fix(tests): clean up sys.modules pollution in training fixtures (#2168)

Two cooperating fixture bugs caused order-dependent failures in
tests/unit_tests/training/:

1. test_timers.py:67 — `sys.modules["torch.distributed"] = dist_stub`
   bypassed pytest's monkeypatch finalizer, leaking the stub for the
   rest of the session. Any later test that imported torchao failed
   with `ModuleNotFoundError: No module named 'torch.distributed.<X>';
   'torch.distributed' is not a package`. Fixed by switching to
   `monkeypatch.setitem(sys.modules, ...)` which is auto-reverted.

2. test_train_ft_mlflow_logging.py:_install_fake_wandb — the fake
   wandb stub had `__spec__ = None`, so accelerate's
   `importlib.util.find_spec("wandb")` raised
   `ValueError: wandb.__spec__ is None`, breaking the test in
   isolation. Fixed by setting a valid ModuleSpec via
   `importlib.util.spec_from_loader`.

Verified: `pytest tests/unit_tests/training/` 55 passed (previously
2 failed, both fixture bugs visible).

Signed-off-by: Robert Luke <code@robertluke.net>

Author: rob-luke

tests/unit_tests/training/test_timers.py

@@ -64,7 +64,7 @@ def _all_gather(dest: torch.Tensor, src: torch.Tensor):  # noqa: D401
     dist_stub._all_gather_base = _all_gather
 
     monkeypatch.setattr(torch, "distributed", dist_stub, raising=False)
-    sys.modules["torch.distributed"] = dist_stub
+    monkeypatch.setitem(sys.modules, "torch.distributed", dist_stub)
 
     # Import the module *after* stubs are in place so it picks them up.
     # Force reload in case it was imported by another test module

tests/unit_tests/training/test_train_ft_mlflow_logging.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib.util
 import sys
 import types
 from unittest.mock import Mock
@@ -26,6 +27,8 @@ def _install_fake_wandb():
     Provide a minimal 'wandb' package so train_ft can be imported without the real dependency.
     """
     wandb = types.ModuleType("wandb")
+    # accelerate calls importlib.util.find_spec("wandb"), which raises if __spec__ is None
+    wandb.__spec__ = importlib.util.spec_from_loader("wandb", loader=None)
     wandb.run = None
 
     class Settings: