SRKs now support forward-mode autodiff.

diffrax/_adjoint.py

@@ -16,7 +16,12 @@
 
 from ._heuristics import is_sde, is_unsafe_sde
 from ._saveat import save_y, SaveAt, SubSaveAt
-from ._solver import AbstractItoSolver, AbstractRungeKutta, AbstractStratonovichSolver
+from ._solver import (
+    AbstractItoSolver,
+    AbstractRungeKutta,
+    AbstractSRK,
+    AbstractStratonovichSolver,
+)
 from ._term import AbstractTerm, AdjointTerm
 
 
@@ -272,7 +277,7 @@ def loop(
         if is_unsafe_sde(terms):
             raise ValueError(
                 "`adjoint=RecursiveCheckpointAdjoint()` does not support "
-                "`UnsafeBrownianPath`. Consider using `adjoint=DirectAdjoint()` "
+                "`UnsafeBrownianPath`. Consider using `adjoint=ForwardMode()` "
                 "instead."
             )
         if self.checkpoints is None and max_steps is None:
@@ -376,7 +381,10 @@ def loop(
             msg = None
         # Support forward-mode autodiff.
         # TODO: remove this hack once we can JVP through custom_vjps.
-        if isinstance(solver, AbstractRungeKutta) and solver.scan_kind is None:
+        if (
+            isinstance(solver, (AbstractRungeKutta, AbstractSRK))
+            and solver.scan_kind is None
+        ):
             solver = eqx.tree_at(
                 lambda s: s.scan_kind, solver, "bounded", is_leaf=_is_none
             )
@@ -888,7 +896,10 @@ def loop(
         outer_while_loop = eqx.Partial(_outer_loop, kind="lax")
         # Support forward-mode autodiff.
         # TODO: remove this hack once we can JVP through custom_vjps.
-        if isinstance(solver, AbstractRungeKutta) and solver.scan_kind is None:
+        if (
+            isinstance(solver, (AbstractRungeKutta, AbstractSRK))
+            and solver.scan_kind is None
+        ):
             solver = eqx.tree_at(lambda s: s.scan_kind, solver, "lax", is_leaf=_is_none)
         final_state = self._loop(
             solver=solver,

diffrax/_solver/srk.py

@@ -1,6 +1,6 @@
 import abc
 from dataclasses import dataclass
-from typing import Any, Generic, Optional, TYPE_CHECKING, TypeVar, Union
+from typing import Any, Generic, Literal, Optional, TYPE_CHECKING, TypeVar, Union
 from typing_extensions import TypeAlias
 
 import equinox as eqx
@@ -255,6 +255,8 @@ class AbstractSRK(AbstractSolver[_SolverState]):
     as well as $b^H$, $a^H$, $b^K$, and $a^K$ if needed.
     """
 
+    scan_kind: Union[None, Literal["lax", "checkpointed"]] = None
+
     interpolation_cls = LocalLinearInterpolation
     term_compatible_contr_kwargs = (dict(), dict(use_levy=True))
     tableau: AbstractClassVar[StochasticButcherTableau]
@@ -583,7 +585,7 @@ def compute_and_insert_kg_j(_w_kgs_in, _levylist_kgs_in):
             scan_inputs,
             len(b_sol),
             buffers=lambda x: x,
-            kind="checkpointed",
+            kind="checkpointed" if self.scan_kind is None else self.scan_kind,
             checkpoints="all",
         )
 

test/test_adjoint.py

@@ -366,10 +366,7 @@ def run(model):
     run(mlp)
 
 
-@pytest.mark.parametrize(
-    "diffusion_fn",
-    ["weak", "lineax"],
-)
+@pytest.mark.parametrize("diffusion_fn", ["weak", "lineax"])
 def test_sde_against(diffusion_fn, getkey):
     def f(t, y, args):
         del t
@@ -427,3 +424,31 @@ def test_implicit_runge_kutta_direct_adjoint():
         adjoint=diffrax.DirectAdjoint(),
         stepsize_controller=diffrax.PIDController(rtol=1e-3, atol=1e-6),
     )
+
+
+@pytest.mark.parametrize("solver", (diffrax.Tsit5(), diffrax.GeneralShARK()))
+def test_forward_mode_runge_kutta(solver, getkey):
+    # Totally fine that we're using Tsit5 with an SDE, it should converge to the
+    # Stratonovich solution.
+    bm = diffrax.UnsafeBrownianPath((), getkey(), levy_area=diffrax.SpaceTimeLevyArea)
+    drift = diffrax.ODETerm(lambda t, y, args: -y)
+    diffusion = diffrax.ControlTerm(lambda t, y, args: 0.1 * y, bm)
+    terms = diffrax.MultiTerm(drift, diffusion)
+
+    def run(y0):
+        sol = diffrax.diffeqsolve(
+            terms,
+            solver,
+            0,
+            1,
+            0.01,
+            y0,
+            adjoint=diffrax.ForwardMode(),
+        )
+        return sol.ys
+
+    @jax.jit
+    def run_jvp(y0):
+        return jax.jvp(run, (y0,), (jnp.ones_like(y0),))
+
+    run_jvp(jnp.array(1.0))