added has_log_probs function

src/gfn/containers/trajectories.py

@@ -14,6 +14,7 @@
 
 from gfn.containers.base import Container
 from gfn.containers.transitions import Transitions
+from gfn.utils.common import has_log_probs
 
 
 def is_tensor(t) -> bool:
@@ -325,11 +326,14 @@ def to_transitions(self) -> Transitions:
                 ],
                 dim=0,
             )
+
+        # Only return logprobs if they exist.
         log_probs = (
             self.log_probs[~self.actions.is_dummy]
-            if self.log_probs is not None and self.log_probs.nelement() > 0
+            if has_log_probs(self)
             else None
         )
+
         return Transitions(
             env=self.env,
             states=states,

src/gfn/containers/transitions.py

@@ -11,6 +11,7 @@
     from gfn.states import States
 
 from gfn.containers.base import Container
+from gfn.utils.common import has_log_probs
 
 
 class Transitions(Container):
@@ -186,11 +187,10 @@ def __getitem__(self, index: int | Sequence[int]) -> Transitions:
         log_rewards = (
             self._log_rewards[index] if self._log_rewards is not None else None
         )
-        log_probs = (
-            self.log_probs[index]
-            if self.log_probs is not None and self.log_probs.nelement() > 0
-            else None
-        )
+
+        # Only return logprobs if they exist.
+        log_probs = self.log_probs[index] if has_log_probs(self) else None
+
         return Transitions(
             env=self.env,
             states=states,

src/gfn/env.py

@@ -393,7 +393,7 @@ class DiscreteEnvStates(DiscreteStates):
 
     def make_actions_class(self) -> type[Actions]:
         env = self
-        self.n_actions
+        n_actions = self.n_actions
 
         class DiscreteEnvActions(Actions):
             action_shape = env.action_shape

src/gfn/gflownet/base.py

@@ -13,6 +13,7 @@
 from gfn.modules import GFNModule
 from gfn.samplers import Sampler
 from gfn.states import States
+from gfn.utils.common import has_log_probs
 
 TrainingSampleType = TypeVar(
     "TrainingSampleType", bound=Union[Container, tuple[States, ...]]
@@ -164,11 +165,7 @@ def get_pfs_and_pbs(
         if valid_states.batch_shape != tuple(valid_actions.batch_shape):
             raise AssertionError("Something wrong happening with log_pf evaluations")
 
-        if (
-            trajectories.log_probs is not None
-            and trajectories.log_probs.nelement() > 0
-            and not recalculate_all
-        ):
+        if has_log_probs(trajectories) and not recalculate_all:
             log_pf_trajectories = trajectories.log_probs
         else:
             if trajectories.estimator_outputs is not None and not recalculate_all:

src/gfn/gflownet/detailed_balance.py

@@ -8,6 +8,7 @@
 from gfn.env import Env
 from gfn.gflownet.base import PFBasedGFlowNet
 from gfn.modules import GFNModule, ScalarEstimator
+from gfn.utils.common import has_log_probs
 
 
 class DBGFlowNet(PFBasedGFlowNet[Transitions]):
@@ -72,11 +73,8 @@ def get_scores(
 
         if states.batch_shape != tuple(actions.batch_shape):
             raise ValueError("Something wrong happening with log_pf evaluations")
-        if (
-            transitions.log_probs is not None
-            and transitions.log_probs.nelement() > 0
-            and not recalculate_all
-        ):
+
+        if has_log_probs(transitions) and not recalculate_all:
             valid_log_pf_actions = transitions.log_probs
         else:
             # Evaluate the log PF of the actions
@@ -179,11 +177,8 @@ def get_scores(
         all_log_rewards = transitions.all_log_rewards[mask]
         module_output = self.pf(states)
         pf_dist = self.pf.to_probability_distribution(states, module_output)
-        if (
-            transitions.log_probs is not None
-            and transitions.log_probs.nelement() > 0
-            and not recalculate_all
-        ):
+
+        if has_log_probs(transitions) and not recalculate_all:
             valid_log_pf_actions = transitions[mask].log_probs
         else:
             # Evaluate the log PF of the actions sampled off policy.

src/gfn/utils/common.py

@@ -15,3 +15,11 @@ def set_seed(seed: int, performance_mode: bool = False) -> None:
     if not performance_mode:
         torch.backends.cudnn.deterministic = True
         torch.backends.cudnn.benchmark = False
+
+
+def has_log_probs(obj):
+    """Returns True if the submitted object has the log_probs attribute populated."""
+    if not isinstance(obj, "log_probs"):
+        return False
+
+    return obj.log_probs is not None and obj.log_probs.nelement() > 0