Added strict=True

Author: patrick-kidger

diffrax/_adjoint.py

@@ -107,7 +107,7 @@ def get_ys(_final_state):
     return final_state
 
 
-class AbstractAdjoint(eqx.Module):
+class AbstractAdjoint(eqx.Module, strict=True):
     """Abstract base class for all adjoint methods."""
 
     @abc.abstractmethod
@@ -167,7 +167,7 @@ def _uncallable(*args, **kwargs):
     assert False
 
 
-class RecursiveCheckpointAdjoint(AbstractAdjoint):
+class RecursiveCheckpointAdjoint(AbstractAdjoint, strict=True):
     """Backpropagate through [`diffrax.diffeqsolve`][] by differentiating the numerical
     solution directly. This is sometimes known as "discretise-then-optimise", or
     described as "backpropagation through the solver".
@@ -318,7 +318,7 @@ def loop(
 """
 
 
-class DirectAdjoint(AbstractAdjoint):
+class DirectAdjoint(AbstractAdjoint, strict=True):
     """A variant of [`diffrax.RecursiveCheckpointAdjoint`][]. The differences are that
     `DirectAdjoint`:
 
@@ -434,7 +434,7 @@ def _frozenset(x: Union[object, Iterable[object]]) -> frozenset[object]:
         return frozenset(iter_x)
 
 
-class ImplicitAdjoint(AbstractAdjoint):
+class ImplicitAdjoint(AbstractAdjoint, strict=True):
     r"""Backpropagate via the [implicit function theorem](https://en.wikipedia.org/wiki/Implicit_function_theorem#Statement_of_the_theorem).
 
     This is used when solving towards a steady state, typically using
@@ -705,7 +705,7 @@ def __get(__aug):
     return a_y1, a_diff_args1, a_diff_terms1
 
 
-class BacksolveAdjoint(AbstractAdjoint):
+class BacksolveAdjoint(AbstractAdjoint, strict=True):
     """Backpropagate through [`diffrax.diffeqsolve`][] by solving the continuous
     adjoint equations backwards-in-time. This is also sometimes known as
     "optimise-then-discretise", the "continuous adjoint method" or simply the "adjoint

diffrax/_brownian/base.py

@@ -8,7 +8,7 @@
 from .._path import AbstractPath
 
 
-class AbstractBrownianPath(AbstractPath):
+class AbstractBrownianPath(AbstractPath, strict=True):
     """Abstract base class for all Brownian paths."""
 
     levy_area: AbstractVar[LevyArea]

diffrax/_brownian/path.py

@@ -19,7 +19,7 @@
 from .base import AbstractBrownianPath
 
 
-class UnsafeBrownianPath(AbstractBrownianPath):
+class UnsafeBrownianPath(AbstractBrownianPath, strict=True):
     """Brownian simulation that is only suitable for certain cases.
 
     This is a very quick way to simulate Brownian motion, but can only be used when all

diffrax/_brownian/tree.py

@@ -60,7 +60,7 @@
 _Spline: TypeAlias = Literal["sqrt", "quad", "zero"]
 
 
-class _State(eqx.Module):
+class _State(eqx.Module, strict=True):
     level: IntScalarLike  # level of the tree
     s: RealScalarLike  # starting time of the interval
     w_s_u_su: FloatTriple  # W_s, W_u, W_{s,u}
@@ -109,7 +109,7 @@ def _split_interval(
     return x_s, x_u, x_su
 
 
-class VirtualBrownianTree(AbstractBrownianPath):
+class VirtualBrownianTree(AbstractBrownianPath, strict=True):
     """Brownian simulation that discretises the interval `[t0, t1]` to tolerance `tol`.
 
