Fixes configclass dict conversion for torch tensors (#1530)

# Description

Fix configclass dict conversion for torch tensors

Up to v1.2.0 if a configclass would contain a list/tuple of torch
tensors it would be left as is.

\#1227 changed the behavior of converting lists/tuples in a dict, which
means that currently torch tensors are converted to an empty dict,
effectively losing all contained data.

The underlying issue is that `torch.tensor.__dict__` returns an empty
dict, which was (luckily) ignored previously because we did not convert
the contents of lists.

This MR fixes this by treating torch tensors specially. I don't like
having a special case for a non-builtin class but given that
IsaacLab is heavily married with torch tensors I think it's ok in this
case.

Since currently the behavior is different between 1.2 and 1.3: can we
cherry pick this change to the 1.3 branch?

## Type of change

- Bug fix (non-breaking change which fixes an issue)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there
Co-authored-by: Kelly Guo <kellyg@nvidia.com>

source/extensions/omni.isaac.lab/omni/isaac/lab/utils/dict.py

@@ -8,6 +8,7 @@
 import collections.abc
 import hashlib
 import json
+import torch
 from collections.abc import Iterable, Mapping
 from typing import Any
 
@@ -40,6 +41,11 @@ def class_to_dict(obj: object) -> dict[str, Any]:
     # convert object to dictionary
     if isinstance(obj, dict):
         obj_dict = obj
+    elif isinstance(obj, torch.Tensor):
+        # We have to treat torch tensors specially because `torch.tensor.__dict__` returns an empty
+        # dict, which would mean that a torch.tensor would be stored as an empty dict. Instead we
+        # want to store it directly as the tensor.
+        return obj
     elif hasattr(obj, "__dict__"):
         obj_dict = obj.__dict__
     else:
@@ -57,6 +63,7 @@ def class_to_dict(obj: object) -> dict[str, Any]:
         # check if attribute is a dictionary
         elif hasattr(value, "__dict__") or isinstance(value, dict):
             data[key] = class_to_dict(value)
+        # check if attribute is a list or tuple
         elif isinstance(value, (list, tuple)):
             data[key] = type(value)([class_to_dict(v) for v in value])
         else:

source/extensions/omni.isaac.lab/test/utils/test_configclass.py

@@ -19,6 +19,7 @@ simulation_app = app_launcher.app
 
 import copy
 import os
+import torch
 import unittest
 from collections.abc import Callable
 from dataclasses import MISSING, asdict, field
@@ -134,6 +135,14 @@ class BasicDemoPostInitCfg:
         self.add_variable = 3
 
 
+@configclass
+class BasicDemoTorchCfg:
+    """Dummy configuration class with a torch tensor ."""
+
+    some_number: int = 0
+    some_tensor: torch.Tensor = torch.Tensor([1, 2, 3])
+
+
 """
 Dummy configuration to check type annotations ordering.
 """
@@ -515,6 +524,12 @@ class TestConfigClass(unittest.TestCase):
         self.assertDictEqual(cfg.to_dict(), basic_demo_cfg_correct)
         self.assertDictEqual(cfg.env.to_dict(), basic_demo_cfg_correct["env"])
 
+        torch_cfg = BasicDemoTorchCfg()
+        torch_cfg_dict = torch_cfg.to_dict()
+        # We have to do a manual check because torch.Tensor does not work with assertDictEqual.
+        self.assertEqual(torch_cfg_dict["some_number"], 0)
+        self.assertTrue(torch.all(torch_cfg_dict["some_tensor"] == torch.tensor([1, 2, 3])))
+
     def test_dict_conversion_order(self):
         """Tests that order is conserved when converting to dictionary."""
         true_outer_order = ["device_id", "env", "robot_default_state", "list_config"]