[BugFix] inline TDParams kwargs in prob modules

tensordict/nn/params.py

@@ -341,6 +341,9 @@ def __init__(
         self._locked_tensordicts = []
         self._get_post_hook = []
 
+    def __iter__(self):
+        yield from self._param_td.__iter__()
+
     def register_get_post_hook(self, hook):
         """Register a hook to be called after any get operation on leaf tensors."""
         if not callable(hook):

tensordict/nn/probabilistic.py

@@ -8,10 +8,6 @@
 import re
 import warnings
 
-try:
-    from enum import StrEnum
-except ImportError:
-    from .utils import StrEnum
 from textwrap import indent
 from typing import Any, Dict, List, Optional
 
@@ -30,6 +26,16 @@
 
 from torch.utils._contextlib import _DecoratorContextManager
 
+try:
+    from torch.compiler import is_compiling
+except ImportError:
+    from torch._dynamo import is_compiling
+
+try:
+    from enum import StrEnum
+except ImportError:
+    from .utils import StrEnum
+
 __all__ = ["ProbabilisticTensorDictModule", "ProbabilisticTensorDictSequential"]
 
 
@@ -350,11 +356,13 @@ def get_dist(self, tensordict: TensorDictBase) -> D.Distribution:
                 if isinstance(dist_key, tuple):
                     dist_key = dist_key[-1]
                 dist_kwargs[dist_key] = tensordict.get(td_key)
-            dist = self.distribution_class(**dist_kwargs, **self.distribution_kwargs)
+            dist = self.distribution_class(
+                **dist_kwargs, **_dynamo_friendly_to_dict(self.distribution_kwargs)
+            )
         except TypeError as err:
             if "an unexpected keyword argument" in str(err):
                 raise TypeError(
-                    "distribution keywords and tensordict keys indicated by ProbabilisticTensorDictModule.dist_keys must match."
+                    "distribution keywords and tensordict keys indicated by ProbabilisticTensorDictModule.dist_keys must match. "
                     f"Got this error message: \n{indent(str(err), 4 * ' ')}\nwith dist_keys={self.dist_keys}"
                 )
             elif re.search(r"missing.*required positional arguments", str(err)):
@@ -623,3 +631,14 @@ def forward(
     ) -> TensorDictBase:
         tensordict_out = self.get_dist_params(tensordict, tensordict_out, **kwargs)
         return self.module[-1](tensordict_out, _requires_sample=self._requires_sample)
+
+
+def _dynamo_friendly_to_dict(data):
+    if not is_compiling():
+        return data
+    if isinstance(data, TensorDictBase):
+        items = list(data.items())
+        if not items:
+            return {}
+        return dict(items)
+    return data

test/test_compile.py

@@ -24,6 +24,8 @@
 )
 from tensordict.nn import (
     CudaGraphModule,
+    InteractionType,
+    ProbabilisticTensorDictModule as Prob,
     TensorDictModule,
     TensorDictModule as Mod,
     TensorDictSequential as Seq,
@@ -662,6 +664,27 @@ def test_dispatch_tensor(self, mode):
         mod_compile = torch.compile(mod, fullgraph=_v2_5, mode=mode)
         torch.testing.assert_close(mod(x=x, y=y), mod_compile(x=x, y=y))
 
+    def test_prob_module_with_kwargs(self, mode):
+        kwargs = TensorDictParams(TensorDict(scale=1.0), no_convert=True)
+        dist_cls = torch.distributions.Normal
+        mod = Mod(torch.nn.Linear(3, 3), in_keys=["inp"], out_keys=["loc"])
+        prob_mod = Seq(
+            mod,
+            Prob(
+                in_keys=["loc"],
+                out_keys=["sample"],
+                return_log_prob=True,
+                distribution_class=dist_cls,
+                distribution_kwargs=kwargs,
+                default_interaction_type=InteractionType.RANDOM,
+            ),
+        )
+        # check that the scale is in the buffers
+        assert len(list(prob_mod.buffers())) == 1
+        prob_mod(TensorDict(inp=torch.randn(3)))
+        prob_mod_c = torch.compile(prob_mod, fullgraph=True, mode=mode)
+        prob_mod_c(TensorDict(inp=torch.randn(3)))
+
 
 @pytest.mark.skipif(
     TORCH_VERSION <= version.parse("2.4.0"), reason="requires torch>2.4"