[Refactor] Use empty_like in storage construction

ghstack-source-id: 28cd569bd4abf472991b82b3eba9fe333b5cd68f
Pull Request resolved: #2455

torchrl/data/replay_buffers/samplers.py

@@ -982,22 +982,20 @@ def _find_start_stop_traj(
 
             # faster
             end = trajectory[:-1] != trajectory[1:]
-            end = torch.cat([end, trajectory[-1:] != trajectory[:1]], 0)
+            if not at_capacity:
+                end = torch.cat([end, torch.ones_like(end[:1])], 0)
+            else:
+                end = torch.cat([end, trajectory[-1:] != trajectory[:1]], 0)
             length = trajectory.shape[0]
         else:
-            # TODO: check that storage is at capacity here, if not we need to assume that the last element of end is True
-
             # We presume that not done at the end means that the traj spans across end and beginning of storage
             length = end.shape[0]
+            if not at_capacity:
+                end = end.clone()
+                end[length - 1] = True
+        ndim = end.ndim
 
-        if not at_capacity:
-            end = torch.index_fill(
-                end,
-                index=torch.tensor(-1, device=end.device, dtype=torch.long),
-                dim=0,
-                value=1,
-            )
-        else:
+        if at_capacity:
             # we must have at least one end by traj to individuate trajectories
             # so if no end can be found we set it manually
             if cursor is not None:
@@ -1019,7 +1017,6 @@ def _find_start_stop_traj(
                 mask = ~end.any(0, True)
                 mask = torch.cat([torch.zeros_like(end[:-1]), mask])
                 end = torch.masked_fill(mask, end, 1)
-        ndim = end.ndim
         if ndim == 0:
             raise RuntimeError(
                 "Expected the end-of-trajectory signal to be at least 1-dimensional."
@@ -1126,7 +1123,7 @@ def _get_stop_and_length(self, storage, fallback=True):
                             "Could not get a tensordict out of the storage, which is required for SliceSampler to compute the trajectories."
                         )
                 vals = self._find_start_stop_traj(
-                    trajectory=trajectory,
+                    trajectory=trajectory.clone(),
                     at_capacity=storage._is_full,
                     cursor=getattr(storage, "_last_cursor", None),
                 )

torchrl/data/replay_buffers/storages.py

@@ -902,9 +902,7 @@ def max_size_along_dim0(data_shape):
 
         if is_tensor_collection(data):
             out = data.to(self.device)
-            out = out.expand(max_size_along_dim0(data.shape))
-            out = out.clone()
-            out = out.zero_()
+            out = torch.empty_like(out.expand(max_size_along_dim0(data.shape)))
         else:
             # if Tensor, we just create a MemoryMappedTensor of the desired shape, device and dtype
             out = tree_map(