Improve the vector-field API: Move vector-fields back into constraints, read off ode_order from the Callable, and more

docs/examples_advanced/equinox_while_loop.py

@@ -26,12 +26,15 @@
 
 from probdiffeq import ivpsolve, probdiffeq, taylor
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
 
 def solution_routine(while_loop):
     """Construct a parameter-to-solution function and an initial value."""
 
     @jax.jit
-    def vf(y, *, t):  # noqa: ARG001
+    def vf(y, /, *, t):  # noqa: ARG001
         """Evaluate the vector field."""
         return 0.5 * y * (1 - y)
 
@@ -40,12 +43,10 @@ def vf(y, *, t):  # noqa: ARG001
 
     tcoeffs = taylor.odejet_padded_scan(lambda y: vf(y, t=t0), (u0,), num=1)
     init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact="isotropic")
-    ts0 = probdiffeq.constraint_ode_ts0(ode_order=1, ssm=ssm)
+    ts0 = probdiffeq.constraint_ode_ts0(vf, ssm=ssm)
 
     strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
-    solver = probdiffeq.solver(
-        vf, strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm
-    )
+    solver = probdiffeq.solver(strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm)
     errorest = probdiffeq.errorest_local_residual_cached(prior=ibm, ssm=ssm)
     solve_adaptive = ivpsolve.solve_adaptive_terminal_values(
         solver=solver, errorest=errorest, while_loop=while_loop

docs/examples_advanced/neural_ode.py

@@ -25,6 +25,9 @@
 
 from probdiffeq import ivpsolve, probdiffeq
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
 
 def main(num_data=100, epochs=500, print_every=50, hidden=(20,), lr=0.2):
     """Train a neural ODE using diffusion tempering."""
@@ -96,7 +99,7 @@ def vf_neural_ode(*, hidden: tuple, t0: float, t1: float):
     u0 = jnp.asarray([0.0])
 
     @jax.jit
-    def vf(y, *, t, p):
+    def vf(y, /, *, t, p):
         """Evaluate the neural ODE vector field."""
         y_and_t = jnp.concatenate([y, t[None]])
         return mlp(p, y_and_t)
@@ -167,14 +170,14 @@ def loss(
         init, ibm, ssm = probdiffeq.prior_wiener_integrated(
             tcoeffs, output_scale=output_scale, ssm_fact="isotropic"
         )
-        ts0 = probdiffeq.constraint_ode_ts0(ssm=ssm)
+
+        def vf_p(y, /, *, t):
+            return vf(y, t=t, p=p)
+
+        ts0 = probdiffeq.constraint_ode_ts0(vf_p, ssm=ssm)
         strategy = probdiffeq.strategy_smoother_fixedinterval(ssm=ssm)
         solver_ts0 = probdiffeq.solver(
-            lambda *a, **kw: vf(*a, **kw, p=p),
-            strategy=strategy,
-            prior=ibm,
-            constraint=ts0,
-            ssm=ssm,
+            strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm
         )
 
         # Solve

docs/examples_advanced/parameter_estimation_blackjax.py

@@ -153,7 +153,7 @@
 
 
 @jax.jit
-def vf(y, *, t):  # noqa: ARG001
+def vf(y, /, *, t):  # noqa: ARG001
     """Evaluate the Lotka-Volterra vector field."""
     return f(y, *f_args)
 
@@ -180,9 +180,9 @@ def plot_solution(t, u, *, ax, marker=".", **plotting_kwargs):
 init, ibm, ssm = probdiffeq.prior_wiener_integrated(
     tcoeffs, output_scale=10.0, ssm_fact="isotropic"
 )
-ts0 = probdiffeq.constraint_ode_ts0(ssm=ssm)
+ts0 = probdiffeq.constraint_ode_ts0(vf, ssm=ssm)
 strategy = probdiffeq.strategy_filter(ssm=ssm)
-solver = probdiffeq.solver(vf, strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm)
+solver = probdiffeq.solver(strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm)
 
 
 @jax.jit

docs/examples_advanced/parameter_estimation_optax.py

@@ -36,6 +36,10 @@
 if not backend.has_been_selected:
     backend.select("jax")  # ivp examples in jax
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
+
 # -
 
 
@@ -64,15 +68,13 @@ def solve(p):
     init, ibm, ssm = probdiffeq.prior_wiener_integrated(
         tcoeffs, output_scale=10.0, ssm_fact="isotropic"
     )
-    ts0 = probdiffeq.constraint_ode_ts0(ssm=ssm)
+
+    def vf_p(y, /, *, t):
+        return vf(y, t=t, p=p)
+
+    ts0 = probdiffeq.constraint_ode_ts0(vf_p, ssm=ssm)
     strategy = probdiffeq.strategy_smoother_fixedinterval(ssm=ssm)
-    solver = probdiffeq.solver(
-        jax.jit(lambda y, t: vf(y, t, p=p)),
-        strategy=strategy,
-        prior=ibm,
-        constraint=ts0,
-        ssm=ssm,
-    )
+    solver = probdiffeq.solver(strategy=strategy, prior=ibm, constraint=ts0, ssm=ssm)
     solve = ivpsolve.solve_fixed_grid(solver=solver)
     return solve(init, grid=ts)
 

docs/examples_advanced/solve_pde.py

@@ -25,32 +25,32 @@
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
 
-from probdiffeq import ivpsolve, probdiffeq, taylor
+from probdiffeq import ivpsolve, probdiffeq
 
-jax.config.update("jax_enable_x64", True)
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
 
 
 def main():
     """Simulate a PDE."""
     key = jax.random.PRNGKey(1)
     f, (u0,), (t0, t1) = fhn_2d(key, num=40, t1=10.0)
 
-    @jax.jit
-    def vf(y, *, t):  # noqa: ARG001
+    def vf(y, /, *, t):  # noqa: ARG001
         """Evaluate the dynamics of the PDE."""
         return f(y)
 
     print("Problem dimension:", u0.size)
 
     # Set up a state-space model
-    tcoeffs = taylor.odejet_padded_scan(lambda y: vf(y, t=t0), (u0,), num=1)
+    tcoeffs = [u0, vf(u0, t=t0)]
     init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact="blockdiag")
 
     # Build a solver
-    ts = probdiffeq.constraint_ode_ts1(ssm=ssm)
+    ts = probdiffeq.constraint_ode_ts1(vf, ssm=ssm)
     strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
     solver = probdiffeq.solver_dynamic(
-        vf, ssm=ssm, strategy=strategy, prior=ibm, constraint=ts
+        ssm=ssm, strategy=strategy, prior=ibm, constraint=ts
     )
     errorest = probdiffeq.errorest_local_residual_cached(prior=ibm, ssm=ssm)
 

docs/examples_basic/conditioning_on_zero_residual.py

@@ -22,6 +22,8 @@
 
 """Demonstrate how probabilistic solvers work via conditioning on constraints."""
 
+import functools
+
 import jax
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
@@ -32,6 +34,11 @@
 if not backend.has_been_selected:
     backend.select("jax")  # ivp examples in jax
 
+
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
+
 # -
 
 # Create an ODE problem.
@@ -40,8 +47,9 @@
 
 
 @jax.jit
-def vector_field(y, t):  # noqa: ARG001
+def vector_field(y, /, *, t):
     """Evaluate the logistic ODE vector field."""
+    del t
     return 10.0 * y * (2.0 - y)
 
 
@@ -78,11 +86,9 @@ def vector_field(y, t):  # noqa: ARG001
 init, ibm, ssm = probdiffeq.prior_wiener_integrated(
     tcoeffs, output_scale=1.0, ssm_fact="dense"
 )
-ts1 = probdiffeq.constraint_ode_ts1(ssm=ssm)
+ts1 = probdiffeq.constraint_ode_ts1(vector_field, ssm=ssm)
 strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
-solver = probdiffeq.solver(
-    vector_field, strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
-)
+solver = probdiffeq.solver(strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm)
 errorest = probdiffeq.errorest_local_residual_cached(prior=ibm, ssm=ssm)
 
 dt0 = ivpsolve.dt0(lambda y: vector_field(y, t=t0), (u0,))
@@ -126,7 +132,8 @@ def vector_field(y, t):  # noqa: ARG001
 
 def residual(x, t):
     """Evaluate the ODE residual."""
-    return x[1] - jax.vmap(jax.vmap(vector_field), in_axes=(0, None))(x[0], t)
+    vf_wrapped = functools.partial(vector_field, t=t)
+    return x[1] - jax.vmap(jax.vmap(vf_wrapped))(x[0])
 
 
 residual_prior = residual(samples_prior, ts)

docs/examples_basic/custom_information_operator.py

@@ -33,6 +33,10 @@
 
 from probdiffeq import ivpsolve, probdiffeq
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
+
 # -
 
 
@@ -43,7 +47,7 @@
 
 
 @jax.jit
-def vf_1st(y, *, t):
+def vf_1st(y, /, *, t):
     """Evaluate the harmonic oscillator dynamics."""
     u, du = jnp.split(y, 2)
     return jnp.concatenate([du, vf_2nd(u, du, t=t)])
@@ -90,10 +94,10 @@ def hamiltonian_2nd(u, du):
 tcoeffs = [u0_1st, zeros, zeros]
 tcoeffs_std = [zeros, ones, ones]
 init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, tcoeffs_std=tcoeffs_std)
-ts1 = probdiffeq.constraint_ode_ts1(ssm=ssm)
+ts1 = probdiffeq.constraint_ode_ts1(vf_1st, ssm=ssm)
 strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
 solver_1st = probdiffeq.solver_mle(
-    vf_1st, strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
+    strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
 )
 errorest = probdiffeq.errorest_local_residual_cached(prior=ibm, ssm=ssm)
 solve = ivpsolve.solve_adaptive_save_at(solver=solver_1st, errorest=errorest)
@@ -115,9 +119,9 @@ def hamiltonian_2nd(u, du):
 # +
 
 
-def root(vf, u, du, ddu):
+def root(u, du, ddu, /, *, t):
     """Evaluate a custom root for the harmonic oscillator."""
-    deriv = ddu - vf(u, du)
+    deriv = ddu - vf_2nd(u, du, t=t)
     hamil = hamiltonian_2nd(u, du) - H0
     return [deriv, hamil]  # any PyTree goes
 
@@ -145,10 +149,10 @@ def root(vf, u, du, ddu):
 
 # +
 
-ts1 = probdiffeq.constraint_root_ts1(root, ssm=ssm, ode_order=2)
+ts1 = probdiffeq.constraint_root_ts1(root, ssm=ssm)
 strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
 solver_2nd = probdiffeq.solver_mle(
-    vf_2nd, strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
+    strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
 )
 
 # -

docs/examples_basic/dynamic_output_scales.py

@@ -44,6 +44,9 @@
 if not backend.has_been_selected:
     backend.select("jax")  # ivp examples in jax
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
 
 # -
 
@@ -53,9 +56,10 @@
 
 
 @jax.jit
-def vf(*ys, t):  # noqa: ARG001
+def vf(y, /, *, t):
     """Evaluate the affine vector field."""
-    return f(*ys, *f_args)
+    del t
+    return f(y, *f_args)
 
 
 # -
@@ -68,12 +72,12 @@ def vf(*ys, t):  # noqa: ARG001
 init, ibm, ssm = probdiffeq.prior_wiener_integrated(
     tcoeffs, output_scale=1.0, ssm_fact="dense"
 )
-ts1 = probdiffeq.constraint_ode_ts1(ssm=ssm)
+ts1 = probdiffeq.constraint_ode_ts1(vf, ssm=ssm)
 strategy = probdiffeq.strategy_filter(ssm=ssm)
 dynamic = probdiffeq.solver_dynamic(
-    vf, strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
+    strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm
 )
-mle = probdiffeq.solver_mle(vf, strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm)
+mle = probdiffeq.solver_mle(strategy=strategy, prior=ibm, constraint=ts1, ssm=ssm)
 
 # -
 

docs/examples_basic/posterior_uncertainties.py

@@ -29,9 +29,12 @@
 
 from probdiffeq import ivpsolve, probdiffeq, taylor
 
+# Fail this notebook on NaN detection (to catch those in the CI)
+jax.config.update("jax_debug_nans", True)
+
 
 @jax.jit
-def vf(y, *, t):  # noqa: ARG001
+def vf(y, /, *, t):  # noqa: ARG001
     """Evaluate the Lotka-Volterra vector field."""
     y0, y1 = y[0], y[1]
 
@@ -58,10 +61,10 @@ def vf(y, *, t):  # noqa: ARG001
 
 tcoeffs = taylor.odejet_padded_scan(lambda y: vf(y, t=t0), (u0,), num=3)
 init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact="blockdiag")
-ts = probdiffeq.constraint_ode_ts1(ssm=ssm)
+ts = probdiffeq.constraint_ode_ts1(vf, ssm=ssm)
 strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
 
-solver = probdiffeq.solver_mle(vf, strategy=strategy, prior=ibm, constraint=ts, ssm=ssm)
+solver = probdiffeq.solver_mle(strategy=strategy, prior=ibm, constraint=ts, ssm=ssm)
 errorest = probdiffeq.errorest_local_residual_cached(prior=ibm, ssm=ssm)
 solve = ivpsolve.solve_adaptive_save_at(solver=solver, errorest=errorest)