Refactor metadata handling in variable step integrators

src/jaxsim/integrators/variable_step.py

@@ -14,7 +14,6 @@
 
 import jaxsim.utils.tracing
 from jaxsim import typing as jtp
-from jaxsim.utils import Mutability
 
 from .common import (
     ExplicitRungeKutta,
@@ -271,30 +270,27 @@ def init(
             # Inject this key to signal that the integrator is initializing.
             # This is used to allocate the arrays of the metadata dictionary,
             # that are then filled with NaNs.
-            integrator.metadata = {EmbeddedRungeKutta.InitializingKey: jnp.array(True)}
+            metadata = {EmbeddedRungeKutta.InitializingKey: jnp.array(True)}
 
             # Run a dummy call of the integrator.
             # It is used only to get the metadata so that we know the structure
             # of the corresponding pytree.
             _ = integrator(
-                x0, jnp.array(t0, dtype=float), jnp.array(dt, dtype=float), **kwargs
+                x0,
+                jnp.array(t0, dtype=float),
+                jnp.array(dt, dtype=float),
+                **(kwargs | {"metadata": metadata}),
             )
 
         # Remove the injected key.
-        _ = integrator.metadata.pop(EmbeddedRungeKutta.InitializingKey)
+        _ = metadata.pop(EmbeddedRungeKutta.InitializingKey)
 
         # Make sure that all leafs of the dictionary are JAX arrays.
         # Also, since these are dummy parameters, set them all to NaN.
         metadata_after_init = jax.tree.map(
-            lambda l: jnp.nan * jnp.zeros_like(l), integrator.metadata
+            lambda l: jnp.nan * jnp.zeros_like(l), metadata
         )
 
-        # Store the zero parameters in the integrator.
-        # When the integrator is stepped, this is used to check if the passed
-        # parameters are valid.
-        with self.mutable_context(mutability=Mutability.MUTABLE_NO_VALIDATION):
-            self.metadata = metadata_after_init
-
         return metadata_after_init
 
     def __call__(
@@ -307,7 +303,7 @@ def __call__(
         #    The metadata is a dictionary of float JAX arrays, that are initialized
         #    with the right shape and filled with NaNs.
         # 2. During the first step, this method operates on the Nan-filled
-        #    `self.metadata` attribute, and it populates with the actual metadata.
+        #    `metadata` argument, and it populates with the actual metadata.
         # 3. After the first step, this method operates on the actual metadata.
         #
         # In particular, we store the following information in the metadata:
@@ -318,8 +314,10 @@ def __call__(
         #   evaluate the dynamics at the final state of the previous step, that matches
         #   the initial state of the current step.
         #
+        metadata = kwargs.pop("metadata", {})
+
         integrator_init = jnp.array(
-            self.metadata.get(self.InitializingKey, False), dtype=bool
+            metadata.get(self.InitializingKey, False), dtype=bool
         )
 
         # Close f over optional kwargs.
@@ -335,24 +333,23 @@ def __call__(
 
         # The value of dt0 is NaN (or, at least, it should be) only after initialization
         # and before the first step.
-        self.metadata["dt0"], self.metadata["dxdt0"] = jax.lax.cond(
-            pred=("dt0" in self.metadata)
-            & ~jnp.isnan(self.metadata.get("dt0", 0.0)).any(),
+        metadata["dt0"], metadata["dxdt0"] = jax.lax.cond(
+            pred=("dt0" in metadata) & ~jnp.isnan(metadata.get("dt0", 0.0)).any(),
             true_fun=lambda metadata: (
                 metadata.get("dt0", jnp.array(0.0, dtype=float)),
-                self.metadata.get("dxdt0", f(x0, t0)[0]),
+                metadata.get("dxdt0", f(x0, t0)[0]),
             ),
             false_fun=lambda aux: estimate_step_size(
                 x0=x0, t0=t0, f=f, order=p, atol=self.atol, rtol=self.rtol
             ),
-            operand=self.metadata,
+            operand=metadata,
         )
 
         # Clip the estimated initial step size to the given bounds, if necessary.
-        self.metadata["dt0"] = jnp.clip(
-            self.metadata["dt0"],
-            jnp.minimum(self.dt_min, self.metadata["dt0"]),
-            jnp.minimum(self.dt_max, self.metadata["dt0"]),
+        metadata["dt0"] = jnp.clip(
+            metadata["dt0"],
+            jnp.minimum(self.dt_min, metadata["dt0"]),
+            jnp.minimum(self.dt_max, metadata["dt0"]),
         )
 
         # =========================================================
@@ -364,7 +361,7 @@ def __call__(
         carry0: Carry = (
             x0,
             jnp.array(t0).astype(float),
-            self.metadata,
+            metadata,
             jnp.array(0, dtype=int),
             jnp.array(False).astype(bool),
         )
@@ -392,9 +389,10 @@ def while_loop_body(carry: Carry) -> Carry:
             # Run the underlying explicit RK integrator.
             # The output z contains multiple solutions (depending on the rows of b.T).
             with self.editable(validate=True) as integrator:
-                integrator.metadata = metadata
-                z, _ = integrator._compute_next_state(x0=x0, t0=t0, dt=Δt0, **kwargs)
-                metadata_next = integrator.metadata
+                z, aux_dict = integrator._compute_next_state(
+                    x0=x0, t0=t0, dt=Δt0, **kwargs
+                )
+                metadata_next = aux_dict["metadata"]
 
             # Extract the high-order solution xf and the low-order estimate x̂f.
             xf = jax.tree.map(lambda l: l[self.row_index_of_solution], z)
@@ -510,12 +508,7 @@ def reject_step():
             init_val=carry0,
         )
 
-        # Store the parameters.
-        # They will be returned to the caller in a functional way in the step method.
-        with self.mutable_context(mutability=Mutability.MUTABLE):
-            self.metadata = metadata_tf
-
-        return xf, {}
+        return xf, {"metadata": metadata_tf}
 
     @property
     def order_of_solution(self) -> int: