[BugFix, Feature] tensorclass.to_dict and from_dict (#707)

docs/source/reference/tensorclass.rst

@@ -6,10 +6,13 @@ tensorclass
 The ``@tensorclass`` decorator helps you build custom classes that inherit the
 behaviour from :class:`~tensordict.TensorDict` while being able to restrict
 the possible entries to a predefined set or implement custom methods for your class.
-Like :class:`~tensordict.TensorDict`, ``@tensorclass`` supports nesting, indexing, reshaping,
-item assignment. It also supports tensor operations like clone, squeeze, cat,
-split and many more. ``@tensorclass`` allows non-tensor entries,
+
+Like :class:`~tensordict.TensorDict`, ``@tensorclass`` supports nesting,
+indexing, reshaping, item assignment. It also supports tensor operations like
+``clone``, ``squeeze``, ``torch.cat``, ``split`` and many more.
+``@tensorclass`` allows non-tensor entries,
 however all the tensor operations are strictly restricted to tensor attributes.
+
 One needs to implement their custom methods for non-tensor data.
 It is important to note that ``@tensorclass`` does not enforce strict type matching
 
@@ -69,9 +72,12 @@ It is important to note that ``@tensorclass`` does not enforce strict type match
     device=None,
     is_shared=False)
 
+As it is the case with :class:`~tensordict.TensorDict`, from v0.4 if the batch size
+is omitted it is considered as empty.
 
-``@tensorclass`` supports indexing. Internally the tensor objects gets indexed,
-however the non-tensor data remains the same
+If a non-empty batch-size is provided, ``@tensorclass`` supports indexing.
+Internally the tensor objects gets indexed, however the non-tensor data
+remains the same
 
 .. code-block::
 
@@ -150,7 +156,7 @@ Here is an example:
      is_shared=False)
 
 Serialization
-~~~~~~~~~~~~~
+-------------
 
 Saving a tensorclass instance can be achieved with the `memmap` method.
 The saving strategy is as follows: tensor data will be saved using memory-mapped
@@ -168,7 +174,7 @@ the `tensorclass` is available in the working environment:
 
 
 Edge cases
-~~~~~~~~~~
+----------
 
 ``@tensorclass`` supports equality and inequality operators, even for
 nested objects. Note that the non-tensor/ meta data is not validated.
@@ -212,11 +218,12 @@ thrown
   >>> data[0] = data2[0]
   UserWarning: Meta data at 'non_tensordata' may or may not be equal, this may result in undefined behaviours
 
-Even though ``@tensorclass`` supports torch functions like cat and stack, the
-non-tensor / meta data is not validated. The torch operation is performed on the
-tensor data and while returning the output, the non-tensor / meta data of the first
-tensor class object is considered. User needs to make sure that all the
-list of tensor class objects have the same non-tensor data to avoid discrepancies
+Even though ``@tensorclass`` supports torch functions like :func:`~torch.cat`
+and :func:`~torch.stack`, the non-tensor / meta data is not validated.
+The torch operation is performed on the tensor data and while returning the
+output, the non-tensor / meta data of the first tensor class object is
+considered. User needs to make sure that all the list of tensor class objects
+have the same non-tensor data to avoid discrepancies
 
 Here is an example:
 
@@ -273,3 +280,78 @@ Here is an example:
 
     tensorclass
     NonTensorData
+    NonTensorStack
+
+Auto-casting
+------------
+
+.. warning:: Auto-casting is an experimental feature and subject to changes in
+  the future. Compatibility with python<=3.9 is limited.
+
+``@tensorclass`` partially supports auto-casting as an experimental feature.
+Methods such as ``__setattr__``, ``update``, ``update_`` and ``from_dict`` will
+attempt to cast type-annotated entries to the desired TensorDict / tensorclass
+instance (except in cases detailed below). For instance, following code will
+cast the `td` dictionary to a :class:`~tensordict.TensorDict` and the `tc`
+entry to a :class:`MyClass` instance:
+
+    >>> @tensorclass
+    ... class MyClass:
+    ...     tensor: torch.Tensor
+    ...     td: TensorDict
+    ...     tc: MyClass
+    ...
+    >>> obj = MyClass(
+    ...     tensor=torch.randn(()),
+    ...     td={"a": torch.randn(())},
+    ...     tc={"tensor": torch.randn(()), "td": None, "tc": None})
+    >>> assert isinstance(obj.tensor, torch.Tensor)
+    >>> assert isinstance(obj.tc, TensorDict)
+    >>> assert isinstance(obj.td, MyClass)
+
+.. note:: Type annotated items that include an ``typing.Optional`` or
+  ``typing.Union`` will not be compatible with auto-casting, but other items
+  in the tensorclass will:
+
+    >>> @tensorclass
+    ... class MyClass:
+    ...     tensor: torch.Tensor
+    ...     tc_autocast: MyClass = None
+    ...     tc_not_autocast: Optional[MyClass] = None
+    >>> obj = MyClass(
+    ...     tensor=torch.randn(()),
+    ...     tc_autocast={"tensor": torch.randn(())},
+    ...     tc_not_autocast={"tensor": torch.randn(())},
+    ... )
+    >>> assert isinstance(obj.tc_autocast, MyClass)
+    >>> # because the type is Optional or Union, auto-casting is disabled for
+    >>> # that variable.
+    >>> assert not isinstance(obj.tc_not_autocast, MyClass)
+
+  If at least one item in the class is annotated using the ``type0 | type1``
+  semantic, the whole class auto-casting capabilities are deactivated.
+  Because ``tensorclass`` supports non-tensor leaves, setting a dictionary in
+  these cases will lead to setting it as a plain dictionary instead of a
+  tensor collection subclass (``TensorDict`` or ``tensorclass``):
+
+    >>> @tensorclass
+    ... class MyClass:
+    ...     tensor: torch.Tensor
+    ...     td: TensorDict
+    ...     tc: MyClass | None
+    ...
+    >>> obj = MyClass(
+    ...     tensor=torch.randn(()),
+    ...     td={"a": torch.randn(())},
+    ...     tc={"tensor": torch.randn(()), "td": None, "tc": None})
+    >>> assert isinstance(obj.tensor, torch.Tensor)
+    >>> # tc and td have not been cast
+    >>> assert isinstance(obj.tc, dict)
+    >>> assert isinstance(obj.td, dict)
+
+.. note:: Auto-casting isn't enabled for leaves (tensors).
+  The reason for this is that this feature isn't compatible with type
+  annotations that contain the ``type0 | type1`` type hinting semantic, which
+  is widespread. Allowing auto-casting would result in very similar codes to
+  have drastically different behaviours if the type annotation differs only
+  slightly.

tensordict/_lazy.py

@@ -115,7 +115,10 @@ def newfunc(self, *args, **kwargs):
             raise RuntimeError(
                 f"the method {func.__name__} cannot complete when there are exclusive keys."
             )
-        return getattr(TensorDictBase, func.__name__)(self, *args, **kwargs)
+        parent_func = getattr(TensorDictBase, func.__name__, None)
+        if parent_func is None:
+            parent_func = getattr(TensorDict, func.__name__)
+        return parent_func(self, *args, **kwargs)
 
     return newfunc
 
@@ -2539,6 +2542,7 @@ def _unsqueeze(self, dim):
     reshape = TensorDict.reshape
     split = TensorDict.split
     _to_module = TensorDict._to_module
+    from_dict_instance = TensorDict.from_dict_instance
 
 
 class _CustomOpTensorDict(TensorDictBase):
@@ -3071,6 +3075,7 @@ def _unsqueeze(self, dim):
     expand = TensorDict.expand
     _unbind = TensorDict._unbind
     _get_names_idx = TensorDict._get_names_idx
+    from_dict_instance = TensorDict.from_dict_instance
 
 
 class _UnsqueezedTensorDict(_CustomOpTensorDict):

