Enable more complex tests, fix related errors

diffrax/_brownian/path.py

@@ -9,6 +9,7 @@
 import jax.tree_util as jtu
 import lineax.internal as lxi
 from jaxtyping import Array, PRNGKeyArray, PyTree
+from lineax.internal import complex_to_real_dtype
 
 from .._custom_types import (
     AbstractBrownianIncrement,
@@ -130,7 +131,7 @@ def _evaluate_leaf(
     ):
         w_std = jnp.sqrt(t1 - t0).astype(shape.dtype)
         w = jr.normal(key, shape.shape, shape.dtype) * w_std
-        dt = jnp.asarray(t1 - t0, dtype=shape.dtype)
+        dt = jnp.asarray(t1 - t0, dtype=complex_to_real_dtype(shape.dtype))
 
         if levy_area is SpaceTimeLevyArea:
             key, key_hh = jr.split(key, 2)

diffrax/_brownian/tree.py

@@ -10,7 +10,8 @@
 import jax.random as jr
 import jax.tree_util as jtu
 import lineax.internal as lxi
-from jaxtyping import Array, Float, PRNGKeyArray, PyTree
+from jaxtyping import Array, Inexact, PRNGKeyArray, PyTree
+from lineax.internal import complex_to_real_dtype
 
 from .._custom_types import (
     AbstractBrownianIncrement,
@@ -54,9 +55,9 @@
 # For the midpoint rule for generating space-time Levy area see Theorem 6.1.6.
 # For the general interpolation rule for space-time Levy area see Theorem 6.1.4.
 
-FloatDouble: TypeAlias = tuple[Float[Array, " *shape"], Float[Array, " *shape"]]
+FloatDouble: TypeAlias = tuple[Inexact[Array, " *shape"], Inexact[Array, " *shape"]]
 FloatTriple: TypeAlias = tuple[
-    Float[Array, " *shape"], Float[Array, " *shape"], Float[Array, " *shape"]
+    Inexact[Array, " *shape"], Inexact[Array, " *shape"], Inexact[Array, " *shape"]
 ]
 _Spline: TypeAlias = Literal["sqrt", "quad", "zero"]
 _BrownianReturn = TypeVar("_BrownianReturn", bound=AbstractBrownianIncrement)
@@ -90,7 +91,7 @@ def _levy_diff(_, x0: tuple, x1: tuple) -> AbstractBrownianIncrement:
         assert len(x1) == 2
         dt0, w0 = x0
         dt1, w1 = x1
-        su = jnp.asarray(dt1 - dt0, dtype=w0.dtype)
+        su = jnp.asarray(dt1 - dt0, dtype=complex_to_real_dtype(w0.dtype))
         return BrownianIncrement(dt=su, W=w1 - w0)
 
     elif len(x0) == 4:  # space-time levy area case
@@ -99,12 +100,13 @@ def _levy_diff(_, x0: tuple, x1: tuple) -> AbstractBrownianIncrement:
         dt1, w1, hh1, bhh1 = x1
 
         w_su = w1 - w0
-        su = jnp.asarray(dt1 - dt0, dtype=w0.dtype)
+        su = jnp.asarray(dt1 - dt0, dtype=complex_to_real_dtype(w0.dtype))
         _su = jnp.where(jnp.abs(su) < jnp.finfo(su).eps, jnp.inf, su)
         inverse_su = 1 / _su
-        u_bb_s = dt1 * w0 - dt0 * w1
-        bhh_su = bhh1 - bhh0 - 0.5 * u_bb_s  # bhh_su = H_{s,u} * (u-s)
-        hh_su = inverse_su * bhh_su
+        with jax.numpy_dtype_promotion("standard"):
+            u_bb_s = dt1 * w0 - dt0 * w1
+            bhh_su = bhh1 - bhh0 - 0.5 * u_bb_s  # bhh_su = H_{s,u} * (u-s)
+            hh_su = inverse_su * bhh_su
         return SpaceTimeLevyArea(dt=su, W=w_su, H=hh_su)
     else:
         assert False
@@ -283,9 +285,10 @@ def _evaluate_leaf(
         tuple[RealScalarLike, Array], tuple[RealScalarLike, Array, Array, Array]
     ]:
         shape, dtype = struct.shape, struct.dtype
+        tdtype = complex_to_real_dtype(dtype)
 
-        t0 = jnp.zeros((), dtype)
-        r = jnp.asarray(r, dtype)
+        t0 = jnp.zeros((), tdtype)
+        r = jnp.asarray(r, tdtype)
 
         if self.levy_area is SpaceTimeLevyArea:
             state_key, init_key_w, init_key_la = jr.split(key, 3)
@@ -394,27 +397,31 @@ def _body_fun(_state: _State):
             a = d_prime * sr3 * sr_ru_half
             b = d_prime * ru3 * sr_ru_half
 
-            w_sr = sr / su * w_su + 6 * sr * ru / su3 * bhh_su + 2 * (a + b) / su * x1
-            w_r = w_s + w_sr
-            c = jnp.sqrt(3 * sr3 * ru3) / (6 * d)
-            bhh_sr = sr3 / su3 * bhh_su - a * x1 + c * x2
-            bhh_r = bhh_s + bhh_sr + 0.5 * (r * w_s - s * w_r)
+            with jax.numpy_dtype_promotion("standard"):
+                w_sr = (
+                    sr / su * w_su + 6 * sr * ru / su3 * bhh_su + 2 * (a + b) / su * x1
+                )
+                w_r = w_s + w_sr
+                c = jnp.sqrt(3 * sr3 * ru3) / (6 * d)
+                bhh_sr = sr3 / su3 * bhh_su - a * x1 + c * x2
+                bhh_r = bhh_s + bhh_sr + 0.5 * (r * w_s - s * w_r)
 
-            inverse_r = 1 / jnp.where(jnp.abs(r) < jnp.finfo(r).eps, jnp.inf, r)
-            hh_r = inverse_r * bhh_r
+                inverse_r = 1 / jnp.where(jnp.abs(r) < jnp.finfo(r).eps, jnp.inf, r)
+                hh_r = inverse_r * bhh_r
 
         elif self.levy_area is BrownianIncrement:
-            w_mean = w_s + sr / su * w_su
-            if self._spline == "sqrt":
-                z = jr.normal(final_state.key, shape, dtype)
-                bb = jnp.sqrt(sr * ru / su) * z
-            elif self._spline == "quad":
-                z = jr.normal(final_state.key, shape, dtype)
-                bb = (sr * ru / su) * z
-            elif self._spline == "zero":
-                bb = jnp.zeros(shape, dtype)
-            else:
-                assert False
+            with jax.numpy_dtype_promotion("standard"):
+                w_mean = w_s + sr / su * w_su
+                if self._spline == "sqrt":
+                    z = jr.normal(final_state.key, shape, dtype)
+                    bb = jnp.sqrt(sr * ru / su) * z
+                elif self._spline == "quad":
+                    z = jr.normal(final_state.key, shape, dtype)
+                    bb = (sr * ru / su) * z
+                elif self._spline == "zero":
+                    bb = jnp.zeros(shape, dtype)
+                else:
+                    assert False
             w_r = w_mean + bb
             return r, w_r
 
@@ -497,8 +504,8 @@ def _brownian_arch(
 
             w_t = w_s + w_st
             w_stu = (w_s, w_t, w_u)
-
-            bhh_t = bhh_s + bhh_st + 0.5 * (t * w_s - s * w_t)
+            with jax.numpy_dtype_promotion("standard"):
+                bhh_t = bhh_s + bhh_st + 0.5 * (t * w_s - s * w_t)
             bhh_stu = (bhh_s, bhh_t, bhh_u)
             bkk_stu = None
             bkk_st_tu = None

diffrax/_global_interpolation.py

@@ -143,7 +143,10 @@ def _index(_ys):
         next_t = self.ts[index + 1]
         diff_t = next_t - prev_t
 
-        return (prev_ys**ω + (next_ys**ω - prev_ys**ω) * (fractional_part / diff_t)).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (
+                prev_ys**ω + (next_ys**ω - prev_ys**ω) * (fractional_part / diff_t)
+            ).ω
 
