diff --git a/tensordict/base.py b/tensordict/base.py
index 3666b6772..3dea5a7d0 100644
--- a/tensordict/base.py
+++ b/tensordict/base.py
@@ -55,6 +55,7 @@
     _CloudpickleWrapper,
     _DTYPE2STRDTYPE,
     _GENERIC_NESTED_ERR,
+    _is_dataclass as is_dataclass,
     _is_non_tensor,
     _is_number,
     _is_tensorclass,
@@ -9452,6 +9453,7 @@ def _validate_value(
         if device is not None and value.device != device:
             if _device_recorder.marked and device.type != "cuda":
                 _device_recorder.record_transfer(device)
+            assert not non_blocking
             value = value.to(device, non_blocking=non_blocking)
         if check_shape:
             if is_tc is None:
@@ -9874,16 +9876,15 @@ def from_any(cls, obj, *, auto_batch_size: bool = False):
             return cls.from_dict(obj, auto_batch_size=auto_batch_size)
         if isinstance(obj, np.ndarray) and hasattr(obj.dtype, "names"):
             return cls.from_struct_array(obj, auto_batch_size=auto_batch_size)
-        from dataclasses import is_dataclass
 
-        if is_dataclass(obj):
-            return cls.from_dataclass(obj, auto_batch_size=auto_batch_size)
-        if is_namedtuple(obj):
-            return cls.from_namedtuple(obj, auto_batch_size=auto_batch_size)
         if isinstance(obj, tuple):
             return cls.from_tuple(obj, auto_batch_size=auto_batch_size)
         if isinstance(obj, list):
             return cls.from_tuple(tuple(obj), auto_batch_size=auto_batch_size)
+        if is_dataclass(obj):
+            return cls.from_dataclass(obj, auto_batch_size=auto_batch_size)
+        if is_namedtuple(obj):
+            return cls.from_namedtuple(obj, auto_batch_size=auto_batch_size)
         if _has_h5:
             import h5py
 
@@ -9942,7 +9943,7 @@ def from_dataclass(
             from tensordict.tensorclass import from_dataclass
 
             return from_dataclass(dataclass, auto_batch_size=auto_batch_size)
-        from dataclasses import fields, is_dataclass
+        from dataclasses import fields
 
         from tensordict import TensorDict
 
diff --git a/tensordict/tensorclass.py b/tensordict/tensorclass.py
index 1fb7d3049..e1c8e77b4 100644
--- a/tensordict/tensorclass.py
+++ b/tensordict/tensorclass.py
@@ -46,6 +46,7 @@
     CompatibleType,
 )
 from tensordict.utils import (  # @manual=//pytorch/tensordict:_C
+    _is_dataclass as is_dataclass,
     _is_json_serializable,
     _is_tensorclass,
     _LOCK_ERROR,
@@ -450,7 +451,7 @@ def from_dataclass(
             by default, this method will return a tensorclass instance or type.
 
     """
-    from dataclasses import asdict, is_dataclass, make_dataclass
+    from dataclasses import asdict, make_dataclass
 
     if isinstance(obj, type):
         if is_tensorclass(obj):
diff --git a/tensordict/utils.py b/tensordict/utils.py
index 54e00a174..81ab2fa0c 100644
--- a/tensordict/utils.py
+++ b/tensordict/utils.py
@@ -20,6 +20,7 @@
 from collections import defaultdict
 from collections.abc import KeysView
 from copy import copy
+from dataclasses import _FIELDS, GenericAlias
 from functools import wraps
 from importlib import import_module
 from numbers import Number
@@ -2813,3 +2814,13 @@ def _mismatch_keys(keys1, keys2):
     if sub2 is not None:
         main.append(sub2)
     raise KeyError(r" ".join(main))
+
+
+def _is_dataclass(obj):
+    """Like dataclasses.is_dataclass but compatible with compile."""
+    cls = (
+        obj
+        if isinstance(obj, type) and not isinstance(obj, GenericAlias)
+        else type(obj)
+    )
+    return hasattr(cls, _FIELDS)
diff --git a/test/test_tensordict.py b/test/test_tensordict.py
index 372e1af6a..73d401c03 100644
--- a/test/test_tensordict.py
+++ b/test/test_tensordict.py
@@ -10661,7 +10661,8 @@ def test_non_tensor_call(self):
 
     def test_nontensor_dict(self, non_tensor_data):
         assert (
-            TensorDict.from_dict(non_tensor_data.to_dict()) == non_tensor_data
+            TensorDict.from_dict(non_tensor_data.to_dict(), auto_batch_size=True)
+            == non_tensor_data
         ).all()
 
     def test_nontensor_tensor(self):
@@ -11202,7 +11203,7 @@ def _to_float(td, td_name, tmpdir):
         td._source = td._source.float()
     elif td_name in ("td_h5",):
         td = PersistentTensorDict.from_dict(
-            td.float().to_dict(), filename=tmpdir + "/file.t"
+            td.float().to_dict(), filename=tmpdir + "/file.t", auto_batch_size=True
         )
     elif td_name in ("td_params",):
         td = TensorDictParams(td.data.float())