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Feature/linalg mixed precision #275

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Merged
Jammy2211 merged 2 commits into from
Feb 8, 2026
Merged

Feature/linalg mixed precision #275

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3d108ab
update to use mixed preicison option
Feb 7, 2026
2a1d963
finish
Feb 8, 2026
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4 changes: 3 additions & 1 deletion autogalaxy/abstract_fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,9 @@ def linear_light_profile_intensity_dict(
reconstruction = self.inversion.reconstruction_dict[linear_obj_func]

for i, light_profile in enumerate(linear_obj_func.light_profile_list):
linear_light_profile_intensity_dict[light_profile] = float(reconstruction[i])
linear_light_profile_intensity_dict[light_profile] = float(
reconstruction[i]
)

return linear_light_profile_intensity_dict

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1 change: 0 additions & 1 deletion autogalaxy/analysis/chaining_util.py
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Expand Up @@ -500,7 +500,6 @@ def mass_light_dark_basis_from(
lmp_model.centre = light_profile.centre
lmp_model.ell_comps = light_profile.ell_comps


print(light_profile.intensity)
print(type(light_profile.intensity))

Comment on lines 503 to 505
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Copilot AI Feb 8, 2026

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The print(...) statements will emit output during normal library usage and can be very noisy in pipeline runs. Please remove these debug prints or replace them with optional logging behind a logger / verbosity flag.

Suggested change
print(light_profile.intensity)
print(type(light_profile.intensity))

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2 changes: 2 additions & 0 deletions autogalaxy/galaxy/to_inversion.py
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Expand Up @@ -323,6 +323,7 @@ def cls_light_profile_func_list_galaxy_dict_from(
psf=self.dataset.psf,
light_profile_list=light_profile_list,
regularization=light_profile.regularization,
settings=self.settings_inversion,
xp=self._xp,
)

Expand Down Expand Up @@ -486,6 +487,7 @@ def mapper_from(
return mapper_from(
mapper_grids=mapper_grids,
regularization=regularization,
settings=self.settings_inversion,
preloads=self.preloads,
xp=self._xp,
)
Comment on lines 487 to 493
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The factory call mapper_from(..., settings=self.settings_inversion, ...) introduces a new keyword argument. With autoarray unpinned, this can break for users on versions where mapper_from doesn’t accept settings. Please align this change with an explicit autoarray minimum version (or guard the kwarg for backwards compatibility).

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5 changes: 4 additions & 1 deletion autogalaxy/profiles/basis.py
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Expand Up @@ -215,7 +215,10 @@ def deflections_yx_2d_from(
"""
if len(self.mass_profile_list) > 0:
return sum(
[mass.deflections_yx_2d_from(grid=grid, xp=xp) for mass in self.profile_list]
[
mass.deflections_yx_2d_from(grid=grid, xp=xp)
for mass in self.profile_list
]
)
return xp.zeros((grid.shape[0], 2))

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10 changes: 8 additions & 2 deletions autogalaxy/profiles/light/linear/abstract.py
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Expand Up @@ -145,6 +145,7 @@ def __init__(
psf: Optional[aa.Kernel2D],
light_profile_list: List[LightProfileLinear],
regularization=Optional[aa.reg.Regularization],
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regularization=Optional[aa.reg.Regularization] sets the default value to the typing.Optional[...] object rather than None, which can lead to unexpected truthiness / attribute errors if callers rely on the default. This should be a type annotation (e.g. regularization: Optional[...] = None) instead of an assigned default.

Suggested change
regularization=Optional[aa.reg.Regularization],
regularization: Optional[aa.reg.Regularization] = None,