     @eqx.filter_jit
     def derivative(self, t: RealScalarLike, left: bool = True) -> PyTree[Array]:
@@ -165,10 +168,11 @@ def derivative(self, t: RealScalarLike, left: bool = True) -> PyTree[Array]:
 
         index, _ = self._interpret_t(t, left)
 
-        return (
-            (ω(self.ys)[index + 1] - ω(self.ys)[index])
-            / (self.ts[index + 1] - self.ts[index])
-        ).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (
+                (ω(self.ys)[index + 1] - ω(self.ys)[index])
+                / (self.ts[index + 1] - self.ts[index])
+            ).ω
 
 
 LinearInterpolation.__init__.__doc__ = """**Arguments:**
@@ -254,10 +258,11 @@ def evaluate(
 
         d, c, b, a = self.coeffs
 
-        return (
-            ω(a)[index]
-            + frac * (ω(b)[index] + frac * (ω(c)[index] + frac * ω(d)[index]))
-        ).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (
+                ω(a)[index]
+                + frac * (ω(b)[index] + frac * (ω(c)[index] + frac * ω(d)[index]))
+            ).ω
 
     @eqx.filter_jit
     def derivative(
@@ -283,7 +288,8 @@ def derivative(
 
         d, c, b, _ = self.coeffs
 
-        return (ω(b)[index] + frac * (2 * ω(c)[index] + frac * 3 * ω(d)[index])).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (ω(b)[index] + frac * (2 * ω(c)[index] + frac * 3 * ω(d)[index])).ω
 
 
 CubicInterpolation.__init__.__doc__ = """**Arguments:**
@@ -622,8 +628,9 @@ def _hermite_forward(
 ]:
     prev_ti, prev_yi, prev_deriv_i = carry
     ti, yi, next_ti, next_yi = value
-    first_deriv_i = (next_yi - yi) / (next_ti - ti)
-    later_deriv_i = (yi - prev_yi) / (ti - prev_ti)
+    with jax.numpy_dtype_promotion("standard"):
+        first_deriv_i = (next_yi - yi) / (next_ti - ti)
+        later_deriv_i = (yi - prev_yi) / (ti - prev_ti)
     deriv_i = jnp.where(jnp.isnan(prev_yi), first_deriv_i, later_deriv_i)
     cond = jnp.isnan(yi)
     carry_ti = jnp.where(cond, prev_ti, ti)
@@ -635,13 +642,15 @@ def _hermite_forward(
 
 def _hermite_coeffs(t0, y0, deriv0, t1, y1):
     t_diff = t1 - t0
-    deriv1 = (y1 - y0) / t_diff
-    d_deriv = deriv1 - deriv0
 
-    a = y0
-    b = deriv0
-    c = 2 * d_deriv / t_diff
-    d = -d_deriv / t_diff**2
+    with jax.numpy_dtype_promotion("standard"):
+        deriv1 = (y1 - y0) / t_diff
+        d_deriv = deriv1 - deriv0
+
+        a = y0
+        b = deriv0
+        c = 2 * d_deriv / t_diff
+        d = -d_deriv / (t_diff**2)
 
     return d, c, b, a
 
@@ -684,7 +693,8 @@ def _backward_hermite_coefficients(
     else:
         y0 = jnp.broadcast_to(replace_nans_at_start, ys[0].shape)
     if deriv0 is None:
-        deriv0 = (next_ys[0] - y0) / (next_ts[0] - t0)
+        with jax.numpy_dtype_promotion("standard"):
+            deriv0 = (next_ys[0] - y0) / (next_ts[0] - t0)
     else:
         deriv0 = jnp.broadcast_to(deriv0, ys[0].shape)
     ts = ts[:-1]

diffrax/_integrate.py

@@ -155,7 +155,8 @@ def _check(term_cls, term, term_contr_kwargs, yi):
             # If we've got to this point then the term is compatible
 
     try:
-        jtu.tree_map(_check, term_structure, terms, contr_kwargs, y)
+        with jax.numpy_dtype_promotion("standard"):
+            jtu.tree_map(_check, term_structure, terms, contr_kwargs, y)
     except ValueError:
         # ValueError may also arise from mismatched tree structures
         return False

diffrax/_local_interpolation.py

@@ -1,6 +1,7 @@
 from collections.abc import Callable
 from typing import cast, Optional, TYPE_CHECKING
 