     Can be initialised with `levy_area` set to `""`, or `"space-time"`.

diffrax/_custom_types.py

@@ -56,7 +56,7 @@
 LevyArea: TypeAlias = Literal["", "space-time"]
 
 
-class LevyVal(eqx.Module):
+class LevyVal(eqx.Module, strict=True):
     dt: PyTree
     W: PyTree
     H: Optional[PyTree]

diffrax/_event.py

@@ -10,7 +10,7 @@
 from ._step_size_controller import AbstractAdaptiveStepSizeController
 
 
-class AbstractDiscreteTerminatingEvent(eqx.Module):
+class AbstractDiscreteTerminatingEvent(eqx.Module, strict=True):
     """Evaluated at the end of each integration step. If true then the solve is stopped
     at that time.
     """
@@ -30,7 +30,7 @@ def __call__(self, state, **kwargs) -> BoolScalarLike:
         """
 
 
-class DiscreteTerminatingEvent(AbstractDiscreteTerminatingEvent):
+class DiscreteTerminatingEvent(AbstractDiscreteTerminatingEvent, strict=True):
     """Terminates the solve if its condition is ever active."""
 
     cond_fn: Callable[..., BoolScalarLike]
@@ -50,7 +50,7 @@ def __call__(self, state, **kwargs):
 """
 
 
-class SteadyStateEvent(AbstractDiscreteTerminatingEvent):
+class SteadyStateEvent(AbstractDiscreteTerminatingEvent, strict=True):
     """Terminates the solve once it reaches a steady state."""
 
     rtol: Optional[float] = None

diffrax/_global_interpolation.py

@@ -23,7 +23,7 @@
 from ._path import AbstractPath
 
 
-class AbstractGlobalInterpolation(AbstractPath):
+class AbstractGlobalInterpolation(AbstractPath, strict=True):
     ts: AbstractVar[Real[Array, " times"]]
     ts_size: AbstractVar[IntScalarLike]
 
@@ -52,7 +52,7 @@ def t1(self):
         return self.ts[-1]
 
 
-class LinearInterpolation(AbstractGlobalInterpolation):
+class LinearInterpolation(AbstractGlobalInterpolation, strict=True):
     """Linearly interpolates some data `ys` over the interval $[t_0, t_1]$ with knots
     at `ts`.
 
@@ -178,7 +178,7 @@ def derivative(self, t: RealScalarLike, left: bool = True) -> PyTree[Array]:
 """
 
 
-class CubicInterpolation(AbstractGlobalInterpolation):
+class CubicInterpolation(AbstractGlobalInterpolation, strict=True):
     """Piecewise cubic spline interpolation over the interval $[t_0, t_1]$."""
 
     ts: Real[Array, " times"]
@@ -302,7 +302,7 @@ def derivative(
 """
 
 
-class DenseInterpolation(AbstractGlobalInterpolation):
+class DenseInterpolation(AbstractGlobalInterpolation, strict=True):
     ts: Real[Array, " times"]
     # DenseInterpolations typically get `ts` and `infos` that are way longer than they
     # need to be, and padded with `nan`s. This means the normal way of measuring how

diffrax/_integrate.py

@@ -47,14 +47,14 @@
 from ._term import AbstractTerm, MultiTerm, ODETerm, WrapTerm
 
 
-class SaveState(eqx.Module):
+class SaveState(eqx.Module, strict=True):
     saveat_ts_index: IntScalarLike
     ts: eqxi.MaybeBuffer[Real[Array, " times"]]
     ys: PyTree[eqxi.MaybeBuffer[Inexact[Array, "times ..."]]]
     save_index: IntScalarLike
 
 
-class State(eqx.Module):
+class State(eqx.Module, strict=True):
     # Evolving state during the solve
     y: PyTree[Array]
     tprev: FloatScalarLike

diffrax/_local_interpolation.py

@@ -1,5 +1,6 @@
 from typing import Optional, TYPE_CHECKING
 
+import equinox as eqx
 import jax.numpy as jnp
 import jax.tree_util as jtu
 import numpy as np
@@ -17,11 +18,11 @@
 from ._path import AbstractPath
 
 
-class AbstractLocalInterpolation(AbstractPath):
+class AbstractLocalInterpolation(AbstractPath, strict=True):
     pass
 
 
-class LocalLinearInterpolation(AbstractLocalInterpolation):
+class LocalLinearInterpolation(AbstractLocalInterpolation, strict=True):
     t0: RealScalarLike
     t1: RealScalarLike
     y0: Y
@@ -39,7 +40,7 @@ def evaluate(
             return (coeff * (self.y1**ω - self.y0**ω)).call(jnp.asarray).ω
 
 
-class ThirdOrderHermitePolynomialInterpolation(AbstractLocalInterpolation):
+class ThirdOrderHermitePolynomialInterpolation(AbstractLocalInterpolation, strict=True):
     t0: RealScalarLike
     t1: RealScalarLike
     coeffs: PyTree[Shaped[Array, "4 ?*dims"], "Y"]
@@ -83,7 +84,9 @@ def _eval(_coeffs):
         return jtu.tree_map(_eval, self.coeffs)
 
 
-class FourthOrderPolynomialInterpolation(AbstractLocalInterpolation):
+class FourthOrderPolynomialInterpolation(
+    AbstractLocalInterpolation, strict=eqx.StrictConfig(allow_abstract_name=True)
+):
     t0: RealScalarLike
     t1: RealScalarLike
     coeffs: PyTree[Shaped[Array, "5 ?*y"], "Y"]

diffrax/_path.py

@@ -15,7 +15,7 @@
 from ._custom_types import RealScalarLike
 
 
-class AbstractPath(eqx.Module):
+class AbstractPath(eqx.Module, strict=True):
     """Abstract base class for all paths.
 
     Every path has a start point `t0` and an end point `t1`. In between these values

diffrax/_root_finder/_verychord.py

@@ -30,7 +30,7 @@ def _converged(factor: Scalar, tol: float) -> Bool[Array, ""]:
     return (factor > 0) & (factor < tol)
 
 
-class _VeryChordState(eqx.Module):
+class _VeryChordState(eqx.Module, strict=True):
     linear_state: tuple[lx.AbstractLinearOperator, PyTree[Any]]
     diff: Y
     diffsize: Scalar
@@ -39,7 +39,7 @@ class _VeryChordState(eqx.Module):
     step: Scalar
 
 
-class _NoAux(eqx.Module):
+class _NoAux(eqx.Module, strict=True):
     fn: Callable
 
     def __call__(self, y, args):
@@ -48,7 +48,7 @@ def __call__(self, y, args):
         return out
 
 
-class VeryChord(optx.AbstractRootFinder):
+class VeryChord(optx.AbstractRootFinder, strict=True):
     """The Chord method of root finding.
 
     As `optimistix.Chord`, except that in Runge--Kutta methods, the linearisation point

diffrax/_saveat.py

@@ -21,7 +21,7 @@ def _convert_ts(
         return jnp.asarray(ts)
 
 
-class SubSaveAt(eqx.Module):
+class SubSaveAt(eqx.Module, strict=True):
     """Used for finer-grained control over what is saved. A PyTree of these should be
     passed to `SaveAt(subs=...)`.
 
@@ -53,7 +53,7 @@ def __check_init__(self):
 """
 
 
-class SaveAt(eqx.Module):
+class SaveAt(eqx.Module, strict=True):
     """Determines what to save as output from the differential equation solve.
 
     Instances of this class should be passed as the `saveat` argument of

diffrax/_solution.py

@@ -1,5 +1,6 @@
 from typing import Any, Optional
 
+import equinox as eqx
 import jax
 import optimistix as optx
 from jaxtyping import Array, Bool, PyTree, Real, Shaped
@@ -55,7 +56,7 @@ def update_result(old_result: RESULTS, new_result: RESULTS) -> RESULTS:
     return RESULTS.where(pred, old_result, out_result)
 
 
-class Solution(AbstractPath):
+class Solution(AbstractPath, strict=eqx.StrictConfig(allow_method_override=True)):
     """The solution to a differential equation.
 
     **Attributes:**

diffrax/_solver/base.py

@@ -49,7 +49,7 @@ def __instancecheck__(cls, obj):
 _set_metaclass = dict(metaclass=_MetaAbstractSolver)
 
 
-class AbstractSolver(eqx.Module, Generic[_SolverState], **_set_metaclass):
+class AbstractSolver(eqx.Module, Generic[_SolverState], strict=True, **_set_metaclass):
     """Abstract base class for all differential equation solvers.
 
     Subclasses should have a class-level attribute `terms`, specifying the PyTree
@@ -179,7 +179,7 @@ def func(
         """
 
 
-class AbstractImplicitSolver(AbstractSolver[_SolverState]):
+class AbstractImplicitSolver(AbstractSolver[_SolverState], strict=True):
     """Indicates that this is an implicit differential equation solver, and as such
     that it should take a root finder as an argument.
     """
@@ -188,25 +188,25 @@ class AbstractImplicitSolver(AbstractSolver[_SolverState]):
     root_find_max_steps: AbstractVar[int]
 
 
-class AbstractItoSolver(AbstractSolver[_SolverState]):
+class AbstractItoSolver(AbstractSolver[_SolverState], strict=True):
     """Indicates that when used as an SDE solver that this solver will converge to the
     Itô solution.
     """
 
 
-class AbstractStratonovichSolver(AbstractSolver[_SolverState]):
+class AbstractStratonovichSolver(AbstractSolver[_SolverState], strict=True):
     """Indicates that when used as an SDE solver that this solver will converge to the
     Stratonovich solution.
     """
 
 
-class AbstractAdaptiveSolver(AbstractSolver[_SolverState]):
+class AbstractAdaptiveSolver(AbstractSolver[_SolverState], strict=True):
     """Indicates that this solver provides error estimates, and that as such it may be
     used with an adaptive step size controller.
     """
 
 
-class AbstractWrappedSolver(AbstractSolver[_SolverState]):
+class AbstractWrappedSolver(AbstractSolver[_SolverState], strict=True):
     """Wraps another solver "transparently", in the sense that all `isinstance` checks
     will be forwarded on to the wrapped solver, e.g. when testing whether the solver is
     implicit/adaptive/SDE-compatible/etc.

diffrax/_solver/dopri5.py

@@ -32,7 +32,7 @@
 )
 
 
-class _Dopri5Interpolation(FourthOrderPolynomialInterpolation):
+class _Dopri5Interpolation(FourthOrderPolynomialInterpolation, strict=True):
     c_mid: ClassVar[np.ndarray] = np.array(
         [
             6025192743 / 30085553152 / 2,

diffrax/_solver/dopri8.py

@@ -188,7 +188,7 @@
 _vmap_polyval = jax.vmap(jnp.polyval, in_axes=(0, None))
 
 
-class _Dopri8Interpolation(AbstractLocalInterpolation):
+class _Dopri8Interpolation(AbstractLocalInterpolation, strict=True):
     t0: RealScalarLike
     t1: RealScalarLike
     y0: Y

diffrax/_solver/kencarp3.py

@@ -89,7 +89,7 @@
 )
 
 
-class KenCarpInterpolation(AbstractLocalInterpolation):
+class AbstractKenCarpInterpolation(AbstractLocalInterpolation, strict=True):
     t0: RealScalarLike
     t1: RealScalarLike
     y0: Y
@@ -120,7 +120,7 @@ def evaluate(
         return (self.y0**ω + vector_tree_dot(coeffs, k) ** ω).ω
 
 
-class _KenCarp3Interpolation(KenCarpInterpolation):
+class _KenCarp3Interpolation(AbstractKenCarpInterpolation, strict=True):
     coeffs = np.array(
         [
             [-215264564351 / 13552729205753, 4655552711362 / 22874653954995],

diffrax/_solver/kencarp4.py

@@ -6,7 +6,7 @@
 
 from .._root_finder import VeryChord, with_stepsize_controller_tols
 from .base import AbstractImplicitSolver
-from .kencarp3 import KenCarpInterpolation
+from .kencarp3 import AbstractKenCarpInterpolation
 from .runge_kutta import (
     AbstractRungeKutta,
     ButcherTableau,
@@ -102,7 +102,7 @@
 )
 
 
-class _KenCarp4Interpolation(KenCarpInterpolation):
+class _KenCarp4Interpolation(AbstractKenCarpInterpolation, strict=True):
     coeffs = np.array(
         [
             [