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settings=aa.SettingsInversion(),
xp=np,
):
"""
Expand Down Expand Up @@ -202,7 +203,9 @@ def __init__(
"""
)

super().__init__(grid=grid, regularization=regularization, xp=xp)
super().__init__(
grid=grid, regularization=regularization, settings=settings, xp=xp
)

self.blurring_grid = blurring_grid
self.psf = psf
Expand Down Expand Up @@ -303,7 +306,10 @@ def operated_mapping_matrix_override(self) -> Optional[np.ndarray]:
)

blurred_image_2d = self.psf.convolved_image_from(
image=image_2d, blurring_image=blurring_image_2d, xp=self._xp
image=image_2d,
blurring_image=blurring_image_2d,
use_mixed_precision=self.settings.use_mixed_precision,
xp=self._xp,
)
Comment on lines 308 to 313
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Kernel2D.convolved_image_from(...) is now called with use_mixed_precision=.... Because autoarray is an unpinned dependency, this will raise TypeError: got an unexpected keyword argument in environments using an older autoarray that doesn’t support this kwarg. Consider (1) pinning/bumping the minimum autoarray version that introduces this parameter, or (2) adding a backward-compatible call path (e.g. only passing the kwarg when supported).

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Comment on lines 308 to 313
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The mixed-precision behavior isn’t covered by tests: existing LightProfileLinearObjFuncList tests construct the class without settings, so they don’t validate that settings.use_mixed_precision is forwarded into the PSF convolution. Add a unit test that sets SettingsInversion(use_mixed_precision=True) (or equivalent) and asserts the mixed-precision path is exercised (e.g. via a spy/mocked convolved_image_from).

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blurred_image_2d_list.append(blurred_image_2d.array)
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42 changes: 28 additions & 14 deletions autogalaxy/profiles/mass/stellar/gaussian.py
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Expand Up @@ -197,19 +197,18 @@ def zeta_from(self, grid: aa.type.Grid2DLike, xp=np):

ind_pos_y = y >= 0

scale = q / (xp.asarray(self.sigma, dtype=xp.float64)
* xp.sqrt(xp.asarray(2.0, dtype=xp.float64) * (1.0 - q2)))
scale = q / (
xp.asarray(self.sigma, dtype=xp.float64)
* xp.sqrt(xp.asarray(2.0, dtype=xp.float64) * (1.0 - q2))
)

xs = x * scale
ys = xp.abs(y) * scale

z1 = xs + 1j * ys
z2 = q * xs + 1j * ys / q

exp_term = xp.exp(
-(xs * xs) * (1.0 - q2)
- (ys * ys) * (1.0 / q2 - 1.0)
)
exp_term = xp.exp(-(xs * xs) * (1.0 - q2) - (ys * ys) * (1.0 / q2 - 1.0))

core = -1j * (self.wofz(z1, xp=xp) - exp_term * self.wofz(z2, xp=xp))

Expand Down Expand Up @@ -243,10 +242,13 @@ def wofz(self, z, xp=np):
w_large = 1j * inv_sqrt_pi / t

# ---------- Region 5 ----------
U5 = xp.asarray([1.320522, 35.7668, 219.031,
1540.787, 3321.990, 36183.31], dtype=xp.float64)
V5 = xp.asarray([1.841439, 61.57037, 364.2191,
2186.181, 9022.228, 24322.84, 32066.6], dtype=xp.float64)
U5 = xp.asarray(
[1.320522, 35.7668, 219.031, 1540.787, 3321.990, 36183.31], dtype=xp.float64
)
V5 = xp.asarray(
[1.841439, 61.57037, 364.2191, 2186.181, 9022.228, 24322.84, 32066.6],
dtype=xp.float64,
)

t = inv_sqrt_pi
for u in U5:
Expand All @@ -259,10 +261,22 @@ def wofz(self, z, xp=np):
w5 = xp.exp(-z2) + 1j * z * t / s

# ---------- Region 6 ----------
U6 = xp.asarray([5.9126262, 30.180142, 93.15558,
181.92853, 214.38239, 122.60793], dtype=xp.float64)
V6 = xp.asarray([10.479857, 53.992907, 170.35400,
348.70392, 457.33448, 352.73063, 122.60793], dtype=xp.float64)
U6 = xp.asarray(
[5.9126262, 30.180142, 93.15558, 181.92853, 214.38239, 122.60793],
dtype=xp.float64,
)
V6 = xp.asarray(
[
10.479857,
53.992907,
170.35400,
348.70392,
457.33448,
352.73063,
122.60793,
],
dtype=xp.float64,
)

t = inv_sqrt_pi
for u in U6:
Expand Down
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