[Refactor] Better handling of params and buffers in bytes

tensordict/base.py

@@ -94,7 +94,7 @@
     unravel_key_list,
 )
 from torch import distributed as dist, multiprocessing as mp, nn, Tensor
-from torch.nn.parameter import UninitializedTensorMixin
+from torch.nn.parameter import Parameter, UninitializedTensorMixin
 from torch.utils._pytree import tree_map
 
 try:
@@ -3196,21 +3196,22 @@ def count_bytes(tensor):
                 if isinstance(tensor, MemoryMappedTensor):
                     add(tensor)
                     return
-                if type(tensor) is not torch.Tensor:
-                    try:
-                        attrs, ctx = tensor.__tensor_flatten__()
-                        for attr in attrs:
-                            t = getattr(tensor, attr)
-                            count_bytes(t)
-                        return
-                    except AttributeError:
-                        warnings.warn(
-                            "The sub-tensor doesn't ot have a __tensor_flatten__ attribute, making it "
-                            "impossible to count the bytes it contains. Falling back on regular count.",
-                            category=UserWarning,
-                        )
-                        count_bytes(torch.as_tensor(tensor))
-                        return
+                if type(tensor) in (Tensor, Parameter, Buffer):
+                    pass
+                elif hasattr(tensor, "__tensor_flatten__"):
+                    attrs, ctx = tensor.__tensor_flatten__()
+                    for attr in attrs:
+                        t = getattr(tensor, attr)
+                        count_bytes(t)
+                    return
+                else:
+                    warnings.warn(
+                        "The sub-tensor doesn't ot have a __tensor_flatten__ attribute, making it "
+                        "impossible to count the bytes it contains. Falling back on regular count.",
+                        category=UserWarning,
+                    )
+                    count_bytes(torch.as_tensor(tensor))
+                    return
 
                 grad = getattr(tensor, "grad", None)
                 if grad is not None: