[BugFix] Fix tensorclass indexing

tensordict/_lazy.py

@@ -63,6 +63,7 @@
     _get_shape_from_args,
     _getitem_batch_size,
     _is_number,
+    _maybe_correct_neg_dim,
     _parse_to,
     _renamed_inplace_method,
     _shape,
@@ -292,6 +293,49 @@ def __init__(
         if stack_dim_name is not None:
             self._td_dim_name = stack_dim_name
 
+    @classmethod
+    def _new_lazy_unsafe(
+        cls,
+        *tensordicts: T,
+        stack_dim: int = 0,
+        hook_out: callable | None = None,
+        hook_in: callable | None = None,
+        batch_size: Sequence[int] | None = None,
+        device: torch.device | None = None,
+        names: Sequence[str] | None = None,
+        stack_dim_name: str | None = None,
+        strict_shape: bool = False,
+    ) -> None:
+        self = cls.__new__(cls)
+        self._is_locked = None
+
+        # sanity check
+        num_tds = len(tensordicts)
+        batch_size = torch.Size(batch_size) if batch_size is not None else None
+        if not num_tds:
+            # create an empty tensor
+            td0 = TensorDict(batch_size=batch_size, device=device, names=names)
+            self._device = torch.device(device) if device is not None else None
+        else:
+            td0 = tensordicts[0]
+            # device = td0.device
+        _batch_size = td0.batch_size
+
+        for td in tensordicts[1:]:
+            _bs = td.batch_size
+            if _bs != _batch_size:
+                _batch_size = torch.Size(
+                    [s if _bs[i] == s else -1 for i, s in enumerate(_batch_size)]
+                )
+        self.tensordicts: list[TensorDictBase] = list(tensordicts)
+        self.stack_dim = stack_dim
+        self._batch_size = self._compute_batch_size(_batch_size, stack_dim, num_tds)
+        self.hook_out = hook_out
+        self.hook_in = hook_in
+        if stack_dim_name is not None:
+            self._td_dim_name = stack_dim_name
+        return self
+
     # These attributes should never be set
     @property
     def _is_shared(self):
@@ -633,7 +677,9 @@ def _split_index(self, index):
         encountered_tensor = False
         for i, idx in enumerate(index):  # noqa: B007
             cursor_incr = 1
-            if idx is None:
+            # if idx is None:
+            #     idx = True
+            if idx is None or idx is True:
                 out.append(None)
                 num_none += cursor <= self.stack_dim
                 continue
@@ -1675,6 +1721,8 @@ def _iterate_over_keys(self) -> None:
 
     @cache  # noqa: B019
     def _key_list(self):
+        if not self.tensordicts:
+            return []
         keys = set(self.tensordicts[0].keys())
         for td in self.tensordicts[1:]:
             keys = keys.intersection(td.keys())
@@ -2099,15 +2147,6 @@ def assign(converted_idx, value=value):
                         value_unbind,
                     ):
                         if mask.any():
-                            assert (
-                                self.tensordicts[i][_idx].shape
-                                == torch.zeros(self.tensordicts[i].shape)[_idx].shape
-                            ), (
-                                self.tensordicts[i].shape,
-                                _idx,
-                                self.tensordicts[i][_idx],
-                                torch.zeros(self.tensordicts[i].shape)[_idx].shape,
-                            )
                             self.tensordicts[i][_idx] = _value
                 else:
                     for (i, _idx), _value in _zip_strict(
@@ -2160,7 +2199,7 @@ def __getitem__(self, index: IndexType) -> Any:
                     batch_size = _getitem_batch_size(self.batch_size, index)
                 else:
                     batch_size = None
-                return LazyStackedTensorDict(
+                return self._new_lazy_unsafe(
                     *result,
                     stack_dim=cat_dim,
                     device=self.device,
@@ -3203,10 +3242,7 @@ def _unsqueeze(self, dim):
         return result
 
     def split(self, split_size: int | list[int], dim: int = 0) -> list[TensorDictBase]:
-        if dim < 0:
-            dim = self.ndim + dim
-        if dim < 0 or dim > self.ndim - 1:
-            raise ValueError(f"Out-of-bounds dim value: {dim}.")
+        dim = _maybe_correct_neg_dim(dim, shape=self.shape)
         if dim == self.stack_dim:
             if isinstance(split_size, int):
                 split_size = [split_size] * -(len(self.tensordicts) // -split_size)
@@ -3217,15 +3253,15 @@ def iter_across_tds():
                 for s in split_size:
                     if s == 0:
                         batch_size = list(self._batch_size)
-                        batch_size.pop(self.stack_dim)
+                        batch_size[self.stack_dim] = 0
                         yield LazyStackedTensorDict(
                             batch_size=batch_size,
                             device=self.device,
                             stack_dim=self.stack_dim,
                         )
                         continue
                     stop = start + s
-                    yield LazyStackedTensorDict(
+                    yield self._new_lazy_unsafe(
                         *self.tensordicts[slice(start, stop)], stack_dim=self.stack_dim
                     )
                     start = stop

