[BugFix] Pass type directly during reduction

ghstack-source-id: 737f03775bc98090172e397ad4a65c8e777302e5
Pull Request resolved: #1225

tensordict/_lazy.py

@@ -381,17 +381,6 @@ def from_dict(
         stack_dim_name=None,
         stack_dim=0,
     ):
-        # if batch_size is not None:
-        #     batch_size = list(batch_size)
-        #     if stack_dim is None:
-        #         stack_dim = 0
-        #     n = batch_size.pop(stack_dim)
-        #     if n != len(input_dict):
-        #         raise ValueError(
-        #             "The number of dicts and the corresponding batch-size must match, "
-        #             f"got len(input_dict)={len(input_dict)} and batch_size[{stack_dim}]={n}."
-        #         )
-        #     batch_size = torch.Size(batch_size)
         return LazyStackedTensorDict(
             *(
                 TensorDict.from_dict(
@@ -1992,11 +1981,22 @@ def _apply_nest(
         if all(r is None for r in results) and filter_empty in (None, True):
             return
         if not inplace:
-            out = type(self)(
-                *results,
-                stack_dim=self.stack_dim,
-                stack_dim_name=self._td_dim_name,
-            )
+            if not results or any(r is not None for r in results):
+                try:
+                    out = type(self)(
+                        *results,
+                        stack_dim=self.stack_dim,
+                        stack_dim_name=self._td_dim_name,
+                    )
+                except Exception as e:
+                    raise RuntimeError(
+                        f"Failed to reconstruct the lazy stack of tensordicts with class: {type(self)}. "
+                        f"One common issue is that the outputs of apply are a mix of None and non-None "
+                        f"values. Check that the outputs of apply() are all None or all non-None. "
+                        f"Otherwise, please report this bug on tensordict github."
+                    ) from e
+            else:
+                out = None
         else:
             out = self
         if names is not NO_DEFAULT:

tensordict/_reductions.py

@@ -11,12 +11,14 @@
 from tensordict._lazy import LazyStackedTensorDict
 from tensordict._td import TensorDict
 
-from tensordict.tensorclass import NonTensorData
-from tensordict.utils import _STRDTYPE2DTYPE
+from tensordict.tensorclass import NonTensorData, NonTensorStack
+from tensordict.utils import _is_tensorclass, _STRDTYPE2DTYPE
 
 CLS_MAP = {
     "TensorDict": TensorDict,
     "LazyStackedTensorDict": LazyStackedTensorDict,
+    "NonTensorData": NonTensorData,
+    "NonTensorStack": NonTensorStack,
 }
 
 
@@ -57,7 +59,9 @@ def from_metadata(metadata=metadata_dict, prefix=None):
             d[k] = from_metadata(
                 v, prefix=prefix + (k,) if prefix is not None else (k,)
             )
-        result = CLS_MAP[cls]._from_dict_validated(d, **cls_metadata)
+        if isinstance(cls, str):
+            cls = CLS_MAP[cls]
+        result = cls._from_dict_validated(d, **cls_metadata)
         if is_locked:
             result.lock_()
         # if is_shared:
@@ -121,10 +125,15 @@ def from_metadata(metadata=metadata, prefix=None):
             d[k] = from_metadata(
                 v, prefix=prefix + (k,) if prefix is not None else (k,)
             )
-        result = CLS_MAP[cls]._from_dict_validated(d, **cls_metadata)
+        if isinstance(cls, str):
+            cls = CLS_MAP[cls]
+        result = cls._from_dict_validated(d, **cls_metadata)
         if is_locked:
             result = result.lock_()
-        result._consolidated = consolidated
+        if _is_tensorclass(cls):
+            result._tensordict._consolidated = consolidated
+        else:
+            result._consolidated = consolidated
         return result
 
     return from_metadata()

tensordict/_td.py

@@ -2064,6 +2064,8 @@ def from_dict(
         batch_dims=None,
         names=None,
     ):
+        if _is_tensor_collection(type(input_dict)):
+            return input_dict
         if others:
             if batch_size is not None:
                 raise TypeError(
@@ -2120,14 +2122,7 @@ def from_dict(
         )
         if batch_size is None:
             if auto_batch_size is None and batch_dims is None:
-                warn(
-                    "The batch-size was not provided and auto_batch_size isn't set either. "
-                    "Currently, from_dict will call set auto_batch_size=True but this behaviour "
-                    "will be changed in v0.8 and auto_batch_size will be False onward. "
-                    "To silence this warning, pass auto_batch_size directly.",
-                    category=DeprecationWarning,
-                )
-                auto_batch_size = True
+                auto_batch_size = False
             elif auto_batch_size is None:
                 auto_batch_size = True
             if auto_batch_size:

tensordict/base.py

@@ -23,7 +23,7 @@
 
 from concurrent.futures import Future, ThreadPoolExecutor, wait
 from copy import copy
-from functools import partial, wraps
+from functools import wraps
 from pathlib import Path
 from textwrap import indent
 from threading import Thread
@@ -2188,7 +2188,6 @@ def _from_dict_validated(cls, *args, **kwargs):
 
         By default, falls back on :meth:`~.from_dict`.
         """
-        kwargs.setdefault("auto_batch_size", True)
         return cls.from_dict(*args, **kwargs)
 
     @abc.abstractmethod
@@ -4994,8 +4993,15 @@ def _reduce_vals_and_metadata(self, *, dtype=NO_DEFAULT, requires_metadata):
 
         if requires_metadata:
             # metadata is nested
+            cls = type(self)
+            from tensordict._reductions import CLS_MAP
+
+            if cls.__name__ in CLS_MAP:
+                cls = cls.__name__
+            else:
+                pass
             metadata_dict = {
-                "cls": type(self).__name__,
+                "cls": cls,
                 "non_tensors": {},
                 "leaves": {},
                 "cls_metadata": self._reduce_get_metadata(),
@@ -5055,18 +5061,27 @@ def assign(
             elif _is_tensor_collection(cls):
                 metadata_dict_key = None
                 if requires_metadata:
+                    from tensordict._reductions import CLS_MAP
+
+                    if cls.__name__ in CLS_MAP:
+                        cls = cls.__name__
+                    else:
+                        pass
                     metadata_dict_key = metadata_dict[key] = {
-                        "cls": cls.__name__,
+                        "cls": cls,
                         "non_tensors": {},
                         "leaves": {},
                         "cls_metadata": value._reduce_get_metadata(),
                     }
-                local_assign = partial(
-                    assign,
-                    track_key=total_key,
-                    metadata_dict=metadata_dict_key,
-                    flat_size=flat_size,
-                )
+
+                def local_assign(*t):
+                    return assign(
+                        *t,
+                        track_key=total_key,
+                        metadata_dict=metadata_dict_key,
+                        flat_size=flat_size,
+                    )
+
                 value._fast_apply(
                     local_assign,
                     named=True,
@@ -5254,7 +5269,15 @@ def consolidate(
                 storage.share_memory_()
         else:
             # Convert the dict to json
-            metadata_dict_json = json.dumps(metadata_dict)
+            try:
+                metadata_dict_json = json.dumps(metadata_dict)
+            except TypeError as e:
+                raise RuntimeError(
+                    "Failed to convert the metatdata to json. "
+                    "This is usually due to a nested class that is unaccounted for by the serializer, "
+                    "such as custom TensorClass. "
+                    "If you encounter this error, please file an issue on github."
+                ) from e
             # Represent as a tensor
             metadata_dict_json = torch.as_tensor(
                 bytearray(metadata_dict_json), dtype=torch.uint8