Add recursion guard and tidy tests

tensordict/tensordict.py

@@ -131,6 +131,22 @@ def is_memmap(datatype: type) -> bool:
 _NestedKey = namedtuple("_NestedKey", ["root_key", "nested_key"])
 
 
+def _recursion_guard(fn):
+    # catches RecursionError and warns of auto-nesting
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        try:
+            return fn(*args, **kwargs)
+        except RecursionError as e:
+            raise RecursionError(
+                f"{fn.__name__.lstrip('_')} failed due to a recursion error. It's possible the "
+                "TensorDict has auto-nested values, which are not supported by this "
+                f"function."
+            ) from e
+
+    return wrapper
+
+
 class _TensorDictKeysView:
     """
     _TensorDictKeysView is returned when accessing tensordict.keys() and holds a
@@ -635,6 +651,7 @@ def apply_(self, fn: Callable) -> TensorDictBase:
         """
         return _apply_safe(lambda _, value: fn(value), self, inplace=True)
 
+    @_recursion_guard
     def apply(
         self,
         fn: Callable,
@@ -1249,6 +1266,7 @@ def zero_(self) -> TensorDictBase:
             self.get(key).zero_()
         return self
 
+    @_recursion_guard
     def unbind(self, dim: int) -> Tuple[TensorDictBase, ...]:
         """Returns a tuple of indexed tensordicts unbound along the indicated dimension.
 
@@ -1668,6 +1686,7 @@ def split(
             for i in range(len(dictionaries))
         ]
 
+    @_recursion_guard
     def gather(self, dim: int, index: torch.Tensor, out=None):
         """Gathers values along an axis specified by `dim`.
 
@@ -1925,6 +1944,7 @@ def __iter__(self) -> Generator:
         for i in range(length):
             yield self[i]
 
+    @_recursion_guard
     def flatten_keys(
         self, separator: str = ".", inplace: bool = False
     ) -> TensorDictBase:
@@ -3086,7 +3106,12 @@ def masked_fill(self, mask: Tensor, value: Union[float, bool]) -> TensorDictBase
         return td_copy.masked_fill_(mask, value)
 
     def is_contiguous(self) -> bool:
-        return all([value.is_contiguous() for _, value in self.items()])
+        return all(
+            self.get(key).is_contiguous()
+            for key in _TensorDictKeysView(
+                self, include_nested=True, leaves_only=True, error_on_loop=False
+            )
+        )
 
     def contiguous(self) -> TensorDictBase:
         if not self.is_contiguous():
@@ -3122,8 +3147,17 @@ def select(
                 d[key] = value
             except KeyError:
                 if strict:
+                    # TODO: in the case of auto-nesting, this error will not list all of
+                    # the (infinitely many) keys, and so there would be valid keys for
+                    # selection that do not appear in the error message.
+                    keys_view = _TensorDictKeysView(
+                        self,
+                        include_nested=True,
+                        leaves_only=False,
+                        error_on_loop=False,
+                    )
                     raise KeyError(
-                        f"Key '{key}' was not found among keys {set(self.keys(True))}."
+                        f"Key '{key}' was not found among keys {set(keys_view)}."
                     )
                 else:
                     continue
@@ -3295,11 +3329,13 @@ def assert_allclose_td(
 
 
 @implements_for_td(torch.unbind)
+@_recursion_guard
 def _unbind(td: TensorDictBase, *args, **kwargs) -> Tuple[TensorDictBase, ...]:
     return td.unbind(*args, **kwargs)
 
 
 @implements_for_td(torch.gather)
+@_recursion_guard
 def _gather(
     input: TensorDictBase,
     dim: int,
@@ -3627,6 +3663,7 @@ def recurse(list_of_tds, out, dim, prefix=()):
     return out
 
 
+@_recursion_guard
 def pad(tensordict: TensorDictBase, pad_size: Sequence[int], value: float = 0.0):
     """Pads all tensors in a tensordict along the batch dimensions with a constant value, returning a new tensordict.