tensordict/_td.py

@@ -448,7 +448,9 @@ def _quick_set(swap_dict, swap_td):
     def __ne__(self, other: object) -> T | bool:
         if _is_tensorclass(other):
             return other != self
-        if isinstance(other, (dict,)) or _is_tensor_collection(other.__class__):
+        if isinstance(other, (dict,)):
+            other = self.from_dict_instance(other)
+        if _is_tensor_collection(other.__class__):
             keys1 = set(self.keys())
             keys2 = set(other.keys())
             if len(keys1.difference(keys2)) or len(keys1) != len(keys2):
@@ -470,7 +472,9 @@ def __ne__(self, other: object) -> T | bool:
     def __xor__(self, other: object) -> T | bool:
         if _is_tensorclass(other):
             return other ^ self
-        if isinstance(other, (dict,)) or _is_tensor_collection(other.__class__):
+        if isinstance(other, (dict,)):
+            other = self.from_dict_instance(other)
+        if _is_tensor_collection(other.__class__):
             keys1 = set(self.keys())
             keys2 = set(other.keys())
             if len(keys1.difference(keys2)) or len(keys1) != len(keys2):
@@ -492,7 +496,9 @@ def __xor__(self, other: object) -> T | bool:
     def __or__(self, other: object) -> T | bool:
         if _is_tensorclass(other):
             return other | self
-        if isinstance(other, (dict,)) or _is_tensor_collection(other.__class__):
+        if isinstance(other, (dict,)):
+            other = self.from_dict_instance(other)
+        if _is_tensor_collection(other.__class__):
             keys1 = set(self.keys())
             keys2 = set(other.keys())
             if len(keys1.difference(keys2)) or len(keys1) != len(keys2):
@@ -515,7 +521,7 @@ def __eq__(self, other: object) -> T | bool:
         if is_tensorclass(other):
             return other == self
         if isinstance(other, (dict,)):
-            other = self.empty(recurse=True).update(other)
+            other = self.from_dict_instance(other)
         if _is_tensor_collection(other.__class__):
             keys1 = set(self.keys())
             keys2 = set(other.keys())
@@ -570,7 +576,7 @@ def __setitem__(
         if isinstance(value, (TensorDictBase, dict)):
             indexed_bs = _getitem_batch_size(self.batch_size, index)
             if isinstance(value, dict):
-                value = TensorDict.from_dict(value, batch_size=indexed_bs)
+                value = self.from_dict_instance(value, batch_size=indexed_bs)
                 # value = self.empty(recurse=True)[index].update(value)
             if value.batch_size != indexed_bs:
                 if value.shape == indexed_bs[-len(value.shape) :]:
@@ -1299,6 +1305,7 @@ def from_dict(cls, input_dict, batch_size=None, device=None, batch_dims=None):
             )
 
         batch_size_set = torch.Size(()) if batch_size is None else batch_size
+        input_dict = copy(input_dict)
         for key, value in list(input_dict.items()):
             if isinstance(value, (dict,)):
                 # we don't know if another tensor of smaller size is coming
@@ -1318,6 +1325,41 @@ def from_dict(cls, input_dict, batch_size=None, device=None, batch_dims=None):
             out.batch_size = batch_size
         return out
 
+    def from_dict_instance(
+        self, input_dict, batch_size=None, device=None, batch_dims=None
+    ):
+        if batch_dims is not None and batch_size is not None:
+            raise ValueError(
+                "Cannot pass both batch_size and batch_dims to `from_dict`."
+            )
+        from tensordict import TensorDict
+
+        batch_size_set = torch.Size(()) if batch_size is None else batch_size
+        input_dict = copy(input_dict)
+        for key, value in list(input_dict.items()):
+            if isinstance(value, (dict,)):
+                cur_value = self.get(key, None)
+                if cur_value is not None:
+                    input_dict[key] = cur_value.from_dict_instance(
+                        value, batch_size=[], device=device, batch_dims=None
+                    )
+                    continue
+                # we don't know if another tensor of smaller size is coming
+                # so we can't be sure that the batch-size will still be valid later
+                input_dict[key] = TensorDict.from_dict(
+                    value, batch_size=[], device=device, batch_dims=None
+                )
+        out = TensorDict.from_dict(
+            input_dict,
+            batch_size=batch_size_set,
+            device=device,
+        )
+        if batch_size is None:
+            _set_max_batch_size(out, batch_dims)
+        else:
+            out.batch_size = batch_size
+        return out
+
     @staticmethod
     def _parse_batch_size(
         source: T | dict,
@@ -2330,6 +2372,8 @@ def _convert_inplace(self, inplace, key):
             raise RuntimeError(_LOCK_ERROR)
         return inplace
 
