Update

[ghstack-poisoned]

tensordict/base.py

@@ -220,6 +220,30 @@ class TensorDictBase(MutableMapping):
     def __bool__(self) -> bool:
         raise RuntimeError("Converting a tensordict to boolean value is not permitted")
 
+    def __abs__(self) -> T:
+        """Returns a new TensorDict instance with absolute values of all tensors.
+
+        Returns:
+            A new TensorDict instance with the same key set as the original,
+            but with all tensors having their absolute values computed.
+
+        .. seealso:: :meth:`~.abs`
+
+        """
+        return self.abs()
+
+    def __neg__(self) -> T:
+        """Returns a new TensorDict instance with negated values of all tensors.
+
+        Returns:
+            A new TensorDict instance with the same key set as the original,
+            but with all tensors having their values negated.
+
+        .. seealso:: :meth:`~.neg`
+
+        """
+        return self.neg()
+
     @abc.abstractmethod
     def __ne__(self, other: object) -> T:
         """NOT operation over two tensordicts, for evey key.
@@ -237,7 +261,7 @@ def __ne__(self, other: object) -> T:
         ...
 
     @abc.abstractmethod
-    def __xor__(self, other: TensorDictBase | float):
+    def __xor__(self, other: TensorDictBase | torch.Tensor | float):
         """XOR operation over two tensordicts, for evey key.
 
         The two tensordicts must have the same key set.
@@ -252,6 +276,13 @@ def __xor__(self, other: TensorDictBase | float):
         """
         ...
 
+    def __rxor__(self, other: TensorDictBase | torch.Tensor | float):
+        """XOR operation over two tensordicts, for evey key.
+
+        Wraps `__xor__` as it is assumed to be commutative.
+        """
+        return self.__xor__(other)
+
     @abc.abstractmethod
     def __or__(self, other: TensorDictBase | torch.Tensor) -> T:
         """OR operation over two tensordicts, for evey key.
@@ -268,6 +299,71 @@ def __or__(self, other: TensorDictBase | torch.Tensor) -> T:
         """
         ...
 
+    def __ror__(self, other: TensorDictBase | torch.Tensor) -> T:
+        """Right-side OR operation over two tensordicts, for evey key.
+
+        This is a wrapper around `__or__` since it is assumed to be commutative.
+        """
+        return self | other
+
+    def __invert__(self) -> T:
+        """Returns a new TensorDict instance with all tensors inverted (i.e., bitwise NOT operation).
+
+        Returns:
+            A new TensorDict instance with the same key set as the original,
+            but with all tensors having their bits inverted.
+        """
+        keys, vals = self._items_list(True, True)
+        vals = [~v for v in vals]
+        items = dict(zip(keys, vals))
+
+        def get(name, val):
+            return items.get(name, val)
+
+        return self._fast_apply(
+            get,
+            named=True,
+            nested_keys=True,
+            is_leaf=_NESTED_TENSORS_AS_LISTS,
+            propagate_lock=True,
+        )
+
+    def __and__(self, other: TensorDictBase | torch.Tensor | float) -> T:
+        """Returns a new TensorDict instance with all tensors performing a logical or bitwise AND operation with the given value.
+
+        Args:
+            other: The value to perform the AND operation with.
+
+        Returns:
+            A new TensorDict instance with the same key set as the original,
+            but with all tensors having performed a AND operation with the given value.
+        """
+        keys, vals = self._items_list(True, True)
+        if _is_tensor_collection(type(other)):
+            new_keys, other_val = other._items_list(True, True, sorting_keys=keys)
+            vals = [(v1 & v2) for v1, v2 in zip(vals, other_val)]
+        else:
+            vals = [(v & other) for v in vals]
+        items = dict(zip(keys, vals))
+
+        def pop(name, val):
+            return items.pop(name, None)
+
+        result = self._fast_apply(
+            pop,
+            named=True,
+            nested_keys=True,
+            is_leaf=_NESTED_TENSORS_AS_LISTS,
+            propagate_lock=True,
+            filter_empty=True,
+            default=None,
+        )
+        if items:
+            result.update(items)
+        return result
+
+    __rand__ = __and__
+
     @abc.abstractmethod
     def __eq__(self, other: object) -> T:
         """Compares two tensordicts against each other, for every key. The two tensordicts must have the same key set.
@@ -8903,21 +8999,27 @@ def record(tensor):
     def __add__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.add(other)
 
