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[feat] Relax strict autograd.numpy requirement (#285)

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* [feat] Relax strict autograd.numpy requirement

* [Fix] Re-impose --Werror for python 3.14 runs after new autograd release
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Fabian Joswig 2026-06-30 08:55:49 +02:00 • committed by GitHub
commit 229ea45ac7
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5 changed files with 71 additions and 17 deletions
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58
tests/fits_test.py
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@ -698,13 +698,27 @@ def test_fit_no_autograd():
y = a[0] * np.exp(-a[1] * x)
return y
with pytest.raises(Exception):
pe.least_squares(x, oy, func)
def func_autograd(a, x):
return a[0] * anp.exp(-a[1] * x)
pe.least_squares(x, oy, func, num_grad=True)
# Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd
# wrappers (ArrayBox.__array_ufunc__), so a function using numpy.exp now
# yields exactly the same result as one using autograd.numpy.exp.
for r_np, r_ag in zip(pe.least_squares(x, oy, func), pe.least_squares(x, oy, func_autograd)):
assert r_np == r_ag
for r_np, r_ag in zip(pe.total_least_squares(oy, oy, func), pe.total_least_squares(oy, oy, func_autograd)):
assert r_np == r_ag
# A function that genuinely cannot be traced by autograd must still raise a
# clear error pointing the user to autograd.numpy.
def func_invalid(a, x):
return np.array(a[0] * np.exp(-a[1] * x), dtype=np.float64)
with pytest.raises(Exception):
pe.total_least_squares(oy, oy, func)
pe.least_squares(x, oy, func_invalid)
with pytest.raises(Exception):
pe.total_least_squares(oy, oy, func_invalid)
def test_invalid_fit_function():
@ -818,7 +832,14 @@ def test_combined_fit_no_autograd():
def func_b(a,x):
return a[0]*np.exp(a[2]*x)
def func_a_autograd(a,x):
return a[0]*anp.exp(a[1]*x)
def func_b_autograd(a,x):
return a[0]*anp.exp(a[2]*x)
funcs = {'a':func_a, 'b':func_b}
funcs_autograd = {'a':func_a_autograd, 'b':func_b_autograd}
xs = {'a':xvals_a, 'b':xvals_b}
ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
@ -826,8 +847,17 @@ def test_combined_fit_no_autograd():
for key in funcs.keys():
[item.gamma_method() for item in ys[key]]
# Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd
# wrappers, so the fit using numpy now matches the one using autograd.numpy.
for r_np, r_ag in zip(pe.least_squares(xs, ys, funcs), pe.least_squares(xs, ys, funcs_autograd)):
assert r_np == r_ag
# A function that genuinely cannot be traced by autograd must still raise.
def func_a_invalid(a, x):
return np.array(a[0] * np.exp(a[1] * x), dtype=np.float64)
with pytest.raises(Exception):
pe.least_squares(xs, ys, funcs)
pe.least_squares(xs, ys, {'a': func_a_invalid, 'b': func_b})
pe.least_squares(xs, ys, funcs, num_grad=True)
@ -930,7 +960,14 @@ def test_combined_fit_no_autograd():
def func_b(a,x):
return a[0]*np.exp(a[2]*x)
def func_a_autograd(a,x):
return a[0]*anp.exp(a[1]*x)
def func_b_autograd(a,x):
return a[0]*anp.exp(a[2]*x)
funcs = {'a':func_a, 'b':func_b}
funcs_autograd = {'a':func_a_autograd, 'b':func_b_autograd}
xs = {'a':xvals_a, 'b':xvals_b}
ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)],
'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]}
@ -938,8 +975,17 @@ def test_combined_fit_no_autograd():
for key in funcs.keys():
[item.gamma_method() for item in ys[key]]
# Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd
# wrappers, so the fit using numpy now matches the one using autograd.numpy.
for r_np, r_ag in zip(pe.least_squares(xs, ys, funcs), pe.least_squares(xs, ys, funcs_autograd)):
assert r_np == r_ag
# A function that genuinely cannot be traced by autograd must still raise.
def func_a_invalid(a, x):
return np.array(a[0] * np.exp(a[1] * x), dtype=np.float64)
with pytest.raises(Exception):
pe.least_squares(xs, ys, funcs)
pe.least_squares(xs, ys, {'a': func_a_invalid, 'b': func_b})
pe.least_squares(xs, ys, funcs, num_grad=True)