+import jax
 import jax.numpy as jnp
 import jax.tree_util as jtu
 import numpy as np
@@ -35,12 +36,15 @@ def evaluate(
         self, t0: RealScalarLike, t1: Optional[RealScalarLike] = None, left: bool = True
     ) -> PyTree[Array]:
         del left
-        if t1 is None:
-            coeff = linear_rescale(self.t0, t0, self.t1)
-            return (self.y0**ω + coeff * (self.y1**ω - self.y0**ω)).call(jnp.asarray).ω
-        else:
-            coeff = (t1 - t0) / (self.t1 - self.t0)
-            return (coeff * (self.y1**ω - self.y0**ω)).call(jnp.asarray).ω
+        with jax.numpy_dtype_promotion("standard"):
+            if t1 is None:
+                coeff = linear_rescale(self.t0, t0, self.t1)
+                return (
+                    (self.y0**ω + coeff * (self.y1**ω - self.y0**ω)).call(jnp.asarray).ω
+                )
+            else:
+                coeff = (t1 - t0) / (self.t1 - self.t0)
+                return (coeff * (self.y1**ω - self.y0**ω)).call(jnp.asarray).ω
 
 
 class ThirdOrderHermitePolynomialInterpolation(AbstractLocalInterpolation):
@@ -82,7 +86,8 @@ def evaluate(
         t = linear_rescale(self.t0, t0, self.t1)
 
         def _eval(_coeffs):
-            return jnp.polyval(_coeffs, t)
+            with jax.numpy_dtype_promotion("standard"):
+                return jnp.polyval(_coeffs, t)
 
         return jtu.tree_map(_eval, self.coeffs)
 
@@ -104,7 +109,8 @@ def __init__(
         k: PyTree[Shaped[Array, "order ?*y"], "Y"],
     ):
         def _calculate(_y0, _y1, _k):
-            _ymid = _y0 + jnp.tensordot(self.c_mid, _k, axes=1)
+            with jax.numpy_dtype_promotion("standard"):
+                _ymid = _y0 + jnp.tensordot(self.c_mid, _k, axes=1)
             _f0 = _k[0]
             _f1 = _k[-1]
             # TODO: rewrite as matrix-vector product?
@@ -127,6 +133,7 @@ def evaluate(
         t = linear_rescale(self.t0, t0, self.t1)
 
         def _eval(_coeffs):
-            return jnp.polyval(_coeffs, t)
+            with jax.numpy_dtype_promotion("standard"):
+                return jnp.polyval(_coeffs, t)
 
         return jtu.tree_map(_eval, self.coeffs)

diffrax/_root_finder/_verychord.py

@@ -11,6 +11,7 @@
 import optimistix as optx
 from equinox.internal import ω
 from jaxtyping import Array, Bool, PyTree, Scalar
+from lineax.internal import complex_to_real_dtype
 
 from .._custom_types import Y
 
@@ -97,11 +98,12 @@ def init(
                 y_dtype = lxi.default_floating_dtype()
             else:
                 y_dtype = jnp.result_type(*y_leaves)
+            diff_dtype = complex_to_real_dtype(y_dtype)
             init_state = _VeryChordState(
                 linear_state=linear_state,
                 diff=jtu.tree_map(lambda x: jnp.full(x.shape, jnp.inf, x.dtype), y),
-                diffsize=jnp.array(jnp.inf, dtype=y_dtype),
-                diffsize_prev=jnp.array(1.0, dtype=y_dtype),
+                diffsize=jnp.array(jnp.inf, dtype=diff_dtype),
+                diffsize_prev=jnp.array(1.0, dtype=diff_dtype),
                 result=optx.RESULTS.successful,
                 step=jnp.array(0),
             )
@@ -127,8 +129,10 @@ def step(
         )
         diff = sol.value
         new_y = (y**ω - diff**ω).ω
-        scale = (self.atol + self.rtol * ω(new_y).call(jnp.abs)).ω
-        diffsize = self.norm((diff**ω / scale**ω).ω)
+
+        with jax.numpy_dtype_promotion("standard"):
+            scale = (self.atol + self.rtol * ω(new_y).call(jnp.abs)).ω
+            diffsize = self.norm((diff**ω / scale**ω).ω)
         new_state = _VeryChordState(
             linear_state=state.linear_state,
             diff=diff,

diffrax/_solver/dopri8.py

@@ -298,7 +298,8 @@ def evaluate(
             return self.evaluate(t1) - self.evaluate(t0)
         t = linear_rescale(self.t0, t0, self.t1)
         coeffs = _vmap_polyval(jnp.asarray(self.eval_coeffs, dtype=t.dtype), t) * t
-        return (self.y0**ω + vector_tree_dot(coeffs, self.k) ** ω).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (self.y0**ω + vector_tree_dot(coeffs, self.k) ** ω).ω
 
 
 class Dopri8(AbstractERK):

diffrax/_solver/kencarp3.py

@@ -117,7 +117,8 @@ def evaluate(
         explicit_k, implicit_k = self.k
         k = (explicit_k**ω + implicit_k**ω).ω
         coeffs = t * jax.vmap(lambda row: jnp.polyval(row, t))(self.coeffs)
-        return (self.y0**ω + vector_tree_dot(coeffs, k) ** ω).ω
+        with jax.numpy_dtype_promotion("standard"):
+            return (self.y0**ω + vector_tree_dot(coeffs, k) ** ω).ω
 
 
 class _KenCarp3Interpolation(KenCarpInterpolation):

diffrax/_solver/milstein.py

@@ -214,7 +214,8 @@ def step(
                 leaf = jnp.tensordot(l1[..., None], l2[None, ...], axes=1)
                 if i1 == i2:
                     eye = jnp.eye(l1.size).reshape(l1.shape + l1.shape)
-                    leaf = leaf - Δt * eye
+                    with jax.numpy_dtype_promotion("standard"):
+                        leaf = leaf - Δt * eye
                 leaves_ΔwΔw.append(leaf)
         tree_ΔwΔw = tree_Δw.compose(tree_Δw)
         ΔwΔw = jtu.tree_unflatten(tree_ΔwΔw, leaves_ΔwΔw)
@@ -236,7 +237,9 @@ def _to_vmap(_g0):
             # _g0 has structure (tree(y0), leaf(y0))
             _, _jvp = jax.jvp(_to_vjp, (y0,), (_g0,))
             # jvp has structure (tree(g0), leaf(g0))
-            _jvp_matrix = jax.jacfwd(lambda _Δw: diffusion.prod(_jvp, _Δw))(Δw)
+            _jvp_matrix = jax.jacfwd(
+                lambda _Δw: diffusion.prod(_jvp, _Δw), holomorphic=jnp.iscomplexobj(Δw)
+            )(Δw)
             # _jvp_matrix has structure (tree(y0), tree(Δw), leaf(y0), leaf(Δw))
             return _jvp_matrix
 
@@ -282,7 +285,9 @@ def _to_treemap(_Δw, _g0):
         Δw_treedef = jtu.tree_structure(Δw)
         # g0 has structure (tree(g0), leaf(g0))
         # Which we now transform into its isomorphic matrix form, as above.
-        g0_matrix = jax.jacfwd(lambda _Δw: diffusion.prod(g0, _Δw))(Δw)
+        g0_matrix = jax.jacfwd(
+            lambda _Δw: diffusion.prod(g0, _Δw), holomorphic=jnp.iscomplexobj(Δw)
+        )(Δw)
         # g0_matrix has structure (tree(y0), tree(Δw), leaf(y0), leaf(Δw))
         g0_matrix = jtu.tree_transpose(y_treedef, Δw_treedef, g0_matrix)
         # g0_matrix has structure (tree(Δw), tree(y0), leaf(y0), leaf(Δw))