+    def __radd__(self, other: TensorDictBase | torch.Tensor) -> T:
+        return self.add(other)
+
     def __iadd__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.add_(other)
 
-    def __abs__(self):
-        return self.abs()
-
     def __truediv__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.div(other)
 
     def __itruediv__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.div_(other)
 
+    def __rtruediv__(self, other: TensorDictBase | torch.Tensor) -> T:
+        return other * self.reciprocal()
+
     def __mul__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.mul(other)
 
+    def __rmul__(self, other: TensorDictBase | torch.Tensor) -> T:
+        return self.mul(other)
+
     def __imul__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.mul_(other)
 
@@ -8927,9 +9029,18 @@ def __sub__(self, other: TensorDictBase | torch.Tensor) -> T:
     def __isub__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.sub_(other)
 
+    def __rsub__(self, other: TensorDictBase | torch.Tensor) -> T:
+        return self.sub(other)
+
     def __pow__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.pow(other)
 
+    def __rpow__(self, other: TensorDictBase | torch.Tensor) -> T:
+        raise NotImplementedError(
+            "rpow isn't implemented for tensordict yet. Make sure both elements are wrapped "
+            "in a tensordict for this to work."
+        )
+
     def __ipow__(self, other: TensorDictBase | torch.Tensor) -> T:
         return self.pow_(other)
 
@@ -9661,6 +9772,110 @@ def pop(name, val):
             result.update(items)
         return result
 
+    def bitwise_and(
+        self,
+        other: TensorDictBase | torch.Tensor,
+        *,
+        default: str | CompatibleType | None = None,
+    ) -> TensorDictBase:  # noqa: D417
+        r"""Performs a bitwise AND operation between ``self`` and :attr:`other`.
+
+        .. math::
+            \text{{out}}_i = \text{{input}}_i \land \text{{other}}_i
+
+        Args:
+            other (TensorDictBase or torch.Tensor): the tensor or TensorDict to perform the bitwise AND with.
+
+        Keyword Args:
+            default (torch.Tensor or str, optional): the default value to use for exclusive entries.
+                If none is provided, the two tensordicts key list must match exactly.
+                If ``default="intersection"`` is passed, only the intersecting key sets will be considered
+                and other keys will be ignored.
+                In all other cases, ``default`` will be used for all missing entries on both sides of the
+                operation.
+        """
+        keys, vals = self._items_list(True, True)
+        if _is_tensor_collection(type(other)):
+            new_keys, other_val = other._items_list(
+                True, True, sorting_keys=keys, default=default
+            )
+            if default is not None:
+                as_dict = dict(zip(keys, vals))
+                vals = [as_dict.get(key, default) for key in new_keys]
+                keys = new_keys
+            vals = [(v1.bitwise_and(v2)) for v1, v2 in zip(vals, other_val)]
+        else:
+            vals = [v.bitwise_and(other) for v in vals]
+        items = dict(zip(keys, vals))
+
+        def pop(name, val):
+            return items.pop(name, None)
+
+        result = self._fast_apply(
+            pop,
+            named=True,
+            nested_keys=True,
+            is_leaf=_NESTED_TENSORS_AS_LISTS,
+            propagate_lock=True,
+            filter_empty=True,
+            default=None,
+        )
+        if items:
+            result.update(items)
+        return result
+
+    def logical_and(
+        self,
+        other: TensorDictBase | torch.Tensor,
+        *,
+        default: str | CompatibleType | None = None,
+    ) -> TensorDictBase:  # noqa: D417
+        r"""Performs a logical AND operation between ``self`` and :attr:`other`.
+
+        .. math::
+            \text{{out}}_i = \text{{input}}_i \land \text{{other}}_i
+
+        Args:
+            other (TensorDictBase or torch.Tensor): the tensor or TensorDict to perform the logical AND with.
+
+        Keyword Args:
+            default (torch.Tensor or str, optional): the default value to use for exclusive entries.
+                If none is provided, the two tensordicts key list must match exactly.
+                If ``default="intersection"`` is passed, only the intersecting key sets will be considered
+                and other keys will be ignored.
+                In all other cases, ``default`` will be used for all missing entries on both sides of the
+                operation.
+        """
+        keys, vals = self._items_list(True, True)
+        if _is_tensor_collection(type(other)):
+            new_keys, other_val = other._items_list(
+                True, True, sorting_keys=keys, default=default
+            )
+            if default is not None:
+                as_dict = dict(zip(keys, vals))
+                vals = [as_dict.get(key, default) for key in new_keys]
+                keys = new_keys
+            vals = [(v1.logical_and(v2)) for v1, v2 in zip(vals, other_val)]
+        else:
+            vals = [v.logical_and(other) for v in vals]
+        items = dict(zip(keys, vals))
+
+        def pop(name, val):
+            return items.pop(name, None)
+
+        result = self._fast_apply(
+            pop,
+            named=True,
+            nested_keys=True,
+            is_leaf=_NESTED_TENSORS_AS_LISTS,
+            propagate_lock=True,
+            filter_empty=True,
+            default=None,
+        )
+        if items:
+            result.update(items)
+        return result
+
     def add(
         self,
         other: TensorDictBase | torch.Tensor,
@@ -9719,7 +9934,12 @@ def pop(name, val):
             result.update(items)
         return result
 