+    from_dict_instance = TensorDict.from_dict_instance
+
     def _set_str(
         self,
         key: NestedKey,

tensordict/base.py

@@ -369,6 +369,57 @@ def auto_batch_size_(self, batch_dims: int | None = None) -> T:
         _set_max_batch_size(self, batch_dims)
         return self
 
+    @abc.abstractmethod
+    def from_dict_instance(
+        self, input_dict, batch_size=None, device=None, batch_dims=None
+    ):
+        """Instance method version of :meth:`~tensordict.TensorDict.from_dict`.
+
+        Unlike :meth:`~tensordict.TensorDict.from_dict`, this method will
+        attempt to keep the tensordict types within the existing tree (for
+        any existing leaf).
+
+        Examples:
+            >>> from tensordict import TensorDict, tensorclass
+            >>> import torch
+            >>>
+            >>> @tensorclass
+            >>> class MyClass:
+            ...     x: torch.Tensor
+            ...     y: int
+            >>>
+            >>> td = TensorDict({"a": torch.randn(()), "b": MyClass(x=torch.zeros(()), y=1)})
+            >>> print(td.from_dict_instance(td.to_dict()))
+            TensorDict(
+                fields={
+                    a: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                    b: MyClass(
+                        x=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                        y=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
+                        batch_size=torch.Size([]),
+                        device=None,
+                        is_shared=False)},
+                batch_size=torch.Size([]),
+                device=None,
+                is_shared=False)
+            >>> print(td.from_dict(td.to_dict()))
+            TensorDict(
+                fields={
+                    a: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                    b: TensorDict(
+                        fields={
+                            x: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                            y: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False)},
+                        batch_size=torch.Size([]),
+                        device=None,
+                        is_shared=False)},
+                batch_size=torch.Size([]),
+                device=None,
+                is_shared=False)
+
+        """
+        ...
+
     # Module interaction
     @classmethod
     def from_module(
@@ -672,13 +723,12 @@ def _batch_size_setter(self, new_batch_size: torch.Size) -> None:
             )
         if not isinstance(new_batch_size, torch.Size):
             new_batch_size = torch.Size(new_batch_size)
-        for key in self.keys():
-            if _is_tensor_collection(self.entry_class(key)):
-                tensordict = self.get(key)
-                if len(tensordict.batch_size) < len(new_batch_size):
+        for key, value in self.items():
+            if _is_tensor_collection(type(value)):
+                if len(value.batch_size) < len(new_batch_size):
                     # document as edge case
-                    tensordict.batch_size = new_batch_size
-                    self._set_str(key, tensordict, inplace=True, validated=True)
+                    value.batch_size = new_batch_size
+                    self._set_str(key, value, inplace=True, validated=True)
         self._check_new_batch_size(new_batch_size)
         self._change_batch_size(new_batch_size)
         if self._has_names():

tensordict/nn/params.py

@@ -817,6 +817,12 @@ def _exclude(
     ) -> TensorDictBase:
         ...
 
+    @_carry_over
+    def from_dict_instance(
+        self, input_dict, batch_size=None, device=None, batch_dims=None
+    ):
+        ...
+
     @_carry_over
     def _legacy_transpose(self, dim0, dim1):
         ...

tensordict/persistent.py

@@ -1124,6 +1124,7 @@ def _unsqueeze(self, dim):
     _to_module = TensorDict._to_module
     _unbind = TensorDict._unbind
     _get_names_idx = TensorDict._get_names_idx
+    from_dict_instance = TensorDict.from_dict_instance
 
 
 def _set_max_batch_size(source: PersistentTensorDict):