tensordict/_td.py

@@ -2518,7 +2518,23 @@ def _set_at_str(self, key, value, idx, *, validated, non_blocking: bool):
         tensor_in = self._get_str(key, NO_DEFAULT)
 
         if is_non_tensor(value) and not (self._is_shared or self._is_memmap):
+            if isinstance(idx, tuple) and len(idx) == 1:
+                idx = idx[0]
             dest = tensor_in
+            if (
+                isinstance(idx, torch.Tensor)
+                and idx.shape == ()
+                and self.shape == ()
+                and idx.dtype == torch.bool
+                and idx
+            ):
+                self._set_str(
+                    key,
+                    dest.squeeze(0),
+                    validated=True,
+                    inplace=False,
+                    ignore_lock=True,
+                )
             is_diff = dest[idx].tolist() != value.tolist()
             if is_diff:
                 dest_val = dest.maybe_to_stack()

tensordict/base.py

@@ -6593,7 +6593,7 @@ def update(
                 value = tree_map(torch.clone, value)
             # the key must be a string by now. Let's check if it is present
             if target is not None:
-                if not is_leaf(type(target)):
+                if not is_leaf(type(target)) and not is_leaf(type(value)):
                     if subkey:
                         sub_keys_to_update = _prune_selected_keys(
                             keys_to_update, firstkey

tensordict/tensorclass.py

@@ -1571,7 +1571,6 @@ def wrapped_func(self, *args, **kwargs):
                 td = super(type(self), self).__getattribute__("_tensordict")
             else:
                 td = self._tensordict
-
             result = getattr(td, funcname)(*args, **kwargs)
             if no_wrap:
                 return result
@@ -1776,10 +1775,26 @@ def _setitem(self, item: NestedKey, value: Any) -> None:  # noqa: D417
         value (any): value to set for the item
 
     """
-    if isinstance(item, str) or (
-        isinstance(item, tuple) and all(isinstance(_item, str) for _item in item)
-    ):
-        raise ValueError(f"Invalid indexing arguments: {item}.")
+    istuple = isinstance(item, tuple)
+    if istuple or isinstance(item, str):
+        # _unravel_key_to_tuple will return an empty tuple if the index isn't a NestedKey
+        idx_unravel = _unravel_key_to_tuple(item)
+        if idx_unravel:
+            raise ValueError(f"Invalid indexing arguments: {item}.")
+
+    if istuple and len(item) == 1:
+        return _setitem(self, item[0], value)
+    if (
+        (
+            isinstance(item, torch.Tensor)
+            and item.dtype == torch.bool
+            and not item.shape
+            and item
+        )
+        or (item is True)
+        or (item is None)
+    ) and self.batch_size == ():
+        return self.update(value.squeeze(0))
 
     if not is_tensorclass(value) and not isinstance(
         value, (TensorDictBase, numbers.Number, Tensor)

tensordict/utils.py

@@ -1243,7 +1243,11 @@ def func_as_decorator(_self, *args, **kwargs):
                 if out is not None:
                     if _attr_post is not _attr_pre:
                         ref = weakref.ref(_self)
-                        out._last_op = (
+                        if is_tensorclass(out):
+                            out_lo = out._tensordict
+                        else:
+                            out_lo = out
+                        out_lo._last_op = (
                             func.__name__,
                             (
                                 args,
@@ -1262,7 +1266,11 @@ def func_as_decorator(_self, *args, **kwargs):
                 out = func(_self, *args, **kwargs)
                 if out is not None:
                     ref = weakref.ref(_self)
-                    out._last_op = (func.__name__, (args, kwargs, ref))
+                    if is_tensorclass(out):
+                        out_lo = out._tensordict
+                    else:
+                        out_lo = out
+                    out_lo._last_op = (func.__name__, (args, kwargs, ref))
                 return out
 
         return func_as_decorator
@@ -2023,7 +2031,7 @@ def _getitem_batch_size(batch_size, index):
     out = []
     count = -1
     for i, idx in enumerate(index):
-        if idx is None:
+        if idx is True or idx is None:
             out.append(1)
             continue
         count += 1 if not bools[i] else idx.ndim

test/test_tensordict.py

@@ -11299,6 +11299,18 @@ def test_set(self, non_tensor_data):
             == "another string!"
         )
 
+    def test_setitem_edge_case(self):
+        s = NonTensorStack("a string")
+        t = NonTensorStack("another string")
+        s[0][True] = t
+        assert s[0].data == "another string"
+        for i in (None, True):
+            s = NonTensorStack("0", "1")
+            t = NonTensorStack(NonTensorStack("2", "3"), stack_dim=1)
+            assert t.batch_size == (2, 1)
+            s[:, i] = t
+            assert s.tolist() == ["2", "3"]
+
     def test_stack(self, non_tensor_data):
         assert (
             LazyStackedTensorDict.lazy_stack([non_tensor_data, non_tensor_data], 0).get(