-    def add_(self, other: TensorDictBase | float, *, alpha: float | None = None):
+    def add_(
+        self,
+        other: TensorDictBase | torch.Tensor | float,
+        *,
+        alpha: float | None = None,
+    ):
         """In-place version of :meth:`~.add`.
 
         .. note::
@@ -9781,7 +10001,11 @@ def get(name, val):
             propagate_lock=True,
         )
 
-    def lerp_(self, end: TensorDictBase | float, weight: TensorDictBase | float):
+    def lerp_(
+        self,
+        end: TensorDictBase | torch.Tensor | float,
+        weight: TensorDictBase | torch.Tensor | float,
+    ):
         """In-place version of :meth:`~.lerp`."""
         if _is_tensor_collection(type(end)):
             end_val = end._values_list(True, True)
@@ -9917,7 +10141,7 @@ def addcmul_(self, other1, other2, *, value: float | None = 1):
 
     def sub(
         self,
-        other: TensorDictBase | float,
+        other: TensorDictBase | torch.Tensor | float,
         *,
         alpha: float | None = None,
         default: str | CompatibleType | None = None,
@@ -9976,7 +10200,9 @@ def pop(name, val):
             result.update(items)
         return result
 
-    def sub_(self, other: TensorDictBase | float, alpha: float | None = None):
+    def sub_(
+        self, other: TensorDictBase | torch.Tensor | float, alpha: float | None = None
+    ):
         """In-place version of :meth:`~.sub`.
 
         .. note::

tensordict/tensorclass.py

@@ -166,19 +166,30 @@ def __subclasscheck__(self, subclass):
 _FALLBACK_METHOD_FROM_TD = [
     "__abs__",
     "__add__",
+    "__and__",
     "__bool__",
     "__eq__",
     "__ge__",
     "__gt__",
     "__iadd__",
     "__imul__",
+    "__invert__",
     "__ipow__",
     "__isub__",
     "__itruediv__",
     "__mul__",
     "__ne__",
+    "__neg__",
     "__or__",
     "__pow__",
+    "__radd__",
+    "__rand__",
+    "__rmul__",
+    "__ror__",
+    "__rpow__",
+    "__rsub__",
+    "__rtruediv__",
+    "__rxor__",
     "__sub__",
     "__truediv__",
     "__xor__",
@@ -228,6 +239,7 @@ def __subclasscheck__(self, subclass):
     "atan_",
     "auto_batch_size_",
     "auto_device_",
+    "bitwise_and",
     "ceil",
     "ceil_",
     "chunk",
@@ -291,10 +303,8 @@ def __subclasscheck__(self, subclass):
     "log2",
     "log2_",
     "log_",
-    "map",
+    "logical_and" "map",
     "map_iter",
-    "to_namedtuple",
-    "to_pytree",
     "masked_fill",
     "masked_fill_",
     "max",
@@ -356,6 +366,8 @@ def __subclasscheck__(self, subclass):
     "tanh_",
     "to",
     "to_module",
+    "to_namedtuple",
+    "to_pytree",
     "transpose",
     "trunc",
     "trunc_",