFP=2 seed is projected to each active max_mode, so max_mode=1 zeroes the seed's mode-2 boundary harmonics before optimizing. For QI, the listed wall time includes all repeated stages using the same constrained least-squares residual definition.

Target	Backend	Policy	max_mode	ESS	QP preseed	Final J	QI legacy	Mirror	Elong.	Aspect	Iota	Wall time
QA	CPU	continuation	3	yes		2.33e-04				5.000	0.4200	6.1 min
QH	CPU	continuation	3	yes		9.68e-03				4.999	-1.6595	4.0 min
QP	CPU	continuation	3	no		6.76e-02				5.019	-0.6255	3.7 min
QI	CPU	continuation	3	yes	no	1.05e-03	1.04e-03	0.211	4.78	5.000	-0.4553	6.6 min

Recreate the four displayed runs:

PYTHONPATH=. JAX_PLATFORMS=cpu python examples/optimization/generate_qs_ess_sweep.py --backend-label cpu --solver-device cpu --policy continuation --problems qa --modes 3 --ess off
PYTHONPATH=. JAX_PLATFORMS=cpu python examples/optimization/generate_qs_ess_sweep.py --backend-label cpu --solver-device cpu --policy continuation --problems qh --modes 3 --ess on
PYTHONPATH=. JAX_PLATFORMS=cpu python examples/optimization/generate_qs_ess_sweep.py --backend-label cpu --solver-device cpu --policy continuation --problems qp --modes 3 --ess off
PYTHONPATH=. JAX_PLATFORMS=cpu python examples/optimization/QI_optimization.py

Regenerate the README panels and the compact CSV used for the table:

PYTHONPATH=. python examples/optimization/render_readme_best_optimizations.py

Performance vs parity

• Default runs select the fastest stable path for each input automatically.
• Use --parity (or performance_mode=False in Python) to force the conservative VMEC2000 loop.
• Use --solver-mode accelerated to force the optimized fixed-boundary controller.
• For GPU benchmarking, compare both first-process and cache-warm timings; the first GPU process pays XLA compilation, while later processes reuse the persistent cache automatically.

Details, profiling guidance, and parity methodology:

• docs/performance.rst
• docs/validation.rst
• tools/diagnostics/parity_manifest.toml + tools/diagnostics/parity_sweep_manifest.py

CLI reference

vmec_jax input.*                run the equilibrium solver → wout_*.nc
vmec_jax --plot wout.nc         generate diagnostic plots (4 output files)
vmec_jax --parity input.*       force conservative VMEC2000 loop
vmec_jax --help                 full option list

VMEC++ notes

The current runtime benchmark compares vmec_jax against VMEC2000. VMEC++ is not included in this benchmark.

When VMEC++ is available, it can be added to the runtime plot via --cpu-summary entries with backend=vmecpp. Some inputs are not supported or do not converge under the same single-grid settings:

VMEC++ unsupported inputs (lasym=True):

• LandremanSenguptaPlunk_section5p3_low_res
• basic_non_stellsym_pressure
• cth_like_free_bdy_lasym_small
• up_down_asymmetric_tokamak

VMEC++ known non-convergence on these lasym=False cases under the same single-grid settings:

• DIII-D_lasym_false
• LandremanPaul2021_QA_reactorScale_lowres
• LandremanPaul2021_QH_reactorScale_lowres
• LandremanSengupta2019_section5.4_B2_A80
• cth_like_fixed_bdy

CLI output and NSTEP

The VMEC-style iteration loop prints every NSTEP iterations. Larger NSTEP means fewer print callbacks and faster runs.

To disable live printing:

export VMEC_JAX_SCAN_PRINT=0

Quiet runs (--quiet or verbose=False) default the scan path to minimal history mode to reduce host/device traffic. Override with:

export VMEC_JAX_SCAN_MINIMAL=0  # keep full scan diagnostics even when quiet