autolens.Settings

class Settings

Bases: object

The settings of an Inversion, customizing how a linear set of equations are solved for.

An Inversion is used to reconstruct a dataset, for example the luminous emission of a galaxy.

Parameters:

• use_mixed_precision (bool) –

  If True, a targeted subset of the inversion’s linear algebra runs in single precision (float32 / complex64) instead of double precision (float64 / complex128). This is intended to reduce VRAM use and speed up the FFT-heavy and bandwidth-bound steps on GPU and CPU; only the JAX (xp=jnp) paths honor the flag — the NumPy backend always runs in fp64.

  Paths that honor the flag:

  • PSF FFT convolution in Convolver.convolved_image_from (the light-profile blurring path, used by linear MGE bases and similar): the input image, kernel multiply and inverse FFT all run in complex64 / float32 end to end. This is the headline GPU win for MGE imaging pipelines.

  • PSF FFT convolution in Convolver.convolved_mapping_matrix_from (the pixelization mapping matrix path): the input cube is fp32 and the forward rfft2 runs in complex64, but the kernel multiply intentionally upcasts back to complex128 so the inverse FFT and downstream linear algebra stay fp64. Pixelization meshes with K ≫ 40 source pixels accumulate enough fp32 round-off through NNLS / log-determinant to shift figure_of_merit by O(1) units; the upcast preserves precision while the cheaper fp32 scatter and forward FFT are kept.

  • The mapping matrix native cube allocation in autoarray.inversion.mappers.mapper_util.mapping_matrix_from — output dtype becomes fp32.

  • The internal compute dtype of the curvature matrix accumulation in autoarray.inversion.inversion.inversion_util.curvature_matrix_via_mapping_matrix_from — the noise-weighted A.T @ A is formed in fp32 then cast to fp64 for downstream stability.

  Empirical platform notes:

  • GPU: full pipeline single-JIT roughly matches the fp64 baseline; vmap-batched evaluation (the production sampler hot path) shows 25–30% speedup on RTX 2060-class hardware.

  • CPU: the per-call FFT itself is ~1.6× faster in fp32, but JAX/XLA’s CPU FFT lowering does not always re-compose well across ~40-call MGE-basis pipelines, so the single-JIT measurement can be neutral or slightly slower than fp64. vmap remains comparable to or slightly faster than fp64. The flag is most beneficial for GPU users.

  Paths that intentionally stay in fp64:

  • The NNLS reconstruction (jaxnnls / Cholesky factor + cho_solve) in autoarray.inversion.inversion.inversion_util.reconstruction_positive_only_from. Active-set and PDIP solvers are sensitive to fp32 noise on ill-conditioned source meshes.

  • The log-determinant of the curvature regularization matrix used by figure_of_merit: condition numbers can exceed 1e6 on fine pixelizations and fp32 silently loses 1+ digit there.

  • Light profile evaluation on the (over-)sampled grid; only the resulting mapping matrix is downcast.

  Empirical numerical impact on the MGE imaging regression (HST-shaped, 15k masked pixels, 40 linear Gaussians): Δlog-likelihood ≈ 1e-4 absolute at log-likelihood ≈ 27,400. Well below the natural χ² sampling noise floor (σ ≈ √(2N) ≈ 175). Pixelization paths with K ≫ 40 source pixels are more sensitive — verify on representative integration tests before turning on for production fits.

  If False (default), all paths run in fp64.

• use_positive_only_solver (Optional[bool]) – Whether to use a positive-only linear system solver, which requires that every reconstructed value is positive but is computationally much slower than the default solver (which allows for positive and negative values).

• use_border_relocator (Optional[bool]) – If True, all coordinates of all pixelization source mesh grids have pixels outside their border relocated to their edge.

• no_regularization_add_to_curvature_diag_value (float) – If a linear func object does not have a corresponding regularization, this value is added to its diagonal entries of the curvature regularization matrix to ensure the matrix is positive-definite.

Methods

Attributes

no_regularization_add_to_curvature_diag_value
use_border_relocator
use_edge_zeroed_pixels
use_positive_only_solver

property use_positive_only_solver

property use_edge_zeroed_pixels

property use_border_relocator

property no_regularization_add_to_curvature_diag_value