Fix optimizer freeze support in HMC

fortuna/prob_model/posterior/sgmcmc/hmc/hmc_trainer.py

@@ -24,6 +24,11 @@
     Array,
     Batch,
 )
+from fortuna.utils.freeze import (
+    has_multiple_opt_state,
+    get_trainable_opt_state,
+    update_trainable_opt_state,
+)
 
 
 class HMCTrainer(MAPTrainer):
@@ -46,9 +51,12 @@ def training_step(
             unravel=unravel,
             **kwargs,
         )
-        state = state.replace(
-            opt_state=state.opt_state._replace(log_prob=aux["loss"]),
-        )
+        if has_multiple_opt_state(state):
+            opt_state = get_trainable_opt_state(state)._replace(log_prob=aux["loss"])
+            state = update_trainable_opt_state(state, opt_state)
+        else:
+            opt_state = state.opt_state._replace(log_prob=aux["loss"])
+            state = state.replace(opt_state=opt_state)
         return state, aux
 
     def __str__(self):

fortuna/utils/freeze.py

@@ -16,7 +16,9 @@
 from optax import (
     multi_transform,
     set_to_zero,
+    MultiTransformState,
 )
+from optax._src.wrappers import MaskedState
 
 from fortuna.typing import (
     AnyKey,
@@ -25,6 +27,8 @@
     Params,
 )
 
+from fortuna.prob_model.posterior.state import PosteriorState
+
 
 def all_values_in_labels(values: Iterable, labels: Any) -> None:
     """
@@ -79,6 +83,65 @@ def freeze_optimizer(
     return multi_transform(partition_optimizers, partition_params)
 
 
+def has_multiple_opt_state(state: PosteriorState):
+    """
+    Check if a given posterior state containts multiple optimizer states.
+
+    Parameters
+    ----------
+    state: PosteriorState
+        An instance of `PosteriorState`.
+
+    Returns
+    -------
+    bool
+    """
+    return isinstance(state.opt_state, MultiTransformState)
+
+
+def get_trainable_opt_state(state: PosteriorState):
+    """
+    Get a trainable optimizer state.
+
+    Parameters
+    ----------
+    state: PosteriorState
+        An instance of `PosteriorState`.
+
+    Returns
+    -------
+    opt_state: Any
+        An instance of trainable optimizer state.
+    """
+    return state.opt_state.inner_states["trainable"].inner_state
+
+
+def update_trainable_opt_state(state: PosteriorState, opt_state: Any):
+    """
+    Update a trainable optimizer state.
+
+    Parameters
+    ----------
+    state: PosteriorState
+        An instance of `PosteriorState`.
+    opt_state: Any
+        An instance of trainable optimizer state.
+
+    Returns
+    -------
+    PosteriorState
+        An updated posterior state.
+    """
+    trainable_state = MaskedState(inner_state=opt_state)
+    opt_state = MultiTransformState(
+        inner_states={
+            k:(trainable_state if k == "trainable" else v)
+            for k, v in state.opt_state.inner_states.items()
+        }
+    )
+    return state.replace(opt_state=opt_state)
+
+
 def get_trainable_paths(
     params: Params,
     freeze_fun: Optional[Callable[[Tuple[AnyKey, ...], Array], str]],

tests/fortuna/prob_model/test_train.py

@@ -386,7 +386,7 @@ def dryrun_task(task, method):
     )
     state = (
         prob_model.posterior.state.get()
-        if method not in ["deep_ensemble", "sghmc", "cyclical_sgld"]
+        if method not in ["deep_ensemble", "sghmc", "cyclical_sgld", "hmc"]
         else prob_model.posterior.state.get(-1)
     )
     model_editor_params = state.params["model_editor"]["params"].unfreeze()